Randall Kramer

Objective: Cardiometabolic diseases are rising in low‐ and middle‐income countries, which also bear high burdens of helminth infection and malnutrition. Untangling the relationships among helminth infection, energy status, and cardiovascular disease is important for reducing these burdens. Methods: We investigated the interacting risks and moderating variables associated with cardiometabolic health using demographic, health, and hookworm infection data from 874 participants in rural northeast Madagascar. Results: We observed wide variation in body‐mass index (BMI) and blood pressure, alongside high prevalences of two helminth infections: the human hookworm Necator americanus (53.6%) and the zoonotic hookworm Ancylostoma ceylanicum (20.4%). We hypothesized that hookworm infections would be associated with reduced BMI and lower blood pressure through energetic and immunomodulatory pathways. Yet, the two hookworm species showed different associations, with N. americanus infection negatively associated with BMI and A. ceylanicum infection positively associated with blood pressure. Wealthier individuals had higher BMI, but this positive effect was attenuated if they were infected with A. ceylanicum . Coinfection with both hookworms did not significantly modify these associations with BMI or blood pressure. Structural equation modeling indicated that N. americanus infection was associated with reduced blood pressure both directly and through its negative association with BMI, while A. ceylanicum infection was associated with increased blood pressure independent of BMI. Conclusions: Our findings shed new light on the interacting burdens of malnutrition, parasitism, and chronic diseases with implications for public health, highlighting that these associations are dependent on parasite species.

Hookworms infect half a billion people worldwide, disproportionately impacting those living in poverty. Previous research has focused on sociodemographic predictors of infection, while less is known about how human land use influences infection. Moreover, recent molecular research has revealed that humans are frequently infected by multiple hookworm species that likely differ in their transmission patterns. In this study, we combined DNA metabarcoding of human and domesticated animal parasites with fine-scale GPS movement data to investigate how hookworm infection relates to people's land use and sociodemographic characteristics in three villages in rural Madagascar. We found high hookworm infection prevalence (52% in women and 69% in men; n = 881). Necator americanus predominated (53% prevalence), but the zoonotic hookworm Ancylostoma ceylanicum was also common (20% prevalence). Domesticated animals hosted several parasite species typically absent in humans. However, the zoonotic hookworm A. ceylanicum was highly prevalent in dogs (67-88% across villages). Consistent with previous findings, men had double the odds of Necator americanus infection than women, and prevalence was higher in those with lower material wealth (64% prevalence in the bottom quartile versus 47% prevalence in the top quartile). Zoonotic hookworm infections were more common in men than women (OR = 1.54), those with less formal education, and those reporting close contact with dogs (OR = 1.61). Regarding land use by people, we found that larger home ranges and greater use of vegetated areas outside villages was linked with increased N. americanus , but not A. ceylanicum, infection risk. Network analyses revealed that infected individuals were more strongly connected to each other than to uninfected individuals on networks of shared vegetated patches outside of the village. However, in one village with low levels of vegetation cover, we found significant spatial associations between people’s dwellings and human hookworm infections, illustrating the importance of ecological context for hookworm transmission. These findings underscore the importance of integrating fine-scale land use patterns with sociodemographic risk factors to better understand and mitigate human and zoonotic hookworm transmission.

Identifying individuals at high risk of infection is critical to guiding interventions during infectious disease outbreaks. Network centrality can characterize infection risk, but its utility varies across pathogens and social systems. We used equivalence-based blockmodeling to identify social-epidemiological roles associated with viral exposure among 1,297 adults in northeast Madagascar. Roles were determined based on social networks derived from reported shared free time and exchanges of food and farmwork. We identified three distinct role categories, including individuals with many reciprocated social ties (Popular), individuals who sent many ties with few reciprocated (Hangers-On), and individuals who had few connections (Periphery). To assess whether the role categories covaried with viral exposure, we performed Phage ImmunoPrecipitation Sequencing with VirScan using dried blood spot samples to determine exposure to 337 virus species and subtypes. Individuals in the Popular ( ß [95% CI]: 0.29 [−0.06-0.63]) and Hangers-On ( ß [95% CI]: 0.36 [0.12-0.60]) role categories had greater viral exposure than individuals in the Periphery role category. Roles performed better at predicting exposure than single measures of centrality. Equivalence-based blockmodeling extends the utility of network centrality for characterizing infection risk and provides new insights into how social roles relate to both pathogen exposure and susceptibility to infection.

Objectives: Understanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystis spp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar.Materials and Methods: We obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened for Blastocystis using DNA metabarcoding. We also tested 133 domesticated animals for Blastocystis. We used network autocorrelation models and permutation tests (network k‐test) to determine whether networks reflecting different transmission pathways predicted infection.Results: We identified six distinct Blastocystis subtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least one Blastocystis subtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33–1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82–1.10]). No human participant was infected with the same subtype as the domesticated animals they owned.Discussion: Our findings suggest that Blastocystis is most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human‐pathogen ecology and mechanisms for reducing disease transmission in rural, low‐income settings.

Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.

Microbial larviciding can be an effective component of integrated vector management malaria control schemes, although it is not commonly implemented. Moreover, quality control and evaluation of intervention activities are essential to evaluate the potential of community-based larviciding interventions. We conducted a process evaluation of a larval source management intervention in rural Tanzania where local staff were employed to apply microbial larvicide to mosquito breeding habitats with the aim of long-term reductions in malaria transmission. We developed a logic model to guide the process evaluation and then established quantitative indicators to measure intervention success. Quantitative analysis of intervention reach, exposure, and fidelity was performed to assess larvicide application, and interviews with larviciding staff were reviewed to provide context to quantitative results. Results indicate that the intervention was successful in terms of reach, as staff applied microbial larvicide at 80% of identified mosquito breeding habitats. However, the dosage of larvicide applied was sufficient to ensure larval elimination at only 26% of sites, which does not meet the standard set for intervention fidelity. We propose that insufficient training and protocol adaptation, environment and resource issues, and human error contributed to low larvicide application rates. This demonstrates how several small, context-specific details in sum can result in meaningful differences between intervention blueprint and execution. These findings may serve the design of other larval source management interventions by demonstrating the value of additional training, supervision, and measurement and evaluation of protocol adherence.

As countries transition toward malaria elimination, malaria programs rely on surveillance–response systems, which are often supported by web- and mobile phone–based reporting tools. Such surveillance–response systems are interventions for elimination, making it important to determine if they are operating optimally. A metric to measure this by is timeliness. This study used a mixed-methods approach to investigate the response time of Zanzibar’s malaria elimination surveillance–response system, Malaria Case Notification (MCN). MCN conducts both passive and reactive case detection, supported by a mobile phone–based reporting tool called Coconut Surveillance. Using data obtained from RTI International and the Zanzibar Malaria Elimination Program (ZAMEP), analysis of summary statistics was conducted to investigate the association of response time with geography, and time series techniques were used to investigate trends in response time and its association with the number of reported cases. Results indicated that response time varied by the district in Zanzibar (0.6–6.05 days) and that it was not associated with calendar time or the number of reported cases. Survey responses and focus groups with a cadre of health workers, district malaria surveillance officers, shed light on operational challenges faced during case investigation, such as incomplete health records and transportation issues, which stem from deficiencies in aspects of ZAMEP’s program management. These findings illustrate that timely response for malaria elimination depends on effective program management, despite the automation of web-based or mobile phone–based tools. For surveillance–response systems to work optimally, malaria programs should ensure that optimal management practices are in place.

Measurements of anti-malarial antibodies are increasingly used as a proxy of transmission intensity. Most serological surveys are based on the use of cross-sectional data that, when age-stratified, approximates historical patterns of transmission within a population. Comparatively few studies leverage longitudinal data to explicitly relate individual infection events with subsequent antibody responses. The occurrence of seroconversion and seroreversion events for two Plasmodium falciparum asexual stage antigens (MSP-1 and AMA-1) was examined using three annual measurements of 691 individuals from a cohort of individuals in a malaria-endemic area of rural east-central Tanzania. Mixed-effect logistic regression models were employed to determine factors associated with changes in serostatus over time. While the expected population-level relationship between seroprevalence and disease incidence was observed, on an individual level the relationship between individual infections and the antibody response was complex. MSP-1 antibody responses were more dynamic in response to the occurrence and resolution of infection events than AMA-1, while the latter was more correlated with consecutive infections. The MSP-1 antibody response to an observed infection seemed to decay faster over time than the corresponding AMA-1 response. Surprisingly, there was no evidence of an age effect on the occurrence of a conversion or reversion event. While the population-level results concur with previously published sero-epidemiological surveys, the individual-level results highlight the more complex relationship between detected infections and antibody dynamics than can be analysed using cross-sectional data. The longitudinal analysis of serological data may provide a powerful tool for teasing apart the complex relationship between infection events and the corresponding immune response, thereby improving the ability to rapidly assess the success or failure of malaria control programmes.