Peer Reviewed: Comparison of Small-Area Analysis Techniques for Estimating Prevalence by Race

Introduction

The Behavioral Risk Factor Surveillance System (BRFSS) is commonly used for estimating the prevalence of chronic disease. One limitation of the BRFSS is that valid estimates can only be obtained for states and larger geographic regions. Limited health data are available on the county level and, thus, many have used small-area analysis techniques to estimate the prevalence of disease on the county level using BRFSS data.

Methods

This study compared the validity and precision of 4 small-area analysis techniques for estimating the prevalence of 3 chronic diseases (asthma, diabetes, and hypertension) by race on the county level. County-level reference estimates obtained through local data collection were compared with prevalence estimates produced by direct estimation, synthetic estimation, spatial data smoothing, and regression. Discrepancy statistics used were Pearson and Spearman correlation coefficients, mean square error, mean absolute difference, mean relative absolute difference, and rank statistics.

Results

The regression method produced estimates of the prevalence of chronic disease by race on the county level that had the smallest discrepancies for a large number of counties.

Conclusion

Regression is the preferable method when applying small-area analysis techniques to obtain county-level prevalence estimates of chronic disease by race using a single year of BRFSS data.     

Peer Reviewed: Misclassification of Survey Responses and Black-White Disparity in Mammography Use, Behavioral Risk Factor Surveillance System, 1995-2006

Introduction

The validity of self-reported data for mammography differ by race. We assessed the effect of racial differences in the validity of age-adjusted, self-reported mammography use estimates from the Behavioral Risk Factor Surveillance System (BRFSS) from 1995 through 2006 to determine whether misclassification (inaccurate survey question response) may have obscured actual racial disparities.

Methods

We adjusted BRFSS mammography use data for age by using 2000 census estimates and for misclassification by using the following formula: (estimated prevalence − 1 + specificity) / (sensitivity + specificity − 1). We used values reported in the literature for the formula (sensitivity = 0.97 for both black and white women, specificity = 0.49 and 0.62, respectively, for black and white women).

Results

After adjustment for misclassification, the percentage of women aged 40 years or older in 1995 who reported receiving a mammogram during the previous 2 years was 54% among white women and 41% among black women, compared with 70% among both white and black women after adjustment for age only. In 2006, the percentage after adjustment for misclassification was 65% among white women and 59% among black women compared with 77% among white women and 78% among black women after adjustment for age only.

Conclusion

Self-reported data overestimate mammography use — more so for black women than for white women. After adjustment for respondent misclassification, neither white women nor black women had attained the Healthy People 2010 objective (≥70%) by 2006, and a disparity between white and black women emerged.     

Peer Reviewed: Using Small-Area Estimation Method to Calculate County-Level Prevalence of Obesity in Mississippi, 2007-2009

Introduction

Obesity is one of Mississippi''s pressing public health problems. Since 2005, the state has ranked first in the nation in adult obesity prevalence. For authorities to take targeted action against the obesity epidemic, counties, regions, and subpopulations that are most affected by obesity need to be identified. The objective of this study was to assess the scope, socioeconomic and geographic characteristics, and temporal trends of the obesity epidemic in Mississippi.

Methods

Using 2007-2009 Mississippi Behavioral Risk Factor Surveillance System data and auxiliary data, we applied a small-area estimation method to estimate county-level obesity prevalence in 2007 through 2009, to assess the association between obesity and socioeconomic factors and to evaluate temporal trends. We determined geographic patterns by mapping obesity prevalence. We appraised the precision of estimates by the width of 95% confidence intervals, and we validated our small-area estimates by comparing them with direct estimates.

Results

In 2009, the county prevalence of obesity ranged from 30.5% to 44.2%. Counties with the highest prevalence of obesity were in the Delta region and along the Mississippi River. The obesity prevalence increased from 2007 through 2009. Age, sex, race, education, and employment status were associated with obesity.

Conclusion

The 2009 obesity prevalence in all Mississippi counties was substantially higher than the national average and differed by geography and race. Although urgent intervention measures are needed in the entire state, policies and programs giving higher priority to higher-risk areas and subpopulations identified by this study may be better strategies.     

Where You Live Matters: Quality and Racial/Ethnic Disparities in Schizophrenia Care in Four State Medicaid Programs

Objective

To determine whether (a) quality in schizophrenia care varies by race/ethnicity and over time and (b) these patterns differ across counties within states.

Data Sources

Medicaid claims data from California, Florida, New York, and North Carolina during 2002–2008.

Study Design

We studied black, Latino, and white Medicaid beneficiaries with schizophrenia. Hierarchical regression models, by state, quantified person and county effects of race/ethnicity and year on a composite quality measure, adjusting for person-level characteristics.

Principal Findings

Overall, our cohort included 164,014 person-years (41–61 percent non-whites), corresponding to 98,400 beneficiaries. Relative to whites, quality was lower for blacks in every state and also lower for Latinos except in North Carolina. Temporal improvements were observed in California and North Carolina only. Within each state, counties differed in quality and disparities. Between-county variation in the black disparity was larger than between-county variation in the Latino disparity in California, and smaller in North Carolina; Latino disparities did not vary by county in Florida. In every state, counties differed in annual changes in quality; by 2008, no county had narrowed the initial disparities.

Conclusions

For Medicaid beneficiaries living in the same state, quality and disparities in schizophrenia care are influenced by county of residence for reasons beyond patients’ characteristics.     

Radiation Therapy Resources and Guideline‐Concordant Radiotherapy for Early‐Stage Breast Cancer Patients in an Underserved Region

Objective

To examine the relationship between radiation therapy resources and guideline-concordant radiotherapy after breast-conserving surgery (BCS) in Kentucky.

Data Sources

The SEER registry and Area Resource File provided county-level data describing cancer care resources and socioeconomic conditions of Kentucky residents.

Study Design

The outcome variable was rate of BCS without radiotherapy in each county for 2000–2007. Eight-year weighted average rates of radiation therapy providers and hospitals per 100,000 residents were explanatory variables of interest. Exploratory spatial data analyses and spatial econometric models were estimated.

Principal Findings

Appalachian counties in Kentucky had significantly fewer radiation oncologists, hospitals with radiation therapy facilities, and surgeons per 100,000 residents than non-Appalachian counties. The likelihood of BCS without radiation was significantly higher among Appalachian compared to non-Appalachian women (42.5 percent vs. 29.0 percent, p < .001). Higher proportions of women not receiving recommended radiotherapy after BCS were clustered in Eastern Kentucky around Lexington. This geographic disparity was partially explained by significantly fewer radiation therapy facilities in Appalachian Kentucky in adjusted analyses.

Conclusions

Scarce radiation therapy resources in Appalachian Kentucky are associated with disparities in receipt of guideline-concordant radiotherapy, suggesting that policy action is needed to improve the cancer treatment infrastructure in disadvantaged mountainous areas.     

Geographic and Demographic Disparities in Late-stage Breast and Colorectal Cancer Diagnoses Across the US

Problem

In 2009, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, the majority of colorectal and almost 1/3 of breast cancers are diagnosed at an advanced stage in the US, which results in higher morbidity and mortality than would obtain with earlier detection. The incidence of late-stage cancer diagnoses varies considerably across the US, and few analyses have examined the entire US.

Purpose

Using the newly available US Cancer Statistics database representing 98% of the US population, we perform multilevel analysis of the incidence of late-stage cancer diagnoses and translate the findings via bivariate mapping, answering questions related to both Why and Where demographic and geographic disparities in these diagnoses are observed.

Methods

To answer questions related to Why disparities are observed, we utilize a three-level, random-intercepts model including person-, local area-, and region- specific levels of influence. To answer questions related to Where disparities are observed, we generate county level robust predictions of late-stage cancer diagnosis rates and map them, contrasting counties ranked in the upper and lower quantiles of all county predicted rates. Bivariate maps are used to spatially translate the geographic variation among US counties in the distribution of both BC and CRC late-stage diagnoses.

Conclusions

Empirical modeling results show demographic disparities, while the spatial translation of empirical results shows geographic disparities that may be quite useful for state cancer control planning. Late stage BC and CRC diagnosis rates are not spatially random, manifesting as place-specific patterns that compare counties in individual states to counties across all states. Providing a relative comparison that enables assessment of how results in one state compare with others, this paper is to be disseminated to all state cancer control and central cancer registry program officials.     

Peer Reviewed: Socioeconomic Disparities in Breast Cancer Screening in Hawaii

Introduction

Despite evidence that breast cancer screening reduces morbidity and mortality, many women do not obtain mammograms. Our objective was to analyze the relationship between income and mammography screening for members enrolled in a large health plan in Hawaii.

Methods

We analyzed claims data for women (N = 46,328) aged 50 to 70 years during 2003 and 2004. We used parametric and nonparametric regression techniques. We used probit estimation to conduct multivariate analysis.

Results

At the 5th percentile of the earnings distribution, the probability of mammography is 57.1%, and at the 95th percentile, it is 67.7%. Movement from the 5th percentile to the 35th percentile of the earnings distribution increases the probability of mammography by 0.0378 percentage points. A similar movement from the 65th percentile to the 95th percentile increases the probability by 0.0394 percentage points. Also, we observed an income gradient within narrowly defined geographic regions where physical access to medical care providers is not an issue.

Conclusion

We observed a steep income gradient in mammography screening in Hawaii. Because of the prevalence of measurement error, this gradient is probably far greater than our estimate. We cannot plausibly attribute our findings to disparities in coverage because 100% of our sample had health insurance coverage. The gradient also does not appear to result from poorer people residing in areas that are geographically isolated from providers of medical care.     

Peer Reviewed: A Comparison of Depression and Mental Distress Indicators,Rhode Island Behavioral Risk Factor Surveillance System, 2006

Introduction

Depression is a public health concern that warrants accurate population estimates. The patient health questionnaire 8 (PHQ-8) offers high sensitivity and specificity for assessing depression but is time-consuming to administer, answer, and score. We sought to determine whether 1 of 3 simpler instruments — the shorter PHQ-2 or 2 single questions from the health-related quality of life (HRQOL) module of the Behavioral Risk Factor Surveillance System (BRFSS) — could offer accuracy comparable to the PHQ-8.

Methods

We compared the depression and mental distress indicators of 2006 Rhode Island BRFSS data by using 4 types of analyses: 1) sensitivity and specificity estimates, 2) prevalence estimates, 3) multivariable logistic regression modeling of the relationship between each of the 4 indicators and 11 demographic and health risk variables, and 4) geographic distribution of prevalence.

Results

Compared with the PHQ-8, the 3 other measures have high levels of specificity but lower sensitivity. Depression prevalence estimates ranged from 8.6% to 10.3%. The adjusted odds ratios from logistic regression modeling were consistent. Each of the indicators was significantly associated with low income, being unable to work, current smoking, and having a disability.

Conclusion

The PHQ-8 indicator is the most sensitive and specific and can assess depression severity. The HRQOL and PHQ-2 indicators are adequate to obtain population prevalence estimates if questionnaire length is limited.     

Geographic and Racial Disparities in Breast Cancer–Related Outcomes in Georgia

Objective

To measure the effects of race/ethnicity, area measures of socioeconomic status (SES) and geographic residency status, and health care supply (HCS) characteristics on breast cancer (BC)-related outcomes.

Data Sources/Study Setting

Female patients in Georgia diagnosed with BC in the years 2000–2009.

Study Design

Multilevel regression analysis with adjustment for variables at the county, census tract (CT), and individual level. The county represents the spatial unit of analysis for HCS. SES and geographic residency status were grouped at the CT level.

Principal Findings

Even after controlling for area-level characteristics, racial and ethnic minority women suffered an unequal BC burden. Despite inferior outcomes for disease stage and receipt of treatment, Hispanics had a marginally significant decreased risk of death compared with non-Hispanics. Higher CT poverty was associated with worse BC-related outcomes. Residing in small, isolated rural areas increased the odds of receiving surgery, decreased the odds of receiving radiotherapy, and decreased the risk of death. A higher per-capita availability of BC care physicians was significantly associated with decreased risk of death.

Conclusions

Race/ethnicity and area-level measures of SES, geographic residency status, and HCS contribute to disparities in BC-related outcomes.     

Peer Reviewed: Increasing Prevalence of Gestational Diabetes and Pregnancy-Related Hypertension in Los Angeles County,California, 1991–2003

Introduction

Gestational diabetes and pregnancy-related hypertension can lead to adverse health effects in mothers and infants. We assessed recent trends in the rates of these conditions in Los Angeles County, California.

Methods

Hospital discharge data were used to identify all women aged 15–54 years who resided in the county, had a singleton delivery from 1991 through 2003, and had gestational diabetes or pregnancy-related hypertension listed as a discharge diagnosis at the time of delivery. The prevalence of each condition was calculated by calendar year, race/ethnicity, and age group. Temporal trends in the rates were assessed by using negative binomial regression models, controlling for race/ethnicity and age. Separate models were run for each racial/ethnic and age group.

Results

The age-adjusted prevalence of gestational diabetes increased more than threefold (from 14.5 cases per 1000 women in 1991 to 47.9 cases per 1000 in 2003). The age-adjusted prevalence of pregnancy-related hypertension also increased (from 40.5 cases per 1000 in 1991 to 54.4 cases per 1000 in 2003). In the multivariable regression analysis, the annual rate increase for gestational diabetes was 8.3% overall and was highest among Hispanics (9.9%). The annual rate increase for pregnancy-related hypertension was 2.8% overall and was highest among blacks (4.8%).

Conclusion

The rates of gestational diabetes and pregnancy-related hypertension are increasing in Los Angeles County. Further research is needed to determine the causes of the observed increases and the growing racial/ethnic disparities in those rates.     

Prevalence of physical activity and obesity in US counties, 2001–2011: a road map for action

Background

Obesity and physical inactivity are associated with several chronic conditions, increased medical care costs, and premature death.

Methods

We used the Behavioral Risk Factor Surveillance System (BRFSS), a state-based random-digit telephone survey that covers the majority of United States counties, and the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of the US civilian noninstitutionalized population. About 3.7 million adults aged 20 years or older participated in the BRFSS from 2000 to 2011, and 30,000 adults aged 20 or older participated in NHANES from 1999 to 2010. We calculated body mass index (BMI) from self-reported weight and height in the BRFSS and adjusted for self-reporting bias using NHANES. We calculated self-reported physical activity—both any physical activity and physical activity meeting recommended levels—from self-reported data in the BRFSS. We used validated small area estimation methods to generate estimates of obesity and physical activity prevalence for each county annually for 2001 to 2011.

Results

Our results showed an increase in the prevalence of sufficient physical activity from 2001 to 2009. Levels were generally higher in men than in women, but increases were greater in women than men. Counties in Kentucky, Florida, Georgia, and California reported the largest gains. This increase in level of activity was matched by an increase in obesity in almost all counties during the same time period. There was a low correlation between level of physical activity and obesity in US counties. From 2001 to 2009, controlling for changes in poverty, unemployment, number of doctors per 100,000 population, percent rural, and baseline levels of obesity, for every 1 percentage point increase in physical activity prevalence, obesity prevalence was 0.11 percentage points lower.

Conclusions

Our study showed that increased physical activity alone has a small impact on obesity prevalence at the county level in the US. Indeed, the rise in physical activity levels will have a positive independent impact on the health of Americans as it will reduce the burden of cardiovascular diseases and diabetes. Other changes such as reduction in caloric intake are likely needed to curb the obesity epidemic and its burden.

     

Breast Cancer Screening,Area Deprivation,and Later-Stage Breast Cancer in Appalachia: Does Geography Matter?

Objective

To model the relationship of an area-based measure of a breast cancer screening and geographic area deprivation on the incidence of later stage breast cancer (LSBC) across a diverse region of Appalachia.

Data Source

Central cancer registry data (2006–2008) from three Appalachian states were linked to Medicare claims and census data.

Study Design

Exploratory spatial analysis preceded the statistical model based on negative binomial regression to model predictors and effect modification by geographic subregions.

Principal Findings

Exploratory spatial analysis revealed geographically varying effects of area deprivation and screening on LSBC. In the negative binomial regression model, predictors of LSBC included receipt of screening, area deprivation, supply of mammography centers, and female population aged >75 years. The most deprived counties had a 3.31 times greater rate of LSBC compared to the least deprived. Effect of screening on LSBC was significantly stronger in northern Appalachia than elsewhere in the study region, found mostly for high-population counties.

Conclusions

Breast cancer screening and area deprivation are strongly associated with disparity in LBSC in Appalachia. The presence of geographically varying predictors of later stage tumors in Appalachia suggests the importance of place-based health care access and risk.     

Peer Reviewed: Applying the Small-Area Estimation Method to Estimate a Population Eligible for Breast Cancer Detection Services

Introduction

Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California''s Cancer Detection Programs: Every Woman Counts.

Methods

We used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model.

Results

Our method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races.

Conclusion

The small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.     

Peer Reviewed: The Prevalence of Multiple Sclerosis in 3 US Communities

Introduction

We estimated the prevalence of multiple sclerosis (MS) in 3 large geographic areas in the southern, middle, and northern United States.

Methods

The primary data source was medical records from office visits to private neurologists'' practices or to neurology departments in tertiary care facilities during a 3-year period. Additional data sources included patient advocacy groups, nursing homes, and general practitioners.

Results

Three-year US age-adjusted prevalence estimates for the study areas varied substantially. The prevalence was lowest (47.2 per 100,000 population) in the Texas study area (33°30′ north latitude), intermediate (86.3 per 100,000 population) in the Missouri study area (39°07′ north latitude), and highest (109.5 per 100,000 population) in the Ohio study area (41°24′ north latitude). The geographic differences remained strong after age-adjustment to the world standard population. The inverse association between UV light exposure and MS prevalence estimates was consistent with this observed latitude gradient. In all 3 areas, MS prevalence was highest among women, people aged 40 to 59 years, and non-Hispanics.

Conclusion

These results provide necessary prevalence estimates for community cluster investigations and establish baseline estimates for future studies to evaluate temporal trends in disease prevalence.     

Peer Reviewed: Tracking Chronic Disease and Risk Behavior Prevalence as Survey Participation Declines: Statistics From the Behavioral Risk Factor Surveillance System and Other National Surveys

Introduction

Response rates for the Behavioral Risk Factor Surveillance System (BRFSS) have declined in recent years. The response rate in 1993 was approximately 72%; in 2006, the response rate was approximately 51%. To assess the impact of this decline on the quality of BRFSS estimates, we compared selected health and risk factor estimates from BRFSS with similar estimates from the National Health Interview Survey (NHIS) and the National Health and Nutrition Examination Survey (NHANES).

Methods

We reviewed questionnaires from the 3 surveys and identified a set of comparable measures related to smoking prevalence, alcohol consumption, medical conditions, vaccination, health status, insurance coverage, cost barriers to medical care, testing for human immunodeficiency virus, and body measurements (height and weight).We compared weighted estimates for up to 15 outcome measures, including overall measures and measures for 12 population subgroups. We produced design-appropriate point estimates and carried out statistical tests of hypotheses on the equality of such estimates. We then calculated P values for comparisons of NHIS and NHANES estimates with their BRFSS counterparts.

Results

Although BRFSS and NHIS estimates were statistically similar for 5 of the 15 measures examined, BRFSS and NHANES estimates were statistically similar for only 1 of 6 measures. The observed differences for some of these comparisons were small, however.

Conclusion

These surveys produced similar estimates for several outcome measures, although we observed significant differences as well. Many of the observed differences may have limited consequences for implementing related public health programs; other differences may require more detailed examination. In general, the range of BRFSS estimates examined here tends to parallel those from NHIS and NHANES, both of which have higher rates of participation.     

Peer Reviewed: Geographic and Sociodemographic Disparities in Drive Times to Joint Commission–Certified Primary Stroke Centers in North Carolina,South Carolina,and Georgia

Introduction

Timely access to facilities that provide acute stroke care is necessary to reduce disabilities and death from stroke. We examined geographic and sociodemographic disparities in drive times to Joint Commission–certified primary stroke centers (JCPSCs) and other hospitals with stroke care quality improvement initiatives in North Carolina, South Carolina, and Georgia.

Methods

We defined boundaries for 30- and 60-minute drive-time areas to JCPSCs and other hospitals  by  using geographic information systems (GIS) mapping technology and calculated the proportions of the population living in these drive-time areas by sociodemographic characteristics. Age-adjusted county-level stroke death rates were overlaid onto the drive-time areas.

Results

Approximately 55% of the population lived within a 30-minute drive time to a JCPSC; 77% lived within a 60-minute drive time. Disparities in percentage of the population within 30-minute drive times were found by race/ethnicity, education, income, and urban/rural status; the disparity was largest between urban areas (70% lived within 30-minute drive time) and rural areas (26%). The rural coastal plains had the largest concentration of counties with high stroke death rates and the fewest JCPSCs.

Conclusion

Many areas in this tri-state region lack timely access to JCPSCs. Alternative strategies are needed to expand provision of quality acute stroke care in this region. GIS modeling is valuable for examining and strategically planning the distribution of hospitals providing acute stroke care.     

Peer Reviewed: Residence in a Distressed County in Appalachia as a Risk Factor for Diabetes,Behavioral Risk Factor Surveillance System, 2006-2007

Introduction

We compared the risk of diabetes for residents of Appalachian counties to that of residents of non-Appalachian counties after controlling for selected risk factors in states containing at least 1 Appalachian county.

Methods

We combined Behavioral Risk Factor Surveillance System data from 2006 and 2007 and conducted a logistic regression analysis, with self-reported diabetes as the dependent variable. We considered county of residence (5 classifications for Appalachian counties, based on economic development, and 1 for non-Appalachian counties), age, sex, race/ethnicity, education, household income, smoking status, physical activity level, and obesity to be independent variables. The classification "distressed" refers to counties in the worst 10%, compared with the nation as a whole, in terms of 3-year unemployment rate, per capita income, and poverty.

Results

Controlling for covariates, residents in distressed Appalachian counties had 33% higher odds (95% confidence interval, 1.10-1.60) of reporting diabetes than residents of non-Appalachian counties. We found no significant differences between other classifications of Appalachian counties and non-Appalachian counties.

Conclusions

Residents of distressed Appalachian counties are at higher risk of diabetes than are residents of other counties. States with distressed Appalachian counties should implement culturally sensitive programs to prevent diabetes.     

Peer Reviewed: Workers’ Health Risk Behaviors by State,Demographic Characteristics,and Health Insurance Status

Introduction

Employers often lack data about their workers'' health risk behaviors. We analyzed state-level prevalence data among workers for 4 common health risk behaviors: obesity, physical inactivity, smoking, and missed influenza vaccination (among workers older than 50 years).

Methods

We analyzed 2007 and 2008 Behavioral Risk Factor Surveillance System data, restricting the sample to employed respondents aged 18 to 64 years. We stratified health risk behavior prevalence by annual household income, educational attainment, health insurance status, and race/ethnicity.

Results

For all 4 health risk behaviors, we found significant differences across states and significant disparities related to social determinants of health — income, education, and race/ethnicity. Among uninsured workers, prevalence of smoking was high and influenza vaccinations were lacking.

Conclusion

In this national survey study, we found that workers'' health risk behaviors vary substantially by state and by workers'' socioeconomic status, insurance status, and race/ethnicity. Employers and workplace health promotion practitioners can use the prevalence tables presented in this article to inform their workplace health promotion programs.