Small-area estimation and prioritizing communities for tobacco control efforts in Massachusetts

Adverse Childhood Experiences Among U.S. Adults: National and State Estimates by Adversity Type, 2019-2020

INTRODUCTION

Although adverse childhood experiences (ACEs) are associated with lifelong health harms, current surveillance data on exposures to childhood adversity among adults are either unavailable or incomplete for many states. In this study, recent data from a nationally representative survey were used to obtain the current and complete estimates of ACEs at the national and state levels.

METHODS

Current, complete, by-state estimates of adverse childhood experiences were obtained by applying small area estimation technique to individual-level data on adults aged ≥18 years from 2019-2020 Behavioral Risk Factor Surveillance System survey. The standardized questions about childhood adversity included in the 2019-2020 survey allowed for obtaining estimates of ACE consistent across states. All missing responses to childhood adversity questions (states did not offer such questions or offered them to only some respondents; respondents skipped questions) were predicted through multilevel mixed-effects logistic small area estimation regressions. The analyses were conducted between October 2022 and May 2023.

RESULTS

An estimated 62.8% of U.S. adults had past exposure to ACEs (range: 54.9% in Connecticut; 72.5% in Maine). Emotional abuse (34.5%) was the most common; household member incarceration (10.6%) was the least common. Sexual abuse varied markedly between females (22.2%) and males (5.4%). Exposure to most types of adverse childhood experiences was lowest for adults who were non-Hispanic White, had the highest level of education (college degree) or income (annual income ≥$50,000), or had access to a personal healthcare provider.

CONCLUSIONS

Current complete estimates of ACEs demonstrate high countrywide exposures and stark sociodemographic inequalities in the burden, highlighting opportunities to prevent adverse childhood experiences by focusing social, educational, medical, and public health interventions on populations disproportionately impacted.

Validation of a small-area model for estimation of smoking prevalence at a subnational level

INTRODUCTION

Small-area estimation methods are an alternative to direct survey-based estimates in cases where a survey's sample size does not suffice to ensure representativeness. Nevertheless, the information yielded by small-area estimation methods must be validated. The objective of this study was thus to validate a small-area model.

METHODS

The prevalence of smokers, ex-smokers, and never smokers by sex and age group (15-34, 35-54, 55-64, 65-74, ≥75 years) was calculated in two Spanish Autonomous Regions (ARs) by applying a weighted ratio estimator (direct estimator) to data from representative surveys. These estimates were compared against those obtained with a small-area model applied to another survey, specifically the Spanish National Health Survey, which did not guarantee representativeness for these two ARs by sex and age. To evaluate the concordance of the estimates, we calculated the intraclass correlation coefficient (ICC) and the 95% confidence intervals of the differences between estimates. To assess the precision of the estimates, the coefficients of variation were obtained.

RESULTS

In all cases, the ICC was ≥0.87, indicating good concordance between the direct and small-area model estimates. Slightly more than eight in ten 95% confidence intervals for the differences between estimates included zero. In all cases, the coefficient of variation of the small-area model was <30%, indicating a good degree of precision in the estimates.

CONCLUSIONS

The small-area model applied to national survey data yields valid estimates of smoking prevalence by sex and age group at the AR level. These models could thus be applied to a single year's data from a national survey, which does not guarantee regional representativeness, to characterize various risk factors in a population at a subnational level.

Small-area models to assess the geographical distribution of tobacco consumption by sex and age in Spain

INTRODUCTION

Complete and accurate data on smoking prevalence at a local level would enable health authorities to plan context-dependent smoking interventions. However, national health surveys do not generally provide direct estimates of smoking prevalence by sex and age groups at the subnational level. This study uses a small-area model-based methodology to obtain precise estimations of smoking prevalence by sex, age group and region, from a population-based survey.

METHODS

The areas targeted for analysis consisted of 180 groups based on a combination of sex, age group (15-34, 35-54, 55-64, 65-74, and ≥75 years), and Autonomous Region. Data on tobacco use came from the 2017 Spanish National Health Survey (2017 SNHS). In each of the 180 groups, we estimated the prevalence of smokers (S), ex-smokers (ExS) and never smokers (NS), as well as their coefficients of variation (CV), using a weighted ratio estimator (direct estimator) and a multinomial logistic model with random area effects.

RESULTS

When smoking prevalence was estimated using the small-area model, the precision of direct estimates improved; the CV of S and ExS decreased on average by 26%, and those of NS by 25%. The range of S prevalence was 11-46% in men and 4-37% in women, excluding the group aged ≥75 years.

CONCLUSIONS

This study proposes a methodology for obtaining reliable estimates of smoking prevalence in groups or areas not covered in the survey design. The model applied is a good alternative for enhancing the precision of estimates at a detailed level, at a much lower cost than that involved in conducting large-scale surveys. This method could be easily integrated into routine data processing of population health surveys. Having such estimates directly after completing a health survey would help characterize the tobacco epidemic and/or any other risk factor more precisely.

Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation

Background

Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy and precision may not carry over for smaller areas that are sparsely represented by non-random samples.

Methods

We developed small-area estimation models using a combination of EHR data drawn from MDPHnet, an EHR-based public health surveillance network in Massachusetts, the American Community Survey, and state hospitalization data. We estimated municipality-specific prevalence rates of asthma, diabetes, hypertension, obesity, and smoking in each of the 351 municipalities in Massachusetts in 2016. Models were compared against Behavioral Risk Factor Surveillance System (BRFSS) state and small area estimates for 2016.

Results

Integrating progressively more variables into prediction models generally reduced mean absolute error (MAE) relative to municipality-level BRFSS small area estimates: asthma (2.24% MAE crude, 1.02% MAE modeled), diabetes (3.13% MAE crude, 3.48% MAE modeled), hypertension (2.60% MAE crude, 1.48% MAE modeled), obesity (4.92% MAE crude, 4.07% MAE modeled), and smoking (5.33% MAE crude, 2.99% MAE modeled). Correlation between modeled estimates and BRFSS estimates for the 13 municipalities in Massachusetts covered by BRFSS’s 500 Cities ranged from 81.9% (obesity) to 96.7% (diabetes).

Conclusions

Small-area estimation using EHR data is feasible and generates estimates comparable to BRFSS state and small-area estimates. Integrating EHR data with survey data can provide timely and accurate disease monitoring tools for areas with sparse data coverage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13809-2.

Development and approbation of a tool for assessing municipal infrastructure affecting behavioral risk factors for cardiovascular and other noncommunicable diseases

Aim. To present first results of the study on the assessment of municipal infrastructure using specialized software in a pilot subject of the Russian Federation (Sverdlovsk Oblast).

Material and methods. The following infrastructure elements of four municipalities of the Sverdlovsk Oblast have been assessed in the period from January to March 2022: sports facilities, grocery stores, fairs/markets with fresh vegetables and fruits, alcoholic drinks in shops and supermarkets, special liquor stores, tobacco outlets, fast food restaurants). The assessment was made with specialized software developed using Open Street Map (OSM) that allows real-time recording of objects for interactive map data visualization.

Results. The density of sales outlets for tobacco, alcohol, fresh vegetables and fruits was 2,3, 3,4 and 2,4 per 1 km², respectively; the density of sports facilities, fast food and restaurants — 1,3, 0,9 and 0,5, respectively. The number of sales outlets for tobacco, alcohol, fresh vegetables and fruits per 100 thousand people was 111,6, 167,7 and 116,5, respectively, while the number of sports facilities, fast food and restaurants — 63,5, 44,1, 25,0, respectively.

Conclusion. Creation and implementation of integrated system for assessing a unified preventive environment for promoting health and maintaining a healthy lifestyle at the municipal level using a combination of objective (GIS technologies, paraclinical tests) and subjective assessment methods (personal survey), as well as the introduction of interactive maps of municipal infrastructure, will make possible use of evidence to make decisions on the development and implementation of programs for the prevention of cardiovascular diseases and health promotion, as well as their management at the regional/municipal level.

Real-Time Context of Tobacco Marketing Exposure and Community Vulnerability-An Ecological Momentary Assessment Among Young Adults

BACKGROUND

Exposure to tobacco product marketing increases tobacco use among young adults, especially those from vulnerable communities (VCs).

PURPOSE

This study examined real-time tobacco marketing exposure among young adults from vulnerable and non-vulnerable communities using Ecological Momentary Assessment (EMA).

METHODS

This study used EMA data to assess context (e.g., location and activity) of tobacco marketing exposure using four text-messaging surveys per day over 2 weeks. Young adult non-current tobacco users living in Washington, D.C. (n = 146; ages 18-24) recorded 5,285 surveys, including 20 participants (13.2%) from VCs with high proportions of lower income and racial/ethnic minorities, and high smoking rates. Unadjusted and adjusted multilevel logistic regressions were used to assess the associations between exposure to any and flavored tobacco marketing, VC residence, and real-time context.

RESULTS

Fifty-nine participants (40.4%) reported at least one tobacco marketing exposure and recorded 94 exposure moments. In adjusted models, odds of exposure were higher among VC residents (AOR = 2.6, 95% CI = 1.2-5.4), in the presence of anyone using tobacco versus no use (AOR = 4.0, 95% CI = 2.4-6.7), at store/retail (AOR = 17.0, 95% CI = 6.4-44.8), or outside/in transit (AOR = 4.1, 95% CI = 2.1-7.8) versus at home. VC residence (AOR = 7.2, 95% CI = 2.3-22.2) was the strongest predictor of flavored tobacco marketing exposure among all covariates examined.

CONCLUSIONS

Young adults are predominantly exposed to tobacco marketing in their daily lives through retail advertisements. Young adults from VCs are at increased risks of seeing any tobacco and especially flavored tobacco marketing. Policies that curtail tobacco retailer density and advertisement displays may reduce overall and differential tobacco marketing exposure.

Associations of County Tobacco Retailer Availability With U.S. Adult Smoking Behaviors, 2014-2015

INTRODUCTION

Greater availability of tobacco product retailers in an area may be associated with smoking behaviors, and the majority of people who smoke purchase their cigarettes at gas stations and convenience stores. This cross-sectional study investigates the associations of overall tobacco retailer density and gas/convenience density with adult smoking behaviors.

METHODS

This study built a list of tobacco retailers in 2014 and calculated the county-level number of retailers per 1,000 people. Individual-level smoking behavior data were drawn from the 2014-2015 Tobacco Use Supplement for a sample of adults (n=88,850) residing in metropolitan counties across the U.S. General estimating equation models were fit to investigate the associations between retailer density and cigarette smoking behaviors (smoking status, quit attempt, quit length). Analyses were conducted in 2020.

RESULTS

A greater number of tobacco retailers (AOR=1.63, 95% CI=1.35, 1.96) and gas stations and convenience stores (AOR=3.29, 95% CI=2.39, 4.52) per 1,000 people were each associated with a higher odds of a respondent smoking every day than the odds of a respondent not smoking. In addition, both measures were associated with a higher odds of a respondent being an every-day than being a some-day smoker. Associations for gas/convenience density were similar in models that additionally controlled for other tobacco retailers (excluding gas/convenience). Study results did not support associations between retailer density and cessation.

CONCLUSIONS

Tobacco retailer density, especially gas/convenience density, is correlated with daily smoking, the most harmful tobacco use behavior. Calculating tobacco retailer density using gas/convenience stores may be a feasible proxy for overall tobacco retailer density.

Tobacco retail availability and risk of relapse among smokers who make a quit attempt: a population-based cohort study

INTRODUCTION

The availability of tobacco is thought to influence smoking behaviour, but there are few longitudinal studies examining if the location and number of tobacco outlets has a prospective impact on smoking cessation.

METHODS

The Ontario Tobacco Survey, a population-representative sample of Ontario adult smokers who were followed every 6 months for up to 3 years, was linked with tobacco outlet location data from the Ontario Ministry of Health. Proximity (distance), threshold (at least one outlet within 500 m) and density (number of outlets within 500 m) with respect to a smokers' home were calculated among urban and suburban current smokers (n=2414). Quit attempts and risk of relapse were assessed using logistic regression and survival analysis, adjusted for neighbourhood effects and individual characteristics.

RESULTS

Increased density of tobacco outlets was associated with decreased odds of making a quit attempt (OR: 0.54; 95% CI 0.35 to 0.85) in high-income neighbourhoods, but not in lower income ones. There was an increased risk of relapse among those who had at least one store within 500 m (HR: 1.41 (95% CI 1.06 to 1.88). Otherwise, there was no association of proximity with quit attempts or relapse.

CONCLUSIONS

The existence of a tobacco retail outlet within walking distance from home was associated with difficulty in succeeding in a quit attempt, while the increased density of stores was associated with decreased attempts in higher income neighbourhoods. The availability of tobacco may influence tobacco use through multiple mechanisms.

Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside

Electronic Health Records (EHR) are rapidly becoming accepted as tools for planning and population health^1,2. With the national dialogue around Medicaid expansion¹², the role of EHR data has become even more important. For their potential to be fully realized and contribute to these discussions, techniques for creating accurate small area estimates is vital. As such, we examined the efficacy of developing small area estimates for Medicaid patients in two locations, Albuquerque and Chicago, by using a Monte Carlo/Gaussian technique that has worked in accurately locating registered voters in North Carolina¹¹. The Albuquerque data, which includes patient address, will first be used to assess the accuracy of the methodology. Subsequently, it will be combined with the EHR data from Chicago to develop a regression that predicts Medicaid patients by US Block Group. We seek to create a tool that is effective in translating EHR data's potential for population health studies.

The Moderating Role of Gender in the Relationship Between Tobacco Outlet Exposure and Tobacco Use Among African American Young Adults

Tobacco outlet exposure is a correlate of tobacco use with potential differences by gender that warrant attention. The aim of this study is to explore the moderating role of gender in the relationship between tobacco outlet exposure and past month tobacco use among African American young adults 21 to 24 years old. This cross-sectional study (n = 283) used geospatial methods to determine the number of tobacco outlets within walking distance (i.e., a quarter mile) of participants' homes and distance to the nearest outlet. Logistic regression models were used to test interactions between gender and tobacco outlet exposure (i.e., density and proximity). Tobacco outlets were classified based on whether or not they were licensed to sell tobacco only (TO outlets) or tobacco and alcohol (TA outlets). Neither density nor proximity was associated with past month tobacco use in the pooled models. However, gender modified the relationship between TO outlet density and tobacco use, and this relationship was significant only among women (OR = 1.02; p < 0.01; adjusted OR = 1.01; p < 0.05). This study underscores the importance of reducing tobacco outlet density in residential neighborhoods, especially TO outlets, as well as highlights potential gender differences in the relationship between tobacco outlet density and tobacco use.

Density and Proximity of Licensed Tobacco Retailers and Adolescent Smoking

Adolescent smoking prevention is an important issue in health care. This literature review describes the theoretical concept of ecological model for adolescent smoking and tobacco retailers and summarizes previous studies on the association between the density and proximity of tobacco retailers and adolescent smoking. We reviewed nine studies on tobacco retailer density and proximity in relation to adolescent smoking, published in peer-reviewed journals between 2004 and 2014. The tobacco retailer density and proximity were correlated with adolescent lifetime smoking, past 12-month smoking, past 30-day smoking, and susceptibility to smoking. School nurses or other school health professionals may need to include the density and proximity of tobacco retailer factors around schools in school-based tobacco-use prevention programs. Health policy makers may need to consider zoning or licensing restrictions of tobacco retailers around schools for adolescent smoking prevention.

Predicting Periodontitis at State and Local Levels in the United States

The objective of the study was to estimate the prevalence of periodontitis at state and local levels across the United States by using a novel, small area estimation (SAE) method. Extended multilevel regression and poststratification analyses were used to estimate the prevalence of periodontitis among adults aged 30 to 79 y at state, county, congressional district, and census tract levels by using periodontal data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, population counts from the 2010 US census, and smoking status estimates from the Behavioral Risk Factor Surveillance System in 2012. The SAE method used age, race, gender, smoking, and poverty variables to estimate the prevalence of periodontitis as defined by the Centers for Disease Control and Prevention/American Academy of Periodontology case definitions at the census block levels and aggregated to larger administrative and geographic areas of interest. Model-based SAEs were validated against national estimates directly from NHANES 2009-2012. Estimated prevalence of periodontitis ranged from 37.7% in Utah to 52.8% in New Mexico among the states (mean, 45.1%; median, 44.9%) and from 33.7% to 68% among counties (mean, 46.6%; median, 45.9%). Severe periodontitis ranged from 7.27% in New Hampshire to 10.26% in Louisiana among the states (mean, 8.9%; median, 8.8%) and from 5.2% to 17.9% among counties (mean, 9.2%; median, 8.8%). Overall, the predicted prevalence of periodontitis was highest for southeastern and southwestern states and for geographic areas in the Southeast along the Mississippi Delta, as well as along the US and Mexico border. Aggregated model-based SAEs were consistent with national prevalence estimates from NHANES 2009-2012. This study is the first-ever estimation of periodontitis prevalence at state and local levels in the United States, and this modeling approach complements public health surveillance efforts to identify areas with a high burden of periodontitis.

Local spatial clustering in youths' use of tobacco, alcohol, and marijuana in Boston

BACKGROUND

Understanding geographic variation in youth drug use is important for both identifying etiologic factors and planning prevention interventions. However, little research has examined spatial clustering of drug use among youths by using rigorous statistical methods.

OBJECTIVES

The purpose of this study was to examine spatial clustering of youth use of tobacco, alcohol, and marijuana.

METHODS

Responses on tobacco, alcohol, and marijuana use from 1,292 high school students ages 13-19 who provided complete residential addresses were drawn from the 2008 Boston Youth Survey Geospatial Dataset. Response options on past month use included "none," "1-2," "3-9," and "10 or more." The response rate for each substance was approximately 94%. Spatial clustering of youth drug use was assessed using the spatial Bernoulli model in the SatScan™ software package.

RESULTS

Approximately 12%, 36%, and 18% of youth reported any past-month use of tobacco, alcohol, and/or marijuana, respectively. Two clusters of elevated past tobacco use among Boston youths were generated, one of which was statistically significant. This cluster, located in the South Boston neighborhood, had a relative risk of 5.37 with a p-value of 0.00014. There was no significant localized spatial clustering in youth past alcohol or marijuana use in either the unadjusted or adjusted models.

CONCLUSION

Significant spatial clustering in youth tobacco use was found. Finding a significant cluster in the South Boston neighborhood provides reason for further investigation into neighborhood characteristics that may shape adolescents' substance use behaviors. This type of research can be used to evaluate the underlying reasons behind spatial clustering of youth substance and to target local drug abuse prevention interventions and use.

Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

The added value of accounting for activity space when examining the association between tobacco retailer availability and smoking among young adults

BACKGROUND

Despite a declining prevalence in many countries, smoking rates remain consistently high among young adults. Targeting contextual influences on smoking, such as the availability of tobacco retailers, is one promising avenue of intervention. Most studies have focused on residential or school neighbourhoods, without accounting for other settings where individuals spend time, that is, their activity space. We investigated the association between tobacco retailer availability in the residential neighbourhood and in the activity space, and smoking status.

METHODS

Cross-sectional baseline data from 1994 young adults (aged 18-25) participating in the Interdisciplinary Study of Inequalities in Smoking (Montreal, Canada, 2011-2012) were analysed. Residential and activity locations served to derive two measures of tobacco retailer availability: counts within 500 m buffers and proximity to the nearest retailer. Prevalence ratios for the association between each tobacco retailer measure and smoking status were estimated using log-binomial regression.

RESULTS

Participants encountering high numbers of tobacco retailers in their residential neighbourhood, and both medium and high retailer counts in their activity space, were more likely to smoke compared to those exposed to fewer retailers. While residential proximity was not associated with smoking, we found 36% and 42% higher smoking prevalence among participants conducting activities within medium and high proximity to tobacco retailers compared to those conducting activities further from such outlets.

CONCLUSIONS

This study adds to the sparse literature on contextual correlates of smoking among young adults, and illustrates the added value of considering individuals' activity space in contextual studies of smoking.

Fall Risk is Not Black and White

OBJECTIVE

To determine whether previously reported racial differences in fall rates between White and Black/African American is explained by differences in health status and neighborhood characteristics.

DESIGN

Prospective cohort.

SETTING

Community.

PARTICIPANTS

The study included 550 White and 116 Black older adults in the Greater Boston area (mean age: 78 years; 36% men) who were English-speaking, able to walk across a room, and without severe cognitive impairment.

MEASUREMENTS

Falls were prospectively reported using monthly fall calendars. The location of each fall and fall-related injuries were asked during telephone interviews. At baseline, we assessed risk factors for falls, including sociodemographic characteristics, physiologic risk factors, physical activity, and community-level characteristics.

RESULTS

Over the mean follow-up of 1,048 days, 1,539 falls occurred (incidence: 806/1,000 person-years). Whites were more likely than Blacks to experience any falls (867 versus 504 falls per 1,000 person-years; RR [95% CI]: 1.77 [1.33, 2.36]), outdoor falls (418 versus 178 falls per 1,000 person-years; 1.78 [1.08, 2.92]), indoor falls (434 versus 320 falls per 1,000 person-years; 1.44 [1.02, 2.05]), and injurious falls (367 versus 205 falls per 1,000 person-years; 1.79 [1.30, 2.46]). With exception of injurious falls, higher fall rates in Whites than Blacks were substantially attenuated with adjustment for risk factors and community-level characteristics: any fall (1.24 [0.81, 1.89]), outdoor fall (1.57 [0.86, 2.88]), indoor fall (1.08 [0.64, 1.81]), and injurious fall (1.77 [1.14, 2.74]).

CONCLUSION

Our findings suggest that the racial differences in fall rates may be largely due to confounding by individual-level and community-level characteristics.

Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system

A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state- and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran's I=0.452, P=0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=-0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=-0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.

Cigarette smoking prevalence in US counties: 1996-2012

BACKGROUND

Cigarette smoking is a leading risk factor for morbidity and premature mortality in the United States, yet information about smoking prevalence and trends is not routinely available below the state level, impeding local-level action.

METHODS

We used data on 4.7 million adults age 18 and older from the Behavioral Risk Factor Surveillance System (BRFSS) from 1996 to 2012. We derived cigarette smoking status from self-reported data in the BRFSS and applied validated small area estimation methods to generate estimates of current total cigarette smoking prevalence and current daily cigarette smoking prevalence for 3,127 counties and county equivalents annually from 1996 to 2012. We applied a novel method to correct for bias resulting from the exclusion of the wireless-only population in the BRFSS prior to 2011.

RESULTS

Total cigarette smoking prevalence varies dramatically between counties, even within states, ranging from 9.9% to 41.5% for males and from 5.8% to 40.8% for females in 2012. Counties in the South, particularly in Kentucky, Tennessee, and West Virginia, as well as those with large Native American populations, have the highest rates of total cigarette smoking, while counties in Utah and other Western states have the lowest. Overall, total cigarette smoking prevalence declined between 1996 and 2012 with a median decline across counties of 0.9% per year for males and 0.6% per year for females, and rates of decline for males and females in some counties exceeded 3% per year. Statistically significant declines were concentrated in a relatively small number of counties, however, and more counties saw statistically significant declines in male cigarette smoking prevalence (39.8% of counties) than in female cigarette smoking prevalence (16.2%). Rates of decline varied by income level: counties in the top quintile in terms of income experienced noticeably faster declines than those in the bottom quintile.

CONCLUSIONS

County-level estimates of cigarette smoking prevalence provide a unique opportunity to assess where prevalence remains high and where progress has been slow. These estimates provide the data needed to better develop and implement strategies at a local and at a state level to further reduce the burden imposed by cigarette smoking.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.

Tailoring community-based wellness initiatives with latent class analysis--Massachusetts Community Transformation Grant projects

INTRODUCTION

Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts' municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach.

METHODS

Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains.

RESULTS

Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges.

CONCLUSION

Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.

Examination of how neighborhood definition influences measurements of youths' access to tobacco retailers: a methodological note on spatial misclassification

Measurements of neighborhood exposures likely vary depending on the definition of "neighborhood" selected. This study examined the extent to which neighborhood definition influences findings regarding spatial accessibility to tobacco retailers among youth. We defined spatial accessibility to tobacco retailers (i.e., tobacco retail density, closest tobacco retailer, and average distance to the closest 5 tobacco retailers) on the basis of circular and network buffers of 400 m and 800 m, census block groups, and census tracts by using residential addresses from the 2008 Boston Youth Survey Geospatial Dataset (n = 1,292). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in measurements of youths' access to tobacco retailers according to the selected neighborhood definitions, and these were marked for the 2 spatial proximity measures (both P < 0.01 for all differences). For example, the median average distance to the closest 5 tobacco retailers was 381.50 m when using specific home addresses, 414.00 m when using census block groups, and 482.50 m when using census tracts, illustrating how neighborhood definition influences the measurement of spatial accessibility to tobacco retailers. These analyses suggest that, whenever possible, egocentric neighborhood definitions should be used. The use of larger administrative neighborhood definitions can bias exposure estimates for proximity measures.

The geography of recreational open space: influence of neighborhood racial composition and neighborhood poverty

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood socio-demographic characteristics (all p value = 0.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I = 0.082; p value = 0.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S = -0.22; conventional p value = 0.005; spatially adjusted p value = 0.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (>60 % non-Hispanic Black in a census tract) and recreational open space density (r S = -0.23; conventional p value = 0.003; spatially adjusted p value = 0.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (p value < 0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

Human papillomavirus vaccine coverage among females aged 11 to 17 in Texas counties: an application of multilevel, small area estimation

BACKGROUND

Local data are often used to plan and evaluate public health interventions and policy. With increasingly fewer public resources to collect sufficient data to support direct estimation of local outcomes, methods for deriving small area estimates are vital. The purpose of this study is to describe the county-level geographic distribution of human papillomavirus (HPV) vaccine coverage among adolescent females in Texas using multilevel small area estimation.

METHODS

Multilevel (individual, county, public health region) random-intercept logit models were fit to HPV vaccination data (≥1 dose Gardasil) from the 2008 Behavioral Risk Factor Surveillance System. Using the parameter estimates from the final model, we simulated 10,000 data sets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county.

RESULTS

County-level coverage estimates ranged from 7% to 29%, compared with the state average of 18% (95% confidence interval [CI], 13.59-21.88). Many Southwestern border and metropolitan counties exhibited high coverage estimates. Low coverage estimates were noted in the Panhandle, Southeastern border region, and Northeast. Significant correlations were observed between HPV vaccination and Hispanic ethnicity, county poverty, and public health region poverty.

CONCLUSION

Harnessing the flexibility of multilevel small area models to estimate HPV vaccine coverage at the county level, we have provided data that may inform the development of health education programs/policies, the provision of health services, and the planning of new research studies. Additionally, we have provided a framework for modeling other health outcomes at the county level using national survey data.

Use of practice-based research network data to measure neighborhood smoking prevalence

Introduction

Practice-Based Research Networks (PBRNs) and health systems may provide timely, reliable data to guide the development and distribution of public health resources to promote healthy behaviors, such as quitting smoking. The objective of this study was to determine if PBRN data could be used to make neighborhood-level estimates of smoking prevalence.

Methods

We estimated the smoking prevalence in 32 greater Boston neighborhoods (population = 877,943 adults) by using the electronic health record data of adults who in 2009 visited one of 26 Partners Primary Care PBRN practices (n = 77,529). We compared PBRN-derived estimates to population-based estimates derived from 1999–2009 Behavioral Risk Factor Surveillance System (BRFSS) data (n = 20,475).

Results

The PBRN estimates of neighborhood smoking status ranged from 5% to 22% and averaged 11%. The 2009 neighborhood-level smoking prevalence estimates derived from the BRFSS ranged from 5% to 26% and averaged 13%. The difference in smoking prevalence between the PBRN and the BRFSS averaged −2 percentage points (standard deviation, 3 percentage points).

Conclusion

Health behavior data collected during routine clinical care by PBRNs and health systems could supplement or be an alternative to using traditional sources of public health data.

A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level

Introduction

Traditional survey methods for obtaining nationwide small-area estimates (SAEs) of childhood obesity are costly. This study applied a geocoded national health survey in a multilevel modeling framework to estimate prevalence of childhood obesity at the census block-group level.

Methods

We constructed a multilevel logistic regression model to evaluate the influence of individual demographic characteristics, zip code, county, and state on the childhood obesity measures from the 2007 National Survey of Children’s Health. The obesity risk for a child in each census block group was then estimated on the basis of this multilevel model. We compared direct survey and model-based SAEs to evaluate the model specification.

Results

Multilevel models in this study explained about 60% of state-level variances associated with childhood obesity, 82.8% to 86.5% of county-level, and 93.1% of zip code-level. The 95% confidence intervals of block- group level SAEs have a wide range (0.795-20.0), a low median of 2.02, and a mean of 2.12. The model-based SAEs of childhood obesity prevalence ranged from 2.3% to 54.7% with a median of 16.0% at the block-group level.

Conclusion

The geographic variances among census block groups, counties, and states demonstrate that locale may be as significant as individual characteristics such as race/ethnicity in the development of the childhood obesity epidemic. Our estimates provide data to identify priority areas for local health programs and to establish feasible local intervention goals. Model-based SAEs of population health outcomes could be a tool of public health assessment and surveillance.

Overcoming data challenges examining oral health disparities in appalachia

Objective

The objective of our study of oral health disparities in Appalachia was to use existing data sources to geographically analyze suspected disparities in oral health status in the 420 counties of Appalachia, and to make sub-state comparisons within Appalachia and to the rest of the nation. The purpose of this manuscript is to describe the methods used to overcome challenges associated with using limited oral health data to make inferences about oral health status.

Methods:

Oral health data were obtained from the Behavioral Risk Factor Surveillance System (BRFSS). Because the BRFSS was designed for state-level analysis, there were inadequate numbers of responses to study Appalachia by county. We set out to determine the smallest possible unit we could use, aggregating data to satisfy CDC minimum requirements for spatially identified responses. For sub-state comparisons, data were first aggregated to Appalachian and non-Appalachian regions within Appalachian states. Next, urban versus rural areas within Appalachian and non-Appalachian regions were examined. Beale codes were used to define metropolitan and non-metropolitan statistical regions for the United States.

Results:

Aggregating the data as described proved useful for smoothing the data used to analyze oral health disparities, while still revealing important sub-state differences. Using geographic information systems to map data throughout the process was very useful for determining an effective approach for our analysis.

Discussion:

Studying oral health disparities on a regional or national level is difficult given a lack of appropriate data. The BRFSS can be adapted for this purpose; however, there is a limited number of oral health questions and because they are also optional, they are not routinely asked by all states. Expanding the BRFSS to include a larger sampling frame would be very helpful for studying oral health disparities.

Conclusions:

Novel techniques were introduced to use BRFSS data to study oral health disparities in Appalachia, which provided informative sub-state results, useful to health planners for targeting intervention strategies.

Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association

BACKGROUND

Poor lifestyle behaviors, including suboptimal diet, physical inactivity, and tobacco use, are leading causes of preventable diseases globally. Although even modest population shifts in risk substantially alter health outcomes, the optimal population-level approaches to improve lifestyle are not well established.

METHODS AND RESULTS

For this American Heart Association scientific statement, the writing group systematically reviewed and graded the current scientific evidence for effective population approaches to improve dietary habits, increase physical activity, and reduce tobacco use. Strategies were considered in 6 broad domains: (1) Media and educational campaigns; (2) labeling and consumer information; (3) taxation, subsidies, and other economic incentives; (4) school and workplace approaches; (5) local environmental changes; and (6) direct restrictions and mandates. The writing group also reviewed the potential contributions of healthcare systems and surveillance systems to behavior change efforts. Several specific population interventions that achieved a Class I or IIa recommendation with grade A or B evidence were identified, providing a set of specific evidence-based strategies that deserve close attention and prioritization for wider implementation. Effective interventions included specific approaches in all 6 domains evaluated for improving diet, increasing activity, and reducing tobacco use. The writing group also identified several specific interventions in each of these domains for which current evidence was less robust, as well as other inconsistencies and evidence gaps, informing the need for further rigorous and interdisciplinary approaches to evaluate population programs and policies.

CONCLUSIONS

This systematic review identified and graded the evidence for a range of population-based strategies to promote lifestyle change. The findings provide a framework for policy makers, advocacy groups, researchers, clinicians, communities, and other stakeholders to understand and implement the most effective approaches. New strategic initiatives and partnerships are needed to translate this evidence into action.

Assessing the relationship between work-family conflict and smoking

OBJECTIVES

We examined the relationship between smoking and work-family conflict among a sample of New England long-term-care facility workers.

METHODS

To collect data, we conducted in-person, structured interviews with workers in 4 extended-care facilities.

RESULTS

There was a strong association between smoking likelihood and work-family conflict. Workers who experienced both stress at home from work issues (i.e., work-to-home conflict) and stress at work from personal issues (i.e., home-to-work conflict) had 3.1 times higher odds of smoking than those who did not experience these types of conflict. Workers who experienced home-to-work conflict had an odds of 2.3 compared with those who did not experience this type of conflict, and workers who experienced work-to-home conflict had an odds of 1.6 compared with workers who did not experience this type of conflict.

CONCLUSIONS

The results of this study indicate that there is a robust relationship between work-family conflict and smoking, but that this relationship is dependent upon the total amount of conflict experienced and the direction of the conflict.

Small area estimates reveal high cigarette smoking prevalence in low-income cities of Los Angeles county

Los Angeles County has among the lowest smoking rates of large urban counties in the USA. Nevertheless, concerning disparities persist as high smoking prevalence is found among certain subgroups. We calculated adult smoking prevalence in the incorporated cities of Los Angeles County in order to identify cities with high smoking prevalence. The prevalence was estimated by a model-based small area estimation method with utilization of three data sources, including the 2007 Los Angeles County Health Survey, the 2000 Census, and the 2007 Los Angeles County Population Estimates and Projection System. Smoking prevalence varied considerably across cities, with a more than fourfold difference between the lowest (5.3%) and the highest prevalence (21.7%). Higher smoking prevalence was generally found in socioeconomically disadvantaged cities. The disparities identified here add another layer of data to our knowledge of the health inequities experienced by low-income urban communities and provide much sought data for local tobacco control. Our study also demonstrates the feasibility of providing credible local estimates of smoking prevalence using the model-based small area estimation method.

[Regionalization of health indicators. Results from the GEDA-Study 2009]

The representative health surveys conducted by the Department of Epidemiology and Health Reporting weren't used before to provide estimates for the spatial distribution of health outcomes. We are discussing the possibilities of providing these outcomes using methods for 'Small-Area-Estimation'. In the study we are using data of the "German Health Update 2009" (GEDA) to analyze regional inequalities for self-assessed health status, smoking and obesity on the district level in Germany. The small area estimates are provided by multilevel logistic regression models using additional regional statistical data from the official INKAR 2009 database of regional indicators for Germany. We are mapping the results of our analysis for the district level (NUTS-3) using simple thematic maps. Afterwards we compared the results of our small area models with conventional estimates that were based on the official German small scale census. The results showed that our estimates are in line with the prevalences of the census. Overall the results suggest that Small-Area-Estimation methods have a big potential to provide regionalized health indicators for the health reporting in Germany.

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

BACKGROUND

Multiple and varied benefits have been suggested for increased neighborhood walkability. However, spatial inequalities in neighborhood walkability likely exist and may be attributable, in part, to residential segregation.

OBJECTIVE

Utilizing a spatial demographic perspective, we evaluated potential spatial inequalities in walkable neighborhood amenities across census tracts in Boston, MA (US).

METHODS

The independent variables included minority racial/ethnic population percentages and percent of families in poverty. Walkable neighborhood amenities were assessed with a composite measure. Spatial autocorrelation in key study variables were first calculated with the Global Moran’s I statistic. Then, Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were calculated as well as Spearman correlations accounting for spatial autocorrelation. We fit ordinary least squares (OLS) regression and spatial autoregressive models, when appropriate, as a final step.

RESULTS

Significant positive spatial autocorrelation was found in neighborhood socio-demographic characteristics (e.g. census tract percent Black), but not walkable neighborhood amenities or in the OLS regression residuals. Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were not statistically significant, nor were neighborhood socio-demographic characteristics significantly associated with walkable neighborhood amenities in OLS regression models.

CONCLUSIONS

Our results suggest that there is residential segregation in Boston and that spatial inequalities do not necessarily show up using a composite measure.

COMMENTS

Future research in other geographic areas (including international contexts) and using different definitions of neighborhoods (including small-area definitions) should evaluate if spatial inequalities are found using composite measures but also should use measures of specific neighborhood amenities.

Smokeless tobacco marketing and sales practices in Appalachian Ohio following federal regulations

INTRODUCTION

Smokeless tobacco (ST) use is increasingly prevalent among poor and vulnerable groups, especially rural males. Access to tobacco products, as well as marketing messages, is associated with tobacco usage. In June 2010, the Tobacco Control Act (TCA) marked the beginning of federal regulation of the sale and marketing of tobacco products--including ST. The goal of this study was to describe marketing practices over time and to provide early assessment of the federal regulation in rural tobacco-licensed retail outlets.

METHODS

Observational data were collected from a sample of retail outlets within three Ohio Appalachian counties. From an estimated 300 retail establishments, a stratified random sample was drawn (n = 86). Trained observers surveyed the sales and marketing of tobacco products. Baseline surveys were conducted between November 2009 and May 2010 before the TCA; follow-up surveys were repeated in August 2010.

RESULTS

Follow-up surveys were completed for 79 tobacco-licensed retail outlets. The majority of retail outlets were gas stations or convenience stores. Compared with baseline, there was a significant reduction in the frequency of exterior and interior advertisements observed after the TCA (p < .01). Despite the lack of change in the proportion of stores advertising ST, the number of ST brands being advertised doubled between baseline and follow-up.

CONCLUSION

Initial compliance with certain elements of the federal restrictions appears to be high in Appalachian Ohio. The significant increase in ST brands advertised suggests that advertising remains a clear presence in retail outlets in Appalachian Ohio.

Regional surveillance of social and geographic inequalities in smoking: the case of Montréal, Canada

Surveillance of social inequalities in health is a platform for action. We examined the trends in smoking behaviours (current and ever smoking, quit ratios) according to education and place of residence and we quantified the observed inequalities. Data were from repeated cross-sectional surveys (2003-2009) of Montreal (Canada) residents ≥15 years (n=12,053). Trends in smoking behaviours according to education were measured with logistic and log-binomial regressions. Spatial distribution of smoking behaviours across local areas was assessed with Morans' Index. Observed inequalities were quantified with prevalence ratio and difference, population attributable risk, and slope index of inequality. Results showed that ever smoking rose among low-educated individuals. Among their high-educated fellow-citizens, current smoking decreased and quit ratios increased. Adverse smoking behaviours (current and ever smoking) were clustered in south-central areas. We conclude that smoking inequalities in Montreal are growing. We thus encourage a closer monitoring of smoking social differentials.

The German multi-centre study on smoking-related behavior-description of a population-based case-control study

Tobacco smoking is a major risk factor for most of the diseases leading in mortality. Nicotine dependence (ND), which sustains regular smoking, is now acknowledged to be under substantial genetic control with some environmental contribution. At present, however, genetic studies on ND are mostly conducted in populations that have been poorly characterized with regard to ND-related phenotypes for the simple reason that the respective populations were not primarily collected to study ND. The German multi-centre study 'Genetics of Nicotine Dependence and Neurobiological Phenotypes', which is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) as part of the Priority Program (Schwerpunktprogramm) SPP1226: 'Nicotine-Molecular and Physiological Effects in CNS', was intended to overcome some of these inherent problems of current genetic studies of ND. The multi-centre study is a population-based case-control study of smokers and never-smokers (n = 2396). The study was unique worldwide because it was the first large-scale genetic study specifically addressing ND with the collection of a wide range of environmental, psychosocial and neurobiological phenotypes. Study design and major population characteristics with emphasis on risk prediction of smoking status were presented in this paper.

The impact of local U.S. tobacco policies on youth tobacco use: A critical review

Tobacco use continues to be the leading preventable cause of premature death in the United States, killing over 430,000 people annually. Tobacco initiation and use among youth remains a significant public health concern. Despite declines in U.S. youth tobacco use in recent years, state and national survey results are still cause for alarm. Although traditional school-based curricular programs are the most common strategy to prevent or reduce youth tobacco use, their effectiveness may be limited because young people are immersed in a broader social context in which tobacco is readily available. Environmental strategies change this social context by focusing on policy, enforcement, and media. A compelling body of evidence suggests that interventions at the state and federal levels can, when implemented in combination, reduce youth tobacco use. The impact of policies implemented at the local levels is less well understood and effects of environmental strategies on smokeless tobacco consumption have been largely ignored. The purpose of this paper is to review the literature on environmental strategies implemented at the local level on youth use of both cigarettes and smokeless tobacco. We highlight results of the extant literature, hypothesize possible effects where research is lacking, and suggest where future studies might be warranted.

Data and measurement issues in the analysis of health disparities

OBJECTIVE

To describe measurement challenges and strategies in identifying and analyzing health disparities and inequities.

METHODS

We discuss the limitations of existing data sources for measuring health disparities and inequities, describe current strategies to address those limitations, and explore the potential of emerging strategies.

PRINCIPAL FINDINGS

Larger national sample sizes are necessary to identify disparities for major population subgroups. Collecting self-reported race and granular ethnicity data may reduce some measurement errors, but it raises other methodological questions. The assessment of health inequities presents particular challenges, requiring analysis of the interactive effects of multiple determinants of health. Indirect estimation and modeling methods are likely to be important tools for estimating health disparities and inequities for the foreseeable future.

CONCLUSIONS

Interdisciplinary training and collaborative research models will be essential for future disparities research. Evaluation of evolving methodologies for assessing health disparities should be a priority for health services researchers in the next decade.

A novel framework for validating and applying standardized small area measurement strategies

BACKGROUND

Local measurements of health behaviors, diseases, and use of health services are critical inputs into local, state, and national decision-making. Small area measurement methods can deliver more precise and accurate local-level information than direct estimates from surveys or administrative records, where sample sizes are often too small to yield acceptable standard errors. However, small area measurement requires careful validation using approaches other than conventional statistical methods such as in-sample or cross-validation methods because they do not solve the problem of validating estimates in data-sparse domains.

METHODS

A new general framework for small area estimation and validation is developed and applied to estimate Type 2 diabetes prevalence in US counties using data from the Behavioral Risk Factor Surveillance System (BRFSS). The framework combines the three conventional approaches to small area measurement: (1) pooling data across time by combining multiple survey years; (2) exploiting spatial correlation by including a spatial component; and (3) utilizing structured relationships between the outcome variable and domain-specific covariates to define four increasingly complex model types - coined the Naive, Geospatial, Covariate, and Full models. The validation framework uses direct estimates of prevalence in large domains as the gold standard and compares model estimates against it using (i) all available observations for the large domains and (ii) systematically reduced sample sizes obtained through random sampling with replacement. At each sampling level, the model is rerun repeatedly, and the validity of the model estimates from the four model types is then determined by calculating the (average) concordance correlation coefficient (CCC) and (average) root mean squared error (RMSE) against the gold standard. The CCC is closely related to the intraclass correlation coefficient and can be used when the units are organized in groups and when it is of interest to measure the agreement between units in the same group (e.g., counties). The RMSE is often used to measure the differences between values predicted by a model or an estimator and the actually observed values. It is a useful measure to capture the precision of the model or estimator.

RESULTS

All model types have substantially higher CCC and lower RMSE than the direct, single-year BRFSS estimates. In addition, the inclusion of relevant domain-specific covariates generally improves predictive validity, especially at small sample sizes, and their leverage can be equivalent to a five- to tenfold increase in sample size.

CONCLUSIONS

Small area estimation of important health outcomes and risk factors can be improved using a systematic modeling and validation framework, which consistently outperformed single-year direct survey estimates and demonstrated the potential leverage of including relevant domain-specific covariates compared to pure measurement models. The proposed validation strategy can be applied to other disease outcomes and risk factors in the US as well as to resource-scarce situations, including low-income countries. These estimates are needed by public health officials to identify at-risk groups, to design targeted prevention and intervention programs, and to monitor and evaluate results over time.

Temporal trends in geographic disparities in small-area breast cancer incidence and mortality, 1988 to 2005

BACKGROUND

A goal of Healthy People 2010 was to reduce health disparities. We determined the extent of reductions in geographic disparities in five breast cancer screening indicators.

METHODS

We examined the extent of reductions in geographic disparities in five breast cancer screening indicators using data about women ages 40 years and older from 200 counties in the 1988 to 2005 Surveillance, Epidemiology, and End Results Program database. County-level trends in five breast cancer indicators (in situ, stage I, lymph node-positive, locally advanced, and mortality) were summarized using the estimated annual percentage change. Observed county rates were smoothed using hierarchical Bayesian spatiotemporal methods to calculate measures of absolute and relative geographic disparity and their changes over time.

RESULTS

For in situ breast cancer, absolute disparity increased 93.7% during 1988 to 2005. Relative disparity declined 61.5% during the entire study period. Absolute and relative disparity for stage I breast cancer declined 18.5% and 41.4%, respectively. Absolute disparity for lymph node-positive breast cancer declined 37.9% during the study period, whereas relative disparity declined 17.6%. Absolute disparity for locally advanced breast cancer declined 66.5%, whereas relative disparity declined 17.8% during the study period. Absolute disparity in breast cancer mortality declined 60.5%, whereas relative disparity declined 19.8%.

CONCLUSIONS

Absolute and relative geographic disparities narrowed over time for all breast cancer indicators except for in situ breast cancer.

IMPACT

Progress has been made toward reducing geographic disparities in breast cancer outcomes, particularly in advanced-stage breast cancer incidence and mortality rates, although disparities remain.

Small-area estimation and prioritizing communities for obesity control in Massachusetts

OBJECTIVES

We developed a method to evaluate geographic and temporal variations in community-level obesity prevalence and used that method to identify communities in Massachusetts that should be considered high priority communities for obesity control.

METHODS

We developed small-area estimation models to estimate community-level obesity prevalence among community-living adults 18 years or older. Individual-level data from the Behavioral Risk Factors Surveillance System from 1999 to 2005 were integrated with community-level data from the 2000 US Census. Small-area estimation models assessed the associations of obesity (body mass index >or= 30 kg/m(2)) with individual- and community-level characteristics. A classification system based on level and precision of obesity prevalence estimates was then used to identify high-priority communities.

RESULTS

Estimates of the prevalence of community-level obesity ranged from 9% to 38% in 2005 and increased in all communities from 1999 to 2005. Fewer than 7% of communities met the Healthy People 2010 objective of prevalence rates below 15%. The highest prevalence rates occurred in communities characterized by lower income, less education, and more blue-collar workers.

CONCLUSIONS

Similar to the rest of the nation, Massachusetts faces a great challenge in reaching the national obesity control objective. Targeting high-priority communities identified by small-area estimation may maximize use of limited resources.