Coxiella burnetii, the causative agent of Query fever (Q fever), is among the most highly infectious zoonotic pathogens transmitted among livestock, with chronic effects challenging to veterinary and medical detection and care systems. Transmission among domestic livestock species can vary regionally due to herd management practices that determine which livestock species are raised, whether or not livestock are in contact with wildlife, and the susceptibility of these livestock to infection. To explore how different livestock management practices are associated with the risk of infection in multispecies environments, we carried out a comparative study of three types of herd management systems in the central Kenyan county of Laikipia: agro‐commercial, mixed conservancy/commercial, and smallholder ranches. We tested C. burnetii antibody seroprevalence in four common livestock species. Across all management types, the highest seroprevalence was in camels (20%), followed by goats (18%), sheep (13%), and cattle (6%). We observed a lower odds of testing seropositive for young compared to adult animals (adjusted OR = 0.44 [95% CI 0.24, 0.76]), and for males compared to females (adjusted OR = 0.52 [95% CI 0.33, 0.80]). Animals from mixed conservancy/commercial and smallholder operations had a higher odds of testing seropositive compared to animals from agro‐commercial ranches (adjusted OR = 5.17 [95% CI 2.71, 10.44] and adjusted OR = 2.21 [95% CI 1.17, 4.43] respectively). These data suggest that herd management practices might affect the transmission dynamics of C. burnetiiin arid African ecosystems like those seen in Kenya where several transmission modes are possible, risk of drought has promoted new livestock species such as camels, and multiple wildlife species may co‐occur with livestock on the landscape. Further longitudinal studies are needed to disentangle the mechanisms underlying these patterns, and further explore transmission patterns between wildlife, domestic animal, and human populations.

Introduction: Short-term exposures to air pollutants such as particulate matter (PM) have been associated with increased risk for symptoms of acute respiratory infections (ARIs). Less well understood is how long-term exposures to fine PM (PM${}_{2.5}$) might increase risk of ARIs and their symptoms. This research uses georeferenced Demographic Health Survey (DHS) data from Kenya (2014) along with a remote sensing based raster of PM${}_{2.5}$ concentrations to test associations between PM${}_{2.5}$ exposure and ARI symptoms in children for up to 12 monthly lags. Methods: Predicted PM${}_{2.5}$ concentrations were extracted from raster of monthly averages for latitude/longitude locations of survey clusters. These data and other environmental and demographic data were used in a logistic regression model of ARI symptoms within a distributed lag nonlinear modeling framework (DLNM) to test lag associations of PM${}_{2.5}$ exposure with binary presence/absence of ARI symptoms in the previous two weeks. Results: Out of 7036 children under five for whom data were available, 46.8% reported ARI symptoms in the previous two weeks. Exposure to PM${}_{2.5}$ within the same month and as an average for the previous 12 months was 18.31 and 22.1 µg/m${}^3$, respectively, far in excess of guidelines set by the World Health Organization. One-year average PM${}_{2.5}$ exposure was higher for children who experienced ARI symptoms compared with children who did not (22.4 vs. 21.8 µg/m³, p < 0.0001.) Logistic regression models using the DLNM framework indicated that while PM exposure was not significantly associated with ARI symptoms for early lags, exposure to high concentrations of PM${}_{2.5}$ (90th percentile) was associated with elevated odds for ARI symptoms along a gradient of lag exposure time even when controlling for age, sex, types of cooking fuels, and precipitation. Conclusions: Long-term exposure to high concentrations of PM${}_{2.5}$ may increase risk for acute respiratory problems in small children. However, more work should be carried out to increase capacity to accurately measure air pollutants in emerging economies such as Kenya.

Climate change is increasing the frequency and intensity of extreme precipitation events, the impacts of which disproportionately impact urban populations. Pluvial flooding and flooding related sewer backups are thought to result in an increase in potentially hazardous human-pathogen encounters. However, the extent and nature of associations between flooding events and non-communicable respiratory diseases such as chronic bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) are not well understood. This research seeks to characterize the state of research on flooding and NCRDs through a systematic review of the scientific literature. We conducted a systematic search of PubMed, Web of Science, and Scopus for published scholarly research papers using the terms flooding, monsoon, and tropical storm with terms for common NCRDs such as asthma, COPD, and chronic bronchitis. Papers were included if they covered research studies on individuals with defined outcomes of flooding events. We excluded review papers, case studies, and opinion pieces. We retrieved 200 articles from PubMed, 268 from Web of Science and 203 from Scopus which comprised 345 unique papers. An initial review of abstracts yielded 38 candidate papers. A full text review of each left 16 papers which were included for the review. All papers except for one found a significant association between a severe weather event and increased risk for at least one of the NCRDs included in this research. Our findings further suggest that extreme weather events may worsen pre-existing respiratory conditions and increase the risk of development of asthma. Future work should focus on more precisely defining measure of health outcomes using validated tools to describe asthma and COPD exacerbations. Research efforts should also work to collect granular data on patients’ health status and family history and assess possible confounding and mediating factors such as neighborhood water mitigation infrastructure, housing conditions, pollen counts, and other environmental variables.

Frequent enteric infections in children may be an important cause of growth faltering; however, we do not fully understand the mechanisms by which pathogen infections and the physiological responses to these infections result in poorer growth. Commonly used protein fecal biomarkers (anti-alpha trypsin, neopterin, and myeloperoxidase) provide broad immunological information on an inflammatory response; however, they do not provide information on non-immune processes (e.g., gut integrity) that may be important indicators of chronic end states such as environmental enteric dysfunction (EED). To explore how additional biomarkers will better inform which physiological pathways (both immune and non-immune) are impacted by pathogen exposure we added to the traditional panel of 3 protein fecal biomarkers 4 novel fecal mRNA transcript biomarkers (sucrase isomaltase, caudal homeobox 1, S100A8, and mucin 12) and analyzed stool samples from infants living in informal settlements in Addis Ababa, Ethiopia. To test how this expanded biomarker panel captures distinct pathogen exposure processes we used two different scoring systems. First, we used a theory-based approach to assign each biomarker to specific physiological attributes based on prior understanding of each biomarker. Second, we used data reduction methods to categorize biomarkers and then assign physiological attributes to those categories. We used linear models to examine the association between the derived biomarker scores (based on mRNA and protein levels) and stool pathogen gene counts to determine pathogen specific effects on gut physiology and immune responses. Inflammation scores were positively associated with Shigella and enteropathogenic E.Coli (EPEC) infection, while gut integrity scores were negatively associated with Shigella, EPEC and, shigatoxigenic E.coli (STEC) infection. Our expanded panel of biomarkers hold promise as tools to measure systemic outcomes of enteric pathogen infection. mRNA biomarkers complement established protein biomarkers by providing important cell-specific physiological and immunological consequences of pathogen carriage that can lead to chronic end states such as EED.

Despite growing urbanization, our understanding of the impacts of water and sanitation on human health has largely come from studies in rural sectors. To this end, we collected data at both regional (water quality measures from water treatment systems) and community (cross-sectional surveys) scales to examine determinants of enteric pathogen infection and diarrheal disease among infants in Addis Ababa, Ethiopia. Regionally, the Legedadi water treatment plant had significantly lower heterotrophic plate counts, total coliform counts, and fecal coliform counts compared with the Gefersa water treatment plant. The number of pathogen types in infant stool also differed by plant. Decreases in chlorine levels and increases in the relative abundance of Gammaproteobacteria with distance from treatment plants suggest a compromised water distribution system. In communities, infants in households that obtained water from yard pipes or public taps had significantly lower odds of diarrhea compared to households that had water piped into their dwellings (OR = 0.35, 95% CI 0.16, 0.76, and OR = 0.39, 95% CI 0.15, 1.00, respectively). Similarly, infants in households that boiled or filtered water had significantly lower odds of diarrhea compared to households that did not treat water (OR = 0.40, 95% CI 0.19, 0.86 and OR = 0.23, 95% CI 0.06, 0.84, respectively). Integrating multiscalar data better informs the health impacts of water in urban settings.

Recent results from water, sanitation, and hygiene interventions highlight the need to better understand environmental influences on enteropathogen transmission. We quantified a range of viral, bacterial, and protozoal pathogens and one indicator, Enterococcus faecalis in soil and water from urban and rural sites in and around Yangon, Myanmar. We found that environmental characteristics associated with contamination differed by pathogens and substrates. In soil, bacterial pathogen gene counts were associated with elevation and drainage ditches (compared to stagnant water) (RR = 0.96, 95% CI 0.93, 0.99 and RR = 1.70, 95% CI 1.18, 2.45, respectively), while viral gene counts were associated with the presence of sanitation facilities within 50 m of the collection point (RR = 3.99, 95% CI 1.12, 14.24). In water, E. faecalis, total pathogen, and bacterial pathogen gene counts were associated with drainage ditches (RR = 1.86, 95% CI 1.27, 2.72, RR = 1.38 95% CI 1.09, 1.74, and RR = 1.38 95% CI 1.07, 1.77, respectively). E. faecalis, total pathogen, bacterial pathogen, and viral gene counts were associated with the presence of uncollected garbage within 50 m of the collection point (RR = 1.57, 95% CI 1.00, 2.47, RR = 1.52, 95% CI 1.16, 2.00, RR = 1.52, 95% CI 1.13, 2.06, and RR = 1.75, 95% CI 1.17, 2.61 respectively). Measuring the environment provides added specificity toward identifying important environmental pathways that require mitigation.