Ashley Hazel

Background: Although healthcare-associated transmission of Clostridioides difficile is a recognized public health concern, community-onset infections represent an important component of the overall disease burden. This paradox likely reflects the underappreciated interplay between these settings. We aimed to quantify in-hospital transmission and the hospital’s contribution to community colonization by estimating the intrinsic reproduction number (R _i ) and introducing the colonization amplification index (A _i ), defined as the ratio of colonized patients at discharge to those at admission. Given the potential contribution of external cases, we also evaluated interventions targeting asymptomatic carriers at admission to reduce disease burden. Methods and findings: We developed a compartmental model informed by data from UCSF Medical Center to capture C. difficile transmission dynamics among symptomatic and asymptomatic patients. Across simulations, the median R _i was 0.61 (Q1–Q3: 0.53, 0.71), consistently indicating limited sustained in-hospital transmission (R _i < 1). In contrast, A _i was 1.9 (Q1–Q3: 1.7, 2.1), suggesting substantial amplification of colonization during hospital stay. Amplification of colonization persisted in sensitivity analyses, with R _i exceeding 1 under boundary values of low colonization prevalence at admission, a low proportion of colonized patients progressing to symptomatic disease, and prolonged incubation periods. Redefining healthcare-associated C. difficile infection (CDI) using thresholds from ≥1-day post-admission instead of the standard threshold of >3 days post-admission increased A _i and R _i by 11% and 21%, respectively. A threshold of ≥5 days post-admission reduced these metrics by 5% and 8%, respectively. Interventions targeting asymptomatic carriers through contact precautions and/or prophylactic treatment reduced both A _i and CDI incidence, with combined interventions yielding the greatest reductions, followed by contact precautions alone. Our main limitation stems from uncertainty in some parameters describing the disease’s natural history, particularly colonization prevalence at admission, progression to symptomatic disease, and the incubation period, which may affect the precision of our estimates despite sensitivity analyses. Conclusions: Our findings indicate that in most potential scenarios, in-hospital transmission of C. difficile is limited (R _i < 1) and likely sustained by continuous importation of cases from the community. Nevertheless, hospitalization amplifies colonization (A _i > 1), which potentially contributes to community transmission. These results underscore the importance of interventions addressing asymptomatic carriers, a currently overlooked source of spread. Our study highlights the need to broaden metrics beyond R _i to capture hospitals’ contribution to the C. difficile burden. Future infection control strategies should address colonization dynamics at admission and potentially at discharge to mitigate transmission and reduce the overall burden of C. difficile.

To enumerate people experiencing homelessness in the United States, the federal Department of Housing and Urban Development (HUD) mandates its designated local jurisdictions regularly conduct a crude census of this population. This Point-in-Time (PIT) body count, typically conducted on a January night by volunteers with flashlights and clipboards, is often followed by interviews with a separate convenience sample. Here, we propose employing a network-based (peer-referral) respondent-driven sampling (RDS) method to generate a representative sample of unsheltered people, accompanied by a novel method to generate a statistical estimate of the number of unsheltered people in the jurisdiction. First, we develop a power analysis for the sample size of our RDS survey to count unsheltered people experiencing homelessness. Then, we conducted 3 large-scale population-representative samples in King County, WA (Seattle metro) in 2022, 2023, and 2024. We describe the data collection and the application of our new method, comparing the 2020 PIT count (the last visual PIT count performed in King County) to the new method of 2022 and 2024 PIT counts. We conclude with a discussion and future directions. This article is part of a Special Collection on Methods in Social Epidemiology.

: Azithromycin mass drug administration decreases child mortality but also selects for antibiotic resistance. Herein, we evaluate macrolide resistance of nasopharyngeal Streptococcus pneumoniae after azithromycin MDA. In a cluster-randomized trial, children 1–59 months received azithromycin or placebo biannually. Fifteen villages from each arm were randomly selected for antimicrobial resistance testing, and 10–15 randomly selected swabs from enrolled children at each village were processed for S. pneumoniae isolation and resistance testing. The primary prespecified outcome was macrolide resistance fraction for azithromycin versus placebo villages at 36 months. Secondary non-prespecified outcomes were comparisons of azithromycin and placebo for: 1) macrolide resistance at 12, 24, and 36 months; 2) nonmacrolide resistance at 36 months; and 3) suspected-erm mutation. At 36 months, 423 swabs were obtained and 322 grew S. pneumoniae, (azithromycin: 146/202, placebo: 176/221). Mean resistance prevalence was non-significantly higher in treatment than placebo (mixed-effects model: 14.6% vs. 8.9%; OR = 2.0, 95% CI: 0.99–3.97). However, when all time points were evaluated, macrolide resistance prevalence was significantly higher in the azithromycin group (β = 0.102, 95% CI: 0.04–0.167). For all nonmacrolides, resistance prevalence at 36 months was not different between the two groups. Azithromycin and placebo were not different for suspected-erm mutation prevalence. Macrolide resistance was higher in the azithromycin group over all time points, but not at 36 months. Although this suggests resistance may not continue to increase after biannual MDA, more studies are needed to clarify when MDA can safely decrease mortality and morbidity in lower- and middle-income countries.

Background: Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission. Methods: We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction. Results: Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%–79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination. Conclusion: In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities’ resiliency to TB introduction decreases as their interconnectivity increases. “Top down” population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.

Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations.

Background: Transmission by unreported cases has been proposed as a reason for the 2013–2016 Ebola virus (EBOV) epidemic decline in West Africa, but studies that test this hypothesis are lacking. We examined a transmission chain within social networks in Sukudu village to assess spread and transmission burnout. Methods: Network data were collected in 2 phases: (1) serological and contact information from Ebola cases (n = 48, including unreported); and (2) interviews (n = 148), including Ebola survivors (n = 13), to identify key social interactions. Social links to the transmission chain were used to calculate cumulative incidence proportion as the number of EBOV-infected people in the network divided by total network size. Results: The sample included 148 participants and 1522 contacts, comprising 10 social networks: 3 had strong links (>50% of cases) to the transmission chain: household sharing (largely kinship), leisure time, and talking about important things (both largely non-kin). Overall cumulative incidence for these networks was 37 of 311 (12%). Unreported cases did not have higher network centrality than reported cases. Conclusions: Although this study did not find evidence that explained epidemic decline in Sukudu, it excluded potential reasons (eg, unreported cases, herd immunity) and identified 3 social interactions in EBOV transmission.

People experiencing homelessness (PEH) face extreme weather exposure and limited social support. However, few studies have empirically assessed biophysical and social drivers of health outcomes among unsheltered PEH. Social network, health, and outdoor exposure data were collected from a convenience sample of unsheltered PEH (n = 246) in Nashville, TN, from August 2018–June 2019. Using multivariate fixed-effects linear regression models, we examined associations between biophysical and social environments and self-reported general health and emotional well-being. We found that study participants reported the lowest general health scores during winter months—Nashville’s coldest season. We also found a positive association between the number of nights participants spent indoors during the previous week and general health. Participants who spent even one night indoors during the past week had 1.8-point higher general health scores than participants who spent zero nights indoors (p < 0.01). Additionally, participants who experienced a conflict with a social contact in the past 30 days had lower emotional well-being scores than participants who experienced no conflict. Finally, women had worse general health and emotional well-being than men. Ecologically framed research about health and well-being among PEH is critically needed, especially as climate change threatens to increase the danger of many homeless environments.

Objective: We describe the composition and variation of women's resource strategies in an arid‐living Southern African agro‐pastoralist society to gain insights into adaptation to climate‐change‐induced increased aridity.Methods: Using cross‐sectional data from 210 women collected in 2009 across 28 agro‐pastoralist villages in Kaokoveld Namibia, we conducted principal‐component (PC) analysis of resource variables and constructed profiles of resource strategies from the major PCs. Next, we explored associations between key resource strategies and demographic measures and fitness proxies.Results: The first two PCs accounted for 43% of women's overall resource variation. PC1 reflects women's ability to access market resources via livestock trading, while PC2 captured women's direct food access. We found that market strategies were more common among married women and less common among women who have experienced child mortality. Women with higher subsistence security were more likely to be from the OvaHimba tribe and had a higher risk of gonorrhea exposure. We also qualitatively explored drought‐induced pressure on women's livestock. Finally, we show that sexual networks were attenuated during drought, indicating strain on social support.Conclusions: Our results highlight how agro‐pastoralist women manage critical resources in unpredictable environments, and how resource strategies distribute among the women in our study. Goats as a commodity to obtain critical resources suggests that some women have flexibility during drought when gardens fail and cattle die. However, increased aridity and drought may eventually overwhelm husbandry practices in this region.

Objectives: With our diverse training, theoretical and empirical toolkits, and rich data, evolutionary and biological anthropologists (EBAs) have much to contribute to research and policy decisions about climate change and other pressing social issues. However, we remain largely absent from these critical, ongoing efforts. Here, we draw on the literature and our own experiences to make recommendations for how EBAs can engage broader audiences, including the communities with whom we collaborate, a more diverse population of students, researchers in other disciplines and the development sector, policymakers, and the general public. These recommendations include: (1) playing to our strength in longitudinal, place‐based research, (2) collaborating more broadly, (3) engaging in greater public communication of science, (4) aligning our work with open‐science practices to the extent possible, and (5) increasing diversity of our field and teams through intentional action, outreach, training, and mentorship.Conclusions: We EBAs need to put ourselves out there: research and engagement are complementary, not opposed to each other. With the resources and workable examples we provide here, we hope to spur more EBAs to action.

OBJECTIVES. With our diverse training, theoretical and empirical toolkits, and rich data, evolutionary and biological anthropologists (EBAs) have much to contribute to research and policy decisions about climate change and other pressing social issues. However, we remain largely absent from these critical, ongoing efforts. Here, we draw on the literature and our own experiences to make recommendations for how EBAs can engage broader audiences, including the communities with whom we collaborate, a more diverse population of students, researchers in other disciplines and the development sector, policymakers, and the general public. These recommendations include: (1) playing to our strength in longitudinal, place-based research, (2) collaborating more broadly, (3) engaging in greater public communication of science, (4) aligning our work with open-science practices to the extent possible, and (5) increasing diversity of our field and teams through intentional action, outreach, training, and mentorship. CONCLUSIONS. We EBAs need to put ourselves out there: research and engagement are complementary, not opposed to each other. With the resources and workable examples we provide here, we hope to spur more EBAs to action.