# CDC Surveillance Summaries **December 11, 1998 / Vol. 47 / No. SS-5**

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES **Centers for Disease Control and Prevention (CDC) Atlanta, Georgia 30333**  

# Surveillance for Waterborne-Disease Outbreaks — United States, 1995–1996 Cardiovascular Disease Risk Factors and Preventive Practices Among Adults — United States, 1994: A Behavioral Risk Factor Atlas **TM**

# Copies can be purchased from Superintendent of Documents, U.S. Government **Printing Office, Washington, DC 20402-9325. Tel ephone: (2**  **02) 512-1800. The**  **MMWR series of publications is published by the Epidemiology Program Office,** **Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Hu man Services, Atlanta, GA 30333.** 

**Centers for Disease Control and Prevention**
**.................... Jeffrey P. Koplan, M.D., M.P.H.**  **Director The production of this report as an**  **MMWR serial publication was coordinated in**  **Epidemiology Program Office.................................... Stephen B. Thacker, M.D., M.Sc.**  **Director Denise Koo, M.D., M.P.H.**  **Associate Editor , CDC Surveillance Summaries**  **Office of Scientific and Health Communi**  **cations ........ ..............John W. Ward, M.D.**  **Director Editor**  **, MMWR Series**  **CDC Surveillance Summaries ...................................... Suzanne M. Hewitt, M.P.A.**  **Managing Editor Patricia A. McGee**  **Project Editor Peter M. Jenkins**  **Visual Information Specialist SUGGESTED CITATION**  **General: Centers for Disease Control and Prevention. CDC Surveillance Sum**  **maries , December 11, 1998. MMWR 1998;47(No. SS-5).**  **Specific: [Author(s)]. [Title of particular article]. In CDC Surveillance Sum**  **maries , December 11, 1998. MMWR 1998;47(No. SS-5):[inclusive**  **page numbers].**

**Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services.** 

# Contents Surveillance for Waterborne-Disease Outbreaks — United States, 1995–1996 **................................................................................1 Introduction......................................................................................................3**  **EPA Regulations for Drinking Water .............................................................3 Methods............................................................................................................4**  **Results ..............................................................................................................6 Discussion ......................................................................................................12**  **Conclusions....................................................................................................18 References......................................................................................................19**  Cardiovascular Disease Risk Factors and Preventive Practices Among Adults — United States, 1994: A Behavioral Risk Factor Atlas **......................................................................35 Introduction....................................................................................................36**  **Methods..........................................................................................................38 Results ............................................................................................................41**  **Discussion ......................................................................................................44 References......................................................................................................46**  Vol. 47 / No. SS-5 MMWR i

# Abortion NCCDPHP 1997; Vol. 46, No. SS-4 **AIDS/HIV Distribution by Racial/Ethnic Group NCID 1988; Vol. 37, No. SS-3**  **Among Black & Hispanic Children & Women of Childbearing Age NCEHIC 1990; Vol. 39, No. SS-3**  **Behavioral Risk Factors NCCDPHP 1997; Vol. 46, No. SS-3 Birth Defects**  **B.D. Monitoring Program (see also Malformations) NCEH 1993; Vol. 42, No. SS-1 Contribution of B.D. to Infant Mortality**  **Among Minority Groups NCEHIC 1990; Vol. 39, No. SS-3 Breast & Cervical Cancer NCCDPHP 1992; Vol. 41, No. SS-2**  **Campylobacter NCID 1988; Vol. 37, No. SS-2**  **Cardiovascular Disease EPO, NCCDPHP 1998; Vol. 47, No. SS-5 Chancroid NCPS 1992; Vol. 41, No. SS-3**  **Chlamydia NCPS 1993; Vol. 42, No. SS-3 Cholera NCID 1992; Vol. 41, No. SS-1**  **Chronic Fatigue Syndrome NCID 1997; Vol. 46, No. SS-2 Congenital Malformations, Minority Groups NCEHIC 1988; Vol. 37, No. SS-3**  **Contraception Practices NCCDPHP 1992; Vol. 41, No. SS-4 Cytomegalovirus Disease, Congenital NCID 1992; Vol. 41, No. SS-2**  **Dengue NCID 1994; Vol. 43, No. SS-2 Dental Caries & Periodontal Disease Among**  **Mexican-American Children NCPS 1988; Vol. 37, No. SS-3 Developmental Disabilities NCEH 1996; Vol. 45, No. SS-2**  **Diabetes Mellitus NCCDPHP 1993; Vol. 42, No. SS-2 Dracunculiasis NCID 1992; Vol. 41, No. SS-1**  **Ectopic Pregnancy NCCDPHP 1993; Vol. 42, No. SS-6 Elderly, Hospitalizations Among NCCDPHP 1991; Vol. 40, No. SS-1**  **Endometrial & Ovarian Cancers EPO, NCCDPHP 1986; Vol. 35, No. 2SS Escherichia coli**  **O157 NCID 1991; Vol. 40, No. SS-1 Evacuation Camps EPO 1992; Vol. 41, No. SS-4**  **Family Planning Services at Title X Clinics NCCDPHP 1995; Vol. 44, No. SS-2 Foodborne Disease NCID 1996; Vol. 45, No. SS-5**  **Food-Handling Preparation, and Consumption Behaviors NCID 1998; Vol. 47, No. SS-4**  **Gonorrhea & Syphilis, Teenagers NCPS 1993; Vol. 42, No. SS-3 Hazardous Substances Emergency Events ATSDR 1994; Vol. 43, No. SS-2**  **Health Surveillance Systems IHPO 1992; Vol. 41, No. SS-4 Hepatitis NCID 1985; Vol. 34, No. 1SS**  **Homicide NCEHIC 1992; Vol. 41, No. SS-3 Homicides, Black Males NCEHIC 1988; Vol. 37, No. SS-1**  **Hysterectomy NCCDPHP 1997; Vol. 46, No. SS-4 Infant Mortality (see also National Infant Mortality;**  **Birth Defects; Postneonatal Mortality) NCCDPHP 1998; Vol. 47, No. SS-2 Influenza NCID 1997; Vol. 46, No. SS-1**  **Injury Death Rates, Blacks & Whites NCEHIC 1988; Vol. 37, No. SS-3**  **Drownings NCEHIC 1988; Vol. 37, No. SS-1 Falls, Deaths NCEHIC 1988; Vol. 37, No. SS-1**  **Firearm-Related Deaths, Unintentional NCEHIC 1988; Vol. 37, No. SS-1** 

# *Abbreviations **ATSDR Agency for Toxic Substances and Disease Registry CIO Centers/Institute/Offices**  **EPO Epidemiology Program Office IHPO International Health Program Office**  **NCCDPHP National Center for Chronic Disease Pr evention and Health Promotion**  **NCEH National Center for Environmental Health NCEHIC National Center for Environmental Health and Injury Control**  **NCID National Center for Infectious Diseases NCIPC National Center for Injury Pr**  **evention and Control NCPS National Center for Prevention Services**  **NIOSH National Institute for Occupational Safety and Health NIP National Immunization Program**  

# Reports Published in CDC Surveillance Summaries Since January 1, 1985 Responsible Subject CIO/Agency* Most Recent Report ii MMWR December 11, 1998

# Head & Neck NCIPC 1993; Vol. 42, No. SS-5 **In Developing Countries NCEHIC 1992; Vol. 41, No. SS-1 In the Home, Persons <15 Years of Age NCEHIC 1988; Vol. 37, No. SS-1**  **Motor Vehicle-Related Deaths NCEHIC 1988; Vol. 37, No. SS-1 Objectives of Injury Control, State & Local NCEHIC 1988; Vol. 37, No. SS-1**  **Objectives of Injury Control, National NCEHIC 1988; Vol. 37, No. SS-1 Residential Fires, Deaths NCEHIC 1988; Vol. 37, No. SS-1**  **Tap Water Scalds NCEHIC 1988; Vol. 37, No. SS-1 Lead Poisoning, Childhood NCEHIC 1990; Vol. 39, No. SS-4**  **Low Birth Weight NCCDPHP 1990; Vol. 39, No. SS-3 Malaria NCID 1997; Vol. 46, No. SS-2**  **Measles NCPS 1992; Vol. 41, No. SS-6 Meningococcal Disease NCID 1993; Vol. 42, No. SS-2**  **Mining NIOSH 1986; Vol. 35, No. 2SS Mumps NIP 1995; Vol. 44, No. SS-3**  **National Infant Mortality (see also Infant Mortality; Birth Defects) NCCDPHP 1998; Vol. 47, No. SS-2**  **Neisseria gonorrhoeae , Antimicrobial Resistance in NCPS 1993; Vol. 42, No. SS-3**  **Neural Tube Defects NCEH 1995; Vol. 44, No. SS-4 Nosocomial Infection NCID 1986; Vol. 35, No. 1SS**  **Occupational Injuries/Disease Asthma NIOSH 1994; Vol. 43, No. SS-1**  **Hazards, Occupational NIOSH 1985; Vol. 34, No. 2SS In Meatpacking Industry NIOSH 1985; Vol. 34, No. 1SS**  **Silicosis NIOSH 1993; Vol. 42, No. SS-5 State Activities NIOSH 1987; Vol. 36, No. SS-2**  **Parasites, Intestinal NCID 1991; Vol. 40, No. SS-4 Pediatric Nutrition NCCDPHP 1992; Vol. 41, No. SS-7**  **Pertussis NCPS 1992; Vol. 41, No. SS-8 Plague NCID 1985; Vol. 34, No. 2SS**  **Plague, American Indians NCID 1988; Vol. 37, No. SS-3 Poliomyelitis NCPS 1992; Vol. 41, No. SS-1**  **Postneonatal Mortality NCCDPHP 1998; Vol. 47, No. SS-2 Pregnancy Nutrition NCCDPHP 1992; Vol. 41, No. SS-7**  **Pregnancy-Related Mortality NCCDPHP 1997; Vol. 46, No. SS-4 Pregnancy, Teenage NCCDPHP 1993; Vol. 42, No. SS-6**  **Rabies NCID 1989; Vol. 38, No. SS-1 Racial/Ethnic Minority Groups Various 1990; Vol. 39, No. SS-3**  **Respiratory Disease NCEHIC 1992; Vol. 41, No. SS-4 Rotavirus NCID 1992; Vol. 41, No. SS-3**  **Salmonella NCID 1988; Vol. 37, No. SS-2**  **School Health Education NCCDPHP 1998; Vol. 47, No. SS-4 Sexually Transmitted Diseases in Italy NCPS 1992; Vol. 41, No. SS-1**  **Silicosis 1997; Vol. 46, No. SS-1 Smoking NCCDPHP 1990; Vol. 39, No. SS-3**  **Smoking-Attributable Mortality NCCDPHP 1994; Vol. 43, No. SS-1 Tobacco Control Laws, State NCCDPHP 1995; Vol. 44, No. SS-6**  **Tobacco-Use Behaviors NCCDPHP 1994; Vol. 43, No. SS-3 Spina Bifida NCEH 1996; Vol. 45, No. SS-2**  **Streptococcal Disease (Group B) NCID 1992; Vol. 41, No. SS-6 Sudden Unexplained Death Syndrome Among**  **Southeast Asian Refugees NCEHIC, NCPS 1987; Vol. 36, No. 1SS Suicides, Persons 15–24 Years of Age NCEHIC 1988; Vol. 37, No. SS-1**  **Syphilis, Congenital NCPS 1993; Vol. 42, No. SS-6 Syphilis, Primary & Secondary NCPS 1993; Vol. 42, No. SS-3**  **Tetanus NIP 1997; Vol. 46, No. SS-2 Trichinosis NCID 1991; Vol. 40, No. SS-3**  **Tuberculosis NCPS 1991; Vol. 40, No. SS-3 Waterborne-Disease Outbreaks NCID 1998; Vol. 47, No. SS-5**  **Years of Potential Life Lost EPO 1992; Vol. 41, No. SS-6 Youth Risk Behaviors NCCDPHP 1996; Vol. 47, No. SS-3**  **Youth Risk Behaviors, College Students NCCDPHP 1997; Vol. 46, No. SS-6** 

# Reports Published in CDC Surveillance Summaries Since January 1, 1985 — Continued Responsible Subject CIO/Agency* Most Recent Report Vol. 47 / No. SS-5 MMWR iii

# iv MMWR December 11, 1998

# Surveillance for Waterborne-Disease Outbreaks — United States, 1995–1996

**Deborah A. Levy, Ph.D., M.P.H. 1,2**  

# Michelle S. Bens, M.P.H. **2** 

# Gunther F. Craun, M.P.H. **3** 

# Rebecca L. Calderon, Ph.D., M.P.H. **4** 

# Barbara L. Herwaldt, M.D., M.P.H. **2 1**  

# Epidemic Intelligence Service, Epidemiology Program Office, CDC **2** 

# Division of Parasitic Diseases, National Center for Infectious Diseases, CDC **3** 

# Gunther F. Craun & Associates, Staunton, Virginia **4** 

# Human Studies Division, National Health and Environmental Effects **Laboratory, U.S. Environmental Protection Agency** Abstract Problem/Condition: **Since 1971, CDC and the U.S. Environmental Protection Agency have maintained a collaborative surveillance system for collecting and periodically**  **reporting data that relate to occurrences and causes of waterborne-disease outbreaks (WBDOs).**  Reporting Period Covered: **This summary includes data for January 1995 through De cember 1996 and previously unreported outbreaks in 1994.**  Description of the System: **The surveillance system includes data about outbreaks associated with drinking water and recreational water. State, territorial, and local pub**  **lic health departments are primarily r esponsible for detecting and investigating**  **WBDOs and for voluntarily reporting them to CDC on a standard form.** Results: **For the period 1995–1996, 13 states reported a total of 22 outbreaks associ ated with drinking water. These outbreaks caused an**  **estimated total of 2,567 persons to become ill. No deaths were reported. The m**  **icrobe or chemical that c aused the out**  **break was identified for 14 (63.6%) of the 22 outbreaks. Giardia lamblia**  **and Shigella**  **sonnei each caused two (9.1%) of the 22 outbreaks;**  **Escherichia coli O157:H7,**  **Plesio monas shigelloides**  **, and a small round structured virus were implicated for one outbreak (4.5%) each. One of the two outbreaks of giardiasis involved the largest num**  **ber of cases, with an estimated 1,449 ill persons. Seven outbreaks (31.8% of 22) of chemical poisoning, which involved a total of 90 persons, were reported. Copper and**  **nitrite were associated with two outbreaks (9.1% of 22) each and sodium hydroxide, chlorine, and concentrated liquid soap with one outbreak (4.5%) each. Eleven (50.0%)**  **of the 22 outbreaks were linked to well water, eight in nonc ommunity and three in**  **community systems. Only three of the 10 outbreaks associated with c**  **ommunity water systems were** **caused by problems at water treatment plants; the other seven resulted from prob lems in the water distribution systems and plumbing of individual facilities (e.g., a**  **restaurant). Six of the seven outbreaks were associated with chemical contamination of the drinking water; the seventh outbreak was attributed to a small ro**  **und structured virus. Four of the seven outbreaks occurred because of backflow or backsip**  **honage** Vol. 47 / No. SS-5 MMWR 1

# through a cross-connection, and two occurred because of high levels of copper that**leached into water after the installation of new plumbing. For three of the four out breaks caused by co**  **ntamination from a c ross-connection, an**  **improperly installed vacuum breaker or a faulty backflow prevention device was identified; no protection**  **against backsiphonage was found for**  **the fourth outbreak. Thirty-seven outbreaks from 17 states were a**  **ttri buted to recreational water expo**  **sure and affected an estimated 9,129 persons, including 8,449 persons in two large outbreaks of cryptosporidiosis. Twenty-two (59.5%) of these 37 were outbreaks of gas**  **troenteritis; nine (24.3%) were outbreaks of dermatitis; and six (16.2%) were single cases of primary amebic meningoencephalitis caused by**  **Naegleria fowleri , all of**  **which were fatal. The etiologic agent was identified for 33 (89.2%) of the 37 outbreaks. Six (27.3%) of the 22 outbreaks of gastroenteritis were caused by**  **Cryptosporidium parvum**  **and six (27.3%) by E. coli**  **O157:H7. All of the latter were associated with unchlorinated water (i.e., in lakes) or inadequately chlorinated water (i.e., in a pool).**  **Thirteen (59.1%) of these 22 outbreaks were associated with lake water, eight (36.4%) with swimming or wading pools, and one(4.5%) with a hot spring. Of the nine out**  **breaks of dermatitis, seven (77.8%) were outbreaks of Pseudomonas**  **dermatitis associated with hot tubs, and two (22.2%) were lake-associated outbreaks of swim**  **mer’s itch caused by Schistosoma**  **species.** Interpretation: **WBDOs caused by E. coli**  **O157:H7 were reported more frequently than in previous years and were associated primarily with recreational lake water. This find**  **ing suggests the need for better monitoring of water quality and identifi cation of**  **sources of contamination. Al though protozoan parasites, especially**  **Cryptosporidium and**  **Giardia , were associated with fewer reported outbreaks than in previous years,**  **they caused large outbreaks that affected a total of approximately 10,000 persons; all of the outbreaks of cryptosporidiosis were associated with recreational water, primar**  **ily swimming pools. Prevention of pool-associated outbreaks caused by chlorine-resistant parasites (e.g.,**  **Cryptosporidium and to a lesser extent**  **Giardia ) is** **particularly difficult because it requires improved filtration methods as well as educa tion of patrons about hazards associated with fecal accidents, especially in pools** **frequented by diaper-aged children. The proportion of reported drinking water out breaks associated with community water systems that were attributed to problems at**  **water treatment plants has steadily declined since 1989 (i.e., 72.7% for 1989–1990, 62.5% for 1991–1992, 57.1% for 1993–1994, and 30.0% for 1995–1996). This decrease** **might reflect improvements in water treatment and in operation of plants. The out breaks attributed to contamination in the distrib**  **ution system suggest that efforts should be increased to prevent cross-connections, especially by installing and moni**  **toring backflow prevention devices.** Actions Taken: **Surveillance data that identify the types of water systems, their defi ciencies, and the etiologic agents associated with outbreaks are used to evaluate the**  **adequacy of current technologies for providing safe drinking and recreational water. In addition, they are used to establish research priorities and can lead to improved**  **water-quality regulations.** 2 MMWR December 11, 1998

# INTRODUCTION **Since 1920, national statistics on outbreaks associated with drinking water have been available (**  **1 ). Since 1971, CDC, the U.S. Environmental Protection Agency (EPA),** **and the Council of State and Territorial Epidemiologists have maintained a collabora tive surveillance system consisting of the collection and periodic reporting of data on**  **the occurrences and causes of waterborne-disease outbreaks (WBDOs) ( 2–4** **). The sur veillance system includes data about outbreaks associated**  **with drinking and recreational water. This summary includes data for 1995 and 1996 and for previously**  **unreported outbreaks in 1994. CDC’s and EPA’s efforts related to waterborne-disease surveillance have the follow**  **ing goals: a) to characterize the epidemiology of WBDOs; b) to identify the etiologic agents that caused WBDOs and to determine why the outbreaks occurred; c) to train**  **public health personnel in how to detect and investigate WBDOs; and d) to collaborate with local, state, federal, and international agencies on initiatives to prevent water**  **borne diseases. The data gathered through this surveillance system are useful for evaluating the adequacy of current technologies for providing safe drinking and rec**  **reational water. Surveillance information also influences research priorities and can lead to improved water-quality regulations.**  

# EPA REGULATIONS FOR DRINKING WATER **Public water systems are regulated under the Safe Drinking Water Act of 1974 ( 5**  **), as amended in 1986 (**  **6 ) and 1996 (**  **7 ). Microbial contamination is regulated under the**  **Surface Water Treatment Rule of 1989 ( 8**  **) and the Total Coliform Rule of 1989 ( 8–10**  **). The Surface Water Treatment Rule includes regulations for filtration, disinfection, and**  **turbidity, as well as trea tment criteria**  **for removing Giardia lamblia**  **, viruses, Le**  **gionella , and heterotrophic plate-count**  **bacteria. All public community and**  **noncommunity water sys tems that use surface-water sources or gr**  **oundwater sources** **under the direct influence of surface water are covered by the Surface Water Treat ment Rule, which requires these water systems to disinfect their water and to**  **maintain a disinfectant residual in their distribution system. Filtration must also be provided unless water sources meet specified criteria for water quality and source**  **water protection. The Total Coliform Rule was promulgated specifically to identify public water sys**  **tems that are contaminated or vulnerable to contamination. The total coliform group of organisms (see Glossary), which includes but is not limited to fecal coliforms and**  **Escherichia coli , is used to indicate the possible presence or absence of pathogens** **and thus, provides a general indication of whether water is contaminated. The pres ence of fecal coliforms or**  **E. coli provides stronger evidence than does a positive total**  **coliform test of fecal contamination and the likely presence of pathogens ( 9**  **). Additional rules are being developed. The Enhanced Surface Water Treatment Rule,**  **proposed July 29, 1994 ( 11**  **), and the Ground Water Disinfection Rule, which has yet to be proposed, also will addr**  **ess the preve ntion of waterborne diseases. The En**  **hanced Surface Water Treatment Rule will propose changes to the Surface Water Treatment Rule to provide additional protection against**  **Cryptosporidium parvum and**  **other waterborne pathogens, including Giardia** **and viruses. The regulation will be im** Vol. 47 / No. SS-5 MMWR 3

# plemented in two stages: an interim rule and a final rule. Treatment requirements un**der the interim Enhanced Surface Water Treatment Rule are scheduled to be announced in l**  **ate 1998. The Ground Water Disinfect**  **ion Rule w ill apply to both community and noncommu**  **nity water systems served by groundwater. Maximum contaminant level goals w ill be**  **set for pathogens. A maximum contaminant level goal of zero w ill be set for viruses**  **and possibly Legionella** **. No maximum contaminant level goal is expected for het erotrophic plate-count bacteria. In lieu of monitoring for pathogens, perform**  **ance criteria for water treatment will be established that are expected to produce the de**  **sired reduction in levels of pathogens. Minimum levels of disinfection will be proposed for viruses and**  **Legionella , if included in the new rule.**  **To fill gaps in existing data on the occurrence of microbial pathogens and other indicators of microbial contamination, the EPA promulgated the Information Collec**  **tion Rule ( 12**  **). This rule requires treatment plants that use surface water and supply communities of**  ≥ **100,000 persons or that use gr oundwater sys**  **tems and supply com munities of**  ≥ **50,000 persons to monitor their source water for specific microbes and chemicals beginning in July 1997. If the concentration of microbes or chemicals ex**  **ceeds a predetermined threshold, then the utility must also monitor its finished water.**  **Microbial monitoring is intended a) to provide data on the occurrence of pathogens and the effectiveness of treatment for the removal of pathogens and b) to evaluate the** **adequacy of the Surface Water Treatment Rule and the Total Coliform Rule. The Infor mation Collection Rule requires utilities to m**  **onitor for the presence of Cryptosporidium**  **and Giardia**  **, total culturable viruses, and total and fecal coliforms or E. coli**  **at least once a month for 18 months.** 

# METHODS Sources of Data **State, territorial, and local public health agencies have the primary responsibility for detecting and investigating WBDOs and voluntarily reporting them to CDC on a**  **standard form (CDC form 52.12 [ 4**  **]). CDC annually requests reports from st ate and** **territorial epidemiologists or from persons designated as the WBDO surveillance co ordinators. When needed, additional information**  **about water quality and treatment is obtained from the state’s drinking water agency.**  

# Definition of Terms* **The unit of analysis for the WBDO surveillance system is an outbreak rather than an individual case of a particular disease. Two criteria must be met for an event to be** **defined as a WBDO. First, two or more persons must have experienced a similar ill ness after either ingestion of drinking water or exposure to water used for recreational**  **purposes. This stipulation that at least two persons be ill is waived for single cases of laboratory-confirmed primary amebic meningoencephalitis and for single cases of**  **chemical poisoning if water-quality data indicate contamination by the chemical. Sec-** ***Additional terms are defined in the glossary.** 4 MMWR December 11, 1998

# ond, epidemiologic evidence must implicate water as the probable source of the ill**ness. Outbreaks caused by contamination of water or ice at the point of use (e.g., a contaminated water faucet or serving container) are not classified as WBDOs.**  **If primary cases (among persons exposed to contaminated water) and secondary cases (among persons who became ill after contact with primary case-patients) are**  **distinguished on the outbreak report form, only primary cases are included in the total** **number of cases. If both actual and estimated case counts are included on the out break report form, the estimated case count is**  **used if the study population was sampled**  **randomly or the estimated count was calculated by using the attack rate.**  **Public water systems — classified as either community or noncommunity water systems — provide piped water to the public for general consumption and are regu**  **lated under the Safe Drinking Water Act. A community w**  **ater system serves year-round residents of a community, subdivision, or mobile-home park that has**  ≥ **15 service connections or an average of**  ≥ **25 residents. A noncommunity water system is defined as any public water system that is not a community water system and is used**  **by the general public for** ≥ **60 days per year and has** ≥ **15 service connections or serves an average of**  ≥ **25 persons. Noncommunity water systems are divided into nontran sient and transient categories, with nontransient nonc**  **ommunity water systems (e.g.,**  **in factories and schools) serving** ≥ **25 persons** ≥ **6 months of the year. Of the approxi mately 170,000 public water systems in the United States, 115,000 (68%) are**  **noncommunity systems, serving transients (95,000 systems) and nontransients (20,000); 55,000 (32%) are community systems (EPA Safe Drinking Water Information**  **System data base, 1998). Community water systems serve approximately 243 million persons in the United States (91% of the U.S. p**  **opulation); approximately 24 million persons (9%) rely on private or individual water systems, which are small systems, not**  **owned or operated by a water utility, that serve <15 connections or <25 persons. In addition, mi**  **llions of pers ons use noncommunity systems while traveling or working.**  **Each drinking water system associated with a WBDO is classified as having one of the following deficiencies:**  **1 = untreated surface water; 2 = untreated gr**  **oundwater; 3 = treatment deficiency (e.g., temporary interruption of disinfection, chronically in**  **adequate disinfection, and inadequate or no filtration); 4 = distribution system deficiency (e.g., cross-connection, contamination of water**  **mains during construction or repair, and contamination of a storage facility); and**  **5 = unknown or miscellaneous deficiency (e.g., contaminated bottled water). If more than one deficiency is noted on the report form for an outbreak, the deficiency**  **that most likely caused the outbreak is noted. Recreational waters include swimming pools, whirlpools, hot tubs, spas, water** **parks, and naturally occurring fresh and marine surface waters. Although the surveil lance system includes whirlpool- and hot tub-associated outbreaks of dermatitis**  **caused by Pseudomonas aeruginosa**  **, it does not include wound infections resulting from waterborne organisms (e.g.,**  **Aeromonas species).**  Vol. 47 / No. SS-5 MMWR 5

# Classification of Outbreaks**This surveillance system classifies WBDOs according to the strength of the evi dence implicating water (Table 1). The classification**  **numbers (i.e., I–IV) are based on the epidemiologic and water-quality data provided on the report form. Epidemiologic** **data are weighted more heavily than water-quality data. Thus, although some out breaks without water-quality data w**  **ere included in this summary, reports without**  **supporting epidemiologic data were excluded. Outbreaks of Pseudomonas**  **dermatitis and single cases of primary amebic meningoencephalitis or of illness resulting from**  **chemical poisoning are not classified according to this scheme. A classification of I**  **means that adequ ate epidemiologic and water-quality data** **were reported but does not necessarily imply that the investigation was optimal. Clas sification numbers of II–IV do not necessarily imply that the**  **investigations were flawed; the circumstances of each outbreak differ, and not all outbreaks can or should**  **be rigorously investigated.** 

# RESULTS 1995–1996 Outbreaks Associated with Drinking Water **For the period 1995–1996, 13 states reported a total of 22 outbreaks associated with drinking water. Sixteen outbreaks were reported for 1995 and six for 1996. Wisconsin**  **reported five outbreaks (22.7%), followed by Idaho, which reported three (13.6%). The outbreaks caused an estimated total of 2,567 persons to become ill. The median out**  **break size was 22 persons (range: 1–1,449). No deaths were attributed to these outbreaks. Outbreaks were most common in the summer and fall months (Figure 1).**  **Seventeen (77.3%) of the 22 outbreaks occurred during June thr ough O**  **ctober. Twelve (54.5%) of the 22 outbreaks were assigned a classification of I based on**  **epidemiologic and water-quality data, three (13.6%) Class II, and six (27.**  **3%) Class III;** **an individual case of chlorine poisoning was not classified. Outbreaks are listed indi vidually by state (Tables 2 and 3) and are tabulated by the etiologic agent and type of**  **water system (Table 4) and by the type of deficiency and type of water system (Table 5).**  

# Etiologic Agents**Fifteen (68.2%) of the 22 outbreaks were of infectious or suspected infectious etiol ogy, and seven (31.8%) were attributed to chemical poiso**  **ning. Two (9.1%) of the 22 outbreaks were caused by the parasite**  **Giardia , four (18.2%) by bacteria, one (4.5%)**  **by a small round structured virus, and eight (36.4%) were of u nknown etiology**  **(Figure 2).** Parasites . **Two outbreaks in 1995 were caused by Giardia,**  **one in Alaska and the other in New York. The outbreak of giardiasis in Alaska occurred in August, a**  **ffected 10 persons, and was associated with untreated surface water. The outbreak in New York**  **occurred in December, affected an estimated 1,449 persons, and was associated with surface water that was both chlorinated and filtered. A dose-response relation was**  **found between consumption of municipal water and illness. No i**  **nterruptions in chlori** 6 MMWR December 11, 1998

# nation were identified at the water plant. Postfilter water turbidity readings, which **serve as an index of the effectiveness of filtration, exceeded the regulated limit (i.e., 95% of water samples must be <0.5 nephelometric turbidity units [NTUs] for**  **conven tional and direct filtration) before and during the outbreak.**  Bacteria . **Four outbreaks were caused by bacteria: two were attributed to Shigella**  **sonnei , one to**  **E. coli O157:H7, and one to**  **Plesiomonas s higelloides**  **. The outbreak of shigellosis in Idaho, which affected 83 persons, occurred at a resort supplied by un**  **treated well water, which became contaminated by sewage from a line that was draining poorly (**  **13 ). The outbreak of shigellosis in Oklahoma, which affected 10 per**  **sons, was associated with tap water in a convenience store that was supplied by**  **chlorinated well water. Although the factors contributing to contamination of the water were not determined, the water was thought to have been inadequately chlorin**  **ated. The outbreak of**  **E. coli O157:H7 infection occurred at a summer camp in Minnesota**  **that was supplied by chlorinated spring water. Several of the 33 affected persons had stool cultures that also were positive for**  **Campylobacter jejuni and**  **Salmonella sero**  **type London. W**  **ater samples from the spring and distribution system were positive for coliforms and**  **E. coli . The contamination was**  **attributed to flooding from heavy rains**  **and to an improperly constructed spring. The outbreak of**  **Plesiomonas shigelloides infection occurred in a restaurant in New**  **York that was supplied by a noncommunity water system. The outbreak affected 60 persons and is thought to be the largest outbreak of**  **Plesiomonas infection re**  **ported in the United States ( 14**  **). Chlorinated spring water that supplied a kitchen tap in the restaurant had a high coliform count, including**  **E. coli , and the disinfectant re**  **sidual was zero. The chlorinator was found to be depleted of disinf ectant, and cultures**  **of water from the river adjacent to the uncovered reservoir where treated water was stored grew**  **Plesiomonas**
**.** Viruses . **One outbreak in 1995 was documented to have been caused by a virus, specifically a small round structured virus. The outbreak occurred in September at a**  **high school in Wisconsin and affected 148 persons. The school received its drinking water from a community water supply. Contamination of the potable water system**  **likely occurred from backsiphonage of water through hoses submerged in a flooded football field. However, the source of the virus on the field was not determined.**  Chemicals . **Seven outbreaks of chemical poisoning were reported, one of which**  **was an individual case of chlorine poisoning. A person became ill immediately after drinking water obtained from a Florida restaurant’s drive-through window; symptoms**  **included a burning sensation in the throat and vomiting. Water samples obtained on the day of the incident had chlorine levels ranging from 1.2 mg/L to 4.7 mg/L (median:**  **4.5 mg/L). The source of the excessive levels of chlorine in the drinking water at the restaurant remained unclear.** **In the two outbreaks of nitrite poisoning (i.e., in California and New Jersey), defec tive check valves for prevention of backflow allowed chemicals to contaminate**  **drinking water. In California, three persons at a school became ill after consuming water from a system that had a double-check backflow prevention valve that did not**  Vol. 47 / No. SS-5 MMWR 7

# meet the industry standard and that allowed chemicals used to treat a cooling tower **and chilling system for the school’s air conditioning unit to contaminate the drinking water. Furthermore, the valve had rubber gaskets that were badly deteriorated. In New**  **Jersey, the drinking water was contaminated with boiler-conditioning fluids through a faulty backflow check valve. Six persons developed acute onset of cyanosis and were**  **diagnosed with methemoglobinemia caused by nitrites, which are strong oxidizing agents (**  **15 ).** **Elevated copper levels in tap water in Wisconsin were associated with gastrointes tinal illness in at least 37 persons in private homes. The homes in this community had** **recently been built or remodeled, and new copper plumbing was thought to have con tributed to the contamination of the water.**  **Thirty persons in Florida developed chemical burns in their mouths after they drank water contaminated with sodium hydroxide. The water became contaminated when**  **an operator at the treatment plant unintentionally released the chemical into the**  **water. The pH of the finished water was 11.8. Thirteen persons at a health-care facility in Iowa developed burning in their mouths**  **and flu-like symptoms after drinking water contaminated with a concentrated liquid soap. A valve on the water supply hose to the soap dispenser had been left open,** **allowing the soap to enter the water system. Vacuum breakers to prevent backsiphon age were installed incorrectly at the soap dispensers.**  Unidentified Etiologic Agent . **The etiologic agent was not identified for**  **eight (36.4%) of the 22 WBDOs associated with drinking water. The**  **illnesses associated with at least four of these outbreaks had incubation periods, durations, and symptom com**  **plexes that were consistent with viral syndromes. For five of the six outbreaks for which testing was done, including three of the four outbreaks of suspected viral etiol**  **ogy, stool specimens were negative for bacterial and parasitic p athogens. One of**  **these outbreaks (i.e., an outbreak in 1995 at the restaurant of a resort in Wisconsin) might have been caused by a rotavirus. The state laboratory reported identifying rota**  **virus in two of six stool specimens with an enzyme immunoassay. Eight stool**  **specimens were tested for enteric pathogens and three for enteroviruses. However, no testing was done for Norwalk-like caliciviruses, which have been more commonly as**  **sociated with WBDOs than the rotaviruses have been. Of the eight outbreaks for which the etiologic agent was not identified, three out**  **breaks were associated with untreated well water, three with inadequate chlorination of unfiltered well water, and one with possible short-term cross-connection and back**  **siphonage pr oblems in the distribution system. The other outbreak was associated**  **with water from an outside tap at a waste-water treatment plant that was not marked as nonpotable. Even though the water was not intended**  **for drinking, this outbreak was categorized as a drinking water outbreak for lack of a better category. Hikers ac**  **cessed the tap by entering fenced property. The water from the tap was waste water that had been through a sedimentation process twice but still required additional**  **treatment; the water had levels of fecal coliforms >23 most probable number/100 mL.** 

# Water-Quality Data **Water-quality data (e.g., information about the presence of coliform bacteria,**  **pathogens, or chemical contaminants) were available for 20 (90.9%) of the 22 out**  8 MMWR December 11, 1998

# breaks. The relevant chemical was found in water samples for all seven outbreaks of**chemical poisoning. Water samples were tested for coliform bacteria during the inves tigation of 13 (86.7%) of the 15 outbreaks that had a known or suspected infectious**  **etiology and were positive for total coliforms or fecal coliforms for 11 (84.6%) of the 13 outbreaks. No information about the presence of coliforms was available for an**  **outbreak of giardiasis and an outbreak of shigellosis. Coli forms were detected for** **three (75.0%) of the four bacterial outbreaks, and all eight outbreaks of unknown etiol ogy, including one for which water samples collected 1 month after the outbreak (at a**  **campground using untreated well water) did not exceed prescribed limits. Coliforms were not detected for two outbreaks.**  **Giardia -like cysts and**  **Cryptosporidium -like oo**  **cysts were found in a filte red, chlorinated water system during the investigation of an** **outbreak of giardiasis; but no coliforms were detected in water samples from the dis tribution system. During the investigation of an outbreak caused by a small r**  **ound structured virus at a school, no coliforms could be detected 4 days after the water,**  **which was chlorinated and filtered, had been contaminated by backsiphonage.** 

# Water System and Water Source **Ten (45.5%) of the 22 WBDOs were associated with c ommunity systems, 10 (45.5%)**  **with noncommunity sys tems, and two (9.1%) with individual water systems (Tables**  **4 and 5; Figure 2). Only three of the 10 outbreaks associated with c ommunity water**  **systems were caused by problems at water treatment plants, and the other seven were the result of problems in the water distribution systems and plumbing of individ**  **ual facilities (e.g., offices, schools, and restaurants). Six of these seven outbreaks were associated with chemical co**  **ntamination of the dr inking**  **water. Two of the six out breaks were attributed to nitrites that contaminated the water when check valves for**  **prevention of backflow at the facilities malfunctioned; two resulted from copper leach**  **ing into the water after plumbing was installed in new homes; one occurred when concentrated liquid soap contaminated the water because of backsiphonage thr**  **ough an improperly installed vacuum breaker; and in one outbreak, the source of the exces**  **sive levels of chlorine in the drinking water at a restaurant remained unclear. The seventh outbreak was attributed to a small round structured virus that likely contami**  **nated the drinking water when backsiphonage of water through hoses submerged in a flooded football field occurred. During 1995–1996, outbreaks in noncommunity sys**  **tems were more likely than those in community sy stems to be associated with** **untreated water (40.0% versus 0.0%). Eight (80.0%) of the 10 outbreaks in noncommu nity systems were associated with well-water sources, as were three (30.0%) of the**  **10 community outbreaks. Of the 15 outbreaks with a known or suspected infectious etiology, nine (60.0%)**  **were associated with well-water sources, three (20.0%) with surface-water sources, two (13.3%) with spring-water sources, and one (6.7%) with partially treated sewage.**  **For four (44.4%) of the nine well-water systems, the water was untreated. For another four (44.4%), the identified deficiency was inadequate chlorination or interrupted dis**  **infection (e.g., coliforms, which are chlorine sensitive, were present in tap water). For one (11.1%), the deficiency occurred in the distribution system. The identified defi**  **ciency for both outbreaks associated with spr ing-water sys**  **tems was inadequate or interrupted chlorination. For the outbreaks associated with surface-water systems, the**  **identified deficiencies were inadequate filtration, backsi phonage, and no tre**  **atment.** Vol. 47 / No. SS-5 MMWR 9

# 1995–1996 Outbreaks Associated with Recreational Water**For the period 1995–1996, 17 states reported 37 outbreaks associated with recrea tional water (Tables 6–8). Twenty-three outbreaks were reported for 1995 and 14 for**  **1996. The states that reported the most outbreaks were Minnesota (six outbreaks) and Texas (five outbreaks). The 37 outbreaks caused illness in an estimated 9,129 per**  **sons. The median outbreak size was 10 persons (range: 1–5,449). All but one of the 22 out**  **breaks of gastroenteritis occurred during the summer (Figure 1). The six cases of primary amebic meningoencephalitis, all of which were fatal, occurred in summer and**  **early fall. Five (55.6%) of the nine outbreaks of dermatitis (i.e., rash or folliculitis), which all were associated with hot tubs and lakes, occurred during the relatively**  **colder months of September through February.** 

# Etiologic Agents**Thirty-three (89.2%) of the 37 recreational water outbreaks were of infectious etiol ogy (Tables 6–8; Figure 3). Seven (31.8%) of the 22 outbreaks of gastroenteritis were**  **caused by parasites, 10 (45.5%) by bacteria, one (4.5%) by a virus, and four (18.2%) were of unknown etiology (Tables 6 and 7).**  Parasites . **Six (27.3%) of the 22 outbreaks of gastroenteritis were caused by Cryp**  **tosporidium and one (4.5%) by**  **Giardia . Six of these seven outbreaks were associated**  **with swimming pools and water parks. In 1995, a large outbreak of cryptosporidiosis at a water park in Georgia caused an estimated 5,449 persons to become ill after a**  **probable fecal accident in the children’s pool. Some stools were positive for both**  **Cryptosporidium and**  **Giardia . Similarly, in 1996, an estimated 3,000 persons acquired**  **cryptosporidiosis after visiting a water park in California. Again, a few of the stool specimens were also positive for**  **Giardia . Park patrons were exposed to untreated**  **water both at the swimming pool and when water from a jet-ski sprayed an audience watching a show.**  **At a swimming pool in Kansas, 24 persons acquired cryptospor idiosis, one of**  **whom was hospitalized ( 16**  **). Inadequate filtration and possible fecal accidents in the pool led to an outbreak of cryptosporidiosis associated with a shallow wading pool in**  **Florida. Twenty-two persons became ill, six of whom were hospitalized. Other condi**  **tions that might have contributed to the outbreak included overcrowding of the area around the pool, an excess**  **ive number of swimmers, loss of water clarity, and an**  **ozonator that was not operational. After heavy rains, runoff that contained cattle feces and that passed from a pasture**  **into a lake led to an outbreak in Indiana in which three persons became ill with cryptosporidiosis. One of the stool specimens was also posi**  **tive for Giardia**  **cysts. Run-off containing livestock feces might have been responsible for an outbreak of cryptosporidiosis in Nebraska, which affected 14 persons.**  **Seventy-seven persons in Florida became ill after visiting a children’s wading pool.**  **Sixty persons had stool specimens that tested positive for Giardia**  **, 17 had specimens that tested positive for**  **Cryptosporidium , and eight had specimens that were positive**  **for both organisms. The wading pool was supplied by municipal well water that was coagulated, settled, filtered, and disinfected with chlorine.**  **In 1995, six cases of primary amebic me ningoencephal**  **itis were attribu ted to**  **Naegleria . All six of the infected children, who ranged in age from 4 to 11 years, died.**  10 MMWR December 11, 1998

# Infection was acquired when the children swam in a shallow lake, river, pond, or canal. **Five of the six cases were associated with exposure in Texas and one in Florida.** Bacteria . **Ten (45.5%) of the 22 outbreaks of gastroenteritis were attributed to bac teria, and eight of these outbreaks were associated with lakes. Six outbreaks (27.3%)**  **were caused by E. coli**  **O157:H7 ( 17**  **), three (13.6%) by Shigella sonnei**  **, and one (4.5%) by**  **Salmonella serotype Java. At a swimming pool in Georgia, 18 persons became ill**  **after ingesting water contaminated with E. coli**  **O157:H7. Inadequate chlorination was thought to have allowed the**  **E. coli to multiply. Thirty-four persons became ill from**  **five outbreaks of E. coli**  **O157:H7 infection associated with lakes. All three outbreaks of shigellosis were associated with lakes. In Colorado, a lake**  **contaminated with human feces caused a total of 120 persons to become ill in two**  **outbreaks. In Pennsylvania, soiled diapers were found near the implic**  **ated lake, and most of the laboratory-confirmed cases were in children aged <10 years who were**  **playing in the sand close to the water.** Other . **In an outbreak in Idaho in 1996, 55 persons became ill from infection with Norwalk virus. Lake water tested positive for coliforms, and the outbreak was pre**  **sumptively attributed to fecal contamination by swimmers. No a gent could be** **identified for four (18.2%) of the 22 outbreaks of gastroenteritis; all four of these out breaks were associated with lakes.**  **An estimated 169 persons were affected in nine outbreaks of dermatitis that were associated with hot tubs or lakes. All nine outbreaks had a known or suspected infec**  **tious etiology (Table 8). Pseudomonas aeruginosa**  **was confirmed as the etiologic**  **agent for four of the seven Pseudomonas**  **outbreaks and was suspected (based on the clinical syndrome) for the other three.**  **Schistosoma sp. was the presumptive etiologic**  **agent of the two outbreaks of swimmer’s itch.** 

# Previously Unreported Outbreaks **Reports of three previously unpublished**  **WBDOs for 1994 also were received (Table 9). In the one outbreak associated with drinking water, well water at a small mobile**  **home park in Florida was inadequately treated, and coliforms were detected in samples of chlorinated water. Two persons were reported to have been**  **ill, one of whom was hospitalized. The other two outbreaks were associated with recreational**  **water and affected an estimated 312 persons. The etiologic agent was not identified for one of these two outbreaks. In the outbreak in Florida, 12 persons had gastroenteri**  **tis, two of whom were hospitalized. This outbreak occurred among children and adults who attended a birthday party at a park and swam in a lake, which was fed by an** **artesian well and had a history of high-quality water. During the investigation, sam ples of lake water had 80–230 total coliforms/mL and up to 40 fecal coliforms/mL. In a**  **New Jersey state park, at least 300 cases of shigello sis were repor**  **ted among bathers in the swimming area of a reservoir where numerous fecal accidents were reported**  **and persons were seen rinsing diapers in lake water.** Vol. 47 / No. SS-5 MMWR 11

# Outbreaks Not Classified as Waterborne-Disease Outbreaks **Outbreaks attributed to drinking water contaminated at its point of use, rather than at the source or in the distribution system, traditionally are not classified as WBDOs.**  **Seven point-of-use outbreaks that affected a total of 200 persons were reported, one from 1991 and six from the 1995–1996 reporting period. Twenty-one persons became**  **ill with symptoms suggestive of viral gastroenteritis after consuming ice at a picnic in New York in 1991. In Florida, consumption of water from an outdoor faucet equipped**  **with a hose by attendees at a day camp resulted in 77 primary cases of c rypto**  **sporidio sis, as well as 24 probable s**  **econdary c ases in household members (**  **18 ).**  **Portable water coolers were filled with water from the hose, which was also used for rinsing garbage cans. Water samples from the o**  **utdoor faucet were positive for total coliforms and**  **Cryptosporidium , whereas samples collected at other sites at the school**  **where the camp was held and at the municipal water plant were negative or below detectable limits for total coliforms,**  **E. coli , and parasites. Feces were observed on**  **several occasions near the faucet and attached hose; thus, the most likely**  **source of contamination of the water was the hose nozzle.**  **Three outbreaks in the 1995–1996 reporting period were thought to be associated with consumption of contaminated ice. At a church festival in Wisconsin, 27 cases of**  **E. coli O157:H7-related gastroenteritis were associated with consumption of ice from**  **plastic water containers that had been filled with water and then frozen. The possible sources of contamination of the ice included a water faucet, which might have been**  **contaminated while preparing ground beef, and the plastic water containers, which might have been contaminated when they were previously used to store the gr**  **ound beef. Bagged ice was linked to infection with**  **Campylobacter sp. in seven persons who**  **became ill at a pri vate home in Ohio. The third ice-related outbreak occurred in New**  **Jersey and affected 39 persons who became ill with symptomatology suggestive of viral gastroenteritis after consuming ice that might have been contaminated with hu**  **man sewage. In Florida, two persons became ill after consuming iced tea that had been left in a water cooler overnight. The acid in the tea caused the metal co**  **ils in the water cooler to corrode and release metals into the tea. Seventeen possible WBDOs** **that occurred during 1995–1996 were not included in this surveillance summary be cause of insufficient epidemiologic data (i.e., the outbreaks did not meet the criteria**  **for Classes I–IV).** 

# DISCUSSION General Considerations About Surveillance Data for Waterborne-Disease Outbreaks**The waterborne-disease surveillance data, which identify the types of water sys tems, their deficiencies,**  **and the respective etiologic agents**  **associated with the** **outbreaks, are useful for evaluating the adequacy of current technologies for provid ing safe drinking and recreational water. However,**  **the data in this surveillance summary are subject to at least one important limitation: they probably do not reflect**  **the true incidence of WBDOs or the relative incidence of outbreaks caused by various etiologic agents. Not all WBDOs are recognized, investigated, and reported to CDC or**  12 MMWR December 11, 1998

# EPA; and the extent to which WBDOs are unrecognized and underreported is un**known. The likelihood**  **that individual cases of illness will be detected, epidemiologically linked, and associated with water varies considerably among locales and is dependent**  **on factors such as public awareness, the likelihood that multiple ill persons consult the same rather than different health-care providers, the interest of health-care providers,**  **availability of laboratory-testing facilities, local requirements for reporting cases of particular diseases, and surveillance and investigative activities and capacities of state**  **and local health and environmental agencies. Therefore, the states that report the most outbreaks might not be those in which the most outbreaks occur. Recognition of** **WBDOs also is dependent on certain outbreak characteristics; outbreaks involving se rious illness are most likely to receive the attention of health authorities. Outbreaks of**  **acute diseases, particularly those characterized by a short incubation period, are more readily identified than those associated with disease from chronic, low-level exposure**  **to an agent (e.g., a chemical). Outbreaks associated with community water systems are more likely to be recognized than those associated with noncommunity systems**  **because the latter serve mostly nonresidential areas and transient populations. Out**  **breaks associated with i ndividual systems are the most likely to be underreported**  **because they generally involve relatively few persons. The identification of the etiologic agent of a WBDO is dependent on the timely rec**  **ognition of the outbreak so that appropriate clinical and environment samples can be obtained. The interests and expertise of investigators and the routine practices of local**  **laboratories can also influence whether the etiologic agent is identified. For example, diarrheal stool specimens generally are examined for bacterial pathogens, but not for**  **viruses. In most laboratories, testing for Cryptosporidium**  **is done only if requested and is not included in routine**  **stool examinations for ova and parasites ( 19**  **). The water-quality data that are collected vary widely among outbreak investigations, de**  **pending on such factors as available fiscal, investigative, and laboratory resources. Furthermore, a few large**  **outbreaks can subs tantially alter the relative proportion of**  **cases of waterborne disease a ttri**  **buted to a particular agent. Finally, the number of**  **reported cases is generally an approximate figure, and the method and accuracy of the approximation vary among outbreaks.**  

# 1995–1996 Outbreaks Associated with Drinking Water**The number of outbreaks reported for 1995 (i.e., 16) is comparable with those re ported for each year during 1987–1994, except for an increase in 1992 (**  **2–4 ; Figures**  **4 and 5). However, the number of outbreaks reported for 1996 (i.e., six) is much lower than for previous years. WBDO reports peaked during 1979–1983. The increase and**  **subsequent decrease in the number of reports might reflect, at least in part, c hanges**  **in surveillance activities ( 20**  **). The decrease in the number of outbreaks reported for**  **1996 might indicate the beginning of a new trend or simply might reflect a reporting artifact.**  **The number of outbreaks attributed to various etiologic agents changed for this**  **reporting period. The number caused by parasites decreased substantially from the previous reporting period (i.e., 1993 and 1994), during which 10 reported outbreaks**  **were caused by parasites (i.e., five outbreaks each attributed to Cryptosporidium**  **and** Vol. 47 / No. SS-5 MMWR 13

# Giardia **). For the 1995–1996 reporting period, only two outbreaks were caused by parasites. However, one of these two outbreaks (i.e., the**  **outbreak of gi ardiasis in New**  **York) affected the largest number of persons of any outbreak for this reporting period. The outbreak of giardiasis in New York was associated with surface water that was**  **filtered and disinfected with chlorine. Al though no**  **interruptions in chlorination at the water plant were identified, postfilter water turbidity readings, which serve as an in**  **dex of the effectiveness of filtration, exceeded the regulated limit before and during the outbreak. The occurrence of the two outbreaks of giardiasis underscores the im**  **portance of requiring water systems to monitor turbidity, to meet turbidity standards, and to provide an adequate chlorine concentration and contact time (as specified by**  **the Surface Water Treatment Rule) to inactivate Giardia**  **and other organisms that are relatively chlorine-resistant, especially if the surface water is unfiltered (**  **21 ).**  **Giardia can be inactivated by disinfection without filtration, but only if stringent conditions are**  **consistently maintained. Providing both filtration and chlorination is an example of using multiple barriers to protect water supplies. The**  **outbreak of giardiasis in New York reportedly was the first attributed to a filtered**  **municipal water system in that state and demonstrates that this organism remains a public health**  **risk e ven in chlori**  **nated and filtered water systems if levels of water turbidity are not consistently maintained (i.e., 95% of water samples must be <0.5 NTUs for conventional and direct**  **filtration). Optimal filtration requires frequent, if not continuous, moni**  **toring of the tur bidity of the water both be**  **fore filtration (i.e., after coagulation, flocculation, and/or settling) and after filtration.**  **No outbreaks in 1995 or 1996 were attributed to Cryptosporidium**  **, a protozoan parasite that is >50-fold more chlorine-resist**  **ant than Giardia**  **. More st ringent EPA**  **standards for acceptable turbidity values have become eff ective in all states since the**  **outbreak of cryptosporidiosis in Milwaukee in 1993 ( 22** **). Many of the large water utili ties have joined the Partnership For Safe Water (**  **23 ), which is an American Water**  **Works Association/EPA activity that helps treatment plants consistently achieve low water turbidity values, reducing the risk for outbreaks of cryptosporidiosis and giardi**  **asis. The number of small utilities that have joined the partnership is unknown. During 1995–1996, all four outbreaks caused by bacteria were associated with non**  **community systems that used either untreated well water or inadequately chlorinated surface or groundwater. Adequate, continuous disinfection of surface water and**  **groundwater used for drinking water should reduce the occurrence of WBDOs, par**  **ticularly for small systems in which intermittent contamination of wells and springs is difficult to detect or prevent. In addition, wells and springs should be protected from**  **sources of contamination such as surface run-off, septic-tank drainage, and sewage discha**  **rges. Unlike in recent years (i.e., 1991–1994), for which no viral outbreaks were reported,** **one reported outbreak in 1995 was documented to have been caused by a virus. Re searchers used electron microscopy to identify a small round structured virus in stool**  **specimens. Methods for detection of enteric viruses have improved greatly in recent years (**  **24 ). State health departments should be encouraged to submit clinical speci**  **mens for viral testing, either at CDC or at state laboratories that have developed the capab**  **ility to conduct these tests. Seven outbreaks of chemical poisoning were reported to CDC for 1995–1996, which**  **is similar to the number of outbreaks for**  **the 1993–1994 reporting period (i.e., eight).** 14 MMWR December 11, 1998

# The two outbreaks associated with nitrite poisoning highlight the importance of pre**venting backflow when potable water systems are connected to boilers and water chillers; approved backflow prevention devices should be used and periodically moni**  **tored for effectiveness. EPA has established a maximum contaminant level of 1.0 mg/L for nitrites; this regulation applies to**  **public water systems but not individual water systems. The two outbreaks of copper poisoning underscore the fact that corrosive** **water can cause leaching of metals from household plumbing and the water distribu tion system. EPA requires moni**  **toring for copper (and lead) at the tap rather than at the treatment plant, and EPA’s action level for copper is 1.3 mg/mL (**  **25 ). The results of this**  **monitoring are used to determine whether treatment to control corrosion is needed or is being applied properly. Occupants of new and older homes in communities that**  **could have corrosive water might be able to reduce**  **their risk for chemical poisoning by adequately flushing water t**  **hrough the household system before drinking the water, especially if the water has stood overnight.**  **Several reasons could help explain why waterborne chemical poisonings are rarely reported to CDC: a) most poisonings of this nature (e.g., those associated with the** **leaching of copper from plumbing systems) probably occur in private residences, af fect relatively few persons and thus, might not come to the attention of public health**  **officials; b) exposure to chemicals via drinking water can cause illness that is difficult to attribute to chemical intoxication, or it can cause**  **nons pecific symptoms that are**  **difficult to link to a specific chemical; and c) the mechanisms for detecting waterborne chemical poisonings and reporting them to the WBDO surveillance system are not as**  **well established as they are for WBDOs caused by infectious agents. Future efforts should be tailored to improve the sensitivity of surveillance activities, the detection of**  **associations b etween environmental releases or exposure incidents and individual** **health events, and the assessment of the public health burden associated with water related chemical exposures.**  **As in previous reporting periods, except for 1993–1994, a large proportion (8 or 36.4%) of the WBDOs were of unknown etiology (Figures 3 and 4). Seven (87.5%) of**  **these eight outbreaks were associated with groundwater sources, three (42.9%) of which involved untreated water; three (42.9%) a treatment deficiency; and one (14.3%)**  **a deficiency in the distribution system. Of the eight outbreaks for which the etiologic agent was not identified, most (i.e., seven) were associated with noncommunity or** **individual systems; this finding reflects the difficulty of investigating outbreaks affect ing the transient**  **populations that use water from these systems.** **The relative proportion of outbreaks associated with various types of water sys tems has remained fairly constant; the proportions associated with c**  **ommunity water**  **systems were 42.3% for 1989–1990, 23.5% for 1991–1992, 46.7% for 1993–1994, and 45.5% for 1995–1996. However, the proportion of reported outbreaks associated with**  **community water systems that were attributed to problems at water treatment plants, and thus affected entire communities, has steadily declined since 1989 (i.e., 72.7% for**  **1989–1990, 62.5% for 1991–1992, 57.1% for 1993–1994, and 30.0% for 1995–1996). This decrease might reflect improvements in water-treatment practices and in operation of**  **plants. For the 1995–1996 reporting period, most outbreaks (i.e., 7 of 10) associated with community water systems were the result of problems in the distribution system** **at individual facilities (e.g., a restaurant). Four of these seven outbreaks were associ ated with problems with backflow prevention devices (i.e., they had not been installed**  Vol. 47 / No. SS-5 MMWR 15

# or had been inappropriately installed or inadequately maintained). Unfortu **nately, such problems at individual facilities are not amenable to actions taken by treatment** **plants. However, they can be remedied by effective cross-connection control regula tions that require inspection and testing. Monitoring, regulating, and standardizing** **the practices of the multitudinous individual facilities (e.g., offices, schools, and res taurants) in this country is a daunting task.**  

# 1995–1996 Outbreaks Associated with Recreational Water **The most frequently reported W**  **BDOs caused by exposure to recreational water were outbreaks of gastroenteritis. Swimming and other recre**  **ational activities in which the unintentional ingestion of water can occur are k**  **nown to increase the risk for**  **gastrointestinal illness, even in nonoutbr**  **eak settings ( 26,27** **). The number of out breaks of gastroenteritis for 1995–1996 (i.e., 22) was greater than reported for previous**  **years (i.e, 14 each for 1993–1994 and 1991–1992, and 13 for 1989–1990). Although the number of outbreaks caused by parasites decreased from 10 for the**  **previous reporting period to seven for this period, two of these seven, both attributed to**  **Cryptosporidium , accounted for a total of >8,000 ill persons. Each of these recrea**  **tional water outbreaks affected more persons than the total number of persons**  **affected by all of the reported drinking water outbreaks. In both of these outbreaks, which occurred at water parks, some stool specimens also tested positive for**  **Giardia**
**. The setting for six of the seven outbreaks was a swimming pool or water park. Inves**  **tigators for only one of the six outbreaks re ported that the pool water was**  **inadequately chlorinated. All six outbreaks of cryptosporidiosis reported for 1995–1996 were associated with**  **recreational water (i.e., none with drinking water), five of which were associated with swimming pools.**  **Cryptosporidium , and to a lesser extent**  **Giardia , is resistant to disin**  **fection by chlorine at levels generally used in swimming pools. Because Cryptosporidium**  **oocysts measure only 4–6** μ **m in diameter, pool filtration systems that use sand or other granular materials (without the special chemical pretreatment** **that is commonly used by the drinking water industry) might not be effective in re moving oocysts. Infection can occur after swallowing as few as 10–100 oocysts**  **( 28,29**  **). Therefore, presumably swallowing a single mouthful of contaminated water could cause illness.** **Prevention of recreational water-associated cryptosporidiosis is particularly diffi cult. Effective prevention measures require efforts to improve filtration methods and**  **the design of pools and to educate patrons. The prolonged time required to filter all the water in a pool, problems in the design of pools that result in areas with poor**  **water circulation (i.e., “dead spots”), and mixing of water from different pools during filtration increase the risk for cryp**  **tosporidiosis ( 30**  **). Improving filtration systems, having specific pools designated**  **for children to reduce the risk of contaminating the entire facility, and having separate filtration systems for the pools for children and** **those for adults might reduce risk. However, such changes can be costly, and the de gree to which they reduce risk is unk**  **nown. Behavi oral changes, such as showering**  **before entering the pool and restricting access of diaper- aged**  **children to certain pools, will r**  **equire education of both patrons and facility management because few facilities currently enforce such measures. Development and enforcement of clear and**  16 MMWR December 11, 1998

# effective policies regarding fecal accidents in recreational water facilities is needed, **but the effectiveness of various approaches in this regard is unclear and should be tested. Questions that still need to be addressed include a) how long should a pool be**  **vacated after a fecal accident? b) is it beneficial to drain a pool after a fecal acc**  **ident? and c) what hyperchlorination strategy should be used, especially for**  **Crypto sporidium**  **? The number of reported outbreaks caused by bacteria increased from four during** **1993–1994 to 10 during this reporting period. The same number of outbreaks of swim ming-associated shigellosis (i.e., three) were reported during 1993–1994 and this**  **reporting period. The probable source of the pathogen for the three outbreaks during 1995–1996, as for previous outbreaks, was fecal contamination of lake water by swim**  **mers. Five of the six outbreaks caused by E. coli**  **O157:H7 also were associated with lake water, which suggests the need for better monitoring of water quality and identi**  **fication of sources of contamination; the sixth was associated with water from a**  **swimming pool that was inadequately chlorinated. E. coli**  **O157:H7, like Shigella**  **spp., apparently has a low infectious dose (**  **31,32 ). Thus, infection can be acquired by swal**  **lowing water with low concentrations of these bacteria. In contrast to the outbreaks caused by parasites, most of which were associated with adequately chlorinated**  **water, all of the outbreaks attributed to bacteria were associated with unchlorinated water (i.e., in lakes) or inadequately chlorinated water (i.e., in pools). The outbreaks**  **associated with swimming pools underline the critical need for maintaining adequate**  **chlorination ( 30**  **). EPA has published criteria**  **for evaluating the quality of both marine and fresh water used for recreation (**  **33,34 ). Microbial monitoring has been recommended for recrea**  **tional areas potentially contaminated by sewage. However, the health risk associated with various levels of fecal coliforms has not been establi**  **shed. Prevention efforts have focused on providing adequate bathroom facilities, including diaper-changing**  **areas, at recreational areas and on limiting the number of swimmers per unit area. An**  **additional important me asure, although difficult to enforce, is to prevent persons (es**  **pecially young, nontoilet -trained children) from entering recreational water if they are**  **either experiencing or convalescing from a diarrheal illness. For the period 1995–1996, most of the reported outbreaks of dermatitis associated** **with hot tubs were directly related to inadequate operation and maintenance proce dures. Outbreaks of**  **Pseudomonas dermatitis associ**  **ated with hot tubs are preventable if water is maintained at a pH of 7.2–7.8 with free, residual chlorine levels**  **in the range of 2.0–5.0 mg/L ( 35**  **). A person’s susceptibility and immersion time, along with the number of bathers per u**  **nit area, also could influence the risk for infec tion (**  **36 ).**  **For the period 1995–1996, all six deaths associated with recreational water were caused by primary amebic meningoencephalitis, a r**  **arely reported disease in the United States. Naegleria infections are generally acquired during the summer months,**  **when the temperature of fresh water is favorable for multiplication of the organism (**  **37,38 ).**  Vol. 47 / No. SS-5 MMWR 17

# CONCLUSIONS **Information from the nationwide surveillance of WBDOs is used to characterize the epidemiology of waterborne diseases in the United States. Data about the types of**  **water systems and deficiencies associated with outbreaks are needed to evaluate the adequacy of current regulations for water treatment and monitoring of water quality.**  **The identification of the etiologic agents of outbreaks is particularly critical because agents newly associated with WBDOs could require new methods of control. Trends**  **in the incidence of WBDOs caused by various etiologic agents can lead to changes in policies or resource allotment.** **For agents that are recognized as important waterborne pathogens, rapid recogni tion and control of WBDOs are facilitated by surveillance at the local and state levels.**  **Close communication between local health departments and water utilities is crucial.** **For example, if epidemiologic evidence suggests the possibility of waterborne trans mission, water utilities should be contacted promptly and asked about such factors as**  **recent treatment deficiencies and changes in source-water quality. Similarly, local policies should be developed that specify the thresholds for reporting various water**  **quality data to health departments. Timely water testing and environm ental**  **investigations can facilitate the identification of an outbreak’s etiologic agent and the correctable source(s) of water contamination, as well as establish**  **whether control measures (e.g., boil-water advisories) are indicated.**  **Means of improving the surveillance system for WBDOs should be explored. The review of information that has been gathered through other mechanisms (e.g., issu**  **ances of boil-water advisories and computerized data on water quality) could facilitate the detection of WBDOs. Special epidemiologic studies are**  **needed that supplement the findings of this surveillance system by addressing such issues as the**  **public health importance of newly identified agents of waterborne disease, the effectiveness of pre**  **vention strategies in nonoutbreak s ettings, and the timeliness with which state and**  **local health departments act in response to these pathogens. State health departments can request**  **epidemiologic assistance and laboratory testing from CDC for the investigation of WBDOs. CDC and EPA can be consulted**  **about the engineering and environmental aspects of water treatment and about col**  **lecting large-volume water samples to identify pathogenic viruses and parasites, which require special methods for recovery. Requests for testing for viruses should be**  **addressed to CDC’ s Viral Gastroenteritis Section, Respiratory and Enterovirus Branch, Division of Viral and Rickettsial Diseases at (404) 639-3577. Requests for testing for**  **parasites should be addressed to CDC’s Division of Parasitic Diseases at (770) 488- 7760.** **Additional information is available from EPA’s Safe Drinking Water Hotline (tele phone [800]**  **426-4791; e-mail sdwa@epamail.epa.gov), CDC’s Cryptospor idiosis**  **Information Line of the Parasitic Diseases Information Line (voice telephone system [888] 232-3228, fax [888] 232-3299), and the CDC/National Center for Infectious** **Diseases’ home page on the Internet at <http://www.cdc.gov/ncidod/ncid.htm>. Infor mation about cryptosporidiosis is available at <http://www.cdc.gov/ncidod/dpd/** **list_crp.htm>. WBDOs should be reported to CDC’s Division of Parasitic Diseases (tele phone [770] 488-7760), and reports may be faxed to (770) 488-7761.**  18 MMWR December 11, 1998

# Acknowledgments**The authors thank the state waterborne-disease surveillance coordinators; the state epidemi ologists; the state drinking water administrators; the Office of Ground Water and Drinking Water,**  **U.S. Environmental Protection Agency; the Division of Bacterial and Mycotic Diseases and the Division of Viral and Rickettsial Diseases, NCID, CDC; the Division of Environmental Hazards and**  **Health Effects, NCEH, CDC; and Dennis Juranek, Division of Parasitic Diseases, NCID, CDC, for contributing to the waterborne-disease surveillance summary.**  **References**

**1. Craun GF, ed. Waterborne diseases in the United States. Boca Raton, FL: CRC Press, 1986. 2. Kramer MH, Herwaldt BL, Craun GF, Calderon RL, Juranek DD. Surveillance for waterborne**  **disease outbreaks—United States, 1993–1994. MMWR 1996;45(No. SS-1):1–33. 3. Moore AC, Herwaldt BL, Craun GF, Calderon RL, Highsmith AK, Juranek DD. Surveillance for**  **waterborne disease outbreaks—United States, 1991–1992. MMWR 1993;42(No. SS-5):1–22. 4. Herwaldt BL, Craun GF, Stokes SL, Juranek DD. Waterborne-disease outbreaks, 1989–1990.**  **MMWR 1991;40(No. SS-3):1–21. 5. US Environmental Protection Agency. 40 CFR Part 141. Water programs: national interim pri**  **mary drinking water regulations. Federal Regis ter 1975;40:59566–74.**  **6. Pontius FW, Roberson JA. The current regulatory agenda: an update. Journal of the Am erican**  **Water Works Association 1994;86:54–63. 7. Pontius FW. Implementing the 1996 SDWA amendments. Journal of the American Water Works**  **Association 1997;89:18–36. 8. US Environmental Protection Agency. 40 CFR Parts 141 and 142. Drinking water: national**  **primary drinking water regulations; filtrati on, disinfection; turbidity,**  **Giardia lamblia , viruses,**  **Legionella , and heterotrophic bacteria; final rule. Federal Register 1989;54:27486–541.**  **9. US Environmental Protection Agency. 40 CFR Parts 141 and 142. Drinking water: national primary drinking water regulations; total coliforms (including fecal coliforms and**  **E. coli );**  **final rule. Federal Register 1989;54:27544–68. 10. US Environmental Protection Agency. 40 CFR Parts 141 and 142. Drinking water; national**  **primary drinking water regulations: total coliforms; corrections and technical amendments; final rule. Federal Register 1990;55:25064–5.**  **11. US Environmental Protection Agency. 40 CFR Parts 141 and 142. National pr imary drinking**  **water regulations: enhanced surface water treatment requirements; proposed rule. Federal Register 1994;59:38832–58.** **12. US Environmental Protection Agency. 40 CFR Part 141. National primary drinking water regu lations: monitoring requirements for public drinking water supplies:**  **Cryptosporidium, Giardia, viruses, disinfection byproducts, water treatment plant data and other information require**  **ments; proposed rule. Federal Register 1994;59:6332–444. 13. CDC.**  **Shigella sonnei outbreak associated with contaminated drinking water—Island Park,**  **Idaho, August 1995. MMWR 1996;45:229–31. 14. CDC.**  **Plesiomonas shigelloides and**  **Salmonella serotype Hartford infections associated with**  **a contaminated water supply—Livingston County, New York, 1996. MMWR 1998;47:394–6. 15. CDC. Methemoglobinemia attributable to nitrite contamination of potable water through boiler**  **fluid additives—New Jersey, 1992 and 1996. MMWR 1997;46:202–4. 16. Wilberschied L. A swimming-pool-associated outbreak of cryptosporidiosis. Kansas Medicine**  **1995;96:67–8. 17. CDC. Lake-associated outbreak of**  **Escherichia coli O157:H7—Illinois, 1995. MMWR**  **1996;45:437–9. 18. CDC. Outbreak of cryptosporidiosis at a day camp—Florida, July–August 1995. MMWR**  **1996;45:442–4. 19. Boyce TG, Pemberton AG, Addiss DG.**  **Cryptosporidium testing practices among clinical labo**  **ratories in the United States. Pediatr Infect Dis J 1996;15:87–8. 20. Craun GF, ed. Methods for the investigation and prevention of waterborne disease outbreaks.**  **Cincinnati, OH: US Environmental Protection Agency, Health Effects Research Laboratory, 1990; EPA publication no. 600/1-90/005a.**  Vol. 47 / No. SS-5 MMWR 19

# 21. Hoff JC. Inactivation of microbial agents by chemical disinfectants. Cincinnati, OH: US En**vironmental Protection Agency, Drinking Water Research Division, Water Engineering Research Laboratory, 1986; EPA publication no. 600/2-86/067.**  **22. Mac Kenzie WR, Hoxie NJ, Proctor ME, et al. A massive outbreak in Milwaukee of Crypto**  **sporidium infection transmitted thr**  **ough the public water supply. N Engl J Med 1994;331:161–7.**  **23. Renner RC, Hegg BA. Self-assessment guide for surface water treatment plant optimization. Denver: American Water Works Association Research Foundation and American Water Works**  **Association, 1997; catalog no. 90736. 24. Ando T, Monroe SS, Gentsch JR, Jin Q, Lewis DC, Glass RI. Detection and differentiation of** **antigenically distinct small round-structured viruses (Norwalk-like viruses) by reverse tran scription-PCR and southern hybridization. J Clin Microbiol 1995;33:64–71.**  **25. US Environmental Protection Agency. 40 CFR Parts 141 and 142. Drinking water regulations: maximum contaminant level goals and national primary drinking water regulations for lead**  **and copper; final rule. Federal Register 1991;56:26460–4. 26. Calderon RL, Mood EW, Dufour AP. Health effects of swimmers and nonpoint sources of con**  **taminated water. International Journal of Environmental Health Research 1991;1:21–31. 27. Seyfried PL, Tobin RS, Brown NE, Ness PF. A prospective study of swimming-related illness:**  **I. Swimming-associated health risk. Am J Public Health 1985;75:1068–70. 28. DuPont HL, Chappell CL, Sterling CR, Okhuysen PC, Rose JB, Jakubowski W. The infectivity**  **of Cryptosporidium parvum**  **in healthy volunteers. N Engl J Med 1995;332:855–9. 29. Haas CN, Rose JB. Reconciliation of microbial risk models and outbreak epidemiology: the**  **case of the Milwaukee outbreak. In Proceedings of the American Water Works Association 1994 Annual Conference: Water Quality. Denver: American Water Works Association,**  **1994:517–23. 30. CDC. Swimming pools: safety and disease control through proper design and operation. At**  **lanta: US Department of Health and Human Services, Public Health Service, CDC, Center for Environmental Health, 1976; DHHS publication no. (CDC)88-8319.**  **31. DuPont HL, Levine MM, Hornick RB, Formal SB. Inoculum size in shigellosis and implications for expected mode of transmission. J Infect Dis 1989;159:1126–8.**  **32. Griffin PM, Tauxe RV. The epidemiology of infections caused by Escherichia coli**  **O157:H7, other enterohemorrhagic**  **E. coli , and the associated hemolytic uremic syndrome. Epidemiol**  **Rev 1991;13:60–98. 33. Dufour AP. Health effects criteria for fresh recreational waters. Research Triangle Park, NC:**  **US Environmental Protection Agency, Office of Research and Development, Health Effects Research Laboratory, 1984; EPA publication no. 600/1-84-004.**  **34. Cabelli VJ. Health effects criteria for marine recreational waters. Research Triangle Park, NC: US Environmental Protection Agency, Office of Research and Development, Health Effects**  **Research Laboratory, 1983; EPA publication no. 600/1-80-031. 35. CDC. Suggested health and safety guidelines for public spas and hot tubs. Atlanta: US De**  **partment of Health and Human Services, Public Health S ervice, CDC, 1981; DHHS publication**  **no. 99-960. 36. Highsmith AK, McNamara AM. Microbiology of recreational and therapeutic whirlpools. Tox**  **icity Assessment 1988;3:599–611. 37. Visvesvara GS, Stehr-Green JK. Epidemiology of free-living ameba infections. J Protozool**  **1990;37(suppl):25S–33S. 38. John DT, Howard MJ. Seasonal distribution of pathogenic free-living amebae in Oklahoma**  **waters. Parasitol Res 1995;81:193–201.** 20 MMWR December 11, 1998

# Glossary **In this glossary, italicized terms that are not names of microorganisms are defined elsewhere in the glossary.**  Action level: **A specified concentration of a contaminant in water; if this concentration is reached or exceeded, certain actions (e.g., further treatment and monitoring)**  **must be taken to comply with a drinking water regulation.** Boil-water advisory: **A statement to the public advising persons to boil tap water be fore drinking it.**  Class: **Refer to the Classification of Outbreaks section in the text and to Table 1 for a comprehensive definition.**  Coagulation: **The process of adding chemicals to water to destabilize charges on natu**  **rally occurring particles to facilitate their subsequent aggregation and removal by flocculation**  **and/or filtration** 
**.** Coliforms: **All facultative anaerobic, gram -neg**  **ative, no nsporeforming, rod-shaped**  **bacteria that ferment lactose with gas and acid formation within 48 hours at 35 C.** Community water system: **A public water system**  **that serves year-round residents of a community,**  **subdivision, or mobile-home park that has** ≥ **15 service connections or an average of**  ≥ **25 residents.** Contact time: **The length of time water is exposed to a disinfectant (e.g., chlorine con tact time).**  Cross-connection: **Any actual or potential connection between a drinking water sup ply and a possible source of contamination or pollution (e.g., a waste-water line).**  Cyst: **The infectious stage of Giardia lamblia**  **and some other protozoan parasites that has a protective wall, which facilitates survival in water and other environments.**  Disinfection by-products: **Chemicals formed in water through reactions between or ganic matter and disinfectants.**  Distri bution system: **Water pipes, storage reservoirs, tanks, and other means used to deliver drinking water to consumers or to store it before delivery.**  Excystation: **The release of the internal (i.e., encysted) contents (e.g., trophozoites or sporozoites) from**  **cysts or**  **oocysts .**  Fecal coliforms: **Coliforms that grow and produce gas from lactose at 44.5 C in 24 hours.**  Filter backwash: **The water containing the material obtained by reversing the flow of water through a filter to dislodge the particles that have been retained on it.**  Vol. 47 / No. SS-5 MMWR 21

# Filtrat ion: **The process of removing suspended particles from water by passing it through one or more permeable membranes or media of small diameter (e.g.,**  **sand, anthracite, or diatomaceous earth).** Finished water: **The water (i.e., drinking water) delivered to the distribution system**  **after treatment, if any.** Flocculation: **The water treatment process after coagulation**  **that uses gentle stirring to cause suspended particles to form larger, aggregated masses (floc). The aggre**  **gates are removed from the water by a separation process (e.g., sedimentation, flotation, or**  **filtration ).**  Free, residual chlorine level: **The concentration of chlorine in water that is not com bined with other constituents and thus serves as an effective disinfectant.**  Groundwater system: **A system that uses water extracted from the gr ound (i.e., a well**  **or spring).** Heterotrophic microflora: **Microorganisms that use organic material for energy and growth.**  Individual water system: **A small water system, not owned or operated by a water**  **utility , that serves <15 residences or farms that do not have access to a**  **public water system**  **.** Maximum-contaminant level: **The maximum permissible concentration (level) of a contaminant in water supplied to any user of a**  **public water system**
**.** Nephelometric turbidity units: **The units in which the turbidity**  **of a sample of water is measured when the degree to which light is scattered is assessed with a**  **nephelometric turbidimeter.** Noncommunity water system: **A public water system**  **that a) serves an institution, industry, camp, park, hotel, or business that is used by the**  **public for** ≥ **60 days per year; b) has**  ≥ **15 service connections or serves an average of** ≥ **25 persons; and c) is not a**  **community water system.** Oocyst: **The infectious stage of Cryptosporidium parvum**  **and some other coccidian parasites that has a protective wall, which facilitates survival in water and other**  **environments.** Public water system: **A system, classified as either a community**  **or a noncommunity**  **water system , that provides piped water to the public for human consumption and**  **is regulated under the Safe Drinking Water Act.** Raw water: **Surface water or groundwater that has not been treated in any way.** Reverse osmosis: **A filtration process that removes dissolved salts and metallic ions from water by forcing it thr**  **ough a semiper meable membrane. This process is also**  **highly effective in removing microbes from water.** 22 MMWR December 11, 1998

# Siphonage: **A reversal of the normal flow of water or other liquid caused by a nega tive-pressure gradient (e.g., within a water system).**  Source water: **Untreated water (i.e., raw water) used to produce drinking water.** Surface water: **The water in lakes, rivers, reservoirs, and oceans.** Total coliforms: **Nonfecal and fecal coliforms**  **that are detected with a standard test.** Turbidity: **The quality (e.g., of water) of having suspended matter (e.g., clay, silt, or**  **plankton), which results in loss of clarity or transparency.** Untreated water: **Refer to raw water**  **.** Water-quality indicator: **A microbial, chemical, or physical parameter that indicates** **the potential risk for infectious diseases associated with use of the water for drink ing, bathing, or recreational purposes. The best indicator is one whose density or**  **concentration correlates best with health hazards associated with a given type of hazard or pollution.**  Water utility: **A water provider that distributes drinking water to a community thr ough**  **a network of pipes.** Watershed: **An area from which water drains to a single point; in a natural basin, the area contributing flow (i.e., water) to a given place or a given point on a stream.**  Watershed-control program: **A program to protect a watershed from sources of con tamination or pollution.** 

**Chemical poisoning Infectious or suspected infectious etiology**  

# Drinking water (N = 22) Recreational water (N = 37)

**Gastroenteritis Dermatitis**  **Meningo encephalitis** 

**J FMAMJ J A SOND 0**  **2 4**  **6 8**  **10 12** 



# Months in 1995 and 1996**Number of outbreaks**



# J FMAMJ J A SOND **0 2**  **4 6**  **8 10**  **12**



# Months in 1995 and 1996**Number of outbreaks** 

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AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA 

# Unidentified **36.4%**

**Chemical 31.8%**  **Viral 4.5%**  **Parasitic**

**9.1% Bacterial**  **18.2% Community**  **45.5%**

**Individual**

**9.1%**
**Noncommunity 45.5%** 

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA 

# Treatment **deficiency**

**36.4%**
**Distribution system**  **31.8%**

**Untreated groundwater 18.2%**  **Unknown**

**9.1% Untreated** 

**surface water**  **4.5% Well**  **50.0%**

**Other 9.1%** 

**Surface water 31.8%**  

# Etiologic Agent Water System

**Deficiency Water Source** 

**Spring 9.1%**  

# FIGURE 2. Waterborne-disease outbreaks associated with drinking water, by etiologic agent, water system, water source, and deficiency — United States, 1995–1996 (N = 22)

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA 

# Escherichia **coli O157:H7**  **27.3% Cryptosporidium**  **parvum 27.3%** 

**Shigella sonnei 13.6%**  **AGI*** **18.2% Other**  **13.6% Lake**  **59.1%**

**Hot spring**

**4.5%**

**Pool 36.4%**  

# Etiologic Agent **Type of Exposure**

***Acute gastrointestinal illness of unknown etiology.** 

# FIGURE 3. Waterborne-disease outbr eaks of gastroenteritis associated with recreational water, by etiologic agent and type of exposure — United States, 1995–1996 (N = 22) 24 MMWR December 11, 1998AAAA AAAA AAAA 

# AGI* **Chemical Viral**  **Parasitic Bacterial** 

**1971 1974 1977 1980 1983 1986 1989 1992 1995 0**  **10 20**  **30 40**  **50 60** 

AAA A AAA A AAA A AAA A AAA A AAAAAAA AAAA AAAA AAAA AAAAA A A A A AAAA AAA AAAA

AAAA AAAAA A AAAA AAAAA A AAAA AAA A AAA A AAAA AAAA AAAA AAAA AAAAA A A A AAAA AAA AAA AAAA AAAA AAAAAAA A AAA A AAA A AAA A AAA A AAA A AAA
A A A A A A AAA AAAA AAAA AAAA

AAAA AAAA AAAAA A A AAAA A AAAA A
AAAA AAAA AAAA AAAA AAAAA A A AAA A AAA AAAAA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA A


# Year**Number of outbreaks**



# *Acute gastrointestinal illness of unknown etiology. 

# FIGURE 4. Number of waterborne-disease outbreaks associated with drinking water, by year and etiologic agent — United States, 1971–1996 (N = 674)



# 1971 1974 1977 1980 1983 1986 1989 1992 1995 **0 10**  **20 30**  **40 50**  **60**

AAAA AAA A AAA A AAA A AAAA AAAA AAAA AAAAA A A

AAAA AAAA AAAA A AAA A AAA A AAA A AAA A AAA A A A A A AAAA AAAA AAAA AAAA AAAA AAAAA A A A AAAA AAA A AAAA
AAAA A AAA A AAA AAAAA A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A AAA A AAA A AAA A AAA A AAA A AAA AAAA

A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA A AAA A AAA A AAAA A A A AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A
AAAA AAAA AAAA AAA AAAAA A A

AAAA AAAA AAA AAAAA A AAAA AAAA AAAA A AAA AAAAA A A AAAA AAAA AAAA AAAA AAAAA A A A A

AAAA AAAA AAAA AAAA AAAA AAAA
A A A A A A

AAAA AAAA AAAA AAAA AAAAA A

AAAA AAAA


# Year**Number of outbreaks** AAA AAA AAA 

# Individual **Noncommunity Community**  FIGURE 5. Number of waterborne-disease outbreaks associated with drinking water, by year and type of water system — United States, 1971–1996 (N = 674) Vol. 47 / No. SS-5 MMWR 25

# 26 MMWR December 11, 1998TABLE 1. Classification of investigations of waterborne-disease outbreaks — United States*Class†Epidemiologic data Water-quality data**I ADEQUATE §** **a) Data were provided about exposed and unexposed persons; and** **b) the relative risk or odds ratio was**≥**2, or the p-value was <0.05** **PROVIDED AND ADEQUATE Could be historical information or laboratory data (e.g., the** **history that a chlorinator malfunctioned or a water main broke, no detectable free-chlorine residual, or the presence** **of coliforms in the water) II ADEQUATE NOT PROVIDED OR INADEQUATE** **(e.g., stating that a lake was crowded) III PROVIDED, BUT LIMITED** **a) Epidemiologic data were provided that did not meet the criteria for Class I; or b) the claim was made that ill persons** **had no exposures in common besides water, but no data were provided.** **PROVIDED AND ADEQUATE IV PROVIDED, BUT LIMITED NOT PROVIDED OR INADEQUATE** ***Outbreaks of** 

# Pseudomonas**dermatitis and single cases of pr imary amebic m** **eningoencephalitis or of illness resulting from chemical poisoning are not classified according to this scheme.** **† The classification is based on the epidemiologic and water-quality data that were provided on the form.** **§ Adequate data were provided to implicate water as the source of the** **outbreak.**

# Vol. 47 / No. SS-5 MMWR 27TABLE 2. Waterborne-disease outbreaks associated with drinking water — United States, 1995 (N = 16)*State Month Class†Etiologic agentNo.casesTypeof system§Deficiency¶Source Setting**Alaska Aug II**  

# Giardia lamblia**10 Ind** **1 Surface**  Rural area****Florida Apr —** **†† Chlorine** **1 Com** **5 Lake Restaurant** **Florida May III** **Sodium hydroxide 30** **Com 3** **River Water utility Idaho Aug** **I** 

# Shigella sonnei**83 NCom** **2 Well Resort** **Idaho Sept I** **AGI §§** **18 Com** **3 Well Community** **Iowa Oct III Concentrated liquid soap 13 Com** **4 Lake** **Health-care facility** **Minnesota Jul I**  

# Escherichia coli**O157:H7 33** **NCom 3** **Spring Camp Montana Aug** **II AGI** **450 NCom** **2 Well Campground** **New York Dec I**  

# G. lamblia**1,449 Com** **3 Lake Water utility** **Oklahoma Oct II**  

# S. sonnei**10 NCom** **3 Well Store** **Pennsylvania Aug I** **AGI 19** **NCom 2** **Well Inn South Dakota Jun** **I AGI** **48 NCom** **2 Well Camp** **Wisconsin Aug III** **AGI ¶¶** **26 NCom** **3 Well Restaurant** **Wisconsin Sept I** **Small round structured virus 148** **Com 4** **Lake School Wisconsin Sept** **I Copper** **22 Com** **4 Well Private home** **Wisconsin Oct I Copper 15 Com 4 Well Private home*****Refer to the Methods section for a description of the reporting variables. †** **Refer to Table 1 for information concerning the classification of outbreaks. §** **Com = community; NCom = noncommunity; Ind = individual; refer to the Methods section for definitions of the types of water systems. ¶** **Refer to the Methods section for the classification of water-system deficiencies.******Surface water from an unknown source. ††** **Not applicable; see Table 1. §§** **AGI = acute gastrointestinal illness of unknown etiology. ¶¶** **See text about the possibility that this outbreak was caused by a rotavirus.**

# 28 MMWR December 11, 1998TABLE 3. Waterborne-disease outbreaks associated with drinking water — United States, 1996 (N = 6)*State Month Class†Etiologic agentNo.casesTypeof system§Deficiency¶Source Setting**California Apr III** **Nitrite 3** **Com 4** **River School California Sept I AGI**  8 Ind 5 Outside tap Waste-water plant****Idaho Jul III AGI 94 NCom 3 Well Camp New Jersey Mar I Nitrite 6 Com 4 Mixed** **†† Office** **New York Jun** **I** 

# Plesiomonas **shigelloides 60 NCom 3 Spring Restaurant** **Wisconsin Jun III AGI 21 NCom 4 Well Restaurant*****Refer to the Methods section for a description of the reporting variables. †** **Refer to Table 1 for information concerning the classification of outbreaks. §** **Com = community; NCom = noncommunity; Ind = individual; refer to the Methods section for definitions of the types of water systems. ¶** **Refer to the Methods section for the classification of water-system deficiencies.******AGI = acute gastrointestinal illness of unknown etiology. ††** **The source was both surface water and groundwater.**

# Vol. 47 / No. SS-5 MMWR 29TABLE 4. Waterborne-disease outbreaks associated with drinking water, by etiologic agent and type of water system —United States, 1995–1996 (N = 22)*Etiologic agentType of water system†TotalCommunityNoncommunityIndividualOutbreaks Cases Outbreaks Cases Outbreaks Cases Outbreaks Cases**AGI §** **1 18 6 658 1 8 8 684**



# Giardia lamblia**1 1,449 0 0 1 10 2 1,459**



# Shigella sonnei**0 0 2 93 0 0 2 93 Copper** **2 37 0 0 0 0 2 37 Nitrite** **2 9 0 0 0 0 2 9 Small round structured** **virus 1 148 0 0 0 0 1 148**  

# Plesiomonas shigelloides**0 0 1 60 0 0 1 60**



# Escherichia coli**O157:H7 0 0 1 33 0 0 1 33** **Sodium hydroxide 1 30 0 0 0 0 1 30** **Concentrated liquid soap 1 13 0 0 0 0 1 13** **Chlorine 1 1 0 0 0 0 1 1** Total 10 1,705 10 844 2 18 22 2,567**(Percentage ¶** **) (45.5) (66.4) (45.5) (32.9) (9.1) (0.7) (100.0) (100.0)** ***Ordered by total number of outbreaks and secondarily by total number of cases. †** **Refer to the Methods section for definitions of the types of water systems. §** **AGI = acute gastrointestinal illness of unknown etiology. ¶** **The percentage is based on 22 outbreaks or 2,567 cases.**

# 30 MMWR December 11, 1998TABLE 5. Waterborne-disease outbreaks associated with drinking water, by type of deficiency and type of water system —United States, 1995–1996 (N = 22)Type of deficiency†Type of water system*TotalCommunityNoncommunityIndividualNo. (%) No. (%) No. (%) No. (%)**Untreated surface water 0 ( 0) 0 ( 0) 1 ( 50.0) 1 ( 4.5)** **Untreated groundwater 0 ( 0) 4 ( 40.0) 0 ( 0) 4 ( 18.2)** **Inadequate treatment 3 ( 30.0) 5 ( 50.0) 0 ( 0) 8 ( 36.4)** **Distr ibution system** **6 ( 60.0) 1 ( 10.0) 0 ( 0) 7 ( 31.8) Miscellaneous or unknown** **1 ( 10.0) 0 ( 0) 1 ( 50.0) 2 ( 9.1)**Total 10 (100.0) 10 (100.0) 2 (100.0) 22 (100.0)***Refer to the Methods section for definitions of the types of water systems. †** **Refer to the Methods section for the classification of water-system deficiencies.**

# Vol. 47 / No. SS-5 MMWR 31TABLE 6. Waterborne-disease outbreaks of gastroenteritis and meningoencephalitis associated with recreational water —United States, 1995 (N = 17)State Month Class* Etiologic agent Illness No. cases Source Setting**Florida Aug —** **†** 

# Naegleria fowleri**Meningoencephalitis 1 Canal** **Canal Georgia Jul** **I** 

# Cryptosporidium parvum**Gastroenteritis 5,449 Pool** **Water park Idaho Mar** **I** 

# Salmonella**serotype Java Gastroenteritis 3 Pool** **Park Illinois Jun** **I** 

# Escherichia coli**O157:H7 Gastroenteritis 12 Lake** **Beach Kansas Jun** **III** 

# C. parvum**Gastroenteritis 24 Pool** **Park Minnesota Jun** **II AGI** **§ Gastroenteritis** **12 Lake Beach** **Minnesota Jul IV**  

# E. coli**O157:H7 Gastroenteritis 6 Lake** **Beach Minnesota Jul** **IV** 

# E. coli**O157:H7 Gastroenteritis 2 Lake** **Beach Nebraska Jul** **IV** 

# C. parvum**Gastroenteritis 14 Pool** **Water park Pennsylvania Aug** **I AGI Gastroenteritis** **17 Lake Park** **Pennsylvania Aug III**  

# Shigella sonnei**Gastroenteritis 70 Lake** **Beach Texas Jul** **—** 

# N. fowleri**Meningoencephalitis 1 River** **River Texas Jul** **—** 

# N. fowleri**Meningoencephalitis 1 Pond** **Pond Texas Aug** **—** 

# N. fowleri**Meningoencephalitis 1 Lake** **Lake Texas Aug** **—** 

# N. fowleri**Meningoencephalitis 1 Lake** **Lake Texas Sept** **—** 

# N. fowleri**Meningoencephalitis 1 Lake** **Lake Wisconsin Jun III**  

# E. coli**O157:H7 Gastroenteritis 8 Lake Beach*****Refer to Table 1 for information concerning the classification of outbreaks. †** **Not applicable; see Table 1. §** **AGI = acute gastrointestinal illness of unknown etiology.**

# 32 MMWR December 11, 1998TABLE 7. Waterborne-disease outbreaks of gastroenteritis associated with recreational water — United States, 1996 (N = 11)State Month Class* Etiologic agent No. cases Source Setting**California Aug II**  

# Cryptosporidium parvum**3,000 Pool Amusement park** **Colorado Jul I**  

# Shigella sonnei**39 Lake Recreation area** **Colorado Jul I**  

# S. sonnei**81 Lake Recreation area** **Florida Jun I**  

# C. parvum**22 Pool Community** **Florida Jun I**  

# Giardia lamblia**† 77** **Pool Community Georgia Jul** **I** 

# Escherichia coli**O157:H7 18** **Pool Mobile-home park Idaho Jun** **II Norwalk** **55 Hot spring Camp** **Indiana Aug IV**  

# C. parvum**3 Lake Beach** **Indiana Aug IV** **AGI §** **4 Lake Beach** **Minnesota Jun IV**  

# E. coli**O157:H7 6** **Lake Beach Oregon Aug IV AGI 32 Lake Camp** ***Refer to Table 1 for information concerning the classification of outbreaks. †** **Sixty persons had stool specimens that tested positive only for** 

# G. lamblia;**17 had specimens that tested positive only for** 

# C. parvum;**and eight had specimens that were positive for both organisms. §** **AGI = acute gastrointestinal illness of unknown etiology.**TABLE 8. Waterborne-disease outbreaks of dermatitis associated with recreational water — United States, 1995–1996 (N = 9)State Year Month Class* Etiologic agent No. cases Source Setting**Maine 1995** **Dec —** **†** 

# Pseudomonas aeruginosa**10 Hot tub Hotel** **Minnesota 1995** **May —**  

# P. aeruginosa**4 Hot tub Hotel** **Minnesota 1995** **Oct —**  

# P. aeruginosa**6 Hot tub Hotel** **New Mexico 1995** **Sept —**  

# P. aeruginosa**4 Hot tub Apartment complex** **Oregon 1996** **Jun III**  

# Schistosoma**sp. 71** **Lake Beach Oregon** **1996 Jul** **III** 

# Schistosoma**sp. 50** **Lake Beach Washington** **1995 Feb** **—** 

# P. aeruginosa**2 Hot tub Resort** **Washington 1995** **May —**  

# P. aeruginosa**5 Hot tub Spa** **Washington 1996 Nov —**  

# P. aeruginosa**17 Hot tub Motel*****Refer to Table 1 for information concerning the classification of outbreaks. †** **Not applicable; see Table 1.**

# Vol. 47 / No. SS-5 MMWR 33TABLE 9. Waterborne-disease outbreaks of gastroenteritis associated with drinking or recreational water that were notincluded in the previous surveillance summaries — United States, 1994 (N = 3)*State Month Year Exposure†Class§Etiologic agentNo.casesType ofsystem¶Deficiency** Source Setting**Florida Jun 1994 Wid III**  

# Escherichia coli**O157:H7 2 NCom 3** **Well Mobile-** **home park Florida** **Jul 1994 Rec I AGI** **†† 12** **— §§** **— §§** **Lake Park New Jersey Jun** **1994 Rec III** 

# Shigella sonnei**300 — — Reservoir Park*****Refer to the Methods section for a description of the reporting variables. †** **Rec = recreational water; Wid = water intended for drinking. §** **Refer to Table 1 for information concerning the classification of outbreaks. ¶** **Ncom = noncommunity; refer to the Methods section for definitions of the types of water systems.******Refer to the Methods section for the classification of water-system deficiencies. ††** **AGI = acute gastrointestinal illness of unknown etiology. §§** **Recreational water outbreaks are not categorized by type of system or water deficiency.**

# Behavioral Risk Factor Surveillance System State & Territorial Coordinators State/Territory Coordinator **Alabama Janice Cook, MBA Alaska Patricia Owen**  **Arizona Brian Bender, MBA Arkansas John Senner, PhD**  **California Bonnie Davis, PhD Colorado Marilyn Leff, MSPH**  **Connecticut Mary Adams, MPH Delaware Fred Breukelman**  **District of Columbia Cynthia Mitchell Florida Scott Hoecherl**  **Georgia Linda Martin, MPH Guam Cynthia Naval**  **Hawaii Alvin T. Onaka, PhD Idaho James Aydelotte**  **Illinois Bruce Steiner, MS Indiana Kathy Horvath**  **Iowa Andrew Wineski Kansas Michael Perry**  **Kentucky Karen Asher Louisiana Ruth Jiles, PhD**  **Maine Dorean Maines Maryland Alyse Weinstein, MA**  **Massachusetts Daniel Brooks, MPH Michigan Harry McGee, MPH**  **Minnesota Nagi Salem, PhD Mississippi Dick Jo**  **hnson Missouri Theophile Murayi, PhD**  **Montana Pete Feigley, PhD Nebraska Martha Metroka**  **Nevada Emil DeJan, MPH New Hampshire Larry Powers, MA**  **New Jersey Georgette Boeselager, MS New Mexico Wayne Honey, MPH**  **New York Thomas A. Melnik, DrPH North Carolina Kristi Passaro, PhD**  **North Dakota Jill Kaske, MPH Ohio Patricia Pullen**  **Oklahoma Neil Hann, MPH Oregon Joyce Grant-Worley, MS**  **Pennsylvania Linda Mann Puerto Rico Yvette Cintron, MPH**  **Rhode Island Jana Hesser, PhD South Carolina**  **Dennis Shepard South Dakota Mark Gildem**  **aster Tennessee David Ridings**  **Texas Ken Condon Utah Rebecca Giles**  **Vermont Cheryl Roe, MS Virgin Islands Julia Sheen**  **Virginia Linda Redman, MPH Washington**  **Katrina Wynkoop-Simmons, PhD West Virginia Fred King**  **Wisconsin Pamela Imm, MS Wyoming Menlo Futa, MA**  34 MMWR December 11, 1998

# Cardiovascular Disease Risk Factors and Preventive Practices Among Adults — United States, 1994: A Behavioral Risk Factor Atlas **Robert A. Hahn, Ph.D., M.P.H. 1**  

# Gregory W. Heath, D.H.Sc., M.P.H. **2** 

# Man-Huei Chang, M.P.H. **1** 

# Behavioral Risk Factor Surveillance System State Coordinators **1** 

# Division of Prevention Research and Analytic Methods **Epidemiology Program Office 2**  

# Division of Adult and Community Health **National Center for Chronic Disease Prevention and Health Promotion** Abstract Problem/Conditions: **Cardiovascular disease (CVD), including coronary heart disease (CHD) and stroke, is the leading cause of death in the United States, and state r**  **ates of CVD vary by state and by region of the country. Several behavioral risk factors (i.e.,**  **overweight, phys ical inactivity, smoking, hypertension, and diabetes mellitus) and**  **preventive practices (i.e., weight loss and smoking cessation) are associated with the development of CVD and also vary geographically. This summary displays and ana**  **lyzes geographic variation in the prevalences of selected CVD risk factors.** Reporting Period: **1994 (1992 for prevalence of hypertension).** Description of System: **The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random-digit–dialing telephone survey of noninstitutionalized adults**  **aged** ≥ **18 years; 50 states and the District of Columbia participated in BRFSS in 1994, and 48 states and the District of Columbia participated in 1992.**  Methods: **Several different analyses were conducted: a) analysis of state risk factor and preventive practice prevalences by sex and race (i.e., black and white); b) map**  **ping; c) cluster analysis; d) correlations of state prevalence rates by sex and race; and e) regression of state risk factor prevalences on state CHD and stroke mortality rates.**  Results: **Mapping the prevalence of selected CVD risk factors and preventive health practices indicates sub**  **stantial geographic variation for black and white men and**  **women, as confirmed by cluster analysis. Data for blacks are limited by small sample size, especially in western states. Geographic clustering is found for physical inactiv**  **ity, smoking, and risk factor combi**  **nations. Risk factor prevalences are generally lower in the West and higher in the East. White men and white women are more similar in**  **state risk factor rates than other race-sex pairs; white w**  **omen and black w**  **omen ranked second in similarity. State prevalences of physical inactivity and hypertension**  **are strongly associated with state mortality rates of CVD.** Interpretation: **Geographic patterns of risk factor prevalence suggest the presence (or absence) of sociocultural environments that promote (or inhibit) the given**  **risk factor or preventive behavior. Because the risk factors examined in this summary are associ**  **ated with CVD, further exploration of the reasons underlying observed geographic patterns might be useful. The BRFSS will continue to provide geographic data about**  Vol. 47 / No. SS-5 MMWR 35

# cardiovascular health behaviors with a possible emphasis on more data-based small**area analyses and mapping. This will permit states to more adequately monitor trends that affect the burden of CVD in their regions and the United States. Mapping also**  **facilitates the exploration of patterns of morbidity, health-care use, and mortality, as well as the epidemiology of**  **risk fac tors. Finally, by identifying those segments of the**  **population with high levels of these risk factors and lower levels of the preventive health practices, public health personnel can better allocate re**  **sources and target in tervention efforts for the prevention of CVD.**  

# INTRODUCTION **Although age-adjusted cardiovascular mortality declined by 58% from 1950 through 1995 (**  **1 ), cardiovascular disease (CVD), including stroke and coronary heart**  **disease (CHD), remains a major public health problem in the United States. In 1995, CVD was the principal diagnosis in 5 million (16.2%) hospital patient discharge records**  **in the United States ( 2**  **) and was the leading cause of death, accounting for 38.7% of all deaths in the United States (**  **1 ).**  **The prevalence, mortality, and health-care use associated with CVD in the United States vary substantially by geographic region and state (**  **2,3,4 ). In 1994, stroke was**  **51% more prevalent in the South than in the Northeast, and CHD was 29% more prevalent in the South than in the West (**  **4 ). In 1994, the ratios of the highest to the**  **lowest age-adjusted state mortality rates for CHD and stroke in the United States were 4.1 and 4.4, respectively (**  **5 ). Health-care use also varies substantially by region. In**  **1995, rates of hospital discharge for CVD were between 40% and 69% times greater in the Northeast, South, and Midwest than in the West (**  **2 ). Geographic variations in CVD**  **prevalen ce, mortality, and health-care use might correspond to differences in a)**  **demographic or risk behavior profiles (e.g., smoking [ 6**  **], physical inactivity [ 7**  **], or risk factor combinations [**  **8 ] among s**  **tate residents); b) physical environment (e.g., exces sive heat [9 ] and air pollution [10 ]); and c) social environ**  **ment (e.g., laws taxing**  **cigarettes or restricting cigarette use) ( 11,12**  **). This atlas displays distribution of major behavioral risk factors and preventive prac**  **tices for CVD among black and white men and women in the 50 states and the District of Columbia. The atlas presents maps for five risk fac**  **tors (i.e., overweight, physical inactivity, smoking, hypertension, and diabetes me**  **llitus), risk factor combinations, and two preventive practices (i.e., weight loss and smoking cessation). Racial differ**  **ences in risk factor prevalence are examined to facilitate exploration of well-recognized racial differences in health status. Only blacks and whites are exam**  **ined in this analysis because of inadequate sample sizes in the data source for other populations. In this summary, we review the association of known behavioral risk fac**  **tors and preventive practices with CVD and specify the criteria used to assess each risk factor analyzed. We then analyze the geographic clustering of CVD risk factors**  **among states and examine the association of state risk factor prevalence rates with state rates**  **of stroke mortality and heart disease mortality. Use of this atlas might facilitate the exploration of geographic patterns of CVD and of the risk factors as well. The atlas**  **might also indicate the need for interventions to reduce cardiovascular risk factors in specific regions and enhance analysis of trends and evaluation of interventions.**  36 MMWR December 11, 1998

# Risk Factors **CHD risk factors analyzed in this report have been chosen for the reasons discussed in the following sections.**  

# Overweight **Overweight is associated with high rates of CVD deaths, especially sudden death among men and congestive**  **heart failure among women ( 13**  **). The high death rate might occur largely as a consequence of the influence of overweight on blood pres**  **sure, blood lipid levels, and the onset of diabetes ( 13**  **); however, a report from the Framingham Study indicates that overweight is also an independent risk factor for**  **CVD ( 14** **). With rare exceptions, overweight develops from eating too much and exer cising too little. The prevalence of overweight has increased substantially in the U.S.**  **population during the last 10 years ( 15**  **).** 

# Physical Inactivity**A review of 43 epidemiologic studies in 1987 indicated that physical activity re duces the risk of CHD (**  **16 ). The relative risk for CHD associated with physical inactivity**  **is approximately 1.9, slightly lower than the relative risks associ**  **ated with increased systolic blood pressure (2.1), cigarette smoking (2.5), and elevated serum cholesterol**  **levels (2.4) ( 17** **). Several studies indicate that endurance exercise training among pa tients with documented CHD is associated with reduced morbidity and mortality**  **( 18,19** **) and that physical activity might improve the likelihood of survival from a myo cardial infarction (i.e., heart attack) (**  **20 ). In addition, evidence documents an**  **association between regular, moderate-intensity physical activity and the lowering of several other risk factors for CVD, including blood lipid levels, resting blood pressure**  **among persons with borderline hypertension, body composition and overweight, and**  **glucose tolerance and insulin sensitivity ( 21**  **).** 

# Smoking **Evidence indicating cigarette smoking as a risk factor for CVD is substantial ( 12**  **). Overall, smokers have a 70% greater level of CVD risk than nonsmokers; persons who**  **smoke** ≥ **2 packs of cigarettes per day have a two- to threefold greater risk for CVD**  **( 17**  **). The risk for CVD also increases with greater depth of inhalation and with increas ing years of smoking, al**  **though per sons who stop smoking eventually reduce their risk**  **for CVD to a level approaching that of nonsmokers (22 ). Cigarette smoking has been**  **reported to act synergistically with other k nown risk fac**  **tors for CVD ( 23**  **).** 

# Hypertension **High blood pressure is another major risk factor for CVD ( 24**  **). Some evidence documents that blood pressure-related risk**  **for CVD increases continuously from low est to highest values for either systolic or diastolic blood pressure (**  **25 ). Elevated**  **blood pressure is often associated with other well-known risk facto**  **rs, inc luding die**  **tary intake, elevated blood lipid levels, obesity, smoking, diab**  **etes mellitus, and physical inactivity (**  **24 ). The prevalence of hypertension has declined substantially in**  **the last 20 years ( 26**  **).** Vol. 47 / No. SS-5 MMWR 37

# Diabetes Mellitus **The glucose intolerance that acc ompanies diabetes melli**  **tus is a direct effect of overweight and is often associated with hypertriglyceridemia, hypertension, elevated**  **LDL cholesterol, and depressed HDL ( 27**  **). Some evidence documents that diabetes mellitus has a vasculotoxic effect, which is greatest for occlusive peripheral vascular**  **disease; however, CHD and stroke are its most c ommon manifesta**  **tions ( 27**  **). The risk for CVD is three times as high among diabetic women as it is**  **among women without diabetes mellitus. Similarly, the risk for CVD is twice as high among di**  **abetic men as it is among men without diabetes mellitus (**  **27 ).**  

# Risk Factor Combinations **Many CVD risk factors i nteract physiologically in the etiology of CVD (**  **28,29 ). Per**  **sons with risk factor combinations are at an increased risk for CVD. Obesity is an**  **example of a risk factor for CHD that influences other risk factors, including hyper**  **lipidemia, hypertension, and diabetes mellitus. Physical inactivity has been related to obesity, lipid abnormalities, hypertension, and diabe**  **tes mellitus ( 21,30**  **).** 

# Preventive Practices **Several preventices practices have been demonstrated to reduce the risk of CHD. The preventive practices analyzed in this report have been chosen for the reasons**  **discussed in the following sections.**  

# Weight Loss **Weight control is a first step in the control of mild hypertension, hyperlipidemia, and impaired glucose tolerance and might eliminate the necessity of lifel**  **ong drug therapy for these conditions (**  **13 ). Efforts to control weight generally have not been**  **effective ( 31**  **); however, researchers have demonstrated recently the effectiveness of combined programs of behavior modification of diet and exercise (**  **31,32 ). Therapeu**  **tic approaches to weight control that emphasize increased physical activity have other benefits in addition to increasing caloric expenditure (**  **16 ).**  

# Smoking Cessation **Persons who stop smoking, es pecially before age 50 years, live longer than those**  **who continue to smoke. After 15 years of abstinence from smoking, the risk for CVD approaches that of persons who have never smoked.**  **Among per sons with previously**  **diagnosed myocardial infarction or stroke, smoking cessation reduces the risk for re**  **current heart attack and death from stroke by 50% ( 22**  **).** 

# METHODS Sampling **We used the Behavioral Risk Factor Surveillance System (BRFSS) data from 1992 and 1994. Data for 1992 were used to assess the prevalence of hypertension because**  **data on hypertension in many states were not available in 1994. BRFSS data for 1995** 38 MMWR December 11, 1998

# were not used because they did not include information on physical activity. Arkansas **and Wyoming did not participate in BRFSS in 1992; all 50 states and the District of Columbia participated in 1994. Using random-digit–dialing telephone survey tech**  **niques, participating states select a probability sample of their noninstitutionalized adult population (aged**  ≥ **18 years) with telephones. The Waksberg method ( 34** **), a mul tistage cluster-sampling design, was used in most states (37 states in 1994), whereas** **other states have chosen different sampling methods (e.g., simple-random or strati fied sample designs) to meet their special needs.**  **The standard BRFSS questionnaire includes questions from previously conducted national surveys (e.g., Hea**  **lth Promo tion Disease Prevention Supplement to the National Health Interview Survey [**  **35 ]).**  **Modules of questions on additional topics are developed by CDC and added at the discretion of each state (**  **36 ). BRFSS also contains basic demographic and socioeco**  **nomic information (e.g., age, race/ethnicity, family income, and educational attainment).**  Definitions of Risk Factors and Preventive Health Practices 

# • Overweight **: Body mass index (BMI = weight [kg]/height [m 2**  

# ]) ≥ **27.8 for men and**  ≥ **27.3 for women. These values approximate the sex s**  **pecific 85th percentile of BMI that was estimated from NHANES II (**  **1 ) for persons aged 20–29 years in**  **the United States ( Healthy People 2000**  **, objective 2.3 [ 33**  **]).** 

# • Physical Inactivity **: No reported exercise, recreation, or physical activities (other**

**than regular job duties) during the previous month (**  **Healthy People 2000 , objec**  **tive 1.5).** 

# • Smoking **: Current regular use of cigarettes by persons who have ever smoked**  **at least 100 cigarettes ( Healthy People 2000**  **, objective 3.4).** 

# • Hypertension **: Survey participants having ever been told by a health profes sional that they have high blood pressure (**  **Healthy People 2000 , objective 15.5).**  

# • Diabetes Mellitus **: Survey participants having ever been told by a physician that**

**they have diabetes ( Healthy People 2000**  **, objective 17.11).** 

# • Risk Factor Combinations **: Survey participants having** ≥ **2 of the following risk factors: ove**  **rweight, physical inactivity, smoking, and diabetes mellitus. Hyper tension is not included because information on this risk factor was not available**  **in 1994, the year for which risk factor combinations are examined.** 

# • Weight Loss **: Survey participants trying to lose or maintain or keep from gain ing weight and who are either eating fewer calories or eating less fat or using**  **physical activity or exercise to maintain, lose, or keep from gaining**  **weight (**  **Healthy People 2000 , objective 2.7).**  

# • Smoking Cessation **: Survey participants ever having smoked 100 cigarettes and having quit smoking for**  ≥ **12 months ( Healthy People 2000**  **, objective 3.6).** Vol. 47 / No. SS-5 MMWR 39

# Analysis **This summary provides state risk factor prevalences estimated from the survey participant’s probability of selection in each state, weighted by the distribution of the** **state population by age, race, and sex. Prevalence estimates for each risk factor in clude only participants who gave specific r**  **esponses for this risk factor. State risk factor prevalence rates were not reported if the sample size for a given**  **population (e.g., black women) was <50. Data were drawn from the BRFSS electronic source by**  **using Statistical Analyses Software (SAS ®**  

# ) (37 ). **Epi-Info (**  **38 ) and**  **Epi-Map (**  **39 ) were**  **applied to produce maps of the prevalence of risk fac**  **tors and preventive practices in the 50 states and the District of Columbia. We do not present age-standardized state**  **prevalences because we were interested in the burden of risk behavior in states. State prevalences were stratified into quartiles.**  **We examined three questions regarding the geographic distribution of risk factors. First, does a geographic pattern of the distribution of risk factors exist (e.g., higher**  **rates in one region of the country than in another)? In addition to visual review, we used the Ohno method (**  **40 ) in CLUSTER Software (**  **41 ) to detect the presence of clus**  **tering of state prevalence rates of the risk factors and preventive practices for each race and sex group. The Ohno method uses a Chi-square test to determine whether**  **the observed number of bordering states with similar r ates is more than expected to**  **occur by chance under the null hypothesis of no geographic clustering. Similar rates are defined as rates within the same quartile. We omitted from the cluster analysis**  **noncontiguous states (i.e., Alaska and Hawaii) and states with unreliable prevalence rates (i.e., sample sizes <50).**  **Second, does race, sex, or some combination of these account for greater similarity in the distribution of state prevalence rates (e.g., are state prevalence rates for black**  **women closer to those for white women or to those for black men)? For each risk factor and preventive practice and for all risk factors combined, we correlated state**  **prevalence rates using Pearson correlation coefficients for each race-sex pair using the SAS**  **®** 

# software. The analysis excluded states without prevalence estimates. We **compared the magnitude of correlation for each race-sex pair among statistically sig**  **nificant correlations. Third, does the distribution of risk factor prevalences among states correspond to**  **state CVD mortality patterns? For each sex-race group, we conducted linear multiple regression analyses in which 1994 state mortality rates for all ages were the depend**  **ent variables and the ind ependent**  **variables were state behavi**  **oral risk factor prevalences. For heart disease, the International Classification of Diseases, Ninth Re**  **vision, Clinical Modification (ICD-9-CM) codes were 390–8, 402, 404–29; for stroke, the ICD-9-CM codes were 430–8. We used mortality data from CDC’s WONDER data sys**  **tem ( 42**  **) and the Shapiro-Wilks statistic in SAS to assess the normality of mortality rates for regression analyses. Because state**  **risk factor prevale nces were not age-ad**  **justed, we did not age-adjust mortality rates. We began with a model that included all of the risk factors and eliminated the risk factor with the highest p value in each suc**  **ceeding model, until all p values were <0.05 in the final model. State risk factor prevalence rates were excluded from this analysis if the sample size for a given popu**  **lation was <50.** 40 MMWR December 11, 1998

# RESULTS**BRFSS sample sizes used for state prevalence estimates varied by risk factor. Ex cluding states for which race-sex group sample s**  **izes were <50, the study sample included a maximum of 38,089 white men; 53,205 white women; 3,028 black men; and**  **5,989 black women. Because of small sample sizes, 28–29 states**  **among black men and 19–21 states among black women were excluded from analysis for**  **the five study risk factors. The states with missing information among blacks varied by risk factor; in general, preva**  **lence infor mation was not available for many western and northeastern states. All**  **states had sufficient sample sizes to calculate reliable prevalence estimates among**  **whites for all risk factors, except as noted for hypertension, for which information was not available for Wyoming and Arkansas.**  

# Prevalence of Risk Factor and Preventive Measures Overweight **Median state prevalen ces of overw**  **eight are higher for blacks than for whites, higher for white men (28.5%) than for white women (24.**  **9%), and higher for black women (42.6%) than for black men (37.0%) (Table 1). No clear geographic pattern of**  **the prevalence of overweight among white men or black men exists (Figure 1a and c). Among white and black women, the higher prevalence r**  **ates are found in**  **the north eastern quadrant of the nation (Figure 1b and d). No statistically determined clusters**  **for this risk factor exist among whites, for w hom clusters could be assessed.**  

# Physical Inactivity **Median state prevalences of physical inactivity are higher for blacks than for whites and higher for women than for men (Table 1). State prevalences of physical inactivity**  **among white men are generally higher in the East and lower in the West (Figure 2a).** **A similar pattern among white women is evident (Figure 2b) and is confirmed by sig nificant clustering. Available data for blacks suggest that state prevalences of physical**  **inactivity are generally higher in the South and lower in the North (Figures 2c and d); however, patterns are not clear be**  **cause of insufficient data from several states.** 

# Smoking **Median state prevalences of smoking are similar for blacks and whites but higher for men than for**  **women (Table 1). Among white men, state prevale nces of smoking**  **are generally higher in the East and lower in the West, as confirmed by significant clustering (Figure 3a). Among white women, the geographic pattern is similar, but not**  **as clearly defined as for white men (Figure 3b). Among black**  **men, no clear geographic pattern exists (Figure 3c). Among black**  **women, prevalences are generally higher in northern states and lower in southern states (Figure 3d).**  

# Hypertension **Median state prevalences of hypertension are higher for blacks than for whites and higher for women than for men (Table 1). State prevalences of hypertension**  **among white men are generally higher in the northeastern quadrant and lower in the West**  Vol. 47 / No. SS-5 MMWR 41

# (with the exceptions of Nevada and Arizona) (Figure 4a). No clear geographic patterns **exist among white women, black men, or black women (Figure 4b, 4c, and 4d). No** **statistically determined clusters exist for this risk factor among whites, for whom clus ters could be assessed.**  

# Diabetes Mellitus **Median state prevalences of diabetes mellitus are higher for blacks than for whites, slightly higher for white men (4.0%) than for white women (3.7%), and slightly higher** **for black women (6.8%) than for black men (6.1%) (Table 1). State prevalences of dia betes mellitus**  **among white men are generally higher in southern states (Figure 5a). State prevalences of diabetes mellitus**  **among white women are generally higher in a band of states from Connecticut, Rhode Island, and Michigan to Texas, and lower in**  **the West (Figure 5b). No apparent geographic pattern of state prevalences of diabetes mellitus exists among black men or black women (Figure 5c and d). No s**  **tatistically determined clusters for this risk factor exist**  **among whites, for whom clusters could be assessed.**  

# Risk Factor Combinations **Median state prevalences of risk factor combinations are higher for blacks than for whites, higher for white men than for white women, and higher for black women than**  **for black men (Table 1). Among the study sample**  **for 1994, 40.1% have no risk factors, 38.2% have just one, 18.0% have just two, 3.4% have just three, and 0.2% have four**  **risk factors. State prevalences of risk factor combinations among white men and white**  **women are generally higher in the East and lower in the West; geographic patterns for both white men and white women are confirmed by significant clustering (Figure 6a** **and b). No clear geographic pattern of the state prevalences of risk factor combina tions exists among black men and black women (Figure 6c and d).**  

# Weight Loss **Median state prevalences of weight-loss practices are higher for whites than for blacks and higher for women than for men (Table 1). Prevale**  **nce rates are generally higher in the W**  **est for white men and white women (Figure 7a and b). No apparent**  **geographic pattern of state prevalences of weight-loss practices exists among black**  **men and women (Figure 7c and d). No statistically determined clusters for this risk**  **factor exist among whites, for w hom clusters could be assessed.**  

# Smoking Cessation **Median state prevalences of smoking cessation are higher**  **for whites than for**  **blacks and higher for men than for women (Table 1). No apparent geographic pattern of state prevalen**  **ces of smoking ces sation exi**  **sts among white men (Figure 8a).**  **Among white women, state prevalences of smoking cessation are highest in coastal and border states (Figure 8b). No apparent geographic pattern of state prevalences of**  **smoking cessation exists among black men and women (Figure 8c and d). No s tatisti**  **cally determined clusters exist for this preventive practice among whites, for whom**  **clusters could be assessed.** 42 MMWR December 11, 1998

# Associations Among State Risk Factor Prevalences by Race and Sex **Because of the small sample sizes of blacks in many western and northeastern states, comparison of similarity in state prevalences**  **among white men, white women, black men, and black women is possible principally for the eastern st**  **ates. Comparison of correlations of state risk factor profiles**  **among all race-sex pairs indicates that white men and white women are most similar to each other; correlations of prevalence rates**  **for all risk factors among whites are s**  **tatistically significant (p** ≤ **0.05). Among signifi cant correlations, second in similarity**  **among race-sex pairs are white women and black women; this correlation is significant for overweight, physical inactivity,**  **hyper tension, weight loss, and smoking cessation — but not for diabetes mellitus. For**  **diabetes mellitus, correlations are highest for the black men and white men pair. Among race-sex pairs, black men and black women are significantly correlated only**  **for physical inactivity and weight loss; however, these correlations are lower (i.e., fourth and sixth out of six pairs, respectively) than those of most other race-sex pairs.**  

# Associations Between State Risk Factor Prevalences and State CVD Mortality **Regression analysis of state risk factor prevalences on state rates of mortality from CHD and stroke among the four race-sex pairs are limited to states with sufficient**  **sample sizes; thus, regression analyses of risk factors and mortality among blacks are** **restricted to a relatively small number of states. Mortality distributions are not signifi cantly different from normal for CHD in any race-sex group and are significantly**  **different from normal for stroke mortality only among black men. Thus, we do not**  **transform the dependent variable in regression analyses. Analysis indicates that state rates of physical inactivity are predictive of state CHD mortality rates**  **among each of the four race-sex groups. In addition, state rates of diabetes are predictive of state** **CHD mortality rates among white women, and state rates of hypertension are predic tive of state CHD mortality rates**  **among white men. State rates of hypertension are**  **predictive of stroke mortality among white men and women, and state r ates of physi**  **cal inactivity are predictive of state rates of stroke mortality among white women. No** Regression models* of effects of state risk factor prevalences on CHD and stroke among adults — United States, 1994 Outcome CHD Mortality

**White men: CHD = –56.74 + 317.97 * INACTIVE + 1354.73 * HYPERTENSION** **White women: CHD = 56.50 + 476.93 * INACTIVE + 2114.71 * DIABETES** **Black men: CHD = 111.06 + 418.70 * INACTIVE** **Black women: CHD = 41.46 + 424.76 * INACTIVE** Stroke mortality

**White men: Stroke=12.87 + 37.94 * INACTIVE + 126.88 * HYPERTENSION** **White women: Stroke= 12.76 + 290.24 * HYPERTENSION** ***Included models with p < 0.05 for** ≥ **1 risk factors.** Vol. 47 / No. SS-5 MMWR 43

# risk factors are predictive of stroke mortality among black men or black women. State **rates of overweight and smoking are not predictive of mortality from either cause.** 

# DISCUSSION**This atlas of state prevalences of cardiovascular risk factors and preventive prac tices for white and black men and w**  **omen in the United States indicates little**  **geographic patterning for some risk factors and preventive practices (e.g., weight loss and smoking cessation), moderate patterning for others (e.g., diabe**  **tes mellitus and hypertension), and marked patterning for others (e.g., physical inactivity, smoking,**  **and risk factor combinations). The presence (or absence) of geographic patterning among white men and white women is confi**  **rmed by cluster analysis. The atlas indicates an eastern concentration of high state prevalences of several**  **CVD risk factors (i.e., physical inactivity, smoking, and risk factor combinations) and low state prevalences of two preventive practices (i.e., weight loss and smoking ces**  **sation). Thus, risk factor prevalences are lower and preventive practice prevalences are higher among western states. Among blacks, such comparison is not possible be**  **cause of inadequate information on most western states. Correlation of state risk factor prevalences for four race-sex pairs indicates that**  **among six possible comparisons, a) white men and white women are most similar in**  **state risk factor profiles; b) white women and black women are second in similarity; and c) black men and black women are among the least similar. This finding suggests**  **that neither race nor sex is uniformly a predictor of similarity in behavior among racial**  **sex groups. The targeting of prevention messages might require specification of racial sex group combinations. These associations might be**  **confounded (e.g., by age or**  **socioeconomic position). For example, the proportion of persons aged** ≥ **65 years in the West is smaller than proportions in the Midwest, Northeast, and South (**  **43 ), and** **greater age might be associated with greater risk factor prevalence. However, com parison of state prevalence maps**  **unadjusted for age (in this summary) and**  **adjusted for age (not presented in this summary) reveals modified rates for some states with**  **large proportions of younger or older populat**  **ions (e.g., Alaska and Florida) but indi cates little difference in overall national patterns. Regarding socioeconomic position,**  **western states do not systematically exceed the national median of household income (**  **43 ). However, western states do have higher proportions of the population who have**  **completed high school ( 44** **). A plausible explanation of East-West prevalence differ ences is that the East and the West differ in their sociocultural environments related to** **risk factor avoidance, health promotion, and preventive behavior; however, no evi dence supports this**  **hypothesis. Regression analysis indicates that state prevalences of some cardiovascular risk**  **factors, particularly physical inactivity and hypertension, are predictive of mortality**  **from CHD and stroke. A mong whites, risk fac**  **tor maps correspond to detailed maps of health-service areas with high mortality rates for CHD along the Mississippi and Ohio**  **valleys; for blacks, insufficient information on risk factor prevalences in many states prevents comparison (**  **3 ). Except for physical inactivity, little apparent correspon**  **dence exists between high state risk factor prevalences and high stroke mortality as depicted in detailed mortality maps. Although s**  **troke mortality is highest in the South east and southeastern quadrant for all race-sex groups, high risk factor prevalences**  44 MMWR December 11, 1998

# other than physical inactivity are not predominantly concentrated in this region ( **3 ).**  **Findings here differ from conclusions of a recent analysis of BRFSS data for 1991– 1992 (**  **8 ). However, our study included all persons aged**  ≥ **18 years (rather than only those aged 45–74 years), and our analysis was stratified by race as well as sex.**  **The data in this report indicate that, among blacks, higher prevalences of CHD risk**  **factors (except for smoking) and lower prevalences of preventive practices exist. This finding is consistent with higher national rates of CHD morbidity and mortality**  **among blacks. These risk factors can be changed or managed for healthier outcomes.** **In addition to problems of misclassification associated with the ecological compari son of state prevalences and state mortality, between 1993 and 1994, approximately**  **2.7% of the U.S. population moved from one state to another, and national trends existed in interregional migration as well (**  **45 ). Because rates of interstate migration**  **have been similar or higher during the past 20 years, current state risk fa**  **ctor preva lence rates cannot be assumed to reflect the prevalences of risk factors among**  **long-term state residents. Several studies indicate the reliab**  **ility or validity of BRFSS data. One study com pared estimates from BRFSS telephone qu**  **estions with measured physical characteristics for several cardiovascular risk factors (**  **46 ). The validity of self-reported**  **BMI was assessed using a cutoff value between the standards for men and women. Using measured height and weight as standards, researchers reported a sensitivity of**  **77% and a specificity of 99% for self-report of BMI among men and a sensitivity of 72%**  **and a specificity of 99% for self-report of BMI among women. For cigar**  **ette smoking, as validated by lung capacity, researchers reported a sensitivity of 78% and a specific**  **ity of 97% among men and a sensitivity of 86% and a specificity of 96% among women. For diabetes, as validated by fasting serum glucose, researchers reported a**  **sensitivity of 67% and a specificity of 98% among men and a sensitivity of 80% and a specificity of 98% among women. For hypertension, as validated by measurement,**  **researchers reported a sensitivity of 40% and a specificity of 87% among men and a**  **sensitivity of 46% and a specificity of 87% among women. The study population (in** **upper New York State) was 99% white and thus precluded racial comparisons of valid ity. Another study**  **reported higher sensi tivity and specificity for self-reported**  **hypertension among both blacks and whites ( 47**  **). One study compared self-reported cigarette consumption with estimates of cigarette sales from excise taxes and indi**  **cated self-reported consumption to be approximately 72% of true consumption ( 48**  **). Another study indicated that BRFSS accurately reported smoking status, but substan**  **tially underreported obesity ( 49**  **). The prevalence estimates of self-reported health-risk behaviors in this analysis**  **might be underestimated because data were collected thr ough teleph**  **one interviews; previous studies indicate substantial differences in the characteristics of persons who**  **reside in households without a telephone compared with those who res ide in house**  **holds with a telephone ( 50**  **). One problem with the local use of BRFSS, apparent in this summary, is the small**  **sample si zes for blacks in approximately half the states. Available information on**  **blacks in BRFSS does not correspond precisely with state populations of blacks. For example, the 1994 black population of Hawaii was 29,000 (**  **43 ), and BRFSS has preva**  **lence data on overweight among black men in Hawaii; whereas in Ohio, which had a black population of 1,**  **235,000 ( 43**  **), BRFSS sample size for black men was not large** Vol. 47 / No. SS-5 MMWR 45

# enough to permit es **timation of the prevalence of overweight. Achievement of reliable annual s**  **tate information on blacks w ill require**  **oversampling in states with small black populations. This additional information might facilitate understanding risk fa**  **ctors for cardiovascular disease among blacks and the design of appropriate prevention pro**  **grams. The primary intended use of this risk factor atlas is as a reference. It might indicate**  **regions of the nation that are in particular need of risk factor reduction and health promotion programs. The atlas might also serve as a baseline for the analysis of** **trends and the assessment of intervention programs. It might serve to assist in plan ning for future data collection efforts (e.g., demonstrating where alternative methods**  **might be beneficial in collecting information previously unavailable or unanalyzable). Finally, the atlas might serve to generate hypotheses and stimulate the development**  **of risk factor epidemiology, which explores the causes and consequences of risk factor**  **distributions in the population. Understanding the determinants for risk factors might**  **facilitate their control for pub lic health.**  **References**

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# edition). Wash**ington, DC, 1997. 44. Day JC, Curry AE. Educational attainment in the United States: March 1996 (Update). Wash**  **ington, DC: US Department of Commerce, Economics and Statistics Administration, Bureau of the Census, July 1997. (Current population reports no. P20-493).** **45. Hansen KA. Geographical mobility: March 1993 to March 1994. Washington, DC: US Depart ment of Commerce, Economics and Statistics Administration, Bureau of the Census, August**  **1995. (Current population reports no. P20-485). 46. Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular**  **disease risk factors assessed by telephone survey: the Behavior Risk Factor Survey. J Clin Epidemiol 1993;46:561–71.**  **47. Giles WH, Croft JB, Keenan NL, Lane MJ, Wheeler FC. The validity of self-reported hypertension and correlates of hypertension awareness among blacks and whites within the stroke belt.**  **Am J Prev Med 1995;11:163–9. 48. Hatziandreu EJ, Pierce JP, Fiore MC, Grise V, Novotny TE, Davis RM. The reliability of self**  **reported cigarette consumption in the United States. Am J Public Health 1989;79:1020–3. 49. Jackson C, Jatulis DE, Fortmann SP. The Behavioral Risk Factor Survey and the Stanford Five**  **City Project Survey: a comparison of cardiovascular risk behavior estimates. Am J Public Health 1992;82:412–6.** **50. Thornberry OT, Massey JT. Trends in United States telephone coverage across time and sub groups. In Groves RM, Biemer PP, Lyberg LE, Massey JT, Nicholls WL, Waksberg J, eds.**  **Telephone survey methodology. New York: John Wiley & Sons, 1989.** 48 MMWR December 11, 1998

# Vol. 47 / No. SS-5 MMWR 49TABLE 1. State-, race-, and sex-specific prevalence ofselected characteristics — Behavioral Risk Factor SurveillanceSystem, 1994 — ContinuedStateOverweightPhysical inactivitySmokingWhiteBlackWhiteBlackWhiteBlackMen (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%)**Alabama 28.5 26.9 37.9 45.0 42.0 45.7 42.8 58.6 26.1 20.5 22.2 11.3 Alaska 31.0 28.0 — — 20.6 21.7 — — 30.1 24.1 — —** **Arizona 28.7 20.5 — — 24.2 23.3 — — 21.6 25.1 — — Arkansas 32.7 26.0 40.3 37.0 33.2 36.1 32.4 47.0 29.4 24.9 31.9 15.8** **California 27.2 23.1 36.8 37.4 16.2 22.3 26.4 38.9 20.6 16.6 20.0 12.5 Colorado 22.3 16.3 — — 14.6 17.1 — — 24.3 21.6 — —** **Connecticut 30.1 20.4 — — 16.9 24.6 — 40.3 20.8 18.8 — 24.6 Delaware 25.7 25.4 45.5 36.5 31.1 36.5 37.4 57.6 28.4 24.3 21.2 24.9** **District of Columbia** **12.8 10.6 22.2 41.7 42.4 38.9 50.3 56.3 15.4 7.3 18.6 16.5 Florida 25.3 22.2 35.6 36.6 23.7 29.1 34.4 43.0 26.8 22.9 18.2 16.6** **Georgia 23.3 23.9 35.5 40.9 30.5 33.4 25.5 43.2 25.9 22.6 19.4 15.5 Hawaii 21.5 17.6 52.6 33.7 12.8 15.0 20.4 26.4 20.9 20.1 34.1 28.6** **Idaho 29.0 28.8 — — 22.0 21.8 — — 18.7 19.8 — — Illinois 29.9 24.7 37.4 42.6 31.8 33.4 29.0 47.6 25.9 22.9 28.7 24.5** **Indiana 30.1 28.5 39.9 54.5 25.0 32.7 34.6 45.6 27.9 23.1 26.6 20.6 Iowa 30.4 28.1 — — 35.6 31.0 — — 21.8 20.0 — —** **Kansas 28.5 19.1 — — 34.1 33.9 — — 23.9 20.1 — — kentucky 29.9 28.4 — 54.9 45.2 46.9 — 45.2 29.9 27.6 — 21.7** **Louisiana 25.5 25.3 31.4 46.4 30.6 34.7 30.2 39.0 28.9 23.6 35.1 16.3 Maine 29.8 27.5 — — 42.9 38.8 — — 25.1 22.4 — —** **Maryland 27.6 23.8 34.5 41.2 26.7 29.8 30.6 41.7 21.1 20.1 25.4 18.3 Massachusetts 28.6 20.1 — 37.2 22.1 24.6 — 34.9 22.4 19.8 — 19.8** **Michigan 31.9 27.6 37.2 51.6 19.5 24.3 20.2 39.2 24.8 25.9 22.6 20.8 Minnesota 29.6 24.9 — 39.4 21.3 21.9 — 19.2 21.4 21.3 — 25.1** **Mississippi 29.4 24.5 46.1 46.1 35.5 36.7 37.8 46.8 27.9 20.6 26.2 13.1 Missouri 33.0 27.2 — 33.6 27.4 34.4 — 46.2 26.9 21.3 — 30.7** **Montana 27.8 24.9 — — 21.3 20.0 — — 21.8 21.3 — — Nebraska 32.7 25.9 — — 26.7 21.5 — — 19.8 18.0 — —** **Nevada 31.2 23.8 — — 18.6 24.1 — — 32.1 26.8 — — New Hampshire 27.8 23.0 — — 22.9 27.4 — — 24.0 21.2 — —** **New Jersey 28.3 21.3 29.6 49.7 28.3 33.1 20.2 45.9 22.2 24.2 28.8 26.6 New Mexico 23.7 18.4 — — 14.8 21.4 — — 19.5 24.1 — —** **New York 28.5 24.3 32.6 42.5 32.5 36.3 34.7 50.9 22.8 20.6 23.5 19.1**

# 50 MMWR December 11, 1998TABLE 1. State-, race-, and sex-specific prevalence ofselected characteristics — Behavioral Risk Factor SurveillanceSystem, 1994 — ContinuedStateOverweightPhysical inactivitySmokingWhiteBlackWhiteBlackWhiteBlackMen (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%)**North Carolina 32.0 25.9 37.6 48.2 38.8 39.9 49.6 62.9 29.5 25.5 37.9 22.5 North Dakota 30.2 23.8 — — 34.2 29.5 — — 19.9 19.5 — —** **Ohio 30.3 26.8 — 47.3 41.1 35.9 — 41.7 27.6 25.3 — 25.4 Oklahoma 24.8 24.6 — 34.5 27.6 30.9 — 27.9 24.6 21.5 — 30.7** **Oregon 26.4 26.3 — — 20.8 21.0 — — 21.4 20.8 — — Pennsylvania 30.9 27.6 27.3 49.1 22.0 28.8 20.2 39.7 24.8 23.1 24.5 31.6** **Rhode Island 25.8 23.7 — — 22.1 30.4 — — 23.9 21.0 — — South Carolina 25.1 25.1 39.7 49.1 28.4 29.5 31.6 43.3 30.4 21.9 21.8 15.5** **South Dakota 28.1 27.7 — — 29.1 31.9 — — 20.1 19.8 — — Tennessee 26.8 25.4 27.9 46.2 38.6 39.9 37.6 47.9 28.8 26.0 24.1 21.6** **Texas 33.1 24.9 — 51.6 24.6 27.9 — 32.6 23.4 20.7 — 19.4 Utah 21.6 26.3 — — 18.2 22.9 — — 16.7 14.2 — —** **Vermont 26.2 24.8 — — 23.0 23.6 — — 23.3 22.0 — — Virginia 28.3 20.4 46.4 39.0 18.4 23.1 31.4 33.0 27.5 23.6 28.4 22.6** **Washington 26.8 25.6 — — 16.2 19.4 — — 23.9 19.4 — — West Virginia 32.2 31.0 — — 43.2 47.3 — — 27.7 26.8 — —** **Wisconsin 35.7 26.7 26.7 45.0 27.4 23.5 26.3 39.8 24.0 22.3 19.7 31.4 Wyoming 31.0 22.8 — — 21.8 19.8 — — 20.7 21.9 — —** Median 28.5 24.9 37.0 42.6 26.7 29.5 31.5 43.1 24.0 21.6 24.3 21.2Low 12.8 10.6 22.2 33.6 12.8 15.0 20.2 19.2 15.4 7.3 18.2 11.3High 35.7 31.0 52.6 54.9 45.2 47.3 50.3 62.9 32.1 27.6 37.9 31.6

# Vol. 47 / No. SS-5 MMWR 51TABLE 1. State-, race-, and sex-specific prevalence ofselected characteristics — Behavioral Risk Factor SurveillanceSystem, 1994 — ContinuedStateHypertension*Diabetes MellitusMultiple Risk FactorsWhiteBlackWhiteBlackWhiteBlackMen (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%)**Alabama 19.1 18.0 20.7 31.1 3.5 2.7 2.1 6.2 26.6 25.5 28.4 36.2 Alaska 14.8 20.4 — — 3.2 2.8 — — 19.9 17.7 — —** **Arizona 21.1 21.0 — — 4.5 2.8 — — 19.4 15.3 — — Arkansas — — — — 4.1 5.4 6.0 12.5 26.8 24.0 29.7 33.6** **California 19.2 22.5 30.4 35.8 4.0 3.3 6.9 8.6 14.7 13.7 19.6 25.2 Colorado 15.6 19.4 — — 3.5 2.6 — — 13.5 10.7 — —** **Connecticut 21.3 23.9 — 29.5 3.8 5.4 — 6.7 14.5 13.7 — 29.6 Delaware 19.4 20.9 29.5 30.8 5.0 4.4 13.4 8.2 23.6 23.0 35.7 39.4** **District of Columbia** **13.0 15.4 22.1 25.9 2.7 1.9 5.0 5.7 15.6 9.2 23.1 36.6 Florida 19.7 21.6 31.8 32.7 5.0 4.2 7.6 6.4 17.7 18.3 24.6 29.9** **Georgia 15.9 17.4 21.2 27.1 3.1 3.3 6.7 9.6 21.3 20.7 19.5 27.3 Hawaii 14.5 16.4 27.6 25.7 2.7 2.5 0.5 6.6 11.1 9.0 31.4 24.1** **Idaho 14.3 22.9 — — 3.9 4.6 — — 17.5 16.4 — — Illinois 18.5 20.1 24.4 29.3 5.0 4.1 7.9 10.8 25.4 21.2 31.7 39.1** **Indiana 21.0 27.3 24.2 34.1 4.1 4.5 2.0 6.1 21.8 22.0 28.6 37.7 Iowa 20.0 21.2 — — 3.5 4.7 — — 24.8 21.1 — —** **Kansas 18.2 23.9 — — 4.8 3.1 — — 25.4 17.5 — — kentucky 23.5 26.7 — 30.3 3.4 4.8 — 6.3 32.0 30.9 — 35.0** **Louisiana 19.4 19.6 17.9 30.9 5.0 3.2 5.2 9.3 22.4 22.6 24.8 32.4 Maine 20.7 22.8 — — 3.3 3.9 — — 29.1 23.5 — —** **Maryland 17.1 20.1 19.6 30.4 4.5 4.2 6.3 7.1 18.2 18.8 24.2 30.9 Massachusetts 19.3 21.4 — — 3.5 3.5 — 18.2 17.6 15.4 — 32.4** **Michigan 24.0 21.7 28.4 27.1 4.0 5.3 3.5 4.7 18.9 20.7 19.6 32.3 Minnesota 20.7 22.7 — — 3.5 4.0 — 4.5 18.6 16.2 — 17.1** **Mississippi 21.2 24.9 30.7 44.8 5.3 3.2 6.6 10.4 27.6 19.4 36.4 32.7 Missouri 22.0 23.7 — 38.9 5.7 5.1 — 11.6 24.1 21.0 — 37.1** **Montana 19.4 21.4 — — 2.0 2.2 — — 16.2 16.0 — — Nebraska 20.3 20.6 — — 3.9 5.4 — — 19.5 14.8 — —** **Nevada 21.4 21.1 — 30.5 4.9 3.1 — — 19.6 17.7 — — New Hampshire 19.9 19.8 — — 4.3 4.5 — — 18.8 17.8 — —** **New Jersey 24.9 18.8 — 24.9 4.8 3.2 5.9 — 22.5 19.2 18.2 32.8 New Mexico 15.0 14.3 — — 4.9 4.1 — — 13.6 13.5 — —** **New York 20.5 19.2 19.2 38.5 3.4 3.5 7.1 6.8 20.2 20.9 24.4 33.9**

# 52 MMWR December 11, 1998TABLE 1. State-, race-, and sex-specific prevalence ofselected characteristics — Behavioral Risk Factor SurveillanceSystem, 1994 — ContinuedStateHypertension*Diabetes MellitusMultiple Risk FactorsWhiteBlackWhiteBlackWhiteBlackMen (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%)**North Carolina 19.0 17.3 13.0 27.5 4.2 3.4 5.5 6.1 30.5 25.9 39.7 42.9 North Dakota 17.3 21.4 — — 3.9 3.3 — — 22.6 17.4 — —** **Ohio 18.1 22.4 — 32.6 3.8 5.2 — 6.4 28.9 23.2 — 34.9 Oklahoma 20.8 25.3 — — 2.4 2.9 — 3.3 19.5 18.5 — 26.0** **Oregon 18.9 21.0 — — 4.0 3.5 — — 17.6 15.7 — — Pennsylvania 22.8 22.1 23.5 31.0 4.8 4.9 7.5 12.2 18.7 21.3 16.0 39.7** **Rhode Island 23.4 22.1 — — 4.4 4.7 — — 18.4 18.3 — — South Carolina 18.5 24.7 27.1 34.4 6.2 4.3 6.9 8.2 22.3 18.7 27.5 30.5** **South Dakota 15.5 19.3 — — 4.9 3.2 — — 19.6 19.5 — — Tennessee 20.5 24.0 24.7 27.4 4.9 4.9 6.4 11.0 28.7 26.1 21.3 38.1** **Texas 17.3 18.8 24.8 29.1 5.5 4.7 — 1.1 22.4 17.9 — 26.4 Utah 16.9 20.4 — — 4.1 3.6 — — 13.5 13.3 — —** **Vermont 19.6 19.2 — — 3.6 4.2 — — 17.8 18.1 — — Virginia 16.9 19.7 25.2 37.2 2.9 3.7 5.6 12.6 18.8 15.8 32.5 31.4** **Washington 19.2 19.9 — — 4.1 3.6 — — 15.4 14.0 — — West Virginia 22.9 24.5 — — 4.2 6.1 — — 28.2 33.1 — —** **Wisconsin 20.2 19.3 — — 3.7 2.6 4.5 4.1 20.0 18.0 21.7 34.2 Wyoming — — — — 3.1 3.2 — — 17.3 15.1 — —** Median 19.4 21.0 24.6 30.7 4.0 3.7 6.1 6.8 19.6 18.3 24.7 32.8Low 13.0 14.3 13.0 24.9 2.0 1.9 0.5 1.1 11.1 9.0 16.0 17.1High 24.9 27.3 31.8 44.8 6.2 6.1 13.4 18.2 32.0 33.1 39.7 42.9*** Hypertension data for 1992 only.**

# TABLE 1. State-, race-, and sex-specific prevalence of selected characteristics — Behavioral Risk Factor Surveillance System, 1994 — Continued

State Weight Loss Smoking Cessation White Black White Black Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) **Alabama 84.4 92.3 81.7 90.0 45.1 38.8 35.5 36.5**  **Alaska 90.5 94.1 — — 29.6 36.3 — —**  **Arizona 83.8 89.2 — — 44.3 27.6 — —**  **Arkansas 80.8 88.2 83.7 82.0 45.9 37.2 — —**  **California 90.9 93.4 88.5 94.7 49.3 51.9 — —**  **Colorado 89.4 92.7 — — 49.1 41.6 — —**  **Connecticut 80.6 89.3 — — 53.4 53.3 — —**  **Delaware 76.9 84.3 77.5 78.5 45.6 46.4 39.7 34.7**  **District of Columbia 82.0 82.5 67.3 81.2 40.7 58.3 30.6 32.7**  **Florida 84.5 88.7 83.5 89.1 48.7 47.1 38.5 29.9**  **Georgia 81.7 87.4 85.8 88.2 31.7 28.7 31.6 26.1**  **Hawaii 87.4 94.0 90.5 89.9 54.2 45.7 16.4 28.0**  **Idaho 81.5 91.5 — — 31.8 23.1 — —**  **Illinois 81.4 88.9 83.3 82.1 31.4 23.5 22.1 17.9**  **Indiana 87.0 91.5 — 93.4 45.7 38.4 — —**  **Iowa 80.6 90.2 — — 36.3 29.1 — —**  **Kansas 87.0 91.3 — — 46.5 37.9 — —**  **kentucky 80.3 90.0 — 88.4 39.7 31.7 — —**  **Louisiana 76.7 88.1 81.1 83.5 48.1 38.4 27.7 36.2**  **Maine 84.4 91.3 — — 37.8 34.7 — —**  **Maryland 85.2 92.4 85.3 90.2 48.9 45.1 32.2 37.8**  **Massachusetts 91.1 92.7 — — 47.9 48.0 — —**  **Michigan 87.5 91.6 88.5 89.8 48.2 39.4 — 33.4**  **Minnesota 85.0 89.2 — — 51.6 44.5 — —**  **Mississippi 71.2 85.2 68.6 86.7 48.2 39.2 33.2 22.0**  **Missouri 85.3 87.9 — 82.3 47.7 42.4 — —**  **Montana 85.7 88.9 — — 44.5 35.5 — —**  **Nebraska 83.6 90.2 — — 53.1 41.0 — —**  **Nevada 79.5 90.2 — — 41.9 38.2 — —**  **New Hampshire 87.6 92.6 — — 47.8 44.4 — —**  **New Jersey 83.1 88.0 — 83.6 49.1 41.6 — —**  **New Mexico 82.7 88.2 — — 54.9 40.3 — —**  **New York 81.5 87.0 77.0 84.2 47.6 43.4 — 28.0**  **North Carolina 77.2 86.5 68.6 82.0 43.4 37.6 25.2 21.9**  **North Dakota 80.9 89.4 — — 54.4 41.1 — —**  **Ohio 70.8 88.7 — 86.7 33.2 22.9 — —**  **Oklahoma 75.7 82.6 — 80.1 36.4 38.6 — —**  **Oregon 86.0 92.0 — — 56.0 46.7 — —**  **Pennsylvania 87.6 92.9 91.1 90.1 50.2 42.2 41.1 26.2**  **Rhode Island 88.4 90.3 — — 50.4 49.2 — —**  **South Carolina 74.2 81.6 76.5 79.0 13.4 21.2 23.9 14.8**  **South Dakota 83.8 89.9 — — 52.8 37.1 — —**  **Tennessee 82.2 87.6 84.1 83.9 35.5 30.1 26.2 31.1**  **Texas 85.6 90.4 — 75.2 49.4 38.8 — —**  **Utah 88.6 93.0 — — 42.8 32.3 — —**  **Vermont 82.5 89.2 — — 51.6 45.7 — —**  **Virginia 85.1 90.0 87.2 95.3 44.2 38.9 35.8 23.2**  **Washington 87.5 90.9 — — 51.4 50.7 — —**  **West Virginia 79.3 87.9 — — 35.1 23.8 — —**  **Wisconsin 88.7 92.0 — 85.8 50.1 44.5 — 22.6**  **Wyoming 87.3 93.7 — — 33.5 27.2 — —**  Median 83.8 90.0 83.5 85.8 47.6 38.9 31.6 28.0 Low 70.8 81.6 67.3 75.2 13.4 21.2 16.4 14.8 High 91.1 94.1 91.1 95.3 56.0 58.3 41.1 37.8 

# Vol. 47 / No. SS-5 MMWR 53AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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# D.C.
**12.8 to 26.3 26.4 to 28.5**  **28.6 to 30.3 30.4 to 35.7**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA



# *Overweight: Body mass index (BMI = weight[kg]/height[m **2** 

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# Percent FIGURE 1a. Prevalence of overweight* among white men — Behavioral Risk Factor Surveillance System, 1994

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# 10.6 to 23.0

**23.1 to 24.8 24.9 to 26.7**  **26.8 to 31.0 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

***Overweight: Body mass index (BMI = weight[kg]/height[m 2**  

# ]) ≥ **27.8 for men and** ≥ **27.3 for**

**women.** 

# Percent FIGURE 1b. Prevalence of overweight* among white women — Behavioral Risk Factor Surveillance System, 1994 54 MMWR December 11, 1998

# 22.2 to 32.5 **32.6 to 37.1 37.2 to 39.8**  **39.9 to 52.6 Data missing /**  **Sample <50** AAAA AAAA AAAAA A AA AA 

# D.C. AA AA A AA AA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA



# *Overweight: Body mass index (BMI = weight[kg]/height[m **2** 

# ]) ≥ **27.8 for men and** ≥ **27.3 for**

**women.** 

# Percent FIGURE 1c. Prevalence of overweight* among black men — Behavioral Risk Factor Surveillance System, 1994

**33.6 to 37.3 37.4 to 42.5**  **42.6 to 49.0 49.1 to 54.9**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AA AA A AA AA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

A AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAA AA A AAA



# *Overweight: Body mass index (BMI = weight[kg]/height[m **2** 

# ]) ≥ **27.8 for men and** ≥ **27.3 for**

**women.** 

# Percent FIGURE 1d. Prevalence of overweight* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 55

# 12.8 to 21.2 **21.3 to 26.6 26.7 to 33.1**  **33.2 to 45.2 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA

AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A



# *Physical inactivity: No reported exercise, recreation, or physical activities (other than regular

**job duties) during the previous month.** 

# Percent FIGURE 2a. Prevalence of physical inactivity* among white men — Behavioral Risk Factor Surveillance System, 1994

**15.0 to 23.0 23.1 to 29.4**  **29.5 to 34.6 34.7 to 47.3**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAA AA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA A A AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA



# *Physical inactivity: No reported exercise, recreation, or physical activities (other than regular

**job duties) during the previous month.** 

# Percent FIGURE 2b. Prevalence of physical inactivity* among white women — Be havioral Risk Factor Surveillance System, 1994 56 MMWR December 11, 1998

# 20.2 to 26.3 **26.4 to 31.5 31.6 to 37.3**  **37.4 to 50.3 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAA AA AA AA AA 

# D.C. AA AA AA A AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

A A A A A AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA



# *Physical inactivity: No reported exercise, recreation, or physical activities (other than regular

**job duties) during the previous month.** 

# Percent FIGURE 2c. Prevalence of physical inactivity* among black men — B ehavioral Risk Factor Surveillance System, 1994

**19.2 to 39.1 39.2 to 43.1**  **43.2 to 46.9 47.0 to 62.9**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AAAA AAAA AAAA AAAA

A AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

A A A A A AAA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA



# *Physical inactivity: No reported exercise, recreation, or physical activities (other than regular

**job duties) during the previous month.** 

# Percent FIGURE 2d. Prevalence of physical inactivity* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 57

# *Smoking: Current regular use of cigarettes by persons who have **ever smoked at least 100 cigarettes.**  **15.4 to 21.3 21.4 to 23.9**  **24.0 to 27.5 27.6 to 32.1**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A AA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# Percent FIGURE 3a. Prevalence of smoking* among white men — Behavioral Risk Factor Surveillance System, 1994

**7.3 to 20.0 20.1 to 21.8**  **21.9 to 24.0 24.1 to 27.6**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A

AA AA

A AAA AAA AA AA AA AA
AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Smoking: Current regular use of cigarettes by persons who have ever smoked at least 100

**cigarettes.** 

# Percent FIGURE 3b. Prevalence of smoking* among white women — Behavioral Risk Factor Surveillance System, 1994 58 MMWR December 11, 1998

# 18.2 to 21.7 **21.8 to 24.4 24.5 to 28.6**  **28.7 to 37.9 Data missing /**  **Sample <50** AAAA AAAA AAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AA AAA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Smoking: Current regular use of cigarettes by persons who have **ever smoked at least 100**

**cigarettes.** 

# Percent FIGURE 3c. Prevalence of smoking* among black men — Behavioral Risk Factor Surveillance System, 1994

**11.3 to 16.5 16.6 to 21.5**  **21.6 to 25.0 25.1 to 31.6**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAA A A A A 

# D.C.

AA A A AA AA

AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA A A AAAA AAAA AAAA AAAA AAAAAA AA AA AA AA

A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA A AAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA



# *Smoking: Current regular use of cigarettes by persons who have ever smoked at least 100

**cigarettes.** 

# Percent FIGURE 3d. Prevalence of smoking* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 59

# 13.0 to 17.2 **17.3 to 19.3 19.4 to 20.9**  **21.0 to 24.9 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA AA A AA AA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA A AA AAAA AAAA AAAA AAAAAAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Hypertension: Survey participants having ever been told by a health professional that they

**have high blood pressure.** 

# Percent FIGURE 4a. Prevalence of hypertension* among white men — Behavioral Risk Factor Surveillance System, 1992

**14.3 to 19.3 19.4 to 20.9**  **21.0 to 22.7 22.8 to 27.3**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAA A A A 

# D.C. AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A
AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

A AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AA AA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAA AAA AAA AA AA AA

AA AA AA 

# *Hypertension: Survey participants having ever been told by a health professional that they

**have high blood pressure.** 

# Percent FIGURE 4b. Prevalence of hypertension* among white women — Behavioral Risk Factor Surveillance System, 1992 60 MMWR December 11, 1998

# 13.0 to 21.1 **21.2 to 24.6 24.7 to 28.3**  **28.4 to 31.8 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AA AA AA AA AA AA AA

AAA AA A AAA



# *Hypertension: Survey participants having ever been told by a health professional that they

**have high blood pressure.** 

# Percent FIGURE 4c. Prevalence of hypertension* among black men — Behavioral Risk Factor Surveillance System, 1992

**24.9 to 27.4 27.5 to 30.7**  **30.8 to 34.3 34.4 to 44.8**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A AAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
A A A A A A A AA AA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Hypertension: Survey participants having ever been told by a health professional that they

**have high blood pressure.** 

# Percent FIGURE 4d. Prevalence of hypertension* among black women — Behavioral Risk Factor Surveillance System, 1992 Vol. 47 / No. SS-5 MMWR 61

# 2.0 to 3.4 **3.5 to 4.0 4.1 to 4.7**  **4.8 to 6.2 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AA AA AA A AA AA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

A AA AAAA AAAA AAAA AAAAAA AA AA AA AA AAA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A



# *Diabetes mellitus: Survey participants having ever been told by a physician that they have

**diabetes.** 

# Percent FIGURE 5a. Prevalence of diabetes mellitus* among white men — Behavioral Risk Factor Surveillance System, 1994

**1.9 to 3.1 3.2 to 3.8**  **3.9 to 4.6 4.7 to 6.1**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AA AA A AA AA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AA AA AA AAAA AAAA AAAA AAAAAAA AAA AAA AAA

A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

A AAA AAA AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A



# *Diabetes mellitus: Survey participants having ever been told by a physician that they have

**diabetes.** 

# Percent FIGURE 5b. Prevalence of diabetes mellitus* among white women — Behavioral Risk Factor Surveillance System, 1994 62 MMWR December 11, 1998

# 0.5 to 5.1 **5.2 to 6.2 6.3 to 6.8**  **6.9 to 13.4 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A

AA AA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA



# *Diabetes mellitus: Survey participants having ever been told by a physician that they have

**diabetes.** 

# Percent FIGURE 5c. Prevalence of diabetes mellitus* among black men — Behavioral Risk Factor Surveillance System, 1994

**1.1 to 6.0 6.1 to 6.7**  **6.8 to 10.7 10.8 to 18.2**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AA AA

A A A AA AA AA AA AA
AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA

A AAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Diabetes mellitus: Survey participants having ever been told by a physician that they have

**diabetes.** 

# Percent FIGURE 5d. Prevalence of diabetes mellitus* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 63

# 11.1 to 17.6 **17.7 to 19.5 19.6 to 24.0**  **24.1 to 32.0 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

A A A A A A AAA A A AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AA AA AA AA AA AA AA AA AA AA

A AAA AAA AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA



# *Risk factor combinations: Survey participants having ≥ **2 of the following risk factors: over**

**weight, physical inactivity, smoking, and diabetes mellitus.** 

# Percent FIGURE 6a. Prevalence of risk factor combinations* among white men — Behavioral Risk Factor Surveillance System, 1994

**9.0 to 15.7 15.8 to 18.2**  **18.3 to 21.1 21.2 to 33.1**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A AAA AAA AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAA AAA AA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA

AAA AAA AAA AAA AAA AAA AAA AA AA AA 

# *Risk factor combinations: Survey participants having ≥ **2 of the following risk factors: over**

**weight, physical inactivity, smoking, and diabetes mellitus.** 

# Percent FIGURE 6b. Prevalence of risk factor combinations* among white women — Behavioral Risk Factor Surveillance System, 1994 64 MMWR December 11, 1998

# 16.0 to 21.6 **21.7 to 24.7 24.8 to 31.3**  **31.4 to 39.7 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

A A A A A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAA AA A AAA



# *Risk factor combinations: Survey participants having ≥ **2 of the following risk factors: over**

**weight, physical inactivity, smoking, and diabetes mellitus.** 

# Percent FIGURE 6c. Prevalence of risk factor combinations* among black men — Be havioral Risk Factor Surveillance System, 1994

**17.1 to 30.4 30.5 to 32.7**  **32.8 to 36.5 36.6 to 42.9**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA A A AAAA AAAA AAAA AAAAAAA AAA AAA AAA

AA A AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA A A A A A AAA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AA AA AA AA AA AA AA AAA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA



# *Risk factor combinations: Survey participants having ≥ **2 of the following risk factors: over**

**weight, physical inactivity, smoking, and diabetes mellitus.** 

# Percent FIGURE 6d. Prevalence of risk factor combinations* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 65

# 70.8 to 80.8 **80.9 to 84.3 84.4 to 87.2**  **87.3 to 91.1 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAA A A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA
AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA A AA AAA



# *Weight loss: Survey participants trying to lose or maintain or keep from gaining weight and

**who are either eating fewer calories or eating less fat or are using physical activity or exercise to maintain, lose, or keep from gaining weight.**  

# Percent FIGURE 7a. Prevalence of weight loss* among white men — Behavioral Risk Factor Surveillance System, 1994

**81.6 to 88.1 88.2 to 89.9**  **90.0 to 91.9 92.0 to 94.1**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA



# *Weight loss: Survey participants trying to lose or maintain or keep from gaining weight and

**who are either eating fewer calories or eating less fat or are using physical activity or exercise to maintain, lose, or keep from gaining weight.**  

# Percent FIGURE 7b. Prevalence of weight loss* among white women — Behavioral Risk Factor Surveillance System, 1994 66 MMWR December 11, 1998

# 67.3 to 77.4 **77.5 to 83.6 83.7 to 87.1**  **87.2 to 91.1 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAA AAA AAA AAA

A AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA



# *Weight loss: Survey participants trying to lose or maintain or keep from gaining weight and

**who are either eating fewer calories or eating less fat or are using physical activity or exercise to maintain, lose, or keep from gaining weight.**  

# Percent FIGURE 7c. Prevalence of weight loss* among black men — Behavioral Risk Factor Surveillance System, 1994

**75.2 to 82.0 82.1 to 86.6**  **86.7 to 89.8 89.9 to 95.3**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C. AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA



# *Weight loss: Survey participants trying to lose or maintain or keep from gaining weight and

**who are either eating fewer calories or eating less fat or are using physical activity or exercise to maintain, lose, or keep from gaining weight.**  

# Percent FIGURE 7d. Prevalence of weight loss* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 67

# 13.4 to 40.6 **40.7 to 47.6 47.7 to 50.0**  **50.1 to 56.0 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAA AA A A A A



# *Smoking cessation: Survey participants ever having smoked 100 cigarettes and having quit

**smoking for** ≥ **12 months.** 

# Percent FIGURE 8a. Prevalence of smoking cessation* among white men — Behavioral Risk Factor Surveillance System, 1994

**21.2 to 35.4 35.5 to 39.1**  **39.2 to 44.4 44.5 to 58.3**  **Data missing / Sample <50**  AAAA AAAA AAAA AAAAA A A A 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A

A

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

A A A A A A A A A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA A AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA

AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAA AAA AAA AA AA AA AA AA AA AA 

# *Smoking cessation: Survey participants ever having smoked 100 cigarettes and having quit

**smoking for** ≥ **12 months.** 

# Percent FIGURE 8b. Prevalence of smoking cessation* among white women — Behavioral Risk Factor Surveillance System, 1994 68 MMWR December 11, 1998

# 16.4 to 26.1 **26.2 to 32.1 32.2 to 35.7**  **35.8 to 41.1 Data missing /**  **Sample <50** AAAA AAAA AAAA AAAAAA AA AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA

AA A A AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA AA A AAA



# *Smoking cessation: Survey participants ever having smoked 100 cigarettes and having quit

**smoking for** ≥ **12 months.** 

# Percent FIGURE 8c. Prevalence of smoking cessation* among black men — Behavioral Risk Factor Surveillance System, 1994

**14.8 to 22.5 22.6 to 27.9**  **28.0 to 34.6 34.7 to 37.8**  **Data missing / Sample <50**  AAAA AAAA AAAAA A AA AA 

# D.C.

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA AA AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA
AAA AAA AAA AAA AAA AAA AAA

AA AA AA AA
AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AA AA AA AA AA AA AA AA AA AA AA



# *Smoking cessation: Survey participants ever having smoked 100 cigarettes and having quit

**smoking for** ≥ **12 months.** 

# Percent FIGURE 8d. Prevalence of smoking cessation* among black women — Behavioral Risk Factor Surveillance System, 1994 Vol. 47 / No. SS-5 MMWR 69

# 70 MMWR December 11, 1998

# Vol. 47 / No. SS-5 MMWR 71

# 72 MMWR December 11, 1998

# State and Territorial Epidemiologists and Laboratory Directors **State and Territorial Epidemiologists and Laboratory Directors are acknowledged for their contributions to**  **CDC Surveillance Summaries . The epidemiologists listed below were in the po**  **sitions shown as of November 1998, and the laboratory directors listed below were in the positions shown as of November 1998.**  State/Territory Epidemiologist Laboratory Director **Alabama John P. Lofgren, MD William J. Callan, PhD Alaska John P. Middaugh, MD Gregory V. Hayes, DrPH**  **Arizona Robert W. England, Jr, MD, MPH Barbara J. Erickson, PhD Arkansas Thomas C. McChesney, DVM Michael G. Foreman**  **California Stephen H. Waterman, MD, MPH Paul Kimsey, PhD Colorado Richard E. Hoffman, MD, MPH**  **Ronald L. Cada, DrPH**  **Connecticut James L. Hadler, MD, MPH Sanders F.**  **Hawkins, PhD Delaware A. LeRoy Hathcock, PhD Roy Almeida, DrPH**  **District of Columbia Martin E. Levy, MD, MPH James B. Thomas, ScD**  **Florida Richard S. Hopkins, MD, MSPH E. Charles Hart**  **wig, ScD Georgia Kathleen E. Toomey, MD, MPH Elizabeth A. Franko, DrPH**  **Hawaii Paul Effler, MD, MPH Vernon K. Miyamoto, PhD Idaho Christine G. Hahn, MD Richard H. Hudson, PhD**  **Illinois Byron J. Francis, MD, MPH David F. Carpenter, PhD Indiana Gregory K. Steele, DrPH, MPH David E. Nauth**  **Iowa M. Patricia Quinlisk, MD, MPH Mary J. R. Gilchrist, PhD Kansas Gianfranco Pezzino, MD, MPH**  **Roger H. Carls on, PhD**  **Kentucky Glyn G. Caldwell, MD Samuel Gregorio, DrPH (Acting) Louisiana Louise McFarland, DrPH Henry B. Bradford, Jr, PhD**  **Maine Kathleen F. Gensheimer, MD, MPH John A. Krueger Maryland Diane M. Dwyer, MD, MPH J. Mehsen Joseph, PhD**  **Massachusetts Alfred DeMaria, Jr, MD Ralph J. Timperi, MPH Michigan Mathew L. Boul**  **ton, MD, MPH Robert Mart**  **in, DrPH, PhD Minnesota Michael T. Osterholm, PhD, MPH Norman Crouch, PhD (Acting)**  **Mississippi Mary Currier, MD, MPH Joe O. Graves, PhD Missouri H. Denny Donnell, Jr, MD, MPH Eric C. Blank, DrPH**  **Montana Todd A. Damrow, PhD, MPH Mike Spence, MD**  **Nebraska Thomas J. Safranek, MD Steve Hinrichs, MD Nevada Randall L. Todd, DrPH**  **L.D. Brown, MD, MPH New Hampshire Jesse Gr**  **eenblatt, MD, MPH Ver onica C. Malmberg, MSN**  **New Jersey John H. Brook, MD, MPH Thomas J. Domenico, PhD New Mexico C. Mack Sewell, DrPH, MS David E. Mills, PhD**  **New York City Benjamin A. M ojica, MD, MPH**  **Alex Ramon, MD, MPH New York State Perry F. Smith, MD Ann Willey, PhD**  **North Carolina J. Newton MacCormack, MD, MPH Lou F. Turner, DrPH North Dakota Larry A. Shir**  **eley, MPH, MS James D. Anders, MPH Ohio Thomas J. Halpin, MD, MPH William Becker, DO**  **Oklahoma J. Michael Crutcher, MD, MPH Richard Baltaro, MD Oregon David W. Fleming, MD Michael R. Skeels, PhD, MPH**  **Pennsylvania James T. Rankin, Jr, DVM, PhD, MPH Bruce Kleger, DrPH Rhode Island Utpala**  **Bandyopadhyay, MD, MPH Walter S. Combs Jr, PhD**  **South Carolina James J. Gibson, MD, MPH Harold Dowda, PhD South Dakota Susan E. Lance, DVM, PhD, MPH Michael Smith**  **Tennessee William L. Moore, Jr, MD Michael W. Kimberly, DrPH Texas Diane M. Simpson, MD, PhD David L. Maserang, PhD**  **Utah Craig R. Nichols, MPA Charles D. Brokopp, DrPH Vermont Peter D. Galbraith, DMD, MPH Burton W. Wilcke, Jr, PhD**  **Virginia Robert B. Stroube, MD, MPH James L. Pearson, DrPH Washington Juliet V**  **anEe nwyk, PhD (Acting) Jon M. Counts, DrPH**  **West Virginia Loretta E. Haddy, MS, MA Frank W. Lambert, Jr, DrPH**  **Wisconsin Jeffrey P. Davis, MD Ronald H.**  **Laessig, PhD Wyoming Gayle L. Miller, DVM, MPH Garry McKee, PhD, MPH**  **American Samoa Edgar C. Reid,**  **DSM, MPH (Acting) Edgar C. Reid, MO, MPH Federated States of**  **Micronesia Jean-Paul Chaine — Guam Robert L. Haddock, DVM, MPH Florencia Nocon (Acting)**  **Marshall Islands Tom D. Kijiner — Northern Mariana Islands Jose L. Chong, MD Joseph Villagomez**  **Palau Jill McCready, MS, MPH — Puerto Rico Carmen C. Deseda, MD, MPH José Luis Miranda Arroyo, MD**  **Virgin Islands Jose Poblete, MD (Acting) Norbert Mantor, PhD** 

# Vol. 47 / No. SS-5

# The Morbidity and Mortality Weekly Report (MMWR) Series is prepared by the Centers for Disease Control and Prevention (CDC) and is available free of charge in elect ronic format and on a paid subscription basis for paper copy. To receive an electronic copy on Friday of each week, send an e-mail message to listserv@listserv.cdc.gov

. The body content should read SUBscribe mmwr-toc

. Electronic copy also is available from CDC’s World-Wide Web server at http://www.cdc.gov/ or from CDC’s file transfer protocol server at ftp.cdc .gov
. To subscribe for paper copy, contact Superi ntendent of Documents, U.S. Government Printing Office, Washington, DC 20402; telephone (202) 512-1800. Data in the weekly MMWR are provisional, based on weekly reports to CDC by state health departments. The report ing week concludes at close of business on Friday; compiled data on a national basis are officially released to the public on the following Friday. Address inquiries about the MMWR Series, including material to be considered for publication, to: Editor, MMWR Series, Mailstop C-08, CDC, 1600 Clifton Rd., N. E., Atlanta, GA 30333; telephone (888) 232-3228. All material in the MMWR Series is in the public domain and may be used and reprinted without permission; citation as to source, however, is appreciated.
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# MMWR