This community‐based research aims to enhance local‐level flood management by utilising participatory geographic information systems (PGIS) methods to capture the spatial dimensions of community member flooding concerns in Hopkins Village, Belize. We offer a mixed methodology, applying participatory sketch mapping as a way to collect local knowledge about community perceptions of flooding in this data‐scarce context. We combine this local knowledge with quantitative geostatistical hot spot analysis of basic village infrastructure characteristics to reveal insights about community perceptions of and response to flood risk. The significance of this research lies in the application of PGIS methods to create two different primary datasets, which when analysed together offer a more complete story about community understanding and needs for flood management. One set of data (more qualitative in nature) originated from sketch maps with community members and answers descriptive questions about how people spatially conceptualise hydro‐meteorological hazards within their community. The other (more quantitative in nature) is the village's first publicly accessible infrastructure dataset (including information on building structures, roads, and drainage infrastructure) digitised by our research team from high‐resolution drone imagery. Attributes for the infrastructure dataset were developed in collaboration with community members to reflect their desires for data and information suited to conduct flood vulnerability assessment. Application of thematic coding and hot spot analysis to the data reveals concerns about hazards within their community and the measures they have taken to protect themselves in the absence of resources to develop community‐wide mitigation plans. Our work offers contributions to the participatory GIS literature by providing an example of sketch mapping methods to facilitate multi‐level management of community‐level flood risk in a data‐scarce setting, and demonstrating the multiple benefits of applied participatory GIS methods as a means of overcoming data scarcity.

Introduction Malawi’s malaria burden is primarily assessed via cross-sectional national household surveys. However, malaria is spatially and temporally heterogenous and no analyses have been performed at a subdistrict level throughout the course of a year. The WHO recommends mass distribution of long-lasting insecticide-treated bed nets (LLINs) every 3 years, but a national longitudinal evaluation has never been conducted in Malawi to determine LLIN effectiveness lifespans. Methods Using District Health Information Software 2 (DHIS2) health facility data, available from January 2018 to June 2020, we assessed malaria risk before and after a mass distribution campaign, stratifying by age group and comparing risk differences (RDs) by LLIN type or annual application of indoor residual spraying (IRS). Results 711 health facilities contributed 20 962 facility reports over 30 months. After national distribution of 10.7 million LLINs and IRS in limited settings, malaria risk decreased from 25.6 to 16.7 cases per 100 people from 2018 to 2019 high transmission seasons, and rebounded to 23.2 in 2020, resulting in significant RDs of −8.9 in 2019 and −2.4 in 2020 as compared with 2018. Piperonyl butoxide (PBO)-treated LLINs were more effective than pyrethroid-treated LLINs, with adjusted RDs of −2.3 (95% CI −2.7 to −1.9) and −1.5 (95% CI −2.0 to −1.0) comparing 2019 and 2020 high transmission seasons to 2018. Use of IRS sustained protection with adjusted RDs of −1.4 (95% CI −2.0 to −0.9) and −2.8% (95% CI −3.5 to −2.2) relative to pyrethroid-treated LLINs. Overall, 12 of 28 districts (42.9%) experienced increases in malaria risk in from 2018 to 2020. Conclusion LLINs in Malawi have a limited effectiveness lifespan and IRS and PBO-treated LLINs perform better than pyrethroid-treated LLINs, perhaps due to net repurposing and insecticide-resistance. DHIS2 provides a compelling framework in which to examine localised malaria trends and evaluate ongoing interventions.

SARS-CoV-2 testing data in North Carolina during the first three months of the state's COVID-19 pandemic were analyzed to determine if there were disparities among intersecting axes of identity including race, Latinx ethnicity, age, urban-rural residence, and residence in a medically underserved area. Demographic and residential data were used to reconstruct patterns of testing metrics (including tests per capita, positive tests per capita, and test positivity rate which is an indicator of sufficient testing) across race-ethnicity groups and urban-rural populations separately. Across the entire sample, 13.1% (38,750 of 295,642) of tests were positive. Within racial-ethnic groups, 11.5% of all tests were positive among non-Latinx (NL) Whites, 22.0% for NL Blacks, and 66.5% for people of Latinx ethnicity. The test positivity rate was higher among people living in rural areas across all racial-ethnic groups. These results suggest that in the first three months of the COVID-19 pandemic, access to COVID-19 testing in North Carolina was not evenly distributed across racial-ethnic groups, especially in Latinx, NL Black and other historically marginalized populations, and further disparities existed within these groups by gender, age, urban-rural status, and residence in a medically underserved area.

PCR-confirmed SARS-CoV-2 cases underestimate true prevalence. Few robust community-level SARS-CoV-2 ethnoracial and overall prevalence estimates have been published for North Carolina in 2020.

Deep tubewells are a key component of arsenic mitigation programs in rural Bangladesh. Compared to widely prevalent shallow tubewells, deep tubewells reduce ground-water arsenic exposure and provide better microbial water quality at source. However, the benefits of clean drinking-water at these more distant sources may be abated by higher levels of microbial contamination at point-of-use. One such potential pathway is the use of contaminated surface water for washing drinking-water storage containers. The aim of this study is to compare the prevalence of surface water use for washing drinking-water storage containers among deep and shallow tubewell users in a cohort of 499 rural residents in Matlab, Bangladesh. We employ a multi-level logistic regression model to measure the effect of tubewell type and ownership status on the odds of washing storage containers with surface water. Results show that deep tubewell users who do not own their drinking-water tubewell, have 6.53 times the odds [95% CI: 3.56, 12.00] of using surface water for cleaning storage containers compared to shallow tubewell users, who own their drinking-water source. Even deep tubewell users who own a private well within walking distance have 2.53 [95% CI: 1.36, 4.71] times the odds of using surface water compared to their shallow tubewell counterparts. These results highlight the need for interventions to limit risk substitution, particularly the increased use of contaminated surface water when access to drinking water is reduced. Increasing ownership of and proximity to deep tubewells, although crucial, is insufficient to achieve equity in safe drinking-water access across rural Bangladesh.