Surveillance and diagnosis of Plasmodium falciparum malaria relies predominantly on rapid diagnostic tests (RDT). However, false-negative (FN) RDT results are known to occur for a variety of reasons, including operator error, poor storage conditions, pfhrp2/3 gene deletions, poor performance of specific RDT brands and lots, and low-parasite density infections. We used RDT and microscopy results from 85 000 children enrolled in Demographic Health Surveys and Malaria Indicator Surveys from 2009 to 2015 across 19 countries to explore the distribution of and risk factors for FN-RDTs in sub-Saharan Africa, where malaria’s impact is greatest. We sought to (1) identify spatial and demographic patterns of FN-RDT results, defined as a negative RDT but positive gold standard microscopy test, and (2) estimate the percentage of infections missed within community-based malaria surveys due to FN-RDT results. Across all studies, 19.9% (95% CI 19.0% to 20.9%) of microscopy-positive subjects were negative by RDT. The distribution of FN-RDT results was spatially heterogeneous. The variance in FN-RDT results was best explained by the prevalence of malaria, with an increase in FN-RDT results observed at lower transmission intensities, among younger subjects, and in urban areas. The observed proportion of FN-RDT results was not predicted by differences in RDT brand or lot performance alone. These findings characterise how the probability of detection by RDTs varies in different transmission settings and emphasise the need for careful interpretation of prevalence estimates based on surveys employing RDTs alone. Further studies are needed to characterise the cost-effectiveness of improved malaria diagnostics (eg, PCR or highly sensitive RDTs) in community-based surveys, especially in regions of low transmission intensity or high urbanicity.

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM_2.5 and O₃) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM_2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O₃ and PM_2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m⁻³, and, on occasion, exceeded 180 µg m⁻³. PM_2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM_2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM_2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM_2.5 during the periods studied at the four locations averaged about 30 µg m⁻³, representing approximately 30 % and 20 % of total PM_2.5 levels in the rural and urban stations, respectively.

The Democratic Republic of the Congo (DRC) bears a large share of global malaria burden despite efforts to control and eliminate the disease. More detailed understanding of individual and household level characteristics associated with malaria are needed, as is an understanding of how these characteristics vary spatiotemporally and across different community-level malaria endemicities. An ongoing study in Kinshasa Province is designed to address gaps in prior malaria surveillance in the DRC by monitoring malaria across seasons, age groups and in high and low malaria sites. Across seven sites, 242 households and 1591 individuals are participating in the study. Results of the enrollment questionnaire, rapid diagnostic tests and PCR testing of dried blood spots are presented. Overall malaria prevalence in the study cohort is high, 27% by rapid diagnostic test and 31% by polymerase chain reaction, and malaria prevalence is highly varied across very small geographic distances. Malaria prevalence is highest in children aged 6–15. While the majority of households own bed nets, bed net usage is less than 50%. The study cohort will provide an understanding of how malaria persists in populations that have varying environmental exposures, varying community-level malaria, and varying access to malaria control efforts.

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.