United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Information

Bulletin

Number 57

August 2009

Health Status and Health 

Care Access of Farm and 

Rural Populations

Carol Adaire Jones, Timothy S. Parker, Mary Ahearn, 

Ashok K. Mishra, and Jayachandran N. Variyam
www.ers.usda.gov

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Health status and health care access of farm and rural populations.

 (Economic information bulletin; no. 57)

 1. Rural health—United States.

 2. Rural population—Health and hygiene—United States.

 3. Farmers—Health and hygiene—United States.

 4. Rural health services—United States.

 I. Jones, Carol Adaire. 

 II. United States. Dept. of Agriculture. Economic Research Service.

 III. Title.

RA771.5

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www.ers.usda.gov www.ers.usda.gov
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Rural residents have higher rates of age-adjusted mortality, disability, and chronic disease 

than their urban counterparts, though mortality and disability rates vary more by region 

than by metro status. Contributing negatively to the health status of rural residents are their 

lower socioeconomic status, higher incidence of both smoking and obesity, and lower 

levels of physical activity. Contributing negatively to the health status of farmers are the 

high risks from workplace hazards, which also affect other members of farm families who 

live on the premises and often share in the work; contributing positively are farmers’ higher 

socioeconomic status, lower incidence of smoking, and more active lifestyle. Both farm and 

rural populations experience lower access to health care along the dimensions of affordability, proximity, and quality, compared with their nonfarm and urban counterparts. 

Keywords: agriculture safety and health, electronic health records, farmer health, 

health, health care access, health care affordability, health care quality, health disparities, health IT, health status, mortality, rural health, telehealth, uninsured

Acknowledgments

We would like to acknowledge the contributions of Dayton Lambert, University of 

Tennessee, who initiated the research on indicators of rural health access; Tracey Farrigan, 

USDA, Economic Research Service (ERS), who conducted a literature review that 

informed the analysis; and Didem Bernard, U.S. Department of Health and Human Services 

(USDHHS), Agency for Healthcare Research and Quality, and Lora Fleming and William 

LeBlanc, University of Miami Miller School of Medicine, who provided special tabulations. This report benefi ted substantially from the comments of fi ve external peer reviewers, 

including Thomas C. Ricketts, University of North Carolina, Chapel Hill; Joan Van Nostrand 

(and others), USDHHS, Offi ce of Rural Health Policy; Mark Rukavina, Brandeis University; 

and two anonymous reviewers; as well as from the following members of USDA, ERS: Mary 

Bohman, Robert Gibbs, and Patrick Sullivan. Finally we gratefully acknowledge John Weber 

for editorial assistance, Linda Beeler and Kathleen Kassel for support in creating the graphics, 

Wynnice Pointer-Napper for graphics and layout, and Cynthia A. Ray for cover design. 

Carol Adaire Jones, cjones@ers.usda.gov,

Timothy S. Parker, Mary Ahearn, Ashok K. Mishra, 

and Jayachandran N. Variyam

Health Status and Health Care 

Access of Farm and Rural 

Populations

Economic

Information

Bulletin

Number 57

August 2009
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Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Chapter 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter 2

Profi les of Farm and Rural Populations . . . . . . . . . . . . . . . . . . . . . . . . . . 3

 Similarities and Differences Between Farm and Rural Households 

 in Factors Affecting Health Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . 3

Chapter 3

Health Status and Health Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

 Mortality and Disability Rates, by Metro Status and Region . . . . . . . . . 7

 Farm and Farm Family Health Risks From the Work Environment. . . . 10

 Incidence of Health Risk Behaviors, Chronic Disease, and Physical 

 Limitations, by Farmer and Metro Status . . . . . . . . . . . . . . . . . . . . . . 12

Chapter 4

Health Care Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 Affordability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 Geographical Access: Medical Resource Availability, 

 by County of Residence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Implications for Quality of Rural Care . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 5

Health Care Quality: Accountability and Coordination . . . . . . . . . . . . 35

 Rural Opportunities and Challenges With Use 

 of Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

 Rural Opportunities and Challenges With Health Information 

 and Communications Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 6

Implications for Research and Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Appendix A: Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Appendix B: Maps Illustrating Measures of Rurality

 and Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Recommended citation format for this publication:

Jones, Carol Adaire, Timothy S. Parker, Mary Ahearn, Ashok K. 

Mishra, and Jayachandran N. Variyam, Health Status and Health 

Care Access of Farm and Rural Populations, EIB-57, U.S. Dept. 

of Agri., Econ. Res. Serv. August 2009.
iii

Health Status and Health Care Access of Farm and Rural Populations / EIB-57

Economic Research Service/USDA

Summary

Health is a critical component of household well-being, and reforming the U.S. 

health care system is high on the national policy agenda. Health care access 

and health status are a particular concern in rural areas, where the population is 

older, has lower education and income levels, and is more likely to be living in 

medically underserved areas than is the case in urban areas. 

What Is the Issue?

U.S. health policy debates have focused on expanding health insurance 

coverage, improving health care quality and value, and achieving greater 

effi ciencies and sustainable fi nancing. Information on current geographic and 

demographic disparities in both health outcomes and access to high-quality 

and cost-effective health care can aid in the design and implementation of 

effective policy solutions. This report focuses on the health status and health 

care access of members of the Nation’s rural households and farm-operator 

households in comparison with those of urban and nonfarm households. 

What Did the Study Find?

Health status. Rural (nonmetro) residents have higher rates of age-adjusted 

mortality, disability, and chronic disease than their urban (metro) counterparts, though mortality and disability rates vary more by region than by metro 

status. The recently identifi ed gap between metro and nonmetro mortality rates 

opened in 1990 and has widened continually since then. Farming has one of 

the highest occupational fatality rates of all occupations, and farm children also 

have high fatal accident rates. In addition, farmers are at high risk for workrelated lung diseases, noise-induced hearing loss, skin diseases, and certain 

cancers associated with chemical use and prolonged sun exposure. 

Socioeconomic status and behavioral health risks. The nonmetro population 

is older, is less likely to be from a minority group, and has lower education 

and income levels than the metro population. (Higher socioeconomic status, 

including education, income, and nonminority status, tends to be positively 

associated with health status.) However, within nonmetro areas, farm operators are more likely to have college degrees and greater economic resources 

and are less likely to be from a minority group than their nonfarm counterparts. Farmers whose major occupation is farming are less likely to smoke 

than nonfarmers, whereas nonmetro adults overall are more likely to smoke, 

to be obese, and to be physically inactive than metro adults. 

Health insurance coverage and health care expenditures. Among 

nonmetro and metro populations, about 15 percent of all individuals had 

no health insurance coverage during 2007—this includes about 17 percent 

of the nonelderly population and 2 percent of the elderly population. (The 

elderly share is low because Medicare coverage starts at age 65.) The rates 

of uninsurance are considerably higher in the South and West (21 percent 

and 19 percent, respectively) than in the Northeast and Midwest (both 

are 13 percent). The study found no statistically signifi cant disparities 

in coverage or in level of health expenditures by metro status; however, 

because nonmetro incomes are lower than metro incomes, nonmetro 
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nonelderly populations pay a greater share of household income for health 

care than their metro counterparts. 

Among all farm-operator households, 14 percent of all members did not have 

health insurance during 2007—this includes 15 percent of nonelderly and 7 

percent of elderly household members. Lack of coverage is higher for members 

of households in which farming was the primary occupation of the operator (20 

percent and 6 percent for nonelderly and elderly, respectively). The study did not 

fi nd statistically signifi cant disparities in coverage of nonelderly farm household 

members by metro status, and the regional variations are much smaller than those 

among the general population (lack of coverage is slightly elevated in the West 

relative to the South and the Midwest). 

Nonmetro households are more likely than metro households to report that 

health care costs limit their medical care. In contrast, households of farmers 

who cite farming as a primary occupation are less likely to report that health 

care costs limit their medical care than households of nonfarmers. 

Health care resources—quantity and quality. The accessibility of health 

care resources generally declines as population density declines and 

geographic isolation increases. In smaller and more remote counties where 

small patient volumes will not support full-service hospitals, the rural health 

care model focuses on providing primary care and emergency care locally, 

and referring patients to (often distant) regional health care centers for 

specialized care. As a result, rural residents in more remote areas incur higher 

fi nancial and travel-time costs than urban residents for specialized treatment. 

As an alternative, they may substitute local generalists for specialists, or 

reduce their usage of health care. 

Nonmetro hospitals, particularly the smaller, more remote Critical Access 

Hospitals, performed less well on average for process-of-care quality indicators for treatment of some conditions, though for other conditions their 

performance was comparable with metro hospital performance. Adoption 

rates for health information technology—widely touted to improve coordination of services and thereby improve quality and reduce costs—remain low 

at this point among all providers. Though high-speed connectivity to the 

Internet is becoming less of a stumbling block in nonmetro areas than it once 

was, nonmetro hospitals report lower adoption rates for electronic health 

record systems than their metro counterparts. Proposed national policy initiatives to improve health care quality and contain costs raise opportunities for 

rural health care. These initiatives, however, may also pose challenges for 

health care providers serving farmers and rural residents unless policies take 

into account distinctive features of the rural context. With smaller patient 

volumes, rural hospitals and other rural providers tend to provide a different 

portfolio of health care services and have a higher cost structure and lower 

levels of fi nancial and human capital relative to urban providers. 

How Was the Study Conducted? 

This study used household-level data for various measures of health status, 

risk behavior, insurance coverage, and care expenditures, as well as for 

nonoccupational health risks and health care usage rates. (All health status 

and nonoccupational health risk variables are age adjusted.) For farm 
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Health Status and Health Care Access of Farm and Rural Populations / EIB-57

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households, USDA’s Agricultural Resource Management Survey was the 

primary source of data on sociodemographic characteristics, insurance 

coverage, and health expenses. For all U.S. households, the U.S. Census 

Bureau’s American Community Survey was the source for demographic 

information, and the Census Bureau’s Current Population Survey was the 

source for economic and health insurance coverage information. The Medical 

Expenditure Panel Survey, developed by the U.S. Department of Health and 

Human Services (HHS), Agency for Healthcare Research and Quality, was 

the source for health expenditure data for all U.S. households. The National 

Health Interview Survey, developed by HHS’s National Center for Health 

Statistics, was the source of measures of health status, behavior, and use of 

health care for nonmetro households and for farm households (identifi ed by 

having a household member who indicates farming as an occupation, a subset 

of all farm households identifi ed by USDA). Measures of health resources 

were drawn from the Area Resource File, a county-level fi le developed by HHS’s 

Health Resources and Services Administration, which contains health-related 

data from a wide variety of sources. 

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CHAPTER 1

Introduction

Health is a critical component of well-being. Healthy individuals are able to 

enjoy leisure and gain satisfaction in life. They also have a greater ability to 

learn new skills, earn more income, and generate wealth to support current and 

future consumption. 

Reforming the U.S. health care system is high on the national policy 

agenda. U.S. health policy debates have focused on expanding health 

insurance coverage, improving health care quality and value, and 

achieving greater effi ciencies and sustainable fi nancing. Because the 

three objectives are interconnected, addressing all three at the same 

time would be most effi cient. Costs grow too rapidly because the system 

pays for volume, not quality. Quality indicators like lifespan and infant 

mortality remain lower than desired because too many individuals are 

left out of the health care system. Some families do not have health care 

coverage because health costs grow faster than labor productivity. And, 

health insurance costs increase partly due to providers shifting the cost of 

uncompensated care to their paying customers. 

Information on current disparities in health outcomes and in access to highquality and cost-effective health care can help in the design and implementation of effective policy solutions. The disparities in health status across 

rural-urban geography have been explored extensively (see Ricketts, 1999; 

Glasgow et al., 2004; Eberhardt et al., 2001; and Gamm et al., 2003). Yet, 

there is extensive variation in health status within metro and nonmetro 

areas and across regions. Moreover, the patterns, extent, and determinants of the geographical differentials observed are not well understood. 

Researchers are beginning to explore the factors associated with regional 

variations in health status, and how the role of metro status needs to be 

understood within the regional context (Ricketts, 2007).

This report focuses on the health care access and health outcomes of rural residents and farm operator households, in comparison with those of their urban 

and nonfarm counterparts. In this analysis, “farm population” refers to principal 

operators of family farms and their households. These households represent twothirds of the farm workforce and account for half of all hours worked in farming. 

Hired farm workers, who account for the rest of the farm workforce, are not 

covered; they have a substantially different demographic and economic profi le 

from operators (see Kandel, 2008; Variyam and Mishra, 2005). 

Numerous defi nitions of rural exist.1 Nonetheless, the key dimensions of 

rural—regardless of defi nition—are geographic dispersion of population and 

lesser access to markets for services and jobs. Both features have signifi cant 

implications for the delivery of health care. This report primarily relies on the 

U.S. Offi ce of Management and Budget’s (OMB) county-based defi nitions of 

metropolitan, micropolitan, and noncore areas because data on attributes of 

the population and the economy are more available on a county basis than on 

noncounty-based measures of “rural.”2

1ERS briefi ng room on Measuring 

Rurality: www.ers.usda.gov/briefi ng/

rurality/

2Data on the characteristics of rural 

and urban residents based on the Census defi nition, for which the building 

block is Census tracts, are available 

only from the decennial censuses.
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Limited information has been published examining the health status and health 

care access of farm households relative to their nonfarm counterparts in rural 

and urban areas. The comparison is informative because rural farm households 

share the health challenges of their nonfarm rural counterparts due to their 

geography but differ in other advantages and challenges as a result of their 

different social and economic status and occupational risks. 
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CHAPTER 2

Profi les of Farm and Rural Populations

Farming is a predominantly rural, or nonmetro, activity. However, as a result 

of a tremendous transformation in U.S. agriculture and rural life during the 

twentieth century, today most rural economies and populations are no longer 

tightly aligned with farming (see box, “Who Is a Farmer?”). The dramatic 

productivity increases in U.S. agriculture over the last century have freed a 

large share of the population to enter nonfarm occupations as well as to relocate to urban, or metro, areas. Today, agriculture employs less than 2 percent 

of the employed labor force. As the rural share of the U.S. population has 

declined, the share of the U.S. population living on farms has declined even 

more, so that the farm population now represents a small share of the rural 

population. While farming dependence once characterized most U.S. rural 

counties, by 2000, only 20 percent of rural counties were considered farming 

dependent. Geographically, these counties are mostly located down the 

middle of the country from North Dakota to northern Texas, with some 

dispersed in the South and the West 1 (Dimitri et al., 2005).

Today, nonmetro counties lag behind metro counties in nonfarming 

economic opportunities: a lower proportion of nonmetro jobs are in higherpaying professional and managerial positions. And the gap between 

nonmetro and metro earnings per job has expanded over the past three 

decades: by 2004, nonmetro earnings per job had dropped to 67 percent of 

metro earnings per job. Further, population growth has been lowest in the 

least densely populated counties: the population in noncore counties grew 

by 8 percent during the 1990s, compared with growth rates of 10 percent in 

micro areas and 14 percent in metro areas. 

With urban sprawl, farming is increasingly occurring within metro counties 

(see box, “What Is Rural?”). Nonetheless, the geographical distribution of the 

households of farm principal operators is quite different from that of all U.S. 

households, which are predominantly located in metro counties (83 percent), 

with slightly more than half in large metro countries (populations of 1 million 

or more). Within nonmetro counties, the U.S. population is more concentrated in micro counties than in noncore counties (fi g. 2.1). In contrast, farm 

operator households are predominantly located in rural areas, and among 

rural households, farm households are more likely to be in the lower density, 

more remote rural areas. Farm households located in metro areas are concentrated in smaller metro areas while nonfarm households are more likely to be 

in large metro areas. 

Similarities and Differences Between Farm and Rural 

Households in Factors Affecting Health Outcomes

The literature identifi es three critical factors contributing to health outcomes: 

access to health care resources (including proximity, affordability, and quality); 

the community and occupational environment; and personal behavior, such 

as smoking and diet (United Health Foundation, 2006). Further, the contributions of these three factors are mediated by age, geography, and socioeconomic 

status. Current research on health disparities associated with socioeconomic 

1To be defi ned as a farming-dependent county in 2000, a county had to 

meet one of two conditions: (a) 15 

percent or more of average annual labor 

and proprietors’ earnings was derived 

from farming during 1998-2000, or

(b) 15 percent or more of employed 

residents worked in farm occupations in 

2000. See www.ers.usda.gov/briefi ng/

rurality/typology/
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status (delineated by education, race/ethnicity, and fi nancial resources) indicates that causal relationships vary, both across the dimensions and across 

the phases of the lifecycle. As summarized by Cutler et al. (2008), parental 

resources—education and income—as well as minority status appear to have 

powerful effects on the health of children. A surprising fi nding is that once 

childhood health is set, income and wealth no longer appear to have a large 

effect. In fact, the direction of causality tends to work in the opposite direction: 

poor health tends to limit the ability of individuals to work, resulting in lower 

This analysis focuses on two farm household populations: households of all 

principal farm operators, and households of the subset of principal farm operators who identify farming as their principal occupations. 

Households of all principal farm operators. To capture the population of principal operators of family farms, the analysis starts with the population of farms 

as defi ned by USDA (“any place from which $1,000 or more of agricultural 

products were produced and sold, or normally would have been sold, during 

the year”). Because the interest here is farm operator households, the analysis 

focuses on the approximately 2 million “family farms,” defi ned as those in 

which the majority of ownership of the farm business is held by related individuals.1 Nearly all U.S. farms (98 percent in 2007) are family farms.2 The USDA 

data sets—the Census of Agriculture and the annual Agricultural Resource 

Management Survey—capture this population. (See appendix A for a description of data sources.) 

Households of operators with farming as their principal occupation. In national 

surveys of the U.S. population, such as the National Health Interview Survey 

(NHIS) (which this analysis uses for data on health status, health risk behaviors, 

and health care usage by farm and metro status), a farmer is typically identifi ed 

based on his or her self-identifi cation of farming as a primary occupation. Many 

farmers hold off-farm jobs in addition to operating their farms. To identify a 

subgroup of farms from USDA data sets comparable to the farmer population 

available in NHIS data, this study reports USDA data for family farms that are 

headed by principal operators who identify farming as their principal occupation, 

which represented about 40 percent of all family farms in 2007.

Relative to all USDA farmers, “farm-occupation” farmers are more likely to be 

over age 65 and live in Midwest noncore counties. Economically, they are more 

reliant on farm income than other farm households, with lower median income 

but higher median wealth than all USDA farmers.3

1For more information, see the glossary in the ERS briefi ng room on Farm Household 

Economics and Well-Being: www.ers.usda.gov/briefi ng/wellbeing/glossary.htm

2About 40 percent of farms have more than one operator; however, for three-quarters 

of the farms with multiple operators, the farm is operated by a husband-and-wife team, 

so that both operators are part of the principal operator household on which we focus. 

About 10 percent of family farms have other operator households associated with the 

farm, which are not included in this analysis. For more information, see www.ers.usda.

gov/briefi ng/wellbeing/.

3Over the last 10 years, U.S. farm households earned on average between 80 and 95 

percent of their household income from nonfarm sources. The average share of household 

income from farming increases with the sales class of the farm. For more information, 

see www.ers.usda.gov/briefi ng/wellbeing/. 

Who Is a Farmer?
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income and wealth accumulation. In adulthood, education and membership in a 

minority group continue to be powerful determinants. 

Nonmetro residents, particularly those living in more remote areas, face 

special health care challenges due to geography. Lower population densities mean that nonmetro residents must typically travel longer distances 

for health services, especially for specialty care. The nonmetro population 

also is older and has lower education and income levels than the metro 

population. (See appendix A for descriptive data on the farm and all U.S. 

populations by metro status.) On the other hand, nonmetro farm operator 

households are more likely to be of higher socioeconomic status than all 

nonmetro households: a larger share has household heads that graduated 

from college, a lower share has minority status, and median household 

income is higher. 

The two most commonly used defi nitions of rural are (1) the county-based defi -

nitions of metropolitan, micropolitan, and noncore areas of the U.S. Offi ce of 

Management and Budget (OMB), and (2) the census-tract based defi nitions of 

rural and urban areas of the U.S. Census Bureau. 

Metro and nonmetro areas. In 2003, OMB updated its defi nition of metro areas 

(based on 2000 census data) as (1) central counties with one or more urbanized 

areas, and (2) outlying counties that are economically tied to the core counties 

as measured by work commuting. Nonmetro counties are outside the boundaries 

of metro areas and are further subdivided into two types: micropolitan, or micro, 

areas, centered on urban clusters with at least 10,000 but no more than 50,000 

persons, and all remaining noncore counties. In the 2003 update, nonmetro 

America covered 75 percent of the Nation’s land but included only 17 percent 

(49 million) of the U.S. population. 

Given that 83 percent of the U.S. population lives in metro counties, a number 

of systems exist to distinguish metro counties by size class. In this report, the 

National Health Interview Survey data split metro counties into large and small 

Metropolitan Statistical Areas (MSAs), where the dividing line is 1 million 

people; 54 percent of the U.S. population lives in large metro countries. 

RUCA codes. The rural-urban commuting area (RUCA) codes are based on 

similar concepts and measures of population density, urbanization, and daily 

commuting as used by OMB to defi ne county-level metro and micro areas. 

However, RUCA codes use census tracts as their building blocks and so allow 

a detailed and fl exible scheme for delineating subcounty components of the 

U.S. settlement system. In a travel time study, Chan et al. (2006) aggregated 

the system of RUCA codes to create four categories: urban (a census-defi ned 

urbanized area of population 50,000 or greater); large rural city (in or associated with a large rural city of 10,000-49,999); small rural town (in or associated with a rural town of 2,500-9,999); and isolated rural town (in a town of 

less than 2,500 population and/or not associated signifi cantly with a large 

town via work commuting fl ows). For a map illustrating the four-part RUCA 

coding of counties, see appendix B. 

Source: Measuring Rurality briefi ng room on the ERS website: www.ers.usda.

gov/briefi ng/rurality/. 

What Is Rural?
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In contrast, the relative socioeconomic status of metro farm operators is 

mixed, compared with that of metro nonfarmers. Metro farmers as a group 

are less likely to have college degrees, and metro farm-occupation farmers 

(though not all farmers) have slightly lower median household incomes than 

their all-U.S. counterparts. On the other hand, metro farmers are much less 

likely to be a member of a minority group. But on another critical measure of 

socioeconomic status—wealth—farm households (and, particularly, metro 

and farm-occupation farmers) dominate their nonfarm household counterparts. At the national level, median farm household wealth is fi ve times that 

of all U.S. households (see appendix A). Farmers are self-employed entrepreneurs in a capital-intensive sector. Consequently, a large share of their net 

worth is in farm business wealth, including farmland.2

As relates to health status, other special challenges farmers confront include 

higher occupational risks of accidents and disease than their nonfarm metro 

and nonmetro counterparts. 

2For more analysis on farm household 

economic well-being, see Jones et al. 

(2006) or Mishra et al. (2002).

Figure 2.1

Metro distribution of households for all farm operators, 

farm-occupation operators, and all U.S., 2007

Percent

Note: Nonmetro is subdivided into micro and noncore.

Sources: USDA, Economic Research Service using all versions of USDA’s Agricultural 

Resource Management Survey 2007, (NASS and ERS), and the U.S. Census Bureau’s 2007 

American Community Survey.

All U.S. All farm 

operators

Farm-occupation 

operators

0

20

40

60

80

100

Large metro Small metro Micro Noncore

Household heads
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CHAPTER 3

Health Status and Health Risks

The principal indicators of health status summarized in this chapter are ageadjusted mortality rates, disability rates, farm accident fatality rates, and 

incidence of self-reported disease and self-assessed physical limitations. 

The analysis of disease incidence focuses on the leading causes of death in 

the United States. The analysis of health risk factors identifi es self-assessed 

personal risk behaviors (and discusses their links to the leading causes of 

death) and risks of fatal injuries on the farm relative to other work environments. National data do not exist on mortality and disability by occupation, 

which would allow comparisons between farmers and other workers to 

complement comparisons between metro and nonmetro populations. 

Mortality and Disability Rates, 

by Metro Status and Region 

Mortality Trends by Metro Status, 1968-2005

The dominant trend in recent U.S. mortality has been one of decreasing death 

rates. During 1968-89, mortality rates for metro and nonmetro counties were 

similar and declined at about the same rates (fi g. 3.1). 

Data reveal that nonmetro mortality rates made a transition in 1990 (Cosby et 

al., 2008). Since then, metro rates have improved at twice the annual rate of 

nonmetro improvement. As a result, by 2001-05, the metro/nonmetro mortality 

disparity had steadily increased to around 70 deaths per 100,000. However, 

within the metro and nonmetro county groups, rates vary signifi cantly across 

regions associated in part with differences in patterns of persistent poverty, race, 

and ethnicity of the populations (Cossman et al., 2003; Murray et al., 2006).

Figure 3.1

Annual metro and nonmetro age-adjusted mortality per 100,000 

for the United States, 1968-2005

Mortality rates per 100,000 

Note: 1974 nonmetro codes were used for 1968-75, 1983 nonmetro codes were used 

for 1976-85, 1993 nonmetro codes were used for 1986-95, and 2003 nonmetro codes 

were used for 1996-2005. 

Sources: Cosby et al., 2008; USDA, Economic Research Service calculations for 2005 

mortality estimates, using the Compressed Mortality File 1999-2005 from the Centers for 

Disease Control and Prevention, National Center for Health Statistics. 

Nonmetro

Metro

1968 71 74 77 80 83 86 89 92 95 98 2001 04

750

800

850

900

950

1,000

1,050

1,100

1,150

1,200

1,250

1,300

1,350
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Mortality Rates: Regional Variations by Metro Status, 2005

The difference in 2005 age-adjusted mortality rates is substantially larger 

across regions than between metro and nonmetro counties (table 3.1). The 

difference from the lowest (West) to the highest (South) mortality-rate 

region is 108 per 100,000, though the difference doubles if the comparison is 

across region and metro status. The level of divergence between metro and 

nonmetro counties varies across regions, with the greatest metro-nonmetro 

gap occurring in the South, while in the Midwest, there was essentially no 

difference by metro status. For context, four of fi ve U.S. farm households 

live in the South or the Midwest (compared with three of fi ve for all U.S. 

households). Further, half of all farm households live in nonmetro counties 

in either the South or the Midwest, and 30 percent live in noncore counties 

in the two regions, compared with 13 and 6 percent, respectively, for all U.S. 

households. (See appendix A.)

The regional nature of the mortality rates so apparent in fi gure 3.2 is corroborated by spatial statistical analysis of mortality clusters (Cossman et al., 

2003). A low mortality-rate cluster is located in the Farm Belt portion of the 

Table 3.1

Age-adjusted mortality rates, by region and metro status, 2005

 Nonmetro

U.S. region Total Metro Total Micro Noncore

 Number of deaths per 100,000

Northeast 762.7 757.2 809.0 804.5 820.9

Midwest 813.0 813.5 814.1 816.4 812.9

South 845.6 818.7 938.8 922.5 960.6

West 737.4 730.0 796.7 799.5 792.3

Total 798.8 784.2 866.1 857.1 880.0

Source: USDA, Economic Research Service using data from the National Center for Health 

Statistics Compressed Mortality File, 2005. 

Figure 3.2

Age-adjusted mortality rate, by county, 2005

Source: USDA, Economic Research Service using data from the National Center 

for Health Statistics, Compressed Mortality File, 1999-2005.

Number of deaths 

per 100,000 persons

980.4 - 2,300.3

889.1 - 980.3

817.3 - 889.0

741.0 - 817.2

260.7 - 740.9

Data not available
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Midwest, from Wisconsin and Iowa west to the Dakotas, and south through 

Nebraska and Kansas to northeastern Colorado. (The area includes many 

of the farming-dependent counties identifi ed by ERS.) The high mortalityrate clusters are in the South, including the Mississippi River Delta, the 

Black Belt of the southern coastal plane from Virginia through Alabama, 

and Appalachia. Factors associated with higher mortality across the clusters 

include high persistent-poverty rates, high shares of Black or Appalachian 

population, and low rates of high school graduation (Cossman et al., 2003). 

Disability Rates: Regional Variations by Metro Status, 2000

In 2000, 18.6 percent of the U.S. population age 16-64 reported having at 

least one of six possible disabilities (table 3.2).1 The distribution of disability 

by region and metro status is similar to that of mortality. The nonmetro rate 

of disability was somewhat higher than the metro rate. Nonmetro areas with a 

low incidence of disability were concentrated in the Midwestern Farm Belt 

(fi g. 3.3). Areas with high nonmetro disability include the areas of high 

mortality in the South: Appalachia, the Delta, and the Black Belt. 

1The U.S. Census asked questions 

about six different impairments and the 

limitations they imposed of respondents: 1) Does this person have any of 

the following long-lasting conditions: 

a) blindness, deafness, or a severe 

vision or hearing impairment; b) a 

condition that substantially limits one 

or more basic physical activities such 

as walking, climbing stairs, reaching, 

lifting or carrying? and 2) Because of a 

physical, mental or emotional condition 

lasting 6 months or more, does this person have any diffi culty in doing any of 

the following activities: a) learning, remembering, or concentrating? b) dressing, bathing, or getting around inside 

the house? c) going outside the home 

alone to shop or visit a doctor’s offi ce? 

or d) working at a job or business? If 

an affi rmative answer was given to any 

of the six questions, respondents were 

classifi ed as having a disability.

Table 3.2

Disability rates for population age 16-64, by region 

and metro status, 2000

 Nonmetro

U.S. region Total Metro Total Micro Noncore

 Percent of population reporting disability

Northeast 18.2 18.2 18.1 17.6 19.5

Midwest 16.4 16.2 17.0 16.5 17.8

South 20.2 19.2 23.9 23.0 25.1

West 18.4 18.4 19.1 19.2 19.1

Total 18.6 18.2 20.5 19.7 21.6

Source: USDA, Economic Research Service using data from the U.S. Census Bureau, 

Cenus of Population, Summary File 3, 2000.

Figure 3.3

Percent of population disabled, age 16-64, by county, 2000

Source: USDA, Economic Research Service using data from the U.S. Census Bureau, 

Cenus of Population, Summary File 3, 2000.

Percent of

population

24.2 - 60.8

21.0 - 24.1

18.0 - 20.9

15.4 - 17.9

6.5 - 15.3
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Farm and Farm Family Health Risks 

From the Work Environment

Agriculture ranks among the most hazardous industries, according to the 

National Institute of Occupational Safety and Health (NIOSH), charged by 

Congress in 1990 with developing an agricultural safety and health program 

to address the high risks of injuries and illnesses experienced by workers 

and families in agriculture. Farmers face risks from working with machinery 

and animals as well as from potential exposures to high concentrations of 

hazardous substances associated with agricultural chemicals, including 

pesticides, herbicides, and fertilizers. As a result, farmers are at high risk for 

fatal and nonfatal injuries, work-related respiratory diseases, noise-induced 

hearing loss, skin diseases, and certain cancers associated with chemical use 

and prolonged sun exposure (NIOSH, 2009). Different types of farms have 

different patterns of exposures and accident risks, resulting in different patterns 

of elevated diseases and accident types (see the meta analyses of studies 

conducted by Blair et al., 1992; and Acquavella and Olsen, 1998). Farming is 

also one of the few industries in which the families (who often share the work 

and live on the premises) are also at risk for injuries, illness, and death.

National data are reported annually on fatal (and nonfatal) accidents, by occupation. However, a national system for calculating rates of total mortality, 

disability rates, and chronic disease by occupational history—necessary to 

compare farmers with other workers—does not exist. Alternatively, the occupational incidence of total mortality and mortality by chronic disease is studied 

by conducting epidemiological studies, which may be based on data collected 

for administrative purposes, including death certifi cates, census registers, and 

tumor registries, or by enrolling selected populations in a longitudinal survey. 

Calculating total mortality rates for an occupation and for all other workers for 

comparison is challenging due to diffi culties in identifying the total population 

of workers who have worked in that occupation.2

Consequently most studies focus on the relative incidence of diseases across 

worker groups, or relative to the general population. Starting with a DHEW 

Vital Statistics Report in 1963 (see Guralnick, 1963), various epidemiological 

studies of farmers in the United States and in other countries have suggested 

that, whereas White male farmers have higher incidences of fatal accidents 

and of certain diseases associated with agricultural exposures, they have had 

a lower incidence of diseases associated with smoking and obesity (including 

heart disease and lung cancer) (Blair et al., 1992; Acquavella and Olsen, 1998, 

Blair et al., 2005). In recent years, however, some studies have raised questions as to whether the incidence of heart disease among White male farmers 

remains lower (Fleming et al., 2003; Brackbill et al., 1994; Lee et al.,2002).3

Incidence of Fatal Farm Accidents, 1992-2007

Farming has one of the highest fatality rates of all occupations, according to the 

U.S. Department of Labor. While the overall fatality rate in the United States in 

2007 was 3.7 per 100,000 workers, the rate for those with farming or ranching 

as a major occupation was more than nine times higher—38.4 per 100,000 

(fi g. 3.4). Furthermore, whereas fatal injuries per 100,000 workers generally 

2One challenge with death certifi cate 

data, for example, from the National 

Occupational Mortality Surveillance 

Data (NOMS), is identifying the total 

population of a particular occupation 

for which NOMS is reporting death 

rates. Lacking population data, ageadjusted mortality rates for specifi c 

diseases can be compared across different occupations represented, but total 

mortality rates for a particular occupation cannot be calculated. Fleming et al. 

(2003) employed an alternate methodology that matched records from a 

sample of respondents in the National 

Health Interview Survey for 1986–94 

with records from the National Death 

Index to estimate mortality rates for 

pesticide-exposed workers and for all 

other workers. However 30 percent of 

their sample was farm workers, rather 

than farm operators.

3To study the relationship between 

exposures and disease incidence in 

pesticide-exposed farmers and farm 

spouses, the National Cancer Institute, 

the National Institute for Environmental 

Health Sciences, and the National Institute for Occupational Safety and Health 

are sponsoring a long-term study of a 

panel of registered pesticide applicators 

(5 percent of whom are commercial 

applicators, the rest are almost entirely 

farmers) and their spouses in North 

Carolina and Iowa. The study enrolled 

participants during 1994-97 and plans 

to follow the population for 20 years. 

Interim fi ndings (Blair et al., 2005) 

comparing the mortality rates of the 

pesticide-exposed population with that 

of the general population in the two 

States indicate the pesticide applicator 

cohort experienced a low mortality rate 

through 2000. The authors suggested 

that a more healthful lifestyle may 

contribute to the lower defi cit but also 

cautioned that part of the mortality 

defi cit may be the result of comparing 

the pesticide-exposed population with 

the general population over a short time 

period (3-7 years), since individuals 

who are currently working typically are 

more healthy than the general population. This “healthy worker” effect, 

typically estimated to be on the order of 

15-20 percent in surveys of workers observed at one point in time, may decline 

in future years of followup.
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declined for all U.S. workers from 1992 to 2006, the fatality rate for farmers 

and ranchers almost doubled during this same period (fi g. 3.5). 

Leading causes of farm fatalities from workplace injuries and accidents are 

transportation incidents (including tractor rollovers), contact with objects or 

equipment, and assaults (including animal attacks). Contributing to the risks 

are the long hours worked during planting and harvesting periods by farmers, 

their family members, and hired workers. The fatal injury rate for those in crop 

production has averaged more than twice that for those in animal production. 

Among agricultural workers, the fatal injury rate for those age 55 and older in 

1995-2002 was 47.9 per 100,000, almost twice the rate for younger agricultural 

workers (Meyer, 2005). Around 60 percent of all farm operators and 70 percent 

of farm-occupation principal operators are age 55 or older. 

Figure 3.4

Farming and other selected occupations with high fatality rates, 2007

Rate = Fatal work injuries/employment x 100,000. 

Source: USDA, Economic Research Service using U.S. Department of Labor, Bureau of 

Labor Statistics, National Census of Fatal Occupational Injuries, All Worker Profile, 1992-2007.

All workers

Police and sheriff patrol officers

Refuse and recyclable material collectors

Drivers/sales workers and truck drivers

Electrical power-line installers and repairers

Roofers

Farmers and ranchers

Structural iron and steel workers

Aircraft pilots and flight engineers

Logging workers

Fishers and related fishing workers

0 20 40 60 80 100 120

Mortality rates per 100,000 

Figure 3.5

Occupational injuries, farmers and ranchers and all workers, 1992-2007

Injuries per 100,000 workers

*Hired managers are excluded for 1998-2007, horticultural managers are excluded for 

1998-2002, and farm workers are included for 1992, 1996, and 1997. 

Source: USDA, Economic Research Service using U.S. Department of Labor, Bureau of 

Labor Statistics, National Census of Fatal Occupational Injuries, All Worker Profile, 1992-2007.

1992 94 96 98 2000 02 04 06

0

10

20

30

40

50

Farmers and ranchers*

All workers*
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Further, farm families who live on the premises and often share the work are also 

at risk. According to NIOSH, an estimated 1.12 million youth under age 20 

resided on farms in 2006, with about 590,000 of these youth performing work on 

the farms. On average, 8 per 100,000 youth died annually from farm-related injuries between 1995 and 2002.4 The leading sources of fatal injuries to youth on 

U.S. farms are machinery, motor vehicles, and drowning (NIOSH, 2009a).

Incidence of Health Risk Behaviors, Chronic Disease, 

and Physical Limitations, by Farmer and Metro Status 

The leading causes of death in the United States in 2005 were heart disease, cancer, 

stroke, chronic respiratory diseases, accidents, and diabetes. The two top causes of 

death, heart disease and cancer, account for roughly half of all U.S. deaths. 

Behavioral decisions by individuals are critical determinants of the burden of 

disease. The risk factors associated with the greatest total disease burden in 

North America are smoking, alcohol use, and overweight and obesity (Murray 

et al., 2006). Cigarette smoking contributes to numerous chronic illnesses, 

including several types of cancers, chronic obstructive pulmonary disease, 

cardiovascular disease (heart disease and stroke), as well as to reduced bone 

density, reduced fertility, and premature death (USDHHS, 2004). Although 

there is some debate over the health benefi ts of small amounts of regular 

alcohol consumption, the negative short- and long-term health effects of excessive alcohol use are well established. Short-term effects include motor vehicle 

injuries, falls, domestic violence, and child abuse. Long-term effects can 

include liver cirrhosis, pancreatitis, various cancers, high blood pressure, and 

psychological disorders, including dependence. Being overweight or obese also 

increases the risk for numerous ailments, including high blood pressure, 

diabetes, heart disease, stroke, arthritis, cancer, and poor reproductive health.5

NHIS asked adults in a nationally representative sample of households about 

the incidence of various health risk factors, self-reported chronic disease, 

and self-assessed physical limitations. The metro/nonmetro comparison is 

based on a sample of all adults, whereas the farmer/other worker comparison 

is based on a sample of working adults categorized by their major occupation at the time of the survey, which excludes people who are sick or have 

a disability and are unable to work (and therefore will be a pool of healthier 

individuals than the total population). 

Nonmetro adults were more likely than their counterparts from large-metro 

and small-metro areas to report engaging in most of the risky health behaviors: higher shares of adults in nonmetro households were current smokers, 

were obese, or were inactive (fi g. 3.6). In contrast, a lower share of nonmetro 

adults was current drinkers. For those whose major occupation was farming, 

the pattern was mixed (fi g. 3.7). The incidence of farmer smoking was less 

than that of other workers. The presumption is that farmers are more active 

on the job. Farmers, however, were more likely than other workers to be 

overweight but less likely to be obese. Lower farmer rates of obesity and of 

current drinking were statistically signifi cant at the 10-percent level.

Relative to adults in metro households, adults in nonmetro households reported 

higher incidence rates of hypertension, heart disease, cancer, and stroke. The 

small difference in rates of respiratory disease was not statistically signifi cant 

4This fatality rate includes the 1.12 

million children and adolescents living 

on the farm, as well as an additional 

307,000 who were hired to work on 

U.S. farms in 2006.

5The Centers for Disease Control 

and Prevention defi nes overweight as 

having a body mass index between 25 

and 30, and obese as having a body 

mass index over 30. Body mass index is 

a measure that adjusts body weight for 

height (USDHHS, CDC, 2004).
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(fi g. 3.8). Relative to nonfarm workers, farmers reported lower incidence rates of 

cardiovascular disease and asthma and emphysema (fi g. 3.9). The differences in 

rates for the other diseases were not statistically signifi cant. In contrast, Brackbill 

et al., (1994) found a statistically signifi cant excess incidence of cardiovascular 

conditions, but not for respiratory disease, when comparing a national sample of 

Figure 3.6

Incidence of behavioral health risk factors by metro status, 2006 

(age standardized)

MSA = Metropolitan Statistical Area.

Source: USDA, Economic Research Service using U.S. DHHS, 2008, Summary Health 

Statistics for U.S. Adults: National Health Interview Survey, 2006. Vital and Health Statistics, 

Series 10, Number 235.

Nonmetro Small MSA Large MSA

Never do vigorous

exercise

Obese

Overweight

Current smoker

Current drinker

0 20 40 60 80

Percent

* = Statistically significant difference from nonmetro at 5-percent level

*

*

*

*

* *

* *

Figure 3.7

Incidence of behavioral health risk factors by farmer status, 1997-2003 

(age standardized)

Source: USDA, Economic Research Service using U.S. DHHS, National Center for Health 

Statistics, National Health Interview Survey, 1997-2003.

Farmers Other U.S. workers

Percent

* = Statistically significant difference at 5-percent level

Obese

Overweight

Current smoker

Current drinker

0 20 40 60 80

*

*
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farmers with other White male workers for the period 1986-90, using a comparable methodology with pooled NHIS data.

Nonmetro adults reported higher incidence rates of chronic joint pain, chronic 

low back pain, and physical limitations, such as diffi culty walking a quarter of 

Figure 3.8

Incidence of chronic disease conditions by metro status, 2006

(age standardized)

MSA = Metropolitan Statistical Area.

Source: USDA, Economic Research Service using U.S. DHHS, 2008, Summary Health 

Statistics for U.S. Adults: National Health Interview Survey, 2006. Vital and Health Statistics, 

Series 10, Number 235.

Nonmetro Small MSA Large MSA

Percent

* = Statistically significant difference from nonmetro at 5-percent level

Diabetes

Cancer

Emphysema

Asthma

Heart disease

Stroke

Hypertension

0 10 15 20 25 5

*

*

*

*

Figure 3.9

Incidence of chronic disease conditions by farmer status, 1997-2003 

(age standardized)

Source: USDA, Economic Research Service using U.S. DHHS, National Center for Health 

Statistics, National Health Interview Survey, 1997-2003.

Farmers Other U.S. workers

Percent

* = Statistically significant difference at 5-percent level

Diabetes

Cancer

Asthma and 

emphysema

Cardiovascular

 (heart, stroke)

Hypertension

0 10 15 20 25 5

*

*
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a mile, climbing 10 steps without stopping, or standing or sitting for 2 hours, 

than their metro counterparts. These results are consistent with the fi ndings 

reported earlier from census data. In contrast, farmers reported a higher incidence of chronic joint pain but are otherwise comparable with other workers in 

terms of chronic injuries and physical limitations (fi gs. 3.10 and 3.11).

Figure 3.10

Incidence of chronic pain and physical difficulties by metro status, 2006 

(age standardized)

MSA = Metropolitan Statistical Area.

Source: USDA, Economic Research Service using U.S. DHHS, 2008, Summary Health 

Statistics for U.S. Adults: National Health Interview Survey, 2006. Vital and Health Statistics, 

Series 10, Number 235.

Nonmetro Small MSA Large MSA

Percent

* = Statistically significant difference from nonmetro at 5-percent level

*

Any physical

difficulties

Chronic low

back pain

Chronic joint

pain

0 10 20 30 40

*

*

*

*

Figure 3.11

Incidence of chronic pain and physical difficulties, by farmer status, 

1997-2003 (age standardized)

Source: USDA, Economic Research Service using U.S. DHHS, National Center for Health 

Statistics, National Health Interview Survey, 1997-2003.

Farmers Other U.S. workers

Percent

* = Statistically significant difference at 5-percent level

Any physical

limitations

Chronic joint

pain

Chronic low

back pain

0 10 20 30 40

*
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CHAPTER 4

Health Care Access

“Access to health care” is the number one rural health priority identifi ed by 

Rural Healthy People 2010 (Gamm et al., 2003). Specifi cally identifi ed as 

priorities were access to health insurance and access to primary, dental, and 

mental health care, as well as emergency services. Rural households confront 

special challenges to achieving healthy outcomes due to socioeconomic 

status and age factors, and due to less healthy behaviors. 

Affordability

Health Insurance Coverage

Lack of health insurance creates a range of consequences, including 

increased incidence of illness, lower quality of life, and lower life expectancy, in tandem with higher fi nancial burdens. In addition to protecting 

households against the fi nancial risks imposed by expensive and unanticipated medical events, health insurance coverage tends to increase the likelihood of timely access to health care, including preventive care, diagnostic 

tests, and prescriptions, which can help prevent escalation of health problems. Coverage may also increase survival rates from life-threatening disease 

and reduce costs, for example, by preventing avoidable hospitalizations for 

chronic conditions, such as congestive heart failure or uncontrolled diabetes 

(Gamm et al., 2003; IOM, 2000; Dorn, 2008).

Senior status is a critical determinant of coverage because Medicare coverage 

is available to U.S. citizens age 65 and older. Because the farmer and 

nonmetro populations have a larger share of elderly than their respective 

counterpart populations, it is important to control for age when comparing 

covered populations.

For nonelderly individuals, the backbone of the U.S. health insurance system 

is employment-based coverage. Nationally, about three-quarters of workers 

are offered employment-based insurance (CBO, 2009). One reason such 

plans are popular is that they are subsidized by the tax code: neither 

employer nor employee payments are subject to income or payroll taxes. 

Larger employers are more likely than small employers to offer insurance to 

their workers because of the economies of scale in administrative costs and 

in risk pooling. Alternatively, individuals may purchase private insurance 

directly but will not generally have access to the group-rate insurance policies that offer lower costs due to risk pooling. In most States, premiums for 

direct purchase policies may vary with age or health status, and applicants 

with particularly high expected costs are generally denied coverage. Also, 

direct purchase insurance generally does not receive favorable tax treatment. 

The exception is for households of the self-employed who can deduct the 

premiums for self-purchased health insurance, yielding a reduction in aftertax health insurance cost equal to their marginal tax rate.1

Major public sources of insurance for nonelderly individuals include the 

Federal/State Medicaid program and the related, but smaller, Children’s 

1The tax benefi ts for direct-purchase 

insurance among the self-employed are 

lower relative to those of employersponsored insurance (ESI) because 

the self-employed tax deduction only 

reduces income taxes while the ESI 

tax exclusion reduces both income and 

payroll taxes.
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Health Insurance Program (CHIP). Both programs provide free or low-priced 

coverage for children in low-income families and, to a more limited degree, 

their parents. Medicaid also covers poor individuals who are blind or have 

other disabilities. Other public sources include Federal health programs for 

military personnel or veterans, and Medicare for nonelderly individuals who 

are disabled or have severe kidney disease. 

It is important to note that comparisons of household out-of-pocket expenditures on premiums across types of insurance can be misleading as an indicator 

of effective costs to the household, as well as of actuarial value of the policy.2 

A useful summary statistic for comparing plans with different designs is their 

actuarial value, which essentially measures the share of health care spending 

for a given population that each plan would cover. Actuarial values for 

employment-based plans typically range between 65 and 95 percent, with an 

average of 80-85 percent; in contrast, for direct purchase plans, they generally 

range from 40-80 percent, with an average of 55-60 percent. Public programs 

vary in the extent of coverage they provide (CBO, 2009). 

Prior literature indicates that insurance coverage is lower in nonmetro areas, in 

large part because working adults living in rural areas are less likely to be 

offered health insurance through their jobs. Employment-based insurance is 

less likely to be offered for low-skilled service jobs and by small employers—

both of which are more prevalent in rural areas. Farmers might be expected to 

have lower coverage because they are self-employed; on the other hand, a 

majority of farm households have someone who works off the farm. Further, 

farm households tend to be in a better fi nancial position than the nonfarm 

population, and consequently are better able to afford the cost of private health 

insurance. ERS research has shown that, in 2006, farm-operator household 

members nationwide were slightly more likely to have insurance coverage than 

the general U.S. population (Ahearn, 2008). Consistent with these national 

results, a recent survey of farmers in seven Great Plains States also found 

higher than average coverage rates for farm households: over 90 percent of 

farm households headed by a nonelderly person in those States indicated that 

all members of their households had been continuously insured during 2006.3

Coverage—nationally and by region and metro status. According to the U.S. 

Census Bureau’s Current Population Survey (CPS), 45.7 million people in the 

United States did not have health care coverage at any time during 2007.4 About 

17 percent of all nonelderly individuals were estimated to be without health 

insurance coverage during the same period. Due to coverage by Medicare, the 

share of elderly individuals without insurance was signifi cantly less, at 2 percent. 

About 15 percent of nonelderly farm household members lack coverage—

slightly lower than that of the general U.S. population. However, among households of primary-occupation farmers (40 percent of all farmers), 20 percent of 

nonelderly members lack insurance coverage (table 4.1). 

The analysis did not fi nd statistically different disparities in coverage by 

nonmetro status for either the whole U.S. population or the farm-operator 

household population (fi g. 4.1). For nonmetro households, it is noteworthy 

that the size (and statistical signifi cance) of the previously observed metro/

nonmetro differences in coverage appear to have dissipated over the last 10 

years since the introduction of CHIP (see box, “Changing Metro/Nonmetro 

Patterns of Insurance Coverage, 1997-2005”), though individuals in the more 

2It is well understood that the employee out-of-pocket share of employmentbased policies understates total premium 

costs. Further, research indicates that 

costs of employers’ payments are passed 

on to employees as a group, mainly in 

the form of lower wages—so employee 

out-of-pocket costs also tend to understate the effective costs of the insurance 

policy to the employee (CBO, 2009).

3The seven States were Montana, 

North Dakota, South Dakota, Minnesota, Nebraska, Iowa, and Missouri, all 

of which (except Montana) were above 

the U.S. average in the share of covered 

individuals (Access Project, 2007).

4For our reporting, we rely on 

USDA’s Agricultural Resource Management Survey (ARMS) for farm 

households and the U.S. Census Bureau’s Current Population Survey (CPS) 

for all U.S. households. The Census 

Bureau cites evidence to support the 

possibility that the supplement to the 

CPS, which collects health insurance 

data for the U.S. population (i.e., the 

Annual Social and Economic supplement (ASEC)), uses a data collection 

approach that leads to underreporting 

the share of households that had health 

insurance coverage at any point during 

the year. In particular, survey research 

suggests that respondents are likely to 

be reporting their insurance coverage at 

the point of time of the data reporting rather than for the entire previous 

year, as is explicit in the wording of 

the survey question. The CPS ACES 

survey question on health insurance is 

similar to the ARMS survey question. 

Hence, the implication of this fi nding 

relating to respondents’ interpretation 

of the survey question likely is similar 

for both the ARMS and the CPS ACES. 

For more information, see appendix C 

of DeNavas-Walt et al., U.S. Census 

Bureau, P60-235, August 2008.
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remote (noncore) counties still have lower coverage than urban residents.5

However, regional location appears to have more impact than rurality, with 

the Northeast and Midwest having consistently higher rates of health insurance coverage than the South and the West. 

Coverage by source of insurance. Among the nonelderly, employment-based 

insurance is the most common type for both farm and nonfarm individuals, 

providing coverage for more than 60 percent of both populations. Among 

nonelderly persons in primary-occupation farming households, close to half 

are covered by employment-based insurance. Although farm operators are 

self-employed on their farm, either the operator or the spouse, or both, of 

two-thirds of farm operator households worked off the farm in 2007. Even in 

cases where the operator cites farming as his or her major occupation, nearly 

20 percent of operators work off the farm. 

5Lenardson et al. (2009). This result 

is based on analysis of pooled December 2004 and December 2005 Medical 

Expenditure Panel Survey (MEPS) data.

Table 4.1

Health insurance coverage of persons in all U.S. and farm households, by metro status 

and by source of coverage, 2007

 Individuals under age 65 (total) Individuals 65 and older Individuals of all ages

 Total Metro Nonmetro Total Metro Nonmetro Total Metro Nonmetro

Percent

All U.S. persons

Uninsured * 17.1 17.1 17.4 1.9 2.0 1.4 15.3 15.3 15.0

Any insurance coverage **

Employer 62.9 63.5 59.4 34.1 35.2 29.8 59.3 60.2 54.9

Private direct 

 purchase 6.5 6.5 6.7 25.9 24.5 31.8 8.9 8.6 10.5

Public (Medicare, 

 Medicaid, other) 18.5 17.8 22.4 93.7 93.1 96.2 27.8 26.6 33.5

Persons in all households of principal farm operators

Uninsured 15.3 15.0 15.5 7.4 8.4 6.7 13.8 13.7 13.9

Any insurance 

 coverage 84.7 85.0 84.5 92.6 91.6 93.3 86.2 86.3 86.1

Employment-based 60.7 60.0 61.1 12.9 12.0 13.5 52.6 51.8 53.0

Private direct 

 purchase 17.9 17.7 18.1 29.5 29.3 29.6 20.1 20 20.2

Public 6.3 6.7 6.1 79.4 78.8 79.8 19.9 20.7 19.4

Persons in households of farming-occupation operators

Uninsured 19.9 23.9 17.6 11.1 na na 16.5 19.6 14.7

Any insurance 

 coverage 80.1 76.1 82.4 93.7 93.3 94.0 83.5 80.4 85.3

Employment-based 47.9 44.1 50.1 11.6 10.5 12.3 40.5 38.2 41.7

Private direct 

 purchase 24.9 23.3 25.4 30.7 30.5 30.9 26.3 25.1 27.1

Public 7.2 6.5 na 82.9 84.1 82.2 26.2 26.0 26.4

na = Insuffi cient sample.

*“Uninsured” refers to no insurance coverage during the last 12 months.

**Sources of coverage add up to more than the total due to multiple sources of insurance.

Source: USDA, Economic Research Service calculations, using data from 2008 March Current Population Survey (ASEC) for all 

50 States, and USDA’s 2007 Agricultural Resource Management Survey, Version 1.
19

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For all U.S. nonelderly households, the second most common type of coverage 

is public insurance. The nonmetro share from public sources is 4 percent higher 

than the metro share, compensating for the difference in employment-related 

shares. In contrast, for nonelderly farm households, the second most common 

source is private direct-purchase of insurance. With higher incomes relative to 

their nonfarm counterparts, farm households are more likely to have the fi nancial 

resources for self-purchase and will typically not qualify for Medicaid or CHIP. 

Even those with low income, perhaps due to a bad year in the farm economy, are 

more likely to have assets that would disqualify them in their low-income years. 

Among the elderly populations, participation in government insurance 

programs is very high, as expected. Many elderly persons also supplement 

their Medicare coverage. For farm households and for all nonmetro households, direct purchase is more common than employment-based sources—the 

reverse of the pattern for metro households.

Health Expenditures 

Health care expenditures are separated into two components: household expenditures on insurance premiums and out-of-pocket costs for care (not covered by 

insurance). Higher health care expenditures do not necessarily imply a higher 

level of medical services—it could refl ect a lower level of insurance coverage, 

for example, direct purchase policies typically have lower coverage relative to 

employment-based plans. Also, for the self-employed (farm and nonfarm), the 

after-tax effective cost of a given level of out-of-pocket household expenditures on insurance premiums will be lower for direct-purchase relative to 

employment-based coverage due to the differential tax treatment.6

Expenditures by farm and metro status. As with insurance coverage, the 

analysis did not fi nd statistically signifi cant disparities in the level of health 

expenditures by nonmetro status—both groups annually spend, on average, 

about $3,300 (table 4.2). However, because the average income of nonmetro 

6In addition, all taxpayers who itemize deductions may take a deduction for 

health care costs above 7.5 percent of 

adjusted gross income.

Figure 4.1

Percent of uninsured nonelderly persons, by region and metro status, 

all U.S. and all farm households, 2007

Percent

na = insufficient numbers to report for Northeast. hh = household head. 

Source: USDA, Economic Research Service using U.S. Department of Commerce, 

Census Bureau, Current Population Survey and USDA’s Agricultural Resource 

Management Survey 2007, Version 1 only.

Northeast Midwest South West All regions

All U.S. Metro, all 

U.S.

Nonmetro, 

all U.S.

All farm Metro, 

farm hh 

Nonmetro, 

farm hh 

0

5

10

15

20

25

30

na na na
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The State Children’s Health Insurance Program (now known as the Children’s 

Health Insurance Program, or CHIP) was created in 1997 as a complement to 

Medicaid to provide coverage to low-income uninsured children not eligible 

for Medicaid. At the time, researchers suggested that the program could have 

a greater impact on lack of coverage for rural children than for urban children 

because more rural families were likely to be in the income range targeted by 

the program (between 100 and 200 percent of the Federal poverty level). 

The share of children without insurance coverage declined substantially between 

1997 and 2005. The increase in public coverage more than compensated for the 

decline in coverage from employment-based insurance. The net rural gains were 

so pronounced relative to urban gains that the rural differential was reversed: 

rural children now have higher insurance coverage rates. 

Although CHIP was designed to improve access for children, some States used 

the program’s fl exibility to expand coverage to parents as well. This does not 

appear to be widespread enough to have reduced uninsurance rates among adults 

age 18-64 between 1997 and 2005. The small increases in public coverage were 

offset by reductions in private coverage. The shares of nonelderly adults without 

coverage increased for metro counties and decreased or were essentially unchanged 

for nonmetro counties through 2005. As a result, the metro/nonmetro gap became 

smaller but did not entirely disappear. The combined effect of CHIP has been the 

dissipation of the metro/nonmetro divergence in nonelderly insurance coverage.

Source: Ziller, E., and A. Coburn. 2009. Rural Coverage Gaps Decline 

Following Public Health Insurance Expansions. Research and Policy Brief, 

Portland. ME: University of Southern Maine, Maine Rural Health Research 

Center, February. 

Changing Metro/Nonmetro Patterns of Insurance 

Coverage, 1997-2005

Insurance coverage for children and nonelderly adults, 

by metro status, 1997 and 2005

Percent

Notes: Calculations use Medical Expenditure Panel Survey (MEPS) data for 

December of each year. Public coverage includes Medicaid, SCHIP, Medicare, 

and TRICARE. Totals may not equal 100% due to rounding.

Source: USDA, Economic Research Service using Ziller and Coburn, 2009. 

1997 2005 1997 2005 1997 2005 1997 2005

0

20

40

60

80

100

Private

Public

Uninsured

Nonmetro Metro Nonmetro Metro

Children Adults, age 18-64
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Table 4.2

Family health expenditures of all U.S. nonelderly individuals, by metro 

status and source of coverage, 2005 (2007$)

Metro status All Metro Nonmetro

Sample size 28,617 23,682 4,935

Population (thousands) 258,708 217,441 41,267

All sources

After-tax family income - mean* 51,709 53,458 42,493

Out-of-pocket health care spending - mean 1,377 1,353 1,505

Out-of-pocket health insurance premiums - mean 1,880 1,912 1,712

Total out-of-pocket health expenses - mean 3,257 3,265 3,216

Percent in families with high burden* 19.1 18.1 24.2

Uninsured

After-tax family income - mean* 29,155 29,832 24,840

Out-of-pocket health care spending - mean 1,133 1,143 1,136

Out-of-pocket health insurance premiums - mean 276 286 252

Total out-of-pocket health expenses - mean 1,409 1,429 1,388

Percent in families with high burden* 15.0 14.5 18.1

Private - all

After-tax family income - mean* 62,989 65,053 52,022

Out-of-pocket health care spending - mean 1,550 1,516 1,673

Out-of-pocket health insurance premiums - mean 2,561 2,595 2,187

Total out-of-pocket health expenses - mean 4,112 4,110 3,866

Percent in families with high burden* 20.5 19.4 23.6

Private - group (employer)

After-tax family income - mean 63,340 65,327 na

Out-of-pocket health care spending - mean 1,517 1,490 na

Out-of-pocket health insurance premiums - mean 2,408 2,450 na

Total out-of-pocket health expenses - mean 3,925 3,939 na

Percent in families with high burden 18.6 17.8 na

Private - nongroup

After-tax family income - mean 56,842 60,084 na

Out-of-pocket health care spending - mean 2,143 1,989 na

Out-of-pocket health insurance premiums - mean 5,247 5,226 na

Total out-of-pocket health expenses - mean 7,389 7,215 na

Percent in families with high burden* 52.9 49.1 na

Public

After-tax family income - mean 22,680 22,618 22,919

Out-of-pocket health care spending - mean 842 813 975

Out-of-pocket health insurance premiums - mean 337 319 417

Total out-of-pocket health expenses - mean 1,179 1,131 1,389

Percent in families with high burden* 16.5 15.4 21.8

na = insuffi cient sample.

“High burden” refers to health expenses exceeding 10 percent of after-tax family income.

* = Signifi cant difference between metro and nonmetro at 5 percent.

#= Signifi cant difference at 10 percent.

Source: USDA, Economic Research Service using MEPS 2005, personal communication from 

Didem Bernard, USDHHS.
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households is lower, nonmetro residents spend a larger share of household 

income on out-of-pocket health expenditures (for health care and health insurance premiums) than metro residents. Overall, for those under age 65, household 

health expenses exceeded 10 percent of after-tax income in 2005 for 24 percent 

of nonmetro households, compared with 18 percent of metro households. The 

metro-nonmetro differential is similar across types of insurance—with the exception of direct purchase insurance, where about half of both metro and nonmetro 

households have expenditures above 10 percent of after-tax income. However, 

small shares of U.S. metro and nonmetro households purchase private insurance. 

Farm operator households spend much more on health care than all U.S. 

households, largely due to their greater reliance on direct-purchase private 

health insurance (table 4.3). In 2007, farm households spent, on average, 

$5,200 for both health insurance premiums and out-of-pocket health costs. 

For the 40 percent of farm operator households for whom farming is their 

primary occupation, health expenses are even higher, averaging nearly 

$6,000 in 2007. Primary-occupation farm households have higher health 

expenses than other farm households, even when they include household 

members working off the farm. For the primary-occupation households that 

rely solely on direct purchase health insurance, total out-of-pocket health 

expenses averaged nearly $10,000 in 2007. 

Table 4.3

Health insurance and expenditures of all farm households and primary-occupation farm households 

by elderly status of principal farm operator and by source of coverage, 2007

Elderly status of principal operator Nonelderly (under age 65) Elderly All ages

 Only Only Combination

 employment direct Only of

Source of coverage Uninsured based purchase public sources All All All 

Households of all principal farm operators

Average dollars

Out-of-pocket health care spending 2,168 1,882 3,075 1,728 2,173 2,106 2,353 2,176

Out-of-pocket health insurance premiums 759 2,543 6,035 1,181 3,641 2,965 3,248 3,046

Total out-of-pocket health care expenses 2,927 4,425 9,110 2,908 5,814 5,071 5,600 5,222

Percent

Health care spending as a share of 

total household spending, net of 

housing and savings 12.2 12.7 24.8 13.6 19.0 15.3 22.0 16.9

Households of farming-occupation operators

Average dollars

Out-of-pocket health care spending 2,778 2,325 3,608 1,087 2,553 2,603 2,533 2,577

Out-of-pocket health insurance premiums 720 2,700 6,222 1,203 4,204 3,350 3,159 3,278

Total out-of-pocket health care expenses 3,498 5,026 9,829 2,290 6,757 5,953 5,692 5,855

Percent

Health care spending as a share of 

total household spending, net of 

housing and savings 17.6 14.4 27.7 11.5 21.2 18.6 24.6 20.4

Source: USDA, Economic Research Service calculations using data from USDA’s 2007 Agricultural Resource Management Survey, 

Version 1.
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Self-assessed health care usage limited by cost. Though farm households 

report higher levels of health care expenditures, they report a lower likelihood 

of delaying medical care due to costs than nonfarm households (fi g. 4.2). In 

contrast, nonmetro households report a higher likelihood of postponing or not 

getting medical care due to costs than metro households (fi g. 4.3).

Figure 4.2

Cost-limited health care access by farm household status, 1997-2003

Source: USDA, Economic Research Service using U.S. DHHS, National Center for Health 

Statistics, National Health Interview Survey, 1997-2003.

Farm family All other families

Percent

* = Statistically significant difference at 5-percent level

*

Household member

did not get medical 

care due to cost, 

during last 12 months

Household member 

delayed medical care

due to cost, during 

last 12 months

0 10 15 5

Figure 4.3

Cost-limited health care access by metro status, 2006

MSA = Metropolitan Statistical Area.

Source: USDA, Economic Research Service using U.S. DHHS, 2008, Summary Health 

Statistics for the U.S. Population: National Health Interview Survey, 2006. Vital and Health 

Statistics, Series 10, Number 236. 

Percent

* = Statistically significant difference from nonmetro at 5-percent level.

Household member 

did not receive medical 

care due to cost

Household member 

delayed medical care 

due to cost

0 8 2 1 4 6 0

*

*

*

Nonmetro Small MSA Large MSA
24

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Geographical Access: Medical Resource Availability, 

by County of Residence

Due to the low density of population and small patient volumes in rural areas, 

the rural health care model—particularly in noncore counties or smaller rural 

areas—focuses on local provision of primary care and emergency care, with 

referrals outside the area for specialized care. The primary care physician—

the backbone of medical services in rural communities—can be supported by 

a population base as small as 2,000 people. In contrast, typical neurosurgeons 

require a population base of 100,000, along with sophisticated hospitals and 

laboratories, and specialty colleagues, to support their practice.7 Rural hospitals tend to be smaller and offer a more limited range of services than their 

counterparts in more densely populated regions—they rely on referrals to 

larger hospitals, which imposes higher travel costs on rural patients and may 

result in discontinuity of care if followup treatment is provided more locally. 

Alternatively, rural patients may rely more extensively on generalists rather 

than specialists for their treatments or forego treatment (Gamm et al., 2003). 

Studies report lower rural utilization of preventive health care services and 

higher rural rates of potentially avoidable hospitalization for conditions 

where timely and appropriate ambulatory care can reduce the frequency of 

hospital admissions (Casey et al., 2000; Casey et al., 2007).

This section presents indicators of the distribution of county-level medical 

resources in the Rural Healthy People 2010 priority areas of primary, dental, 

mental, and emergency care. The fi rst set includes standard indicators of 

access—county-based quantity of medical resources per 10,000 population. 

To assess the adequacy of health care resources, one needs a benchmark. 

Several researchers caution against a simplistic “more is better” approach, 

arguing that the urban supply of physicians is excessive and so does not 

provide an appropriate benchmark.8 Though there is an extensive body of 

research documenting the relationship between quantity of medical resources 

and quality of health outcomes with data on chronically ill Medicare patients, 

benchmarks for local health market care are not included in these studies (see 

Dartmouth Medical School, 2008). An alternative approach is to identify 

benchmarks for shortages. The second set of indicators—population shares 

living in Health Professional Shortage Areas (HPSA)—captures the extent of 

critical shortages of health care professionals. The HPSA designation identifi es populations with too few physicians for primary, dental, and mental 

health care. Providers in HPSAs are eligible for a broad array of governmental assistance, including Medicare bonus payments and allocations of 

health professionals trained through the National Health Service Corps. 

Though commonly employed for reasons of data availability, the countybased quantity per population measures is an imperfect proxy for accessibility for several reasons. For one, individuals can travel across counties, 

and, in fact, the closest health care may be just across a county line. More 

signifi cantly, the relationship between quantity counts and travel distance 

and time varies with population density (population per square mile) and 

with transportation networks. In general, for a given level of per capita health 

resources, travel distance and travel time to access them increase as population density declines—particularly in remote areas that are not served by 

interstate highways. The exception is in the transition from the most densely 

7 Rosenblatt, R., and L.G. Hart, 

“Physicians in Rural America,” in Ricketts, 1999, p. 41.

8Ibid., p. 39.; Ricketts and Holmes, 

2007; Baicker and Chandra, 2004a.
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Health Status and Health Care Access of Farm and Rural Populations / EIB-57

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populated urban areas to outlying urban/suburban areas, where travel time 

actually may decline with population density as travel congestion decreases.

To address these limitations, the next section reports a third set of indicators 

available for a selection of States—travel times to treatment and usage rates 

of all generalist and specialist medical professionals. 

Health Professionals 

As the total number of physicians practicing in the United States grew 

substantially over the last few decades, numerous programs have been 

implemented to redistribute health professionals to rural areas. Alternative 

approaches include educational interventions and economic incentives for 

health professionals to locate in rural areas, as well as direct public provision of primary care to underserved and disadvantaged populations through 

Federally Qualifi ed Health Centers and Rural Health Clinics. During this 

time, the supply of physicians in rural areas grew modestly—primarily in 

the larger rural communities adjacent to metro areas. Although most types 

of specialty care have become more broadly available over time, specialties 

with fewer practitioners are still not generally available in smaller towns 

(Rosenthal et al., 2005).

In this analysis, the counts of medical professionals per 10,000 persons in a 

county9 are limited to the subset of professionals who are non-Federal employees 

and who provide patient care. Unless otherwise noted, the data are for 2005 (see 

box, “Calculating Household Distributions of County-Level Indicators”).

Primary care physicians.10 Primary care physicians are integral to the rural 

health care system. The share of total physicians providing primary, rather than 

specialty, care increases as population density declines and geographic isolation increases—from 35 percent in metro, to 44 percent in micro, to 63 percent 

in noncore counties. The concentration of primary care declines less than that 

of the different specialties (fi g. 4.4). However, there is still a decline from nine 

primary care physicians per 10,000 persons in metro counties, to six per 10,000 

in micro counties, and fi ve per 10,000 in noncore counties. Following the 

pattern of the distribution for all physicians, farm households have lower 

concentrations of primary care physicians in metro counties and comparable 

concentrations in micro and noncore counties, relative to all U.S. households. 

The HPSA threshold level of U.S. primary care physicians is 3.5 per 10,000 

persons, below which a county is designated as an HPSA. The share of the 

population in whole-county HPSAs represents an underestimate of the total 

number of medically underserved.11 Four percent of total U.S. households 

are in a whole-county primary care HPSA (fi g. 4.5). The share of HPSA 

households increases substantially as rurality increases, from 2 percent in 

metro counties to 28 percent in noncore counties. In contrast, 17 percent of 

the farm population is in a whole-county HPSA, ranging from 8 percent in 

micro to 32 percent in noncore counties.

Maternal, infant, and child health care. In remote counties, public policy is 

directed to ensuring local availability of prenatal, obstetric, and neonatal 

services in tandem with linking higher risk women and infants with nonlocal 

specialized care when required. Primary care doctors are critical but so is the 

9County designations are based on 

county of household residence and of 

offi ce location of medical professional.

10Primary care includes general practice, internal medicine (general), and 

pediatrics (general) physicians.

11There are additional persons in 

partial-county HPSAs or specifi c-population HPSAs (e.g., Native Americans 

or homeless in a particular county), 

but counting the population affected is 

challenging.
26

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Health care resource data are compiled on a county basis. Because 

counties vary tremendously in the number of resident households, 

ERS researchers created a household distribution of health care 

resources by assigning to each household the value for the county in 

which it resides. 

A typical approach to compare the quantities of health resources available for two populations—for example, doctors per 10,000 population—

would be to compare averages for the two populations. But an average is 

highly infl uenced by the high and low ends of the distributions, particularly a few observations with a large number of doctors per 10,000. 

Given the great variability in resources across counties, ERS chose the 

median as the measure of central tendency rather than the mean. (At the 

50th percentile of a distribution, half of the households have a higher 

value than the median, and half have a lower value.)

To display the variability of values across the distributions of farm and 

all U.S. households, box plots are a useful tool. The bottom, middle, and 

top of the box represent the 25th, 50th (median), and 75th percentiles of 

the distributions—which are also known as the fi rst, second, and third 

quartiles. (At the 25th percentile, 25 percent of households have a lower 

value, and 75 percent have a higher value.) The “whiskers” above and 

below the box represent the 10th and 90th percentiles.

The box plot above illustrates the distribution of all doctors1 per 

10,000 population across all U.S. households. For each household, its 

doctor/10,000 ratio is determined by the county in which it is located. 

For example, the value for residents of the New York City borough 

of Manhattan (New York County, with a population of 1.6 million) is 

96/10,000, whereas in Loving County, TX (with a population of 55), 

there are no doctors, and so the value is 0. From the box plot, one can 

see that, for the household at the midpoint or median of the distribution, 

23.5 physicians per 10,000 provided patient care per 10,000 persons. The 

interquartile range was (15, 32), which means that 25 percent of U.S. 

households lived in a county with 15 or fewer physicians per 10,000 and 

75 percent lived in a county with 32 or fewer physicians per 10,000. 

1Includes both medical doctors (MDs) and doctors of osteopathy (DOs).

Calculating Household Distributions 

of County-Level Indicators 

0

10

20

30

40

50

All

23.5

90th

10th

25th

50th

75th

Number of 

doctors per 

10,000 population

Percentiles
27

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(a) Medical doctors (MD) and doctors of osteopathy (DO)

Households-Principal

farm operators

HouseholdsAll U.S.

0

10

20

30

40

50

All Metro Micro Noncore Total Metro Micro Noncore

23.5

16.4

12.4

6.6 10.6

7.2

24.9 13.4

Note: The numbers in the boxes are medians represented by the lines inside, the bottom of the 

box represents the fi rst quartile (25 percentile), and top of the box is the medium quartile (75 

percentile) of the distribution. Unless otherwise noted, data are for 2005.

Source: USDA, Economic Research Service using Area Resource File (ARF), 2006. U.S. 

Department of Health and Human Services, Health Resources and Services Administration, 

Bureau of Health Professions, Rockville, MD.

Figure 4.4

Health professionals—household distributions of county counts of

non-Federal employees providing patient care, per 10,000 population

0

1

2

3

All Metro Micro Noncore Total Metro Micro Noncore

(c) MD: Obstetrics/Gynecology

Households-Principal

farm operators

HouseholdsAll U.S.

1.2

0.8

0.7

0.0

0.5

0.0

0.7 1.3

0

2

4

6

8

All Metro Micro Noncore Total Metro Micro Noncore

(d) Dentists, total private practice (1998)

Households-Principal

farm operators

HouseholdsAll U.S.

4.8

4.0

3.5

2.7

3.4

2.6

3.5

5.1

0

5

10

15

All Metro Micro Noncore Total Metro Micro Noncore

(b) MD and DO: Primary care

Households-Principal

farm operators

HouseholdsAll U.S.

8.5

6.7 6.0 4.9 5.8 5.0 6.1

8.9
28

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surgical specialty of obstetrics and gynecology, which operates on the 

borderline between specialty and primary care. Obstetricians/gynecologists 

(ob/gyn) are heavily concentrated in metro counties and are almost nonexistent in noncore counties. Lack of local services imposes economic and traveltime burdens on rural residents and may have an impact on perinatal 

outcomes.12 Similarly, pediatric specialists (part of the medical specialist 

group, not illustrated here) are less well represented in noncore counties. 

Dentist. The median U.S. household lives in a county with 4.8 dentists per 

10,000 persons, compared with 3.4 dentists per 10,000 for the median farmoperator household. In noncore counties, the medians are 2.6 and 2.7, respectively. Relative to the distribution of primary care HPSAs, smaller shares of 

the population live in whole-county dental care HPSAs—3 percent of the 

total U.S. population and 8 percent of the farm population. Among noncore 

county residents, 14 percent of both populations live in HPSA counties. 

In addition to fewer dentists, rural areas face other supply-related challenges 

to accessing dental care, including a relatively low supply of dentists who 

accept Medicaid or other discounted fee schedules, reluctance of dentists 

to participate in managed care programs, and absence of a coordinated 

screening and referral network (National Rural Health Association, 2001). 

Limited dental insurance coverage—Medicare does not cover outpatient 

dental care—is also an impediment (USDHHS, 2000). 

Mental health professionals. Mental health care professionals are substantially underrepresented in rural areas, and farm households are disproportionately affected. The number of psychiatrists practicing in nonmetro counties is 

small—most micro or noncore counties have no psychiatrists. 

Among the HSPA designations, mental health captures the highest shares 

of the population, with 17 percent of the total U.S. population covered, 

including 51 percent and 60 percent, respectively, among micropolitan and 

12Lishner et al., “Rural Maternal and 

Perinatal Health,” in Ricketts (ed.), 

1999, p. 134.

Figure 4.5

Share of households in counties designated Health Professional 

Shortage Areas, by farm and all U.S. status, 2004

Percent

Source: USDA, Economic Research Service using Area Resource File (ARF), 2006. 

U.S. Department of Health and Human Services, Health Resources and Services Administration, 

Bureau of Health Professions, Rockville, MD.

Primary care Dental Mental health

0

20

40

60

80

100

All U.S.

Metro, all U.S.

Micro, all U.S.

Noncore, all U.S.

All farmers

Metro, all farmers

Micro, all farmers

Noncore, all farmers
29

Health Status and Health Care Access of Farm and Rural Populations / EIB-57

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noncore county households. For farm households, the share covered by 

mental health whole-county HPSAs is triple that for all households; the farm 

share in noncore counties is 73 percent. 

As a result, primary care practitioners in more remote rural areas play a 

larger role in mental health care than do their urban counterparts. Primary 

care practitioners treating mental illness face a number of practice and 

professional constraints, including insuffi cient training and skills, heavy 

patient caseload, lack of time, and lack of specialized backup.13

Health Infrastructure

Across the United States, the numbers of hospitals and hospital beds have 

decreased over the last 30 years, refl ecting a national trend toward shortening hospital stays and shifting services to other lower cost inpatient 

facilities—such as skilled nursing facilities—and outpatient services—such 

as home health care. Rural hospitals, with their lower patient volumes and 

consequently higher cost structure, experienced a disproportionate share of 

closures during this period. Rural providers are more dependent on public 

payers—Medicare and Medicaid—which typically have lower rates of reimbursement. Further, to contain costs, Medicare introduced in the 1980s a new 

reimbursement system for hospitals, the Prospective Payment System (PPS), 

which based reimbursements on a predetermined, fi xed amount for each 

diagnosis, rather than the prior system, which reimbursed the allowable full 

cost of services provided. The move to PPS from a system that reimbursed 

the allowable full cost of services exacerbated the fi nancial problems for 

small, low-volume rural hospitals (Ricketts 1999).

According to the National Advisory Committee on Rural Health and Human 

Services (2008), various Medicare policies have been viewed as ‘fi xes’ to 

problems created by the prospective payment system for low-volume rural 

institutions. The biggest change for rural hospitals over the past two decades 

was legislative designation of Critical Access Hospitals (CAH), which established a cost-based reimbursement system for rural hospitals with 25 or fewer 

beds. The primary fi nancial benefi t of conversion to CAH status has been that 

these facilities no longer lose money on Medicare because they are paid for 

101 percent of costs.14 To qualify for conversion to CAH status, hospitals must 

provide a minimum level of emergency care inhouse; in addition, they must 

establish agreements with regional acute care hospitals to provide care for their 

more severely ill patients, including protocols for referral and transfer, communication, and emergency and nonemergency patient transportation. 

Hospital and skilled nursing facility beds. The median number of local (in 

the county) staffed hospital beds per 10,000 population is 29 for all U.S. 

households and 26 for farm households. The medians shift somewhat among 

rural and urban areas but are similar across metro status for the two populations (fi g. 4.6). However, the range for nonmetro households is wide, particularly for households living in noncore areas. In noncore counties, the number 

of beds ranges from 0 at the 10th percentile to 80-100 at the 90th percentile 

for all U.S and farm households, respectively. For Medicare-certifi ed skilled 

nursing facilities, the median number of beds consistently increases from 

metro to micro to noncore counties for both populations. Farm households 

13Gamm et al., p. 167.

14However, the designation does not 

address any fi nancial shortfalls that 

occur when Medicaid or private pay reimbursement falls below hospital costs.
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actually have higher median numbers of beds, third quartiles, and 90th

percentile values across all county types relative to all U.S. households.

Emergency medical services. Prehospital emergency medical services (EMS) 

represent the fi rst stage in a full continuum of emergency care that also 

includes hospital emergency departments (EDs), trauma systems/centers, 

inpatient critical care services, and interfacility transport. EMS encompasses 

9-1-1 dispatch, response to the scene by ambulance, treatment and triage by 

EMS personnel, and transport to a care facility via ground and/or air ambulance. In its 2007 report on the future of emergency care in the U.S. health 

system, the National Academy of Science’s Institute of Medicine (IOM) 

identifi ed major challenges faced across the country, as well as particular 

complications confronting rural EMS providers, including low patient 

volume, vast distances to travel, limited infrastructure, and inadequate 

funding for personnel, vehicles, and advanced medical equipment.15 

15Because States and localities have 

the primary role in design and fi nancing 

of emergency medical services, little 

standardized and quantifi able information exists across a wide geographical 

range, and most available information is localized and anecdotal (GAO, 

2001).

0

20

40

60

80

100

All Metro Micro Noncore Total Metro Micro Noncore

(a) Hospital beds, all hospitals, 2004

Households-Principal

farm operators

HouseholdsAll U.S.

29 24

29 27 24 26 30 29

Note: The numbers in the boxes are medians represented by the lines inside the boxes; the bottom of the box represents the fi rst quartile (25 percentile), and top of the box is the third quartile 

(75 percentile) of the distribution. 

Source: USDA, Economic Research Service using Area Resource File (ARF). 2006. U.S. 

Department of Health and Human Services, Health Resources and Services Administration, 

Bureau of Health Professions, Rockville, MD.

Figure 4.6

Health facilities - household distributions of county counts, 

per 10,000 population

0

50

100

150

200

All Metro Micro Noncore Total Metro Micro Noncore

(b) Skilled nursing facilities beds (Medicare certified), 2005

Households-Principal

farm operators

HouseholdsAll U.S.

51 51

78

97

68

80

71

47
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As a result, the type and quality of available prehospital EMS services varies 

signifi cantly across rural areas—to a greater extent than in urban areas. Relying 

more than their urban counterparts on volunteer staff with highly variable 

levels of expertise, training, and critical care experience, many rural programs 

offer only basic life support services.16 Further, EMS response times from the 

instigating event to arrival at the hospital are signifi cantly longer in rural areas 

than in urban areas due to increased distances. A 2002 survey found that 30 

percent of rural patients fatally injured in a crash (compared with 8.3 percent in 

urban areas) arrived at the hospital more than 60 minutes after the crash, after 

the “golden hour” had expired.17 In addition to the increased distances 

involved, other factors contributing to prolonged response times include the 

greater delay in the discovery of rural crash scenes, as well as the limits of 

9-1-1 availability in sparsely populated areas.18 Four percent of the Nation’s 

counties, all in rural areas, still do not have access to basic 9-1-1, and 55 

percent do not have advanced 9-1-1 systems that can track the location of 

cellular callers, which can be vital to emergency response. 

Travel Time and Care Usage

Data from the one major study recording actual travel times that people incur to 

receive medical care, by degree of rurality, complement the data documenting 

the lower concentrations of physicians in more remote areas (Chan et al., 

2006). The study also reports data examining the relationship between travel 

times and usage of care, including share of primary versus specialist care.

While rural locations are attractive to some because of their less congested 

conditions, various studies have indicated that usage of preventive care 

is adversely affected by longer travel times. Patients have been observed 

to forego free mammograms if the travel distance is greater than 20 miles 

(Brustrom and Hunter, 2001). Several State health departments have 

proposed a standard whereby rural residents should not have to travel more 

than 30 minutes to see a physician (Bosanac et al., 1976). For more severe 

illnesses, the regionalization of services is designed to provide greater access 

to specialists. However, it represents a tradeoff in greater travel time burden, 

which may impose diffi culties for severely ill patients, and which may impair 

outcomes and/or discourage use. In addition, there may also be indirect 

impacts if care is not effectively coordinated during transition to more local 

caregivers for post-operative outpatient care. If EMS transport is involved, 

the travel times reported here measure only a portion of the time from onset 

of the emergency to arrival at hospital because they do not include time to 

call, and time to arrival on scene. 

Travel times to care. Chan et al. (2006) studied a population of Medicare 

patients in fi ve States in different regions. For their rurality measure, they 

aggregated the system of RUCA codes to create four categories: urban (a 

census-defi ned urbanized area of population 50,000 or greater); large rural 

city (in or associated with a large rural city of 10,000-49,999); small rural 

town (in or associated with a rural town of 2,500-9,999); and an isolated rural 

town (in a town of less than 2,500 population and/or not associated signifi -

cantly with a large town via work commuting fl ows).19

For general medical exams (an indicator of preventive services), median 

travel times for all visits across the four groups were less than the 30 minute 

16A recent national assessment found 

that 77 percent of emergency medical 

service personnel in rural areas were 

volunteers, compared with 33 percent 

in urban areas (Minnesota Department of Health, Offi ce of Rural Health 

Primary Care, 2003).

17Developed from medical experience in the Korean War, the concept of 

the “golden hour” refers to the outer 

bound of time to initiate treatment following trauma or onset of acute illness 

to maximize potential for survival and 

recovery (NHTSA, 2005).

18For vehicle accidents, delay in 

discovery may be the single largest 

contributor to prolonged times until 

transport to a hospital (Esposito et al., 

1995).

19The data are from Medicare billing records of patients seen in the 

fee-for-service environment during 

1998 in Alaska, Idaho, North Carolina, South Carolina, and Washington. 

Travel distances and travel times were 

estimated from ZIP Code data for 

patient residence and provider location 

of service, using ARC-View Network 

Analyst, which calculates the shortest 

travel time (and the associated distance) 

between the centroids of the origin and 

destination ZIP Codes. Because of the 

relatively unique spatial dispersion of 

population in Alaska relative to other 

States—with 52 percent of the population in frontier counties—the more 

extreme measures of the distribution 

are unlikely to be representative; consequently, this analysis does not illustrate 

the 10th and 90th percentile whiskers 

associated with the box plots.
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standard (fi g. 4.7). Residents of large rural cities had the shortest median 

travel times; their times were shorter—though more variable—than those 

for urban residents. Residents of both small rural towns and isolated rural 

towns had upper quartile travel times that exceeded 30 minutes; more than 25 

percent of these groups’ visits did not meet the 30 minute standard. This relative pattern was observed across most categories of visits disaggregated by 

diagnosis or procedure, except that the rural city median was higher than the 

urban median for intubation, kidney dialysis, and pulmonary function tests.

Among emergency services, however, median travel times for small and 

isolated rural communities are 41 and 53 minutes, respectively, with travel 

times exceeding 75 minutes for 25 percent of patients. For cardiopulmonary 

resuscitation and critical care services, which generally occur in hospital 

coronary care, intensive care, respiratory care, or emergency care units, 

median travel times are less than 30 minutes across all groups. However, 25 

percent of patients in small and isolated rural areas have travel times greater 

than 30-40 minutes for CPR and greater than 44-50 minutes for critical care 

services, respectively. 

For mental health services (depression, anxiety, and dementia), median rural 

times were somewhat higher, though all were under or met the 30-minute 

mark. However, 25 percent of isolated rural patients experienced travel times 

over 50-60 minutes. 

For various specialized diagnoses or treatment procedures, including treatments for vascular diseases of the heart and brain (ischemic heart and cerebrovascular) and cancer (malignant neoplasms), diagnostic tests for lung 

and heart disease, and specialized treatments, including kidney dialysis and 

digestive surgery, the median rural times exceeded 30 minutes and third 

quartiles times were close to or exceeded 60 minutes.

Use of health care. In this sample of Medicare patients, long median travel 

times are observed in small and isolated rural towns for some emergency 

care procedures, some mental health diagnoses, and some specialized diagnostic tests and treatments. To make inferences about health impacts, the 

researchers look at data on patterns of usage of care to identify possible 

behavioral responses of recipients to the long travel times. They are interested in the extent to which rural residents substitute primary care providers 

for specialists, or forego such care altogether. 

The data on actual visits by Medicare patients indicate that the share of visits 

to generalists rather than specialists increases with rurality, but the differences are far less than implied by the county availability data because rural 

patients travel out of county, particularly for specialty care not available in 

local health care markets. As noted earlier, the share of total physicians in 

the county providing primary rather than specialty care increases from 35 

percent in metro, to 44 percent in micro, and to 63 percent in noncore counties. In contrast, for this sample of actual Medicare patient visits, 18 percent 

of visits by metro county residents were to generalists, relative to 22 percent 

for residents of large rural cities and 26 percent for residents of both small 

and isolated rural towns. Rural residents do not travel to urban areas for the 

majority of their care; only about 30 percent of visits from residents of small 
33

Health Status and Health Care Access of Farm and Rural Populations / EIB-57

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0

10

20

30

40

50

60

70

All Urban Large rural Small rural Isolated

rural

(a) All medical visits (20.6 million visits)

Minutes

11.7

26.9

18.1

10.9 6.4

Figure 4.7

Median Medicare patient travel time from residence to care provider in selected States, 1998

0

10

20

30

40

50

60

70

All Urban Large rural Small rural Isolated

rural

(c) Depression and anxiety (154,454 visits)

Minutes

12.8

30.3

24.0

10.2

11.7

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(a) General medical exam (75,790 visits)

Minutes

10.2

22.8

11.5

3.2

10.0

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(d) Critical care services (41,506 visits)

Minutes

13.3

28.2

18.1

8.7 11.8

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(b) Dialysis (699,542 visits)

Minutes

14.9

41.2 37.1

19.4

12.0

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(e) Cardiopulmonary resuscitation (2,333 visits)

Minutes

11.7

21.1

11.4 11.3 8.5

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(c) Pulmonary function tests (26,571 visits)

Minutes

16.6

42.5 38.7

13.9

13.0

0

20

40

60

80

All Urban Large rural Small rural Isolated

rural

(f) Intubation for emergency (4,814 visits)

Minutes

16.3

52.7

40.7

18.4

12.0

0

10

20

30

40

50

60

70

All Urban Large rural Small rural Isolated

rural

(b) Ischemic heart disease (774,628 visits)

Minutes

14.1

32.6

28.4

10.3

12.0

0

10

20

30

40

50

60

70

All Urban Large rural Small rural Isolated

rural

(d) Malignant neoplasms (861,340 visits)

Minutes

15.8

42.0 38.6

13.9

12.4

Note: The numbers in the boxes are medians represented by the lines inside the boxes; the bottom of the box represents the fi rst quartile 

(25 percentile), and top of the box is the third quartile (75 percentile) of the distribution. Data are reported for Alaska, Idaho, North Carolina, 

South Carolina, and Washington.

Source: USDA, Economic Research Service using Chan et al., 2006.

For selected procedures

For selected diagnosis
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and isolated rural areas were to urban areas. The share dropped to 20 percent 

for residents of large rural areas. 

The median number of trips per patient was the same (seven trips) across all 

rural and urban areas. However, urban areas had a larger share of patients 

with large numbers of trips, and so the average number of trips was higher in 

urban areas. 

Implications for Quality of Rural Care

For traumatic injuries or episodes, longer times to emergency treatment are 

likely to have a negative effect on health outcomes. For other forms of care, 

longer travel times represent a higher cost of care, in addition to the fi nancial costs. Does the lower rural average usage of total visits and of specialist 

visits found in this study signal lower quality care? For total visits, there is a 

longstanding basic presumption that more is better. For the usage of specialists, Chan et al. (2006) cite epidemiological studies suggesting that health 

outcomes are better for certain diseases (rheumatoid arthritis and congestive 

heart failure) when treated by specialists (Reis et al.,1997; MacLean et al., 

2000). A major thrust of medical practice has been to promote increasing 

regionalization of services for which care-effi cacy research indicates that 

higher volumes are associated with lower mortality. 

New research is examining the tradeoffs more closely, including both the 

travel burden, which may affect health outcomes directly, as well as potential 

negative indirect effects on health outcomes if care is not effectively coordinated during transition to more local caregivers for post-operative outpatient 

care. The Dartmouth Atlas project has documented the extensive variation in 

the quantity of care and the specialist share of care given to chronically ill 

Medicare benefi ciaries in different parts of the country, and examined its 

relationship with the quality of care. Project researchers have found that 

people who live in areas with more medical resources receive more intensive 

inpatient care, including higher rates of specialist use, but do not enjoy 

improved survival, better quality of life, or better access to care.20 However, 

their study focuses on the performance at the level of hospitals, States, and 

health resource regions. Additional analysis would be required to evaluate 

the health implications for the rural local health care context. 

20Dartmouth Medical School, 2008, 

p. 17. See also, Fisher et al., 2003a 

and 2003b, and Baicker and Chandra, 

2004a and 2004b.
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CHAPTER 5

Health Care Quality: Accountability 

and Coordination 

In recent years, initiatives to improve the quality of health care have been 

gaining momentum, due in part to a series of landmark studies by the 

National Academy of Science’s Institute of Medicine. The two initial reports 

in the series, To Err is Human (2000) and Crossing the Quality Chasm: A 

New Health System for the 21st Century (2001), provided evidence documenting the high number of medical errors resulting in fatalities1 along with 

other serious shortcomings in the U.S. health care system. Concluding that 

health care was a decade or more behind other high-risk sectors in attention 

to ensuring safety, these reports called for a fundamental redesign of the 

Nation’s fragmented health care delivery system. 

Two major themes for the redesign of health care have been articulated by 

the IOM and other health experts as a way to reduce errors, improve quality, 

and reduce costs: improving the coordination of care across a currently 

fragmented system of service providers, and creating transparency and 

accountability for the performance of service providers. To help the system 

accomplish these goals, current and proposed policies are designed to 

promote adoption of health information and communication technologies, 

including telemedicine, and implementation of public reporting systems for 

indicators of the quality and cost effectiveness of Medicare services to the 

public, envisioned as the foundation for future pay-for-performance reimbursement systems. 

Rural Opportunities and Challenges 

With Use of Performance Indicators

As the payer for all Medicare and Medicaid services, which represented 34 

percent of total U.S. health care expenditures in 2007, the Centers for 

Medicare and Medicaid Services (CMS) of the U.S. Department of Health 

and Human Services have articulated a roadmap for the development of 

performance-reporting initiatives to enhance the quality and effi ciency of 

medical services provided to benefi ciaries. CMS’s goal is to develop a wide 

range of indicators of quality of care and effective resource use, and to cover 

all types of service delivery for Medicare patients. Starting in 2006 with indicators of process-based quality of care in the hospital reporting system, 

Hospital Compare, as of 2009 the performance-reporting system covers 

nearly all types of care institutions and a wide range of indicators.2

This section reports data for the fi rst set of hospital quality indicators, 

process-based quality of care measures, by metro Prospective Payer System 

(PPS), nonmetro PPS and Critical Access Hospital (CAH) status. (CAHs, 

small hospitals located in more remote nonmetro regions, are reimbursed by 

Medicare on a cost-basis rather than through the standard PPS, which bases 

reimbursement on a predetermined, fi xed amount per diagnosis. See discussion in chapter 4). These consensus-based indicators, developed by professional organizations, cover hospital procedures that have been identifi ed as 

1To Err is Human (2000) estimated 

that more people die in a given year 

as a result of medical errors (44,000-

99,000) than from motor vehicle accidents (43,458), breast cancer (42,297), 

or AIDS (16,516).

2The only settings not covered as of 

2009 are those with the smallest shares of 

Medicare expenditures (hospice, labs, and 

durable medical equipment, with each representing 2 percent of total expenditures).
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“effective care”: indicators that are relatively inexpensive, known to have 

desirable medical benefi ts, and rarely contraindicated. 

Hospital Performance on Process-of-Care 

Quality Indicators

By the beginning of 2006, the pay-for-reporting system for PPS hospitals 

included 22 indicators, covering treatment of heart attacks, congestive heart 

failure, pneumonia, and prevention of infections following surgery.3 These 

conditions represent major sources of chronic morbidity and/or mortality and 

health care expenses.4 Among eligible PPS hospitals, 93 percent participated 

and met requirements; 6 percent failed to meet requirements; and 1 percent 

chose not to participate. Among the 1,286 nonmetro hospitals with CAH 

status as of December 2006, 63 percent were participating in Hospital 

Compare nationally.5 Unlike PPS hospitals, CAHs can choose voluntarily to 

submit data for any or all of the specifi ed measures; because they are reimbursed by Medicare on a cost basis, they do not have a fi nancial incentive 

under the quality initiative to submit the quality measures. 

Based on 2006 hospital performance data (shown in table 5.1), none of the three 

hospital groups (metro PPS, nonmetro PPS, and nonmetro CAH) is substantially in compliance with most of these process-of-care quality measures. This 

is surprising given that these “effective care” measures are selected because 

they are relatively inexpensive, are known to have desirable medical benefi ts, 

and are rarely contraindicated. For example, performance rates for providing 

discharge instructions to heart failure patients ranged from 58 percent of CAHs 

to 70 percent of metro hospitals. These low rates are observed, despite the fact 

that substantial improvements in compliance rates have occurred for some of the 

measures over the fi rst 3 years of reporting, 2004-06. 

For heart attack (“acute myocardial infarction (AMI)”) and heart failure 

measures, metro PPS hospitals generally had the best performance and 

nonmetro CAHs the worst, with nonmetro PPS hospitals rated intermediate—

and, in many cases, closer in performance to metro PPS hospitals than CAHs. 

In contrast, for pneumonia and surgical infection prevention, each hospital 

group, including CAHs, scored as well or better than the others on some 

measures, and not as well on other measures, but the differences are generally not large enough to be of import. Exceptions were the indicators for 

pneumonia smoking cessation advice, and for surgical infection prevention 

pre-surgery antibiotics, for which CAH performance was lowest. Because 

improvements tended to occur across all three groups during the fi rst 3 years 

of reporting, relative patterns of performance across hospital type were 

similar during these years.

Challenges With Use of Quality Indicators 

in Rural Local Health Care Market Areas 

In 2007, the hospital indicators were expanded beyond process-of-care 

measures to include quality outcomes (including morbidity and 30-day 

mortality), structure (use of electronic health records), and patient assessments 

of care. Additional categories of indicators to be included in the future include 

physician and provider resource use. In 2007, CMS submitted to Congress 

a plan to implement a pay-for-performance system in hospitals (value-based 

3Acute care hospitals paid under the 

Prospective Payment System that did 

not report the required data faced a 

0.4-percent reduction in their annual 

payment update from Medicare in 

fi scal years 2004 through 2006 and a 

2.0-percent reduction in fi scal years 

2007 and 2008.

4The fi rst three constitute more 

than 15 percent of Medicare hospital medical and surgical admissions; 

heart disease is the top cause of death, 

representing a quarter of all deaths; 

pneumonia combined with infl uenza is 

in the top 10 causes of death. A 2007 

study found that in 2002, 1.7 million 

hospital-acquired infections were associated with 99,000 deaths (see Klevens 

et al., 2007).

5This total does not include 289 

CAHs that submitted quality measure 

data for 2006 discharges to Q-Net Exchange, the national Quality Improvement Organization data warehouse, but 

did not allow the data to be publicly 

reported to Hospital Compare.
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purchasing) that builds from its pay-for-reporting program and ongoing 

demonstration projects evaluating incentive payments for improved performance. To implement the plan, CMS needs additional legislative authority. 

According to the National Advisory Committee on Rural Health and Human 

Services, the reporting systems represent an opportunity to promote quality 

improvement in rural care delivery but will pose challenges unless tailored 

to the rural context. Hospitals face competing demands for many different 

reporting programs, which serve complementary roles in encouraging quality 

improvement. The programs, however, tend to be poorly coordinated and 

Table 5.1

Share of Medicare patients receiving recommended hospital care, 

by hospital type, 2006

 Metro Nonmetro

Type of hospital PPS1 PPS CAHs 

Sample size 2,431 1,004 812

 Percent

Quality measure

Heart attack (acute myocardial infarction, AMI)

Aspirin at arrival 96.9* 94.2* 88.8

Aspirin at discharge 97.0* 93.3* 86.0

ACE inhibitor or ARB for LVS dysfunction2 86.7* 85.6* 79.0

Smoking cessation advice 96.7* 95.0* 66.8

Beta blocker at discharge 96.5* 93.5* 86.8

Beta blocker at arrival 94.0* 90.1* 83.1

Fibrinolytic w/in 30 minutes of arrival 43.3 42.0 37.4

PCI w/in 120 minutes of arrival 60.4* 56.0* **

Heart failure

Discharge instructions 69.7* 67.4* 58.4

Assessment of LVS function 94.2* 85.9* 71.4

ACE inhibitor or ARB for LVS dysfunction 85.8* 82.5* 80.1

Smoking cessation advice 92.2* 88.1* 72.3

Pneumonia

Oxygenation assessment 99.7* 99.3 99.3

Pneumococcal vaccination 74.7* 75.8* 72.8

Blood culture prior to fi rst antibiotic 90.0* 91.1 91.4

Smoking cessation advice 89.3* 86.5* 74.0

Initial antibiotic(s) within 4 hours 78.3* 82.7* 85.2

Most appropriate initial antibiotic(s) 86.6* 83.1 82.7

Infl uenza vaccination 70.2* 73.1* 71.6

Surgical infection prevention

Preventative antibiotic(s) 1 hour before incision 85.4* 81.3* 79.5

Received most appropriate preventative antibiotic(s) 92.1* 90.8 91.3

Preventative antibiotic(s) stopped within 24 hours 

 after surgery 76.4* 74.8* 77.6

* Statistically signifi cant difference from CAH value.

** Insuffi cient sample.

1Critical Access Hospitals (CAH), which are small and located in remote rural areas, are exempt 

from the Prospective Payment System (PPS), a method of reimbursement in which Medicare 

payment is made based on a predetermined, fi xed amount. 

2LVS (left ventricular systolic) function is an indicator of heart performance; ACE (angiotensin 

converting enzyme) and ARB (angiotensin receptor blockers) are medicines used to treat heart 

attacks, heart failure, or decreased heart function; PCI (percutaneous coronary interventions) 

procedures open blocked blood vessels that cause heart attacks.

Source: USDA, Economic Research Service using Casey et al., (2008). 
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command sizable resources, which poses particular burdens on smaller rural 

institutions. Health information technology is seen as a key to achieving 

effi ciencies in reporting. But the reporting systems do not currently take 

into account the distinctive features of rural health care, including the lower 

volume of patients, fewer acute cases, and high rates of transfers to larger 

tertiary hospitals though activities are currently directed at addressing this 

issue. Implementing the program may test the fi nancial viability of rural 

hospitals as CMS shifts to pay-for-performance systems based on these indicators (NACRHHS, 2008; RUPRI Health Panel, 2009). 

Three design elements may need to be addressed to tailor the system to rural 

areas: determining suitable indicators, addressing the challenge of small 

numbers of patients (small sample size), and setting suitable baselines for 

rural communities (calibrating to low economies of scale). 

Given that rural hospitals are smaller, less complex, and more reliant on 

generalists, the set of indicators suited for evaluating rural health care 

diverges somewhat from those suited for urban settings. Indicators for 

specialized medical treatments, for which rural residents typically travel to 

more urban areas, will have small numbers of cases (as seen with the indicators for AMI and, to a lesser extent, prevention of surgical infections). 

Further, additional indicators are needed for processes that are especially 

pertinent to rural settings, such as triage, stabilization, and transfer of emergency patients (Moscovice et al., 2004). CMS is working with Rural Hospital 

Flexibility programs to develop more tailored measures. 

Analysis of the data for CAHs and other rural hospitals is limited because 

of the low numbers of patients for particular procedures at small hospitals. 

Additional research is needed to evaluate alternative methods of assessing 

and comparing quality performance at small rural hospitals. 

Finally, low rural volumes of care have implications for cost structures, and for 

resource quantity benchmarks. As future pay-for-performance systems incorporate performance criteria based on the effi ciency of resource use, rural areas 

may be severely disadvantaged in the absence of rural benchmarks. The 1983 

introduction of a new Medicare prospective payment system that shifted the 

payment basis from cost-plus to predetermined, fi xed amounts for services had 

unintended negative consequences for rural hospitals. Intended to promote cost 

savings, the program did not take into account the different cost structures in 

urban and rural hospitals, resulting in deleterious effects on rural hospital 

viability. To address the negative effects of the prospective payer system on 

rural hospital viability, the Medicare program made midcourse corrections, 

including creating new categories of care centers with more favorable payment 

provisions, such as cost-based reimbursement and bonus payments.6

Rural Opportunities and Challenges With Health 

Information and Communications Technology 

Health care is an information- and knowledge-intensive enterprise. New 

health information and communications technologies are widely regarded to 

have tremendous promise for increasing patient safety, quality of care, and 

organization effi ciency, while reducing the costs of patient care (IOM, 2001; 

Rand, 2005). In addition, telemedicine applications show particular promise 

6Ricketts, 1999, pp. 101-112.
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for improving care in remote areas unable to sustain local medical and pharmaceutical services due to low population density. 

Key elements of health information and communications technology (HICT) 

include electronic records of clinical and administrative information for 

each individual, a system of networks to exchange health information, 

and the IT and communications technology and standards to support both. 

Standardization of the technologies is still in progress. 

Opportunities and Challenges to Adoption

Given the fragmented U.S. health care system, electronic health records 

(EHR) have the potential to improve the coordination of care across 

providers, which can provide particular benefi ts for patients with multiple 

chronic conditions who account for a large portion of health care expenditures. Accessing clinical decision-support technology, in tandem with 

patient data, can facilitate disease diagnosis and treatment management and 

generate reminders for appropriate preventive services, such as vaccinations 

and screenings, based on patient risk factors. Use of computerized physician 

order entry systems can help prevent medication errors, provide warnings of 

harmful drug interactions and possible allergic reactions to prescribed medications, and reduce costs by reducing the duplication of tests and prompting 

use of generic medications rather than more costly name brands.

Drawing upon electronic data sources, telemedicine applications of HICT 

can reduce costs and improve health care by allowing remote health care 

facilities to consult with primary care physicians and specialists at other 

hospitals or regional medical centers through the use of high-resolution 

cameras, digital-imaging equipment, and high-speed connectivity. This technology can reduce the need to transfer patients and possibly save lives. And, 

remote patient monitoring systems (for example, of blood glucose or heart 

ECG) linked with electronic health records can transmit data to providers to 

facilitate early identifi cation and quick response to potential problems (IOM, 

2005; USDHHS, 2008). The benefi ts of these applications are not limited to 

rural patients. 

Though the promise of these technologies is high, experts have identifi ed 

a number of challenges that must be addressed for them to be effective. 

Some impediments are driven by the intensive investments required to adopt 

the technologies, both in fi nancial capital and skilled labor. Rural areas in 

particular face challenges from limited access to capital, weak information 

and communications infrastructure, and lack of workforce expertise (Casey 

et al., 2006). Further, those who invest in the technology do not receive the 

related savings. Patients benefi t from better health, and payers benefi t from 

lower costs. However, providers pay in both higher costs for implementation, 

and lower revenues after implementation (Rand, 2005). Reimbursement for 

telemedicine services is expanding but not universal. Medicare pays for some 

procedures but not for others. State Medicaid programs also have differing 

policies on telemedicine reimbursement, as do private insurers. The 2009 

stimulus program provides $19 billion to improve the HICT infrastructure, 

including $17 billion in temporary incentive payments starting in 2011 for 

doctors and other providers who can demonstrate “meaningful use” of a 

certifi ed electronic records system. In 2015, the incentives shift to fi nancial 
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penalties for failure to use such a system. The remaining $2 billion in stimulus monies are allocated to various programs to promote HICT adoption, 

including community-based extension services. 

The current lack of specifi city about government standards and patient 

privacy rules has delayed adoption of some technologies. The development 

of clear protocols for unifi ed technological infrastructure will help ensure 

effective transfer of information across sites. Professional licensure regulations for physicians or pharmacists will promote the practice of telehealth. 

Protocols for safeguarding the integrity and reliability of the record system 

will help protect patient confi dentiality (Blumenthal et al., 2008).

Adoption Rates

Over the past few years, several surveys estimating HICT adoption rates 

have generated a wide range of estimates due to different approaches to 

accounting for the many potential features that may be included in the 

electronic health record, use of nonrepresentative samples, and low survey 

response rates. To evaluate the effectiveness of policies aimed at accelerating adoption and interoperability, the Offi ce of the National Coordinator 

for Health Information Technology (DHHS) implemented an HIT Adoption 

Initiative to develop consensus-based guidelines for what features an electronic health record (EHR) must include to be counted, and then to collect 

consistent measurements over time. Findings from two national surveys of 

adoption supported by the initiative are presented in this section—one of 

hospitals and the other of physicians’ offi ces. Table 5.2 identifi es the functionalities associated with full and basic implementation of the EHRs in both. 

The fi ndings indicate that U.S. health care providers have been slow to adopt 

electronic health records. 

Hospitals. Among acute care hospitals that are members of the American 

Hospital Association, 1.5 percent reported having a fully implemented electronic record system and an additional 7.6 percent reported having a basic 

system (Jha et al., 2009). Hospitals that are larger, system-affi liated, have 

teaching status, and have a coronary care unit were more likely to report 

they have fully implemented EHRs. As a result, it is not surprising that EHR 

implementation also varies with metro status; metro hospitals were twice 

as likely as nonmetro hospitals to have either a basic system (8.4 percent 

versus 4.0 percent) or a fully implemented system (1.9 percent versus 0.6 

percent). Due to high costs, implementation of HICT often occurs in stages, 

starting with a subset of functions, and a subset of departments within the 

hospital. Administrative functions to support fi nancial reimbursement are 

generally the fi rst to be adopted. Among the clinical functions, the functions 

most frequently adopted in all units are electronic laboratory and radiology 

reporting systems (75 percent of hospitals) (Jha et al., 2009).

A survey conducted solely among rural hospitals found that, consistent with 

the national pattern, adoption of EHRs in rural areas tends to be lower in 

smaller and stand-alone hospitals, including CAHs, relative to larger and 

system-affi liated rural hospitals. As of 2006, 58 percent of non-CAH rural 

hospitals had started implementing EHRs, compared with 45 percent of 

CAHs (Schoenman, 2007). Though the level of adoption is lower in rural 
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areas, the pattern of staging adoption of functions is similar in both urban and 

rural hospitals (Jha, 2009; Schoenman, 2007).

Also lagging is the sharing of data across institutions and with patients. 

About one-half of hospitals shared electronic patient data with others in both 

2005 (53 percent) and 2006 (49 percent). They most commonly share data 

with private-practice physician offi ces, laboratories, payers, and other hospitals (AHA, 2007).

Broadband. By enabling high-speed transfer of information across sites, 

broadband connectivity is essential for achieving effective coordination 

across providers, including telemedicine consultations. High-speed connectivity to the Internet is becoming less of a stumbling block for rural hospitals, 

Table 5.2

Electronic health records: Functionalities of “basic” 

and “full” systems, 2009

 Physician offi ces Hospitals

 Basic Full Basic Full

Electronic clinical information and data

Patient demographics x x x x

Physician notes x x x x

Nursing assessments x x

Problem list x x x x

Current medications x x x x

Medical history and followup x

Discharge summaries x x

Advance directives x

Order entry management

Prescription orders x x x x

Lab orders x x

Radiology orders x x

Prescription orders sent electronically x 

Lab orders sent electronically x 

Consultation requests x

Nursing orders x

Results management

View lab results x x x x

View imaging results x x x x

Images returned x x

View diagnostic test results x

View diagnostic test images x

View consultant report x

Clinical decision support

Drug warnings x x

Drug-lab interactions x

Drug-dosing support x

Out-of-range levels highlighted x 

Clinical guidelines x

Clinical reminders x x

“Full” implementation indicates that all marked features were present in all clinical units; “basic” 

implementation indicates that marked functionalities were present in at least one clinical unit.

Sources: USDA, Economic Research Service using DesRoches et al., 2008; Jha et al., 2009, 

and U.S. DHHS Health Information Adoption Initiative website, http://healthit.hhs.gov/portal/

server.pt?open=512&objID=1152&parentname=CommunityPage&parentid=8&mode=2&in_hi_

userid=10741&cached=true (accessed June 1, 2009).
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with nearly all reporting that high-speed service is available in their area. 

Well over three-fourths of rural hospitals reported using T-1 and/or T-3 lines, 

and over two-thirds reported having wireless capabilities (Schoenman, 2007). 

Transmittal of data for review by specialists was most common for radiology, 

with two-thirds of both urban and rural hospitals indicating they currently 

transmit radiological images for remote review (AHA, 2007).

Telemedicine. Networked telemedicine programs link tertiary care hospitals 

and clinics with outlying clinics and community health centers in rural or 

suburban areas. Approximately 200 such networks are estimated to currently 

be operating, involving close to 2,000 medical institutions throughout the 

country. However, it is estimated that about half are actively providing 

patient care services on a daily basis, while the others are used primarily for 

administrative or educational use.7

More than half of hospitals in both urban and rural areas used some form of 

telemedicine in both 2005 and 2006 to consult with physicians and medical 

personnel at other hospitals or regional medical centers. Among rural hospitals, a quarter reported participating in telecardiology, and about 1 in 10 

reported participating in tele-emergency services (AHA, 2007; Schoenman, 

2007). Technologies used include high-resolution cameras, digital-imaging 

equipment, and high-speed connectivity, with 24 percent indicating full 

implementation. Among rural hospitals, a quarter reported current use of 

video teleconferencing for patient consultations. Less than a tenth reported 

remote monitoring of hospital inpatients by another site, or by the hospital 

of off-site patients. Similar patterns were observed for CAHs and non-CAHs 

(Schoenman, 2007).

Physician offi ces. Among physicians providing care in an offi ce setting, 4 

percent reported having an extensive, fully implemented electronic records 

system, and 13 percent reported having a basic system during 2007-08 

(DesRoches et al., 2008). In a regression analysis controlling for multiple 

variables, DesRoches et al., found that adoption was higher for primary care 

physicians, those practicing in large groups, hospitals, or medical centers, 

those practicing less than 30 years, and those practicing in the West; adoption 

did not vary with metro status. 

Three-quarters of physicians with a fully functional system (or 3 percent of total 

physicians’ offi ces) reported that their system was integrated with the electronic 

system at the hospital where they admit patients, as compared with 56 percent of 

those with a basic system (or 7 percent of total physicians’ offi ces). 

Other settings. For both urban and rural areas, HICT adoption rates have 

been found to vary across care settings. The highest rates of adoption are 

for hospitals; the lowest rates are for the nonacute care settings—skilled 

nursing facilities, rehabilitation hospitals, and home health agencies (Poon 

et al., 2006). Consequently, patients are affected by the lack of coordination 

across the care settings, such as when patients transition from acute care in 

the hospital to nonacute care settings. While these nonacute facilities are 

often small in size, they deliver the bulk of medical care in the United States 

(USDHHS, 2002). 

7American Telemedicine Association, http://www.americantelemed.org/

i4a/pages/index.cfm?pageid=3333, 

accessed March 23, 2009. 
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CHAPTER 6

Implications for Research and Policy

Important research questions remain for understanding the disparities in 

health outcomes for rural and farm populations. It has been widely observed 

that rural populations have higher rates of disability and chronic diseases, 

even controlling for the fact that the rural population is older. Recently 

published fi ndings show that poorer health carries through to higher ageadjusted mortality rates (i.e., lower life expectancy) in rural areas: a gap in 

life expectancy between metro and nonmetro counties opened up in 1990 and 

has grown continuously since then. In addition, there is substantial variation 

across regions in total mortality rates, as well as in metro-nonmetro differentials. Further research that explores the heterogeneity within nonmetro (and 

metro) areas, in a regional context, would be helpful to better understand the 

extent and determinants of these geographical patterns. Because nonmetro 

populations are small relative to their metro counterparts, lack of data has 

been a challenge. A useful contribution to the research would be additional 

survey data that would allow greater disaggregation within nonmetro areas, 

including separate reporting for more remote areas, which face particular 

challenges in health care access as well as in economic opportunities. 

At the same time, farmers and other family members who live on farms face 

particular health and safety challenges associated with farm-related activities—including one of the highest occupational fatality rates, as well as a 

high fatal accident rate among farm children. Further research that takes into 

account the heterogeneity of farm environments would improve the understanding of the relationship between specifi c activities and exposures and the 

resulting patterns of occupational accidents and diseases. With an improved 

understanding, it could be possible to develop more effective strategies for 

prevention of farm-related accidents and diseases.

The U.S. debate on health care reform has focused on expanding health 

insurance coverage, improving health care quality, and achieving greater effi -

ciencies and sustainable fi nancing. The distinctive characteristics of farm and 

nonfarm rural populations, and of rural health care systems, need to be taken 

into account if proposed policies are to achieve these goals in rural areas. 

The nonmetro population as a whole has lower socioeconomic status, higher 

average age, and greater geographical dispersion than the U.S. population 

as a whole; in contrast, nonmetro farmers have greater economic resources 

but are more likely to be located in the more remote counties. Due to low 

population densities and small patient volumes in rural areas, geographical 

access to care—particularly for smaller and more remote counties—is more 

challenging; furthermore, rural hospitals and other rural providers tend to be 

small and more marginal fi nancially than their urban counterparts because 

low population density leads to lower patient volume, which, in turn, leads to 

higher costs per patient. 

A major goal articulated for current health care reform proposals is to 

achieve health care coverage for the 46 million individuals in the United 

States who are uninsured for all or part of the year. Proposed approaches 

typically include elements that build upon the current system, with its mix of 
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employer-based and direct-purchase private insurance, combined with public 

insurance, including means-tested (Medicaid and related programs) as well 

as targeted-population (Medicare and military/veterans insurance) programs. 

Nationally, most of the uninsured are in working families lacking access to 

employer-sponsored insurance. Because the rural economy has a disproportionate share of small businesses and self-employment, this is particularly the 

case for rural residents. Most proposed or enacted employer-based reforms 

have excluded fi rms below a certain size (e.g., COBRA and employer 

mandates). Consequently, expansion of employer-based policies is less likely 

to increase coverage for either farm or nonfarm rural households relative to 

the typical urban household. 

For nonmetro households, policies to expand public insurance may be more 

effective in increasing insurance coverage than policies to promote expansion of direct purchase coverage. This is because the nonmetro uninsured are 

more likely than the metro uninsured to have either low income, a member 

of the household receiving Medicaid or CHIP, or an elderly member of the 

household receiving Medicare. For farm households, expansion of private 

direct purchase coverage and Medicare buy-ins are more likely to yield an 

increase in coverage than means-tested public insurance. The uninsured in 

farm—compared to nonfarm—households are more likely to have resources 

that would disqualify them from means-tested public insurance and enable 

direct purchase of insurance, and they are more likely to have a member of 

the household on Medicare. 

With current coverage options, affordability is an issue for both farm and 

nonmetro households. How can the United States make health care more 

affordable while expanding current coverage? For direct purchase of insurance, affordability could be improved if participants were able to gain access 

to the advantages of group-rate insurance policies that offer lower costs due 

to risk pooling. Comparably priced direct-purchase policies typically cover a 

smaller share of the insured individual’s healthcare spending than do employment-based policies. For public insurance (Medicaid, CHIP, and Medicare), 

a critical issue is balancing the adequacy of program coverage with sustainability of the programs. 

Other reforms proposed to improve quality and reduce costs include 

promoting adoption of health information and communication technologies and establishing pay-for-performance payment systems, building from 

current Medicare performance-reporting systems. The rural health community generally views both strategies as providing potential opportunities for 

improving rural health care. At the same time, these strategies pose challenges unless current approaches are adapted to take into account the distinctive characteristics of rural health care providers. 

Adoption of health information and communications technologies—widely 

promoted to increase patient safety and health, while reducing costs—holds 

particular promise for improving access in remote areas by facilitating coordination of care across geographically dispersed providers. Adoption rates 

remain low at this point, with rural hospitals lagging behind their urban counterparts. Health care organizations may be reluctant to adopt new information 

systems due to the current lack of technology standards and patient privacy 
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liability concerns, as well as the intensive investments required to adopt these 

new technologies, both in fi nancial capital and skilled labor.

In rural areas, hospitals and other providers may face particular challenges 

due to limited fi nancial resources and workforce expertise. Smaller organizations, more typical in rural areas, are likely to have greater needs for not only 

fi nancial assistance, but also technical assistance to implement the systems and 

adjust workfl ow to achieve the potential effi ciencies. The 2009 economic stimulus package (the American Recovery and Reinvestment Act of 2009) allocated $19 billion to reduce some of these impediments, including $17 billion in 

incentive payments through Medicare and Medicaid reimbursements to reward 

health providers that can show meaningful use of new information technologies. In 2015, the incentives shift to fi nancial penalties for failure to use such a 

system. The remaining funds support various programs to promote technology 

adoption, including community-based regional extension services.

Designed to promote greater public accountability, public performancereporting systems for Medicare services are in place for indicators of quality 

process-of-care, quality outcomes, structure (for example, use of electronic 

health records), and patients’ assessments of care. Though nonmetro hospitals, particularly the small, nonmetro Critical Access Hospitals, have not 

performed consistently as well as their metro counterparts on the fi rst set of 

quality indicators included in the system (process-of-care measures), their 

performance has improved during the fi rst 3 years of the hospital reporting 

system. (The relative pattern of urban/rural differentials remains comparable 

because improvements generally occurred across all types of hospitals.) The 

proposed next step is to shift the reimbursement system away from payment 

for quantity of services and toward payment for performance—for example, 

quality of care, quality of outcomes, and effective use of resources. Some 

of the proposals build from the current Medicare public reporting systems. 

However, the current system design does not fully refl ect the distinctive 

features of rural care—lower volume of patients, fewer acute cases, and high 

rates of patient transfers to larger regional hospitals. A major challenge is to 

establish indicators and benchmarks suited to rural care providers; otherwise 

a payment-for-performance system may create unintended consequences 

for rural providers whose economics do not match those of the “typical” 

provider. Adaptations needed to tailor systems to small rural institutions 

could include incorporating indicators that refl ect the different mix of rural 

services, addressing the challenge that small numbers of patients leads to 

small sample sizes, and setting baselines that are calibrated to low-volume, 

high-cost economics. 
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Appendix A: Data Sources 

Agricultural Resource Management Survey 

Sponsored jointly by USDA’s National Agricultural Statistics Service 

(NASS) and Economic Research Service (ERS), the Agricultural Resource 

Management Survey (ARMS) is the only national-level U.S. survey that 

provides annual observations of a nationally representative sample of all U.S. 

farms as defi ned by USDA in the 48 contiguous States. The survey data 

support estimation of household income and wealth, business income and 

performance measures, farm sector income and value-added, production 

costs for crop and livestock enterprises, and chemical use by farmers in the 

production of crop and livestock commodities. To collect the different kinds 

of information, ARMS employs a complex, multiphase, stratifi ed sampling 

procedure using multiple survey instruments. NASS creates weights for each 

observation to address sampling, nonresponse, and undercoverage by calibrating to independent USDA estimates.1 

Analysis samples. ARMS data were used for measures of farm household demographics, economics and geography, as well as for health insurance coverage. 

The ARMS sample was drawn from the population of all farms, as defi ned by 

USDA—any place from which $1,000 or more of agricultural products were 

produced and sold, or normally would have been sold, during the census year. 

Because the study’s focus is on farm households, the 2007 sample was limited 

to the 98 percent of all farms that are “family farms,” defi ned as those in which 

the majority of ownership of the farm business is held by the operator and 

related individuals. Farm household data were reported for all farm households 

(N=17,465) and for the subset of farm households whose operator cited farming 

as his or her primary occupation (N=12,834). The selection criteria for the latter 

subset are more comparable to the National Health Information Survey sample. 

Primary-occupation farm households accounted for 40 percent of total farm 

households. See Appendix table 1 for data broken down by metro status.

Most of the farm household information is collected on all versions of the survey; 

the health insurance and expense information is only collected on version 1. 

Hence, for the health information, the study used only the version 1 observations 

with their unique weights. The sample size for version 1 was 5,736, excluding 

the 435 households who refused to respond to the health-related items. 

National Health Interview Survey 

The National Health Interview Survey (NHIS) is the source of multiple 

measures of health status and health care usage for U.S. households. The 

sampling is designed to represent all U.S. households for the civilian noninstitutionalized population. The basic survey consists of the three components. 

The fi rst is the Family Core, which collects limited information on all family 

members in each sample household, as well as household fi nances and health 

insurance coverage. In addition, each year, one adult (18+ years) and one child 

are selected from each sample household to interview indepth to collect information on health status, use of health care services, and health behaviors. The 

survey is conducted continuously throughout the year. 

1For more information about ARMS, 

see http://www.ers.usda.gov/Data/

ARMS/
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Analysis samples: NHIS data were used for measures of health status and 

health care access. Two samples were analyzed. The fi rst one is based on a 

subsample of working adults (from the Adult sample fi le). Information was 

obtained about the workers’ families from the linked Family sample fi le. 

Workers were classifi ed as farmers or other workers, based on the major 

occupation of the interviewed adult. The second sample, based on published 

data for 2006, covered all adults and their families. Using this sample, health 

indicators were measured by metro and nonmetro status. Metro status was 

further broken down by whether the Metropolitan Statistical Area (MSA) 

was larger or smaller than 1 million. 

Due to the small number of farm families, data were pooled for the 1997-

2003 period (following procedures outlined in National Center for Health 

Statistics, 2003, pp. 93-94), yielding a sample with 774 farm families and 

135,291 nonfarm families. The weighted population of farm households 

of 812,255 is comparable to the ARMS estimate of family farms headed 

by primary-occupation farm operators (803,793 in 2007). Due to the small 

number of farm families, the analysis was not disaggregated by metro or 

regional status. 

For comparison with ARMS data on farmers, descriptive statistics are 

reported in Appendix table 3. Technically, to compare characteristics of the 

NHIS farmer sample against a comparable farmer sample, one would need 

to look at all operators (not just principal operators) who identify farming as 

their principal occupations. However, ARMS only collects data on households of principal operators. The distributions across education and minority 

status are reasonably comparable. The NHIS regional shares diverge somewhat—the Midwest share (53 percent) is 13 percent higher, and the Southern 

share (28 percent) is 9 percent lower than that for the ARMS farm-occupation sample (Appendix table 1B). A substantial divergence occurs in the age 

distribution; while the share of all farm-occupation operators age 65 or older 

is 33 percent in ARMS, it is 15 percent in the NHIS sample. To compensate 

for this difference, the indicators reported are adjusted for age differences 

across the populations to a standard age distribution, using the direct method 

outlined in Klein and Schoenborn (2001). 

American Community Survey 

The American Community Survey (ACS) is the source for demographic 

information for all U.S. households by metropolitan status. The ACS is 

a large, nationwide household survey recently begun by the U.S. Census 

Bureau to replace the long form in future censuses and provide roughly 

the same data every year instead of once in 10 years. It uses a continuous 

measurement technique, combining monthly samples of 250,000 housing 

units to yield data on age, race, education, income, migration, commuting, 

housing characteristics, and other characteristics. The ACS enables 

researchers to break down demographic data by micropolitan and noncore 

counties between the decennial censuses. See the U.S. Census Bureau’s 

website (www.census.gov) for more information. This study uses ACS data 

for descriptive statistics for the U.S. population by metro status. 
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Current Population Survey, Annual Social 

and Economic Supplement

The Annual Social and Economic Supplement (ASEC) of the Current 

Population Survey (CPS), conducted by the Census Bureau, is designed to 

provide timely and detailed estimates of income, poverty, and health insurance coverage, and to measure change in those estimates at the national level. 

The sample is scientifi cally selected to represent the civilian non institutional 

population living in the United States. The unit of observation is the household. For the supplement, a single interview is conducted with about 70,000 

households each year. This study used CPS ASEC data on health insurance 

coverage and sources of insurance for the U.S. population, by metro status. 

Medical Expenditure Panel Survey – 

Household Component 

The Household Component (HC) of the Medical Expenditure Panel Survey, 

co-sponsored by the Agency for Healthcare Research and Quality (AHRQ) and 

the National Center for Health Statistics, both of the U.S. Department of Health 

and Human Services, collects detailed information on health care spending and 

use of services, insurance coverage by type, sources of payment, health status, 

employment, and other socio-demographic measures. The HC is based on an 

overlapping panel design in which data covering a 2-year period are collected 

through a preliminary contact, followed by a series of fi ve rounds of interviews 

over a 2.5-year period. Data on medical expenditures and use for 2 calendar 

years are collected from each household. Table 4.2 on page 21 reports a special 

tabulation prepared by Didem Bernard, using 2005 data for a sample of 28,617 

nonelderly households, and weighted to provide nationally representative estimates. The 2005 estimates of income and health expenditures were adjusted 

to 2007 levels. For income, the study used the CPI-U-RS (see http://www.bls.

gov/cpi/cpiurs1978_2007.pdf). To adjust measures of out-of-pocket expenditures on care and insurance premiums, the study used the rate of increase 

in household expenditures for those measures reported in table 5, National 

Health Expenditures, by Source of Funds, Type of Expenditure and Sponsor: 

Calendar Years 2002-2007, Centers of Medicare & Medicaid Services, Offi ce 

of the Actuary, National Health Statistics Group. (http://www.cms.hhs.gov/

NationalHealthExpendData/downloads/tables.pdf)

Area Resource File 

The Area Resource File (ARF) is a county-level fi le developed by U.S. 

Department of Health and Human Services, Health Resources and Services 

Administration, Bureau of Health Professionals. It contains county-level data 

relating to health issues from a wide variety of sources. This study drew from 

the 2006 ARF: county infant mortality rates from Vital Statistics; physician 

data from the American Medical Association directory; and hospital data 

from the American Hospital Association; Medicare-certifi ed skilled nursing 

facility beds from Centers of Medicare and Medicaid Services; and Health 

Professional Shortage Areas from Bureau of Primary Health Care.
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Vital Statistics Mortality Series

Mortality data are from the National Center for Health Statistics Compressed 

Mortality File.

Sources of Data for Area Weights

For county weights, the study used counts of farm households by county 

from the 2002 Census of Agriculture and counts of all U.S. households from 

the 2000 Census of Population, Summary File 1. 

Appendix table 1A 

Demographic and socioeconomic characteristics of households of all principal farm operators

of family farms, by metro status, 2007 (ARMS)

 All Metro Nonmetro

Item Total Micro Noncore

Number of households 2,143,398 837,730 1,305,668 531,056 774,612

Percent of households 100 39 61 25 36

Sample size 17,465 6,888 10,577 4,194 6,383

Column percent

Region

Northeast 7 8 6 8 4

Midwest 37 28 42 39 44

South 42 44 41 41 42

West 14 20 11 12 10

Demographics, principal farm operator

Age (years)

Less than 35 5 4 6 6 6

35-44 37 35 38 38 37

55-64 31 33 29 27 30

65 and older 28 28 28 29 27

Race/ethnicity

White, non-Hispanic 91 90 92 90 93

Non-White or Hispanic 9 10 8 10 7

Educational attainment

Less than high school 10 9 10 12 9

High school grad., including some college 67 65 67 66 69

College grad., including more 24 25 22 23 22

Dollars

Household economic status

Median household income 53,952 54,125 53,492 56,27 51,944

Median household wealth 537,540 559,851 523,805 525,090 521,951

Source: USDA, Economic Research Service calculations using data from USDA’s 2007 Agricultural Resource Management Survey (ARMS), all 

versions.
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Appendix table 1B 

Demographic and socioeconomic characteristics of households of farm-occupation operators, 

by metro status, 2007 (ARMS)

 All Metro Nonmetro

Item Total Micro Noncore

Number of households 884,903 349,510 535,393 323,297 212,096

Percent of households 100 39 61 37 24

Sample size 12,834 5,000 7,834 4,768 3,066

Column percent

Region

Northeast 7 10 5 2 10

Midwest 40 31 46 49 42

South 37 36 37 38 35

West 16 22 12 11 13

Demographics, principal farm operator 

Age (years)

Less than 35 4 3 4 4 5

35-44 27 27 28 28 27

55-64 34 35 33 34 32

65 and older 35 36 35 34 36

Race/ethnicity 

White, non-Hispanic 88 86 90 91 89

Non-White or Hispanic 12 14 10 9 11

Educational attainment

Less than high school 13 14 12 11 13

High school grad., inc. some college 68 66 69 70 69

College grad., inc. more 19 20 19 19 18

Dollars

Household economic status

Median household income 43,473 43,467 43,473 43,608 42,669

Median household wealth 712,275 707,592 714,680 698,896 748,700

Source: USDA, Economic Research Service calculations using data from 2007 USDA Agricultural Resource Management Survey (ARMS), all 

versions.
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Appendix table 2 

Demographic and socioeconomic characteristics of all U.S. households, by metro status, 2007 (ACS & SCF)

 All Metro Nonmetro

Item Total Micro Noncore

Number of households 112,377,977 93,095,789 19,282,188 11,710,762 7,571,426

Percent of households 100 83.8 17.2 10.4 6.7

Sample size 1,937,659 1,459,951 477,708 253,308 224,400

Column percent

Region

Northeast 18.3 19.9 10.9 13.1 7.6

Midwest 22.9 21.0 32.2 30.5 34.9

South 36.8 35.5 43.3 41.7 45.7

West 21.9 23.2 23.2 23.2 23.2

Demographics, household head

Age (years)

Less than 35 20.7 20.9 19.6 20.6 18.1

35-54 42.0 42.7 38.2 38.5 37.6

55-64 16.8 16.7 17.5 17.1 18.0

65 and older 20.5 19.6 24.7 23.8 26.3

Race/ethnicity*

White, Non-Hispanic 71.8 69.1 84.7 83.8 86.1

Black 11.8 12.7 7.4 7.7 7.0

Hispanic 11.0 12.3 4.5 5.1 3.5

Other 10.4 11.4 5.1 5.4 4.8

Education 

Less than high school 13.7 13.0 17.3 16.3 18.9

High school grad., inc. some college 57.0 55.4 64.8 64.3 65.6

College grad., inc. more 29.3 31.7 17.8 19.3 15.5

Dollars

Household economic status 

Median household income 50,740 53,066 40,080 41,367 37,844

Percent

Poverty rate 12.3 11.6 15.7 15.1 16.5

Dollars

Median household net wealth 120,430 133,700 99,200 

Sample covers all 50 States, including Alaska and Hawaii. 

* Hispanics may be counted in both the Hispanic as well as in the Black or Other categories, so race/ethnicity shares add up 

to more than 100 percent. 

Sources: USDA, Economic Research Service calculations using data from the American Community Survey (ACS), 2007, and for wealth (only), 

from the Survey of Consumer Finance (SCF), 2007 (metro/nonmetro breakout is from personal communication with A. Kennickell, Federal 

Reserve Board).
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Appendix table 3A

Demographic and educational characteristics of all U.S. adults, 2006 

(NHIS)

Population (1,000s) 220,267

Sample size 24,275

 Population count Percent of 

 (1,000s) population

Metro status

Large MSA 110,233 50

Small MSA 70,790 32

Not in MSA (Nonmetro) 39,243 18

Region

Northeast 39,033 18 

Midwest 51,565 23

South 83,511 38

West 46,157 21

Age (years)

18-44 110,391 50

45-64 74,203 34

65 and older 37,674 17 

65-74 19,081 9

75 and older 16,593 8

Race

White 179,456 81

Black 26,223 12

Other 12,081 5

More than one race 2,506 1

Hispanic 28,664 13

Educational attainment

Less than high school 31,750 14

High school diploma or GED 54,586 25

Some college 51,159 23

College degree, inc. more 51,863 24

Source: USDA, Economic Research Service using U.S. DHHS, 2008, Summary Health Statistics

for the U.S. Population: National Health Interview Survey (NHIS), 2006. Vital and Health 

Statistics, Series 10, Number 235. 
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Appendix table 3B

Demographic and educational characteristics of farmers and all other 

adult workers, 1997-2003 (NHIS)

 Farmers Other workers All

Population 812, 255 126,178,827 126,991,082

Sample size 774 135,291 136,065

 Percent of population

Metro status

Metro 32.0 80.4 80.1

Nonmetro 68.0 19.6 19.9

Region

Northeast 6.3 19.1 19.0

Midwest 52.5 26.5 26.6

South 28.3 35.7 35.7

West 12.8 18.8 18.7

Age (years)

18-24 4.0 13.4 13.3

25-34 10.2 23.4 23.3

35-54 48.7 49.8 49.8

55-64 22.2 10.4 10.4

65 and older 15.0 3.0 3.1

Race/ethnicity

White, Non-Hispanic 95.5 74.3 74.4

Black, Non-Hispanic 1.1 10.7 10.7

Other, Non-Hispanic 1.0 4.1 4.1

Hispanic 2.3 10.8 10.8

Education

Less than high school (HS) 16.0 11.8 11.9

HS grad., inc. some college 68.4 60.3 60.3

College grad., inc. more 15.6 27.9 27.8

Note: Adults age 18 and older who report working in the week prior to the interview with occupation coded as “farm operators and managers” and class of worker coded as “self-employed in own 

business/professional practice/farm or working without pay in family business or farm” are identifi ed 

as farmers. The rest of working adults are classifi ed as other workers.

Source: USDA, Economic Research Service calculations; National Health Interview Survey 

(NHIS) sample.
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Economic Research Service/USDA

Appendix B: Maps Illustrating Measures of 

Rurality and Regions

Appendix figure B.1

Metropolitan, micropolitan, and noncore counties, 2003

Source: USDA, Economic Research Service.

Nonmetro noncore

Nonmetro micropolitan

Metro

Appendix figure B.2

Rural-urban commuting area codes by ZIP Code, 2004

Note: See box, “What Is Rural?” on page 5 for definitions of categories.

Source: USDA, Economic Research Service.

Isolated rural

Small rural

Large rural

Urban
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Economic Research Service/USDA

Appendix figure B.3

Census regions and divisions of the United States

Source: U.S. Department of Commerce, Economics and Statistics Administration, 

U.S. Census Bureau. Prepared by the Geography Division.

PA

NJ

NY

ME

NH VT

MA

CT RI

ND

SD

NE

KS

MN

IA

MO

IL

WI

IN OH

MI

AK

TX

OK

AR

LA

MS AL GA

FL

SC

TN NC

KY

WV

VA

MD

DC

DE

HI

WA

MT

WY

ID OR

CA

NV UT

CO

NM AZ

Legend

REGION

DIVISION

STATE

EAST

SOUTH

CENTRAL

SOUTH

ATLANTIC

MIDDLE

ATLANTIC

NEW

ENGLAND

EAST

NORTH

CENTRAL

WEST

SOUTH

CENTRAL

WEST

NORTH

CENTRAL

MOUNTAIN

PACIFIC

PACIFIC

PACIFIC

SOUTH

NORTHEAST

MIDWEST WEST