- Email: [email protected]
Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US Community—Olmsted County, Minnesota
ORIGINAL ARTICLE
Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US CommunitydOlmsted County, Minnesota Chung-Il Wi, MD; Jennifer L. St. Sauver, PhD; Debra J. Jacobson, MS; Richard S. Pendegraft, MS; Brian D. Lahr, MS; Euijung Ryu, PhD; Timothy J. Beebe, PhD; Jeff A. Sloan, PhD; Jennifer L. Rand-Weaver, MS; Elizabeth A. Krusemark, AAS; YuBin Choi, BS; and Young J. Juhn, MD, MPH Abstract Objective: To characterize health disparities in common chronic diseases among adults by socioeconomic status (SES) and ethnicity in a mixed rural-urban community of the United States. Patients and Methods: We conducted a cross-sectional study to assess the association of the prevalence of the 5 most burdensome chronic diseases in adults with SES and ethnicity and their interaction. The Rochester Epidemiology Project medical records linkage system was used to identify the prevalence of coronary heart disease, asthma, diabetes, hypertension, and mood disorder using International Classification of Diseases, Ninth Revision codes recorded from January 1, 2005, through December 31, 2009, among all adult residents of Olmsted County, Minnesota, on April 1, 2009. For SES measurements, an individual HOUsing-based index of SocioEconomic Status (HOUSES) derived from real property data was used. Logistic regression models were used to examine the association of the prevalence of chronic diseases with ethnicity and HOUSES score and their interaction. Results: We identified 88,010 eligible adults with HOUSES scores available, of whom 48,086 (54.6%) were female and 80,699 (91.7%) were non-Hispanic white; the median (interquartile range) age was 45 years (30-58 years). Overall and in the subgroup of non-Hispanic whites, SES measured by HOUSES was inversely associated with the prevalence of all 5 chronic diseases independent of age, sex, and ethnicity (P<.001). While an association of ethnicity with disease prevalence was observed for all the chronic diseases, SES modified the effect of ethnicity for clinically less overt conditions (interaction P<.05 for each condition [diabetes, hypertension, and mood disorder]) but not for coronary heart disease, a clinically more overt condition. Conclusion: In a mixed rural-urban setting with a predominantly non-Hispanic white population, health disparities in chronic diseases still exist across SES. The extent to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. ª 2016 Mayo Foundation for Medical Education and Research
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ecause health disparities across socioeconomic status (SES) and ethnicity have been well documented in the United States and elsewhere, reduction of health disparities has been consistently one of the overarching goals of the Healthy People program in the United States since 1990.1-9 The 2003 Institute of Medicine Report and the 2013 National Healthcare Disparities Report suggest prevailing disparities in health care access and health outcomes across income and ethnicity.10,11 The geographic and
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temporal trends of health disparities among people with different SES and ethnic backgrounds have persisted and may have worsened over time.11-15 For example, data from the National Health and Nutrition Examination Survey from 1999 to 2006 revealed that control of blood pressure and glucose and cholesterol levels has improved since 1999 for adults with cardiovascular disease and diabetes, but gaps in ethnic or socioeconomic disparities have not significantly declined.14 Another recent cross-sectional study among
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From the Department of Pediatric and Adolescent Medicine (C.-I.W., J.L.R.-W., E.A.K., Y.C., Y.J.J.), Division of Epidemiology (J.L.S.), Division of Biomedical Statistics and Informatics (D.J.J., R.S.P., B.D.L., J.A.S.), Division of Health Care Policy and Research (E.R., T.J.B.), and Center for Individualized Medicine (E.R.), Mayo Clinic, Rochester, MN.
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Medicare enrollees in 2006 and 2011 found similar ethnic disparities in control of the same measures as well as significant regional variation.15 Despite the regional variation of health disparities across ethnicity categories in the United States, little is known about the degree and nature of health disparities in a mixed rural-urban setting that mitigates factors known to contribute to health disparities, such as a relatively affluent community, a higher proportion of people working in the health care system (higher access to health care services), lower prevalence of environmental issues (eg, air pollution, pest infestation), homogeneous distribution of people with different ethnicity in the community (lower dissimilarity index), and higher health insurance coverage.16-20 Given these potentially mitigating factors for health disparities at a community level, assessing health disparities in common chronic diseases in a community characterized by these attributes is likely to provide important insight into the degree and nature of health disparities in chronic diseases in a noneinner city setting. Using data from such a setting, we determined whether major health disparities in chronic diseases among people with different ethnicity and SES existed in our community given the mitigating factors at a community level. In addressing this question, the literature is limited because few population-based studies using a well-defined cohort in a mixed rural-urban setting are available. Particularly, most previous studies were based on self-reported SES measures and health outcomes.3-9 One important barrier to largescale health disparities research based on clinical or administrative data sets is the lack of SES measures even during the electronic medical record era. To overcome this barrier, we applied our recently developed individual housing-based SES index (HOUsing-based index of SocioEconomic Status [HOUSES]) to a population-based cohort including nearly all Olmsted County, Minnesota, residents instead of relying on self-reported SES measures.21-25 Olmsted County is a suitable setting for conducting a population-based study such as this because the health care environment is self-contained, and medical records for nearly all residents are available for clinical research. 2
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Using these unusual resources, we examined the degree and nature of health disparities in 5 common chronic diseases among adults with different SES and ethnicity. PATIENTS AND METHODS Study Setting and Population Olmsted County, Minnesota, is a mixed ruralurban setting containing both urban and rural areas defined by the US Census Bureau (16% rural population and 91% rural area) with a low white to black dissimilarity index of 29.5 in 2010 (vs 82.5 for Chicago, Illinois).20,26 According to the 2010 census, the population of Olmsted County was 85.7% white, 4.7% African American, 5.4% Asian, and 4.2% Hispanic.16 Our community has a higher median family income ($66,252 in 2009-2013) than the national average ($53,046) and a larger proportion of residents of Rochester, Minnesota (the largest city in Olmsted County) working in the health care industry (22%).18,19,27 Accordingly, Olmsted County is not categorized as a Medically Underserved Area.28 The level of poverty in Minnesota and in the United States has steadily increased since 2008 (11.9% in Minnesota and 15.9% throughout the nation in 2011), but poverty levels in Olmsted County remain considerably below national and state levels, hovering around 8% over the past 5 years. Of all Olmsted County adults, 95% currently have health insurance (vs 85% in the United States), and 66% routinely seek medical care.29 The Rochester Epidemiology Project (REP) links data on medical care delivered to the population of Olmsted County, Minnesota.30-32 Most medical care in this community is currently provided by Mayo Clinic and its 2 affiliated hospitals and the Olmsted Medical Center and its affiliated hospitals, with limited care provided through the 2 clinicians of the Rochester Family Medicine Clinic, which has recently closed. The health care records from these institutions are linked together through the REP records linkage system.31,32 Patients are categorized as residents or nonresidents of Olmsted County at the time of each health care visit on the basis of their address. The population counts obtained by the REP census are similar to
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those obtained by the US Census Bureau, indicating that virtually the entire population of the county is captured by the system.31,32 For this study, we used the REP census to identify all individuals who resided in Olmsted County on April 1, 2009, but we excluded those individuals who had refused medical record research authorization in at least one health care institution.31 Study Design We conducted a cross-sectional study to assess the association of 5-year prevalence (ie, January 1, 2005, through December 31, 2009) of the 5 most burdensome chronic diseases in adults with different SES and ethnicity and their interaction. Disease Prevalence. We assessed the prevalence of the 5 most burdensome chronic diseases as identified by the Agency for Healthcare Research and Quality among adults over 18 years of age identified by the REP data set.33 These diseases included coronary heart disease (CHD), asthma, diabetes, hypertension, and mood disorder. Detailed identification algorithms for each disease have been described previously.34 In brief, the diagnostic indices of the REP were searched electronically to extract the International Classification of Diseases, Ninth Revision (ICD-9) codes of these 5 chronic diseases in the medical records of the Olmsted County population ever assigned by any health care institution from January 1, 2005, through December 31, 2009 (ie, 5-year prevalence with a single ICD-9 code). These ICD-9 codes were grouped into clinical classification codes proposed by the Agency for Healthcare Research and QualityeHealthcare Cost and Utilization Project.35,36 Individual SES Measured by HOUSES. Socioeconomic status of our study population was measured by individual HOUSES.21-25 Development and initial testing of the index were completed in both Olmsted County, Minnesota, and Jackson County, Missouri, and the index was applied to a study that was conducted in Sioux Falls, South Dakota. Briefly, in formulating HOUSES, the addresses of the eligible study participants in April 2009 were geocoded. Geocoding allowed us to match the study participant’s address to geographic Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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reference data and real property data from the Assessor’s Office of the county government. Our original research work for development and validation of HOUSES (principal components factor analysis) identified 4 real property feature variablesdhousing value, square footage of housing unit, number of bedrooms, and number of bathroomsdin a same factor sharing the underlying construct (SES). We then formulated a standardized HOUSES score by summing the z scores for each variable (ie, standardized index). The higher the HOUSES score (z score), the higher the SES. Our prior work has documented that the HOUSES score is associated with health outcomes in children and adults, such as the risk of low birth weight, obesity, smoking exposure at home, asthma control status, risk of pneumococcal diseases, postemyocardial infarction mortality, and risk of rheumatoid arthritis and posterheumatoid arthritis mortality.21-25 Other Variables. Both Olmsted County’s and Minnesota’s minority populations accounted for 14.3% of the population in 2010.37 For this study, we grouped the self-reported ethnicity into 4 categoriesdnon-Hispanic white, African American, Asian, and Hispanicd according to the racial and ethnic categories suggested by the National Institutes of Health.15,38 As suggested by a previous study, we grouped the other/unknown category with the non-Hispanic white category because we presumed that most of the patients in the other/unknown category were non-Hispanic white (85.7% of the Olmsted County population self-reported white in the 2010 census).34 Statistical Analyses Descriptive statistics were used to summarize demographic characteristics of the study population. Association between demographic variables and HOUSES score in quartiles (Q1-Q4) was assessed by c2 tests. To assess the association of HOUSES score with the prevalence of each chronic condition (CHD, asthma, diabetes, hypertension, and mood disorder), logistic regression models were used among patients in the overall cohort, adjusting for age, sex, and ethnicity. Additional multivariate logistic models further adjusted for pertinent risk factors for CHD
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(diabetes, hypertension, hyperlipidemia, and obesity) and diabetes (hyperlipidemia and obesity). Similar analysis was carried out among non-Hispanic white patients only. Stratified logistic regression models were used to assess associations of different ethnicity groups with each chronic disease. In addition, an interaction effect between ethnicity and HOUSES score on prevalence of each disease was tested under the framework of logistic regression by adding an interaction term between ethnicity group and HOUSES quartiles. Statistical analyses were performed using SAS statistical software (SAS Institute). All tests were 2-sided, and P<.05 was considered statistically significant. RESULTS Characteristics of the Study Population Of the 130,078 eligible patients identified in the REP database, we excluded 9455d330 who were institutionalized, 1186 who provided only a Post Office box for their address, 6099 with unverifiable addresses, and 1840 with unavailable real property data, leaving 120,623 (92.7%) who were successfully geocoded. Of these 120,623 patients, 88,010 were adults and constituted our study group; 48,086 (54.6%) were female, and the median age (interquartile range [IQR]) was 45 years (IQR, 30-58 years) (Table 1). The proportions of non-Hispanic white, African American, Asian, and Hispanic patients were 80,699 (91.7%), 2650 (3.0%), 3065 (3.5%), and 1596 (1.8%), respectively. The median (IQR) HOUSES score was 0.38 (2.28 to 1.84). SES as Measured by HOUSES and Demographic Characteristics Overall, higher SES was observed among patients between 46 and 65 years and males (Table 2). In addition, non-Hispanic white patients had higher SES than their African American and Hispanic counterparts, whereas Asian patients’ SES was relatively similar to that of non-Hispanic whites in this community (highest SES quartile [Q4]: nonHispanic whites, 20,901 of 80,699 [25.9%]; African Americans, 196 of 2650 [7.5%]; Hispanics, 159 of 1596 [10.0%]; and Asians, 745 of 3065 [24.3%]). 4
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TABLE 1. Sociodemographic Characteristics and Prevalence of the 5 Most Burdensome Chronic Diseasesa,b Variable
Data from 88,010 study patients
Age (y), median (IQR) 45 (30-58) Age group (y) 18-45 45,450 (51.6) 46-65 29,011 (33.0) >65 13,549 (15.4) Sex Male 39,924 (45.4) Female 48,086 (54.6) Ethnicity Non-Hispanic white 80,699 (91.7) African American 2650 (3.0) Asian 3065 (3.5) Hispanic 1596 (1.8) HOUSES, median (IQR) 0.38 (2.28 to 1.84) Prevalence of chronic disease Coronary heart disease 9585 (10.9) Asthma 7601 (8.6) Diabetes 7824 (8.9) Hypertension 22,513 (25.6) Mood disorder 22,263 (25.3) HOUSES ¼ HOUsing-based index of SocioEconomic Status; IQR ¼ interquartile range. b Data are presented as No. (percentage) of patients unless indicated otherwise. a
SES and Prevalence of Common Chronic Diseases Overall, lower SES was associated with a higher prevalence of each of the 5 chronic conditions after controlling for age, sex, ethnicity, and additional pertinent risk factors (P<.001) (Table 3). A similar pattern was found in the subgroup of non-Hispanic white patients. The impact of SES on prevalence tended to be greater in diabetes, hypertension, and mood disorder than in CHD and asthma. For example, the adjusted odds ratio for the highest SES group compared with the lowest SES group was 0.56 (95% CI, 0.52-0.60) for diabetes and 0.74 (95% CI, 0.69-0.80) for CHD. Ethnicity and Prevalence of Chronic Diseases The association between ethnicity and prevalence of chronic diseases varied in magnitude but revealed no consistent patterns for distinguishing clinically more overt vs less overt
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TABLE 2. Association Between Demographic Characteristics and HOUSES as Both Continuous Scale and Quartiles Among 88,010 Study Patientsa,b HOUSES by quartile, No. (%) Variable
HOUSES, median (IQR)
Q1
Q2
Q3
Q4
0.57 (2.74 to 1.65) 0.22 (1.72 to 2.57) 0.95 (2.32 to 0.75)
12,999 (28.6) 5454 (18.8) 3551 (26.2)
10,594 (23.3) 6853 (23.6) 4556 (33.6)
11,022 (24.3) 7598 (26.2) 3382 (25.0)
10,835 (23.8) 9106 (31.4) 2060 (15.2)
0.17 (2.12 to 2.02) 0.54 (2.40 to 1.69)
9283 (23.3) 12,721 (26.5)
9862 (24.7) 12,141 (25.2)
10,274 (25.7) 11,728 (24.4)
10,505 (26.3) 11,496 (23.9)
0.24 4.56 0.41 3.51
18,519 1603 956 926
20,754 390 586 273
20,525 461 778 238
20,901 196 745 159
P valuec <.001
Age (y) 18-45 (n¼45,450) 46-65 (n¼29,011) >65 (n¼13,549) Sex Male (n¼39,924) Female (n¼48,086) Ethnicity Non-Hispanic white (n¼80,699) African American (n¼2650) Asian (n¼3065) Hispanic (n¼1596)
<.001
<.001 (2.11 (5.15 (3.21 (4.97
to to to to
1.96) 0.44) 1.74) 0.43)
(22.9) (60.5) (31.2) (58.0)
(25.7) (14.7) (19.1) (17.1)
(25.4) (17.4) (25.4) (14.9)
(25.9) (7.4) (24.3) (10.0)
HOUSES ¼ HOUsing-based index of SocioEconomic Status; IQR ¼ interquartile range; Q ¼ quartile. Q1 is the lowest quartile and Q4 is the highest quartile. Percentages may not equal 100 because of rounding. c P value from c2 test. a
b
diseases. The results are summarized in Table 4. Although mood disorder was more prevalent in non-Hispanic white patients than in minority groups, the odds of diabetes were at least 2-fold higher among minority groups compared with non-Hispanic whites after adjusting for age, sex, hyperlipidemia, and obesity. In comparison to non-Hispanic white patients, Hispanic patients had a reduced rate of both hypertension and CHD, whereas African American patients had increased odds of both diseases after adjusting for pertinent risk factors. Both Asian and Hispanic, but not African American, patients had lower rates of asthma compared with nonHispanic white patients. Interaction Between SES and Ethnicity in Prevalence of Chronic Diseases Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, and the interaction between SES and ethnicity depended on the nature of the disease. The results are summarized in the Figure and the Supplemental Table (available online at http://www.mayoclinicproceedings. org). Specifically, the patterns in which SES modified the effect of ethnicity observed among minority groups for diabetes, hypertension, and mood disorder were comparatively different from CHD. As shown in the Figure and the Supplemental Table, the odds Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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of diabetes, hypertension, and mood disorder were generally increased with higher SES (above the median of HOUSES) in the minority patients, especially African American patients, relative to non-Hispanic white patients after controlling for age, sex, ethnicity, and additional pertinent risk factors for diabetes (ie, obesity and hyperlipidemia) (interaction P<.05 for each condition [diabetes, hypertension, and mood disorder]). However, such patterns appeared to be reversed for CHD (interaction P¼.50). DISCUSSION In this study, notable health disparities still existed among non-Hispanic white residents and the overall adult population with different SES in a mixed rural-urban setting. Also, there were ethnic disparities in the prevalence of common chronic diseases. Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, but such effect modification by SES depended on the nature of the clinical disease (ie, clinically overt diseases such as CHD vs clinically less overt diseases such as diabetes, hypertension, and mood disorder). Despite the potential mitigating factors for health disparities (eg, a relatively affluent community, a higher proportion of people working in the health care system, homogeneous distribution of people with different ethnicity in the community, and higher health
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TABLE 3. Association Between HOUSES and Prevalence of Chronic Diseases in Overall Cohort and Among Non-Hispanic White Patientsa Overall cohort (N¼88,010) HOUSES (quartile) CHD Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Asthma Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Diabetes Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Hypertension Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Mood disorder Q1 (lowest SES) Q2 Q3 Q4 (highest SES)
No. (%) of patients
Adjusted OR (95% CI)
Non-Hispanic white patients (n¼80,699) P value
No. (%) of patients
Adjusted OR (95% CI)
<.001 2535 2867 2429 1754
(11.5) (13.0) (11.0) (8.0)
1.0 0.87 0.86 0.74
(Reference) (0.81-0.93) (0.80-0.92) (0.69-0.80)
2357 2784 2331 1693
(12.7) (13.4) (11.4) (8.1)
1.0 0.84 0.87 0.81
(Reference) (0.78-0.90) (0.81-0.94) (0.75-0.88)
<.001d 2112 1950 1840 1699
(9.6) (8.9) (8.4) (7.7)
1.0 0.90 0.85 0.78
<.001e
(Reference) (0.84-0.96) (0.80-0.91) (0.73-0.83)
1849 1850 1739 1627
(10.0) (8.9) (8.5) (7.8)
1.0 0.91 0.86 0.78
(Reference) (0.85-0.97) (0.80-0.92) (0.73-0.84) <.001g
<.001f 2330 2303 1905 1286
(10.6) (10.5) (8.7) (5.8)
1.0 0.84 0.75 0.56
(Reference) (0.78-0.89) (0.70-0.80) (0.52-0.60)
5672 6789 5651 4401
(25.8) (30.9) (25.7) (20.0)
1.0 0.96 0.80 0.64
(Reference) (0.91-1.01) (0.76-0.85) (0.61-0.68)
2004 2176 1741 1178
(10.8) (10.5) (8.5) (5.6)
1.0 0.83 0.74 0.56
(Reference) (0.78-0.89) (0.69-0.80) (0.52-0.61)
5180 6558 5352 4209
(28.0) (31.6) (26.1) (20.1)
1.0 0.96 0.78 0.64
(Reference) (0.90-1.01) (0.74-0.83) (0.60-0.67)
<.001d
<.001e
<.001e
<.001d 6534 5737 5309 4683
(29.7) (26.1) (24.1) (21.3)
1.0 0.78 0.72 0.61
P value <.001c
b
(Reference) (0.75-0.81) (0.69-0.75) (0.58-0.63)
5872 5520 5057 4531
(31.7) (26.6) (24.6) (21.7)
1.0 0.79 0.72 0.61
(Reference) (0.76-0.83) (0.69-0.75) (0.59-0.64)
CHD ¼ coronary heart disease; HOUSES ¼ HOUsing-based index of SocioEconomic Status; OR ¼ odds ratio; Q ¼ quartile; SES ¼ socioeconomic status. Adjusted for age, sex, ethnicity, hypertension, diabetes, hyperlipidemia, and obesity. c Adjusted for age, sex, hypertension, diabetes, hyperlipidemia, and obesity. d Adjusted for age, sex, and ethnicity. e Adjusted for age and sex. f Adjusted for age, sex, ethnicity, hyperlipidemia, and obesity. g Adjusted for age, sex, hyperlipidemia, and obesity. a
b
insurance coverage),16-19,29,39 there were still substantial disparities in the prevalence of chronic disease among different ethnic groups in this community. Also, health disparities existed in our study’s majority, the nonHispanic white population, with different SES in a dose-response manner across all chronic diseases examined. Furthermore, the ratios of prevalence of each chronic disease between the highest SES stratum (Q4) and the lowest stratum (Q1) in our community (Q4/ Q1) (ie, 0.70 for CHD, 0.80 for asthma, 0.55 for diabetes, 0.78 for hypertension, and 0.72 for mood disorder) were not drastically different from estimates by multiple studies at a national level (ie, 0.4-0.66 for CHD, 0.94-1.04 for asthma, 0.38-0.47 for diabetes, 6
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0.59-0.70 for hypertension, and 0.87 for mood disorder).40-43 Because our study was based on ICD-9 codes for chronic diseases and HOUSES for SES measurement, it is unlikely that our results are due to report biases caused by self-report. The literature has documented that non-Hispanic whites have a reduced risk of multiple chronic diseases.44-48 Because the non-Hispanic white population has often been used as a reference group for analysis, the literature examining health disparities among non-Hispanic white populations across different SES groups is relatively limited.3 Overall, our study results suggest that non-Hispanic white populations do not seem to be exempt from health disparities across different SES.
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TABLE 4. Association Between Ethnicity and Prevalence of Chronic Diseasesa,b Ethnicity
CHD (P¼.01)
Asthma (P<.001)
Diabetes (P<.001)
Hypertensiond (P<.001)
African American Asian Hispanic
1.25 (1.04-1.50) 1.00 (0.84-1.18) 0.71 (0.53-0.97)
1.04 (0.91-1.19) 0.61 (0.52-0.71) 0.61 (0.49-0.75)
2.22 (1.94-2.55) 2.09 (1.83-2.39) 1.97 (1.64-2.38)
1.38 (1.23-1.55) 0.86 (0.77-0.97) 0.86 (0.72-1.01)
c
d
e
Mood disorderd (P<.001) 0.62 (0.56-0.69) 0.44 (0.40-0.49) 0.63 (0.56-0.72)
CHD ¼ coronary heart disease. Data are presented as adjusted odds ratio (95% CI), with non-Hispanic white patients as the reference group. c Adjusted for age, sex, hypertension, diabetes, hyperlipidemia, and obesity. d Adjusted for age and sex. e Adjusted for age, sex, hyperlipidemia, and obesity. a
b
Our study reveals noteworthy findings. As illustrated in the Figure and Supplemental Table, overall, a higher prevalence of clinically less overt diseases (diabetes, hypertension, and mood disorder) was found in the minority groups with higher SES (above the median of HOUSES when stratified by HOUSES) compared with those with lower SES. Such patterns or trends were not observed in clinically overt disease such as CHD. In this respect, the higher prevalence of chronic diseases may reflect identification of diseases (diagnosis or detection) not necessarily representing only a risk of disease per se. For example, clinically less overt chronic diseases might be much more dependent on access to health care and health literacy for identification of such diseases, whereas those clinically more overt diseases such as CHD often require emergency care (because they can be lifethreatening conditions) and thus are less dependent on one’s ability to recognize the diseases and on health care access. In this interpretation, we consider asthma a disease with mixed features between clinically overt and less overt, and thus asthma had a different pattern from clinically overt disease and less overt diseases as illustrated in the Figure. This variability suggests that the extent to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. Therefore, when interpreting our study results in terms of differentiating disease vs diagnosis, one should bear in mind that we used ICD-9 codes for defining chronic diseases. As Shippee et al49 pointed out, given the impact of SES on health care access and literacy, patients with lower SES may not seek timely care for appropriate diagnosis and treatment for clinically less overt Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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disease until clinically apparent manifestations develop. Our study results have a few important implications for health care policy concerning health disparities. First, ethnic disparities in health outcomes should be interpreted in the context of SES and the nature of the disease examined. The complex interaction among ethnicity, SES, and the nature of the disease might not be captured if health disparities across different ethnicity and SES are examined separately, especially without considering the clinical nature of the disease. Second, true health disparities (ie, disease risk) across different SES might be more underestimated in clinically less overt diseases such as diabetes or hypertension regardless of ascertainment methods (ie, lower identification of such diseases in lower SES groups and greater detection in higher SES groups). Therefore, although the health care system makes an effort to prevent these chronic diseases, to reduce health disparities in outcomes of clinically less overt diseases the identification of such diseases through improving health literacy and health care access should be a primary focus of intervention, especially among underserved populations including minorities or lower SES populations. Finally, our study results highlight that non-Hispanic white populations with lower SES still represent underserved populations that are a target population for health promotion. The main strength of our study is the population-based study design. Another strength is that the prevalence of diseases was based on documented physician diagnosis instead of self-report. In addition, we objectively measured SES on the basis of real property data instead of self-reported conventional
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Asthma 1.2 1.0
African American Asian
Odds ratio
Odds ratio
CHD 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0
Hispanic
African American Asian
0.8 0.6 0.4
Hispanic
0.2 0 Below the Above the median median HOUSES
Below the Above the median median HOUSES
Diabetes
Hypertension 2.5
2.0
African American Asian
1.0
Hispanic
3.0
2.0 Odds ratio
Odds ratio
4.0
African American Asian
1.5 1.0
Hispanic
0.5
0
0 Below the Above the median median HOUSES
Below the Above the median median HOUSES
Mood disorder 1.0 Odds ratio
0.8
African American Asian
0.6 0.4
Hispanic
0.2 0 Below the Above the median median HOUSES
FIGURE. Comparison of odds ratios of each chronic condition by ethnicity and socioeconomic status (SES) measured by the HOUsing-based index of SocioEconomic Status (HOUSES). Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, and the interaction between SES and ethnicity depended on the nature of the disease. Specifically, the patterns in which SES modified the effect of ethnicity observed among minority groups for diabetes, hypertension, and mood disorder were comparatively different from coronary heart disease (CHD). The odds of diabetes, hypertension, and mood disorder were generally increased with higher SES (above the median of HOUSES) in the minority patients, especially African American patients, relative to non-Hispanic white patients after controlling for age, sex, and additional pertinent risk factors. However, such patterns appeared to be reversed for CHD.
SES measures. Also in this study, HOUSES scores were available for nearly all eligible patients (92.7%). Our findings should be interpreted with caution, however. The main limitation of our study was the inability to 8
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verify ICD-9 codes.50,51 Because we defined the prevalence of each chronic condition with a single ICD-9 code as suggested by a previous study,34 the prevalence of some conditions might have been overestimated. For
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example, the prevalence of CHD (10.9% in this study vs 6.0% in the United States and 4.9% in Minnesota41) might have changed if different ICD-9 codes were assigned after diagnostic procedures. However, systematic differential misclassification of CHD across different SES or ethnicity is unlikely given the lifethreatening nature of CHD. Also, given the availability of specific diagnostic tests or procedures for hypertension, diabetes, and asthma, our study results are unlikely to be due to systemic (differential) misclassification of diseases. The cross-sectional nature of the analysis (ie, prevalence study) is also a limitation of this study. Although we took into account traditional risk factors for certain diseases (CHD and diabetes), we did not have all pertinent risk factors for certain diseases examined to adjust the main results (eg, smoking data was not available for this study). For this study, we grouped the other/ unknown category of patients with the nonHispanic white category as suggested by previous studies, but this presumption may be inaccurate. Lastly, unavailability of insurance data for the REP population limited our further data analysis for impact of insurance type on the association of SES with prevalence of chronic conditions. CONCLUSION In a mixed rural-urban setting with a predominantly non-Hispanic white population, health disparities in chronic diseases still exist across different SES. The degree to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. ACKNOWLEDGMENTS We thank Mrs Kelly Okeson for administrative assistance and support and Dr Barbara P. Yawn, MD, MSc, for editorial review of the submitted manuscript. The funding agency was not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the submitted manuscript. Dr Juhn had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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SUPPLEMENTAL ONLINE MATERIAL Supplemental material can be found online at http://www.mayoclinicproceedings.org. Supplemental material attached to journal articles has not been edited, and the authors take responsibility for the accuracy of all data. Abbreviations and Acronyms: CHD = coronary heart disease; HOUSES = HOUsing-based index of SocioEconomic Status; ICD-9 = International Classification of Diseases, Ninth Revision; IQR = interquartile range; REP = Rochester Epidemiology Project; SES = socioeconomic status Grant Support: This work was supported by grant R21 AI101277 (Y.J.J.) from the National Institute of Allergy and Infectious Diseases and the Scholarly Clinician Award (Y.J.J.) from the Mayo Foundation. The study was made possible by the Rochester Epidemiology Project (grant number R01-AG034676; Principal Investigators: Walter A. Rocca, MD, MPH, and Jennifer L. St Sauver, PhD), which is supported by the National Institute on Aging of the National Institutes of Health. Correspondence: Address to Young J. Juhn, MD, MPH, Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (Juhn. [email protected]).
REFERENCES 1. US Department of Health and Human Services. Healthy People 2020. https://www.healthypeople.gov/. Updated February 23, 2016. Accessed January 23, 2015. 2. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2020 leading health indicators: progress update. https://www. healthypeople.gov/sites/default/files/LHI-ProgressReport-Exec Sum_0.pdf. Published March 2014. Accessed February 23, 2016. 3. Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: what the patterns tell us. Am J Public Health. 2010;100(suppl 1): S186-S196. 4. Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE. Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001-2006. Ann Epidemiol. 2010; 20(8):617-628. 5. Banks J, Marmot M, Oldfield Z, Smith JP. Disease and disadvantage in the United States and in England. JAMA. 2006;295(17): 2037-2045. 6. Signorello LB, Cohen SS, Williams DR, Munro HM, Hargreaves MK, Blot WJ. Socioeconomic status, race, and mortality: a prospective cohort study. Am J Public Health. 2014; 104(12):e98-e107. 7. Farmer MM, Ferraro KF. Are racial disparities in health conditional on socioeconomic status? Soc Sci Med. 2005;60(1): 191-204. 8. Mackenbach JP, Stirbu I, Roskam AJ, et al; European Union Working Group on Socioeconomic Inequalities in Health. Socioeconomic inequalities in health in 22 European countries [published correction appears in N Engl J Med. 2008;359(12): e14]. N Engl J Med. 2008;358(23):2468-2481. 9. Whitehead M, Townsend P, Davidson N, eds. Inequalities in Health: The Black Report and the Health Divide. London, England: Penguin Books; 1992.
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10. Smedley BD, Stith AY, Nelson AR, eds, Committee on Understanding and Eliminating Racial Disparities in Health Care. Evidence of Healthcare. In: Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: National Academies Press; 2003:5-6. 11. US Department of Health and Human Services. 2013 National Healthcare Disparities Report. Agency for Healthcare Research and Quality website. http://www.ahrq.gov/sites/default/files/ publications/files/2013nhdr.pdf. Published May 2014. Accessed February 13, 2015. 12. Kamangar F, Dores GM, Anderson WF. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol. 2006;24(14):2137-2150. 13. United Health Foundation. America’s Health Rankings: 2014 Annual Report. http://www.americashealthrankings.org/reports/ annual. Accessed February 13, 2015. 14. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: U.S. trends from 1999 to 2006 and effects of Medicare coverage. Ann Intern Med. 2009;150(8):505-515. 15. Ayanian JZ, Landon BE, Newhouse JP, Zaslavsky AM. Racial and ethnic disparities among enrollees in Medicare Advantage plans. N Engl J Med. 2014;371(24):2288-2297. 16. US Census Bureau. 2010 Census interactive population search: Minnesota. http://www.census.gov/2010census/popmap/ ipmtext.php?fl¼27. Accessed February 13, 2015. 17. American Lung Association. American Lung Association State of the Air 2014. http://www.stateoftheair.org/2014/assets/ALASOTA-2014-Full.pdf. Accessed February, 2015. 18. US Department of Labor. Occupational employment statistics: May 2013 metropolitan and nonmetropolitan area occupational employment and wage estimates; Rochester, MN. US Bureau of Labor Statistics website. http://www.bls.gov/oes/ current/oes_40340.htm#otherlinks. Updated March 25, 2015. Accessed February 28, 2015. 19. US Department of Labor. Occupational employment statistics: May 2013 national occupational employment and wage estimates by ownership; cross-industry, private ownership only. US Bureau of Labor Statitstics website. http://factfinder.census. gov/faces/tableservices/jsf/pages/productview.xhtml?src¼CF. Acessed March 11, 2016. 20. US 2010: discover America in a new century; resident segregation. Brown University website. http://www.s4.brown.edu/ us2010/segregation2010/Default.aspx?msa¼10180. Accessed June 29, 2015. 21. Juhn YJ, Beebe TJ, Finnie DM, et al. Development and initial testing of a new socioeconomic status measure based on housing data. J Urban Health. 2011;88(5):933-944. 22. Johnson MD, Urm SH, Jung JA, et al. Housing data-based socioeconomic index and risk of invasive pneumococcal disease: an exploratory study. Epidemiol Infect. 2013;141(4):880-887. 23. Bang DW, Manemann SM, Gerber Y, et al. A novel socioeconomic measure using individual housing data in cardiovascular outcome research. Int J Environ Res Public Health. 2014; 11(11):11597-11615. 24. Harris MN, Lundien MC, Finnie DM, et al. Application of a novel socioeconomic measure using individual housing data in asthma research: an exploratory study. NPJ Prim Care Respir Med. 2014;24:14018. 25. Ghawi H, Crowson CS, Rand-Weaver J, Krusemark E, Gabriel SE, Juhn YJ. A novel measure of socioeconomic status using individual housing data to assess the association of SES with rheumatoid arthritis and its mortality: a population-based case-control study. BMJ Open. 2015;5(4):e006469. 26. US Census Bureau. 2010 Census urban and rural classification and urban area criteria. https://www.census.gov/geo/reference/ ua/urban-rural-2010.html. Updated February 29, 2015. Accessed June 5, 2015.
10
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27. US Census Bureau. State & County QuickFacts: United States. http://quickfacts.census.gov/qfd/states/00000.html. Updated December 2, 2015. Accessed February 28, 2015. 28. US Department of Health and Human Services. MUA find. Health Resources and Services Administration website. http:// muafind.hrsa.gov/index.aspx. Updated February 23, 2016. Accessed October 15, 2014. 29. Olmsted County, Minnesota Community Health Needs Assessment 2013. Olmsted County website. http://www.co. olmsted.mn.us/OCPHS/reports/Documents/Community%20 Health%20Needs%20Assessment%202013.pdf. Accessed October 15, 2014. 30. St Sauver JL, Grossardt BR, Leibson CL, Yawn BP, Melton LJ III, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc. 2012; 87(2):151-160. 31. St Sauver JL, Grossardt BR, Yawn BP, Melton LJ III, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project. Am J Epidemiol. 2011;173(9):1059-1068. 32. Rocca WA, Yawn BP, St Sauver JL, Grossardt BR, Melton LJ III. History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population. Mayo Clin Proc. 2012;87(12):1202-1213. 33. Stanton MW. The high concentration of US health care expenditures. Research in Action, No. 19. AHRQ Publication No. 06e0060. Agency for Health Care Research and Quality website. http://archive.ahrq.gov/research/findings/factsheets/costs/ expriach/expendria.pdf. Published June 2006. Accessed May 19, 2012. 34. St Sauver JL, Warner DO, Yawn BP, et al. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. Mayo Clin Proc. 2013;88(1): 56-67. 35. Agency for Healthcare Quality and Research. Medical Expenditure Panel Survey HC-120, Appendix 3: Clinical Classification Code to ICD-9-CM Code Crosswalk. http://www.bls.gov/oes/ 2013/may/oes_stru.htm. Accessed November 18, 2014. 36. Agency for Healthcare Quality and Research. Healthcare Cost and Utilization Project: clinical classifications software (CCS) for ICD-9-CM. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs. jsp. Updated December 18, 2015. Accessed November 18, 2014. 37. US Census Bureau, 2010. http://factfinder2.census.gov/faces/ tableservices/jsf/pages/productview.xhtml?src¼bkmk. Accessed Sep 8, 2014. 38. National Institutes of Health. Racial and ethnic categories and definitions for NIH diversity programs and for other reporting purposes. http://grants.nih.gov/grants/guide/notice-files/NOTOD-15-089.html#sthash.OD1zPDgj.dpuf. Published April 8, 2015. Accessed March 11, 2016. 39. Segregation: dissimilarity indices. CensusScope website. http:// www.censusscope.org/us/rank_dissimilarity_white_black.html. Accessed November 18, 2014. 40. National Center for Health Statistics. Health, United States, 2012. Table 64. DHHS Publication No. 2013-1232. Centers for Disease Control and Prevention website. http://www.cdc. gov/nchs/data/hus/2012/064.pdf. Published May 2013. Accessed November 18, 2014. 41. Centers for Disease Control and Prevention. Prevalence of coronary heart diseasedUnited States, 2006-2010. MMWR Morb Mortal Wkly Rep. 2011;60(40):1377-1381. 42. Schiller JS, Lucas JW, Peregoy JA. Summary health statistics for U.S. adults: National Health Interview Survey. Vital Health Stat 10. 2011;2012(256):1-218. 43. Horwath E, Johnson J, Klerman GL, Weissman MM. Depressive symptoms as relative and attributable risk factors for first-onset major depression. Arch Gen Psychiatry. 1992; 49(10):817-823.
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44. Jones DW, Chambless LE, Folsom AR, et al. Risk factors for coronary heart disease in African Americans: the Atherosclerosis Risk in Communities Study, 1987-1997. Arch Intern Med. 2002;162(22):2565-2571. 45. Breslau J, Kendler KS, Su M, Gaxiola-Aguilar S, Kessler RC. Lifetime risk and persistence of psychiatric disorders across ethnic groups in the United States. Psychol Med. 2005;35(3):317-327. 46. McDaniel M, Paxson C, Waldfogel J. Racial disparities in childhood asthma in the United States: evidence from the National Health Interview Survey, 1997 to 2003. Pediatrics. 2006;117(5): e868-e877. 47. Heisler M, Smith DM, Hayward RA, Krein SL, Kerr EA. Racial disparities in diabetes care processes, outcomes, and treatment intensity. Med Care. 2003;41(11):1221-1232.
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48. Hertz RP, Unger AN, Cornell JA, Saunders E. Racial disparities in hypertension prevalence, awareness, and management. Arch Intern Med. 2005;165(18):2098-2104. 49. Shippee ND, Shah ND, May CR, Mair FS, Montori VM. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. J Clin Epidemiol. 2012;65(10):1041-1051. 50. Yawn BP, Wollan P, St Sauver J. Comparing shingles incidence and complication rates from medical record review and administrative database estimates: how close are they? Am J Epidemiol. 2011;174(9):1054-1061. 51. Roger VL, Killian J, Henkel M, et al. Coronary disease surveillance in Olmsted County objectives and methodology. J Clin Epidemiol. 2002;55(6):593-601.
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Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US CommunitydOlmsted County, Minnesota Chung-Il Wi, MD; Jennifer L. St. Sauver, PhD; Debra J. Jacobson, MS; Richard S. Pendegraft, MS; Brian D. Lahr, MS; Euijung Ryu, PhD; Timothy J. Beebe, PhD; Jeff A. Sloan, PhD; Jennifer L. Rand-Weaver, MS; Elizabeth A. Krusemark, AAS; YuBin Choi, BS; and Young J. Juhn, MD, MPH Abstract Objective: To characterize health disparities in common chronic diseases among adults by socioeconomic status (SES) and ethnicity in a mixed rural-urban community of the United States. Patients and Methods: We conducted a cross-sectional study to assess the association of the prevalence of the 5 most burdensome chronic diseases in adults with SES and ethnicity and their interaction. The Rochester Epidemiology Project medical records linkage system was used to identify the prevalence of coronary heart disease, asthma, diabetes, hypertension, and mood disorder using International Classification of Diseases, Ninth Revision codes recorded from January 1, 2005, through December 31, 2009, among all adult residents of Olmsted County, Minnesota, on April 1, 2009. For SES measurements, an individual HOUsing-based index of SocioEconomic Status (HOUSES) derived from real property data was used. Logistic regression models were used to examine the association of the prevalence of chronic diseases with ethnicity and HOUSES score and their interaction. Results: We identified 88,010 eligible adults with HOUSES scores available, of whom 48,086 (54.6%) were female and 80,699 (91.7%) were non-Hispanic white; the median (interquartile range) age was 45 years (30-58 years). Overall and in the subgroup of non-Hispanic whites, SES measured by HOUSES was inversely associated with the prevalence of all 5 chronic diseases independent of age, sex, and ethnicity (P<.001). While an association of ethnicity with disease prevalence was observed for all the chronic diseases, SES modified the effect of ethnicity for clinically less overt conditions (interaction P<.05 for each condition [diabetes, hypertension, and mood disorder]) but not for coronary heart disease, a clinically more overt condition. Conclusion: In a mixed rural-urban setting with a predominantly non-Hispanic white population, health disparities in chronic diseases still exist across SES. The extent to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. ª 2016 Mayo Foundation for Medical Education and Research
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ecause health disparities across socioeconomic status (SES) and ethnicity have been well documented in the United States and elsewhere, reduction of health disparities has been consistently one of the overarching goals of the Healthy People program in the United States since 1990.1-9 The 2003 Institute of Medicine Report and the 2013 National Healthcare Disparities Report suggest prevailing disparities in health care access and health outcomes across income and ethnicity.10,11 The geographic and
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temporal trends of health disparities among people with different SES and ethnic backgrounds have persisted and may have worsened over time.11-15 For example, data from the National Health and Nutrition Examination Survey from 1999 to 2006 revealed that control of blood pressure and glucose and cholesterol levels has improved since 1999 for adults with cardiovascular disease and diabetes, but gaps in ethnic or socioeconomic disparities have not significantly declined.14 Another recent cross-sectional study among
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From the Department of Pediatric and Adolescent Medicine (C.-I.W., J.L.R.-W., E.A.K., Y.C., Y.J.J.), Division of Epidemiology (J.L.S.), Division of Biomedical Statistics and Informatics (D.J.J., R.S.P., B.D.L., J.A.S.), Division of Health Care Policy and Research (E.R., T.J.B.), and Center for Individualized Medicine (E.R.), Mayo Clinic, Rochester, MN.
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Medicare enrollees in 2006 and 2011 found similar ethnic disparities in control of the same measures as well as significant regional variation.15 Despite the regional variation of health disparities across ethnicity categories in the United States, little is known about the degree and nature of health disparities in a mixed rural-urban setting that mitigates factors known to contribute to health disparities, such as a relatively affluent community, a higher proportion of people working in the health care system (higher access to health care services), lower prevalence of environmental issues (eg, air pollution, pest infestation), homogeneous distribution of people with different ethnicity in the community (lower dissimilarity index), and higher health insurance coverage.16-20 Given these potentially mitigating factors for health disparities at a community level, assessing health disparities in common chronic diseases in a community characterized by these attributes is likely to provide important insight into the degree and nature of health disparities in chronic diseases in a noneinner city setting. Using data from such a setting, we determined whether major health disparities in chronic diseases among people with different ethnicity and SES existed in our community given the mitigating factors at a community level. In addressing this question, the literature is limited because few population-based studies using a well-defined cohort in a mixed rural-urban setting are available. Particularly, most previous studies were based on self-reported SES measures and health outcomes.3-9 One important barrier to largescale health disparities research based on clinical or administrative data sets is the lack of SES measures even during the electronic medical record era. To overcome this barrier, we applied our recently developed individual housing-based SES index (HOUsing-based index of SocioEconomic Status [HOUSES]) to a population-based cohort including nearly all Olmsted County, Minnesota, residents instead of relying on self-reported SES measures.21-25 Olmsted County is a suitable setting for conducting a population-based study such as this because the health care environment is self-contained, and medical records for nearly all residents are available for clinical research. 2
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Using these unusual resources, we examined the degree and nature of health disparities in 5 common chronic diseases among adults with different SES and ethnicity. PATIENTS AND METHODS Study Setting and Population Olmsted County, Minnesota, is a mixed ruralurban setting containing both urban and rural areas defined by the US Census Bureau (16% rural population and 91% rural area) with a low white to black dissimilarity index of 29.5 in 2010 (vs 82.5 for Chicago, Illinois).20,26 According to the 2010 census, the population of Olmsted County was 85.7% white, 4.7% African American, 5.4% Asian, and 4.2% Hispanic.16 Our community has a higher median family income ($66,252 in 2009-2013) than the national average ($53,046) and a larger proportion of residents of Rochester, Minnesota (the largest city in Olmsted County) working in the health care industry (22%).18,19,27 Accordingly, Olmsted County is not categorized as a Medically Underserved Area.28 The level of poverty in Minnesota and in the United States has steadily increased since 2008 (11.9% in Minnesota and 15.9% throughout the nation in 2011), but poverty levels in Olmsted County remain considerably below national and state levels, hovering around 8% over the past 5 years. Of all Olmsted County adults, 95% currently have health insurance (vs 85% in the United States), and 66% routinely seek medical care.29 The Rochester Epidemiology Project (REP) links data on medical care delivered to the population of Olmsted County, Minnesota.30-32 Most medical care in this community is currently provided by Mayo Clinic and its 2 affiliated hospitals and the Olmsted Medical Center and its affiliated hospitals, with limited care provided through the 2 clinicians of the Rochester Family Medicine Clinic, which has recently closed. The health care records from these institutions are linked together through the REP records linkage system.31,32 Patients are categorized as residents or nonresidents of Olmsted County at the time of each health care visit on the basis of their address. The population counts obtained by the REP census are similar to
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those obtained by the US Census Bureau, indicating that virtually the entire population of the county is captured by the system.31,32 For this study, we used the REP census to identify all individuals who resided in Olmsted County on April 1, 2009, but we excluded those individuals who had refused medical record research authorization in at least one health care institution.31 Study Design We conducted a cross-sectional study to assess the association of 5-year prevalence (ie, January 1, 2005, through December 31, 2009) of the 5 most burdensome chronic diseases in adults with different SES and ethnicity and their interaction. Disease Prevalence. We assessed the prevalence of the 5 most burdensome chronic diseases as identified by the Agency for Healthcare Research and Quality among adults over 18 years of age identified by the REP data set.33 These diseases included coronary heart disease (CHD), asthma, diabetes, hypertension, and mood disorder. Detailed identification algorithms for each disease have been described previously.34 In brief, the diagnostic indices of the REP were searched electronically to extract the International Classification of Diseases, Ninth Revision (ICD-9) codes of these 5 chronic diseases in the medical records of the Olmsted County population ever assigned by any health care institution from January 1, 2005, through December 31, 2009 (ie, 5-year prevalence with a single ICD-9 code). These ICD-9 codes were grouped into clinical classification codes proposed by the Agency for Healthcare Research and QualityeHealthcare Cost and Utilization Project.35,36 Individual SES Measured by HOUSES. Socioeconomic status of our study population was measured by individual HOUSES.21-25 Development and initial testing of the index were completed in both Olmsted County, Minnesota, and Jackson County, Missouri, and the index was applied to a study that was conducted in Sioux Falls, South Dakota. Briefly, in formulating HOUSES, the addresses of the eligible study participants in April 2009 were geocoded. Geocoding allowed us to match the study participant’s address to geographic Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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reference data and real property data from the Assessor’s Office of the county government. Our original research work for development and validation of HOUSES (principal components factor analysis) identified 4 real property feature variablesdhousing value, square footage of housing unit, number of bedrooms, and number of bathroomsdin a same factor sharing the underlying construct (SES). We then formulated a standardized HOUSES score by summing the z scores for each variable (ie, standardized index). The higher the HOUSES score (z score), the higher the SES. Our prior work has documented that the HOUSES score is associated with health outcomes in children and adults, such as the risk of low birth weight, obesity, smoking exposure at home, asthma control status, risk of pneumococcal diseases, postemyocardial infarction mortality, and risk of rheumatoid arthritis and posterheumatoid arthritis mortality.21-25 Other Variables. Both Olmsted County’s and Minnesota’s minority populations accounted for 14.3% of the population in 2010.37 For this study, we grouped the self-reported ethnicity into 4 categoriesdnon-Hispanic white, African American, Asian, and Hispanicd according to the racial and ethnic categories suggested by the National Institutes of Health.15,38 As suggested by a previous study, we grouped the other/unknown category with the non-Hispanic white category because we presumed that most of the patients in the other/unknown category were non-Hispanic white (85.7% of the Olmsted County population self-reported white in the 2010 census).34 Statistical Analyses Descriptive statistics were used to summarize demographic characteristics of the study population. Association between demographic variables and HOUSES score in quartiles (Q1-Q4) was assessed by c2 tests. To assess the association of HOUSES score with the prevalence of each chronic condition (CHD, asthma, diabetes, hypertension, and mood disorder), logistic regression models were used among patients in the overall cohort, adjusting for age, sex, and ethnicity. Additional multivariate logistic models further adjusted for pertinent risk factors for CHD
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(diabetes, hypertension, hyperlipidemia, and obesity) and diabetes (hyperlipidemia and obesity). Similar analysis was carried out among non-Hispanic white patients only. Stratified logistic regression models were used to assess associations of different ethnicity groups with each chronic disease. In addition, an interaction effect between ethnicity and HOUSES score on prevalence of each disease was tested under the framework of logistic regression by adding an interaction term between ethnicity group and HOUSES quartiles. Statistical analyses were performed using SAS statistical software (SAS Institute). All tests were 2-sided, and P<.05 was considered statistically significant. RESULTS Characteristics of the Study Population Of the 130,078 eligible patients identified in the REP database, we excluded 9455d330 who were institutionalized, 1186 who provided only a Post Office box for their address, 6099 with unverifiable addresses, and 1840 with unavailable real property data, leaving 120,623 (92.7%) who were successfully geocoded. Of these 120,623 patients, 88,010 were adults and constituted our study group; 48,086 (54.6%) were female, and the median age (interquartile range [IQR]) was 45 years (IQR, 30-58 years) (Table 1). The proportions of non-Hispanic white, African American, Asian, and Hispanic patients were 80,699 (91.7%), 2650 (3.0%), 3065 (3.5%), and 1596 (1.8%), respectively. The median (IQR) HOUSES score was 0.38 (2.28 to 1.84). SES as Measured by HOUSES and Demographic Characteristics Overall, higher SES was observed among patients between 46 and 65 years and males (Table 2). In addition, non-Hispanic white patients had higher SES than their African American and Hispanic counterparts, whereas Asian patients’ SES was relatively similar to that of non-Hispanic whites in this community (highest SES quartile [Q4]: nonHispanic whites, 20,901 of 80,699 [25.9%]; African Americans, 196 of 2650 [7.5%]; Hispanics, 159 of 1596 [10.0%]; and Asians, 745 of 3065 [24.3%]). 4
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TABLE 1. Sociodemographic Characteristics and Prevalence of the 5 Most Burdensome Chronic Diseasesa,b Variable
Data from 88,010 study patients
Age (y), median (IQR) 45 (30-58) Age group (y) 18-45 45,450 (51.6) 46-65 29,011 (33.0) >65 13,549 (15.4) Sex Male 39,924 (45.4) Female 48,086 (54.6) Ethnicity Non-Hispanic white 80,699 (91.7) African American 2650 (3.0) Asian 3065 (3.5) Hispanic 1596 (1.8) HOUSES, median (IQR) 0.38 (2.28 to 1.84) Prevalence of chronic disease Coronary heart disease 9585 (10.9) Asthma 7601 (8.6) Diabetes 7824 (8.9) Hypertension 22,513 (25.6) Mood disorder 22,263 (25.3) HOUSES ¼ HOUsing-based index of SocioEconomic Status; IQR ¼ interquartile range. b Data are presented as No. (percentage) of patients unless indicated otherwise. a
SES and Prevalence of Common Chronic Diseases Overall, lower SES was associated with a higher prevalence of each of the 5 chronic conditions after controlling for age, sex, ethnicity, and additional pertinent risk factors (P<.001) (Table 3). A similar pattern was found in the subgroup of non-Hispanic white patients. The impact of SES on prevalence tended to be greater in diabetes, hypertension, and mood disorder than in CHD and asthma. For example, the adjusted odds ratio for the highest SES group compared with the lowest SES group was 0.56 (95% CI, 0.52-0.60) for diabetes and 0.74 (95% CI, 0.69-0.80) for CHD. Ethnicity and Prevalence of Chronic Diseases The association between ethnicity and prevalence of chronic diseases varied in magnitude but revealed no consistent patterns for distinguishing clinically more overt vs less overt
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TABLE 2. Association Between Demographic Characteristics and HOUSES as Both Continuous Scale and Quartiles Among 88,010 Study Patientsa,b HOUSES by quartile, No. (%) Variable
HOUSES, median (IQR)
Q1
Q2
Q3
Q4
0.57 (2.74 to 1.65) 0.22 (1.72 to 2.57) 0.95 (2.32 to 0.75)
12,999 (28.6) 5454 (18.8) 3551 (26.2)
10,594 (23.3) 6853 (23.6) 4556 (33.6)
11,022 (24.3) 7598 (26.2) 3382 (25.0)
10,835 (23.8) 9106 (31.4) 2060 (15.2)
0.17 (2.12 to 2.02) 0.54 (2.40 to 1.69)
9283 (23.3) 12,721 (26.5)
9862 (24.7) 12,141 (25.2)
10,274 (25.7) 11,728 (24.4)
10,505 (26.3) 11,496 (23.9)
0.24 4.56 0.41 3.51
18,519 1603 956 926
20,754 390 586 273
20,525 461 778 238
20,901 196 745 159
P valuec <.001
Age (y) 18-45 (n¼45,450) 46-65 (n¼29,011) >65 (n¼13,549) Sex Male (n¼39,924) Female (n¼48,086) Ethnicity Non-Hispanic white (n¼80,699) African American (n¼2650) Asian (n¼3065) Hispanic (n¼1596)
<.001
<.001 (2.11 (5.15 (3.21 (4.97
to to to to
1.96) 0.44) 1.74) 0.43)
(22.9) (60.5) (31.2) (58.0)
(25.7) (14.7) (19.1) (17.1)
(25.4) (17.4) (25.4) (14.9)
(25.9) (7.4) (24.3) (10.0)
HOUSES ¼ HOUsing-based index of SocioEconomic Status; IQR ¼ interquartile range; Q ¼ quartile. Q1 is the lowest quartile and Q4 is the highest quartile. Percentages may not equal 100 because of rounding. c P value from c2 test. a
b
diseases. The results are summarized in Table 4. Although mood disorder was more prevalent in non-Hispanic white patients than in minority groups, the odds of diabetes were at least 2-fold higher among minority groups compared with non-Hispanic whites after adjusting for age, sex, hyperlipidemia, and obesity. In comparison to non-Hispanic white patients, Hispanic patients had a reduced rate of both hypertension and CHD, whereas African American patients had increased odds of both diseases after adjusting for pertinent risk factors. Both Asian and Hispanic, but not African American, patients had lower rates of asthma compared with nonHispanic white patients. Interaction Between SES and Ethnicity in Prevalence of Chronic Diseases Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, and the interaction between SES and ethnicity depended on the nature of the disease. The results are summarized in the Figure and the Supplemental Table (available online at http://www.mayoclinicproceedings. org). Specifically, the patterns in which SES modified the effect of ethnicity observed among minority groups for diabetes, hypertension, and mood disorder were comparatively different from CHD. As shown in the Figure and the Supplemental Table, the odds Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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of diabetes, hypertension, and mood disorder were generally increased with higher SES (above the median of HOUSES) in the minority patients, especially African American patients, relative to non-Hispanic white patients after controlling for age, sex, ethnicity, and additional pertinent risk factors for diabetes (ie, obesity and hyperlipidemia) (interaction P<.05 for each condition [diabetes, hypertension, and mood disorder]). However, such patterns appeared to be reversed for CHD (interaction P¼.50). DISCUSSION In this study, notable health disparities still existed among non-Hispanic white residents and the overall adult population with different SES in a mixed rural-urban setting. Also, there were ethnic disparities in the prevalence of common chronic diseases. Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, but such effect modification by SES depended on the nature of the clinical disease (ie, clinically overt diseases such as CHD vs clinically less overt diseases such as diabetes, hypertension, and mood disorder). Despite the potential mitigating factors for health disparities (eg, a relatively affluent community, a higher proportion of people working in the health care system, homogeneous distribution of people with different ethnicity in the community, and higher health
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TABLE 3. Association Between HOUSES and Prevalence of Chronic Diseases in Overall Cohort and Among Non-Hispanic White Patientsa Overall cohort (N¼88,010) HOUSES (quartile) CHD Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Asthma Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Diabetes Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Hypertension Q1 (lowest SES) Q2 Q3 Q4 (highest SES) Mood disorder Q1 (lowest SES) Q2 Q3 Q4 (highest SES)
No. (%) of patients
Adjusted OR (95% CI)
Non-Hispanic white patients (n¼80,699) P value
No. (%) of patients
Adjusted OR (95% CI)
<.001 2535 2867 2429 1754
(11.5) (13.0) (11.0) (8.0)
1.0 0.87 0.86 0.74
(Reference) (0.81-0.93) (0.80-0.92) (0.69-0.80)
2357 2784 2331 1693
(12.7) (13.4) (11.4) (8.1)
1.0 0.84 0.87 0.81
(Reference) (0.78-0.90) (0.81-0.94) (0.75-0.88)
<.001d 2112 1950 1840 1699
(9.6) (8.9) (8.4) (7.7)
1.0 0.90 0.85 0.78
<.001e
(Reference) (0.84-0.96) (0.80-0.91) (0.73-0.83)
1849 1850 1739 1627
(10.0) (8.9) (8.5) (7.8)
1.0 0.91 0.86 0.78
(Reference) (0.85-0.97) (0.80-0.92) (0.73-0.84) <.001g
<.001f 2330 2303 1905 1286
(10.6) (10.5) (8.7) (5.8)
1.0 0.84 0.75 0.56
(Reference) (0.78-0.89) (0.70-0.80) (0.52-0.60)
5672 6789 5651 4401
(25.8) (30.9) (25.7) (20.0)
1.0 0.96 0.80 0.64
(Reference) (0.91-1.01) (0.76-0.85) (0.61-0.68)
2004 2176 1741 1178
(10.8) (10.5) (8.5) (5.6)
1.0 0.83 0.74 0.56
(Reference) (0.78-0.89) (0.69-0.80) (0.52-0.61)
5180 6558 5352 4209
(28.0) (31.6) (26.1) (20.1)
1.0 0.96 0.78 0.64
(Reference) (0.90-1.01) (0.74-0.83) (0.60-0.67)
<.001d
<.001e
<.001e
<.001d 6534 5737 5309 4683
(29.7) (26.1) (24.1) (21.3)
1.0 0.78 0.72 0.61
P value <.001c
b
(Reference) (0.75-0.81) (0.69-0.75) (0.58-0.63)
5872 5520 5057 4531
(31.7) (26.6) (24.6) (21.7)
1.0 0.79 0.72 0.61
(Reference) (0.76-0.83) (0.69-0.75) (0.59-0.64)
CHD ¼ coronary heart disease; HOUSES ¼ HOUsing-based index of SocioEconomic Status; OR ¼ odds ratio; Q ¼ quartile; SES ¼ socioeconomic status. Adjusted for age, sex, ethnicity, hypertension, diabetes, hyperlipidemia, and obesity. c Adjusted for age, sex, hypertension, diabetes, hyperlipidemia, and obesity. d Adjusted for age, sex, and ethnicity. e Adjusted for age and sex. f Adjusted for age, sex, ethnicity, hyperlipidemia, and obesity. g Adjusted for age, sex, hyperlipidemia, and obesity. a
b
insurance coverage),16-19,29,39 there were still substantial disparities in the prevalence of chronic disease among different ethnic groups in this community. Also, health disparities existed in our study’s majority, the nonHispanic white population, with different SES in a dose-response manner across all chronic diseases examined. Furthermore, the ratios of prevalence of each chronic disease between the highest SES stratum (Q4) and the lowest stratum (Q1) in our community (Q4/ Q1) (ie, 0.70 for CHD, 0.80 for asthma, 0.55 for diabetes, 0.78 for hypertension, and 0.72 for mood disorder) were not drastically different from estimates by multiple studies at a national level (ie, 0.4-0.66 for CHD, 0.94-1.04 for asthma, 0.38-0.47 for diabetes, 6
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0.59-0.70 for hypertension, and 0.87 for mood disorder).40-43 Because our study was based on ICD-9 codes for chronic diseases and HOUSES for SES measurement, it is unlikely that our results are due to report biases caused by self-report. The literature has documented that non-Hispanic whites have a reduced risk of multiple chronic diseases.44-48 Because the non-Hispanic white population has often been used as a reference group for analysis, the literature examining health disparities among non-Hispanic white populations across different SES groups is relatively limited.3 Overall, our study results suggest that non-Hispanic white populations do not seem to be exempt from health disparities across different SES.
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TABLE 4. Association Between Ethnicity and Prevalence of Chronic Diseasesa,b Ethnicity
CHD (P¼.01)
Asthma (P<.001)
Diabetes (P<.001)
Hypertensiond (P<.001)
African American Asian Hispanic
1.25 (1.04-1.50) 1.00 (0.84-1.18) 0.71 (0.53-0.97)
1.04 (0.91-1.19) 0.61 (0.52-0.71) 0.61 (0.49-0.75)
2.22 (1.94-2.55) 2.09 (1.83-2.39) 1.97 (1.64-2.38)
1.38 (1.23-1.55) 0.86 (0.77-0.97) 0.86 (0.72-1.01)
c
d
e
Mood disorderd (P<.001) 0.62 (0.56-0.69) 0.44 (0.40-0.49) 0.63 (0.56-0.72)
CHD ¼ coronary heart disease. Data are presented as adjusted odds ratio (95% CI), with non-Hispanic white patients as the reference group. c Adjusted for age, sex, hypertension, diabetes, hyperlipidemia, and obesity. d Adjusted for age and sex. e Adjusted for age, sex, hyperlipidemia, and obesity. a
b
Our study reveals noteworthy findings. As illustrated in the Figure and Supplemental Table, overall, a higher prevalence of clinically less overt diseases (diabetes, hypertension, and mood disorder) was found in the minority groups with higher SES (above the median of HOUSES when stratified by HOUSES) compared with those with lower SES. Such patterns or trends were not observed in clinically overt disease such as CHD. In this respect, the higher prevalence of chronic diseases may reflect identification of diseases (diagnosis or detection) not necessarily representing only a risk of disease per se. For example, clinically less overt chronic diseases might be much more dependent on access to health care and health literacy for identification of such diseases, whereas those clinically more overt diseases such as CHD often require emergency care (because they can be lifethreatening conditions) and thus are less dependent on one’s ability to recognize the diseases and on health care access. In this interpretation, we consider asthma a disease with mixed features between clinically overt and less overt, and thus asthma had a different pattern from clinically overt disease and less overt diseases as illustrated in the Figure. This variability suggests that the extent to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. Therefore, when interpreting our study results in terms of differentiating disease vs diagnosis, one should bear in mind that we used ICD-9 codes for defining chronic diseases. As Shippee et al49 pointed out, given the impact of SES on health care access and literacy, patients with lower SES may not seek timely care for appropriate diagnosis and treatment for clinically less overt Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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disease until clinically apparent manifestations develop. Our study results have a few important implications for health care policy concerning health disparities. First, ethnic disparities in health outcomes should be interpreted in the context of SES and the nature of the disease examined. The complex interaction among ethnicity, SES, and the nature of the disease might not be captured if health disparities across different ethnicity and SES are examined separately, especially without considering the clinical nature of the disease. Second, true health disparities (ie, disease risk) across different SES might be more underestimated in clinically less overt diseases such as diabetes or hypertension regardless of ascertainment methods (ie, lower identification of such diseases in lower SES groups and greater detection in higher SES groups). Therefore, although the health care system makes an effort to prevent these chronic diseases, to reduce health disparities in outcomes of clinically less overt diseases the identification of such diseases through improving health literacy and health care access should be a primary focus of intervention, especially among underserved populations including minorities or lower SES populations. Finally, our study results highlight that non-Hispanic white populations with lower SES still represent underserved populations that are a target population for health promotion. The main strength of our study is the population-based study design. Another strength is that the prevalence of diseases was based on documented physician diagnosis instead of self-report. In addition, we objectively measured SES on the basis of real property data instead of self-reported conventional
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Asthma 1.2 1.0
African American Asian
Odds ratio
Odds ratio
CHD 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0
Hispanic
African American Asian
0.8 0.6 0.4
Hispanic
0.2 0 Below the Above the median median HOUSES
Below the Above the median median HOUSES
Diabetes
Hypertension 2.5
2.0
African American Asian
1.0
Hispanic
3.0
2.0 Odds ratio
Odds ratio
4.0
African American Asian
1.5 1.0
Hispanic
0.5
0
0 Below the Above the median median HOUSES
Below the Above the median median HOUSES
Mood disorder 1.0 Odds ratio
0.8
African American Asian
0.6 0.4
Hispanic
0.2 0 Below the Above the median median HOUSES
FIGURE. Comparison of odds ratios of each chronic condition by ethnicity and socioeconomic status (SES) measured by the HOUsing-based index of SocioEconomic Status (HOUSES). Socioeconomic status modified the effect of ethnicity on the prevalence of chronic diseases, and the interaction between SES and ethnicity depended on the nature of the disease. Specifically, the patterns in which SES modified the effect of ethnicity observed among minority groups for diabetes, hypertension, and mood disorder were comparatively different from coronary heart disease (CHD). The odds of diabetes, hypertension, and mood disorder were generally increased with higher SES (above the median of HOUSES) in the minority patients, especially African American patients, relative to non-Hispanic white patients after controlling for age, sex, and additional pertinent risk factors. However, such patterns appeared to be reversed for CHD.
SES measures. Also in this study, HOUSES scores were available for nearly all eligible patients (92.7%). Our findings should be interpreted with caution, however. The main limitation of our study was the inability to 8
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verify ICD-9 codes.50,51 Because we defined the prevalence of each chronic condition with a single ICD-9 code as suggested by a previous study,34 the prevalence of some conditions might have been overestimated. For
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HEALTH DISPARITIES IN A MIXED RURAL-URBAN COMMUNITY
example, the prevalence of CHD (10.9% in this study vs 6.0% in the United States and 4.9% in Minnesota41) might have changed if different ICD-9 codes were assigned after diagnostic procedures. However, systematic differential misclassification of CHD across different SES or ethnicity is unlikely given the lifethreatening nature of CHD. Also, given the availability of specific diagnostic tests or procedures for hypertension, diabetes, and asthma, our study results are unlikely to be due to systemic (differential) misclassification of diseases. The cross-sectional nature of the analysis (ie, prevalence study) is also a limitation of this study. Although we took into account traditional risk factors for certain diseases (CHD and diabetes), we did not have all pertinent risk factors for certain diseases examined to adjust the main results (eg, smoking data was not available for this study). For this study, we grouped the other/ unknown category of patients with the nonHispanic white category as suggested by previous studies, but this presumption may be inaccurate. Lastly, unavailability of insurance data for the REP population limited our further data analysis for impact of insurance type on the association of SES with prevalence of chronic conditions. CONCLUSION In a mixed rural-urban setting with a predominantly non-Hispanic white population, health disparities in chronic diseases still exist across different SES. The degree to which SES modifies the effect of ethnicity on the risk of chronic diseases may depend on the nature of the disease. ACKNOWLEDGMENTS We thank Mrs Kelly Okeson for administrative assistance and support and Dr Barbara P. Yawn, MD, MSc, for editorial review of the submitted manuscript. The funding agency was not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the submitted manuscript. Dr Juhn had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Mayo Clin Proc. n XXX 2016;nn(n):1-11 www.mayoclinicproceedings.org
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SUPPLEMENTAL ONLINE MATERIAL Supplemental material can be found online at http://www.mayoclinicproceedings.org. Supplemental material attached to journal articles has not been edited, and the authors take responsibility for the accuracy of all data. Abbreviations and Acronyms: CHD = coronary heart disease; HOUSES = HOUsing-based index of SocioEconomic Status; ICD-9 = International Classification of Diseases, Ninth Revision; IQR = interquartile range; REP = Rochester Epidemiology Project; SES = socioeconomic status Grant Support: This work was supported by grant R21 AI101277 (Y.J.J.) from the National Institute of Allergy and Infectious Diseases and the Scholarly Clinician Award (Y.J.J.) from the Mayo Foundation. The study was made possible by the Rochester Epidemiology Project (grant number R01-AG034676; Principal Investigators: Walter A. Rocca, MD, MPH, and Jennifer L. St Sauver, PhD), which is supported by the National Institute on Aging of the National Institutes of Health. Correspondence: Address to Young J. Juhn, MD, MPH, Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (Juhn. [email protected]).
REFERENCES 1. US Department of Health and Human Services. Healthy People 2020. https://www.healthypeople.gov/. Updated February 23, 2016. Accessed January 23, 2015. 2. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2020 leading health indicators: progress update. https://www. healthypeople.gov/sites/default/files/LHI-ProgressReport-Exec Sum_0.pdf. Published March 2014. Accessed February 23, 2016. 3. Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: what the patterns tell us. Am J Public Health. 2010;100(suppl 1): S186-S196. 4. Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE. Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001-2006. Ann Epidemiol. 2010; 20(8):617-628. 5. Banks J, Marmot M, Oldfield Z, Smith JP. Disease and disadvantage in the United States and in England. JAMA. 2006;295(17): 2037-2045. 6. Signorello LB, Cohen SS, Williams DR, Munro HM, Hargreaves MK, Blot WJ. Socioeconomic status, race, and mortality: a prospective cohort study. Am J Public Health. 2014; 104(12):e98-e107. 7. Farmer MM, Ferraro KF. Are racial disparities in health conditional on socioeconomic status? Soc Sci Med. 2005;60(1): 191-204. 8. Mackenbach JP, Stirbu I, Roskam AJ, et al; European Union Working Group on Socioeconomic Inequalities in Health. Socioeconomic inequalities in health in 22 European countries [published correction appears in N Engl J Med. 2008;359(12): e14]. N Engl J Med. 2008;358(23):2468-2481. 9. Whitehead M, Townsend P, Davidson N, eds. Inequalities in Health: The Black Report and the Health Divide. London, England: Penguin Books; 1992.
http://dx.doi.org/10.1016/j.mayocp.2016.02.011
9
MAYO CLINIC PROCEEDINGS
10. Smedley BD, Stith AY, Nelson AR, eds, Committee on Understanding and Eliminating Racial Disparities in Health Care. Evidence of Healthcare. In: Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: National Academies Press; 2003:5-6. 11. US Department of Health and Human Services. 2013 National Healthcare Disparities Report. Agency for Healthcare Research and Quality website. http://www.ahrq.gov/sites/default/files/ publications/files/2013nhdr.pdf. Published May 2014. Accessed February 13, 2015. 12. Kamangar F, Dores GM, Anderson WF. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol. 2006;24(14):2137-2150. 13. United Health Foundation. America’s Health Rankings: 2014 Annual Report. http://www.americashealthrankings.org/reports/ annual. Accessed February 13, 2015. 14. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: U.S. trends from 1999 to 2006 and effects of Medicare coverage. Ann Intern Med. 2009;150(8):505-515. 15. Ayanian JZ, Landon BE, Newhouse JP, Zaslavsky AM. Racial and ethnic disparities among enrollees in Medicare Advantage plans. N Engl J Med. 2014;371(24):2288-2297. 16. US Census Bureau. 2010 Census interactive population search: Minnesota. http://www.census.gov/2010census/popmap/ ipmtext.php?fl¼27. Accessed February 13, 2015. 17. American Lung Association. American Lung Association State of the Air 2014. http://www.stateoftheair.org/2014/assets/ALASOTA-2014-Full.pdf. Accessed February, 2015. 18. US Department of Labor. Occupational employment statistics: May 2013 metropolitan and nonmetropolitan area occupational employment and wage estimates; Rochester, MN. US Bureau of Labor Statistics website. http://www.bls.gov/oes/ current/oes_40340.htm#otherlinks. Updated March 25, 2015. Accessed February 28, 2015. 19. US Department of Labor. Occupational employment statistics: May 2013 national occupational employment and wage estimates by ownership; cross-industry, private ownership only. US Bureau of Labor Statitstics website. http://factfinder.census. gov/faces/tableservices/jsf/pages/productview.xhtml?src¼CF. Acessed March 11, 2016. 20. US 2010: discover America in a new century; resident segregation. Brown University website. http://www.s4.brown.edu/ us2010/segregation2010/Default.aspx?msa¼10180. Accessed June 29, 2015. 21. Juhn YJ, Beebe TJ, Finnie DM, et al. Development and initial testing of a new socioeconomic status measure based on housing data. J Urban Health. 2011;88(5):933-944. 22. Johnson MD, Urm SH, Jung JA, et al. Housing data-based socioeconomic index and risk of invasive pneumococcal disease: an exploratory study. Epidemiol Infect. 2013;141(4):880-887. 23. Bang DW, Manemann SM, Gerber Y, et al. A novel socioeconomic measure using individual housing data in cardiovascular outcome research. Int J Environ Res Public Health. 2014; 11(11):11597-11615. 24. Harris MN, Lundien MC, Finnie DM, et al. Application of a novel socioeconomic measure using individual housing data in asthma research: an exploratory study. NPJ Prim Care Respir Med. 2014;24:14018. 25. Ghawi H, Crowson CS, Rand-Weaver J, Krusemark E, Gabriel SE, Juhn YJ. A novel measure of socioeconomic status using individual housing data to assess the association of SES with rheumatoid arthritis and its mortality: a population-based case-control study. BMJ Open. 2015;5(4):e006469. 26. US Census Bureau. 2010 Census urban and rural classification and urban area criteria. https://www.census.gov/geo/reference/ ua/urban-rural-2010.html. Updated February 29, 2015. Accessed June 5, 2015.
10
Mayo Clin Proc.
n
27. US Census Bureau. State & County QuickFacts: United States. http://quickfacts.census.gov/qfd/states/00000.html. Updated December 2, 2015. Accessed February 28, 2015. 28. US Department of Health and Human Services. MUA find. Health Resources and Services Administration website. http:// muafind.hrsa.gov/index.aspx. Updated February 23, 2016. Accessed October 15, 2014. 29. Olmsted County, Minnesota Community Health Needs Assessment 2013. Olmsted County website. http://www.co. olmsted.mn.us/OCPHS/reports/Documents/Community%20 Health%20Needs%20Assessment%202013.pdf. Accessed October 15, 2014. 30. St Sauver JL, Grossardt BR, Leibson CL, Yawn BP, Melton LJ III, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc. 2012; 87(2):151-160. 31. St Sauver JL, Grossardt BR, Yawn BP, Melton LJ III, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project. Am J Epidemiol. 2011;173(9):1059-1068. 32. Rocca WA, Yawn BP, St Sauver JL, Grossardt BR, Melton LJ III. History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population. Mayo Clin Proc. 2012;87(12):1202-1213. 33. Stanton MW. The high concentration of US health care expenditures. Research in Action, No. 19. AHRQ Publication No. 06e0060. Agency for Health Care Research and Quality website. http://archive.ahrq.gov/research/findings/factsheets/costs/ expriach/expendria.pdf. Published June 2006. Accessed May 19, 2012. 34. St Sauver JL, Warner DO, Yawn BP, et al. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. Mayo Clin Proc. 2013;88(1): 56-67. 35. Agency for Healthcare Quality and Research. Medical Expenditure Panel Survey HC-120, Appendix 3: Clinical Classification Code to ICD-9-CM Code Crosswalk. http://www.bls.gov/oes/ 2013/may/oes_stru.htm. Accessed November 18, 2014. 36. Agency for Healthcare Quality and Research. Healthcare Cost and Utilization Project: clinical classifications software (CCS) for ICD-9-CM. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs. jsp. Updated December 18, 2015. Accessed November 18, 2014. 37. US Census Bureau, 2010. http://factfinder2.census.gov/faces/ tableservices/jsf/pages/productview.xhtml?src¼bkmk. Accessed Sep 8, 2014. 38. National Institutes of Health. Racial and ethnic categories and definitions for NIH diversity programs and for other reporting purposes. http://grants.nih.gov/grants/guide/notice-files/NOTOD-15-089.html#sthash.OD1zPDgj.dpuf. Published April 8, 2015. Accessed March 11, 2016. 39. Segregation: dissimilarity indices. CensusScope website. http:// www.censusscope.org/us/rank_dissimilarity_white_black.html. Accessed November 18, 2014. 40. National Center for Health Statistics. Health, United States, 2012. Table 64. DHHS Publication No. 2013-1232. Centers for Disease Control and Prevention website. http://www.cdc. gov/nchs/data/hus/2012/064.pdf. Published May 2013. Accessed November 18, 2014. 41. Centers for Disease Control and Prevention. Prevalence of coronary heart diseasedUnited States, 2006-2010. MMWR Morb Mortal Wkly Rep. 2011;60(40):1377-1381. 42. Schiller JS, Lucas JW, Peregoy JA. Summary health statistics for U.S. adults: National Health Interview Survey. Vital Health Stat 10. 2011;2012(256):1-218. 43. Horwath E, Johnson J, Klerman GL, Weissman MM. Depressive symptoms as relative and attributable risk factors for first-onset major depression. Arch Gen Psychiatry. 1992; 49(10):817-823.
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44. Jones DW, Chambless LE, Folsom AR, et al. Risk factors for coronary heart disease in African Americans: the Atherosclerosis Risk in Communities Study, 1987-1997. Arch Intern Med. 2002;162(22):2565-2571. 45. Breslau J, Kendler KS, Su M, Gaxiola-Aguilar S, Kessler RC. Lifetime risk and persistence of psychiatric disorders across ethnic groups in the United States. Psychol Med. 2005;35(3):317-327. 46. McDaniel M, Paxson C, Waldfogel J. Racial disparities in childhood asthma in the United States: evidence from the National Health Interview Survey, 1997 to 2003. Pediatrics. 2006;117(5): e868-e877. 47. Heisler M, Smith DM, Hayward RA, Krein SL, Kerr EA. Racial disparities in diabetes care processes, outcomes, and treatment intensity. Med Care. 2003;41(11):1221-1232.
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48. Hertz RP, Unger AN, Cornell JA, Saunders E. Racial disparities in hypertension prevalence, awareness, and management. Arch Intern Med. 2005;165(18):2098-2104. 49. Shippee ND, Shah ND, May CR, Mair FS, Montori VM. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. J Clin Epidemiol. 2012;65(10):1041-1051. 50. Yawn BP, Wollan P, St Sauver J. Comparing shingles incidence and complication rates from medical record review and administrative database estimates: how close are they? Am J Epidemiol. 2011;174(9):1054-1061. 51. Roger VL, Killian J, Henkel M, et al. Coronary disease surveillance in Olmsted County objectives and methodology. J Clin Epidemiol. 2002;55(6):593-601.
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