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.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.

BACKGROUND: Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. METHODS: Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. RESULTS: We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. CONCLUSIONS: The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

A methodology for small area prevalence estimation based on survey data.

BACKGROUND: Brazil conducts many health surveys to provide estimates by national level, macro-regions, states, metropolitan regions and capitals. However, estimates for smaller areas are lacking due to their high cost. The Health Vulnerability Index (in Portuguese, Índice de Vulnerabilidade em Saúde, IVS) is a measure that combines socioeconomic and environmental variables in the same indicator and allows for the analysis of the characteristics of population groups residing in census tracts, grouping them into four health risk areas (low, medium, high and very high risk) in addition to showing inequalities in the epidemiological profile of different social groups. This index was developed by the Municipal Health Secretariat of Belo Horizonte to guide health planning. OBJECTIVE: The aim of the study is to produce a methodology for obtaining reliable estimates for tobacco smoking in small areas for which the IVS was not designed. METHODS: The Vigitel dataset from 2006 to 2013 was used to obtain estimates of the prevalence of smokers based on the IVS employing small area estimation methods that use data from a larger domain to obtain estimates in smaller areas. For indirect estimates, the covariates included were sanitation, housing, education, income, and social and health factors. Post-stratification weights were used according to the IVS based on the population of the 2010 census. RESULTS: From 2006 to 2009, 16.2% (95% CI: 13.6-14.8%) of the adult population in Belo Horizonte were smokers, and 14.8% (95% CI: 14.0-15.6%) were smokers between 2010 and 2013. The very high-risk population maintained a high prevalence over the same period of 21.1% (95% CI: 17.1-25.0%) between 2006 and 2009 and 20.8% (95% CI: 17.0-24.6%) between 2010 and 2013, while in the low-risk group, the prevalence in the same period fell from 14.9% (95% CI: 13.7-16.2%) to 11.8% (95% CI, 10.6-13.1%). CONCLUSIONS: The present study identified differences in the profile of smokers by the IVS in the city of Belo Horizonte. While the smoking prevalence declined in richer areas, it remained high in poor areas. This methodology can be used to produce reliable estimates for subgroups with greater vulnerability in small areas and thus subsidize the formulation, monitoring and evaluation of public health policies and programmes aimed at smoking.

Small-area estimates of stunting. Mexico 2010: Based on a hierarchical Bayesian estimator.

The Sustainable Development Goal (SDG) 2.2 proposes ending stunting and wasting in children under five years of age by 2025. In Mexico, progress in the reduction of stunting has slowed in the 21st century. One of the challenges in tackling stunting is that it has become more concentrated in certain areas, but there are no data detailing its precise location. This paper produces the first small-area estimates of stunting for the Mexican municipalities by applying a hierarchical Bayesian estimator using data from a nationally representative survey (ENSANUT 2012, in Spanish) and the sample of the National Housing and Population Census 2010. The findings suggest the existence of large within-state differences in the prevalence of stunting and that this phenomenon is highly spatially clustered. The paper also illustrates the value of the small-area stunting estimates by performing a spatial analysis on the relationship between stunting and food insecurity at the municipal level.

Progress toward eliminating TB and HIV deaths in Brazil, 2001-2015: a spatial assessment.

BACKGROUND: Brazil has high burdens of tuberculosis (TB) and HIV, as previously estimated for the 26 states and the Federal District, as well as high levels of inequality in social and health indicators. We improved the geographic detail of burden estimation by modelling deaths due to TB and HIV and TB case fatality ratios for the more than 5400 municipalities in Brazil. METHODS: This ecological study used vital registration data from the national mortality information system and TB case notifications from the national communicable disease notification system from 2001 to 2015. Mortality due to TB and HIV was modelled separately by cause and sex using a Bayesian spatially explicit mixed effects regression model. TB incidence was modelled using the same approach. Results were calibrated to the Global Burden of Disease Study 2016. Case fatality ratios were calculated for TB. RESULTS: There was substantial inequality in TB and HIV mortality rates within the nation and within states. National-level TB mortality in people without HIV infection declined by nearly 50% during 2001 to 2015, but HIV mortality declined by just over 20% for males and 10% for females. TB and HIV mortality rates for municipalities in the 90th percentile nationally were more than three times rates in the 10th percentile, with nearly 70% of the worst-performing municipalities for male TB mortality and more than 75% for female mortality in 2001 also in the worst decile in 2015. The same municipality ranking metric for HIV was observed to be between 55% and 61%. Within states, the TB mortality rate ratios by sex for municipalities in the worst decile versus the best decile varied from 1.4 to 2.9, and HIV varied from 1.4 to 4.2. The World Health Organization target case fatality rate for TB of less than 10% was achieved in 9.6% of municipalities for males versus 38.4% for females in 2001 and improved to 38.4% and 56.6% of municipalities for males versus females, respectively, by 2014. CONCLUSIONS: Mortality rates in municipalities within the same state exhibited nearly as much relative variation as within the nation as a whole. Monitoring the mortality burden at this level of geographic detail is critical for guiding precision public health responses.

Small area estimation of obesity prevalence and dietary patterns: a model applied to Rio de Janeiro city, Brazil.

We propose the use of previously developed small area estimation techniques to monitor obesity and dietary habits in developing countries and apply the model to Rio de Janeiro city. We estimate obesity prevalence rates at the Census Tract through a combinatorial optimization spatial microsimulation model that matches body mass index and socio-demographic data in Brazil's 2008-9 family expenditure survey with Census 2010 socio-demographic data. Obesity ranges from 8% to 25% in most areas and affects the poor almost as much as the rich. Male and female obesity rates are uncorrelated at the small area level. The model is an effective tool to understand the complexity of the problem and to aid in policy design.