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ORIGINAL ARTICLE
3 (
2
); 134-149
doi:
10.21106/ijma.46

Widening Geographical Disparities in Cardiovascular Disease Mortality in the United States, 1969-2011

The Center for Global Health and Health Policy, Global Health and Education Projects, Riverdale, Maryland 20738, USA
University of Nebraska Medical Center, Department of Health Promotion, Social and Behavioral Health, Omaha, NE 68198-4365, USA
US Department of Health and Human Services, Rockville, Maryland 20857, USA

✉Corresponding author email: gsingh@mchandaids.org

Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives:

This study examined trends in geographical disparities in cardiovascular-disease (CVD) mortality in the United States between 1969 and 2011.

Methods:

National vital statistics data and the National Longitudinal Mortality Study were used to estimate regional, state, and county-level disparities in CVD mortality over time. Log-linear, weighted least squares, and Cox regression were used to analyze mortality trends and differentials.

Results:

During 1969-2011, CVD mortality rates declined fastest in New England and Mid-Atlantic regions and slowest in the Southeast and Southwestern regions. In 1969, the mortality rate was 9% higher in the Southeast than in New England, but the differential increased to 48% in 2011. In 2011, Southeastern states, Mississippi and Alabama, had the highest CVD mortality rates, nearly twice the rates for Minnesota and Hawaii. Controlling for individual-level covariates reduced state differentials. State- and county-level differentials in CVD mortality rates widened over time as geographical disparity in CVD mortality increased by 50% between 1969 and 2011. Area deprivation, smoking, obesity, physical inactivity, diabetes prevalence, urbanization, lack of health insurance, and lower access to primary medical care were all significant predictors of county-level CVD mortality rates and accounted for 52.7% of the county variance.

Conclusions and Global Health Implications:

Although CVD mortality has declined for all geographical areas in the United States, geographical disparity has widened over time as certain regions and states, particularly those in the South, have lagged behind in mortality reduction. Geographical disparities in CVD mortality reflect inequalities in socioeconomic conditions and behavioral risk factors. With the global CVD burden on the rise, monitoring geographical disparities, particularly in low- and middle-income countries, could indicate the extent to which reductions in CVD mortality are achievable and may help identify effective policy strategies for CVD prevention and control.

Keywords

CVD mortality
Geography
Deprivation
SES
Inequality
Trend
Longitudinal

Introduction

Reduction of health inequalities, including those between social groups and geographical areas, has been a major health policy goal in the United States (US) for the past 4 decades.[1-5] Cardiovascular diseases (CVD), including heart disease and stroke, have been the number one cause of death in the United States for the past eight decades, and contribute greatly to overall health inequalities for the nation.[6,7] While CVD mortality rates are widely reported by age, sex, and race/ethnicity, geographical disparities in CVD mortality are mostly limited to reporting differences by rural-urban or state of residence.[7-9] Analyses of geographical disparities in CVD mortality over time, especially by region or county of residence, and their socioeconomic and behavioral determinants are less common, although a few recent US studies have examined county- level variations in CVD mortality as a function of area-based deprivation or socioeconomic characteristics.[5,10-14]

Although US data have identified higher rates of CVD morbidity and mortality in several Southern states and the Southeastern region, research on whether the magnitude and patterns of geographical disparities in CVD mortality rates at various levels of geography (such as region, state, and county) have changed over time is either limited or lacking.[5,12-15] While national-level analyses are important in understanding overall social-group disparities in CVD, it is crucial to know from a policy standpoint as to how specific regions, states, or geographical areas are performing in reducing their CVD mortality rates and associated risk factors relative to each other or nation as a whole.[16] In the US, states and local communities such as counties are generally responsible for development and implementation of public policies to tackle public health problems, for collecting social, environmental, and health data, and for providing a broad range of social and health services to their residents.[5] Documenting disparities between geographical areas with the lowest and highest CVD rates can tell us the extent to which mortality reductions can be achieved.[16] Moreover, a spatial-temporal analysis should help identify geographical areas or regions which not only have high rates of CVD mortality but have also experienced slower mortality reductions, indicating the need for urgent action for CVD prevention and control.[5,13,14]

The aim of our study is to examine changes in the extent of geographical disparities in CVD mortality among 9 census regions, 50 states and the District of Columbia, and 3,141 counties of the United States between 1969 and 2011. Using small- area national vital statistics mortality and census data, we model variations in county-level CVD mortality rates as a function of area deprivation, urbanization, racial/ethnic composition, smoking, obesity, physical inactivity, diabetes, and health care access. Additionally, we use the National Longitudinal Mortality Study (NLMS) to model regional and state- level disparities in CVD mortality risks after adjusting for individual-level socioeconomic and demographic characteristics.

Methods

Use of National Vital Statistics and Census Databases to Analyze Trends in Regional, State and County-level Disparities

To analyze geographical disparities in CVD mortality over time, we used the national vital statistics mortality database, which has been the cornerstone of health and disease monitoring among sociodemograhic groups and geographical areas in the US for over a century.[3-9,17] The national mortality database is based on information from death certificates of every death occurring in the United States each year.[8,17,18] While the national mortality database provides the number of deaths (numerator data) by year, age, sex, race, geographic area, and cause of death, the corresponding population statistics developed by the US Census Bureau serve as the denominator for computing mortality rates.[6-9,17,18]

The mainland United States consists of 50 states and the District of Columbia, which are grouped into 9 census regions as shown in Figure 1. States are divided into counties, and the number of counties varies by state. In all, there are 3,143 counties in the United States. In our study, CVD mortality rates were computed annually for all 9 regions between 1969 and 2011. For smaller geographical areas such as states and counties, mortality trends are presented for three time periods due to data availability and space constraints. State-specific CVD mortality rates were computed for 1969, 1990, and 2011. CVD mortality rates were computed for 3,141 counties for the time periods: 1969-1974,1990-1999, and 2003-2007. Mortality rates for all geographic areas were age-adjusted by the direct method using the age-composition of the 2000 US population as the standard.[4-9]

Log-linear regression models were used to estimate annual rates of decrease in CVD mortality for each census region.[4,5] Specifically; the logarithm of region-specific mortality rates were modeled as a linear function of time (calendar year), which yielded annual exponential rates of change in mortality rates.[4,5] In order to summarize state- and county- level disparities in mortality, we used various disparity measures such as the coefficient of variation (CV), interquartile range, quintile and percentile ratios, and absolute and relative mean deviation indices.[16] Moreover, disparities in mortality were described by rate ratios (relative risks) and rate differences (absolute inequalities), which were tested for statistical significance at the 0.05 level.

We used weighted least squares regression to model county-level variations in age-adjusted CVD mortality rates as a function of area deprivation, urbanization, racial/ethnic composition, smoking, obesity, physical inactivity, diabetes, and health uninsurance rates, and availability of primary care physicians. The data on county-level covariates were obtained from several sources such as the Census, Behavioral Risk Factor Surveillance System, and Area Resource File.[19-22] For area deprivation, we used a factor-based deprivation index from the 2000 decennial US census.[5,23] The deprivation index consisted of 22 socioeconomic indicators, which are viewed as broadly representing educational opportunities, labor force skills, economic, and housing conditions in a given county.[23] Selected indicators of education, occupation, wealth, income distribution, unemployment rate, poverty rate, and housing quality were used to construct the 2000 index.[23] Substantive and methodological details of the US deprivation index are provided elsewhere.[4,5,23]

Effects of both continuous and categorical measures of the deprivation index and smoking, obesity, and diabetes prevalence rates were estimated in the regression models. Cardiovascular deaths in each county were used as weights in the weighted regression models because the number of deaths is proportional to the inverse of the variance of mortality rates.[24]

National Longitudinal Mortality Study (NLMS)

To examine regional and state-level variations in CVD mortality, we also used the 1979-2002 NLMS, that allowed us to examine geographical differences in mortality after adjusting for individual-level socioeconomic and demographic characteristics. The NLMS is a longitudinal dataset for examining socioeconomic, occupational, and demographic factors associated with all-cause and cause-specific mortality in the United States.[25-28] The NLMS is conducted by the National Heart, Lung, and Blood Institute (National Institutes of Health [NIH]) in collaboration with the US Census Bureau, the National Cancer Institute (NIH), the National Institute on Aging (NIH), and the National Center for Health Statistics (Centers for Disease Control and Prevention).[25-28] The NLMS consists of 30 Current Population Survey (CPS) and census cohorts between 1973 and 2002 whose survival (mortality) experiences were studied between 1979 and 2002.[25] The CPS is a sample household and telephone interview survey of the civilian non- institutionalized population in the United States and is conducted by the US Census Bureau to produce monthly national statistics on unemployment and the labor force. Data from death certificates on the fact of death and the cause of death are combined with the socioeconomic and demographic characteristics of the NLMS cohorts by means of the National Death Index.[25-28] Detailed descriptions of the NLMS have been provided elsewhere.[25-27]

The full NLMS consists of approximately 3 million individuals drawn from 30 CPS and census cohorts whose mortality experience has been followed from 1979 through 2002, with the total number of deaths during the 23-year follow-up being 341,343.[25] However, our study uses the public-use microdata sample that contains only selected population cohorts between 1979 and 1991, with a maximum mortality follow-up of 11 years.[25] State- and region-level differentials in mortality risks were adjusted by multivariate Cox proportional hazards regression for age and for additional covariates such as sex, race/ethnicity, marital status, metropolitan/non-metropolitan residence, education, income/poverty level, and occupation.[28] The public-use NLMS sample for 1979-2002 included 780,461 individuals aged ≥25 at the baseline and 50,430 CVD deaths during the 11-year mortality follow-up.[25] In estimating the mortality risk, all those surviving beyond the 11-year follow-up (i.e., 4,018 days of follow-up) and those dying from causes other than CVD during the follow-up period were treated as right-censored observations. The Cox models were estimated by the SAS PHREG procedure.[29]

Results

Regional Trends and Differentials in CVD Mortality

Figure 1 shows annual trends in CVD mortality among 9 census regions. During 1969-2011, CVD mortality rates declined at the fastest pace in New England and Mid-Atlantic regions and at the slowest rate in the Southeast and Southwestern regions of the United States. The average annual rates of decline in mortality during 1969-2011 were 2.94% for New England, 2.7% for Mid-Atlantic, 2.23% for Southwest, and 2.12% for Southeast. In 1969, the mortality rate was 9% higher in the Southeast than in New England, but this differential increased to 22% in 1990 and 48% in 2011. A similar increase in relative risk of CVD mortality was seen over time for the Southeast and Southwest regions when compared to New England and Mountain regions (Figure 1). Even after adjusting for individual-level socioeconomic and demographic characteristics in the NLMS, those in the Southeast and East Northcentral regions maintained 18-19% higher CVD mortality risks than their counterparts in the Mountain region (Table 1). The adjusted effects of other individual-level covariates on CVD mortality risks in the NLMS are worth noting (Table 1). Education and income were inversely associated with CVD mortality during 1979-2002. Individuals with low education and incomes had 32-40% higher CVD mortality risks than their counterparts with high education and income levels. Service workers and manual laborers had 17-19% higher CVD mortality risks than those employed in professional and managerial occupations. Divorced/separated and never married individuals had 29-32% higher CVD mortality risks than married individuals. Hispanics and Asian/Pacific Islanders had 35-41% lower CVD mortality risks than their non-Hispanic counterparts of equivalent socioeconomic backgrounds.

Trends in Cardiovascular Disease (CVD) Mortality by Geographic Region, United States, 1969-2011 New England = Maine + New Hampshire + Vermont + Massachusetts + Rhode Island + Connecticut Middle Atlantic = New York + New Jersey + Pennsylvania East North Central = Ohio + Indiana + Illinois + Michigan + Wisconsin West North Central = Minnesota + Iowa + Missouri + North Dakota + South Dakota + Nebraska + Kansas South Atlantic = Delaware + Maryland + District of Columbia + Virginia + West Virginia + North Carolina + South Carolina + Georgia + Florida East South Central = Kentucky + Tennessee + Alabama + Mississippi West South Central = Arkansas + Louisiana + Oklahoma + Texas Mountain = Montana + Idaho + Wyoming + Colorado + New Mexico + Arizona + Utah + Nevada Pacific = Washington + Oregon + California + Alaska + Hawaii
Figure 1.
Trends in Cardiovascular Disease (CVD) Mortality by Geographic Region, United States, 1969-2011
New England = Maine + New Hampshire + Vermont + Massachusetts + Rhode Island + Connecticut
Middle Atlantic = New York + New Jersey + Pennsylvania
East North Central = Ohio + Indiana + Illinois + Michigan + Wisconsin
West North Central = Minnesota + Iowa + Missouri + North Dakota + South Dakota + Nebraska + Kansas
South Atlantic = Delaware + Maryland + District of Columbia + Virginia + West Virginia + North Carolina + South Carolina + Georgia + Florida
East South Central = Kentucky + Tennessee + Alabama + Mississippi
West South Central = Arkansas + Louisiana + Oklahoma + Texas Mountain = Montana + Idaho + Wyoming + Colorado + New Mexico + Arizona + Utah + Nevada
Pacific = Washington + Oregon + California + Alaska + Hawaii
Table 1. Age- and Covariate-Adjusted Relative Risks of Cardiovascular Disease (CVD) Mortality Among US Adults Aged 25+years According to Baseline Socioedemographic Characteristics and Region of Residence: The US National Longitudinal Mortaliy Study, 1979-2002 (N=780,461)
Baseline socio-demographic characteristics Age-adjusted1 Covariate-adjusted2
Hazard ratio 95% confidence interval Hazard ratio 95% confidence interval
Age (years) 1.11 1.11 1.11 1.10 1.10 1.10
Sex
    Male 1.69 1.66 1.72 1.94 1.91 1.98
    Female 1.00 Reference 1.00 Reference
Race/ethnicity
    Non-Hispanic white 1.00 Reference 1.00 Reference
    Hispanic 0.75 0.71 0.79 0.65 0.62 0.69
    Non-Hispanic black 1.24 1.20 1.28 1.03 1.00 1.07
    American Indian/Alaska Native 1.00 0.88 1.14 0.88 0.77 1.01
    Asian/Pacific Islander 0.61 0.55 0.68 0.59 0.53 0.66
    Other 0.83 0.67 1.03 0.84 0.68 1.04
Maritalstatus
    Married 1.00 Reference 1.00 Reference
    Widowed 0.95 0.93 0.97 1.19 1.16 1.21
    Divorced/separated 1.25 1.21 1.30 1.32 1.27 1.36
    Single 1.19 1.15 1.23 1.29 1.24 1.34
Place of residence
    Metropolitan 1.00 Reference 1.00 Reference
    Non-metropolitan 1.02 1.01 1.04 0.98 0.96 1.00
Education (years)
    <12 1.53 1.48 1.58 1.32 1.27 1.37
    12 1.24 1.20 1.29 1.21 1.17 1.26
    13-15 1.15 1.11 1.20 1.14 1.10 1.19
    16+ 1.00 Reference 1.00 Reference
Occupation
    Professional/managerial 1.00 Reference 1.00 Reference
    Sales/Clerical/Admin support 0.99 0.94 1.04 1.03 0.98 1.09
    Service 1.34 1.27 1.42 1.17 1.11 1.24
    Craftand repair 1.52 1.43 1.61 1.11 1.04 1.18
    Laborer 1.53 1.45 1.62 1.19 1.12 1.27
    Military 3.12 1.01 9.68 2.28 0.74 7.02
    Unemployed/outside labor force 1.63 1.56 1.70 1.64 1.56 1.72
Poverty status (ratio of family income to poverty threshold)
    Below 100% 1.63 1.57 1.69 1.40 1.35 1.46
    100-149% 1.54 1.48 1.60 1.31 1.26 1.37
    150-199% 1.52 1.47 1.59 1.31 1.26 1.37
    200-299% 1.39 1.34 1.44 1.22 1.17 1.27
    300-399% 1.29 1.24 1.34 1.17 1.12 1.22
    400-599% 1.20 1.16 1.25 1.12 1.08 1.17
    At or above 600% 1.00 Reference 1.00 Reference
Geographic region
    New England 1.07 1.03 1.12 1.05 1.00 1.09
    Middle Atlantic 1.17 1.13 1.22 1.13 1.09 1.17
    East Northcentral 1.25 1.20 1.30 1.19 1.15 1.24
    West Northcentral 1.08 1.04 1.12 1.05 1.00 1.09
    South Atlantic 1.21 1.16 1.25 1.15 1.11 1.20
    East Southcentral 1.31 1.25 1.37 1.18 1.13 1.24
    West Southcentral 1.20 1.15 1.25 1.15 1.11 1.20
    Mountain 1.00 Reference 1.00 Reference
    Pacific 1.04 1.00 1.09 1.08 1.04 1.13

Notes: Relative risks (hazard ratios) were derived from multivariate Cox proportional hazards regression models. 1Adjusted for age only. 2Adjusted for age, sex, race/ethnicity, marital status, metropolitan/non-metropolitan residence, educational attainment, occupation, income/poverty level, and geographic region

Trends and Differentials in State-Level Disparities in CVD Mortality

In 2011, Southeastern states such as Mississippi and Alabama had the highest CVD mortality rates, nearly two times higher than the rates for Minnesota and Hawaii (Table 2). State patterns were similar in 1969 and 1990, with substantially increased risks of CVD mortality for most Southern states. In 1990, Mississippi and Louisiana had the highest mortality rates, 51%, and 42% higher than the rate for Hawaii. In 1969, South Carolina had the highest mortality rate, 52% higher than the rate for Alaska (Table 2). Controlling for individual-level sociodemographic characteristics in the NLMS reduced state differentials; however, individuals in Indiana, Michigan, Louisiana, and Kentucky maintained 30-35% higher CVD mortality risks than their counterparts in New Mexico (Table 3).

Table 2. Age-Adjusted Cardiovascular Disease Mortality Rates by State: United States, 1969, 1990, and 2011
State 1969 Rate 1969 SE 1969 Deaths 1990 Rate 1990 SE 1990 Deaths 2011 Rate 2011 SE 2011 Deaths % Decline in rate, 1969-2011
Alabama 737.06 6.01 16,747 446.41 3.49 16,714 296.10 2.40 15,496 59.83
Alaska 548.20 35.25 369 338.94 16.49 587 202.89 7.04 970 62.99
Arizona 595.18 8.42 6,043 350.55 3.39 11,192 198.35 1.68 14,043 66.67
Arkansas 690.41 6.79 11,120 424.96 4.16 10,633 279.13 2.91 9,345 59.57
California 672.40 2.43 84,185 396.75 1.35 88,293 211.91 0.76 79,859 68.48
Colorado 633.92 7.12 8,432 336.84 3.77 8,132 178.54 1.97 8,467 71.84
Connecticut 677.40 6.07 13,803 366.39 3.39 11,850 194.72 2.09 9,119 71.25
Delaware 781.07 16.53 2,504 417.74 8.79 2,343 226.44 4.72 2,342 71.01
District of Columbia 750.80 12.78 3,864 424.28 8.79 2,375 248.26 6.54 1,497 66.93
Florida 638.34 3.59 37,139 365.21 1.55 57,781 196.90 0.87 53,048 69.15
Georgia 771.66 5.70 20,919 449.20 3.13 21,359 244.30 1.72 20,938 68.34
Hawaii 554.29 14.70 1,756 325.02 6.37 2,743 178.12 3.26 3,128 67.87
Idaho 654.49 12.17 3,115 344.77 6.40 2,957 210.50 3.65 3,400 67.84
Illinois 816.74 3.43 62,961 424.76 2.03 44,380 230.35 1.29 32,457 71.80
Indiana 780.66 4.91 27,287 434.33 2.94 22,062 246.91 1.88 17,663 68.37
Iowa 676.38 5.34 16,686 377.54 3.41 12,483 216.38 2.35 8,862 68.01
Kansas 671.90 6.14 12,491 381.65 3.84 10,004 217.37 2.58 7,365 67.65
Kentucky 774.29 6.00 18,003 445.99 3.66 15,100 270.86 2.43 12,723 65.02
Louisiana 799.46 6.49 17,285 462.07 3.74 15,669 273.10 2.46 12,589 65.84
Maine 766.83 10.06 6,132 388.26 5.67 4,736 195.45 3.36 3,486 74.51
Maryland 767.09 6.47 16,446 406.60 3.38 14,978 222.58 1.92 13,750 70.98
Massachusetts 698.82 4.11 31,105 370.84 2.49 22,398 186.23 1.53 15,372 73.35
Michigan 748.22 4.01 39,639 437.92 2.37 34,754 253.89 1.49 29,776 66.07
Minnesota 652.43 4.94 18,550 345.55 2.86 14,687 166.52 1.66 10,328 74.48
Mississippi 774.46 7.49 11,711 491.02 4.62 11,485 311.88 3.22 9,562 59.73
Missouri 719.23 4.46 27,661 420.46 2.82 22,502 254.88 1.92 17,880 64.56
Montana 650.16 11.57 3,325 345.32 6.76 2,645 207.18 4.17 2,543 68.13
Nebraska 647.84 7.29 8,234 376.93 4.68 6,577 200.86 3.06 4,468 69.00
Nevada 732.48 20.98 1,606 431.82 7.79 3,578 248.13 3.19 6,308 66.12
New Hampshire 749.10 12.20 4,040 385.69 6.47 3,595 197.41 3.63 3,033 73.65
New Jersey 776.61 4.30 37,562 402.81 2.39 29,146 220.26 1.46 23,229 71.64
New Mexico 576.97 11.82 2,783 334.91 5.53 3,808 196.07 3.01 4,333 66.02
New York 758.58 2.56 100,650 441.80 1.61 75,829 234.67 1.02 54,292 69.06
North Carolina 781.98 5.57 22,991 426.92 2.79 24,272 225.21 1.51 22,749 71.20
North Dakota 643.25 12.07 3,001 364.42 7.26 2,554 194.39 4.76 1,755 69.78
Ohio 771.32 3.45 54,635 430.86 2.09 43,444 248.21 1.35 34,514 67.82
Oklahoma 680.49 5.97 13,872 443.08 3.78 13,883 292.34 2.69 11,957 57.04
Oregon 665.34 6.72 10,513 368.80 3.67 10,271 193.81 2.08 8,995 70.87
Pennsylvania 791.54 3.17 69,905 422.53 1.85 53,830 237.67 1.19 41,264 69.97
Rhode Island 715.24 10.55 5,088 394.62 6.10 4,258 207.05 3.93 2,944 71.05
South Carolina 832.66 8.34 11,889 452.36 4.17 12,471 246.39 2.25 12,349 70.41
South Dakota 665.68 11.31 3,678 378.76 6.99 2,994 212.42 4.58 2,254 68.09
Tennessee 778.25 5.71 20,643 451.01 3.21 20,138 270.31 2.00 18,614 65.27
Texas 670.56 3.31 45,875 405.43 1.81 51,002 227.76 1.03 50,076 66.03
Utah 580.60 11.05 3,072 338.76 5.72 3,606 193.52 3.06 4,060 66.67
Vermont 693.80 14.74 2,323 371.26 8.65 1,854 193.68 5.06 1,520 72.08
Virginia 749.18 5.70 19,841 419.81 3.02 19,976 217.69 1.65 17,799 70.94
Washington 706.81 5.74 16,263 367.65 3.02 15,051 194.97 1.67 14,105 72.42
West Virginia 790.75 7.99 10,716 459.68 4.98 8,736 272.50 3.44 6,414 65.54
Wisconsin 705.43 4.84 22,951 387.57 2.83 18,915 214.06 1.78 14,825 69.66
Wyoming 630.66 18.27 1,340 368.01 10.51 1,254 206.71 6.05 1,205 67.22

Notes: Rates are per 100,000 population and are directly age-adjusted to the 2000 US standard population. SE=standard error.

Table 3. Relative Risks of Cardiovascular Disease (CVD) Mortality Among US Adults Aged ≥25 Years, According to State of Residence: The US National Longitudinal Mortaliy Study, 1979-2002 (N=780,461)
State of residence Age-adjusted1 Covariate-adjusted2
Hazard ratio 95% confidence interval Hazard ratio 95% confidence interval
Alabama 1.44 1.29 1.61 1.20 1.07 1.35
Alaska 1.09 0.92 1.29 1.06 0.90 1.25
Arizona 1.13 1.00 1.27 1.06 0.94 1.19
Arkansas 1.38 1.23 1.54 1.16 1.04 1.30
California 1.22 1.11 1.34 1.18 1.07 1.30
Colorado 1.13 1.00 1.27 1.09 0.96 1.23
Connecticut 1.19 1.05 1.34 1.10 0.97 1.24
Delaware 1.43 1.26 1.62 1.28 1.13 1.45
District of Columbia 1.34 1.18 1.53 1.22 1.07 1.39
Florida 1.28 1.16 1.41 1.18 1.07 1.31
Georgia 1.35 1.20 1.51 1.17 1.05 1.32
Hawaii 0.99 0.86 1.14 1.28 1.10 1.50
Idaho 1.20 1.06 1.35 1.07 0.94 1.20
Illinois 1.41 1.27 1.55 1.28 1.16 1.42
Indiana 1.52 1.36 1.69 1.35 1.21 1.50
lowa 1.22 1.10 1.37 1.12 1.00 1.25
Kansas 1.11 0.99 1.24 1.03 0.92 1.16
Kentucky 1.49 1.33 1.67 1.30 1.16 1.45
Louisiana 1.55 1.38 1.74 1.31 1.16 1.47
Maine 1.24 1.10 1.40 1.10 0.98 1.24
Maryland 1.28 1.14 1.43 1.15 1.03 1.29
Massachusetts 1.16 1.05 1.29 1.07 0.97 1.19
Michigan 1.52 1.38 1.68 1.33 1.20 1.47
Minnesota 1.24 1.11 1.38 1.11 0.99 1.24
Mississippi 1.51 1.35 1.68 1.25 1.12 1.40
Missouri 1.38 1.25 1.54 1.21 1.09 1.35
Montana 1.13 1.00 1.28 1.02 0.90 1.15
Nebraska 1.24 1.11 1.39 1.16 1.03 1.30
Nevada 1.32 1.16 1.50 1.20 1.06 1.37
New Hampshire 1.28 1.13 1.45 1.18 1.04 1.34
New Jersey 1.27 1.15 1.41 1.20 1.08 1.33
New Mexico 1.00 Reference 1.00 Reference
New York 1.28 1.17 1.41 1.17 1.06 1.28
North Carolina 1.47 1.32 1.62 1.29 1.16 1.44
North Dakota 1.13 1.00 1.27 1.00 0.89 1.12
Ohio 1.34 1.21 1.48 1.19 1.07 1.31
Oklahoma 1.38 1.24 1.55 1.23 1.10 1.37
Oregon 1.18 1.05 1.33 1.07 0.95 1.20
Pennsylvania 1.42 1.29 1.57 1.24 1.12 1.37
Rhode Island 1.25 1.11 1.42 1.10 0.97 1.24
South Carolina 1.48 1.31 1.67 1.27 1.12 1.43
South Dakota 1.18 1.05 1.32 1.06 0.94 1.18
Tennessee 1.49 1.33 1.67 1.26 1.12 1.41
Texas 1.29 1.17 1.43 1.21 1.10 1.34
Utah 1.11 0.98 1.25 1.04 0.92 1.17
Vermont 1.26 1.11 1.42 1.16 1.02 1.31
Virginia 1.41 1.26 1.57 1.27 1.13 1.42
Washington 1.12 1.00 1.27 1.03 0.91 1.16
West Virginia 1.48 1.32 1.66 1.24 1.10 1.39
Wisconsin 1.33 1.19 1.48 1.19 1.07 1.33
Wyoming 1.12 0.98 1.28 1.02 0.89 1.17

Notes: Estimated relative risks (hazard ratios) were derived from multivariate Cox proportional hazards regression models. 1Adjusted for age only. 2Adjusted for age, sex, race/ ethnicity, marital status, metro/non-metro residence, educational attainment, occupation, and income/poverty level

Absolute disparities in state-level CVD mortality, as measured by interquartile range and absolute mean deviation, decreased over time. However, relative disparities in state-level CVD mortality rates, as measured by CV, relative mean deviation index, and quintile and percentile ratios, widened over time. The coefficient of variation in state-level CVD mortality increased by 48% from 10.0 in 1969 to 14.8 in 2011. The relative mean deviation index indicated a 43% increase in state-level disparity in CVD mortality between 1969 and 2011 (Table 4).

Table 4. Summary Measures of Geographical Disparities in Cardiovascular Disease (CVD) Mortality, United States, 1969-2011 (50 States and District of Columbia; 3,141 Counties)
Disparity measure 1969 1990 2011
State
Coefficient of variation (%) 9.99 10.25 14.83
Interquartile range 111.58 7.87 7.53
Absolute mean deviation 59.63 16.04 21.60
Relative mean deviation index 8.43 8.86 12.09
Quintile ratio (Q4/QI) 1.19 1.20 1.27
Percentile ratio (P90/P10) 1.24 1.30 1.41
Disparity measure 1969-1974 1990-1999 2003-2007
County
Coefficient of variation (%) 13.79 16.49 22.14
Interquartile range 122.53 82.81 79.76
Absolute mean deviation 74.06 50.56 48.30
Relative mean deviation index 10.86 12.91 16.49
Quintile ratio (Q4/QI) 1.26 1.31 1.42
Percentile ratio (P90/P10) 1.41 1.50 1.69

Interquartile range-3rd quartile - 1st quartile; Q1-First quintile; Q4-Fourth quintile. P10=10th Percentile; P90-90th Percentile

Trends and Differentials in County-Level Disparities in CVD Mortality

County-level variations in area deprivation and CVD mortality rates were closely related, with the weighted correlation being -0.53 (Figure 2 and Table 5). Consistent with high deprivation levels in the Southeast, individuals in this region had the highest CVD mortality rates (Figure 2). Area deprivation, smoking, obesity, physical inactivity, diabetes prevalence, urbanization, racial/ethnic composition, lack of health insurance, and lower access to primary medical care were all significant predictors of county-level CVD mortality rates (Table 5). In the multivariate models, these covariates (excluding health insurance and physician availability because of multicollinearity) accounted for 52.7% of the county variance. A 10-percentage- point increase in obesity prevalence was associated with a 32.2-point increase in the CVD mortality rate. Similarly, a 10-percentage-point increase in diabetes prevalence was associated with a 57.7-point increase in the CVD mortality rate. In multivariate categorical models, consistent gradients in CVD mortality were found by area deprivation and smoking, obesity and diabetes prevalence. Even after adjusting for behavioral risk factors, those in the most deprived counties had 15% higher CVD mortality than those in the most affluent counties. CVD mortality rates were 18% higher in areas with smoking rates ≥36%, compared with areas with smoking rates <12%. Counties with obesity rates ≥40% had 54% higher CVD mortality than counties with an obesity rate <15%. Counties with a diabetes prevalence ≥14% had 19% higher CVD mortality than counties with a diabetes prevalence <6% (Table 5).

Area (County) Socioeconomic Deprivation Index and Age-Adjusted Cardiovascular Disease (CVD) Mortality Rates per 100,000 Population for the United States (2000 US Population Used as Standard; 3,141 Counties)
Figure 2.
Area (County) Socioeconomic Deprivation Index and Age-Adjusted Cardiovascular Disease (CVD) Mortality Rates per 100,000 Population for the United States (2000 US Population Used as Standard; 3,141 Counties)
Table 5. Weighted Least Squares Regression Models Showing the Impacts of the Continuous and Categorical Socioeconomic Deprivation Index, Smoking, Obesity, Physical Activity, Diabetes Prevalence, Rural-Urban Continuum, and Racial/Ethnic Composition on County-Level Age-Adjusted Cardiovascular Disease (CVD) Mortality Rates: United States, 2003-2007 (N=3,141)
Covariate Bivariate models Multivariate model
b β t-stat P-value Adj. R2 b β t-stat P-value Adj. R2
Socioeconomic deprivation index1 −1.19 −0.53 −35.28 <0.001 28.58 −0.36 −0.16 −7.26 <0.001 52.7
Adult smoking prevalence (%)2 4.91 0.53 34.53 <0.001 27.71 1.76 0.19 8.10 <0.001
Adult obesity prevalence (%)3 7.05 0.62 43.79 <0.001 38.13 3.22 0.28 13.52 <0.001
Physical activity prevalence (%)4 −5.03 −0.39 −23.43 <0.001 15.00 −1.12 −0.09 −6.01 <0.001
Adult diabetes prevalence (%)3 16.97 0.64 46.50 <0.001 40.79 5.77 0.22 10.27 <0.001
Rural-urban continuuum5 5.35 0.21 11.97 <0.001 4.41 −1.83 −0.07 −4.34 <0.001
Percentage minority population6 0.16 0.07 4.15 <0.001 0.55 0.32 0.15 6.90 <0.001
Health uninsurance rate7 2.15 0.21 12.26 <0.001 4.61
Availability of primary care doctors8 −0.23 −0.26 −14.76 <0.001 6.55
Age-adjusted CVD mortality Covariate-adjusted CVD mortality9
Rate SE Rate SE
Socioeconomic deprivation, categorical
Quintile 1 (lowest SES/most deprived) 321.04 1.61 322.60 4.20
Quintile 2 291.97 1.45 304.69 4.14
Quintile 3 277.71 1.53 294.69 4.14
Quintile 4 253.55 1.50 282.13 4.11
Quintile 1 (highest SES/least deprived) 247.24 1.50 281.56 4.12
Adult smoking prevalence (%), categorical
<12 213.73 8.05 269.85 7.70
12-17.99 254.89 1.53 285.39 4.15
18-23.99 264.70 1.06 284.89 3.84
24-27.99 296.79 1.47 297.03 3.80
28-31.99 323.23 2.18 309.63 4.05
32-35.99 332.79 5.11 314.65 5.53
236 351.73 9.66 318.47 8.58
Adult obesity prevalence (%), categorical
<15 192.72 14.30 253.61 12.37
15-19.99 229.71 2.48 260.37 3.16
20-24.99 256.55 1.19 270.61 2.64
25-29.99 282.82 0.98 282.36 2.50
30-34.99 324.83 1.76 299.11 2.65
35-39.99 368.73 7.82 322.12 6.87
240 444.03 25.20 391.74 21.74
Adult diabetes prevalence (%), categorical
<6 221.66 3.54 269.73 5.11
6-7.99 250.32 1.15 282.82 4.46
8-9.99 280.02 0.99 296.21 4.38
10-11.99 315.18 1.62 304.21 4.47
12-13.99 338.70 3.08 309.33 4.94
≥14 376.92 9.63 320.49 8.75

Notes: b=Unstandardized regression coefficient; β=Standardized regression coefficient; R2=Percentage variance explained. β is also equal to the correlation coefficient in bivariate regression models. Health uninsurance and primary care physician availability rates were not used as covariates in the multivariate model because of estimation problems due to multicollinearity. 1The 2000 census socioeconomic deprivation index is a continuous variable with a mean of 100 and a standard deviation of 20. Higher index scores denote higher levels of socioeconomic position and lower levels of deprivation. 2Current smoking prevalence among adults aged 18+in 2000-2003. 3Obesity or diabetes prevalence among adults aged 18+in 2006-2008. 4Percentage of physically active adults aged 18+in 2007, where phyically active=at least 150 minutes of moderate physical activity per week, or 75 minutes of vigorous activity per week, or an equivalent comination of moderate and vigorous physical activity 5The 2003 rural-urban continuum is used a continuous variable, with code I being the most urbanized county and code 9 being the most rural county 6Percentage of black, American Indian/Alaska Native, Asian/ Pacific Islander, and Hispanic populations in 2000. 7Percentage of population without health insurance in 2000. 8Number of primary care doctors per 100,000 population in 2005. 9Adjusted for socioeconomic deprivation, smoking, obesity and PA prevalence, rural-urban continuum, and minority concentration. Source: Based on the US National Vital Statistcs System, Behavioral Risk Factor Surveillance System, US Census, and Area Resource File

County-level differentials in CVD mortality rates, as measured by relative disparity indices, widened over time; the relative mean deviation index and coefficient of variation indicated, respectively, a 52% and 61% increase in county-level disparity in CVD mortality rates between 1969 and 2007. Absolute county-level disparities in CVD mortality, however, declined over time (Table 4).

Discussion

Cardiovascular disease mortality rates have decreased for all regions and states in the United States. Yet, geographical disparities in mortality, in relative terms, have widened over time as several areas in the South experienced slower mortality declines than those in the Northeast and Western regions of the country. Geographical disparities are very marked, with several Southern states having nearly twice the risk of CVD mortality than states in the Northeastern and Western United States. Existence of such marked and growing geographical disparities in CVD mortality appears contrary to the goals of the national health initiative that calls for further reductions in cardiovascular disease inequalities in the United States by 2020.[1]

Our results are consistent with the previous studies that have shown historically higher rates of CVD mortality in the Southern region of the United States.[7,12,15] Because of the persistence of this geographical pattern, the “South” is often referred to as the “stroke or heart disease belt” of the United States.[14,15] Since behavioral risk factors such as smoking, unhealthy diet, physical inactivity, and obesity are known to account for about 80% of CVD deaths, geographical disparities in CVD mortality may be understood in terms of geographical distribution of these risk factors.[30] Our analysis confirms the significance of geographical distribution of smoking, obesity, physical inactivity, and diabetes prevalence in explaining county-level disparities in CVD mortality rates. Obesity and diabetes prevalence alone account for nearly 40% of the variance in CVD mortality, and geographical differences in smoking explain about 28% of the variance. Smoking, obesity, and physical inactivity rates are highest in the South, and increases in obesity rates have been more marked in the Southern states.[7,31,32] Moreover, smoking rates have declined more slowly in the South than elsewhere in the United States.[7,31,32]

Patterns and increasing geographical disparities in CVD mortality shown here are consistent with those observed previously for the United States and Europe.[5,13,33-35] A recent study showed widening rural-urban disparities in CVD mortality rates in the United States, with those in rural areas experiencing 16% and 26% higher mortality in 1990 and 2009 respectively than their urban counterparts.[33] Disparities in CVD mortality between most deprived non-metropolitan areas and most affluent metropolitan areas of the United States also increased markedly between 1990 and 2009 in both absolute and relative terms.[33] Coronary heart disease mortality rates have been found to be higher in inner-city areas and in local authority areas in the north of England than those in the south.[34] Another study showed a substantial, widening gap in coronary heart disease mortality between the “worst health” and “best health” areas of Britain over a 10-year period.[35]

Conclusions and Global Health Implications

With the prevalence of many chronic disease risk factors rising in the developing world due to urbanization, development, and globalization, the global burden of cardiovascular diseases is expected to increase further, especially in low- and middle- income countries which account for more than 80% of CVD deaths globally. [30,36-38] Cardiovascular disease is the leading cause of death not only in the industrialized world, but also in low- and middle- income countries.[30,36-38] Globally, a major shift has been occurring in the distribution of disease burden as a number of low- and middle-income countries are experiencing an increasing proportion of deaths and years of life lost due to non-communicable diseases such as heart disease, stroke, and COPD.[30,36-38] Most of the CVD deaths are preventable through policy measures that are aimed at reducing behavioral risk factors such as smoking, physical inactivity, unhealthy diet, and heavy drinking that account for about 80% of cardiovascular diseases globally.[30,36]

Cardiovascular disease burden varies greatly across the world regions, with India and China accounting for >30% of all global CVD deaths.[30,36-38] Similar analyses of geographical disparities in cardiovascular disease prevalence and mortality rates in developing countries can highlight ruralurban, province-, or district-level disparities, thus indicating the need for targeted action and population-wide interventions to reduce cardiovascular disease incidence and associated behavioral risks. The following countries have the highest disease burden (in terms of number of heart disease deaths): US and Germany among high-income countries; China and Indonesia in the East Asia and Pacific region; Russia and Ukraine in Europe and Central Asia; Brazil and Mexico in Latin America and the Caribbean; India and Pakistan in South Asia; and Nigeria and Ethiopia in Sub-Saharan Africa.[30,36-38] Because of macro-societal forces, such as globalization and urbanization, people in developing countries are increasingly being exposed to such CVD risk factors as smoking, drinking, physical inactivity, and unhealthy diet. At the same time, they do not have similar access to public health education and prevention programs and access to primary care as their counterparts in the industrialized world.[30,36]

Geographical inequalities in the United States remain quite marked despite the impressive overall decline in CVD mortality over the past several decades. The growing geographical disparities in CVD mortality are a major public health concern. Because cardiovascular diseases are the leading cause of death and account for nearly one-third of all US deaths, the widening inequalities in CVD mortality contribute greatly to overall health and mortality inequalities in the United States.[1,7,8] These disparities in mortality may indicate significant geographical inequities in CVD prevention and control efforts. Population-wide interventions such as comprehensive tobacco control policies, smoking cessation programs, increased access to primary medical care, physical activity campaigns, and anti-obesity programs can be implemented to reduce CVD risks in the entire population while targeting those in the more disadvantaged areas of the country such as the South.[30] A broad course of policy action related to the wider social determinants can be a particularly effective strategy in reducing CVD inequalities.[5,30,34,35] Health and social policy interventions such as improved access to health services, and reductions in inequalities in education, poverty, unemployment, occupation, housing, and access to health-promoting physical or built environments are essential for tackling long-term inequalities in CVD mortality between geographical areas in the United States.[5,24,30,34,35]

Human Subjects Review:

No IRB approval was required for this study, which is based on the secondary analysis of public-use federal databases.

Financial Disclosure:

None.

Acknowledgments

The views expressed are the authors’ and not necessarily those of their institutions.

Conflicts of Interest:

None.

Funding/Support:

None.

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