An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.

This article introduces bayesian spatial smoothing models for disease mapping-a specific application of small area estimation where the full universe of data is known-to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space-time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers.

Spatial Patterning of Spontaneous and Medically Indicated Preterm Birth in Philadelphia.

Preterm birth (PTB) remains a key public health issue that disproportionately affects Black individuals. Since spontaneous PTB (sPTB) and medically indicated PTB (mPTB) may have different causes and interventions, we quantified racial disparities for sPTB and mPTB, and we characterized the geographic patterning of these phenotypes, overall and according to race/ethnicity. We examined a pregnancy cohort of 83,952 singleton births at 2 Philadelphia hospitals from 2008-2020, and classified each PTB as sPTB or mPTB. We used binomial regression to quantify the magnitude of racial disparities between non-Hispanic Black and non-Hispanic White individuals, then generated small area estimates by applying a Bayesian model that accounts for small numbers and smooths estimates of PTB risk by borrowing information from neighboring areas. Racial disparities in both sPTB and mPTB were significant (relative risk of sPTB = 1.83, 95% confidence interval: 1.70, 1.98; relative risk of mPTB = 2.20, 95% confidence interval: 2.00, 2.42). The disparity was 20% greater in mPTB than sPTB. There was substantial geographic variation in PTB, sPTB, and mPTB risks and racial disparity. Our findings underscore the importance of distinguishing PTB phenotypes within the context of public health and preventive medicine. Future work should consider social and environmental exposures that may explain geographic differences in PTB risk and disparities.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.

In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.

Extent and socioeconomic correlates of small area variations in life expectancy in Canada and the United States.

Background: An extensive literature documents substantial variations in life expectancy (LE) between countries and at various levels of subnational geography. These variations in LE are significantly correlated with socioeconomic covariates, though no analyses have been produced at the finest feasible census tract (CT) level of geographic disaggregation in Canada or designed to compare Canada with the United States. Data and methods: Abridged life tables for each CT where robust estimates were feasible were estimated comparably with U.S. data. Cross-tabulations and graphical visualizations are used to explore patterns of LE across Canada, for Canada's 15 largest cities, and for the 6 largest U.S. cities. Results: LE varies by as much as two decades across CTs in both countries' largest cities. There are notable differences in the strength of associations with socioeconomic status (SES) factors across Canada's largest cities, though these associations with income-poverty rates are noticeably weaker for Canada's largest cities than for the United States' largest cities. Interpretation: Small area geographic variations in LE signal major health inequalities. The association of CT-level LE with SES factors supports and extends similar findings across many studies. The variability in these associations within Canada and compared with those in the United States reinforces the importance for population health of better understanding differences in social structures and public policies not only at the national and provincial or state levels, but also within municipalities to better inform interventions to ameliorate health inequalities.

Exploring the use of experimental small area estimates to examine the relationship between individual-level and area-level community belonging and self-rated health.

Background: Small area estimation refers to statistical modelling procedures that leverage information or "borrow strength" from other sources or variables. This is done to enhance the reliability of estimates of characteristics or outcomes for areas that do not contain sufficient sample sizes to provide disaggregated estimates of adequate precision and reliability. There is growing interest in secondary research applications for small area estimates (SAEs). However, it is crucial to assess the analytic value of these estimates when used as proxies for individual-level characteristics or as distinct measures that offer insights at the area level. This study assessed novel area-level community belonging measures derived using small area estimation and examined associations with individual-level measures of community belonging and self-rated health. Data and methods: SAEs of community belonging within census tracts produced from the 2016-2019 cycles of the Canadian Community Health Survey (CCHS) were merged with respondent data from the 2020 CCHS. Multinomial logistic regression models were run between area-level SAEs, individual-level sense of community belonging, and self-rated health on the study sample of people aged 18 years and older. Results: Area-level community belonging was associated with individual-level community belonging, even after adjusting for individual-level sociodemographic characteristics, despite limited agreement between individual- and area-level measures. Living in a neighbourhood with low community belonging was associated with higher odds of reporting being in fair or poor health, versus being in very good or excellent health (odds ratio: 1.53; 95% confidence interval: 1.22, 1.91), even after adjusting for other factors such as individual-level sense of community belonging, which was also associated with self-rated health. Interpretation: Area-level and individual-level sense of community belonging were independently associated with self-rated health. The novel SAEs of community belonging can be used as distinct measures of neighbourhood-level community belonging and should be understood as complementary to, rather than proxies for, individual-level measures of community belonging.

Trends in chronic childhood undernutrition in Bangladesh for small domains.

Chronic childhood undernutrition, known as stunting, is an important population health problem with short- and long-term adverse outcomes. Bangladesh has made strides to reduce chronic childhood undernutrition, yet progress is falling short of the 2030 Sustainable Development Goals targets. This study estimates trends in age-specific chronic childhood undernutrition in Bangladesh's 64 districts during 1997-2018, using underlying direct estimates extracted from seven Demographic and Health Surveys in the development of small area time-series models. These models combine cross-sectional, temporal, and spatial data to predict in all districts in both survey and non-survey years. Nationally, there has been a steep decline in stunting from about three in five to one in three children. However, our results highlight significant inequalities in chronic undernutrition, with several districts experiencing less pronounced declines. These differences are more nuanced at the district-by-age level, with only districts in more socio-economically advantaged areas of Bangladesh consistently reporting declines in stunting across all age groups.

Spatial Inequities in Life Expectancy in Small Areas of Buenos Aires, Argentina 2015-2017.

Studies of life expectancy (LE) in small areas of cities are relatively common in high-income countries but rare in Latin American countries. Small-area estimation methods can help to describe and quantify inequities in LE between neighborhoods and their predictors. Our objective was to analyze the distribution and spatial patterning of LE across small areas of Ciudad Autónoma de Buenos Aires (CABA), Argentina, and its association with socioeconomic characteristics. As part of the SALURBAL project, we used georeferenced death certificates in 2015-2017 for CABA, Argentina. We used a spatial Bayesian Poisson model using the TOPALS method to estimate age- and sex-specific mortality rates. We used life tables to estimate LE at birth. We obtained data on neighborhood socioeconomic characteristics from the 2010 census and analyzed their associations. LE at birth was higher for women (median of across neighborhoods = 81.1 years) compared to men (76.7 years). We found a gap in LE of 9.3 (women) and 14.9 years (men) between areas with the highest and the lowest LE. Better socioeconomic characteristics were associated with higher LE. For example, mean differences in LE at birth in areas with highest versus lowest values of composite SES index were 2.79 years (95% CI: 2.30 to 3.28) in women and 5.61 years (95% CI: 4.98 to 6.24) in men. We found large spatial inequities in LE across neighborhoods of a large city in Latin America, highlighting the importance of place-based policies to address this gap.

Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach.

BACKGROUND: Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators. METHODS: The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS). RESULTS: The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated. CONCLUSIONS: In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.

BACKGROUND: Local governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA. METHODS: The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. RESULTS: The ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates. CONCLUSIONS: These SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Subnational estimates of maternal mortality in Nigeria: Secondary Data Analysis of female siblings' survivorship histories.

High Maternal Mortality (MM) in Nigeria is complicated by the absence of reliable estimates at subnational levels. Obtaining accurate data at the state and geopolitical region levels is crucial for effective policy-making and targeted interventions. This study employs novel small area estimation techniques to derive plausible estimates of Maternal Mortality rates and ratios for Nigerian states and geopolitical regions. Data from 293,769 female siblings, provided by 114,154 women in the Nigeria Demographic and Health Surveys of 2008, 2013, and 2018, are used. Empirical Bayesian technique and the James-Stein estimator are applied to estimate MM Rates and Ratios, respectively. Maternal Mortality Ratio is highest in rural areas, Northern Nigeria states, and regions. While the South West exhibits lower MMRatio, the Northern regions, particularly the North-East, show consistently higher ratios. Mortality trends have decreased in the North West and South East regions but increased in the South West from 2008 to 2018. Addressing these disparities is essential for achieving sustainable development goals and improving maternal health in Nigeria.

La mortalité maternelle (MM) élevée au Nigeria est compliquée par l'absence d'estimations fiables aux niveaux infranationaux. L'obtention de données précises au niveau des États et des régions géopolitiques est cruciale pour une élaboration de politiques efficaces et des interventions ciblées. Cette étude utilise de nouvelles techniques d'estimation sur petites zones pour dériver des estimations plausibles des taux et ratios de mortalité maternelle pour les États et les régions géopolitiques du Nigeria. Les données de 293 769 frères et soeurs, fournies par 114 154 femmes dans les enquêtes démographiques et sanitaires du Nigeria de 2008, 2013 et 2018, sont utilisées. La technique bayésienne empirique et l'estimateur de James-Stein sont appliqués pour estimer respectivement les taux et les ratios MM. Le taux de mortalité maternelle est le plus élevé dans les zones rurales, dans les États et les régions du nord du Nigéria. Alors que le Sud-Ouest présente un ratio MMR plus faible, les régions du Nord, en particulier le Nord-Est, affichent des ratios systématiquement plus élevés. Les tendances de la mortalité ont diminué dans les régions du Nord- Ouest et du Sud-Est, mais ont augmenté dans le Sud-Ouest de 2008 à 2018. Il est essentiel de remédier à ces disparités pour atteindre les objectifs de développement durable et améliorer la santé maternelle au Nigéria.

Bivariate small-area estimation for binary and gaussian variables based on a conditionally specified model.

Many large-scale surveys collect both discrete and continuous variables. Small-area estimates may be desired for means of continuous variables, proportions in each level of a categorical variable, or for domain means defined as the mean of the continuous variable for each level of the categorical variable. In this paper, we introduce a conditionally specified bivariate mixed-effects model for small-area estimation, and provide a necessary and sufficient condition under which the conditional distributions render a valid joint distribution. The conditional specification allows better model interpretation. We use the valid joint distribution to calculate empirical Bayes predictors and use the parametric bootstrap to estimate the mean squared error. Simulation studies demonstrate the superior performance of the bivariate mixed-effects model relative to univariate model estimators. We apply the bivariate mixed-effects model to construct estimates for small watersheds using data from the Conservation Effects Assessment Project, a survey developed to quantify the environmental impacts of conservation efforts. We construct predictors of mean sediment loss, the proportion of land where the soil loss tolerance is exceeded, and the average sediment loss on land where the soil loss tolerance is exceeded. In the data analysis, the bivariate mixed-effects model leads to more scientifically interpretable estimates of domain means than those based on two independent univariate models.

Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE).

BACKGROUND: The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation. METHODS: We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay-Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts. RESULTS: The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. CONCLUSIONS: HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016-2018.

BACKGROUND: There is a critical need for maternal and child health data at the local level (for example, county), yet most counties lack sustainable resources or capabilities to collect local-level data. In such case, model-based small area estimation (SAE) could be a feasible approach. SAE for maternal or infant health-related behaviors at small areas has never been conducted or evaluated. METHODS: We applied multilevel regression with post-stratification approach to produce county-level estimates using Pregnancy Risk Assessment Monitoring System (PRAMS) data, 2016-2018 (n = 65,803 from 23 states) for 2 key outcomes, breastfeeding at 8 weeks and infant non-supine sleeping position. RESULTS: Among the 1,471 counties, the median model estimate of breastfeeding at 8 weeks was 59.8% (ranged from 34.9 to 87.4%), and the median of infant non-supine sleeping position was 16.6% (ranged from 10.3 to 39.0%). Strong correlations were found between model estimates and direct estimates for both indicators at the state level. Model estimates for both indicators were close to direct estimates in magnitude for Philadelphia County, Pennsylvania. CONCLUSION: Our findings support this approach being potentially applied to other maternal and infant health and behavioral indicators in PRAMS to facilitate public health decision-making at the local level.

A method for small-area estimation of population mortality in settings affected by crises.

BACKGROUND: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. METHODS: We describe here a 'small-area estimation' method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method's implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. RESULTS: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. CONCLUSIONS: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands.

BACKGROUND: Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being of their residents. Surveys are often used to gather data, but many neighborhoods can have only few or even zero respondents. In that case, estimating the status of the local population directly from survey responses is prone to be unreliable. METHODS: Small Area Estimation (SAE) is a technique to provide estimates at small geographical levels with only few or even zero respondents. In classical individual-level SAE, a complex statistical regression model is fitted to the survey responses by using auxiliary administrative data for the population as predictors, the missing responses are then predicted and aggregated to the desired geographical level. In this paper we compare gradient boosted trees (XGBoost), a well-known machine learning technique, to a structured additive regression model (STAR) designed for the specific problem of estimating public health and well-being in the whole population of the Netherlands. RESULTS: We compare the accuracy and performance of these models using out-of-sample predictions with five-fold Cross Validation (5CV). We do this for three data sets of different sample sizes and outcome types. Compared to the STAR model, gradient boosted trees are able to improve both the accuracy of the predictions and the total time taken to get these predictions. Even though the models appear quite similar in overall accuracy, the small area predictions at neighborhood level sometimes differ significantly. It may therefore make sense to pursue slightly more accurate models for better predictions into small areas. However, one of the biggest benefits is that XGBoost does not require prior knowledge or model specification. Data preparation and modelling is much easier, since the method automatically handles missing data, non-linear responses, interactions and accounts for spatial correlation structures. CONCLUSIONS: In this paper we provide new nationwide estimates of health, housing and well-being indicators at neighborhood level in the Netherlands, see 'Online materials'. We demonstrate that machine learning provides a good alternative to complex statistical regression modelling for small area estimation in terms of accuracy, robustness, speed and data preparation. These results can be used to make appropriate policy decisions at a local level and make recommendations about which estimation methods are beneficial in terms of accuracy, time and budget constraints.

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling.

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.

Public perceptions of the health risks of extreme heat across US states, counties, and neighborhoods.

Extreme heat is the leading weather-related cause of death in the United States. Many individuals, however, fail to perceive this risk, which will be exacerbated by global warming. Given that awareness of one's physical and social vulnerability is a critical precursor to preparedness for extreme weather events, understanding Americans' perceptions of heat risk and their geographic variability is essential for promoting adaptive behaviors during heat waves. Using a large original survey dataset of 9,217 respondents, we create and validate a model of Americans' perceived risk to their health from extreme heat in all 50 US states, 3,142 counties, and 72,429 populated census tracts. States in warm climates (e.g., Texas, Nevada, and Hawaii) have some of the highest heat-risk perceptions, yet states in cooler climates often face greater health risks from heat. Likewise, places with older populations who have increased vulnerability to health effects of heat tend to have lower risk perceptions, putting them at even greater risk since lack of awareness is a barrier to adaptive responses. Poorer neighborhoods and those with larger minority populations generally have higher risk perceptions than wealthier neighborhoods with more white residents, consistent with vulnerability differences across these populations. Comprehensive models of extreme weather risks, exposure, and effects should take individual perceptions, which motivate behavior, into account. Understanding risk perceptions at fine spatial scales can also support targeting of communication and education initiatives to where heat adaptation efforts are most needed.

Estimates of Childhood Overweight and Obesity at the Region, State, and County Levels: A Multilevel Small-Area Estimation Approach.

Local-level childhood overweight and obesity data are often used to implement and evaluate community programs, as well as allocate resources to combat overweight and obesity. The most current substate estimates of US childhood obesity use data collected in 2007. Using a spatial multilevel model and the 2016 National Survey of Children's Health, we estimated childhood overweight and obesity prevalence rates at the Census regional division, state, and county levels using small-area estimation with poststratification. A sample of 24,162 children aged 10-17 years was used to estimate a national overweight and obesity rate of 30.7% (95% confidence interval: 27.0%, 34.9%). There was substantial county-to-county variability (range, 7.0% to 80.9%), with 31 out of 3,143 counties having an overweight and obesity rate significantly different from the national rate. Estimates from counties located in the Pacific region had higher uncertainty than other regions, driven by a higher proportion of underrepresented sociodemographic groups. Child-level overweight and obesity was related to race/ethnicity, sex, parental highest education (P < 0.01 for all), county-level walkability (P = 0.03), and urban/rural designation (P = 0.02). Overweight and obesity remains a vital issue for US youth, with substantial area-level variability. The additional uncertainty for underrepresented groups shows surveys need to better target diverse samples.

Mapping subnational HIV mortality in six Latin American countries with incomplete vital registration systems.

BACKGROUND: Human immunodeficiency virus (HIV) remains a public health priority in Latin America. While the burden of HIV is historically concentrated in urban areas and high-risk groups, subnational estimates that cover multiple countries and years are missing. This paucity is partially due to incomplete vital registration (VR) systems and statistical challenges related to estimating mortality rates in areas with low numbers of HIV deaths. In this analysis, we address this gap and provide novel estimates of the HIV mortality rate and the number of HIV deaths by age group, sex, and municipality in Brazil, Colombia, Costa Rica, Ecuador, Guatemala, and Mexico. METHODS: We performed an ecological study using VR data ranging from 2000 to 2017, dependent on individual country data availability. We modeled HIV mortality using a Bayesian spatially explicit mixed-effects regression model that incorporates prior information on VR completeness. We calibrated our results to the Global Burden of Disease Study 2017. RESULTS: All countries displayed over a 40-fold difference in HIV mortality between municipalities with the highest and lowest age-standardized HIV mortality rate in the last year of study for men, and over a 20-fold difference for women. Despite decreases in national HIV mortality in all countries-apart from Ecuador-across the period of study, we found broad variation in relative changes in HIV mortality at the municipality level and increasing relative inequality over time in all countries. In all six countries included in this analysis, 50% or more HIV deaths were concentrated in fewer than 10% of municipalities in the latest year of study. In addition, national age patterns reflected shifts in mortality to older age groups-the median age group among decedents ranged from 30 to 45 years of age at the municipality level in Brazil, Colombia, and Mexico in 2017. CONCLUSIONS: Our subnational estimates of HIV mortality revealed significant spatial variation and diverging local trends in HIV mortality over time and by age. This analysis provides a framework for incorporating data and uncertainty from incomplete VR systems and can help guide more geographically precise public health intervention to support HIV-related care and reduce HIV-related deaths.