International Journal of Health Geographics最新文献

Background: The availability of physical activity (PA) facilities in neighborhoods is hypothesized to influence cardiovascular disease (CVD), but evidence from individual-level long-term cohort studies is limited. We aimed to assess the association between neighborhood exposure to PA facilities and CVD incidence.

Methods: A total of 4658 participants from the Chinese Multi-provincial Cohort Study without CVD at baseline (2007-2008) were followed for the incidence of CVD, coronary heart disease (CHD), and stroke. Availability of PA facilities was defined as both the presence and the density of PA facilities within a 500-m buffer zone around the participants' residential addresses. Time-dependent Cox regression models were performed to estimate the associations between the availability of PA facilities and risks of incident CVD, CHD, and stroke.

Results: During a median follow-up of 12.1 years, there were 518 CVD events, 188 CHD events, and 355 stroke events. Analyses with the presence indicator revealed significantly lower risks of CVD (hazard ratio [HR] 0.80, 95% confidence interval ([CI] 0.65-0.99) and stroke (HR 0.76, 95% CI 0.60-0.97) in participants with PA facilities in the 500-m buffer zone compared with participants with no nearby facilities in fully adjusted models. In analyses with the density indicator, exposure to 2 and ≥ 3 PA facilities was associated with 35% (HR 0.65, 95% CI 0.47-0.91) and 28% (HR 0.72, 95% CI 0.56-0.92) lower risks of CVD and 40% (HR 0.60, 95% CI 0.40-0.90) and 38% (HR 0.62, 95% CI 0.46-0.84) lower risks of stroke compared with those without any PA facilities in 500-m buffer, respectively. Effect modifications between presence of PA facilities and a history of hypertension for incident stroke (P = 0.049), and a history of diabetes for incident CVD (P = 0.013) and stroke (P = 0.009) were noted.

Conclusions: Residing in neighborhoods with better availability of PA facilities was associated with a lower risk of incident CVD. Urban planning intervention policies that increase the availability of PA facilities could contribute to CVD prevention.

Background: National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates.

Results: The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight.

Conclusion: This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

Background: Non-pharmaceutical interventions (NPIs) implemented in one place can affect neighboring regions by influencing people's behavior. However, existing epidemic models for NPIs evaluation rarely consider such spatial spillover effects, which may lead to a biased assessment of policy effects.

Methods: Using the US state-level mobility and policy data from January 6 to August 2, 2020, we develop a quantitative framework that includes both a panel spatial econometric model and an S-SEIR (Spillover-Susceptible-Exposed-Infected-Recovered) model to quantify the spatial spillover effects of NPIs on human mobility and COVID-19 transmission.

Results: The spatial spillover effects of NPIs explain [Formula: see text] [[Formula: see text] credible interval: 52.8-[Formula: see text]] of national cumulative confirmed cases, suggesting that the presence of the spillover effect significantly enhances the NPI influence. Simulations based on the S-SEIR model further show that increasing interventions in only a few states with larger intrastate human mobility intensity significantly reduce the cases nationwide. These region-based interventions also can carry over to interstate lockdowns.

Conclusions: Our study provides a framework for evaluating and comparing the effectiveness of different intervention strategies conditional on NPI spillovers, and calls for collaboration from different regions.

Background: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence.

Methods: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data.

Results: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways.

Conclusion: Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Background: The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities.

Methods: In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level.

Results: The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO₂ and O₃. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric.

Conclusions: This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.

Background: COVID-19 has been characterised by its global and rapid spread, with high infection, hospitalisation, and mortality rates worldwide. However, the course of the pandemic showed differences in chronology and intensity in different geographical areas and countries, probably due to a multitude of factors. Among these, socio-economic deprivation has been supposed to play a substantial role, although available evidence is not fully in agreement. Our study aimed to assess incidence and fatality rates of COVID-19 across the levels of socio-economic deprivation during the first epidemic wave (March-May 2020) in the Italian Province of Foggia, Apulia Region.

Methods: Based on the data of the regional active surveillance platform, we performed a retrospective epidemiological study among all COVID-19 confirmed cases that occurred in the Apulian District of Foggia, Italy, from March 1st to May 5th, 2020. Geocoded addresses were linked to the individual Census Tract (CT) of residence. Effects of socio-economic condition were calculated by means of the Socio-Economic and Health-related Deprivation Index (SEHDI) on COVID-19 incidence and fatality.

Results: Of the 1054 confirmed COVID-19 cases, 537 (50.9%) were men, 682 (64.7%) were 0-64 years old, and 338 (32.1%) had pre-existing comorbidities. COVID-19 incidence was higher in the less deprived areas (p < 0.05), independently on age. The level of socio-economic deprivation did not show a significant impact on the vital status, while a higher fatality was observed in male cases (p < 0.001), cases > 65 years (p < 0.001), cases having a connection with a nursing home (p < 0.05) or having at least 1 comorbidity (p < 0.001). On the other hand, a significant protection for healthcare workers was apparent (p < 0.001).

Conclusions: Our findings show that deprivation alone does not affect COVID-19 incidence and fatality burden, suggesting that the burden of disease is driven by a complexity of factors not yet fully understood. Better knowledge is needed to identify subgroups at higher risk and implement effective preventive strategies.

Background: Child mortality continue to be a major public health issue in most developing countries; albeit there has been a decline in global under-five deaths. The differences in child mortality can best be explained by socioeconomic and environmental inequalities among countries. In this study, we explore the effect of country-level development indicators on under-five mortality rates. Specifically, we examine potential spatio-temporal heterogeneity in the association between major world development indicators on under-five mortality, as well as, visualize the global differential time trend of under-five mortality rates.

Methods: The data from 195 countries were curated from the World Bank's World Development Indicators (WDI) spanning from 2000 to 2017 and national estimates for under-five mortality from the UN Inter-agency Group for Child Mortality Estimation (UN IGME).We built parametric and non-parametric Bayesian space-time interaction models to examine the effect of development indicators on under-five mortality rates. We also used employed Bayesian spatio-temporal varying coefficient models to assess the spatial and temporal variations in the effect of development indicators on under-five mortality rates.

Results: In both parametric and non-parametric models, the results show indicators of good socioeconomic development were associated with a reduction in under-five mortality rates while poor indicators were associated with an increase in under-five mortality rates. For instance, the parametric model shows that gross domestic product (GDP) (β = - 1.26, [CI - 1.51; - 1.01]), current healthcare expenditure (β = - 0.40, [CI - 0.55; - 0.26]) and access to basic sanitation (β = - 0.03, [CI - 0.05; - 0.01]) were associated with a reduction under-five mortality. An increase in the proportion practising open defecation (β = 0.14, [CI 0.08; 0.20]) an increase under-five mortality rate. The result of the spatial components spatial variation in the effect of the development indicators on under-five mortality rates. The spatial patterns of the effect also change over time for some indicators, such as PM2.5.

Conclusion: The findings show that the burden of under-five mortality rates was considerably higher among sub-Saharan African countries and some southern Asian countries. The findings also reveal the trend in reduction in the sub-Saharan African region has been slower than the global trend.

Background: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

Methods: We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

Results: Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

Conclusions: This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Background: Prehospital delay in reaching a percutaneous coronary intervention (PCI) facility is a major problem preventing early coronary reperfusion in patients with ST-elevation myocardial infarction (STEMI). The aim of this study was to identify modifiable factors that contribute to the interval from symptom onset to arrival at a PCI-capable center with a focus on geographical infrastructure-dependent and -independent factors.

Methods: We analyzed data from 603 STEMI patients who received primary PCI within 12 h of symptom onset in the Hokkaido Acute Coronary Care Survey. We defined onset-to-door time (ODT) as the interval from the onset of symptoms to arrival at the PCI facility and we defined door-to-balloon time (DBT) as the interval from arrival at the PCI facility to PCI. We analyzed the characteristics and factors of each time interval by type of transportation to PCI facilities. In addition, we used geographical information system software to calculate the minimum prehospital system time (min-PST), which represents the time required to reach a PCI facility based on geographical factors. We then subtracted min-PST from ODT to find the estimated delay-in-arrival-to-door (eDAD), which represents the time required to reach a PCI facility independent of geographical factors. We investigated the factors related to the prolongation of eDAD.

Results: DBT (median [IQR]: 63 [44, 90] min) was shorter than ODT (median [IQR]: 104 [56, 204] min) regardless of the type of transportation. However, ODT was more than 120 min in 44% of the patients. The min-PST (median [IQR]: 3.7 [2.2, 12.0] min) varied widely among patients, with a maximum of 156 min. Prolongation of eDAD (median [IQR]: 89.1 [49, 180] min) was associated with older age, absence of a witness, onset at night, no emergency medical services (EMS) call, and transfer via a non-PCI facility. If eDAD was zero, ODT was projected to be less than 120 min in more than 90% of the patients.

Conclusions: The contribution of geographical infrastructure-dependent time in prehospital delay was substantially smaller than that of geographical infrastructure-independent time. Intervention to shorten eDAD by focusing on factors such as older age, absence of a witness, onset at night, no EMS call, and transfer via a non-PCI facility appears to be an important strategy for reducing ODT in STEMI patients. Additionally, eDAD may be useful for evaluating the quality of STEMI patient transport in areas with different geographical conditions.