Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators

Impact of the use of small-area models on estimation of attributable mortality at a regional level

The objective of this study is to assess the impact of applying prevalences derived from a small-area model at a regional level on smoking-attributable mortality (SAM). A prevalence-dependent method was used to estimate SAM. Prevalences of tobacco use were derived from a small-area model. SAM and population attributable fraction (PAF) estimates were compared against those calculated by pooling data from three national health surveys conducted in Spain (2011-2014-2017). We calculated the relative changes between the two estimates and assessed the width of the 95% CI of the PAF. Applying surveys-based prevalences, tobacco use was estimated to cause 53 825 (95% CI: 53 182-54 342) deaths in Spain in 2017, a figure 3.8% lower obtained with the small-area model prevalences. The lowest relative change was observed in the Castile-La Mancha region (1.1%) and the highest in Navarre (14.1%). The median relative change between regions was higher for women (26.1%), population aged ≥65 years (6.6%), and cardiometabolic diseases (9.0%). The differences between PAF by cause of death were never greater than 2%. Overall, the differences between estimates of SAM, PAF, and confidence interval width are small when using prevalences from both sources. Having these data available by region will allow decision-makers to implement smoking control measures based on more accurate data.

Socioenvironmental sugar promotion and geographical inequalities in dental health of 5-year-old children in England

OBJECTIVES

To investigate the relationship between socioenvironmental sugar promotion and geographical inequalities in the prevalence of dental caries amongst 5-year-olds living across small areas within England.

METHODS

Ecological data from the National Dental Epidemiology Programme (NDEP) 2018-2019, comprising information on the percentage of 5-year-olds with tooth decay (≥1 teeth that are decayed into dentine, missing due to decay, or filled), and untreated tooth decay (≥1 decayed but untreated teeth), in lower-tier local authorities (LAs) of England. These were analysed for association with a newly developed Index of Sugar-Promoting Environments Affecting Child Dental Health (ISPE-ACDH). The index quantifies sugar-promoting determinants within a child's environment and provides standardized scores for the index, and its component domains that is, neighbourhood-, school- and family-environment, with the highest scores representing the highest levels of sugar promotion in lower-tier LAs (N = 317) of England. Linear regressions, including unadjusted models separately using index and each domain, and models adjusted for domains were built for each dental outcome.

RESULTS

Participants lived across 272 of 317 lower-tier LAs measured within the index. The average percentage of children with tooth decay and untreated tooth decay was 22.5 (SD: 8.5) and 19.6 (SD: 8.3), respectively. The mean index score was (0.1 [SD: 1.01]). Mean domain scores were: neighbourhood (0.02 [SD: 1.03]), school (0.1 [SD: 1.0]), and family (0.1 [SD: 0.9]). Unadjusted linear regressions indicated that the LA-level percentage of children with tooth decay increased by 5.04, 3.71, 4.78 and 5.24 with increased scores of the index, and neighbourhood, school and family domains, respectively. An additional model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.37, and by family domain it increased to 6.33. Furthermore, unadjusted models indicated that the LA-level percentage of children with untreated tooth decay increased by 4.72, 3.42, 4.45 and 4.97 with increased scores of the index, and neighbourhood, school, and family domains, respectively. The model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.24 and by family domain rose to 6.47. School-domain was not significantly associated with either outcome in adjusted models.

CONCLUSIONS

This study reveals that socioenvironmental sugar promotion, particularly within neighbourhood- and family-environments, may contribute to geographical inequalities in dental caries in children. Further research involving data on individual-level dental outcomes and confounders is required.

Chronic back pain prevalence at small area level in England - the design and validation of a 2-stage static spatial microsimulation model

Spatially disaggregated estimates provide valuable insights into the nature of a disease. They highlight inequalities, aid public health planning and identify avenues for further research. Spatial microsimulation is advantageous in that it can be used to create large microdata sets with intact microlevel relationships between variables, which allows analysis of relationships between variables locally. This methodological paper outlines the design and validation of a 2-stage static spatial microsimulation model for chronic back pain prevalence across England, suitable for policy modelling. Data used was obtained from the Health Survey for England and the 2011 Census. Microsimulation was performed using SimObesity, a previously validated static deterministic program, and the synthetic chronic back pain microdataset was internally validated. The paper also highlights modelling considerations for researchers embarking on similar work, as well as future directions for research in this area of microsimulation.

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.

Estimating BMI distributions by age and sex for local authorities in England: a small area estimation study

OBJECTIVES

Rates of overweight and obesity vary across England, but local rates have not been estimated for over 10 years. We aimed to produce new small area estimates of body mass index (BMI) by age and sex for each lower tier and unitary local authority in England, to provide up-to-date and more detailed estimates for the use of policy-makers and academics working in non-communicable disease risk and health inequalities.

DESIGN

We used generalised linear modelling to estimate the relationship between BMI with social/demographic markers in a cross-sectional survey, then used this model to impute a BMI for each adult in locally-representative populations. These groups were then disaggregated by 5-year age group, sex and local authority group.

SETTING

The Health Survey for England 2018 (cross-sectional BMI data for England) and Census microdata 2011 (locally representative).

PARTICIPANTS

A total of 6174 complete cases aged 16 and over were included.

OUTCOME MEASURES

Modelled group-level BMI as mean and SD of log-BMI. Extensive internal validation was performed, against the original data and external validation against the National Diet and Nutrition Survey and Active Lives Survey and previous small area estimates.

RESULTS

In 94% of age-sex are groups, mean BMI was in the overweight or obese ranges. Older and more deprived areas had the highest overweight and obesity rates, which were particularly in coastal areas, the West Midlands, Yorkshire and the Humber. Validation showed close concordance with previous estimates by local area and demographic groups.

CONCLUSION

This work updated previous estimates of the distribution of BMI in England and contributes considerable additional detail to our understanding of the local epidemiology of overweight and obesity. Raised BMI now affects the vast majority of demographic groups by age, sex and area in England, regardless of geography or deprivation.

Standard multiple imputation of survey data didn't perform better than simple substitution in enhancing an administrative dataset: the example of self-rated health in England

Background

Health surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are becoming less representative as response levels fall. Routinely collected administrative data offer more extensive population coverage but typically comprise fewer health topics. We explore whether data combination and multiple imputation of health variables from survey data is a simple and robust way of generating these variables in the general population.

Methods

We use the UK Integrated Household Survey and the English 2011 population census both of which included self-rated general health. Setting aside the census self-rated health data we multiply imputed self-rated health responses for the census using the survey data and compared these with the actual census results in 576 unique groups defined by age, sex, housing tenure and geographic region.

Results

Compared with original census data across the groups, multiply imputed proportions of bad or very bad self-rated health were not a markedly better fit than those simply derived from the survey proportions.

Conclusion

While multiple imputation may have the potential to augment population data with information from surveys, further testing and refinement is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12982-021-00099-z.

Adult diet in England: Where is more support needed to achieve dietary recommendations?

Background

Small-area estimation models are regularly commissioned by public health bodies to identify areas of greater inequality and target areas for intervention in a range of behaviours and outcomes. Such local modelling has not been completed for diet consumption in England despite diet being an important predictor of health status. The study sets out whether aspects of adult diet can be modelled from previously collected data to define and evaluate area-level interventions to address obesity and ill-health.

Methods

Adults aged 16 years and over living in England. Consumption of fruit, vegetables, and sugar-sweetened beverages (SSB) are modelled using small-area estimation methods in English neighbourhoods (Middle Super Output Areas [MSOA]) to identify areas where reported portions are significantly different from recommended levels of consumption. The selected aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008–2016.

Results

Estimates indicate that the average prevalence of adults consuming less than one portion of fruit, vegetables or 100% juice each day by MSOA is 6.9% (range of 4.3 to 14.7%, SE 0.06) and the average prevalence of drinking more than 330ml/day of SSB is 11.5% (range of 5.7 to 30.5%, SE 0.03). Credible intervals around the estimates are wider for SSB consumption. The results identify areas including regions in London, urban areas in the North of England and the South coast which may be prioritised for targeted interventions to support reduced consumption of SSB and/or an increase in portions of fruit and vegetables.

Conclusion

These estimates provide valuable information at a finer spatial scale than is presently feasible, allowing for within-country and locality prioritisation of resources to improve diet. Local, targeted interventions to improve fruit and vegetable consumption such as subsidies or voucher schemes should be considered where consumption of these foods is predicted to be low.

Fruit and vegetable consumption, leisure-time physical activity and binge drinking in Belo Horizonte, Brazil, according to the Health Vulnerability Index

OBJECTIVE

To estimate the prevalence of fruit and vegetable consumption, practice of leisure time physical activity (LTPA) and binge drinking for small areas of Belo Horizonte, Minas Gerais.

METHODS

Ecological study conducted with data from the Surveillance System for Risk and Protection Factors for Noncommunicable Diseases by Telephone Survey (Sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico - Vigitel). The prevalence of risk and protection factors from 2006 to 2013 were estimated and the 95% confidence intervals calculated. "Small areas" corresponded to the municipality division into four strata of health risk classification given by the Health Vulnerability Index 2012 (Índice de Vulnerabilidade à Saúde - IVS).

RESULTS

The mean prevalences for the period were: about 42% of regular intake of fruit and vegetable, 34.7% of leisure time activity and 20.4% of binge drinking. The prevalence of fruit and vegetable consumption was higher in low-risk areas (58.5%; 95%CI 56.8 - 60.2) and lower in very high-risk areas (32.3%; 95%CI 27.7 - 36.9). The practice of LTPA was higher in low-risk areas (40.8%; 95%CI 38.9 - 42.8) and lower in very high risk (25.2%; 95%CI 20.6 - 29.9). Binge drinking was higher in low-risk areas (22.9%; 95%CI 21.7 - 24.2) compared to very high-risk areas (14.3%; 95%CI 11.4 - 17.3).

CONCLUSION

It was identified a gradient in the distribution of risk and protection factors for noncommunicable diseases in Belo Horizonte according to the risk classification. This information can support programs aimed at reducing health inequalities, especially in the most vulnerable areas.

Multilevel modeling of geographic variation in general practice consultations

OBJECTIVE

To test relatively simple and complex models for examining model fit, higher-level variation in, and correlates of, GP consultations, where known nonhierarchical data structures are present.

SETTING

New South Wales (NSW), Australia.

DESIGN

Association between socioeconomic circumstances and geographic remoteness with GP consultation frequencies per participant was assessed using single-level, hierarchical, and multiple membership cross-classified (MMCC) models. Models were adjusted for age, gender, and a range of socioeconomic and demographic confounds.

DATA COLLECTION/EXTRACTION METHODS

A total of 261,930 participants in the Sax Institute's 45 and Up Study were linked to all GP consultation records (Medicare Benefits Schedule; Department of Human Services) within 12 months of baseline (2006-2009).

PRINCIPAL FINDINGS

Deviance information criterion values indicated the MMCC negative binomial regression was the best fitting model, relative to an MMCC Poisson equivalent and simpler hierarchical and single-level models. Between-area variances were relatively consistent across models, even when between GP variation was estimated. Lower rates of GP consultation outside of major cities were only observed once between-GP variation was assessed simultaneously with between-area variation in the MMCC models.

CONCLUSIONS

Application of the MMCC model is necessary for estimation of variances and effect sizes in sources of big data on primary care in which complex nonhierarchical clustering by geographical area and GP is present.

Not belonging where others do: a cross-sectional analysis of multi-level social capital interactions on health and mental well-being in Wales

BACKGROUND

Social capital may be a social good in health terms, but it is not necessarily a universal good. Several studies have shown that while there is a positive association between ecological social capital and health in people with high individual-level social capital, this relationship is weaker or even reversed in those with low individual-level social capital. Such studies, however, have used relatively coarse levels of geography for quantifying ecological social capital. The present study looks at this relationship at a more fine-grained spatial scale.

METHODS

Data from the National Survey for Wales (n=27 828, weighted mean age=48.4) were linked to previously published small-area estimates (n=410) of ecological social capital for Wales. Mixed effects models were then used to assess whether the relationship between mental well-being and self-reported health on one hand, and ecological social capital (sense of belonging) on the other, was moderated by individual-level social capital.

RESULTS

The models found the same moderation of the relationship that has been demonstrated previously: Although ecological social capital is positively associated with health in respondents with high individual-level social capital, the relationship is negative in those with low individual-level social capital.

CONCLUSION

This study replicates this association at a spatial scale orders of magnitude more fine-grained than had been shown previously. Ecological social capital is not an unambiguously positive factor for public health, and may be a risk factor for marginalised people.

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.

Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems

Background: Low health literacy is associated with poorer health. Research has shown that predictive models of health literacy can be developed; however, key variables may be missing from systems where predictive models might be applied, such as health service data. This paper describes an approach to developing predictive health literacy models using variables common to both "source" health literacy data and "target" systems such as health services. Methods: A multilevel synthetic estimation was undertaken on a national (England) dataset containing health literacy, socio-demographic data and geographical (Lower Super Output Area: LSOA) indicators. Predictive models, using variables commonly present in health service data, were produced. An algorithm was written to pilot the calculations in a Family Physician Clinical System in one inner-city area. The minimum data required were age, sex and ethnicity; other missing data were imputed using model values. Results: There are 32,845 LSOAs in England, with a population aged 16 to 65 years of 34,329,091. The mean proportion of the national population below the health literacy threshold in LSOAs was 61.87% (SD 12.26). The algorithm was run on the 275,706 adult working-age people in Lambeth, South London. The algorithm could be calculated for 228,610 people (82.92%). When compared with people for whom there were sufficient data to calculate the risk score, people with insufficient data were more likely to be older, male, and living in a deprived area, although the strength of these associations was weak. Conclusions: Logistic regression using key socio-demographic data and area of residence can produce predictive models to calculate individual- and area-level risk of low health literacy, but requires high levels of ethnicity recording. While the models produced will be specific to the settings in which they are developed, it is likely that the method can be applied wherever relevant health literacy data are available. Further work is required to assess the feasibility, accuracy and acceptability of the method. If feasible, accurate and acceptable, this method could identify people requiring additional resources and support in areas such as medical practice.

The utility of geodemographic indicators in small area estimates of limiting long-term illness

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.

Measuring Geographic "Hot Spots" of Racial/Ethnic Disparities: An Application to Mental Health Care

This article identifies geographic "hot spots" of racial/ethnic disparities in mental health care access. Using data from the 2001-2003 Collaborative Psychiatric Epidemiology Surveys(CPES), we identified metropolitan statistical areas(MSAs) with the largest mental health care access disparities ("hot spots") as well as areas without disparities ("cold spots"). Racial/ethnic disparities were identified after adjustment for clinical need. Richmond, Virginia and Columbus, Georgia were found to be hot spots for Black-White disparities, regardless of method used. Fresno, California and Dallas, Texas were ranked as having the highest Latino-White disparities and Riverside, California and Houston, Texas consistently ranked high in Asian-White mental health care disparities across different methods. We recommend that institutions and government agencies in these "hot spot" areas work together to address key mechanisms underlying these disparities. We discuss the potential and limitations of these methods as tools for understanding health care disparities in other contexts.

Integrating national surveys to estimate small area variations in poor health and limiting long-term illness in Great Britain

OBJECTIVES

This study aims to address, for the first time, the challenges of constructing small area estimates of health status using linked national surveys. The study also seeks to assess the concordance of these small area estimates with data from national censuses.

SETTING

Population level health status in England, Scotland and Wales.

PARTICIPANTS

A linked integrated dataset of 23 374 survey respondents (16+ years) from the 2011 waves of the Health Survey for England (n=8603), the Scottish Health Survey (n=7537) and the Welsh Health Survey (n=7234).

PRIMARY AND SECONDARY OUTCOME MEASURES

Population prevalence of poorer self-rated health and limiting long-term illness. A multilevel small area estimation modelling approach was used to estimate prevalence of these outcomes for middle super output areas in England and Wales and intermediate zones in Scotland. The estimates were then compared with matched measures from the contemporaneous 2011 UK Census.

RESULTS

There was a strong positive association between the small area estimates and matched census measures for all three countries for both poorer self-rated health (r=0.828, 95% CI 0.821 to 0.834) and limiting long-term illness (r=0.831, 95% CI 0.824 to 0.837), although systematic differences were evident, and small area estimation tended to indicate higher prevalences than census data.

CONCLUSIONS

Despite strong concordance, variations in the small area prevalences of poorer self-rated health and limiting long-term illness evident in census data cannot be replicated perfectly using small area estimation with linked national surveys. This reflects a lack of harmonisation between surveys over question wording and design. The nature of small area estimates as 'expected values' also needs to be better understood.

How community-level social and economic developments have changed the patterns of substance use in a transition economy?

Most social changes take place at the community level before indirectly affecting individuals. Although the contextual effect is far-reaching, few studies have investigated the important questions of: how do community-level developments affect drinking and smoking, and how do they change the existing gender and income patterns of drinking and smoking, particularly in transition economies? In this study, I used a Chinese panel dataset between 1991 and 2011 to reveal the moderating effects of community developments. Through multilevel growth curve modeling that controls for age, period, and cohort effects, as well as individual- and community-level covariates, I found that community-level economic development and social development are negatively associated with drinking and smoking. Moreover, economic and social developments also moderate the important influences of income and gender: women start to drink more in communities with higher economic development; the traditionally positive association between income and smoking/drinking is also reversed, i.e. the rich start to smoke and drink less in communities with higher social development. This study concludes that the rapid changes in communal social and economic structures have created new health disparities based on the gender and socioeconomic hierarchy.

The spatial structure of chronic morbidity: evidence from UK census returns

Background

Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity.

Methods

2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power.

Results

The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions.

Conclusions

Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0057-5) contains supplementary material, which is available to authorized users.

Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey

OBJECTIVES

We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small.

METHODS

Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs).

RESULTS

Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs.

CONCLUSIONS

The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.

Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates

This paper examines the secondary data requirements for multilevel small area synthetic estimation (ML-SASE). This research method uses secondary survey data sets as source data for statistical models. The parameters of these models are used to generate data for small areas. The paper assesses the impact of knowing the geographical location of survey respondents on the accuracy of estimates, moving beyond debating the generic merits of geocoded social survey datasets to examine quantitatively the hypothesis that knowing the approximate location of respondents can improve the accuracy of the resultant estimates. Four sets of synthetic estimates are generated to predict expected levels of limiting long term illnesses using different levels of knowledge about respondent location. The estimates were compared to comprehensive census data on limiting long term illness (LLTI). Estimates based on fully geocoded data were more accurate than estimates based on data that did not include geocodes.

Small area synthetic estimates of smoking prevalence during pregnancy in England

Background

Complete and accurate data on maternal smoking prevalence during pregnancy are not available at a local geographical scale in England. We employ a synthetic estimation approach to predict the expected prevalence of smoking during pregnancy and smoking at delivery by Primary Care Trust (PCT).

Methods

Multilevel logistic regression models were used with data from the 2010 Infant Feeding Survey and 2011 Census to predict the probability of mothers (a) smoking at any point during pregnancy and (b) smoking at delivery, according to age, deprivation, and the ethnic profile of the home area. These probabilities were applied to demographic information on mothers giving birth from 2010/11 Hospital Episode Statistics data to produce expected counts, and prevalence figures, of smokers by PCT, with Bayesian 95 % credible intervals. The expected prevalence of smoking at delivery by PCT was compared with midwife-collected Smoking at the Time of Delivery (SATOD) data using a Bland-Altman plot.

Results

The expected prevalence of smoking during pregnancy by PCT ranged from 8.1 % (95 % CI 5.6–1.0) to 31.6 % (27.5–34.8). The expected prevalence of smoking at delivery ranged from 2.5 % (1.4–4.0) to 17.1 % (13.7–20.4). Figures for expected smoking prevalence at delivery showed some agreement with SATOD, though SATOD data were generally higher than the synthetic estimates (mean difference 2.99 %).

Conclusions

It is possible to derive good estimates of expected smoking prevalence during pregnancy for small areas, potentially at much lower cost than conducting large surveys. Such data may be useful to help plan and commission smoking cessation services and monitor their effectiveness.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-015-0067-8) contains supplementary material, which is available to authorized users.

Comparison of estimation methods for creating small area rates of acute myocardial infarction among Medicare beneficiaries in California

Creating local population health measures from administrative data would be useful for health policy and public health monitoring purposes. While a wide range of options--from simple spatial smoothers to model-based methods--for estimating such rates exists, there are relatively few side-by-side comparisons, especially not with real-world data. In this paper, we compare methods for creating local estimates of acute myocardial infarction rates from Medicare claims data. A Bayesian Monte Carlo Markov Chain estimator that incorporated spatial and local random effects performed best, followed by a method-of-moments spatial Empirical Bayes estimator. As the former is more complicated and time-consuming, spatial linear Empirical Bayes methods may represent a good alternative for non-specialist investigators.

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.

The construction of a decision tool to analyse local demand and local supply for GP care using a synthetic estimation model

BACKGROUND

This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match.

METHODS

National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands.

RESULTS

Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P <0.001). The statistical model could estimate GP consultation time for every postcode area with >1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions.

CONCLUSIONS

The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it.

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.

Bringing the individual back to small-area variation studies: a multilevel analysis of all-cause mortality in Andalusia, Spain

We performed a multilevel analysis (including individuals, households, census tracts, municipalities and provinces) on a 10% sample (N=230,978) from the Longitudinal Database of the Andalusian Population (LDAP). We aimed to investigate place effects on 8-year individual mortality risk. Moreover, besides calculating association (yielding odds ratios, ORs) between area socio-economic circumstances and individual risk, we wanted to estimate variance and clustering using the variance partition coefficient (VPC). We explicitly proclaim the relevance of considering general contextual effects (i.e. the degree to which the context, as a whole, affects individual variance in mortality risk) under at least two circumstances. The first of these concerns the interpretation of specific contextual effects (i.e. the association between a particular area characteristic and individual risk) obtained from multilevel regression analyses. The second involves the interpretation of geographical variance obtained from classic ecological spatial analyses. The so-called "ecological fallacy" apart, the lack of individual-level information renders geographical variance unrelated to the total individual variation and, therefore, difficult to interpret. Finally, we stress the importance of considering the familial household in multilevel analyses. We observed an association between percentage of people with a low educational level in the census tract and individual mortality risk (OR, highest v. lowest quintile=1.14; 95% confidence interval, CI 1.08-1.20). However, only a minor proportion of the total individual variance in the probability of dying was at the municipality (M) and census tract (CT) levels (VPC(M)=0.2% and VPC(CT)=0.3%). Conversely, the household (H) level appeared much more relevant (VPC(H)=18.6%) than the administrative geographical areas. Without considering general contextual effects, both multilevel analyses of specific contextual effects and ecological studies of small-area variation may provide a misleading picture that overstates the role of administrative areas as contextual determinants of individual differences in mortality.

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.

Generating small-area prevalence of psychological distress and alcohol consumption: validation of a spatial microsimulation method

PURPOSE

Public mental health surveillance data are rarely available at a fine geographic scale. This study applies a spatial microsimulation procedure to generate small-area (lower super outputs areas [LSOA]) estimates of psychological distress and alcohol consumption. The validity of LSOA estimates and their associations with proximal and broader socioeconomic conditions are examined.

METHODS

A deterministic reweighting methodology assigns prevalence estimates for psychological distress and heavy alcohol consumption through a process of matching individuals from a large, population-representative dataset (Health Survey for England) to known LSOA populations (from the 2001 population Census). 'goodness-of-fit' of LSOA estimates is assessed by their comparison to observed prevalence of these health indicators at higher levels of aggregation (local authority districts [LAD]). Population prevalence estimates are correlated to the mental health needs index (MINI) and other health indicators; ordered logistic regression is applied to investigate their associations with proximal and broader socioeconomic conditions.

RESULTS

Performance of microsimulation models is high with no more than 10% errors in at least 90% of LAD for psychological distress and moderate and heavy alcohol consumption. The MINI is strongly correlated with psychological distress (r = 0.910; p value < 0.001) and moderately with heavy drinking (r = 0.389; p value < 0.001). Psychological distress and heavy alcohol consumption are differently associated with socioeconomic and rurality indicators at the LSOA level. Associations further vary at the LAD level and regional variations are apparent.

CONCLUSION

Spatial microsimulation may be an appropriate methodological approach for replicating social and demographic health patterns at the local level.

Small area estimates of smoking prevalence in London. Testing the effect of input data

Small area estimates (SAEs) can provide information about health behaviour at small area levels that is otherwise not available. Because of its increasing use by policy makers, more attention needs to be paid to the reliability of these estimates. This paper reports on smoking prevalence data generated for London at the neighbourhood level using spatial microsimulation modelling. We test the reliability of smoking prevalence estimates at the neighbourhood level using different input datasets. The paper further underlines the importance of estimating health behaviours at the small area level, particularly in diverse cities such as London, where estimation at the city level can mask significant spatial differences.

Areas of disadvantage: a systematic review of effects of area-level socioeconomic status on substance use outcomes

ISSUES

This review examines whether area-level disadvantage is associated with increased substance use and whether study results are impacted by the size of the area examined, definition of socioeconomic status (SES), age or ethnicity of participants, outcome variables or analytic techniques.

APPROACH

Five electronic databases and the reference sections of identified papers were searched to locate studies of the effects of area-level SES on substance use published through the end of 2007 in English-language, peer-reviewed journals or books. The 41 studies that met inclusion criteria included 238 effects, with a subsample of 34 studies (180 effects) used for the main analyses. Study findings were stratified by methodological characteristics and synthesised using generalised estimating equations to account for clustering of effects within studies.

KEY FINDINGS

There was strong evidence that substance use outcomes cluster by geographic area, but there was limited and conflicting support for the hypothesis that area-level disadvantage is associated with increased substance use. Support for the disadvantage hypothesis appeared to vary by sample age and ethnicity, size of area examined, type of SES measure, specific outcome considered and analysis techniques.

IMPLICATIONS

Future studies should use rigorous methods to yield more definitive conclusions about the effects of area-level SES on alcohol and drug outcomes, including composite measures of SES and both bivariate and multivariate analyses.

CONCLUSION

Further research is needed to identify confounds of the relationship between area-level SES and substance use and to explain why the effects of area-level SES vary by outcome and residents' age.

To what extent can deprivation inequalities in mortality and heart disease incidence amongst the working aged in Scotland be explained by smoking? Relative and absolute approaches

Smoking is important for both population health and inequalities in health. There is a growing recognition that its impact on inequalities can be assessed both by standardising smoking rates across socio-economic groups and by assessing the effect of reducing the prevalence of smoking in all groups, so-called relative and absolute approaches. While national level studies (such as census-linkage studies) give vital information on the socio-economic gradient in health they often lack smoking data. Here, small area smoking estimates are linked to a national level longitudinal study to overcome this problem. Results confirm that in relative and especially absolute terms smoking plays an important role in inequalities.

The geography of recreational physical activity in England

Levels of physical activity have declined considerably over recent decades in England, and there is evidence that activity patterns vary across areas. Previous studies of the geography of physical activity have frequently relied on model based synthetic estimates. Using data from a large population survey this study develops a direct measure of recreational physical activity and investigates variations in activity patterns across English Local Authorities. For both sexes the results show a distinct geography of recreational physical activity associated with north/south variations and urban/rural status. The environmental and behavioural factors driving those patterns are still poorly understood. We conclude that the variations observed might reflect recreational opportunities and the socio-cultural context of areas.

An index of unhealthy lifestyle is associated with coronary heart disease mortality rates for small areas in England after adjustment for deprivation

Indices of socio-economic deprivation are often used as a proxy for differences in the health behaviours of populations within small areas, but these indices are a measure of the economic environment rather than the health environment. Sets of synthetic estimates of the ward-level prevalence of low fruit and vegetable consumption, obesity, raised blood pressure, raised cholesterol and smoking were combined to develop an index of unhealthy lifestyle. Multi-level regression models showed that this index described about 50% of the large-scale geographic variation in CHD mortality rates in England, and substantially adds to the ability of an index of deprivation to explain geographic variations in CHD mortality rates.

Geographical information systems and tropical medicine

In terms of their applicability to the field of tropical medicine, geographical information systems (GIS) have developed enormously in the last two decades. This article reviews some of the pertinent and representative applications of GIS, including the use of such systems and remote sensing for the mapping of Chagas disease and human helminthiases, the use of GIS in vaccine trials, and the global applications of GIS for health-information management, disease epidemiology, and pandemic planning. The future use of GIS as a decision-making tool and some barriers to the widespread implementation of such systems in developing settings are also discussed.

Social inequalities in alcohol consumption in the Czech Republic: a multilevel analysis

Czech Republic traditionally ranks among the countries with the highest alcohol, consumption. This paper examines both risk and protective factors for frequent of alcohol, consumption in the Czech population using multilevel analysis. Risk factors were measured at the, individual level and at the area level. The individual-level data were obtained from a survey for a, sample of 3526 respondents aged 18-64 years. The area-level data were obtained from the Czech, Statistical Office. The group most inclinable to risk alcohol consumption and binge drinking are mainly, men, who live as single, with low education and also unemployed. Only the variable for divorce rate, showed statistical significance at both levels, thus the individual and the aggregated one. No cross-level interactions were found to be statistically significant.

Area disadvantage, socioeconomic position and women's contraception use: a multilevel study in the UK

BACKGROUND AND METHODOLOGY

This study aimed to investigate associations between area-level socioeconomic disadvantage (central heating, car ownership and residents in professional occupations), individual-level socioeconomic position (social class and educational qualifications) and contraception use in the UK for the period 1990-1991. Multilevel logistic regression analysis was conducted on cross-sectional data from the National Survey of Attitudes and Lifestyles of 9793 women, 16-59 years of age, residing in 646 postcode districts throughout the UK.

RESULTS

Women with lower levels of formal education were less likely to use contraception than women with higher education [odds ratio (OR) 0.50, 95% CI 0.44-0.57]. Women in the middle and low social class groups were less likely to use contraception than women in the higher social class group (OR 0.84, 95% CI 0.74-0.97 and OR 0.66, 95% CI 0.56-0.79, respectively). The association between social class and contraception use varied significantly across postcode districts (p < 0.001). The contraception use of women in the lowest social class group varied the most geographically. Women in the lowest quintiles of disadvantage were less likely to use contraception than women in the most advantaged quintiles according to all three measures, namely central heating (OR 0.76, 95% CI 0.61-0.94), car ownership (OR 0.67, 95% CI 0.53-0.84) and residents in professional occupations (OR 0.75, 95% CI 0.61-0.93).

DISCUSSION AND CONCLUSION

Although more information is needed to understand how area and individual socioeconomic characteristics are associated with contraceptive use, this study suggests that policy on contraceptive use needs to be extended beyond individually targeted approaches and needs to take into account socioeconomic determinants of contraceptive use.

Smoking cessation in England: intentionality, anticipated ease of quitting and advice provision

Smoking prevalence in England continues to reduce but further reduction is increasingly difficult. Cessation policy has successfully targeted those who want to quit but further reduction will need to shift attention to more difficult 'core smoker' populations. Following the established 'stages of change' perspective, this paper considers the characteristics of people who do not intend to quit smoking, anticipate difficulties in quitting and have not received advice about quitting. We deploy multilevel models of data drawn from the Health Survey for England years 2002-2004, and the NHS Primary Care Trust Patient Surveys for 2004 and 2005. It was found that variations in intentionality and anticipated ease of quitting are associated with individual factors such as smoking intensity, parental smoking, age/length of time as a smoker and the nature of the advice-giving consultation. Household composition and household income are also implicated in the intention to quit and anticipated difficulties in quitting. Once individual and household factors are taken into account the only identifiable area-level variation is reduced intentionality towards quitting in rural areas. We conclude by arguing that further gains in smoking cessation must focus on understanding the characteristics of 'hard-to-engage' populations.

Validation of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for wards in England

Several sets of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for small areas in England have been developed. These have been used in policy documents to indicate which areas are in need of intervention. In general, these models have not been subjected to validity assessment. This paper describes a validity assessment of 16 sets of synthetic estimates, by comparison of the models with national, regional and local survey-based estimates, and local mortality rate estimates. Model-based estimates of the prevalence of smoking, low fruit and vegetable consumption, obesity, hypertension and raised cholesterol are found to be valid.

Use of a Local Worker Survey as a Source of Neighborhood Information

Researchers increasingly recognize the potential influence of the neighborhood environment on individual health and social behavior. To examine these influences, it is important to use varying measures and sources of neighborhood characteristics. Though neighborhood residents are often surveyed, the perceptions of neighborhood workers have been largely ignored. The current study documents procedures and findings from two longitudinal studies in which workers in 60 neighborhoods were surveyed about neighborhood social cohesion and social control (collective efficacy), and neighborhood problems. Results indicated that workers within neighborhoods were more homogeneous in their views of neighborhood collective efficacy and neighborhood problems than were workers across neighborhoods. In addition, workers' perceptions of their neighborhoods were similar to the perceptions of neighborhood residents, but also provided unique information. Overall, this study demonstrates the viability and usefulness of local workers as an additional source of neighborhood information.

Life-course socioeconomic environment and health risk behaviours. A multilevel small-area analysis of young-old persons in an urban neighbourhood in Lausanne, Switzerland

With a life expectancy at the age of 65 of around 20 years, damaging health risk behaviours of young-old adults have become a target for preventive actions. Such risk factors necessitate an accurate understanding of the present and past socioeconomic conditions associated with health risk behaviours. The aim of our study is to assess the impact of certain life events as well as economic and environmental factors on health risk behaviours. We included 1309 participants of the Lausanne Cohort Lc65+ aged 65-70 years and employed logistic regression analyses, with individuals nested within areas. The results illustrate the influences of socioeconomic factors from childhood to young-old age. Life experiences in adulthood and economic resources in young-old age are both associated with unfavourable health behaviours. Neighbourhood is a modest determinant as well, particularly regarding alcohol consumption. Therefore, prevention against health risk behaviours should focus on population subgroups defined on the basis of their socioeconomic and living contexts.

Toward the next generation of research into small area effects on health: a synthesis of multilevel investigations published since July 1998

To map out area effects on health research, this study had the following aims: (1) to inventory multilevel investigations of area effects on self rated health, cardiovascular diseases and risk factors, and mortality among adults; (2) to describe and critically discuss methodological approaches employed and results observed; and (3) to formulate selected recommendations for advancing the study of area effects on health. Overall, 86 studies were inventoried. Although several innovative methodological approaches and analytical designs were found, small areas are most often operationalised using administrative and statistical spatial units. Most studies used indicators of area socioeconomic status derived from censuses, and few provided information on the validity and reliability of measures of exposures. A consistent finding was that a significant portion of the variation in health is associated with area context independently of individual characteristics. Area effects on health, although significant in most studies, often depend on the health outcome studied, the measure of area exposure used, and the spatial scale at which associations are examined.

Smoking in context - a multilevel approach to smoking among females in Helsinki

Background

Smoking is associated with disadvantage. As people with lower social status reside in less privileged areas, the extent of contextual influences for smoking remains unclear. The aims were to examine the spatial patterning of daily smoking within the city of Helsinki, to analyse whether contextual variation can be observed and which spatial factors associate with current daily smoking in the employed female population.

Methods

Data from a cross-sectional questionnaire were collected for municipal employees of Helsinki (aged 40–60 years). The response rate was 69%. As almost 4/5 of the employees are females, the analyses were restricted to women (n = 5028). Measures included smoking status, individual level socio-demographic characteristics (age, occupational social class, education, family type) and statistical data describing areas in terms of social structure (unemployment rate, proportion of manual workers) and social cohesion (proportions of single parents and single households). Logistic multilevel analysis was used to analyse data.

Results

After adjusting for the individual-level composition, smoking was significantly more prevalent according to all social structural and social cohesion indicators apart from the proportion of manual workers. For example, high unemployment in the area of domicile increased the risk of smoking by almost a half. The largest observed area difference in smoking – 8 percentage points – was found according to the proportion of single households.

Conclusion

The large variation in smoking rates between areas appears mainly to result from variation in the characteristics of residents within areas. Yet, living in an area with a high level of unemployment appears to be an additional risk for smoking that cannot be fully accounted for by individual level characteristics even in a cohort of female municipal employees.

Estimating health conditions for small areas: asthma symptom prevalence for state legislative districts

RESEARCH OBJECTIVE

To create prevalence estimates of asthma symptoms for California legislative districts.

DATA SOURCES

Three main data sources were used for this study: 2001 California Health Interview Survey, 2000 Census, and 2000-2002 March Current Population Surveys.

STUDY DESIGN

Secondary data analyses were conducted from cross-sectional data to distribute the joint probability of ever having an asthma diagnosis and symptoms in the last 12 months within an Assembly district. We applied hierarchical logistic regressions to estimate the parameters for selected survey and census data that predicted the probabilities of diagnosed asthmatics with asthma symptoms. Predictors included individual-level variables and contextual variables at zip code levels.

PRINCIPAL FINDINGS

Asthma symptom prevalence geographically varied by age within and across Assembly districts throughout California.

CONCLUSIONS

With modest investments in establishing analytic data files and estimating regression parameters for target conditions, small area estimation (SAE) procedures can create health data estimates not otherwise available at the sub-county level. Applying SAE procedures to asthma symptom prevalence suggest that these data can become essential reference tools for advocates and policy makers currently addressing this and other public health concerns in the state.

Flaccid theory and the geographies of sexual health in the age of Viagra™

The discipline of geography is largely absent in discussions and debates about drug use practices and their relationships to sexual health. Given the important relationships among the use of drugs, performances of sexualized identities, and the practices of sex, it behooves medical and health geographers particularly, and social and cultural geographers more generally, to engage in the wider interdisciplinary debates about these relationships. Through a discussion of one drug, Viagra, this brief intervention offers an agenda for studying the geographies of sex, sexuality, and drug use. It is argued that drug use is an inherently geographic practice that reshapes how places are resituated in relation to the fluid and dynamic meanings of sex, sexuality, and sexual health, areas of research and practice that medical and health geographers ought to consider more seriously.

Spatial risk for gender-specific adult mortality in an area of southern China

Background

Although economic reforms have brought significant benefits, including improved health care to many Chinese people, accessibility to improved care has not been distributed evenly throughout Chinese society. Also, the effects of the uneven distribution of improved healthcare are not clearly understood. Evidence suggests that mortality is an indicator for evaluating accessibility to improved health care services. We constructed spatially smoothed risk maps for gender-specific adult mortality in an area of southern China comprising both urban and rural areas and identified ecological factors of gender-specific mortality across societies.

Results

The study analyzed the data of the Hechi Prefecture in southern in China. An average of 124,204 people lived in the area during the study period (2002–2004). Individual level data for 2002–2004 were grouped using identical rectangular cells (regular lattice) of 0.25 km². Poisson regression was fitted to the group level data to identify gender-specific ecological factors of adult (ages 15–<45 years) mortality. Adult male mortality was more than two-fold higher than adult female mortality. Adults were likely to die of injury, poisoning, or trauma. Significantly more deaths were observed in poor areas than in areas with higher incomes. Specifically, higher spatial risk for adult male mortality was clustered in two rural study areas, which did not overlap with neighborhoods with higher risk for adult female mortality. One high-risk neighborhood for adult female mortality was in a poor urban area.

Conclusion

We found a disparity in mortality rates between rural and urban areas in the study area in southern China, especially for adult men. There were also differences in mortality rates between poorer and wealthy populations in both rural and urban areas, which may in part reflect differences in health care quality. Spatial influences upon adult male versus adult female mortality difference underscore the need for more research on gender-related influences on adult mortality in China.