Shared component modelling as an alternative to assess geographical variations in medical practice: gender inequalities in hospital admissions for chronic diseases

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background setting

Traditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.

Methods

The variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.

Results

Compared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.

Conclusions

The proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40163-024-00235-5.

Cancer mortality distribution in South Africa, 1997-2016

Introduction

The mortality data in South Africa (SA) have not been widely used to estimate the patterns of deaths attributed to cancer over a spectrum of relevant subgroups. There is no research in SA providing patterns and atlases of cancer deaths in age and sex groups per district per year. This study presents age-sex-specific geographical patterns of cancer mortality at the district level in SA and their temporal evolutions from 1997 to 2016.

Methods

Individual mortality level data provided by Statistics South Africa were grouped by three age groups (0-14, 15-64, and 65+), sex (male and female), and aggregated at each of the 52 districts. The proportionate mortality ratios (PMRs) for cancer were calculated per 100 residents. The atlases showing the distribution of cancer mortality were plotted using ArcGIS. Spatial analyses were conducted through Moran's I test.

Results

There was an increase in PMRs for cancer in the age groups 15-64 and 65+ years from 2006 to 2016. Ranges were 2.83 (95% CI: 2.77-2.89) -4.16 (95% CI: 4.08-4.24) among men aged 15-64 years and 2.99 (95% CI: 2.93-3.06) -5.19 (95% CI: 5.09-5.28) among women in this age group. The PMRs in men and women aged 65+ years were 2.47 (95% CI: 2.42-2.53) -4.06 (95% CI: 3.98-4.14), and 2.33 (95% CI: 2.27-2.38) -4.19 (95% CI: 4.11-4.28). There were considerable geographical variations and similarities in the patterns of cancer mortality. For the age group 15-64 years, the ranges were 1.18 (95% CI: 0.78-1.71) -8.71 (95% CI: 7.18-10.47), p < 0.0001 in men and 1.35 (95% CI: 0.92-1.92) -10.83 (95% CI: 8.84-13.14), p < 0.0001 in women in 2016. There were higher PMRs among women in the Western Cape, Northern Cape, North West, and Gauteng compared to other areas. Similar patterns were also observed among men in these provinces, except in North West and Gauteng.

Conclusion

The identification of geographical and temporal distributions of cancer mortality provided evidence of periods and districts with similar and divergent patterns. This will contribute to understanding the past, present, future trends and formulating interventions at a local level.

An Offenders-Offenses Shared Component Spatial Model for Identifying Shared and Specific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater Toronto Area

Objectives

We attempted to apply the Bayesian shared component spatial modeling (SCSM) for the identification of hotspots from two (offenders and offenses) instead of one (offenders or offenses) variables and developed three risk surfaces for (1) common or shared by both offenders and offenses; (2) specific to offenders, and (3) specific to offenses.

Methods

We applied SCSM to examine the joint spatial distributions of juvenile delinquents (offenders) and violent crime (offenses) in the York Region of the Greater Toronto Area at the dissemination area level. The spatial autocorrelation, overdispersion, and latent covariates were adjusted by spatially structured and unstructured random effect terms in the model. We mapped the posterior means of the estimated shared and specific risks for identifying the three risk surfaces and types of hotspots.

Results

Results suggest that about 50% and 25% of the relative risks of juvenile delinquents and violent crimes, respectively, could be explained by the shared component of offenders and offenses. The spatially structured terms attributed to 48% and 24% of total variations of the delinquents and violent crimes, respectively. Contrastingly, the unstructured random covariates influenced 3% of total variations of the juvenile delinquents and 51% for violent crimes.

Conclusions

The Bayesian SCSM presented in this study identifies shared and specific hotspots of juvenile delinquents and violent crime. The method can be applied to other kinds of offenders and offenses and provide new insights into the clusters of high risks that are due to both offenders and offenses or due to offenders or offenses only.

Shared component modelling of early childhood anaemia and malaria in Kenya, Malawi, Tanzania and Uganda

Background

Malaria and anaemia contribute substantially to child morbidity and mortality. In this study, we sought to jointly model the residual spatial variation in the likelihood of these two correlated diseases, while controlling for individual-level, household-level and environmental characteristics.

Methods

A child-level shared component model was utilised to partition shared and disease-specific district-level spatial effects.

Results

The results indicated that the spatial variation in the likelihood of malaria was more prominent compared to that of anaemia, for both the shared and specific spatial components. In addition, approximately 30% of the districts were associated with an increased likelihood of anaemia but a decreased likelihood of malaria. This suggests that there are other drivers of anaemia in children in these districts, which warrants further investigation.

Conclusions

The maps of the shared and disease-specific spatial patterns provide a tool to allow for more targeted action in malaria and anaemia control and prevention, as well as for the targeted allocation of limited district health system resources.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03694-4.

Atlas VPM: two decades informing on unwarranted variations in health care in Spain

Since the early 2000's, the Atlas of Variations in Medical Practice in the Spanish National Health System (namely, Atlas VPM) has been analysing and informing unwarranted variations in health care provision and outcomes in the Spanish Health System.Atlas VPM covers a two-fold perspective: a geographic one, where unwarranted variations would reflect the uneven exposure of the population to health care as a consequence of the place of residence; and, a provider-specific approach, where unwarranted variations would reflect differences in utilisation and outcomes that are at provider-level.Building on routine data (hospital and primary care electronic records, administrative data, geographic information, etc.) Atlas VPM has adapted the classical small area methods and has included a large panoply of techniques, such as Bayesian methods, hierarchical modelling or time-series forecasting.Led by the Data Science for Health Services and Policy Research group at the Institute for Health Sciences in Aragon, Atlas VPM implies a linkage and exchange process with the 17 Departments of Health of the Spanish regions where the research agenda is shared and research outcomes are translated into profiling and benchmarking interactive tools meant to facilitate clinical and policy decision-making.

Wide systematic variations in potentially avoidable hospitalisations of chronically ill patients: Ecological study of basic health areas and healthcare areas

BACKGROUND AND OBJECTIVE

Potentially avoidable hospitalisations (PAHs) due to chronic conditions are a healthcare problem that could reflect healthcare of insufficient quality. This study reports the systematic variations in PAHs for the collection of providers of the Spanish National Health System.

MATERIALS AND METHODS

We conducted an ecological study on government data, analysing the systematic variation in PAHs for 6 chronic conditions during 2013-2015. To determine the variation, we performed a small area analysis using Bayesian methodology.

RESULTS

Between 2013 and 2015, 439,878 admissions for PAHs were recorded in the Spanish National Health System. There was an up to 4-fold difference in PAH rates between certain basic health areas (BHA), with highly variable differences depending on the analysed condition. Forty percent of the BHAs showed a greater than expected risk of PAH. Beyond the systematic variation observed between BHAs, the healthcare areas of the patients' residence explained 33% of the variation in PAHs. We observed specific differences in these general results according to clinical condition, age and sex.

CONCLUSIONS

The wide systematic variation in PAHs suggests a problem of quality in the care provided to chronically ill patients by the providers of healthcare areas in Spain. Identifying and analysing these areas and other healthcare areas with better results could provide a reference for improving the care of other suppliers with poorer performance.

Wide systematic variations in potentially avoidable hospitalizations of chronically ill patients: Ecological study of primary care areas and healthcare areas

BACKGROUND AND OBJECTIVE

Potentially avoidable hospitalisations (PAHs) due to chronic conditions are a healthcare problem that could reflect healthcare of insufficient quality. This study reports the systematic variations in PAHs for the collection of providers of the Spanish National Health System.

MATERIALS AND METHODS

We conducted an ecological study on government data, analysing the systematic variation in PAHs for 6 chronic conditions during 2013-2015. To determine the variation, we performed a small area analysis using Bayesian methodology.

RESULTS

Between 2013 and 2015, 439,878 admissions for PAHs were recorded in the Spanish National Health System. There was an up to 4-fold difference in PAH rates between certain basic health areas (BHA), with highly variable differences depending on the analyzed condition. Forty percent of the BHAs showed a greater than expected risk of PAH. Beyond the systematic variation observed between BHAs, the healthcare areas of the patients' residence explained 33% of the variation in PAHs. We observed specific differences in these general results according to clinical condition, age and sex.

CONCLUSIONS

The wide systematic variation in PAHs suggests a problem of quality in the care provided to chronically ill patients by the providers of healthcare areas in Spain. Identifying and analysing these areas and other healthcare areas with better results could provide a reference for improving the care of other suppliers with poorer performance.

Spatial Variation in Lung Cancer Mortality and Related Men-Women Disparities in Iran from 2011 to 2014

Purpose

Lung cancer is considered as a common cause of cancer mortality. The disease represents the second and third causes of deaths from cancer among Iranian women and men, respectively. The present study aimed to evaluate the spatial variations in relative risk of lung cancer mortality in Iran and its relation to common risk factors between men and women and specific risk factors among women.

Methods

In this ecological study, the lung cancer mortality data were analyzed in Iran during 2011–2014. Besag, York, and Mollie’s (BYM) model and shared component model (SCM) were used to compare the spatial variations of the relative risk of lung cancer mortality by applying OpenBUGS version 3.2.3 and R version 3.6.1.

Results

The median age for death due to lung cancer in Iran is 74 years. During 2011–2014, the age-standardized lung cancer mortality rates among men and women were 12 and 5 per 100,000 individuals, respectively. In addition, almost similar spatial patterns were observed for both men and women. Further, risk factors, which are shared between men and women, were considered as the main cause of variation of lung cancer mortality relative risk in the regions under study for both men and women. The highest impact of the women-specific risk factors was estimated in northeastern and southwestern of the country while the lowest was related to Gilan province in northern part of Iran.

Conclusion

Based on the spatial pattern, lung cancer risk factors are at relatively high levels in most parts of Iran, especially in the northwest of the country. Regarding the women, the high-risk regions were considerably extended. Further, the highest concentration of the specific risk factors among women was observed in the eastern, central, and southwestern parts. The smoking effect, and the second-smoking effect and environmental pollutions could play more significant roles for men and women, respectively.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer’s disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Recent technological advances have led to a new generation of studies that interrogate multiple molecular levels in the same target tissue of a set of subjects, generating complex multi-omic datasets with which to study disease mechanism. These datasets of genetic, epigenomic, transcriptomic, and other data have the potential to reveal novel biological insights; however, integrative analyses remain challenging and require new computational methods. We developed an integrative Bayesian approach to detect genes with consistent differences between case and control samples across multiple data types. The method further integrates prior knowledge about gene function in the form of a gene functional similarity network to improve statistical inference by sharing information between related genes. We applied our method to an Alzheimer’s disease dataset of epigenomic and transcriptomic data and detected and then validated several novel and known candidate genes as well as three major disease-related biological processes. One of these processes reflected microglial activation and included the cytokine CSF1. Single-nucleus data revealed that CSF1 was primarily upregulated in astrocytes, implicating the involvement of this cell type in microglial activation. Hence, we demonstrated that integrative analysis approaches to multi-omic datasets can improve candidate gene detection and thereby generate new insights into complex diseases.

Decomposition of the US black/white inequality in premature mortality, 2010-2015: an observational study

OBJECTIVE

Decompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.

SETTING

All 50 US states and the District of Columbia, 2010 to 2015.

PARTICIPANTS

A total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.

PRIMARY AND SECONDARY OUTCOME MEASURES

The race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.

RESULTS

There is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.

CONCLUSION

Understanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

BACKGROUND

In epidemiological studies, several diseases share common risk factors or co-exist in their spatial prevalence. Disease mapping allows the health practitioners and epidemiologists to hypothesize the disease aetiology and gain better understanding of the geographical prevalence of the disease risks.

OBJECTIVE

This paper investigates the differences in small scale geographical variations and the underlying risk factors of child's health outcomes (anemia, stunting and wasting) in Sub-Saharan Africa using spatial epidemiology.

METHOD

The study first carried out an independent univariate analysis on each malnutrition indicator to identify underlying risk factors. A multivariate conditional autoregressive prior was explored to jointly model the spatial correlation between the undernutrition indicators and the small area-geographical disparities at sub-national levels in two sub-Saharan African countries.

RESULTS

The approach was implemented on data from National cross-sectional household- based demographic and health surveys conducted in 17,307 under-five children in Burkina Faso and Mozambique in 2010-012. Out of these children, 31.8% are found to be stunted, 15.5% wasted and 30.9% had anemia among Burkina Faso children, while 42.5% of Mozambican children were stunted, 5.9% wasted and 30.9% suffered from iron-deficiency anemia. The multivariate analysis revealed that the spatial prevalence existed across regions in Burkina Faso with geographical variations in stunting estimated as: 0.7549, CI (0.4693, 1.264); wasting 0.9197; (95%CI: 0.535, 1.591)and anemia: 0.734; (0.4606, 1.214). In additin, the spatial correlation between stunting and wasting was negatively correlated: -0.998; 95% CI (-1.000, -0.984), and a perfect negative correlation;(-1) between stunting and anemia, and positive for wasting and anemia: 0.997; (0.978, 1.000). The spatial occurrence across provinces in Mozambique indicated that there was strong positive correlation between stunting and wasting; 0.986; (0.899, 1.000) and a significant negative correlation between stunting and anemia: -0.720, (-0.934, -0.308) and wasting and anemia: -0.640; (-0.903 -0.174) with individual geographical variability in child stunting: 1427, (913.6, 2268); wasting:1751, (1117, 2803) and anemia: 556, (279.5, 978.9). These extra random effect parameters computed in our multivariate approach would outperform a univariate analysis in similar studies. Our model further detected high prevalent of malnutrition and anemia in the northern Burkina Faso, but high anemia prevalent found in central Mozambique, and high stunting and wasting identified Southern Mozambique. In addition, the risk factors of malnutrition and iron deficiency anemia included household poverty, morbidity, short birth interval (less 18 months), breast feeding, antenatal attendance and maternal literacy.

CONCLUSION

The statistical relevance of the identified risk factors in this study is useful to target specific individual interventions and the maps of the geographical inequalities in sub-national region can be used for designing nutrition interventions and allocation of scarce health resources.

Addressing unwarranted clinical variation: A rapid review of current evidence

INTRODUCTION

Unwarranted clinical variation (UCV) can be described as variation that can only be explained by differences in health system performance. There is a lack of clarity regarding how to define and identify UCV and, once identified, to determine whether it is sufficiently problematic to warrant action. As such, the implementation of systemic approaches to reducing UCV is challenging. A review of approaches to understand, identify, and address UCV was undertaken to determine how conceptual and theoretical frameworks currently attempt to define UCV, the approaches used to identify UCV, and the evidence of their effectiveness.

DESIGN

Rapid evidence assessment (REA) methodology was used.

DATA SOURCES

A range of text words, synonyms, and subject headings were developed for the major concepts of unwarranted clinical variation, standards (and deviation from these standards), and health care environment. Two electronic databases (Medline and Pubmed) were searched from January 2006 to April 2017, in addition to hand searching of relevant journals, reference lists, and grey literature.

DATA SYNTHESIS

Results were merged using reference-management software (Endnote) and duplicates removed. Inclusion criteria were independently applied to potentially relevant articles by 3 reviewers. Findings were presented in a narrative synthesis to highlight key concepts addressed in the published literature.

RESULTS

A total of 48 relevant publications were included in the review; 21 articles were identified as eligible from the database search, 4 from hand searching published work and 23 from the grey literature. The search process highlighted the voluminous literature reporting clinical variation internationally; yet, there is a dearth of evidence regarding systematic approaches to identifying or addressing UCV.

CONCLUSION

Wennberg's classification framework is commonly cited in relation to classifying variation, but no single approach is agreed upon to systematically explore and address UCV. The instances of UCV that warrant investigation and action are largely determined at a systems level currently, and stakeholder engagement in this process is limited. Lack of consensus on an evidence-based definition for UCV remains a substantial barrier to progress in this field.

Mapping Geographic Variation in Infant Mortality and Related Black-White Disparities in the US

BACKGROUND

In the US, black infants remain more than twice as likely as white infants to die in the first year of life. Previous studies of geographic variation in infant mortality disparities have been limited to large metropolitan areas where stable estimates of infant mortality rates by race can be determined, leaving much of the US unexplored.

METHODS

The objective of this analysis was to describe geographic variation in county-level racial disparities in infant mortality rates across the 48 contiguous US states and District of Columbia using national linked birth and infant death period files (2004-2011). We implemented Bayesian shared component models in OpenBUGS, borrowing strength across both spatial units and racial groups. We mapped posterior estimates of mortality rates for black and white infants as well as relative and absolute disparities.

RESULTS

Black infants had higher infant mortality rates than white infants in all counties, but there was geographic variation in the magnitude of both relative and absolute disparities. The mean difference between black and white rates was 5.9 per 1,000 (median: 5.8, interquartile range: 5.2 to 6.6 per 1,000), while those for black infants were 2.2 times higher than for white infants (median: 2.1, interquartile range: 1.9-2.3). One quarter of the county-level variation in rates for black infants was shared with white infants.

CONCLUSIONS

Examining county-level variation in infant mortality rates among black and white infants and related racial disparities may inform efforts to redress inequities and reduce the burden of infant mortality in the US.

Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China

Most previous research on the disparities of hypertension risk has neither simultaneously explored the spatio-temporal disparities nor considered the spatial information contained in the samples, thus the estimated results may be unreliable. Our study was based on the China Health and Nutrition Survey (CHNS), including residents over 12 years old in seven provinces from 1991 to 2011. Bayesian B-spline was used in the extended shared component model (SCM) for fitting temporal-related variation to explore spatio-temporal distribution in the odds ratio (OR) of hypertension, reveal gender variation, and explore latent risk factors. Our results revealed that the prevalence of hypertension increased from 14.09% in 1991 to 32.37% in 2011, with men experiencing a more obvious change than women. From a spatial perspective, a standardized prevalence ratio (SPR) remaining at a high level was found in Henan and Shandong for both men and women. Meanwhile, before 1997, the temporal distribution of hypertension risk for both men and women remained low. After that, notably since 2004, the OR of hypertension in each province increased to a relatively high level, especially in Northern China. Notably, the OR of hypertension in Shandong and Jiangsu, which was over 1.2, continuously stood out after 2004 for males, while that in Shandong and Guangxi was relatively high for females. The findings suggested that obvious spatial-temporal patterns for hypertension exist in the regions under research and this pattern was quite different between men and women.

Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa

Most mortality maps in South Africa and most contried of the sub-Saharan region are static, showing aggregated count data over years or at specific years. Lack of space and temporral dynamanics in these maps may adversely impact on their use and application for vigorous public health policy decisions and interventions. This study aims at describing and modeling sub-national distributions of age-gender specific all-cause mortality and their temporal evolutions from 1997 to 2013 in South Africa. Mortality information that included year, age, gender, and municipality administrative division were obtained from Statistics South Africa for the period. Individual mortality level data were grouped by three ages groups (0-14, 15-64, and 65 and over) and gender (male, female) and aggregated at each of the 234 municipalities in the country. The six age-gender all-cause mortality rates may be related due to shared common social deprivation, health and demographic risk factors. We undertake a joint analysis of the spatial-temporal variation of the six age-gender mortality risks. This is done within a shared component spatial model construction where age-gender common and specific spatial and temporal trends are estiamted using a hierarchical Bayesian spatial model. The results show municipal and temporal differentials in mortality risk profiles between age and gender groupings. High rates were seen in 2005, especially for the 15-64 years age group for both males and females. The dynamic geographical and time distributions of subnational age-gender all-cause mortality contribute to a better understanding of the temporal evolvement and geographical variations in the relationship between demographic composition and burden of diseases in South Africa. This provides useful information for effective monitoring and evaluation of public health policies and programmes targeting mortality reduction across time and sub-populations in the country.

Spatial Associations of Multiple Chronic Conditions Among Older Adults

Multimorbidity, the presence of two or more chronic conditions in an individual, presents a major challenge for meeting the health care needs of older adults. This study advances understanding of multiple chronic conditions by using local colocation quotients to reveal spatial associations for five chronic conditions (arthritis, diabetes, heart disease, hypertension, and pulmonary disease) in a statewide panel of older adults in New Jersey. Among adults with three or more conditions, large concentrations of Arthritis-Heart Disease-Pulmonary Disease, Arthritis-Hypertension-Pulmonary Disease, and Diabetes-Heart Disease-Hypertension were observed, each triad located in different regions of the state. Individuals with other triads of conditions, in contrast, were distributed among all older adults in the sample as expected with no areas of local concentration. The study provides gerontologists with a new and effective method for uncovering geographical patterns in combinations of chronic conditions among the populations they serve, thereby enabling more effective interventions.

Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China

Background

Previous studies have shown that there may be gender disparities in the prevalence of hypertension; however, these studies do not address the spatial information contained in the sample which may limit the analytical results. Our study extends the existing Shared Component Model (SCM) and compares its utility with a logistic regression model to evaluate the significance of spatial information for identifying risk factors for hypertension and other non-rare diseases.

Methods

A total of 1267 residents aged 45 years of age and over were included in our study, of which 48.1 % were males. The overall prevalence of hypertension was 33.2 %, with females experiencing a higher prevalence than males (35.5 % vs. 30.6 %). The research variables included body mass index (BMI), Waist -to-Height Ratio (WHtR), smoking status, alcohol consumption etc. The extended SCM is employed to investigate regional gender variations in the risk of hypertension and assess the gender variation in the middle-aged and elderly populations of Zhejiang Province in eastern China and then its performance is compared with that of a traditional multiple logistic regression model.

Results

Our SCM analysis determined that the spatial pattern of hypertension risk for the middle-aged and elderly populations of Zhejiang Province in eastern China is quite different for males and females. Furthermore, Waist -to-Height Ratio (WHtR) continues to be a simple and effective predictor of hypertension risk for males at the regional level.

Conclusions

We believe that the extended SCM spatial model is a useful tool for identifying risk factors at the regional level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3121-y) contains supplementary material, which is available to authorized users.

Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People's Republic of China

Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases' prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

The impact of the economic downturn on healthcare in Spain: consequences and alternatives

In Spain, the economic downturn has caused big changes in most of the public policies, where healthcare system is the one which is deeply affected too. The objective of the paper is to review some of the recent changes achieved in the system, and to discuss about providing some alternative ideas to the implemented policies. The existing universal coverage previous to the crisis, as acknowledged by the law, has changed last year and the new figure of 'insured person' has been introduced into the system. These persons are now the only ones eligible to receive healthcare under the public coverage. New co-payments have been introduced for drugs, and retired persons must also pay a 10% co-payment (which was 0% before) at the chemist office. Healthcare institutions have also implemented several policies to manage tough budget constraints. Some regions have privatized healthcare management of some hospitals (as Madrid) to control budget and presumably to obtain a higher efficiency. Different initiatives dealing with human resources and external purchases are also presented in this paper to mostly achieve budget control. The majority of the changes have been pure budget cuts and a reorganization of the system and institutions is still needed.