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The association between geographic and medical aspects is a well-known phenomenon, which also occurs in dermatological research. This article reviews the field of health geography, the history of the association between spatial location and health, and focuses on current areas of research. Research focusing on explaining regional variations in health refer to individual aspects and needs, population factors, environmental factors, and health care delivery structures in specific regions, as well as the interaction between them. Regional healthcare research is primarily concerned with access to health services and on the utilisation of those services. Methodologically, the analysis of geodata and the application of geographic information systems (GIS) and spatial modelling play a major role in this field. Dermatological research and dermatological practice can benefit from the findings of the regional analysis of access, utilisation, and variations in order to obtain a more detailed picture of care and thus to optimise care.

BACKGROUND The aim of health geographical research is to identify regional variations in health and care and their causes. Regional differences are also evident in skin cancer incidence, but knowledge about small-scale, inner-city variations is not yet available. OBJECTIVES How can the inner-city variations in skin cancer incidence in Hamburg, Germany, be described and how can initial indications of their causes be identified? METHODS The study area is the city of Hamburg and the data source is the Hamburg City Health Study with N = 10,000 persons aged 45-74 years. Descriptive methods are used to characterise the regional variation in skin cancer prevalence adjusted for age and sex. A correlation analysis provides information on possible associations with sociodemographic conditions in the city districts and district clusters. RESULTS Skin cancer prevalence varies within the urban area with high prevalences (maximum 13.8%) in the north-east and south-west of Hamburg. Weak to medium correlations with selected sociodemographic variables are found. For example, the higher the living space per inhabitant (r = 0.35), the higher the skin cancer prevalence in the district. CONCLUSIONS The Hamburg City Health Study shows regional and inner-city disparities and possible associations between skin cancer prevalence and local sociodemographic conditions. However, further and methodologically more extensive studies are necessary in order to make detailed statements.

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.

Medical Geography today draws on the concepts and techniques of geography, and epitomes the interdisciplinary nature of urban health discipline. Chikungunya is one of the Vector-borne diseases and today it continues to remain an important public health problem in Madurai city. It is a mosquito-borne viral infection caused by the Chikungunya Virus (CHKV). The sudden onset of very high fever along with a rash and severe arthralgia especially in the small joints of hands and toes are the characteristics of the disease. The study area, Madurai is the oldest and second-largest city in Tamil Nadu situated on the banks of river Vaigai in the south-central part of Tamil Nadu, India. The main objectives were to identify the major disease zones concerning Chikungunya cases and to observe the related urban ecological and environmental conditions. The study also aimed to derive a conceptual framework towards strengthening, control and sustainable development of the urban landscape. The data collected were taken by using the method of stratified Random sampling and a total of 600 samples were collected. Standard score (Z-score) technique was used to describe the relationship between the sets of variables and total conditions of selected urban environmental variables. It includes mapping of the disease using GIS software of Arc GIS. Apart from this, the multi-variant statistical technique, factor analysis is used to find the major associations and interrelationship between Chikungunya and people. The twelve factors rotated by explaining the varimax procedure registered more than 89.83 percent of the total variance. Among these, the first factor alone accounted for 33.52 percent of the total variance and eigenvalue of 21.45 was qualified as the primary factor. Our study concludes that in Madurai city’s built-up area, a landscape environment of transmission of Chikungunya epidemics with a high prevalence is noted since it is associated with urban environmental factors.

Background Rurality can reflect many aspects of the community, including community characteristics that may be associated with mental health. In this study, we focused on geographical units to address multiple layers of a rural environment. By evaluating rurality at both the municipality and neighborhood (i.e., a smaller unit within a municipality) levels in Japan, we aimed to elucidate the relationship between depression and rurality. To explore the mechanisms linking rurality and depression, we examined how the association between rurality and depression can be explained by community social capital according to geographical units. Methods We used cross-sectional data from the 2016 wave of the Japan Gerontological Evaluation Study involving 144,822 respondents aged 65 years or older residing in 937 neighborhoods across 39 municipalities. The population density quintile for municipality-level rurality and the quintile for the time required to reach densely inhabited districts for neighborhood-level rurality were used. We calculated the prevalence ratios of depressive symptoms by gender using a three-level (individual, neighborhood, and municipality) Poisson regression. Community social capital was assessed using three components: civic participation, social cohesion, and reciprocity. Results The prevalence of depressive symptoms was higher in municipalities with lower population density than those with the highest population density; the ratios were 1.22 (95% confidence intervals: 1.15, 1.30) for men and 1.22 (1.13, 1.31) for women. In contrast, when evaluating rurality at the neighborhood level, the prevalence of depressive symptoms was 0.9 times lower for men in rural areas; no such association was observed for women. In rural municipalities, community civic participation was associated with an increased risk of depressive symptoms. In rural neighborhoods, community social cohesion and reciprocity were linked to a lower risk of depressive symptoms. Conclusions The association between rurality and depression varied according to geographical unit. In rural municipalities, the risk of depression may be higher for both men and women, and the presence of an environment conducive to civic participation may contribute to a higher risk of depression, as observed in this study. The risk of depression in men may be lower in rural neighborhoods in Japan, which may be related to high social cohesion and reciprocity.

Background Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. This may introduce error into analyses based on the distance between households and providers. Methods We assessed the effect of imprecise household location on quality-adjusted effective coverage of child curative services estimated by linking sick children to providers based on geographic proximity. We used data on care-seeking for child illness and health provider quality in Southern Province, Zambia. The dataset included the location of respondent households, a census of providers, and data on the exact outlets utilized by sick children included in the study. We displaced the central point of each household cluster point five times. We calculated quality-adjusted coverage by assigning each sick child to a provider’s care based on three measures of geographic proximity (Euclidean distance, travel time, and geographic radius) from the household location, cluster point, and displaced cluster locations. We compared the estimates of quality-adjusted coverage to each other and estimates using each sick child’s true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores. Results Fewer children were linked to their true source of care using cluster locations than household locations. Effective coverage estimates produced using undisplaced or displaced cluster points did not vary significantly from estimates produced using household location data or each sick child’s true source of care. However, the sensitivity analyses simulating greater variability in provider quality showed bias in effective coverage estimates produced with the geographic radius and travel time method using imprecise location data in some scenarios. Conclusions Use of undisplaced or displaced cluster location reduced the proportion of children that linked to their true source of care. In settings with minimal variability in quality within provider categories, the impact on effective coverage estimates is limited. However, use of imprecise household location and choice of geographic linking method can bias estimates in areas with high variability in provider quality or preferential care-seeking.

Background: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. Methods: To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio–temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. Results: Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. Conclusion: Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales., SCOPUS: ar.j, info:eu-repo/semantics/published

Background A common approach for measuring place-based exposure is to use geographically-defined administrative boundaries and to link neighborhood characteristics at this level. This approach, however, may not be feasible in low-to middle-income countries where neighborhood-level data are limited or unavailable, and administrative boundaries are often unstandardized and not proportional to population size. Furthermore, such traditional approaches may not be appropriate for marginalized populations whose environments can be more difficult to study. In this paper, we describe two innovative and feasible methods to generate geospatial data to characterize and assess the role of risk environments on drug use among female sex workers living with HIV in the Dominican Republic. Methods Participatory geographic mapping and daily activity space travel diaries were employed. Results The methods presented in this study were feasible to implement, acceptable by study participants, and yielded rich geospatial data to analyze the impact of contextual factors on risk behaviors of female sex workers in a low-to middle-income country. Conclusion Participatory geographic mapping and activity space diaries are two alternative methods for collecting geospatial data among hard-to-reach populations in resource constrained settings. Moreover, the methods are interactive and educational, allowing study participants to take an active role in the data collection process and potentially allowing for a deeper understanding of place-based effects on health and behavior.

Introduction Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one’s neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health. Methods In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety. Results In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (β = − 2.34, p = 0.017), depression scores (β = − 4.22, p = 0.006), and BMI (β = − 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots. Conclusion Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity—via stress reduction and concomitant effects on cardiometabolic health.

Background Accounting for the co-occurrence of multiple environmental influences is a more accurate reflection of population exposure than considering isolated influences, aiding in understanding the complex interactions between environments, behaviour and health. This study examines how environmental ‘goods’ such as green spaces and environmental ‘bads’ such as alcohol outlets co-occur to develop a nationwide area-level healthy location index (HLI) for New Zealand. Methods Nationwide data were collected, processed, and geocoded on a comprehensive range of environmental exposures. Health-constraining ‘bads’ were represented by: (i) fast-food outlets, (ii) takeaway outlets, (iii) dairy outlets and convenience stores, (iv) alcohol outlets, (v) and gaming venues. Health-promoting ‘goods’ were represented by: (i) green spaces, (ii) blue spaces, (iii) physical activity facilities, (iv) fruit and vegetable outlets, and (v) supermarkets. The HLI was developed based on ranked access to environmental domains. The HLI was then used to investigate socio-spatial patterning by area-level deprivation and rural/urban classification. Results Results showed environmental ‘goods’ and ‘bads’ co-occurred together and were patterned by area-level deprivation. The novel HLI shows that the most deprived areas of New Zealand often have the most environmental ‘bads’ and less access to environmental ‘goods’. Conclusions The index, that is now publicly available, is able to capture both inter-regional and local variations in accessibility to health-promoting and health-constraining environments and their combination. Results in this study further reinforce the need to embrace the multidimensional nature of neighbourhood and place not only when designing health-promoting places, but also when studying the effect of existing built environments on population health.

The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. The causation of OO and T2D is complex and highly multifactorial rather than a mere energy intake (food) and expenditure (exercise) imbalance. But previous research into food and physical activity (PA) neighbourhood environments has mainly focused on associating body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, crude and inconsistent assumptions and conclusions that are far from the spirit of 'precision and accuracy public health'. Different people and population groups respond differently to the same food and PA environments, due to a myriad of unique individual and population group factors (genetic/epigenetic, metabolic, dietary and lifestyle habits, health literacy profiles, screen viewing times, stress levels, sleep patterns, environmental air and noise pollution levels, etc.) and their complex interplays with each other and with local food and PA settings. Furthermore, the same food store or fast food outlet can often sell or serve both healthy and non-healthy options/portions, so a simple binary classification into 'good' or 'bad' store/outlet should be avoided. Moreover, appropriate physical exercise, whilst essential for good health and disease prevention, is not very effective for weight maintenance or loss (especially when solely relied upon), and cannot offset the effects of a bad diet. The research we should be doing in the third decade of the twenty-first century should use a systems thinking approach, helped by recent advances in sensors, big data and related technologies, to investigate and consider all these factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention. Supplementary Information The online version contains supplementary material available at 10.1186/s12942-021-00266-0.

Spatial inequalities in health result from different exposures to health risk factors according to the features of geographical contexts, in terms of physical environment, social deprivation, and health care accessibility. Using a common geographical referential, which combines indices measuring these contextual features, could improve the comparability of studies and the understanding of the spatial dimension of health inequalities.We developed the Geographical Classification for Health studies (GeoClasH) to distinguish French municipalities according to their ability to influence health outcomes. Ten contextual scores measuring physical and social environment as well as spatial accessibility of health care have been computed and combined to classify French municipalities through a K-means clustering. Age-standardized mortality rates according to the clusters of this classification have been calculated to assess its effectiveness.Significant lower mortality rates compared to the mainland France population were found in the Wealthy Metropolitan Areas (SMR = 0.868, 95% CI 0.863-0.873) and in the Residential Outskirts (SMR = 0.971, 95% CI 0.964-0.978), while significant excess mortality were found for Precarious Population Districts (SMR = 1.037, 95% CI 1.035-1.039), Agricultural and Industrial Plains (SMR = 1.066, 95% CI 1.063-1.070) and Rural Margins (SMR = 1.042, 95% CI 1.037-1.047).Our results evidence the comprehensive contribution of the geographical context in the constitution of health inequalities. To our knowledge, GeoClasH is the first nationwide classification that combines social, environmental and health care access scores at the municipality scale. It can therefore be used as a proxy to assess the geographical context of the individuals in public health studies.

Background In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in ‘single-aggregation disease maps’ whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. Results We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. Conclusions The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P

Background The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany. Methods We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%. Results The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p Conclusion We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility.

Background There is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile. Methods To deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socio-economic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data. Results We found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other. Conclusions In this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and socioeconomic data. The method explored and uncovered valuable spatial patterns for health decision-making. In turn, spatial microsimulation.

Background Travel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings. Objectives This cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility. Methods Women were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland–Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used. Results Modelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H0: Mean difference = 0)] 0: Mean difference = 0) = 0.31, limits of agreement: [− 187.26; 199.96]]. Conclusion Modelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings. Trial registration ClinicalTrials.gov, NCT01911494. Registered 30 July 2013, https://clinicaltrials.gov/ct2/show/NCT01911494

Although a preponderance of research indicates that increased income inequality negatively impacts population health, several international studies found that a greater income inequality was associated with better population health when measured on a fine geographic level of aggregation. This finding is known as a “Swiss paradox”. To date, no studies have examined variability in the associations between income inequality and health outcomes by spatial aggregation level in the US. Therefore, this study examined associations between income inequality (Gini index, GI) and population health by geographic level using a large, nationally representative dataset of older adults. We geographically linked respondents’ county data from the 2012 Behavioral Risk Factor Surveillance System to 2012 American Community Survey data. Using generalized linear models, we estimated the association between GI decile on the state and county levels and five population health outcomes (diabetes, obesity, smoking, sedentary lifestyle and self-rated health), accounting for confounders and complex sampling. Although state-level GI was not significantly associated with obesity rates (b = − 0.245, 95% CI − 0.497, 0.008), there was a significant, negative association between county-level GI and obesity rates (b = − 0.416, 95% CI − 0.629, − 0.202). State-level GI also associated with an increased diabetes rate (b = 0.304, 95% CI 0.063, 0.546), but the association was not significant for county-level GI and diabetes rate (b = − 0.101, 95% CI − 0.305, 0.104). Associations between both county-level GI and state-level GI and current smoking status were also not significant. These findings show the associations between income inequality and health vary by spatial aggregation level and challenge the preponderance of evidence suggesting that income inequality is consistently associated with worse health. Further research is needed to understand the nuances behind these observed associations to design informed policies and programs designed to reduce socioeconomic health inequities among older adults.

Background Identifying and intervening on health disparities requires representative community public health data. For cities with high vacancy and transient populations, traditional methods of population estimation for refining random samples are not feasible. The aim of this project was to develop a novel method for systematic observations to establish community epidemiologic samples. Results We devised a four-step population randomization observation process for Flint, Michigan, USA: (1) Use recent total population data for community areas (i.e., neighborhoods) to establish the proportional sample size for each area, (2) Randomly select street segments of each community area, (3) Deploy raters to conduct observations about habitation for each randomly selected segment, and (4) Complete observations for second and third street segments, depending on vacancy levels. We implemented this systematic observation process on 400 randomly selected street segments. Of these, 130 (32.5%) required assessment of secondary segments due to high vacancy. Among the 130 primary segments, 28 (21.5%) required assessment of tertiary (or more) segments. For 71.5% of the 400 primary street segments, there was consensus among raters on whether the dwelling inhabited or uninhabited. Conclusion Houses observed with this method could have easily been considered uninhabited via other methods. This could cause residents of ambiguous dwellings (likely to be the most marginalized residents with highest levels of unmet health needs) to be underrepresented in the resultant sample.

In addition to providing critical medical services to communities, hospitals are also forces of broader change when seen from the perspective of neighborhood development. Over the past few decades the obligation on the part of U.S. nonprofit hospitals to positively impact the communities in which they are located has become entrenched in both U.S. tax law and the practices of many hospitals. This article presents findings from a grounded theory qualitative study of the relationship between a non-profit children's hospital in Columbus, Ohio, and the neighborhood in which it is located. Based on in-depth interviews with local residents and community leaders, findings suggest that community members often interpret distance, safety, and transportation in different, and often counter-intuitive ways. Drawing upon literature from medical geography and sociology, the authors argue that hospitals may benefit from working closely with community members to determine how space is understood and constructed when planning community health interventions.

Background Neighbourhood environment characteristics have been found to be associated with residents’ willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators—namely, wealthy, safe, lively, depressing, boring and beautiful—and residents’ time spent on PA in Guangzhou, China. Methods A human–machine adversarial scoring system was developed to predict perceptions of neighbourhood environments based on Tencent Street View imagery and deep learning techniques. Image segmentation was conducted using a fully convolutional neural network (FCN-8s) and annotated ADE20k data. A human–machine adversarial scoring system was constructed based on a random forest model and image ratings by 30 volunteers. Multilevel linear regressions were used to examine the association between each of the six indicators and time spent on PA among 808 residents living in 35 neighbourhoods. Results Total PA time was positively associated with the scores for “safe” [Coef. = 1.495, SE = 0.558], “lively” [1.635, 0.789] and “beautiful” [1.009, 0.404]. It was negatively associated with the scores for “depressing” [− 1.232, 0.588] and “boring” [− 1.227, 0.603]. No significant linkage was found between total PA time and the “wealthy” score. PA was further categorised into three intensity levels. More neighbourhood perceptual indicators were associated with higher intensity PA. The scores for “safe” and “depressing” were significantly related to all three intensity levels of PA. Conclusions People living in perceived safe, lively and beautiful neighbourhoods were more likely to engage in PA, and people living in perceived boring and depressing neighbourhoods were less likely to engage in PA. Additionally, the relationship between neighbourhood perception and PA varies across different PA intensity levels. A combination of Tencent Street View imagery and deep learning techniques provides an accurate tool to automatically assess neighbourhood environment exposure for Chinese large cities.

Background Body Mass Index (BMI) is a common outcome when assessing associations between childhood overweight and obesity and physical activity patterns. However, the fat and fat-free components of BMI, measured by the Fat Mass Index (FMI) and Fat-Free Mass Index (FFMI), may show contrasting associations with physical activity, while ethnic groups may vary in both physical activity patterns and body composition. Body composition must therefore be evaluated when assessing the associations between childhood overweight and obesity and physical activity in multi-ethnic populations. Methods This cross-sectional study investigated associations of BMI, FMI and FFMI z-scores with extracurricular physical activity for 2171 London primary schoolchildren (aged 5–11 years) of black, South Asian and white/other ethnicity. Multilevel mixed-effects ordered logistic modelling was used, adjusting for age, sex and family and neighbourhood socioeconomic status as potential confounders. Results Controlling for ethnicity and individual, family and neighbourhood socioeconomic confounders, actively commuting children had significantly lower Odds Ratios for being in high BMI (Odds Ratio (OR) = 0.678; 95 % Confidence Interval (CI) = 0.531 − 0.865; p − value = 0.002) and FMI z-score groups (OR = 0.679; 95 % CI = 0.499 − 0.922; p = 0.013), but not FFMI z-score groups, than passive commuters. Children doing sports less than once a week had lower Odds Ratios for being in high BMI (OR = 0.435; 95 % CI = 0.236 − 0.802; p = 0.008) and FFMI (OR = 0.455; 95 % CI = 0.214 − 0.969; p = .041) z-score categories compared to daily active children. Differences in FMI between groups did not reach the significance threshold. A trend towards statistical significance was obtained whereby children’s complete inactivity was associated with higher odds for being in higher BMI (OR = 2.222 : 95 % CI = 0.977 − 5.052; p = .057) and FMI z-score groups (OR = 2.485 : 95 % CI = 0.961 − 6.429; p = .060). FFMI z-scores did not show a similar trend with complete inactivity. Conclusions Active commuting was objectively associated with lower adiposity, while more frequent extracurricular sports participation was correlated with greater fat-free mass accretion. These relationships were independent of ethnicity and individual, family or neighbourhood socioeconomic confounding factors. Electronic supplementary material The online version of this article (10.1186/s12889-019-6883-1) contains supplementary material, which is available to authorized users.

The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed.

BACKGROUND: Environmental exposures are related to the risk of some types of cancer, and children are the most vulnerable group of people. This study seeks to present the methodological approaches used in the papers of our group about risk of childhood cancers in the vicinity of pollution sources (industrial and urban sites). A population-based case-control study of incident childhood cancers in Spain and their relationship with residential proximity to industrial and urban areas was designed. Two methodological approaches using mixed multiple unconditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) were developed: (a) "near vs. far" analysis, where possible excess risks of cancers in children living near ("near") versus those living far ("far") from industrial and urban areas were assessed; and (b) "risk gradient" analysis, where the risk gradient in the vicinity of industries was assessed. For each one of the two approaches, three strategies of analysis were implemented: "joint", "stratified", and "individualized" analysis. Incident cases were obtained from the Spanish Registry of Childhood Cancer (between 1996 and 2011). RESULTS: Applying this methodology, associations between proximity (≤ 2 km) to specific industrial and urban zones and risk (OR; 95% CI) of leukemias (1.31; 1.04-1.65 for industrial areas, and 1.28; 1.00-1.53 for urban areas), neuroblastoma (2.12; 1.18-3.83 for both industrial and urban areas), and renal (2.02; 1.16-3.52 for industrial areas) and bone (4.02; 1.73-9.34 for urban areas) tumors have been suggested. CONCLUSIONS: The two methodological approaches were used as a very useful and flexible tool to analyze the excess risk of childhood cancers in the vicinity of industrial and urban areas, which can be extrapolated and generalized to other cancers and chronic diseases, and adapted to other types of pollution sources. This study was funded by Carlos III Institute of Health, Spain Grand EPY1344/16, Spain’s Health Research Fund (Fondo de Investigación Sanitaria—FIS 12/01416), and Scientific Foundation of the Spanish Association Against Cancer, Spain (Fundación Científica de la Asociación Española Contra el Cáncer (AECC)—EVP-1178/14). This article presents independent research. The views expressed are those of the authors and not necessarily those of the Carlos III Institute of Health. Sí

Background Waterborne diseases are one of the leading causes of mortality in developing countries, and diarrhea alone is responsible for over 1.5 million deaths annually. Such waterborne illnesses most often affect those in impoverished rural communities who rely on rivers for their supply of drinking water. Deaths are most common among infants and the elderly. Without knowledge of which communities are upstream of a community, upstream sanitary and bathing behaviors can never be directly linked to downstream health outcomes including disease outbreaks. Although current GIS technologies can answer the upstream question for a limited number of downstream communities, no systematic way existed of labeling each downstream village with all its upstream contributing villages along river networks or within basins at the large national scale, such as in Indonesia. This limitation prohibits macro analyses of waterborne illness across developing world communities globally. Results This novel method approach combines parallel computing, big data, community data, and open source GIS to create a database of upstream communities for 50,000–70,0000 villages in Indonesia across four differing periods. The resultant village database provides information that can be tied to the Indonesian PODES health and behavior surveys in each village to connect upstream sanitary behaviors to downstream health outcomes. We find that the approximately 250,000 communities analyzed across the four periods in Indonesia have a combined total of 13.7 million upstream villages. The average number of upstream villages per village was almost 55, the maximum number of upstream villages for any single village was over 5300. Conclusions Advances in big-data availability, particularly high-resolution elevation data, the lowering of the cost of parallel computing options, mass survey data, and open source GIS algorithms that can utilize parallel processing and big-data, open new opportunities for the study of human health at micro granularities but across entire nations. The database generated has already been used by health researchers to compute the influence of upstream behaviors on downstream diarrhea outbreaks and to monitor avoidance behaviors to upstream water behaviors across all downstream 250,000 Indonesian villages over 4 years, and further waterborne health analyses are underway.

This paper reviews the progress of population geography in China since the 1980s. The review results suggest that contrary to the common perception of its invisibility and marginalized status in the field, tremendous progress has been made in population geography in China since the 1980s. Population geographers have made significant contribution to the understanding of a wide range of population issues from geographical perspectives, including migration, urbanization, population distribution, the relationships between population, environment and resources, aging, marriage patterns, and migrants’ crimes, although such contribution often did not appear in the geographical circle. Furthermore, population geographers have played an indispensable role in revitalizing population studies in China and forging its links to human geography, occupying an important position in this multi-disciplinary field. Population geographers’ contribution to the areas of migration and urbanization research has been particularly significant, reflected in their leading roles in these areas’ research. The paper demonstrates that as latecomers in the field after more than 20 years of isolation, population geographers in China have gone through a process of catching up and increasing engagement with developments in social sciences and increasing interaction with social scientists since the 1980s, and have benefited greatly from it; however, there is a tendency for population geography to be increasingly alienated from the main stream human geography, a phenomenon similar to but not exactly the same as Anglo-American geography in the late 1990s and early 2000s. The paper argues that population geography is only half way in the course to forge the links between population studies and human geography, and it needs to return to geographical sciences to strike a healthy balance between the field of population studies and that of human geography, and promote its further development in a multi-disciplinary field.

Background: The built environment health promotion has attracted notable attention across a wide spectrum of health-related research over the past decade. However, the results about the contextual effects on health and PA are highly heterogeneous. The discrepancies between the results can potentially be partly explained by the diverse use of different spatial units of analysis in assessing individuals' exposure to various environment characteristics. This study investigated whether different residential and activity space units of analysis yield distinct results regarding the association between the built environment and health. In addition, this study examines the challenges and opportunities of the different spatial units of analysis for environmental health-related research. Methods: Two common residential units of analysis and two novel activity space models were used to examine older adults' wellbeing in relation to the built environment features in the Helsinki Metropolitan Area, Finland. An administrative unit, 500 m residential buffer, home range model and individualized residential exposure model were used to assess the associations between the built environment and wellbeing of respondent's (n = 844). Results: All four different spatial units of analysis yield distinct results regarding the associations between the built environment characteristics and wellbeing. A positive association between green space and health was found only when exposure was assessed with individualized residential exposure model. Walkability index and the length of pedestrian and bicycle roads were found to positively correlate with perceived wellbeing measures only with a home range model. Additionally, all units of analysis differed from each other in terms of size, shape, and how they capture different contextual measures. Conclusions: The results show that different spatial units of analysis result in considerably different measurements of built environment. In turn, the differences derived from the use of different spatial units seem to considerably affect the associations between environment characteristics and wellbeing measures. Although it is not easy to argue about the correctness of these measurements, what is evident is that they can reveal different wellbeing outcomes. While some methods are especially usable to determine the availability of environmental opportunities that promote active travel and the related health outcomes, others can provide us with insight into the mechanisms how the actual exposure to green structure can enhance wellbeing.

Background The relationships between food environments and dietary intake have been assessed via a range of methodologically diverse measures of spatial exposure to food outlets, resulting in a largely inconclusive body of evidence, limiting informed policy intervention. Objective This systematic review aims to evaluate the influence of methodological choice on study outcomes by examining the within-study effect of availability (e.g., counts) versus accessibility (e.g., proximity) spatial exposure measures on associations with diet. Methods (PROSPERO registration: CRD42018085250). PubMed, Web of Science, Scopus and ScienceDirect databases were searched for empirical studies from 1980 to 2017, in the English language, involving adults and reporting on the statistical association between a dietary outcome and spatial exposure measures of both availability and accessibility. Studies were appraised using an eight-point quality criteria with a narrative synthesis of results. Results A total of 205 associations and 44 relationships (i.e., multiple measures of spatial exposure relating to a particular food outlet type and dietary outcome) were extracted from 14 eligible articles. Comparative measures were dominated by counts (availability) and proximity (accessibility). Few studies compared more complex measures and all counts were derived from place-based measures of exposure. Sixteen of the 44 relationships had a significant effect involving an availability measure whilst only 8 had a significant effect from an accessibility measure. The largest effect sizes in relationships were mostly for availability measures. After stratification by scale, availability measure had the greatest effect size in 139 of the 176 pairwise comparisons. Of the 33% (68/205) of associations that reached significance, 53/68 (78%) were from availability measures. There was no relationship between study quality and reported study outcomes. Conclusions The limited evidence suggests that availability measures may produce significant and greater effect sizes than accessibility measures. However, both availability and accessibility measures may be important concepts of spatial exposure depending on the food outlet type and dietary outcome examined. More studies reporting on multi-method effects are required to differentiate findings by the type of spatial exposure assessment and build an evidence base regarding the appropriateness and robustness of measures under different circumstances.

Background Dengue fever is a vector-borne infectious disease that is transmitted by contact between vector mosquitoes and susceptible hosts. The literature has addressed the issue on quantifying the effect of individual mobility on dengue transmission. However, there are methodological concerns in the spatial regression model configuration for examining the effect of intercity-scale human mobility on dengue diffusion. The purposes of the study are to investigate the influence of neighborhood structures on intercity epidemic progression from pre-epidemic to epidemic periods and to compare definitions of different neighborhood structures for interpreting the spread of dengue epidemics. Methods We proposed a framework for assessing the effect of model configurations on dengue incidence in 2014 and 2015, which were the most severe outbreaks in 70 years in Taiwan. Compared with the conventional model configuration in spatial regression analysis, our proposed model used a radiation model, which reflects population flow between townships, as a spatial weight to capture the structure of human mobility. Results The results of our model demonstrate better model fitting performance, indicating that the structure of human mobility has better explanatory power in dengue diffusion than the geometric structure of administration boundaries and geographic distance between centroids of cities. We also identified spatial–temporal hierarchy of dengue diffusion: dengue incidence would be influenced by its immediate neighboring townships during pre-epidemic and epidemic periods, and also with more distant neighbors (based on mobility) in pre-epidemic periods. Conclusions Our findings suggest that the structure of population mobility could more reasonably capture urban-to-urban interactions, which implies that the hub cities could be a “bridge” for large-scale transmission and make townships that immediately connect to hub cities more vulnerable to dengue epidemics. Electronic supplementary material The online version of this article (10.1186/s12942-018-0131-2) contains supplementary material, which is available to authorized users.

Background Malaria is highly sensitive to climatic variables and is strongly influenced by the presence of vectors in a region that further contribute to parasite development and sustained disease transmission. Mathematical analysis of malaria transmission through the use and application of the value of the basic reproduction number (R0) threshold is an important and useful tool for the understanding of disease patterns. Methods Temperature dependence aspect of R0 obtained from dynamical mathematical network model was used to derive the spatial distribution maps for malaria transmission under different climatic and intervention scenarios. Model validation was conducted using MARA map and the Annual Plasmodium falciparum Entomological Inoculation Rates for Africa. Results The inclusion of the coupling between patches in dynamical model seems to have no effects on the estimate of the optimal temperature (about 25 °C) for malaria transmission. In patches environment, we were able to establish a threshold value (about α = 5) representing the ratio between the migration rates from one patch to another that has no effect on the magnitude of R0. Such findings allow us to limit the production of the spatial distribution map of R0 to a single patch model. Future projections using temperature changes indicated a shift in malaria transmission areas towards the southern and northern areas of Africa and the application of the interventions scenario yielded a considerable reduction in transmission within malaria endemic areas of the continent. Conclusions The approach employed here is a sole study that defined the limits of contemporary malaria transmission, using R0 derived from a dynamical mathematical model. It has offered a unique prospect for measuring the impacts of interventions through simple manipulation of model parameters. Projections at scale provide options to visualize and query the results, when linked to the human population could potentially deliver adequate highlight on the number of individuals at risk of malaria infection across Africa. The findings provide a reasonable basis for understanding the fundamental effects of malaria control and could contribute towards disease elimination, which is considered as a challenge especially in the context of climate change.

Background Although the incidence of legionellosis throughout North America and Europe continues to increase, public health investigations have not been able to identify a common exposure in most cases. Over 80% of cases are sporadic with no known source. To better understand the role of the macro-environment in legionellosis risk, a retrospective ecological study assessed associations between population-level measures of demographic, socioeconomic, and environmental factors and high-risk areas. Methods Geographic variability and clustering of legionellosis was explored in our study setting using the following methods: unadjusted and standardized incidence rate and SaTScan™ cluster detection methods using default scanning window of 1 and 50% as well as a reliability score methodology. Methods for classification of “high-risk” census tracts (areas roughly equivalent to a neighborhood with average population of 4000) for each of the spatial methods are presented. Univariate and multivariate logistic regression analyses were used to estimate associations with sociodemographic factors: population ≥ 65 years of age, non-white race, Hispanic ethnicity, poverty, less than or some high school education; housing factors: housing vacant, renter-occupied, and built pre-1950 and pre-1970; and whether drinking water is groundwater or surface water source. Results Census tracts with high percentages of poverty, Hispanic population, and non-white population were more likely to be classified as high-risk for legionellosis versus a low-risk census tract. Vacant housing, renter-occupied housing, and homes built pre-1970 were also important positively associated risk factors. Drinking water source was not found to be associated with legionellosis incidence. Discussion Census tract level demographic, socioeconomic, and environmental characteristics are important risk factors of legionellosis and add to our understanding of the macro-environment for legionellosis occurrence. Our findings can be used by public health professionals to target disease prevention communication to vulnerable populations. Future studies are needed to explore the exact mechanisms by which these risk factors may influence legionellosis clustering.

International audience; BackgroundSpatial accessibility indices are increasingly applied when investigating inequalities in health. Although most studies are making mentions of potential errors caused by the edge effect, many acknowledge having neglected to consider this concern by establishing spatial analyses within a finite region, settling for hypothesizing that accessibility to facilities will be under-reported. Our study seeks to assess the effect of edge on the accuracy of defining healthcare provider access by comparing healthcare provider accessibility accounting or not for the edge effect, in a real-world application.MethodsThis study was carried out in the department of Nord, France. The statistical unit we use is the French census block known as ‘IRIS’ (Ilot Regroupé pour l’Information Statistique), defined by the National Institute of Statistics and Economic Studies. The geographical accessibility indicator used is the “Index of Spatial Accessibility” (ISA), based on the E2SFCA algorithm. We calculated ISA for the pregnant women population by selecting three types of healthcare providers: general practitioners, gynecologists and midwives. We compared ISA variation when accounting or not edge effect in urban and rural zones. The GIS method was then employed to determine global and local autocorrelation. Lastly, we compared the relationship between socioeconomic distress index and ISA, when accounting or not for the edge effect, to fully evaluate its impact.ResultsThe results revealed that on average ISA when offer and demand beyond the boundary were included is slightly below ISA when not accounting for the edge effect, and we found that the IRIS value was more likely to deteriorate than improve. Moreover, edge effect impact can vary widely by health provider type. There is greater variability within the rural IRIS group than within the urban IRIS group. We found a positive correlation between socioeconomic distress variables and composite ISA. Spatial analysis results (such as Moran’s spatial autocorrelation index and local indicators of spatial autocorrelation) are not really impacted.ConclusionOur research has revealed minor accessibility variation when edge effect has been considered in a French context. No general statement can be set up because intensity of impact varies according to healthcare provider type, territorial organization and methodology used to measure the accessibility to healthcare. Additional researches are required in order to distinguish what findings are specific to a territory and others common to different countries. It constitute a promising direction to determine more precisely healthcare shortage areas and then to fight against social health inequalities.

Patients transfers are an essential routine in care supplying and are considered a crucial form of inter-hospital collaboration in any Regional Health System. Previous organizational studies about the phenomenon adopted the resource dependency theory, demonstrating that transfers occur for because resources are not homogenously distributed between hospitals. More recently, it has been observed that patient transfers are driven by complex system of determinants and not only by the lack of resources. This paper, adopting a geographical view, observed that the theoretical lens of proximity are useful to better understand the phenomenon. Analyzing data on the patients transfers between the 35 hospitals of Abruzzo (Italy) Regional Health System through Multiple Regression-Quadratic Assignment Procedure, the study demonstrated that the different dimensions of proximity have a significant impact on the inter-hospitals patients transfers. Policy makers, health care executives and health geographers may take into account these logics in designing the territorial system of care.

This article will explore the utility of Swidler’s concepts of cultured capacities and strategies of action in mapping the pathways through which area-based, multiple deprivation and inequality impact upon resources for health, health outcomes and health inequalities. It will be argued that these concepts have the potential to bring the collective and aggregated impacts of resource distribution to the fore in unpicking the processes through which area-based inequalities become manifest in health outcomes. This has the potential to illuminate the some of the economic, social and political processes though which neoliberalism has generated widening health inequalities in the United Kingdom. To that end, these concepts will be employed in a case study of the post-industrial town of Clydebank. It will consider the implications for population health of changes in: the amount and quality of employment on offer; the quality and affordability housing; and the accessibility of social and political resources for those who live in more deprived areas. Swidler’s concepts will be used to theoretically map the relationships between the growing wealth inequalities, widening place-based inequalities and increasing health inequalities observed over the past few decades in the United Kingdom.

Background Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. Methods A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. Results The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2 = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Conclusion Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time. Electronic supplementary material The online version of this article (doi:10.1186/s12942-017-0107-7) contains supplementary material, which is available to authorized users.

Background The prevalence of overweight is increasing and the effectiveness of various weight management and exercise programs varied. An augmented reality smartphone game, Pokémon Go, appears to increase activity levels of players. This study assessed the players and ex-players’ frequencies and durations of staying outdoors, and walking/jogging before and during the time they played Pokémon Go, evaluated the physical activity levels of players, ex-players and non-players, and investigated the potential factors which determined their play statuses. Methods Students in a university answered an online-questionnaire survey. The IPAQ-short form was incorporated to measure vigorous-intensity activities, moderate-intensity activities and walking. Chi square tests were used to compare frequencies and durations of staying outdoors and walking/jogging, health discomforts and physical activity levels between players, ex-players and non-players. Wilcoxon signed ranks tests were performed to assess the changes prior to and during the time when the players and ex-players played Pokémon Go. Logistic regression analyses were performed to assess factors contributing to playing, quitting or not playing Pokémon Go. Results 644 university students answered the questionnaire. Compared with the ex-players, the players were significantly more frequent to stay outdoors when playing Pokémon Go (P

Background Modelling travel time to services has become a common public health tool for planning service provision but the usefulness of these analyses is constrained by the availability of accurate input data and limitations inherent in the assumptions and parameterisation. This is particularly an issue in the developing world where access to basic data is limited and travel is often complex and multi-modal. Improving the accuracy and relevance in this context requires greater accessibility to, and flexibility in, travel time modelling tools to facilitate the incorporation of local knowledge and the rapid exploration of multiple travel scenarios. The aim of this work was to develop simple open source, adaptable, interactive travel time modelling tools to allow greater access to and participation in service access analysis. Results Described are three interconnected applications designed to reduce some of the barriers to the more wide-spread use of GIS analysis of service access and allow for complex spatial and temporal variations in service availability. These applications are an open source GIS tool-kit and two geo-simulation models. The development of these tools was guided by health service issues from a developing world context but they present a general approach to enabling greater access to and flexibility in health access modelling. The tools demonstrate a method that substantially simplifies the process for conducting travel time assessments and demonstrate a dynamic, interactive approach in an open source GIS format. In addition this paper provides examples from empirical experience where these tools have informed better policy and planning. Conclusion Travel and health service access is complex and cannot be reduced to a few static modeled outputs. The approaches described in this paper use a unique set of tools to explore this complexity, promote discussion and build understanding with the goal of producing better planning outcomes. The accessible, flexible, interactive and responsive nature of the applications described has the potential to allow complex environmental social and political considerations to be incorporated and visualised. Through supporting evidence-based planning the innovative modelling practices described have the potential to help local health and emergency response planning in the developing world.

Background Studies exploring associations between food environments and food purchasing behaviours have been limited by the absence of data on where food purchases occur. Determining where food purchases occur relative to home and how these locations differ by individual, neighbourhood and trip characteristics is an important step to better understanding the association between food environments and food behaviours. Methods Conducted in Melbourne, Australia, this study recruited participants within sixteen neighbourhoods that were selected based on their socioeconomic characteristics and proximity to supermarkets. The survey material contained a short questionnaire on individual and household characteristics and a food purchasing diary. Participants were asked to record details related to all food purchases made over a 2-week period including food store address. Fifty-six participants recorded a total of 952 food purchases of which 893 were considered valid for analysis. Households and food purchase locations were geocoded and the network distance between these calculated. Linear mixed models were used to determine associations between individual, neighbourhood, and trip characteristics and distance to each food purchase location from home. Additional analysis was conducted limiting the outcome to: (a) purchase made when home was the prior origin (n. 484); and (b) purchases made within supermarkets (n. 317). Results Food purchases occurred a median distance of 3.6 km (IQR 1.8, 7.2) from participants’ homes. This distance was similar when home was reported as the origin (median 3.4 km; IQR 1.6, 6.4) whilst it was shorter for purchases made within supermarkets (median 2.8 km; IQR 1.6, 5.6). For all purchases, the reported food purchase location was further from home amongst the youngest age group (compared to the oldest age group), when workplace was the origin of the food purchase trip (compared to home), and on weekends (compared to weekdays). Differences were also observed by neighbourhood characteristics. Conclusions This study has demonstrated that many food purchases occur outside what is traditionally considered the residential neighbourhood food environment. To better understand the role of food environments on food purchasing behaviours, further work is needed to develop more appropriate food environment exposure measures. Electronic supplementary material The online version of this article (doi:10.1186/s12942-017-0082-z) contains supplementary material, which is available to authorized users.

This paper focuses on the graphic pathogeographies in David B.'s Epileptic and David Small's Stitches: A Memoir to highlight the significance of geographic concepts in graphic novels of health and disease. Despite its importance in such works, few scholars have examined the role of geography in their narrative and structure. I examine the role of place in Epileptic and Stitches to extend the academic discussion on graphic novels of health and disease and identify how such works bring attention to the role of geography in the individual's engagement with health, disease, and related settings.

Political theorists generally defend the moral importance of health care by appealing to its purported importance in promoting good health and saving lives. Recent research on the social determinants of health demonstrates, however, that health care actually does relatively little to promote good health or save lives in comparison with other social and environmental factors. This article assesses the implications of the social determinants of health literature for existing theories of health care justice, and outlines a new approach that can justify publicly subsidized comprehensive health care despite its limited contribution to good health. Even if health care plays a relatively limited role in promoting good health, it remains morally important because of the care it provides to individuals. As such, it can be justified in terms of care ethics. When health care is justified primarily in terms of care rather than health, however, the goals of a just health-care system shift. The measure of a just health-care system is no longer strictly its ability to generate good health outcomes but also its ability to provide individuals with accessible, good quality daily care. This different focus has important consequences for the way we think about the institutions of a just health-care system as well as for the delivery and allocation of medical goods and services.

Background Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic ‘smart surveillance’ methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently. Methods The approach uses the ‘uncertainty’ map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. Results The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. Conclusion This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas. Electronic supplementary material The online version of this article (doi:10.1186/s12942-016-0064-6) contains supplementary material, which is available to authorized users.

Background Converging international evidence suggests that diabetes incidence is lower among adults living in more walkable neighbourhoods. The association between walkability and physical activity (PA), the presumed mediator of this relationship, has not been carefully examined in adults with type 2 diabetes. We investigated the associations of walkability with total PA occurring within home neighbourhoods and overall PA, irrespective of location. Methods Participants (n = 97; 59.5 ± 10.5 years) were recruited through clinics in Montreal (QC, Canada) and wore a GPS-accelerometer device for 7 days. Total PA was expressed as the total Vector of the Dynamic Body Acceleration. PA location was determined using a Global Positioning System (GPS) device (SIRF IV chip). Walkability (street connectivity, land use mix, population density) was assessed using Geographical Information Systems software. The cross-sectional associations between walkability and location-based PA were estimated using robust linear regressions adjusted for age, body mass index, sex, university education, season, car access, residential self-selection, and wear-time. Results A one standard deviation (SD) increment in walkability was associated with 10.4 % of a SD increment in neighbourhood-based PA (95 % confidence interval (CI) 1.2, 19.7) – equivalent to 165 more steps/day (95 % 19, 312). Car access emerged as an important predictor of neighbourhood-based PA (Not having car access: 38.6 % of a SD increment in neighbourhood-based PA, 95 % CI 17.9, 59.3). Neither walkability nor car access were conclusively associated with overall PA. Conclusions Higher neighbourhood walkability is associated with higher home neighbourhood-based PA but not with higher overall PA. Other factors will need to be leveraged to facilitate meaningful increases in overall PA among adults with type 2 diabetes. Electronic supplementary material The online version of this article (doi:10.1186/s12889-016-3603-y) contains supplementary material, which is available to authorized users.

BackgroundRespiratory infections continue to be a public health threat, particularly to young children in developing countries. Understanding the geographic patterns of diseases and the role of potential risk factors can help improve future mitigation efforts. Toward this goal, this paper applies a spatial scan statistic combined with a zero-inflated negative-binomial regression to re-examine the impacts of a community-based treatment program on the geographic patterns of acute lower respiratory infection (ALRI) mortality in an area of rural Bangladesh. Exposure to arsenic-contaminated drinking water is also a serious threat to the health of children in this area, and the variation in exposure to arsenic must be considered when evaluating the health interventions.MethodsALRI mortality data were obtained for children under 2 years old from 1989 to 1996 in the Matlab Health and Demographic Surveillance System. This study period covers the years immediately following the implementation of an ALRI control program. A zero-inflated negative binomial (ZINB) regression model was first used to simultaneously estimate mortality rates and the likelihood of no deaths in groups of related households while controlling for socioeconomic status, potential arsenic exposure, and access to care. Next a spatial scan statistic was used to assess the location and magnitude of clusters of ALRI mortality. The ZINB model was used to adjust the scan statistic for multiple social and environmental risk factors.ResultsThe results of the ZINB models and spatial scan statistic suggest that the ALRI control program was successful in reducing child mortality in the study area. Exposure to arsenic-contaminated drinking water was not associated with increased mortality. Higher socioeconomic status also significantly reduced mortality rates, even among households who were in the treatment program area.ConclusionCommunity-based ALRI interventions can be effective at reducing child mortality, though socioeconomic factors may continue to influence mortality patterns. The combination of spatial and non-spatial methods used in this paper has not been applied previously in the literature, and this study demonstrates the importance of such approaches for evaluating and improving public health intervention programs

Background In this paper, we demonstrate why and how both temporality and multimodality should be integrated in health related studies that include accessibility perspective, in this case healthy food accessibility. We provide evidence regarding the importance of using multimodal spatio-temporal accessibility measures when conducting research in urban contexts and propose a methodological approach for integrating different travel modes and temporality to spatial accessibility analyses. We use the Helsinki metropolitan area (Finland) as our case study region to demonstrate the effects of temporality and modality on the results. Methods Spatial analyses were carried out on 250 m statistical grid squares. We measured travel times between the home location of inhabitants and open grocery stores providing healthy food at 5 p.m., 10 p.m., and 1 a.m. using public transportation and private cars. We applied the so-called door-to-door approach for the travel time measurements to obtain more realistic and comparable results between travel modes. The analyses are based on open access data and publicly available open-source tools, thus similar analyses can be conducted in urban regions worldwide. Results Our results show that both time and mode of transport have a prominent impact on the outcome of the analyses; thus, understanding the realities of accessibility in a city may be very different according to the setting of the analysis used. In terms of travel time, there is clear variation in the results at different times of the day. In terms of travel mode, our results show that when analyzed in a comparable manner, public transport can be an even faster mode than a private car to access healthy food, especially in central areas of the city where the service network is dense and public transportation system is effective. Conclusions This study demonstrates that time and transport modes are essential components when modeling health-related accessibility in urban environments. Neglecting them from spatial analyses may lead to overly simplified or even erroneous images of the realities of accessibility. Hence, there is a risk that health related planning and decisions based on simplistic accessibility measures might cause unwanted outcomes in terms of inequality among different groups of people.

Background: Despite being one of the world’s most affected regions, only little is known about the social and spatial distributions of malaria in Indonesian Papua. Existing studies tend to be descriptive in nature; their inferences are prone to confounding and selection biases. At the same time, there remains limited malaria-cartographic activity in the region. Analysing a subset (N = 22,643) of the National Basic Health Research 2007 dataset (N = 987,205), this paper aims to quantify the district-specific risk of malaria in Papua and to understand how socio-demographic/economic factors measured at individual and district levels are associated with individual’s probability of contracting the disease. Methods: We adopt a Bayesian hierarchical logistic regression model that accommodates not only the nesting of individuals within the island’s 27 administrative units but also the spatial autocorrelation among these locations. Both individual and contextual characteristics are included as predictors in the model; a normal conditional autoregressive prior and an exchangeable one are assigned to the random effects. Robustness is then assessed through sensitivity analyses using alternative hyperpriors. Results: We find that rural Papuans as well as those who live in poor, densely forested, lowland districts are at a higher risk of infection than their counterparts. We also find age and gender differentials in malaria prevalence, if only to a small degree. Nine districts are estimated to have higher-than-expected malaria risks; the extent of spatial variation on the island remains notable even after accounting for socio-demographic/economic risk factors. Conclusions: Although we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. This means that malaria eradication requires not only biological (proximal) interventions but also social (distal) ones.

Background In recent years, childhood overweight and obesity have become an increasing and challenging phenomenon in Western cities. A lot of studies have focused on the analysis of factors such as individual dispositions and nutrition balances, among others. However, little is known about the intra-urban spatial patterns of childhood overweight and its associations with influencing factors that stretch from an individual to a neighbourhood level. The aim of this paper is to analyse the spatial patterns of childhood obesity in Berlin, and also to explore and test for associations with a complex set of risk factors at the individual, household and neighbourhood levels. Methods We use data from a survey of 5–6 year-olds that includes health status, height, and weight, as well as several socioeconomic and other risk variables. In addition, we use a set of neighbourhood variables, such as distance, and density measures of parks or fast food restaurants. Our outcome variable is the percentage of children of 5–6 years who were reported overweight or obese in 2012. The aggregated data is available for 60 areas in Berlin. We first analyse the outcome and risk factor data descriptively, and subsequently apply a set of regression analyses to test for associations between reported overweight and obesity, and also individual, household and neighbourhood characteristics. Results Our analysis returned a distinct spatial distribution of childhood overweight in Berlin with highest shares in the city centre. Moreover, we were able to identify significant effects regarding the social index, and the percentage of non-German children being obese or overweight; additionally, we identified fast food restaurant density as a possible influencing factor. For the other variables, including the neighbourhood variables, we could not identify a significant association on this aggregated level of analysis. Conclusions Our findings confirm the results of earlier studies, in which the social status and percentage of non-German children is very important in terms of the association with childhood overweight and obesity. Unlike many studies conducted in North America, this study did not reveal an influence of neighbourhood variables. We argue that European urban structures differ from North American structures and highlight the need for a more detailed analysis of the association between the neighbourhood environment and the physical activity of children in urban setting. Electronic supplementary material The online version of this article (doi:10.1186/s12942-016-0041-0) contains supplementary material, which is available to authorized users.

Background Rapid and accurate diagnosis drives evidence-based care in health. Point-of-care testing (POCT) aids diagnosis by bringing advanced technologies closer to patients. Health small-world networks are constrained by natural connectivity in the interactions between geography of resources and social forces. Using a geographic information system (GIS) we can understand how populations utilize their health networks, visualize their inefficiencies, and compare alternatives. Methods This project focuses on cardiac care resource in rural Isaan, Thailand. A health care access analysis was created using ArcGIS Network Analyst 10.1 from data representing aggregated population, roads, health resource facilities, and diagnostic technologies. The analysis quantified cardiac health care access and identified ways to improve it using both widespread and resource-limited strategies. Results Results indicated that having diagnostic technologies closer to populations streamlines critical care paths. GIS allowed us to compare the effectiveness of the implementation strategies and put into perspective the benefits of adopting rapid POCT within health networks. Conclusions Geospatial analyses derive high impact by improving alternative diagnostic placement strategies in limited-resource settings and by revealing deficiencies in health care access pathways. Additionally, the GIS provides a platform for comparing relative costs, assessing benefits, and improving outcomes. This approach can be implemented effectively by health ministries seeking to enhance cardiac care despite limited resources.

Background Characterizing geographic access depends on a broad range of methods available to researchers and the healthcare context to which the method is applied. Globally, travel time is one frequently used measure of geographic access with known limitations associated with data availability. Specifically, due to lack of available utilization data, many travel time studies assume that patients use the closest facility. To examine this assumption, an example using mammography screening data, which is considered a geographically abundant health care service in the United States, is explored. This work makes an important methodological contribution to measuring access—which is a critical component of health care planning and equity almost everywhere. Method We analyzed one mammogram from each of 646,553 women participating in the US based Breast Cancer Surveillance Consortium for years 2005–2012. We geocoded each record to street level address data in order to calculate travel time to the closest and to the actually used mammography facility. Travel time between the closest and the actual facility used was explored by woman-level and facility characteristics. Results Only 35 % of women in the study population used their closest facility, but nearly three-quarters of women not using their closest facility used a facility within 5 min of the closest facility. Individuals that by-passed the closest facility tended to live in an urban core, within higher income neighborhoods, or in areas where the average travel times to work was longer. Those living in small towns or isolated rural areas had longer closer and actual median drive times. Conclusion Since the majority of US women accessed a facility within a few minutes of their closest facility this suggests that distance to the closest facility may serve as an adequate proxy for utilization studies of geographically abundant services like mammography in areas where the transportation networks are well established.

Recently, geographical information systems have been very intensively applied in social life and in public health in particular. A retrospective problem-oriented search on their use in health planning was performed in Web of Science of Web of Knowledge, three versions of MEDLINE, Scopus, EMBASE, and ProQuest Medical in 1990---2010. The annual dynamics of a set of scientometric parameters characterizing several aspects of the abstracted publications, authors' scientific institutions, journals, authors, citations, and languages was comparatively analyzed. It was established that world publication output on such a relatively narrow topic was reflected to a different extent in these data-bases. MEDLINE (PubMed) presented with 484 papers published in 243 journals followed by MEDLINE (WoK) with 360 papers in 215 journals. The abstracted publications were mainly in English, but 14 other languages were present in significant numbers. Publications by authors from 44 countries were abstracted in WoS but from 29 countries in MEDLINE (Ebsco). The most productive authors and institutions as well as the `core' journals were identified. The International Journal of Health Geography occupied the leading position. The Center for Disease Control and Prevention (USA) was one of the most productive research institutions in WoS and in Scopus. Scientific institutions and journals belonged to problem-oriented and to mono-, two- and three-disciplinary thematic profiles as well. Some essential peculiarities of the dynamics of research institutionalization and internationalization in this interdisciplinary field were illustrated. The constellation of specific semantically-loaded indicators could be applied for the purposes of problem-oriented analyses as it could timely identify the essential patterns of scientific advances in rapidly expanding interdisciplinary topics.

Leaders of health professional schools often support community-based education as a means of promoting emerging practitioners' awareness of health disparities and commitment to serving the poor. Yet, most programs do not teach about the causes of health disparities, raising questions regarding what social and political lessons students learn from these experiences. This article examines the ways in which community-based clinical education programs help shape the subjectivities of new dentists as ethical clinician-citizens within the US commodified health care system. Drawing on ethnographic research during volunteer and required community-based programs and interviews with participants, I demonstrate three implicit logics that students learned: (1) dialectical ideologies of volunteer entitlement and recipient debt; (2) forms of justification for the often inferior care provided to "failed" consumers (patients with Medicaid or uninsured); and (3) specific forms of obligations characterizing the ethical clinician-citizen. I explore the ways these messages reflected the structured relations of both student encounters and the overarching health care system, and examine the strategies faculty supervisors undertook to challenge these messages and relations. Finally, I argue that promoting commitments to social justice in health care should not rely on cultivating altruism, but should instead be pursued through educating new practitioners about the lives of poor people, the causal relationships between poverty and poor health, and attention to the structure of health care and provider-patient interactions. This approach involves shining a critical light on America's commodified health care system as an arena based in relations of power and inequality.