Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013

A reanalysis: Do hog farms cause disease in North Carolina neighborhoods?

A 2018 publication reported that communities living near hog Concentrated Animal Feeding Operations (CAFO) in North Carolina, USA have increased negative health outcomes and mortalities. While the authors stated that the associations do not imply causation, speculative interpretation of their results by media and subsequent use as evidence in lawsuits caused detrimental effects on the swine industry. We repeated their study using updated data to evaluate the strength of conclusions and appropriateness of methods used with the ultimate goal of alerting on the impact that study limitations may have when used as evidence. As done in the 2018 study, logistic regression was conducted at the individual level using 2007-2018 data, while presumably correcting for six confounders drawn from zip code or county-level databases. Exposure to CAFOs was defined by categorizing zip codes into three by swine density; where, >1 hogs/km² (G1), > 232 hogs/km² (G2), and no hogs (Control). Association with CAFO exposure resulting in mortality, hospital admissions, and emergency department visits were analyzed related to eight conditions (six from the previous study: anemia, kidney disease, infectious diseases, tuberculosis, low birth weight, and we added HIV and diabetes). Re-evaluation identified shortcomings including ecological fallacy, residual confounding, inconsistency of associations, and overestimation of exposure. HIV and diabetes, which are not causally relatable to CAFOs, were also prominent in these neighborhoods likely reflecting underlying systemic health disparities. Hence, we emphasize the need for improved exposure analysis and the importance of responsible interpretation of ecological studies that affect both public health and agriculture.

Geographic Variation in HIV Testing Among Transgender and Nonbinary Adults in the United States

BACKGROUND

Transgender and nonbinary (TNB) populations are disproportionately affected by HIV and few local health departments or HIV surveillance systems collect/report data on TNB identities. Our objective was to estimate the prevalence of HIV testing among TNB adults by US county and state, with a focus on the Ending the HIV Epidemic (EHE) geographies.

METHODS

We applied a Bayesian hierarchical spatial small area estimation model to data from the 2015 US Transgender Survey, a large national cross-sectional Internet-based survey. We estimated the county- and state-level proportion of TNB adults who ever tested or tested for HIV in the last year by gender identity, race/ethnicity, and age.

RESULTS

Our analysis included 26,100 TNB participants with valid zip codes who resided in 1688 counties (54% of all 3141 counties that cover 92% of the US population). The median county-level proportion of TNB adults who ever tested for HIV was 44% (range 10%-80%) and who tested in the last year was 17% (range 4%-44%). Within most counties, testing was highest among transgender women, black respondents, and people aged ≥25 years. HIV testing was lowest among nonbinary people and young adults aged <25 years. The proportion of TNB adults who tested within the last year was very low in most EHE counties and in all 7 rural states.

CONCLUSIONS

HIV testing among TNB adults is likely below national recommendations in the majority of EHE geographies. Geographic variation in HIV testing patterns among TNB adults indicates that testing strategies need to be tailored to local settings.

From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

OBJECTIVE

Health system data were assessed for how well they can estimate obesity prevalence in census tracts.

METHODS

Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.

RESULTS

The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.

CONCLUSIONS

Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.

Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques

The four rounds of National Family Health Survey (NFHS) conducted during 1992-93, 1998-99, 2005-06 and 2015-16 is main source to track the health and development related indicators including nutritional status of children at national and state level in India. Except NFHS-4, first three rounds of NFHS were unable to provides district-level estimates of childhood stunting due to the insufficient sample sizes. The small area estimation (SAE) techniques offer a viable solution to overcome the problem of small sample size. Therefore, this study uses SAE techniques to derive district level prevalence of childhood stunting corresponding to NFHS-2 (1998-99). Study further estimated GIS maps, univariate Local indicator of spatial autocorrelation (LISA) and Moran's I to understand the trend in district level childhood stunting between NFHS-2 and NFHS-4. Estimates obtained by SAE techniques suggest that prevalence of childhood stunting ranges from 20.7% (95% CI: 18.8-22.7) in South Goa district of Goa to 64.4% (95%CI: 63.1-65.7) in Dhaulpur district of Rajasthan during 1998-99. The diagnostic measures used to validate the reliability of estimates obtained by SAE techniques indicate that the model-based estimates are reliable and representative at district level. Results of geospatial analysis indicates substantial reduction in childhood stunting between 1998 and 2016. Out of 640 district,about 81 district experience reduction of more than 50%. At the same time 60 district experience less than 10% of reduction between 1998 and 2016. Spatial clustering of childhood stunting remains same over the study period except few additional cluster in Maharashtra, Andhra and Meghalaya in 2016. The district level estimates obtained from this study might be helpful in framing decentralized policies and implementation of vertical programs to enhance the efficacy of various nutrition interventions in priority districts of the country.

Potential effects of minimum unit pricing at local authority level on alcohol-attributed harms in North West and North East England: a modelling study

Background

In 2018, Scotland implemented a 50p-per-unit minimum unit price for alcohol. Previous modelling estimated the impact of minimum unit pricing for England, Scotland, Wales and Northern Ireland. Decision-makers want to know the potential effects of minimum unit pricing for local authorities in England; the premise of this study is that estimated effects of minimum unit pricing would vary by locality.

Objective

The objective was to estimate the potential effects on mortality, hospitalisations and crime of the implementation of minimum unit pricing for alcohol at local authority level in England.

Design

This was an evidence synthesis, and used computer modelling using the Sheffield Alcohol Policy Model (local authority version 4.0). This study gathered evidence on local consumption of alcohol from the Health Survey for England, and gathered data on local prices paid from the Living Costs and Food Survey and from market research companies’ actual sales data. These data were linked with local harms in terms of both alcohol-attributable mortality (from the Office for National Statistics) and alcohol-attributable hospitalisations (from Hospital Episode Statistics) for 45 conditions defined by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. These data were examined for eight age–sex groups split by five Index of Multiple Deprivation quintiles. Alcohol-attributable crime data (Office for National Statistics police-recorded crimes and uplifts for unrecorded offences) were also analysed.

Setting

This study was set in 23 upper-tier local authorities in North West England, 12 upper-tier local authorities in the North East region and nine government office regions, and a national summary was conducted.

Participants

The participants were the population of England aged ≥ 18 years.

Intervention

The intervention was setting a local minimum unit price. The base case is 50p per unit of alcohol. Sensitivity analyses were undertaken using minimum unit prices of 30p, 40p, 60p and 70p per unit of alcohol.

Main outcome measures

The main outcome measures were changes in alcohol-attributable deaths, hospitalisations and crime. Savings in NHS costs, changes in alcohol purchasing and consumption, changes in revenue to off-trade and on-trade retailers and changes in the slope index of inequality between most and least deprived areas were also examined.

Results

The modelling has proved feasible at the upper-tier local authority level. The resulting estimates suggest that minimum unit pricing for alcohol at local authority level could be effective in reducing alcohol-attributable deaths, hospitalisations, NHS costs and crime. A 50p minimum unit price for alcohol at local authority level is estimated to reduce annual alcohol-related deaths in the North West region by 205, hospitalisations by 5956 (–5.5%) and crimes by 8528 (–2.5%). These estimated reductions are mostly due to the 5% of people drinking at high-risk levels (e.g. men drinking > 25 pints of beer or five bottles of wine per week, women drinking > 17 pints of beer or 3.5 bottles of wine per week, and who spend around £2500 per year currently on alcohol). Model estimates of impact are bigger in the North West and North East regions than nationally because, currently, more cheap alcohol is consumed in these regions and because there are more alcohol-related deaths and hospitalisations in these areas. A 30p minimum unit price has estimated effects that are ≈ 90% lower than those of a 50p minimum unit price, and a 40p minimum unit price has estimated effects that are ≈ 50% lower. Health inequalities are estimated to reduce with greater health gains in the deprived areas, where more cheap alcohol is purchased and where there are higher baseline harms.

Limitations

The approach requires synthesis of evidence from multiple sources on alcohol consumption; prices paid; and incidence of diseases, mortality and crime. Price elasticities used are from previous UK analysis of price responsiveness rather than specific to local areas. The study has not estimated ‘cross-border effects’, namely travelling to shops outside the region.

Conclusions

The modelling estimates suggest that minimum unit pricing for alcohol at local authority level would be an effective and well-targeted policy, reducing inequalities.

Future work

The Sheffield Alcohol Policy Model for Local Authorities framework could be further utilised to examine the local impact of national policies (e.g. tax changes) or local policies (e.g. licensing or identification and brief advice). As evidence emerges from the Scottish minimum unit price implementation, this will further inform estimates of impact in English localities. The methods used to estimate drinking and purchasing patterns in each local authority could also be used for other topics involving unhealthy products affecting public health, for example to estimate local smoking or high-fat, high-salt food consumption patterns.

Funding

This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 9, No. 4. See the NIHR Journals Library website for further project information.

Small-Area Estimation of Smoke-Free Workplace Policies and Home Rules in US Counties

INTRODUCTION

The workplace and home are sources of exposure to secondhand smoke, a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from secondhand smoke and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small-area estimation techniques are needed to produce such estimates.

METHODS

Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods.

RESULTS

Model-based estimates of smoke-free workplace policies and home rules were produced for 3134 (of 3143) US counties. In 2014-2015, nearly 80% of US adult workers were covered by smoke-free workplace policies, and more than 85% of US adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules.

CONCLUSIONS

The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention.

IMPLICATIONS

Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, for example, to other behavioral, policy, or health related topics.

Health and Health Determinant Metrics for Cities: A Comparison of County and City-Level Data

We evaluated whether using county-level data to characterize public health measures in cities biases the characterization of city populations. We compared 4 public health and sociodemographic measures in 447 US cities (percent of children living in poverty, percent of non-Hispanic Black population, age-adjusted cardiovascular disease mortality, life expectancy at birth) to the same measures calculated for counties that contain those cities. We found substantial and highly variable city-county differences within and across metrics, which suggests that use of county data to proxy city measures could hamper accurate allocation of public health resources and appreciation of the urgency of public health needs in specific locales.

Empirical analysis on the illicit trade of cigarettes in Vietnam

Background

This paper examined how a higher tax on tobacco would affect illicit trade in Vietnam.

Methodology and data

This paper used the gap method to estimate the gap between cigarette domestically tax-paid sales and domestic consumption. Data were from the tax-paid sales by the Vietnam Steering Committee on Smoking and Health (VINACOSH), the Vietnam Tobacco Association, the General Tax Department, as well as two rounds of the Global Adult Tobacco Survey in 2010 and 2015.

Key results

The results indicated that Vietnam had a negative volume of illicit trade, either a result of under-reporting of tobacco use or due to net smuggling of tax-paid cigarettes out of the country. Furthermore, the trend showed an increased negative volume over time, which indicated that increases in tobacco taxes in the interleading years did not result in an increase in illicit trade in tobaccos in Vietnam.

Conclusions

Vietnam’s low prices on domestic cigarettes created favourable conditions for cigarette smugglers and provided easy access to illicit cigarettes for the Vietnamese people, but the absence of a relationship between tax changes and smuggling suggested that potential increases in the excise tax should not be discouraged by the threat of an increase in illicit trade. The government should increase taxes on cigarettes to raise domestic cigarette prices and take strong policy measures to create a more transparent social environment, therefore effectively reducing the prevalence of illicit cigarettes in Vietnam.

Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults

Importance

The prevalence of extreme obesity continues to increase among adults in the US, yet there is an absence of subnational estimates and geographic description of extreme obesity. This shortcoming prevents a thorough understanding of the geographic distribution of extreme obesity, which in turn limits the ability of public health agencies and policy makers to target areas with a known higher prevalence.

Objectives

To use small-area estimation to create county-level estimates of extreme obesity in the US and apply spatial methods to identify clusters of high and low prevalence.

Design, Setting, and Participants

A cross-sectional analysis was conducted using multilevel regression and poststratification with data from the 2012 Behavioral Risk Factor Surveillance System and the US Census Bureau to create prevalence estimates of county-level extreme obesity (body mass index ≥40 [calculated as weight in kilograms divided by height in meters squared]). Data were included on adults (aged ≥18 years) living in the contiguous US. Analysis was performed from June 4 to December 28, 2018.

Main Outcomes and Measures

Multilevel logistic regression models estimated the probability of extreme obesity based on individual-level and area-level characteristics. Census counts were multiplied by these probabilities and summed by county to create county-level prevalence estimates. Moran index values were calculated to assess spatial autocorrelation and identify spatial clusters of hot and cold spots. Estimates of moderate obesity were obtained for comparison.

Results

Overall, the weighted prevalence of extreme obesity was 4.0% (95% CI, 3.9%-4.1%) and the prevalence of moderate obesity was 23.7% (95% CI, 23.4%-23.9%). County-level prevalence of extreme obesity ranged from 1.3% (95% CI, 1.3%-1.3%) to 15.7% (95% CI, 15.3%-16.0%). The Pearson correlation coefficient comparing model-predicted estimates with direct estimates was 0.81 (P < .001). The Moran index I score was 0.35 (P < .001), indicating spatial clustering. Significant clusters of high and low prevalence were identified. Hot spots indicating clustering of high prevalence of extreme obesity in several regions, including the Mississippi Delta region and the Southeast, were identified, as well as clusters of low prevalence in the Rocky Mountain region and the Northeast.

Conclusions and Relevance

Substantial geographic variation was identified in the prevalence of extreme obesity; there was considerable county-level variation even in states generally known as having high or low prevalence of obesity. The results suggest that extreme obesity prevalence demonstrates spatial dependence and clustering and may support the need for substate analysis and benefit of disaggregation of obesity by group. Findings from this study can inform local and national policies seeking to identify populations most at risk from very high body mass index.

Reweighting national survey data for small area behaviour estimates: modelling alcohol consumption in Local Authorities in England

BACKGROUND

There are likely to be differences in alcohol consumption levels and patterns across local areas within a country, yet survey data is often collected at the national or sub-national/regional level and is not representative for small geographic areas.

METHODS

This paper presents a method for reweighting national survey data-the Health Survey for England-by combining survey and routine data to produce simulated locally representative survey data and provide statistics of alcohol consumption for each Local Authority in England.

RESULTS

We find a 2-fold difference in estimated mean alcohol consumption between the lightest and heaviest drinking Local Authorities, a 4.5-fold difference in abstention rates, and a 3.5-fold difference in harmful drinking. The method compares well to direct estimates from the data at regional level.

CONCLUSIONS

The results have important policy implications in itself, but the reweighted data can also be used to model local policy effects. This method can also be used for other public health small area estimation where locally representative data are not available.

Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys

Background

National health surveys, such as the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), collect data on cancer screening and smoking-related measures in the US noninstitutionalized population. These surveys are designed to produce reliable estimates at the national and state levels. However, county-level data are often needed for cancer surveillance and related research.

Methods

To use the large sample sizes of BRFSS and the high response rates and better coverage of NHIS, we applied multilevel models that combined information from both surveys. We also used relevant sources such as census and administrative records. By using these methods, we generated estimates for several cancer risk factors and screening behaviors that are more precise than design-based estimates.

Results

We produced reliable, modeled estimates for 11 outcomes related to smoking and to screening for female breast cancer, cervical cancer, and colorectal cancer. The estimates were produced for 3,112 counties in the United States for the data period from 2008 through 2010.

Conclusion

The modeled estimates corrected for potential noncoverage bias and nonresponse bias in the BRFSS and reduced the variability in NHIS estimates that is attributable to small sample size. The small area estimates produced in this study can serve as a useful resource to the cancer surveillance community.

Social media captures demographic and regional physical activity

Objectives

We examined the use of data from social media for surveillance of physical activity prevalence in the USA.

Methods

We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention.

Results

The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes.

Conclusions

The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

Building a Data Platform for Cross-Country Urban Health Studies: the SALURBAL Study

Studies examining urban health and the environment must ensure comparability of measures across cities and countries. We describe a data platform and process that integrates health outcomes together with physical and social environment data to examine multilevel aspects of health across cities in 11 Latin American countries. We used two complementary sources to identify cities with ≥ 100,000 inhabitants as of 2010 in Argentina, Brazil, Chile, Colombia, Costa Rica, El Salvador, Guatemala, Mexico, Nicaragua, Panama, and Peru. We defined cities in three ways: administratively, quantitatively from satellite imagery, and based on country-defined metropolitan areas. In addition to "cities," we identified sub-city units and smaller neighborhoods within them using census hierarchies. Selected physical environment (e.g., urban form, air pollution and transport) and social environment (e.g., income, education, safety) data were compiled for cities, sub-city units, and neighborhoods whenever possible using a range of sources. Harmonized mortality and health survey data were linked to city and sub-city units. Finer georeferencing is underway. We identified 371 cities and 1436 sub-city units in the 11 countries. The median city population was 234,553 inhabitants (IQR 141,942; 500,398). The systematic organization of cities, the initial task of this platform, was accomplished and further ongoing developments include the harmonization of mortality and survey measures using available sources for between country comparisons. A range of physical and social environment indicators can be created using available data. The flexible multilevel data structure accommodates heterogeneity in the data available and allows for varied multilevel research questions related to the associations of physical and social environment variables with variability in health outcomes within and across cities. The creation of such data platforms holds great promise to support researching with greater granularity the field of urban health in Latin America as well as serving as a resource for the evaluation of policies oriented to improve the health and environmental sustainability of cities.

Estimating missing values in China's official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling

Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country’s basic scale of official statistics data collection. We developed a procedure to impute the missing values under the Bayesian hierarchical modeling framework. The procedure incorporates two novelties. First, it takes into account spatial autocorrelations and temporal trends for those easier-to-impute variables with small missing percentages. Second, it further uses the first-step complete variables as covariate information to improve the modeling of more-difficult-to-impute variables with large missing percentages. We applied this progressive spatiotemporal (PST) method to China’s official socioeconomic statistics during 2002–2011 and compared it with four other widely used imputation methods, including k-nearest neighbors (kNN), expectation maximum (EM), singular value decomposition (SVD) and random forest (RF). The results show that the PST method outperforms these methods, thus proving the effects of sophisticatedly incorporating the additional spatial and temporal information and progressively utilizing the covariate information. This study has an outcome that allows China to construct a complete socioeconomic dataset and establishes a methodology that can be generally useful for estimating missing values in large spatiotemporal datasets.

Sub-County Life Expectancy: A Tool to Improve Community Health and Advance Health Equity

Compared with people in other developed countries, Americans live shorter lives, have more disease and disability, and lag on most population health measures. Recent research suggests that this poor comparative performance is primarily driven by profound local place-based disparities. Several initiatives successfully used sub-county life expectancy estimates to identify geographic disparities, generate widespread interest, and catalyze multisector actions. To explore the feasibility of scaling these efforts, the Centers for Disease Control and Prevention and the Council of State and Territorial Epidemiologists initiated a multiphase project - the Sub-County Assessment of Life Expectancy. Phase I participants reviewed the literature, assessed and identified appropriate tools, calculated locally relevant estimates, and developed methodological guidance. Phase I results suggest that most state and local health departments will be able to calculate actionable sub-county life expectancy estimates despite varying resources, expertise, and population sizes, densities, and geographies. To accelerate widespread scaling, we describe several successful case examples, identify user-friendly validated tools, and provide practical tips that resulted from lessons learned.

Advances in spatial epidemiology and geographic information systems

The field of spatial epidemiology has evolved rapidly in the past 2 decades. This study serves as a brief introduction to spatial epidemiology and the use of geographic information systems in applied research in epidemiology. We highlight technical developments and highlight opportunities to apply spatial analytic methods in epidemiologic research, focusing on methodologies involving geocoding, distance estimation, residential mobility, record linkage and data integration, spatial and spatio-temporal clustering, small area estimation, and Bayesian applications to disease mapping. The articles included in this issue incorporate many of these methods into their study designs and analytical frameworks. It is our hope that these studies will spur further development and utilization of spatial analysis and geographic information systems in epidemiologic research.