Spatial and Spatio-Temporal Epidemiology最新文献

Bogota, the capital and largest city of Colombia, constantly fights against easily transmitted and endemic–epidemic diseases that lead to enormous public health problems. Pneumonia is currently the leading cause of mortality attributable respiratory infections in the city. Its recurrence and impact have been partially explained by biological, medical, and behavioural factors. Against this background, this study investigates Pneumonia mortality rates in Bogota from 2004 and 2014. We identified a set of environmental, socioeconomic, behavioural, and medical care factors whose interaction in space could explain the occurrence and impact of the disease in the Iberoamerican city. We adopted a spatial autoregressive models framework to study the spatial dependence and heterogeneity of Pneumonia mortality rates associated with well-known risk factors. The results highlight the different types of spatial processes governing Pneumonia mortality. Furthermore, they demonstrate and quantify the driving factors that stimulate the spatial spread and clustering of mortality rates. Our study stresses the importance of spatial modelling of context-dependent diseases such as Pneumonia. Likewise, we emphasize the need to develop comprehensive public health policies that consider the space and contextual factors.

Anaemia which is a condition that describes low haemoglobin (Hb) levels has been recognized as a major public health problem amongst pregnant women in many sub-Saharan African countries including Nigeria. The causes of maternal anaemia which are interconnected and complex vary between countries and can vary within a country. This study aimed to investigate the spatial pattern and identify demographic and socio-economic determinants associated with anaemia amongst Nigerian pregnant women aged 15–49 years using data from the 2018 Nigeria Demographic and Health Survey (NDHS). This study utilized chi-square tests of independence and semiparametric structured additive models to describe the relationship between the presumed factors and anaemia status or Hb level while also taking spatial effects at state level into account. The Gaussian and Binomial distributions were used for Hb level and anaemia status respectively. The overall observed prevalence of anaemia in pregnant women and average Hb level in Nigeria were 64% and 10.4 (SD = 1.6) g/dL respectively while the prevalence of mild, moderate and severe anaemia were 27.2%, 34.6% and 2.2% respectively. Higher education, older age, and currently breastfeeding were associated with higher Hb level. Low education, being unemployed and recently having a sexually transmitted infection were identified as risk factors for maternal anaemia. Body mass index (BMI) and household size had a nonlinear effect on Hb level while BMI and age were nonlinearly related to odds of anaemia. Bivariate analysis indicated that living in rural area, low wealth class, using unsafe water and non-usage of internet were significantly associated with increased risk of anaemia. Maternal anaemia prevalence was highest in the South Eastern part of Nigeria with Imo state producing the highest prevalence of maternal anaemia while Cross River state yielded the lowest prevalence of maternal anaemia. The spatial effects associated with states were significant but unstructured indicating that states in close proximity do not necessarily share similar spatial effects. Hence, unobserved characteristics shared by states in close proximity do not influence maternal anaemia and Hb level. The findings from this study can undoubtedly help in the planning and designing of anaemia interventions that match local conditions taking into consideration the aetiology of anaemia in Nigeria.

Background

In the past, deprivation has been mostly captured through simple and univariate measures such as low income or poor educational attainment in research on health and social inequalities in Belgium. This paper presents a shift towards a more complex, multidimensional measure of deprivation at the aggregate level and describes the development of the first Belgian Indices of Multiple Deprivation (BIMDs) for the years 2001 and 2011.

Methods

The BIMDs are constructed at the level of the smallest administrative unit in Belgium, the statistical sector. They are a combination of six domains of deprivation: income, employment, education, housing, crime and health. Each domain is built on a suite of relevant indicators representing individuals that suffer from a certain deprivation in an area. The indicators are combined to create the domain deprivation scores, and these scores are then weighted to create the overall BIMDs scores. The domain and BIMDs scores can be ranked and assigned to deciles from 1 (the most deprived) to 10 (the least deprived).

Results

We show geographical variations in the distribution of the most and least deprived statistical sectors in terms of individual domains and overall BIMDs, and we identify hotspots of deprivation. The majority of the most deprived statistical sectors are located in Wallonia, whereas most of the least deprived statistical sectors are in Flanders.

Conclusion

The BIMDs offer a new tool for researches and policy makers for analyzing patterns of deprivation and identifying areas that would benefit from special initiatives and programs.

Nepal aims to eliminate malaria by 2026. This study analyzed district-level spatio-temporal patterns of malaria in Nepal from 2005 to 2018, following the introduction of Long-Lasting Insecticidal Nets (LLINs) for vector control intervention. The spatial variation in a temporal trend (SVTT) method in SaTScan was used to detect significantly high or low temporal trends of five malaria indicators: Indigenous, Imported, PV (Plasmodium vivax), PF (Plasmodium falciparum), and Total Malaria; results were mapped as clusters with associated trends. Spatial clusters of increasing malaria were found for all five indicators. Indigenous Malaria increased 113.71% in a cluster of three previously non-endemic mountainous districts. The most prominent cluster of Imported Malaria increased by 156.22%, and included the capital, Kathmandu. While some clusters had decreasing malaria, the rate of decrease in clusters was lower than outside the clusters. Overall, malaria burden is decreasing in Nepal as the country progresses closer to the elimination deadline. However, spatial clusters of increasing malaria, and clusters of lower rates of decreasing malaria, point to a need to focus vector control interventions on these clusters.

Childhood cancer incidence is known to vary by age, sex, and race/ethnicity, but evidence is limited regarding external risk factors. We aim to identify harmful combinations of air pollutants and other environmental and social risk factors in association with the incidence of childhood cancer based on 2003–2017 data from the Georgia Cancer Registry. We calculated the standardized incidence ratios (SIR) of Central Nervous System (CNS) tumors, leukemia and lymphomas based on age, gender and ethnic composition in each of the 159 counties in Georgia, USA. County-level information on air pollution, socioeconomic status (SES), tobacco smoking, alcohol drinking and obesity were derived from US EPA and other public data sources. We applied two unsupervised learning tools (self-organizing map [SOM] and exposure-continuum mapping [ECM]) to identify pertinent types of multi-exposure combinations. Spatial Bayesian Poisson models (Leroux-CAR) were fit with indicators for each multi-exposure category as exposure and SIR of childhood cancers as outcomes. We identified consistent associations of environmental (pesticide exposure) and social/behavioral stressors (low socioeconomic status, alcohol) with spatial clustering of pediatric cancer class II (lymphomas and reticuloendothelial neoplasms), but not for other cancer classes. More research is needed to identify the causal risk factors for these associations.

COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.

COVID-19 has spread worldwide with a high variability in cases and mortality between populations. This research aims to assess socioeconomic inequities of COVID-19 in the city of Cali, Colombia, during the first and second peaks of the pandemic in this city. An ecological study by neighborhoods was carried out, were COVID-19 cases were analyzed using a Bayesian hierarchical spatial model that includes potential risk factors such as the index of unsatisfied basic needs and socioeconomic variables as well as random effects to account for residual variation. Maps showing the geographic patterns of the estimated relative risks as well as exceedance probabilities were created. The results indicate that in the first wave, the neighborhoods with the greatest unsatisfied basic needs and low socioeconomic strata, were more likely to report positive cases for COVID-19. For the second wave, the disease begins to spread through different neighborhoods of the city and middle socioeconomic strata presents the highest risk followed by the lower strata. These findings indicate the importance of measuring social determinants in the study of the distribution of cases due to COVID-19 for its inclusion in the interventions and measures implemented to contain contagions and reduce impacts on the most vulnerable populations.

Background

Sobriety checkpoints are a form of proactive policing in which law enforcement officers concentrate at a point on the roadway to systematically perform sobriety tests for all passing drivers. We investigated whether sobriety checkpoints unintentionally reduce assaults in surrounding areas.

Methods

Exposures of interest were sobriety checkpoints conducted by the Los Angeles Police Department between 2012 and 2017. Comparison units were matched 1:2 to sobriety checkpoints, selected as the same point location temporally lagged by exactly ±168 hours. The outcome was the density of police-reported assaults around the checkpoint location.

Results

In mixed effects regression analyses, assault incidence was lower when sobriety checkpoints were in operation compared to the same location ±168 hours [b= -0.0108, 95% CI: (-0.0203, -0.0012)].

Conclusions

Sobriety checkpoints were associated with decreased assault incidence, but estimated effect sizes were small and effects did not endure long after checkpoints ended.

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day – rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.