Spatial and Spatio-Temporal Epidemiology最新文献

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.

While the extant literature has established that transgender people face significant barriers to accessing healthcare, no studies to date have offered an explicitly spatial analysis of their access to trans-specific care. This study aims to fill that gap by providing a spatial analysis of access to gender-affirming hormone therapy (GAHT) using Texas as a case study. We used the three-step floating catchment area method, which relies on census tract-level population data and location data for healthcare facilities to quantify spatial access to healthcare within a specific drive-time window, in our case 120 min. For our tract-level population estimates we adapt estimates of the rates of transgender identification from a recent data source, the Household Pulse Survey, and use these in tandem with a spatial database of GAHT providers of the lead author's creation. We then compare results of the 3SFCA with data on urbanicity and rurality, as well as which areas are deemed medically underserved. Finally, we conduct a hot-spot analysis that identifies specific areas where health services could be planned in ways that could improve both access to GAHT for trans people and access to primary care for the general population. Ultimately, we conclude that our results illustrate that patterns of access to trans-specific medical care, like GAHT, do not neatly follow patterns of access to primary care for the general population and that therefore trans communities’ access to healthcare warrants specific, further investigation.

We constructed county-level models to examine properties of the SARS-CoV-2 B.1.617.2 (Delta) variant wave of infections in North Carolina and assessed immunity levels (via prior infection, via vaccination, and overall) prior to the Delta wave. To understand how prior immunity shaped Delta wave outcomes, we assessed relationships among these characteristics. Peak weekly infection rate and total percent of the population infected during the Delta wave were negatively correlated with the proportion of people with vaccine-derived immunity prior to the Delta Wave, signaling that places with higher vaccine uptake had better outcomes. We observed a positive correlation between immunity via infection prior to Delta and percent of the population infected during the Delta wave, meaning that counties with poor pre-Delta outcomes also had poor Delta wave outcomes. Our findings illustrate geographic variation in outcomes during the Delta wave in North Carolina, highlighting regional differences in population characteristics and infection dynamics.

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042–0.0044) and higher RE (77–80%) compared to SRS designs (MSE: 0.0072–0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Cardiovascular disease (CVD) is the leading cause of death globally, coronary heart disease (CHD) is the main category of it. It has been shown that the urban built environment affects the occurrence of CHD, but most focus on single environmental factors. This study developed two multicomponent Urban Heart Health Environment (UHHE) Indexes (unweighted index and weighted index), which were based on the four main behavioral risk factors for CHD (unhealthy diet, lack of physical activity, smoking, and drinking). And we examined the relationship between the indexes and the prevalence of CHD. The prevalence calculation is based on the database of F Hospital patients, who have had coronary stent implantation (CSI). Furthermore, these single-center data were corrected to reduce underestimation of prevalence. We performed global (Ordinal Least Square) and local (Geographically Weighed Regression) regression analyses to assess the relationship between the two UHHE indexes and CHD prevalence. Both indexes showed a significant negative relationship with CHD prevalence. In its spatial relationship, a non-stationary was discovered. The UHHE indexes may help identify and prioritize geographical areas for CHD prevention and may be beneficial to urban design in China.