Livestock, pathogens, vectors, and their environment: A causal inference-based approach to estimating the pathway-specific effect of livestock on human African trypanosomiasis risk.

Livestock are important reservoirs for many zoonotic diseases, however the effects of livestock on human and environmental health extend well beyond direct disease transmission. In this retrospective ecological cohort study we use pre-existing data and the parametric g-formula, which imputes potential outcomes to quantify mediation, to estimate three hypothesized mechanisms by which livestock can influence human African trypanosomiasis (HAT) risk: the reservoir effect, where infected cattle and pigs are a source of infection to humans; the zooprophylactic effect, where preference for livestock hosts exhibited by the tsetse fly vector of HAT means that their presence protects humans from infection; and the environmental change effect, where livestock keeping activities modify the environment in such a way that habitat suitability for tsetse flies, and in turn human infection risk, is reduced. We conducted this study in four high burden countries: at the point level in Uganda, Malawi, and Democratic Republic of Congo (DRC), and at the county level in South Sudan. Our results indicate cattle and pigs play a reservoir role for the rhodesiense form (rHAT) in Uganda (rate ratio (RR) 1.68, 95% CI 0.84, 2.82 for cattle; RR 2.16, 95% CI 1.18, 3.05 for pigs), however zooprophylaxis outweighs this effect for rHAT in Malawi (RR 0.85, 95% CI 0.68, 1.00 for cattle, RR 0.38, 95% CI 0.21, 0.69 for pigs). For the gambiense form (gHAT) we found evidence that pigs may be a competent reservoir (RR 1.15, 95% CI 0.92, 1.72 in Uganda; RR 1.25, 95% CI 1.11, 1.42 in DRC). Statistical significance was reached for rHAT in Malawi (pigs and cattle) and Uganda (pigs only) and for gHAT in DRC (pigs and cattle). We did not find compelling evidence of an environmental change effect (all effect sizes close to 1).

Association between pertussis vaccination coverage and other sociodemographic factors and pertussis incidence using surveillance data.

Routine vaccination with pertussis vaccines has been successful in driving down pertussis mortality and morbidity globally. Despite high vaccination coverage, countries such as Australia, USA, and UK have experienced increase in pertussis activity over the last few decades. This may be due to local pockets of low vaccination coverage that result in persistence of pertussis in the population and occasionally lead to large outbreaks. The objective of this study was to characterize the association between pertussis vaccination coverage and sociodemographic factors and pertussis incidence at the school district level in King County, Washington, USA. We used monthly pertussis incidence data for all ages reported to the Public Health Seattle and King County between January 1, 2010 and December 31, 2017 to obtain school district level pertussis incidence. We obtained immunization data from the Washington State Immunization Information System to estimate school-district level vaccination coverage as proportion of 19-35 month old children fully vaccinated with ≥4 doses of the Diphtheria-Tetanus-acellular-Pertussis (DTaP) vaccine in a school district. We used two methods to quantify the effects of vaccination coverage on pertussis incidence: an ecological vaccine model and an endemic-epidemic model. Even though the effect of vaccination is modeled differently in the two approaches, both models can be used to estimate the association between vaccination coverage and pertussis incidence. Using the ecological vaccine model, we estimated the vaccine effectiveness of 4 doses of Diphtheria-Tetanus-acellular-Pertussis vaccine to be 83% (95% credible interval: 63%, 95%). In the endemic-epidemic model, under-vaccination was statistically significantly associated with epidemic risk of pertussis (adjusted Relative Risk, aRR: 2.76; 95% confidence interval: 1.44, 16.6). Household size and median income were statistically significantly associated with endemic pertussis risk. The endemic-epidemic model suffers from ecological bias, whereas the ecological vaccine model provides less biased and more interpretable estimates of epidemiological parameters, such as DTaP vaccine effectiveness, for each school district.

Does a One Health approach to human African trypanosomiasis control hasten elimination? A stochastic compartmental modeling approach.

BACKGROUND: . In response to large strides in the control of human African trypanosomiasis (HAT), in the early 2000s the WHO set targets for elimination of both the gambiense (gHAT) and rhodesiense (rHAT) forms as a public health (EPHP) problem by 2020, and elimination of gHAT transmisson (EOT) by 2030. While global EPHP targets have been met, and EOT appears within reach, current control strategies may fail to achieve gHAT EOT in the presence of animal reservoirs, the role of which is currently uncertain. Furthermore, rHAT is not targeted for EOT due to the known importance of animal reservoirs for this form. METHODS: . To evaluate the utility of a One Health approach to gHAT and rHAT EOT, we built and parameterized a compartmental stochastic model, using the Institute for Disease Modeling's Compartmental Modeling Software, to six HAT epidemics: the national rHAT epidemics in Uganda and Malawi, the national gHAT epidemics in Uganda and South Sudan, and two separate gHAT epidemics in Democratic Republic of Congo distinguished by dominant vector species. In rHAT foci the reservoir animal sub-model was stratified on four species groups, while in gHAT foci domestic swine were assumed to be the only competent reservoir. The modeled time horizon was 2005-2045, with calibration performed using HAT surveillance data and Optuna. Interventions included insecticide and trypanocide treatment of domestic animal reservoirs at varying coverage levels. RESULTS: . Validation against HAT surveillance data indicates favorable performance overall, with the possible exception of DRC. EOT was not observed in any modeled scenarios for rHAT, however insecticide treatment consistently performed better than trypanocide treatment in terms of rHAT control. EOT was not observed for gHAT at 0% coverage of domestic reservoirs with trypanocides or insecticides, but was observed by 2030 in all test scenarios; again, insecticides demonstrated superior performance to trypanocides. CONCLUSIONS: EOT likely cannot be achieved for rHAT without control of wildlife reservoirs, however insecticide treatment of domestic animals holds promise for improved control. In the presence of domestic animal reservoirs, gHAT EOT may not be achieved under current control strategies.

The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense: A causal inference-based approach.

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirs-particularly pigs-threatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of the Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk. For rHAT, we found a positive effect for cattle (RR 1.61, 95% CI 0.90, 2.99) and pigs (RR 2.07, 95% CI 1.15, 2.75) in Uganda, and a negative effect for cattle (RR 0.88, 95% CI 0.71, 1.10) and pigs (RR 0.42, 95% CI 0.23, 0.67) in Malawi. For gHAT we found a negative effect for cattle in Uganda (RR 0.88, 95% CI 0.50, 1.77) and South Sudan (RR 0.63, 95% CI 0.54, 0.77) but a positive effect in DRC (1.17, 95% CI 1.04, 1.32). For pigs, we found a positive gHAT effect in both Uganda (RR 2.02, 95% CI 0.87, 3.94) and DRC (RR 1.23, 95% CI 1.10, 1.37), and a negative association in South Sudan (RR 0.66, 95% CI 0.50, 0.98). These effects did not reach significance for the cattle-rHAT effect in Uganda or Malawi, or the cattle-gHAT and pig-gHAT effects in Uganda. While ecological bias may drive the findings in South Sudan, estimated E-values and simulation studies suggest unmeasured confounding and underreporting are unlikely to explain our findings in Malawi, Uganda, and DRC. Our results suggest cattle and pigs may be important reservoirs of rHAT in Uganda but not Malawi, and that pigs-and possibly cattle-may be gHAT reservoirs.

A time-series approach to mapping livestock density using household survey data.

More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo, and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.

Prevalence of current and past COVID-19 in Ohio adults.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

Environmental Transmission of Typhoid Fever in an Urban Slum.

BACKGROUND: Enteric fever due to Salmonella Typhi (typhoid fever) occurs in urban areas with poor sanitation. While direct fecal-oral transmission is thought to be the predominant mode of transmission, recent evidence suggests that indirect environmental transmission may also contribute to disease spread. METHODS: Data from a population-based infectious disease surveillance system (28,000 individuals followed biweekly) were used to map the spatial pattern of typhoid fever in Kibera, an urban informal settlement in Nairobi Kenya, between 2010-2011. Spatial modeling was used to test whether variations in topography and accumulation of surface water explain the geographic patterns of risk. RESULTS: Among children less than ten years of age, risk of typhoid fever was geographically heterogeneous across the study area (p = 0.016) and was positively associated with lower elevation, OR = 1.87, 95% CI (1.36-2.57), p <0.001. In contrast, the risk of typhoid fever did not vary geographically or with elevation among individuals more than ten years of age [corrected]. CONCLUSIONS: Our results provide evidence of indirect, environmental transmission of typhoid fever among children, a group with high exposure to fecal pathogens in the environment. Spatially targeting sanitation interventions may decrease enteric fever transmission.

Geographic distribution of HIV stigma among women of childbearing age in rural Kenya.

OBJECTIVE(S): HIV stigma is considered to be a major driver of the HIV/AIDS pandemic, yet there is a limited understanding of its occurrence. We describe the geographic patterns of two forms of HIV stigma in a cross-sectional sample of women of childbearing age from western Kenya: internalized stigma (associated with shame) and externalized stigma (associated with blame). DESIGN: Geographic studies of HIV stigma provide a first step in generating hypotheses regarding potential community-level causes of stigma and may lead to more effective community-level interventions. METHODS: Spatial regression using generalized additive models and point pattern analyses using K-functions were used to assess the spatial scale(s) at which each form of HIV stigma clusters, and to assess whether the spatial clustering of each stigma indicator was present after adjustment for individual-level characteristics. RESULTS: There was evidence that externalized stigma (blame) was geographically heterogeneous across the study area, even after controlling for individual-level factors (P=0.01). In contrast, there was less evidence (P=0.70) of spatial trend or clustering of internalized stigma (shame). CONCLUSION: Our results may point to differences in the underlying social processes motivating each form of HIV stigma. Externalized stigma may be driven more by cultural beliefs disseminated within communities, whereas internalized stigma may be the result of individual-level characteristics outside the domain of community influence. These data may inform community-level interventions to decrease HIV-related stigma, and thus impact the HIV epidemic.

A Bayesian model for cluster detection.

The detection of areas in which the risk of a particular disease is significantly elevated, leading to an excess of cases, is an important enterprise in spatial epidemiology. Various frequentist approaches have been suggested for the detection of "clusters" within a hypothesis testing framework. Unfortunately, these suffer from a number of drawbacks including the difficulty in specifying a p-value threshold at which to call significance, the inherent multiplicity problem, and the possibility of multiple clusters. In this paper, we suggest a Bayesian approach to detecting "areas of clustering" in which the study region is partitioned into, possibly multiple, "zones" within which the risk is either at a null, or non-null, level. Computation is carried out using Markov chain Monte Carlo, tuned to the model that we develop. The method is applied to leukemia data in upstate New York.

Incorporating spatial variability within epidemiological studies of environmental exposures.

Recently there has been great interest in modelling the association between aggregate disease counts and environmental exposures measured at point locations, for example via air pollution monitors. In such cases, the standard approach is to average the observed measurements from the individual monitors and use this in a log-linear health model. Hence such studies are ecological in nature being based on spatially aggregated health and exposure data. Here we investigate the potential for biases in the estimates of the effects on health in such settings. Such ecological bias may occur if a simple summary measure, such as a daily mean, is not a suitable summary of a spatially variable pollution surface. We assess the performance of commonly used models when confronted with such issues using simulation studies and compare their performance with a model specifically designed to acknowledge the effects of exposure aggregation. In addition to simulation studies, we apply the models to a case study of the short-term effects of particulate matter on respiratory mortality using data from Greater London for the period 2002-2005.

The Combination of Ecological and Case-Control Data.

Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. In order to overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual-level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared to an ecological study, and in efficiency gains relative to a conventional case-control study. An interesting special case of the proposed design is the situation where ecological data are supplemented with case-only data. The design is illustrated using a dataset of county-specific lung cancer mortality rates in the state of Ohio from 1988.

Ecologic studies revisited.

Ecologic studies use data aggregated over groups rather than data on individuals. Such studies are popular because they use existing databases and can offer large exposure variation if the data arise from broad geographical areas. Unfortunately, the aggregation of data that define ecologic studies results in an information loss that can lead to ecologic bias. Specifically, ecologic bias arises from the inability of ecologic data to characterize within-area variability in exposures and confounders. We describe in detail particular forms of ecologic bias so that their potential impact on any particular study may be assessed. The only way to overcome such bias, while avoiding uncheckable assumptions concerning the missing information, is to supplement the ecologic with individual-level information, and we outline a number of proposals that may achieve this aim.

Long-term associations of outdoor air pollution with mortality in Great Britain.

BACKGROUND: Recent studies have indicated long-term effects on mortality of particulate and sulphur dioxide (SO(2)) pollution, but uncertainties remain over the size of any effects, potential latency and generalisability. METHODS: A small area study was performed across electoral wards in Great Britain of mean annual black smoke (BS) and SO(2) concentrations (from 1966) and subsequent all-cause and cause-specific mortality using random effect models within a Bayesian framework adjusted for social deprivation and urban/rural classification. Different latencies and changes in associations over time were assessed. RESULTS: Significant associations were found between BS and SO(2) concentrations and mortality. The effects were stronger for respiratory illness than other causes of mortality for the most recent exposure periods (shorter latency times) and most recent mortality period (lower pollutant concentrations). In pooled analysis across four sequential 4 year mortality periods (1982-98), adjusted excess relative risk for respiratory mortality was 3.6% (95% CI 2.6% to 4.5%) per 10 microg/m(3) BS and 13.2% (95% CI 11.5% to 14.9%) per 10 ppb SO(2), and in the most recent period (1994-8) it was 19.3% (95% CI 5.1% to 35.7%) and 21.7% (95% CI 2.9% to 38.5%), respectively. CONCLUSIONS: These findings add to the evidence that air pollution has long-term effects on mortality and point to continuing public health risks even at the relatively lower levels of BS and SO(2) that now occur. They therefore have importance for policies on public health protection through regulation and control of air pollution.

Hierarchical models for combining ecological and case-control data.

The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures, and confounders. The consequent nonidentifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this article is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious computational challenges. We present a Bayesian implementation based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of county-specific infant mortality data from the state of North Carolina.

An investigation of the association between traffic exposure and the diagnosis of asthma in children.

This study investigated whether proximity to traffic at residence location is associated with being diagnosed with asthma as a young child. A survey of parents of children (aged 5-7) in kindergarten and first-grade in 13 schools was completed in Anchorage, Alaska, and Geographical Information System (GIS) mapping was used to obtain an exposure measure based on traffic density within 100 m of the cross streets closest to the child's residence. Using the range of observed exposure values, a score of low, medium or high traffic exposure was assigned to each child. After controlling for individual level confounders, relative to the low referent group, relative risks (95% confidence intervals) of 1.40 (0.77, 2.55) and 2.83 (1.23,6.51) were obtained in the medium and high exposure groups, respectively. For the null hypothesis of no difference in risk, a significance level of 0.056 was obtained, which suggests that further investigation would be worthwhile. Children without a family history of asthma were more likely to have an asthma diagnosis if they resided in a high traffic area than children who had one or more parents with asthma. The relative risk for children without a family history of asthma is 2.43 (1.12, 5.28) for medium exposure and 5.43 (2.08, 13.74) for high exposure. For children with a family history of asthma, the relative risk is 0.66 (0.25, 1.74) for medium exposure and 0.67 (0.12, 3.69) for high exposure. The P-value for the overall "exposure-effect" (i.e. both main effects AND interaction terms) is 0.0097.