Spatial and Spatio-Temporal Epidemiology最新文献

The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.

Aflatoxins are carcinogenic toxins produced by fungi, and many countries legislate limits in food. Previous research suggests elevated liver cancer (LC) mortality in some areas may be due to aflatoxin exposure, but this has not been investigated spatially. We investigate links between aflatoxin legislation, climate, and LC mortality and other covariates globally. Comparison tests of LC mortality showed expected patterns with legislation and climate. They also showed associations between high LC mortality and high Hepatitis, low alcohol consumption, low health expenditure and high family agriculture rates. Spatial analysis showed latitudinal trend with significant clusters of low LC mortality in Europe and high rates in West Africa, Central America, East and South-East Asia. Only health expenditure and Hepatitis were significant in spatial regression, but climate and family agriculture were also significant in multiple linear regression (MLR). Results suggest that aflatoxin education and legislation should be expanded, particularly in hot/wet climates.

The aim of this study was to describe, through spatial analysis, the cases of arboviruses (dengue and chikungunya), including deaths, during the first epidemic after the circulation of the chikungunya virus (CHIKV) in the state of Pernambuco, Northeastern Brazil. This was an ecological study in both Pernambuco and the state capital, Recife, from 2015 to 2018. The odds ratios (OR) were estimated, and the statistical significance was considered p≤0.05. For the spatial analysis, Kulldorff's space-time scan statistics method was adopted to identify spatial clusters and to provide the relative risk (RR). In order to assess the significance at a level of p < 0.01 of the model, the number of Monte Carlo replications was 999 times. To perform the scan statistics we used the Poisson probability model, with a circular scanning window; annual temporal precision and retrospective analysis. A total of 227 deaths and 158,728 survivors from arboviruses was reported during the study period, with 100 deaths from dengue and 127 from CHIKV. The proportion of deaths from dengue was 0.08% and from chikungunya was 0.35%. The proportion of all those infected (deaths plus survivors) with dengue was 77.42% and with chikungunya was 22.58%. Children aged 0 to 9 years were around 3 times more likely to die than the reference group (OR 2.84; CI95% 1.16–5.00). From the age of 40, the chances of death increased significantly: 40–49 (OR 2.52; CI95% 1.19–5.29), 50–59 (OR 5.55; CI95% 2.76–11.17) and 60 or more (OR 14.90; CI95% 7.79–28.49). Males were approximately twice as likely to die as females (OR 1.77; CI95% 1.36–2.30). White-skinned people were less likely to die compared to non-white (OR 0.60; CI95% 0.41–0.87). The space-time analysis of prevalence in the state of Pernambuco revealed the presence of four clusters in the years 2015 and 2016, highlighting the Metropolitan Macro-region with a relative risk=4 and the Agreste and Hinterland macro-regions with a relative risk=3.3. The spatial distribution of the death rate in the municipality of Recife smoothed by the local empirical Bayesian estimator enabled a special pattern to be identified in the southwest and northeast of the municipality. The spatiotemporal analysis of the death rate revealed the presence of two clusters in the year 2015. In the primary cluster, it may be noted that the aforementioned aggregate presented a RR=7.2, and the secondary cluster presented a RR=6.0. The spatiotemporal analysis with Kulldorff's space-time scan statistics method, proved viable in identifying the risk areas for the occurrence of arboviruses, and could be included in surveillance routines so as to optimize prevention strategies during future epidemics.

Recent studies and reports suggest an increased mortality rate of undocumented border crossers (UBCs) in Arizona is the result of heat extremes and climatic change. Conversely, others have shown that deaths have occurred in cooler environments than in previous years. We hypothesized that human locomotion plays a greater role in heat-related mortality and that such events are not simply the result of exposure. To test our hypothesis, we used a postmortem geographic application of the human heat balance equation for 2,746 UBC deaths between 1990 and 2022 and performed regression and cluster analyses to assess the impacts of ambient temperature and exertion. Results demonstrate exertion having greater explaining power, suggesting that heat-related mortality among UBCs is not simply a function of extreme temperatures, but more so a result of the required physical exertion. Additionally, the power of these variables is not static but changes with place, time, and policy.

Acute respiratory infections (ARI), diarrhea, and fever are three common childhood illnesses, especially in sub-Saharan Africa. This study investigates the marginal and pairwise correlated effects of these diseases across Western African countries in a single analytical framework. Using data from nationally representative cross-sectional Demographic and Health Surveys, the study analyzed specific and correlated effects of each pair of childhood morbidity from ARI, diarrhea, and fever using copula regression models in fourteen contiguous Western African countries. Data concerning childhood demographic and socio-economic conditions were used as covariates. In this cross-sectional analysis of 152,125 children aged 0–59 months, the prevalence of ARI was 6.9%, diarrhea, 13.8%, and fever 19.6%. The results showed a positive correlation and geographical variation in the prevalence of the three illnesses across the study region. The estimated correlation and 95% confidence interval between diarrhea and fever is $0.431(0.300,0.539)$; diarrhea and ARI is $0.270(0.096,0.422)$; and fever and ARI is $0.502(0.350,0.614)$. The marginal and correlated spatial random effects reveal within-country spatial dependence. Source of water and access to electricity was significantly associated with any of the three illnesses, while television, birth order, and gender were associated with diarrhea or fever. The place of residence and access to newspapers were associated with fever or ARI. There was an increased likelihood of childhood ARI, diarrhea, and fever, which peaked at about ten months but decreased substantially thereafter. Mother’s age was associated with a reduced likelihood of the three illnesses. The maps generated could be resourceful for area-specific policy-making to speed up mitigation processes.

Objective

To determine the impact of the police murder of George Floyd in Minneapolis, MN on firearm violence, and examine the spatial and social heterogeneity of the effect.

Methods

We analyzed a uniquely constructed panel dataset of Minneapolis Zip Code Tabulation Areas from 2016–2020 (n = 5742), consisting of Minnesota Hospital Association, Minneapolis Police Department, Minneapolis Public Schools, Census Bureau, and Minnesota Department of Natural Resources data. Interrupted time-series and random effects panel models were used to model the spatiotemporal effects of police killing event on the rate of firearm assault injuries.

Results

Findings reveal a rising and falling temporal pattern post-killing and a spatial pattern in which disadvantaged, historically Black communities near earlier sites of protest against police violence experienced the brunt of the post-killing increase in firearm assault injury. These effects remain after adjusting for changes in police activity and pandemic-related restrictions, indicating that rising violence was not a simple byproduct of changes in police behavior or COVID-19 response.

Conclusions

The results suggest that the increases in firearm violence as a result of police violence are disproportionately borne by underserved communities.

Rapidly emerging research on the mental health consequences of the COVID-19 pandemic shows increasing patterns of psychological distress, including anxiety and depression, and self-harming behaviors, particularly during the early months of the pandemic. Yet, few studies have investigated the spatial and temporal changes in depressive disorders and suicidal behavior during the pandemic. The objective of this retrospective analysis was to evaluate geographic patterns of emergency department admissions for depression and suicidal behavior in North Carolina before (March 2017-February 2020) and during the COVID-19 pandemic (March 2020 - December 2021). Univariate cluster detection examined each outcome separately and multivariate cluster detection was used to examine the co-occurrence of depression and suicide-related outcomes in SatScan; the Rand index evaluated cluster overlap. Cluster analyses were adjusted for age, race, and sex. Findings suggest that the mental health burden of depression and suicide-related outcomes remained high in many communities throughout the pandemic. Rural communities exhibited a larger increase in the co-occurrence of depression and suicide-related ED visits during the pandemic period. Results showed the exacerbation of depression and suicide-related outcomes in select communities and emphasize the need for targeted and sustained mental health interventions throughout the many phases of the COVID-19 pandemic.

While pandemic waves are often studied on the national scale, they typically are not distributed evenly within countries. This study presents a novel approach to analyzing the spatial-temporal dynamics of the COVID-19 pandemic in Germany. By using a composite indicator of pandemic severity and subdividing the pandemic into fifteen phases, we were able to identify similar trajectories of pandemic severity among all German counties through hierarchical clustering. Our results show that the hotspots and cold spots of the first four waves were relatively stationary in space. This highlights the importance of examining pandemic waves on a regional scale to gain a more comprehensive understanding of their dynamics. By combining spatial autocorrelation and spatial-temporal clustering of time series, we were able to identify important patterns of regional anomalies, which can help target more effective public health interventions on a regional scale.

Public health studies routinely use simplistic methods to calculate proximity-based “access” to greenspace, such as by measuring distances to the geographic centroids of parks or, less frequently, to the perimeter of the park area. Although computationally efficient, these approaches oversimplify exposure measurement because parks often have specific entrance points. In this tutorial paper, we describe how researchers can instead calculate more-accurate access measures using freely available open-source methods. Specifically, we demonstrate processes for calculating “service areas” representing street-network-based buffers of access to parks within set distances and mode of transportation (e.g., 1-km walk or 20-minute drive) using OpenRouteService and QGIS software. We also introduce an advanced method involving the identification of trailheads or parking lots with OpenStreetMap data and show how large parks particularly benefit from this approach. These methods can be used globally and are applicable to analyses of a wide range of studies investigating proximity access to resources.

Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.