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. Telephone: (202) 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 Human 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 Communications ......................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 Summaries, December 11, 1998. MMWR 1998;47(No. SS-5).

Specific: [Author(s)]. [Title of particular article]. In CDC Surveillance Summaries, 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 Prevention 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 Prevention 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 December 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 public health departments are primarily responsible 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 associated with drinking water. These outbreaks caused an estimated total of 2,567 persons

to become ill. No deaths were reported. The microbe or chemical that caused the outbreak 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, Plesiomonas 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 number 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 noncommunity and three in

community systems.

Only three of the 10 outbreaks associated with community water systems were

caused by problems at water treatment plants; the other seven resulted from problems 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 round structured

virus. Four of the seven outbreaks occurred because of backflow or backsiphonage

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 outbreaks caused by contamination from a cross-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 attributed to recreational water exposure 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 gastroenteritis; 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 outbreaks of dermatitis, seven (77.8%) were outbreaks of Pseudomonas dermatitis

associated with hot tubs, and two (22.2%) were lake-associated outbreaks of swimmer’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 finding suggests the need for better monitoring of water quality and identification of

sources of contamination. Although 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, primarily 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 education of patrons about hazards associated with fecal accidents, especially in pools

frequented by diaper-aged children. The proportion of reported drinking water outbreaks 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 outbreaks attributed to contamination in the distribution system suggest that efforts

should be increased to prevent cross-connections, especially by installing and monitoring backflow prevention devices.

Actions Taken: Surveillance data that identify the types of water systems, their deficiencies, 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 collaborative surveillance system consisting of the collection and periodic reporting of data on

the occurrences and causes of waterborne-disease outbreaks (WBDOs) (2–4 ). The surveillance 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 following 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 waterborne diseases. The data gathered through this surveillance system are useful for

evaluating the adequacy of current technologies for providing safe drinking and recreational 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 treatment criteria for removing Giardia lamblia, viruses, Legionella, and heterotrophic plate-count bacteria. All public community and

noncommunity water systems that use surface-water sources or groundwater sources

under the direct influence of surface water are covered by the Surface Water Treatment 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 sourcewater protection.

The Total Coliform Rule was promulgated specifically to identify public water systems 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 presence 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 address the prevention of waterborne diseases. The Enhanced 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 imVol. 47 / No. SS-5 MMWR 3
plemented in two stages: an interim rule and a final rule. Treatment requirements under the interim Enhanced Surface Water Treatment Rule are scheduled to be

announced in late 1998.

The Ground Water Disinfection Rule will apply to both community and noncommunity water systems served by groundwater. Maximum contaminant level goals will be

set for pathogens. A maximum contaminant level goal of zero will be set for viruses

and possibly Legionella. No maximum contaminant level goal is expected for heterotrophic plate-count bacteria. In lieu of monitoring for pathogens, performance

criteria for water treatment will be established that are expected to produce the desired 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 Collection Rule (12 ). This rule requires treatment plants that use surface water and supply

communities of ≥100,000 persons or that use groundwater systems and supply communities 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 exceeds 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 Information Collection Rule requires utilities to monitor 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 state and

territorial epidemiologists or from persons designated as the WBDO surveillance coordinators. 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 illness 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 illness. 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 outbreak 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 regulated under the Safe Drinking Water Act. A community water 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 nontransient and transient categories, with nontransient noncommunity water systems (e.g.,

in factories and schools) serving ≥25 persons ≥6 months of the year. Of the approximately 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. population); 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, millions of persons 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 groundwater;

3 = treatment deficiency (e.g., temporary interruption of disinfection, chronically inadequate 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 surveillance 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 evidence 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 outbreaks without water-quality data were 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 adequate epidemiologic and water-quality data

were reported but does not necessarily imply that the investigation was optimal. Classification 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 outbreak 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 through October.

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 individually 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 etiology, and seven (31.8%) were attributed to chemical poisoning. 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 unknown 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, affected 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 interruptions in chlori6 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 conventional 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 shigelloides. The outbreak of

shigellosis in Idaho, which affected 83 persons, occurred at a resort supplied by untreated well water, which became contaminated by sewage from a line that was

draining poorly (13 ). The outbreak of shigellosis in Oklahoma, which affected 10 persons, 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 chlorinated.

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 serotype London. Water 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 reported 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 residual was zero. The chlorinator was found to be depleted of disinfectant, 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), defective 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 gastrointestinal 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 contributed 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 backsiphonage 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 complexes 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 etiology, stool specimens were negative for bacterial and parasitic pathogens. 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 rotavirus 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 associated with WBDOs than the rotaviruses have been.

Of the eight outbreaks for which the etiologic agent was not identified, three outbreaks were associated with untreated well water, three with inadequate chlorination

of unfiltered well water, and one with possible short-term cross-connection and backsiphonage problems 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 accessed 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 out8 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 investigation 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. Coliforms were detected for

three (75.0%) of the four bacterial outbreaks, and all eight outbreaks of unknown etiology, 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 oocysts were found in a filtered, chlorinated water system during the investigation of an

outbreak of giardiasis; but no coliforms were detected in water samples from the distribution system. During the investigation of an outbreak caused by a small round

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 community systems, 10 (45.5%)

with noncommunity systems, and two (9.1%) with individual water systems (Tables

4 and 5; Figure 2). Only three of the 10 outbreaks associated with community 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 individual facilities (e.g., offices, schools, and restaurants). Six of these seven outbreaks were

associated with chemical contamination of the drinking water. Two of the six outbreaks were attributed to nitrites that contaminated the water when check valves for

prevention of backflow at the facilities malfunctioned; two resulted from copper leaching into the water after plumbing was installed in new homes; one occurred when

concentrated liquid soap contaminated the water because of backsiphonage through

an improperly installed vacuum breaker; and in one outbreak, the source of the excessive 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 contaminated the drinking water when backsiphonage of water through hoses submerged in

a flooded football field occurred. During 1995–1996, outbreaks in noncommunity systems were more likely than those in community systems to be associated with

untreated water (40.0% versus 0.0%). Eight (80.0%) of the 10 outbreaks in noncommunity 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 disinfection (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 deficiency for both outbreaks associated with spring-water systems was inadequate or

interrupted chlorination. For the outbreaks associated with surface-water systems, the

identified deficiencies were inadequate filtration, backsiphonage, and no treatment.

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 recreational 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 persons.

The median outbreak size was 10 persons (range: 1–5,449). All but one of the 22 outbreaks 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 etiology (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 Cryptosporidium 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 cryptosporidiosis, 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 conditions that might have contributed to the outbreak included overcrowding of the area

around the pool, an excessive 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 positive 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 meningoencephalitis were attributed 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 bacteria, 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 implicated 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 presumptively attributed to fecal contamination by swimmers. No agent could be

identified for four (18.2%) of the 22 outbreaks of gastroenteritis; all four of these outbreaks 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 infectious 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 mobilehome 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 gastroenteritis, 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, samples 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 shigellosis were reported 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 cryptosporidiosis, as well as 24 probable secondary cases 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 outdoor 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 ground

beef. Bagged ice was linked to infection with Campylobacter sp. in seven persons who

became ill at a private 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 human 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 coils 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 because 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 systems, their deficiencies, and the respective etiologic agents associated with the

outbreaks, are useful for evaluating the adequacy of current technologies for providing 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 unknown.

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 serious 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. Outbreaks associated with individual 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 recognition 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, depending on such factors as available fiscal, investigative, and laboratory resources.

Furthermore, a few large outbreaks can substantially alter the relative proportion of

cases of waterborne disease attributed 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 reported 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, changes

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 giardiasis 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. Although no interruptions in chlorination at the

water plant were identified, postfilter water turbidity readings, which serve as an index of the effectiveness of filtration, exceeded the regulated limit before and during

the outbreak. The occurrence of the two outbreaks of giardiasis underscores the importance 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 even in chlorinated 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, monitoring of the turbidity of the water both before 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-resistant than Giardia. More stringent EPA

standards for acceptable turbidity values have become effective in all states since the

outbreak of cryptosporidiosis in Milwaukee in 1993 (22 ). Many of the large water utilities 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 giardiasis. The number of small utilities that have joined the partnership is unknown.

During 1995–1996, all four outbreaks caused by bacteria were associated with noncommunity 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, particularly 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

discharges.

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. Researchers 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 specimens for viral testing, either at CDC or at state laboratories that have developed the

capability 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 preventing backflow when potable water systems are connected to boilers and water

chillers; approved backflow prevention devices should be used and periodically monitored 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 distribution system. EPA requires monitoring 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 through 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, affect 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 nonspecific 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 between environmental releases or exposure incidents and individual

health events, and the assessment of the public health burden associated with waterrelated 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 affecting the transient populations that use water from these systems.

The relative proportion of outbreaks associated with various types of water systems has remained fairly constant; the proportions associated with community 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 associated 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). Unfortunately,

such problems at individual facilities are not amenable to actions taken by treatment

plants. However, they can be remedied by effective cross-connection control regulations that require inspection and testing. Monitoring, regulating, and standardizing

the practices of the multitudinous individual facilities (e.g., offices, schools, and restaurants) in this country is a daunting task.

1995–1996 Outbreaks Associated with Recreational Water

The most frequently reported WBDOs caused by exposure to recreational water

were outbreaks of gastroenteritis. Swimming and other recreational activities in

which the unintentional ingestion of water can occur are known to increase the risk for

gastrointestinal illness, even in nonoutbreak settings (26,27 ). The number of outbreaks 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 recreational 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. Investigators for only one of the six outbreaks reported 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 disinfection by chlorine at levels generally used in swimming pools. Because

Cryptosporidium oocysts measure only 4–6 μm 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 removing 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 difficult. 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 cryptosporidiosis (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 degree to which they reduce risk is unknown. Behavioral changes, such as showering

before entering the pool and restricting access of diaper-aged children to certain

pools, will require 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 accident?

and c) what hyperchlorination strategy should be used, especially for Cryptosporidium?

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 swimming-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 swimmers. 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 identification 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 swallowing 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 recreational areas potentially contaminated by sewage. However, the health risk associated

with various levels of fecal coliforms has not been established. 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 measure, although difficult to enforce, is to prevent persons (especially 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 procedures. Outbreaks of Pseudomonas dermatitis associated 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 unit area, also could influence the risk for infection (36).

For the period 1995–1996, all six deaths associated with recreational water were

caused by primary amebic meningoencephalitis, a rarely 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 recognition 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 transmission, 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 waterquality data to health departments. Timely water testing and environmental

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., issuances 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 prevention strategies in nonoutbreak settings, 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 collecting 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 (telephone [800] 426-4791; e-mail sdwa@epamail.epa.gov), CDC’s Cryptosporidiosis

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>. Information 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 (telephone [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 epidemiologists; 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 waterbornedisease 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 primary drinking water regulations. Federal Register 1975;40:59566–74.

6. Pontius FW, Roberson JA. The current regulatory agenda: an update. Journal of the American

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; filtration, 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 primary 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 regulations: monitoring requirements for public drinking water supplies: Cryptosporidium, Giardia,

viruses, disinfection byproducts, water treatment plant data and other information requirements; 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 laboratories 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 Environmental 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 Cryptosporidium infection transmitted through 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 transcription-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 contaminated 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. Atlanta: 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 Department of Health and Human Services, Public Health Service, CDC, 1981; DHHS publication

no. 99-960.

36. Highsmith AK, McNamara AM. Microbiology of recreational and therapeutic whirlpools. Toxicity 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 before 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 naturally occurring particles to facilitate their subsequent aggregation and removal by

flocculation and/or filtration.

Coliforms: All facultative anaerobic, gram-negative, nonsporeforming, 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 contact time).

Cross-connection: Any actual or potential connection between a drinking water supply 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 organic matter and disinfectants.

Distribution 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
Filtration: 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 aggregates 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 combined with other constituents and thus serves as an effective disinfectant.

Groundwater system: A system that uses water extracted from the ground (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 through a semipermeable 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 negative-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 drinking, 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 through

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 contamination or pollution.

Chemical poisoning

Infectious or suspected infectious etiology

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

Gastroenteritis

Dermatitis

Meningoencephalitis

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

FIGURE 1. Number of waterborne-disease outbreaks, by type of water, etiologic

agent or illness, and month — United States, 1995–1996 (N = 59)

Vol. 47 / No. SS-5 MMWR 23
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Unidentified

36.4%

Chemical

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4.5%

Parasitic

9.1%

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18.2%

Community

45.5%

Individual

9.1%

Noncommunity

45.5%

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deficiency

36.4%

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system

31.8%

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18.2%

Unknown

9.1%

Untreated

surface

water

4.5%

Well

50.0%

Other

9.1%

Surface water

31.8%

Etiologic Agent Water System

Water Source Deficiency

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)

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Escherichia

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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 outbreaks of gastroenteritis associated with

recreational water, by etiologic agent and type of exposure — United States,

1995–1996 (N = 22)

24 MMWR December 11, 1998
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Chemical

Viral

Parasitic

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1971 1974 1977 1980 1983 1986 1989 1992 1995

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50

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*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

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50

60

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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
TABLE 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 laboratorhistory that a chlorinator malfunctioned or broke, no detectable free-chlorine residual, 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 thecriteria for Class I; or b) the claim was made that ill personshad no exposures in common besides water, but no datawere provided.

PROVIDED AND ADEQUATE

IV PROVIDED, BUT LIMITED NOT PROVIDED OR INADEQUATE*Outbreaks of Pseudomonas dermatitis and single cases of primary amebic meningoencephalitis or of illness resultin

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.
TABLE 2. Waterborne-disease outbreaks associated with drinking water — United States, 1995 (N = 16)* State Month Class† Etiologic agent No. cases Type of system§ Deficiency¶ Source 

Alaska Aug II Giardia lamblia 10 Ind 1 Surface** 

Florida Apr —†† Chlorine 1 Com 5 Lake 

Florida May III Sodium hydroxide 30 Com

3 River 

Idaho Aug I Shigella sonnei 83 NCom 2 Well 

Idaho Sept I AGI§§ 18 Com

3 Well 

Iowa Oct III Concentrated liquid soap 13

Com

4

Lake

Minnesota Jul I Escherichia coli O157:H7 33 NCom

3 Spring 

Montana Aug II AGI 450 NCom 2 Well New York Dec I G. lamblia 1,449 Com

3 Lake 

Oklahoma Oct II S. sonnei 10 NCom

3 Well 

Pennsylvania Aug I AGI 19 NCom 2 Well South Dakota Jun I AGI 48 NCom 2 Well Wisconsin Aug III AGI¶¶ 26 NCom

3 Well 

Wisconsin Sept I Small round structured virus 148 Com

4 Lake 

Wisconsin Sept I Copper 22 Com

4 Well 

Wisconsin Oct I Copper 15 Com 4 Well  *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 o

¶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.
TABLE 3. Waterborne-disease outbreaks associated with drinking water — United States, 1996 (N = 6)* State Month Class† Etiologic agent No. cases Type of system§ Deficiency¶ Source Se

California Apr III Nitrite 3 Com

4 River Sc

California Sept I AGI** 8 Ind 5 Outside tap WIdaho Jul III AGI 94 NCom 3 Well CaNew Jersey Mar I Nitrite 6 Com 4 Mixed†† ONew York

Jun

I Plesiomonas

shigelloides

60 NCom 3 Spring Re

Wisconsin Jun III AGI 21 NCom 4 Well Re *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 o

¶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.
TABLE 4. Waterborne-disease outbreaks associated with drinking water, by etiologic agent and type of wUnited States, 1995–1996 (N = 22)* Etiologic agent Type of water system† Community Noncommunity Individual 

Outbreaks Cases Outbreaks Cases Outbreaks Cases Outbreak

AGI§ 1 18 6 658 1 8 8 

Giardia lamblia 1 1,449 0 0 1 10 2 Shigella sonnei 0 0 2 93 0 0 2 

Copper 2 37 0 0 0 0 2 Nitrite 2 9 0 0 0 0 2 Small round structured virus 1 148 0 0 0 0 1 

Plesiomonas shigelloides 0 0 1 60 0 0 1 Escherichia coli O157:H7 0 0 1 33 0 0 1 

Sodium hydroxide 1 30 0 0 0 0 1 Concentrated liquid soap 1 13 0 0 0 0 1 Chlorine 1 1 0 0 0 0 1 Total 10 1,705 10 844 2 18 22 (Percentage¶) (45.5) (66.4) (45.5) (32.9) (9.1) (0.7) (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.
TABLE 5. Waterborne-disease outbreaks associated with drinking water, by type of deficiency and type of wUnited States, 1995–1996 (N = 22) Type of deficiency† Type of water system* Community Noncommunity Individual 

No. (%) No. (%) No. (%) No. 

Untreated surface water 0 ( 0) 0 ( 0) 1 ( 50.0) 1 Untreated groundwater 0 ( 0) 4 ( 40.0) 0 ( 0) 4 Inadequate treatment 3 ( 30.0) 5 ( 50.0) 0 ( 0) 8 Distribution system 6 ( 60.0) 1 ( 10.0) 0 ( 0) 7 Miscellaneous or unknown 1 ( 10.0) 0 ( 0) 1 ( 50.0) 2 Total 10 (100.0) 10 (100.0) 2 (100.0) 22 

*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.
TABLE 6. Waterborne-disease outbreaks of gastroenteritis and meningoencephalitis associated with recreaUnited States, 1995 (N = 17) State Month Class* Etiologic agent Illness No. cases Source Florida Aug

—† Naegleria fowleri Meningoencephalitis 1 Canal 

Georgia Jul I Cryptosporidium parvum Gastroenteritis 5,449 Pool Idaho Mar I Salmonella serotype Java Gastroenteritis 3 Pool 

Illinois Jun I Escherichia coli O157:H7 Gastroenteritis 12 Lake Kansas Jun III C. parvum Gastroenteritis 24 Pool 

Minnesota Jun II AGI§ Gastroenteritis 12 Lake Minnesota Jul IV E. coli O157:H7 Gastroenteritis 6 Lake Minnesota Jul IV E. coli O157:H7 Gastroenteritis 2 Lake Nebraska Jul IV C. parvum Gastroenteritis 14 Pool Pennsylvania Aug I AGI Gastroenteritis 17 Lake 

Pennsylvania Aug III Shigella sonnei Gastroenteritis 70 Lake 

Texas Jul

— N. fowleri Meningoencephalitis 1 River 

Texas Jul

— N. fowleri Meningoencephalitis 1 Pond 

Texas Aug

— N. fowleri Meningoencephalitis 1 Lake 

Texas Aug

— N. fowleri Meningoencephalitis 1 Lake 

Texas Sept

— N. fowleri Meningoencephalitis 1 Lake 

Wisconsin Jun III E. coli O157:H7 Gastroenteritis 8 Lake 

*Refer to Table 1 for information concerning the classification of outbreaks.

†Not applicable; see Table 1.§AGI = acute gastrointestinal illness of unknown etiology.
TABLE 7. Waterborne-disease outbreaks of gastroenteritis associated with recreational water — United StateState Month Class* Etiologic agent No. cases Source SetCalifornia Aug II Cryptosporidium parvum 3,000 Pool Am

Colorado Jul I Shigella sonnei 39 Lake Rec

Colorado Jul I S. sonnei 81 Lake RecFlorida Jun I C. parvum 22 Pool ComFlorida Jun I Giardia lamblia† 77 Pool ComGeorgia Jul I Escherichia coli O157:H7 18 Pool Mo

Idaho Jun II Norwalk 55 Hot spring CamIndiana Aug IV C. parvum 3 Lake Bea

Indiana Aug IV AGI§ 4 Lake BeaMinnesota Jun IV E. coli O157:H7 6 Lake Bea

Oregon Aug IV AGI 32 Lake Cam*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 on

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, 19State Year Month Class* Etiologic agent No. cases Source SettingMaine 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 Apartme

Oregon 1996 Jun III Schistosoma sp. 71 Lake Beach

Oregon 1996 Jul III Schistosoma sp. 50 Lake BeachWashington 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.
TABLE 9. Waterborne-disease outbreaks of gastroenteritis associated with drinking or recreational water included in the previous surveillance summaries — United States, 1994 (N = 3)* State Month Year Exposure† Class§ Etiologic agent No. cases Type of system¶ Deficiency** Source 

Florida Jun 1994 Wid III Escherichia coli O157:H7 2 NCom 3 Well 

Florida Jul 1994 Rec I AGI†† 12 

—§§

—§§ Lake 

New Jersey Jun 1994 Rec III Shigella sonnei 300 — — Reserv

 *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 Johnson

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 Gildemaster

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 rates of

CVD vary by state and by region of the country. Several behavioral risk factors (i.e.,

overweight, physical 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 analyzes 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) mapping; 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 substantial 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 inactivity, smoking, and risk factor combinations. 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 women and black women

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 associated 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 smallarea 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 factors. 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 resources and target intervention 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

prevalence, 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 state residents); b) physical environment (e.g., excessive heat [9 ] and air pollution [10 ]); and c) social environment (e.g., laws taxing

cigarettes or restricting cigarette use) (11,12 ).

This atlas displays distribution of major behavioral risk factors and preventive practices 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 factors (i.e., overweight, physical

inactivity, smoking, hypertension, and diabetes mellitus), risk factor combinations,

and two preventive practices (i.e., weight loss and smoking cessation). Racial differences in risk factor prevalence are examined to facilitate exploration of

well-recognized racial differences in health status. Only blacks and whites are examined 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 factors 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 pressure, 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 exercising 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 reduces the risk of CHD (16 ). The relative risk for CHD associated with physical inactivity

is approximately 1.9, slightly lower than the relative risks associated 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 patients with documented CHD is associated with reduced morbidity and mortality

(18,19 ) and that physical activity might improve the likelihood of survival from a myocardial 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 increasing years of smoking, although persons 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 known risk factors 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 lowest to highest values for either systolic or diastolic blood pressure (25 ). Elevated

blood pressure is often associated with other well-known risk factors, including dietary intake, elevated blood lipid levels, obesity, smoking, diabetes 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 accompanies diabetes mellitus 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 common manifestations (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 diabetic men as it

is among men without diabetes mellitus (27 ).

Risk Factor Combinations

Many CVD risk factors interact physiologically in the etiology of CVD (28,29 ). Persons 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 hyperlipidemia, hypertension, and diabetes mellitus. Physical inactivity has been related to

obesity, lipid abnormalities, hypertension, and diabetes 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 lifelong 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 ). Therapeutic 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, especially 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 persons with previously

diagnosed myocardial infarction or stroke, smoking cessation reduces the risk for recurrent 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 techniques, participating states select a probability sample of their noninstitutionalized

adult population (aged ≥18 years) with telephones. The Waksberg method (34 ), a multistage 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 stratified sample designs) to meet their special needs. The standard BRFSS questionnaire

includes questions from previously conducted national surveys (e.g., Health Promotion 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 socioeconomic 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 [m2]) ≥27.8 for men

and ≥27.3 for women. These values approximate the sex specific 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, objective 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 professional 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: overweight, physical inactivity, smoking, and diabetes mellitus. Hypertension 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 gaining 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 include only participants who gave specific responses 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 factors 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 clustering 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 rates 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 significant 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 dependent variables and the independent variables were state behavioral risk factor

prevalences. For heart disease, the International Classification of Diseases, Ninth Revision, 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 system (42 ) and the Shapiro-Wilks statistic in SAS to assess the normality of mortality

rates for regression analyses. Because state risk factor prevalences were not age-adjusted, 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 succeeding 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 population was <50.

40 MMWR December 11, 1998
RESULTS

BRFSS sample sizes used for state prevalence estimates varied by risk factor. Excluding states for which race-sex group sample sizes 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, prevalence information 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 prevalences of overweight 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 rates are found in the northeastern quadrant of the nation (Figure 1b and d). No statistically determined clusters

for this risk factor exist among whites, for whom 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 significant 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 because 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 prevalences 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 clusters 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 diabetes 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 statistically

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 combinations 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). Prevalence rates are generally

higher in the West 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 whom 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 prevalences of smoking cessation exists 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 statistically 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 states. 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 statistically significant (p ≤ 0.05). Among significant correlations, second in similarity among race-sex pairs are white women and

black women; this correlation is significant for overweight, physical inactivity, hypertension, 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 significantly 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 predictive of state CHD mortality rates among white men. State rates of hypertension are

predictive of stroke mortality among white men and women, and state rates of physical 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 practices for white and black men and women 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., diabetes 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 confirmed 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 cessation). Thus, risk factor prevalences are lower and preventive practice prevalences

are higher among western states. Among blacks, such comparison is not possible because 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, comparison 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 populations (e.g., Alaska and Florida) but indicates 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 differences is that the East and the West differ in their sociocultural environments related to

risk factor avoidance, health promotion, and preventive behavior; however, no evidence 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. Among whites, risk factor 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 correspondence exists between high state risk factor prevalences and high stroke mortality as

depicted in detailed mortality maps. Although stroke mortality is highest in the Southeast 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 comparison 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 factor prevalence rates cannot be assumed to reflect the prevalences of risk factors among

long-term state residents.

Several studies indicate the reliability or validity of BRFSS data. One study compared estimates from BRFSS telephone questions 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 cigarette smoking,

as validated by lung capacity, researchers reported a sensitivity of 78% and a specificity 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 validity. Another study reported higher sensitivity 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 indicated self-reported consumption to be approximately 72% of true consumption (48 ).

Another study indicated that BRFSS accurately reported smoking status, but substantially underreported obesity (49 ).

The prevalence estimates of self-reported health-risk behaviors in this analysis

might be underestimated because data were collected through telephone interviews;

previous studies indicate substantial differences in the characteristics of persons who

reside in households without a telephone compared with those who reside in households with a telephone (50 ).

One problem with the local use of BRFSS, apparent in this summary, is the small

sample sizes 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 prevalence 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 estimation of the prevalence of overweight. Achievement of reliable

annual state information on blacks will require oversampling in states with small black

populations. This additional information might facilitate understanding risk factors for

cardiovascular disease among blacks and the design of appropriate prevention programs.

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 planning 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 public health.

References

1. NCHS. Health United States, 1995. Hyattsville, MD: US Department of Health and Human Services, Public Health Service, CDC, National Center for Health Statistics, May 1996; DHHS

publication no. (PHS) 96-1232.

2. Gillum BS, Graves EJ, Wood E. National Hospital Discharge Survey: Annual Summary, 1995.

Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for

Health Statistics, 1998; DHHS publication no. (PHS)98-1794. (Vital and health statistics; series

13, no. 133).

3. Pickle LW, Mingiole M, Jones GK, White AA. Atlas of United States mortality. Hyattsville, MD:

US Department of Health and Human Services, Public Health Service, CDC, National Center

for Health Statistics, December 1996; DHHS publication no. (PHS)97-1015.

4. Adams PF, Marano MA. Current estimates from the National Health Interview Survey, 1994.

Hyattsville, MD: US Department of Health and Human Services, Public Health Service, CDC,

National Center for Health Statistics, December 1995; DHHS publication no. (PHS) 96-1521.

(Vital and health statistics: data from the National Health Survey; series 10, no. 193).

5. National Center for Health Statistics. Advance report of final mortality statistics, 1994.

Hyattsville, MD: US Department of Health and Human Services, Public Health Service, CDC,

1996. (Monthly vital statistics report; vol 45, no. 3, suppl).

6. Nelson DE, Kirkendall RS, Lawton RL, et al. Surveillance for smoking-attributable mortality

and years of potential life lost, by state—United States, 1990. In CDC surveillance summaries

(June 10). MMWR 1994;43(No. SS-1):1–8.

7. Yeager KK, Anda RF, Macera CA, Donehoo RS, Eaker ED. Sedentary life style and state variation

in coronary heart disease mortality. Public Health Rep 1995;110:100–2.

8. Byers T, Anda R, McQueen D, et al. The correspondence between coronary heart disease mortality and risk factor prevalence among states in the United States, 1991–1992. Prev Med

1998;27:311–6.

9. CDC. Heat-related illnesses and deaths—United States, 1994–1995. MMWR 1995;44:465–8.

10. Dockery DW, Pope CA III, Xu X, et al. An association between air pollution and mortality in

six U.S. cities. New Engl J Med 1993;329:1753–9.

11. CDC. State tobacco control highlights—1996. Atlanta: CDC, National Center for Chronic Disease

Prevention and Health Promotion, Office on Smoking and Health, 1996; publication no. 099-

4895.

12. CDC. Reducing the health consequences of smoking: 25 years of progress—a report of the

Surgeon General. Rockville, MD: US Department of Health and Human Services, Public Health

Service, CDC, Center for Chronic Disease Prevention and Health Promotion, 1989; DHHS publication no. (CDC)89-8411.

13. Foster WR, Burton BT, eds. National Institutes of Health Consensus Development Panel on

the health implications of obesity. Ann Intern Med 1985;103(suppl 6):1073–7.

46 MMWR December 11, 1998
14. Hubert HB. The importance of obesity in the development of coronary risk factors and disease:

the epidemiological evidence. Annu Rev Public Health 1986;7:493–502.

15. Kuczmarski RJ, Flegal KM, Campbell SM, Johnson CL. Increasing prevalence of overweight

among US adults: the National Health and Nutrition Examination Surveys, 1960 to 1991. JAMA

1994;272:205–11.

16. Powell KE, Thompson PD, Caspersen CJ, et al. Physical activity and the incidence of coronary

heart disease. Annu Rev Public Health 1987;8:253.

17. Pooling Project Research Group. Relationship of blood pressure, serum cholesterol, smoking

habit, relative weight and ECG abnormalities to incidence of major coronary events: final

report of the Pooling Project. J Chronic Dis 1978;31:202–306.

18. May GS, Eberlein KA, Furberg CD, Passamani ER, DeMets DL. Secondary prevention after

myocardial infarction: a review of long term trials. Prog Cardiovasc Dis 1982;24:331.

19. O’Connor GT, Buring JE, Yusaf S, et al. An overview of randomized trials of rehabilitation

with exercise after myocardial infarction. Circulation 1989;80:234–44.

20. Paffenbarger RS Jr, Wing AL, Hyde RT. Physical activity as an index of heart attack risk in

college alumni. Am J Epidemiol 1978;108:161.

21. CDC. Physical activity and health: a report of the Surgeon General. Atlanta: US Department

of Health and Human Services, CDC, National Center for Chronic Disease Prevention and

Health Promotion, President’s Council on Physical Fitness and Sports, 1996.

22. Mulcahy, R. Influence of cigarette smoking on morbidity and mortality after myocardial infarction. Br Heart J 1983;49:410–5.

23. Pathobiological Determinants of Atherosclerosis in Youth (PDAY) Research Group. Relationship of atherosclerosis in young men to serum lipoprotein cholesterol concentrations and

smoking. JAMA 1990;264:3018–24.

24. The sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and

Treatment of High Blood Pressure. Arch Intern Med 1997;157:2413–46.

25. Grundy SM, Balady GJ, Criqui MH, et al. Guide to primary prevention of cardiovascular diseases. Circulation 1997;95:2329–31.

26. Burt VL, Culter JA, Higgins M, et al. Trends in the prevalence, awareness, treatment, and

control of hypertension in the adult US population: data from the Health Examination Surveys,

1960 to 1991. Hypertension 1995;26:60–9.

27. Barrett-Connor E, Orghard T. Diabetes and heart disease. In Diabetes in America. MI Harris,

RF Hamman, eds. Washington, DC: US Department of Health and Human Services, Public

Health Service, National Institutes of Health, National Institute of Arthritis, Diabetes, and Digestive and Kidney Diseases, August 1985; NIH publication no. 85-1468.

28. Criqui MH, Barrett-Connor E, Holdbrook MJ, Austin M, Turner JD. Clustering of cardiovascular

disease risk factors. Prev Med 1980;9:525–33.

29. Gotto AM, Jr. Interactions of the major risk factors for coronary heart disease. Am J Med

1986;80(suppl 2A):48–55.

30. Friedewald WT. Physical activity research and coronary heart disease. Public Health Rep

1985;100:115–7.

31. Jeffery RW. Community programs for obesity prevention: The Minnesota Heart Health Program. Obesity Research 1995;3(suppl;Sept 2):283s–88s.

32. Brownell KD. Public Health approaches to obesity and its management. Annu Rev Public Health

1986;7:521.

33. Public Health Service. Healthy people 2000: national health promotion and disease prevention

objectives—full report, with commentary. Washington, DC: US Department of Health and Human Services, Public Health Service, 1991; DHHS publication no. (PHS) 91-50212.

34. Waksberg JS. Sampling methods for random digit dialing. J Am Stat Assoc 1978;73:40–6.

35. NCHS. Health Promotion and Disease Prevention—United States, 1990. Hyattsville, MD: US

Department of Health and Human Services, Public Health Service, CDC, National Center for

Health Statistics, April 1993; DHHS publication no. (PHS) 93-1513. (Vital and health statistics:

data from the National Health Survey; series 10, no. 185).

36. Marks, JS, Hogelin GC, Gentry EM, et al. The Behavioral Risk Factor Surveys: state-specific

prevalence estimates of behavioral risk factors. Am J Med 1995;1:1–8.

37. SAS Institute Inc. SAS® Companion for the MVS environment. Version 6, First edition, Cary,

NC: SAS Institute Inc, 1990.

Vol. 47 / No. SS-5 MMWR 47
38. CDC. Epi Info, version 6: a word processing, database, and statistics system for epidemiology

on microcomputers. Atlanta: Public Health Service, CDC, Epidemiology Program Office, July

1995.

39. CDC. Epi Map, version 2: an Epi Info- and dBASE-Compatible mapping program. Atlanta: Public

Health Service, CDC, Epidemiology Program Office, September 1995.

40. Ohno Y, Aoki K, Aoki N. A test of significance for geographic clusters of disease. Int J Epidemiol

1979;8:273–81.

41. ATSDR. CLUSTER 3.1 Software system for epidemiologic analysis: instruction manual. Atlanta:

Public Health Service, Agency for Toxic Substances and Disease Registry, February 1993.

42. Friede A, Rosen DH, Reid JA. CDC WONDER: a cooperative processing architecture for public

health. J Am Med Inform Assoc 1994;1:303–12.

43. US Bureau of the Census. Statistical abstract of the United States: 1997 (117th edition). Washington, DC, 1997.

44. Day JC, Curry AE. Educational attainment in the United States: March 1996 (Update). Washington, 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 Department 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 selfreported 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 FiveCity 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 subgroups. 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
TABLE 1. State-, race-, and sex-specific prevalence of selected characteristics — Behavioral Risk FactSystem, 1994 — Continued State Overweight Physical inactivity Smoking White Black White Black White 

Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Me

Alabama 28.5 26.9 37.9 45.0 42.0 45.7 42.8 58.6 26.1 20.5 2Alaska 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 3California 27.2 23.1 36.8 37.4 16.2 22.3 26.4 38.9 20.6 16.6 2Colorado 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 Delaware 25.7 25.4 45.5 36.5 31.1 36.5 37.4 57.6 28.4 24.3 2District of Columbia 12.8 10.6 22.2 41.7 42.4 38.9 50.3 56.3 15.4 7.3 1

Florida 25.3 22.2 35.6 36.6 23.7 29.1 34.4 43.0 26.8 22.9 1Georgia 23.3 23.9 35.5 40.9 30.5 33.4 25.5 43.2 25.9 22.6 1Hawaii 21.5 17.6 52.6 33.7 12.8 15.0 20.4 26.4 20.9 20.1 3Idaho 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 2Indiana 30.1 28.5 39.9 54.5 25.0 32.7 34.6 45.6 27.9 23.1 2Iowa 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 Louisiana 25.5 25.3 31.4 46.4 30.6 34.7 30.2 39.0 28.9 23.6 3Maine 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 2Massachusetts 28.6 20.1 — 37.2 22.1 24.6 — 34.9 22.4 19.8 Michigan 31.9 27.6 37.2 51.6 19.5 24.3 20.2 39.2 24.8 25.9 2Minnesota 29.6 24.9 — 39.4 21.3 21.9 — 19.2 21.4 21.3 Mississippi 29.4 24.5 46.1 46.1 35.5 36.7 37.8 46.8 27.9 20.6 2Missouri 33.0 27.2 — 33.6 27.4 34.4 — 46.2 26.9 21.3 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 2New 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 2
TABLE 1. State-, race-, and sex-specific prevalence of selected characteristics — Behavioral Risk FactSystem, 1994 — Continued State Overweight Physical inactivity Smoking White Black White Black White 

Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Me

North Carolina 32.0 25.9 37.6 48.2 38.8 39.9 49.6 62.9 29.5 25.5 3North 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 Oklahoma 24.8 24.6 — 34.5 27.6 30.9 — 27.9 24.6 21.5 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 2Rhode 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 2South 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 2Texas 33.1 24.9 — 51.6 24.6 27.9 — 32.6 23.4 20.7 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 2Washington 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 1Wyoming 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 2Low 12.8 10.6 22.2 33.6 12.8 15.0 20.2 19.2 15.4 7.3 1High 35.7 31.0 52.6 54.9 45.2 47.3 50.3 62.9 32.1 27.6 3
TABLE 1. State-, race-, and sex-specific prevalence of selected characteristics — Behavioral Risk FactSystem, 1994 — Continued State Hypertension* Diabetes Mellitus Multiple Risk F

White Black White Black White 

Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Me

Alabama 19.1 18.0 20.7 31.1 3.5 2.7 2.1 6.2 26.6 25.5 2Alaska 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 2California 19.2 22.5 30.4 35.8 4.0 3.3 6.9 8.6 14.7 13.7 1Colorado 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 Delaware 19.4 20.9 29.5 30.8 5.0 4.4 13.4 8.2 23.6 23.0 3District of Columbia 13.0 15.4 22.1 25.9 2.7 1.9 5.0 5.7 15.6 9.2 2Florida 19.7 21.6 31.8 32.7 5.0 4.2 7.6 6.4 17.7 18.3 2Georgia 15.9 17.4 21.2 27.1 3.1 3.3 6.7 9.6 21.3 20.7 1Hawaii 14.5 16.4 27.6 25.7 2.7 2.5 0.5 6.6 11.1 9.0 3Idaho 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 3Indiana 21.0 27.3 24.2 34.1 4.1 4.5 2.0 6.1 21.8 22.0 2Iowa 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 Louisiana 19.4 19.6 17.9 30.9 5.0 3.2 5.2 9.3 22.4 22.6 2Maine 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 2Massachusetts 19.3 21.4 — — 3.5 3.5 — 18.2 17.6 15.4 Michigan 24.0 21.7 28.4 27.1 4.0 5.3 3.5 4.7 18.9 20.7 1Minnesota 20.7 22.7 — — 3.5 4.0 — 4.5 18.6 16.2 Mississippi 21.2 24.9 30.7 44.8 5.3 3.2 6.6 10.4 27.6 19.4 3Missouri 22.0 23.7 — 38.9 5.7 5.1 — 11.6 24.1 21.0 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 1New 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 2
TABLE 1. State-, race-, and sex-specific prevalence of selected characteristics — Behavioral Risk FactSystem, 1994 — Continued State Hypertension* Diabetes Mellitus Multiple Risk F

White Black White Black White 

Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Men (%) Women (%) Me

North Carolina 19.0 17.3 13.0 27.5 4.2 3.4 5.5 6.1 30.5 25.9 3North 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 Oklahoma 20.8 25.3 — — 2.4 2.9 — 3.3 19.5 18.5 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 1Rhode 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 2South 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 2Texas 17.3 18.8 24.8 29.1 5.5 4.7 — 1.1 22.4 17.9 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 3Washington 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 2Wyoming — — — — 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 2Low 13.0 14.3 13.0 24.9 2.0 1.9 0.5 1.1 11.1 9.0 1High 24.9 27.3 31.8 44.8 6.2 6.1 13.4 18.2 32.0 33.1 3

* 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 53
AAAA 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

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 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 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 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAAA

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA

AA

AAAA

AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAA

AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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 AAAA

AAAA

AA

AA

*Overweight: Body mass index (BMI = weight[kg]/height[m2]) ≥27.8 for men and ≥27.3 for

women.

Percent

FIGURE 1a. Prevalence of overweight* among white men — Behavioral Risk Factor

Surveillance System, 1994

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

AAAA

AAAAAAAAAA

AAAAAA

AAAAAAAAAAAA

AAAA

AAAAAAAAAA

AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA

AAAAAAAAAA

AAAAAA

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

A

A

AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA

AAAAAA

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 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

AAAAAAAAAA

AAAAAAAAAAAAAA

AAAA AAAA AAAA

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 AAAA AAAA AAAA AAAA

AAAA AAAA

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 AAAA

AA

AA

AA

AA

D.C.

*Overweight: Body mass index (BMI = weight[kg]/height[m2]) ≥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 AAAAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAA

AAAA

AAA AAAA 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

AAAAAAAAAA

AAAAAAAAAAAA

A

A

AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA

*Overweight: Body mass index (BMI = weight[kg]/height[m2]) ≥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 AAAA

AA

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

AAAA

AAAAAAAAAA

AAAAAA

AAAAAAAAAAAA

AAAA

AAAAAAAAAA

AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA

AAAAAAAAAA

AAAAAA

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

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

AAAAAAAAAA

AAAAAAAAAAAA

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

AAAAAAAAAA

AAAA

AAA AAA

AAA

*Overweight: Body mass index (BMI = weight[kg]/height[m2]) ≥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 AAAAAAAA

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

AAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA 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 AAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AA AAAA AAAA AAAAAAA AAA AAA AAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

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

AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAAAAA A

AAAA AAAA AAAA

AAA AAA AAA AAA

A

AAAAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA 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

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

AAAAAAAAAA

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

AAAAAAAAAA

AAAAAAAAAAAA

*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 — Behavioral

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 AAAAAA

AA

AA

AA

D.C. AAAA

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 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 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 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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

AAAAAAAAAA

AAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAAA

A

A

A

A

AA

AAAA

AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAA

*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 — Behavioral 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 AAAA

AA

AA

AA

AA

D.C.

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 AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAAAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA

A

A

A

A

A

A

AAAAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

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

AAAAAAAAAA

AAAAAAAAAAAA

*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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

AAAA AA AAAA AAAA AAAAAAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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 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

A

A

A

A

A

A

A

A

A

A

A

A

AAAA

A

AAA AAA

AA

AAAAAA

AAAA

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

AAAAAAAAAA

AA

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

AAAAAA

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

AAAAAAAAAA

AAAAAAAAAAAAAA

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

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 AAAAA

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

AAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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.

AAA

A

AAAA

AAA AAAA 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

AAA AAA AAA AAA

AAA AAA AAA AAA AAA

AAA AAA AAA

AAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA

AAAAAAAAAAAA

AAAA

AAAA AA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAA

A

A

AAAA AAAA AAAA

AAAA AAAA

AAAA 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

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAAAAAAAA

AAAAAAAAAAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA 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 AAAA

AAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAA

AAAAAAA AAAA AAAA AAAAAAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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

AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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 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

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

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

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

AAAAAAAA

AAAAAAAAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA

AAAAAAAAAAAA

AAAAAAAAAA

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

AAAAAAAAAA

AAAA

AAAA

AAAA AAAA AAAA 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 AAAAAAAAAAAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAAAAAA

AAAAAAAAAA

AAAAAA

AAAAAAAA

AAA AAA

AAA

AAAA

AA

AAAAAA

*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 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

AAA AAA AAA AAA AAA

AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAA

AAA AAA

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 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

AAAA AAAA AAAA

AAAA

A

A

A

A

A

A

A AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA 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

AAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAAAAA AAAA AAAA AAAA

AAAAAA AAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA A AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

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 AAAA

AA

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

AAAA

AAAAAAAAAA

AAAAAA

AAAAAAAAAAAA

AAAA

AAAAAAAAAA

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

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 AAAAAA AAAA AAAA AAAAAAA AAA AAA AAA

AAAAAA

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

AAAA

AAAAAAAAAA

AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

AAAA AAAAAAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAAAAAAAA

AAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AA

AAAA

AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAA

*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

AAAA

AAAA

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

AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA 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

AAA AAA

AAAA AAAA

AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA

AAAAAAAAAAAA

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

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 AAAAAAAA

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 AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA 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 AAAAA

A

A

A

A

A

AAAA

A

AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAA AAAAAA

A

AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA

*Risk factor combinations: Survey participants having ≥2 of the following risk factors: overweight, 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 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

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

AAAAAAAAAA

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

AAAAAAAAAA

AAAAAAAAAAAAAA

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

AAAA

AAAAAAAAAA

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

AAAAAAAAAA

AAAAAAAAAAAA

AAA 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

AAAAAAAA

AAAAAAAAAA

AAAAAA

AAAAAAAA

AAA AAA

AAA

AAA AAA AAA AAA

AAAAAA

*Risk factor combinations: Survey participants having ≥2 of the following risk factors: overweight, 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 AAAA

AAAA

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

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

A

A

AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

A

A A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAA

AAA AAA

AAA

*Risk factor combinations: Survey participants having ≥2 of the following risk factors: overweight, physical inactivity, smoking, and diabetes mellitus.

Percent

FIGURE 6c. Prevalence of risk factor combinations* among black men — Behavioral

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 AAAA

AA

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

AAAAAA

AAAAAA

AAAAAAAA

AAAAAAA A

AAAA AAAA AAAA

AAA AAA AAA AAA

AAAAAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA

A

A

A

A

A

A

AAAAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAAAA 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

AAAAAAAAAA

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

AAAAAAAAAA

AAAA

*Risk factor combinations: Survey participants having ≥2 of the following risk factors: overweight, 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

AAAAAAAAAA

AAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA

AAAA AAAA AAAA AAAA AAAA AAAA

AAA AAA

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 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

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

AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA

AAAAAAAAAA

AAAAAAAAAAAAAA

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 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

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 AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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

AAAAAAAAAA

AAAAAAAAAA

*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

AAAA

AAAA

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

AAAA

AAAAAAAAAA

AAAAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA

AAAA AAAA

AAAA AAAA AAAA AAAA AAAA

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

AAAAAAAA

AAAAAAAAAAAAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAAA AAAA AAAA

AAA AAA AAA

AAA AAA AAA

AA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA

AAAAAAAA

AAAAAAAAAAAAAA

*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 AAAA

AAAA

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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

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 AAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA 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 AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAA

A

A

A

A

A

A

A

A

A

A

A

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA

AA

AAAA

AA

AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAA AAAAAAAAAAAAAAAAAAAAAA

AAA AAA

AAA

*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

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*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

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*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

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*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 positions 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 Hartwig, 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. Carlson, 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. Boulton, MD, MPH Robert Martin, 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 Greenblatt, MD, MPH Veronica 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. Mojica, 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. Shireley, 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 VanEenwyk, 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

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server at ftp.cdc.gov. To subscribe for paper copy, contact Superintendent 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 reporting 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

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MMWR