Trace metal pollution gradients in a tropical seagrass ecosystem.

Trace metals are one of the most serious pollutants in tropical seagrass meadows given their persistence and toxicity. Whereas quantity is frequently measured, there is no information on the spatial extent of metal pollution in these systems. Here, we use an island in Indonesia (Barang Lompo) as a model system to study the impact radius of two major and eight trace metals in sediment and seagrass leaves. We provide evidence for exponential decay in both the metal pollution index and concentrations of most metals with increasing distance from the island (k = -0.01 to -0.08 m-1). Consequently, there is an impact radius of approximately 100 m around the island. The comparative analysis of both seagrass species further revealed interspecific differences in metal loads. This study highlights the importance of assessing the spatial extent of metal pollution in addition to its quantity.

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe.

European agri-food systems must overcome structural lock-ins to achieve more sustainable modes of production and consumption. Yet European regions are highly diverse, and we lack understanding of how different regional characteristics may enable or inhibit sustainability transitions. This hinders the development of context-tailored governance strategies. In this paper, we identify and apply sets of spatial indicators to map the regional potentials for agri-food transitions. We first analyse the strength of lock-in to the incumbent agro-industrial paradigm. We then map the enabling environments for two alternative agri-food networks-multifunctional value chains and civic food networks-that each embed distinct social-ecological qualities of agriculture and food. Results demonstrate a large spatial diversity in transition potential, with stronger lock-ins throughout North and Western Europe and stronger enabling environments for agri-food transitions in Italy, France, Switzerland, and Southwest Germany. We find that lock-ins are strongest in livestock-dominated regions and are associated with higher GHG emissions and excess nitrogen levels. Our study demonstrates the need for coordinated public policies that (1) leverage region-specific transition potentials and (2) enable complementary innovations in market-based and community-led networks. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-024-01480-y.

Socioeconomic risk and the longitudinal child lifetime prevalence of child protection involvement.

BACKGROUND: North American studies find that geographic indicators of disadvantage, such as concentrated poverty, significantly increase the risk of child protection involvement. Despite having one of the most extensive family support systems and progressive income redistribution policies in North America, the Canadian province of Québec still faces geographic variations in socioeconomic conditions that remain a major risk factor for child protection involvement. OBJECTIVE: This study asks how child protection involvement is distributed across socioeconomically distinct geographic areas of the province. Drawing from prior literature, we hypothesize that the highest level of child protection involvement across childhood (age 0-17) is found in the lowest socioeconomic areas. PARTICIPANTS & SETTING: This is a population-based prevalence study using administrative child protection data spanning the years 2000 to 2017 across Québec. METHODS: We constructed cumulative risk life tables of first instances of child protection events (report confirmation, compromised security or development, and out-of-home placement). Prevalence rates were mapped onto 10,650 Census dissemination areas divided into three tiers according to a validated socioeconomic status (SES) index. RESULTS: The highest childhood prevalence of confirmed child protection reports, finding of compromised security or development, and out-of-home placement was found in the lowest SES areas. Rates in low SES areas can be over twice the rates in high SES areas. CONCLUSIONS: Area-level socioeconomic vulnerability remains a robust predictor of child protection involvement even in a socially progressive context. Our findings underscore that without targeted pediatric and family services, as well as poverty-alleviation programs for high-need families in high-need areas, even well-intentioned systems may fall short of reaching the families most in need.

Health map for HealthGap: Defining a geographical catchment to examine cardiovascular risk in Victoria, Australia.

The HealthGap study aimed to understand cardiovascular risk among Indigenous Australians in Victoria using linked administrative data. A key challenge was differing spatial coverages of sources: state-level data for risk factors but cardiovascular outcomes for three hospitals. Catchments were defined based on hospital postcodes to estimate denominator populations for risk modelling: first- and second-order neighbours, and spatial distribution of outcomes ('spatial event distribution'). Catchment coverage was assessed through proportions of patients presenting to study hospitals from catchment postcodes. The spatial event distribution performed best, capturing 82% events overall (first-order:40%; second-order:64%) and 65% Indigenous (27% and 45%). No approach excluded proximal non-study hospitals. Spatial event distributions could help define denominator populations when geographic information on outcome data is available but may not avoid potential misclassification.

Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer.

Background: MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression. Methods: We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis. Results: Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56+ cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p=0.064) and overall survival (OS) (HR=0.496, p=0.041). The OS association strengthened with high counts of both CD56+ and CD8+ cells (HR=0.199, p<1×10-3). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3+CD8+ T cells and CD3-CD56+ NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ+/- NK cells and T cells. We discovered that both IFNγ+ NK and CD8 T cells were more frequently associated with other IFNγ+ lymphocytes in comparison to IFNγ- NK cells and CD8 T cells (p<1×10-30). Moreover, IFNγ+ lymphocytes were most often found clustered near MHC-I+ tumor cells. Conclusions: Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ+ NK cells with other IFNγ+ lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated.

Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis.

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

Spatiotemporal analysis of HPV vaccination and associated neighborhood-level disparities in Texas-an ecological study.

Background: HPV is responsible for most cervical, oropharyngeal, anal, vaginal, and vulvar cancers. The HPV vaccine has decreased cervical cancer incidence, but only 49% of Texas adolescents have initiated the vaccine. Texas shows great variation in HPV vaccination rates. We used geospatial analysis to identify areas with high and low vaccination rates and explored differences in neighborhood characteristics. Methods: Using Anselin's Local Moran's I statistic, we conducted an ecological analysis of hot and cold spots of adolescent HPV vaccination coverage in Texas from 2017 to 2021. Next, we utilized a Mann-Whitney U test to compare neighborhood characteristics of vaccination coverage in hot spots versus cold spots, leveraging data from the Child Opportunity Index (COI) and American Community Survey. Results: In Texas, there are 64 persistent vaccination coverage hotspots and 55 persistent vaccination coverage cold spots. The persistent vaccination coverage hot spots are characterized by ZIP codes with lower COI scores, higher percentages of Hispanic residents, higher poverty rates, and smaller populations per square mile compared to vaccine coverage cold spots. We found a more pronounced spatial clustering pattern for male adolescent vaccine coverage than we did for female adolescent vaccine coverage. Conclusion: In Texas, HPV vaccination coverage rates differ depending on the community's income level, with lower-income areas achieving higher success rates. Notably, there are also gender-based discrepancies in vaccination coverage rates, particularly among male adolescents. This knowledge can aid advocates in customizing their outreach initiatives to address these disparities.

GIS-based methodology for tracking the grazing cattle site use.

Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.

Evaluation of Brain Function Recovery After Traumatic Brain Injury Treatment in a Porcine Model by Cross-Group Temporal-Spatial Correlation Analysis.

Traumatic brain injury (TBI), a significant global health issue, is affecting ∼69 million annually. To better understand TBI's impact on brain function and assess the efficacy of treatments, this study uses a novel temporal-spatial cross-group approach with a porcine model, integrating resting-state functional magnetic resonance imaging (rs-fMRI) for temporal and arterial spin labeling for spatial information. Our research used 18 four-week-old pigs divided into three groups: TBI treated with saline (SLN, n = 6), TBI treated with fecal microbial transplant (FMT, n = 6), and a sham group (sham, n = 6) with only craniectomy surgery as the baseline. By applying machine learning techniques-specifically, independent component analysis and sparse dictionary learning-across seven identified resting-state networks, we assessed the temporal and spatial correlations indicative of treatment efficacy. Both temporal and spatial analyses revealed a consistent increase of correlation between the FMT and sham groups in the executive control and salience networks. Our results are further evidenced by a simulation study designed to mimic the progression of TBI severity through the introduction of variable Gaussian noise to an independent rs-fMRI dataset. The results demonstrate a decreasing temporal correlation between the sham and TBI groups with increasing injury severity, consistent with the experimental results. This study underscores the effectiveness of the methodology in evaluating post-TBI treatments such as the FMT. By presenting comprehensive experimental and simulated data, our research contributes significantly to the field and opens new paths for future investigations into TBI treatment evaluations.

Enhancing resilience: Integrating future flood modeling and socio-economic analysis in the face of climate change impacts.

As climate change intensifies, future floods will become more severe in some areas with geographic variation, necessitating that local and regional governments implement systems to provide information for climate adaptation, particularly for vulnerable populations. Therefore, we aimed to develop a methodology to identify areas that are at an increased risk from future floods and independently socially vulnerable. In this study, 100-year recurrence interval flood extents and depths were estimated using an ensemble of six independent Coupled Model Intercomparison Project Phase 6 climate models for a past and future period under the highest-emissions climate scenario. The flood inundation results were related to social vulnerability for two selected study areas in the Mississippi River Basin. The range of flood extents and depths for both time periods were estimated, and differences were evaluated to determine the effects from climate change. To identify at-risk areas, the relationship between the spatial distribution of flood depths and vulnerability was then assessed. Finally, an analysis of the current and future damages on infrastructure from flooding on residential housing was performed to determine whether damages are correlated with higher vulnerability areas. Results show in every flooding scenario, flood extents and depths are increasing in the future compared with the past, ranging from an increase of 6 to 76 km2 in extent across both locations. A statistically significant relationship between spatial clusters of flooding and of vulnerability was found. The infrastructure analysis found that residential structures in the most vulnerable census tracts are 6 to 59 times more likely to experience moderate damage compared with the least vulnerable tracts depending on scenario. Overall, a framework was established to holistically understand the hydrologic and socioeconomic impacts of climate change, and a methodology was developed to use for allocating resources at the local scale.

Spatial Variation and Determinants of Inadequate Minimum Meal Frequency among Children Aged 6-23 Months in Ethiopia: Spatial and multilevel analysis using Ethiopian Mini Demographic and Health Survey (EMDHS) 2019.

Introduction: Minimum meal frequency is the number of times children eat in a day. Without adequate meal frequency, infants and young children are prone to malnutrition. There is little information on the spatial distribution and determinants of inadequate meal frequency at the national level. Therefore, we aimed to investigate the spatial distribution and determinants of inadequate meal frequency among young children in Ethiopia. Methods: The most recent Ethiopian demographic and health survey data was used. The analysis was conducted using a weighted sample of 1,610 children aged 6-23 months old. The Global Moran's I was estimated to assess the regional variation in minimum meal frequency. Further, a multivariable multilevel logistic regression model was fitted to identify factors associated with inadequate meal frequency. The AOR (adjusted odds ratio) at 95% CI (confidence interval) was computed to assess the strength and significance of the relationship between explanatory variables and the outcome variable. Factors with a p-value of <0.05 are declared statistically significant. Results: This study revealed that the prevalence of inadequate meal frequency was found to be 30.56% (95% CI: 28.33-32.88). We identified statistically significant clusters of high inadequate meal frequency, notably observed in Somalia, northern Amhara, the eastern part of southern nations and nationalities, and the southwestern Oromia regions. Child age, antenatal care (ANC) visit, marital status, and community level illiteracy were significant factors that were associated with inadequate meal frequency. Conclusion: According to the study findings, the proportion of inadequate meal frequency among young children in Ethiopia was higher and also distributed non-randomly across Ethiopian regions. As a result, policymakers and other concerned bodies should prioritize risky areas in designing intervention. Thus, special attention should be given to the Somalia region, the northern part of Amhara, the eastern part of Southern nations and nationalities, and southwestern Oromia.

Epidemiology of Q fever in humans in four selected regions, Spain, 2016 to 2022.

BackgroundQ fever is a bacterial zoonosis caused by Coxiella burnetii. Spain has the highest number of notified human cases in Europe. Small ruminants are a key reservoir for the pathogen, transmission from animals to humans is usually airborne.AimWe aimed at exploring temporal and spatial epidemiological patterns of sporadic and outbreak cases of Q fever in four Spanish regions with the highest number of notified cases.MethodsWe extracted data on Q fever cases in the Canary Islands, Basque Country, La Rioja and Navarre between 2016 and 2022 from the Spanish National Epidemiological Surveillance Network. We calculated standardised incidence ratios (SIR), spatial relative risks (sRR) and posterior probabilities (PP) utilising Besag-York-Mollié models.ResultsThere were 1,059 notifications, with a predominance of males aged 30-60 years. In Basque Country, La Rioja and Navarre area, 11 outbreaks were reported, while no in the Canary Islands. A seasonal increase in incidence rates was observed between March and June. In the Canary Islands, elevated sRR was seen in La Palma, Gran Canaria, Lanzarote and Fuerteventura. In Basque Country, La Rioja and Navarre area, the highest sRR was identified in the south of Biscay province.ConclusionGoats were the main source for humans in outbreaks reported in the literature. Seasonal increase may be related to the parturition season of small ruminants and specific environmental conditions. Local variations in sRR within these regions likely result from diverse environmental factors. Future One Health-oriented studies are essential to deepen our understanding of Q fever epidemiology.

The COVID-19 pandemic in Brazil: space-time approach of cases, deaths, and vaccination coverage (February 2020 - April 2024).

OBJECTIVE: To assess the evolution of the COVID-19 pandemic in Brazil and its macro-regions, considering disease incidence and mortality rates, as well as identifying territories with still rising disease indices and evaluating vaccine coverage and population adherence to COVID-19 immunization. METHODS: An ecological study conducted in Brazil with COVID-19 cases and deaths reported between February 2020 and April 2024, obtained through the Coronavirus Panel. Historical series were constructed from incidence and mortality rates to assess the pandemic's evolution, and temporal trends were estimated using the Seasonal Trend Decomposition using Loess (STL) method. The Spatial Variation in Temporal Trends (SVTT) technique was employed to identify clusters with significant variations in temporal trends. Vaccination was analyzed considering the percentage of vaccinated and unvaccinated population in each municipality of the country. RESULTS: Brazil recorded a total of 38,795,966 cases and 712,038 deaths from COVID-19 during the study period. Incidence and mortality rates showed three waves of the disease, with a fourth wave of smaller amplitude. Four clusters with significant case growth and two with increased deaths were identified. Vaccine coverage varied among municipalities, with some regions showing low vaccination rates and others with high immunization adherence. CONCLUSION: The study provided a comprehensive overview of coronavirus behavior in Brazil, and its results highlight the ongoing importance of vaccination and the need to direct efforts and resources to areas of higher risk.

The impact of COVID-19 national lockdowns on drug-resistant tuberculosis in KwaZulu-Natal, South Africa: a spatial analysis.

PURPOSE: We sought to understand the impact of the initial COVID-19 mitigation strategies in 2020 on drug-resistant (DR) TB diagnoses in KwaZulu-Natal province (KZN), South Africa. METHODS: We compared the number, spatial distribution, and characteristics of DR TB diagnoses before and after the initial COVID-19 lockdown on March 26th, 2020. Information on DR TB diagnoses was collected from the CONTEXT prospective cohort study and municipality characteristics were collected from Statistics South Africa. We used Bayesian conditional autoregressive models and relative-risk surface maps to examine spatial correlates and patterns of DR TB notifications. RESULTS: Between October 2018 and February 2022, there were 693 individuals diagnosed with DR TB in KZN, South Africa. The rate of diagnoses per year was 274 and 155 prior and after to the initial lockdowns, respectively, corresponding to a 43% decrease in the notification rate of cases. Compared to cases diagnosed before the lockdown, cases diagnosed after were less likely to have a fuel source for heating, piped water, a flush toilet, or own a phone (p-values≤0.02). Changes in notifications were not homogenously distributed, with predominantly rural northeastern and southwestern municipalities having significantly greater relative-risks after the lockdown. CONCLUSIONS: We found a reduction in the rate of DR TB diagnoses after the COVID-19 pandemic lockdowns and observed that individuals diagnosed after the lockdowns had worse living conditions, fewer household resources, and more adults living in their household compared to before the pandemic.

Spatial distribution and geographical heterogeneity factors associated with households' enrollment level in community-based health insurance.

Background: Healthcare service utilization is unequal among different subpopulations in low-income countries. For healthcare access and utilization of healthcare services with partial or full support, households are recommended to be enrolled in a community-based health insurance system (CBHIS). However, many households in low-income countries incur catastrophic health expenditure. This study aimed to assess the spatial distribution and factors associated with households' enrollment level in CBHIS in Ethiopia. Methods: A cross-sectional study design with two-stage sampling techniques was used. The 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. STATA 15 software and Microsoft Office Excel were used for data management. ArcMap 10.7 and SaTScan 9.5 software were used for geographically weighted regression analysis and mapping the results. A multilevel fixed-effect regression was used to assess the association of variables. A variable with a p < 0.05 was considered significant with a 95% confidence interval. Results: Nearly three out of 10 (28.6%) households were enrolled in a CBHIS. The spatial distribution of households' enrollment in the health insurance system was not random, and households in the Amhara and Tigray regions had good enrollment in community-based health insurance. A total of 126 significant clusters were detected, and households in the primary clusters were more likely to be enrolled in CBHIS. Primary education (AOR: 1.21, 95% CI: 1.05, 1.31), age of the head of the household >35 years (AOR: 2.47, 95% CI: 2.04, 3.02), poor wealth status (AOR: 0.31, 95% CI: 0.21, 1.31), media exposure (AOR: 1.35, 95% CI: 1.02, 2.27), and residing in Afar (AOR: 0.01, 95% CI: 0.003, 0.03), Gambela (AOR: 0.03, 95% CI: 0.01, 0.08), Harari (AOR: 0.06, 95% CI: 0.02, 0.18), and Dire Dawa (AOR: 0.02, 95% CI: 0.01, 0.06) regions were significant factors for households' enrollment in CBHIS. The secondary education status of household heads, poor wealth status, and media exposure had stationary significant positive and negative effects on the enrollment of households in CBHIS across the geographical areas of the country. Conclusion: The majority of households did not enroll in the CBHIS. Effective CBHIS frameworks and packages are required to improve the households' enrollment level. Financial support and subsidizing the premiums are also critical to enhancing households' enrollment in CBHIS.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019-2021 fatal collisions in the USA.

OBJECTIVE: The USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas or single cities. This study aims to quantify associations between built environment characteristics and traffic collisions in the USA. METHODS: Built environment characteristics were derived from Google Street View images and summarised at the census tract level. Fatal traffic collisions were obtained from the 2019-2021 Fatality Analysis Reporting System. Fatal and non-fatal traffic collisions in Washington DC were obtained from the District Department of Transportation. Adjusted Poisson regression models examined whether built environment characteristics are related to motor vehicle collisions in the USA, controlling for census tract sociodemographic characteristics. RESULTS: Census tracts in the highest tertile of sidewalks, single-lane roads, streetlights and street greenness had 70%, 50%, 30% and 26% fewer fatal vehicle collisions compared with those in the lowest tertile. Street greenness and single-lane roads were associated with 37% and 38% fewer pedestrian-involved and cyclist-involved fatal collisions. Analyses with fatal and non-fatal collisions in Washington DC found streetlights and stop signs were associated with fewer pedestrians and cyclists-involved vehicle collisions while road construction had an adverse association. CONCLUSION: This study demonstrates the utility of using data algorithms that can automatically analyse street segments to create indicators of the built environment to enhance understanding of large-scale patterns and inform interventions to decrease road traffic injuries and fatalities.

A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping.

System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.

Associations between Flood Risk and United States Census Tract-Level Health Outcomes.

Human-induced climate change has led to more frequent and severe flooding throughout the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the UnitedStates, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation by census tract and used principal component analysis to derive a set of five interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on this data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We showed that three flood risk principal components had small but significant associations with each of the health outcomes, across the different stratifications of social vulnerability. Our analysis gives the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards.

Geographic accessibility to physiotherapy care in Aotearoa New Zealand.

Disparities in care access for health conditions where physiotherapy can play a major role are abetting health inequities. Spatial analyses can contribute to illuminating inequities in health yet the geographic accessibility to physiotherapy care across New Zealand has not been examined. This population-based study evaluated the accessibility of the New Zealand physiotherapy workforce relative to the population at a local scale. The locations of 5,582 physiotherapists were geocoded and integrated with 2018 Census data to generate 'accessibility scores' for each Statistical Area 2 using the newer 3-step floating catchment area method. For examining the spatial distribution and mapping, accessibility scores were categorized into seven levels, centered around 0.5 SD above and below the mean. New Zealand has an above-average physiotherapy-to-population ratio compared with other OECD countries; however, this workforce is maldistributed. This study identified areas (and locations) where geographic accessibility to physiotherapy care is relatively low.