Identification, mapping, and self-reported practice patterns of village doctors in Sitakunda subdistrict, Bangladesh.

Background: Informally trained health care providers, such as village doctors in Bangladesh, are crucial in providing health care services to the rural poor in low- and middle-income countries. Despite being one of the primary vendors of antibiotics in rural Bangladesh, village doctors often have limited knowledge about appropriate antibiotic use, leading to varied and potentially inappropriate dispensing and treatment practices. In this study, we aimed to identify, map, and survey village doctors in the Sitakunda subdistrict of Bangladesh to understand their distribution, practice characteristics, clinical behaviours, access to technologies, and use of these technologies for clinical decision-making. Methods: Using a 'snowball' sampling method, we identified and mapped 411 village doctors, with 371 agreeing to complete a structured survey. Results: The median distance between a residential household and the closest village doctor practice was 0.37 km, and over half of the practices (51.2%) were within 100 m of the major highway. Village doctors were predominately male (98.7%), with a median age of 39. After completing village doctor training, 39.4% had completed an internship, with a median of 15 years of practice experience. Village doctors reported seeing a median of 84 patients per week, including a median of five paediatric diarrhoea cases per week. They stocked a range of antibiotics, with ciprofloxacin and metronidazole being the most prescribed for diarrhoea. Most had access to phones with an internet connection and used online resources for clinical decision-making and guidance. Conclusions: The findings provide insights into the characteristics and practices of village doctors and point to the potential for internet and phone-based interventions to improve patient care and reduce inappropriate antibiotic use in this health care provider group.

Analysis of Cholera Risk in India: Insights from 2017-18 Serosurvey Data Integrated with Epidemiologic data and Societal Determinants from 2015-2019.

BACKGROUND: Indian subcontinent being an important region in the fight to eliminate cholera needs better cholera surveillance. Current methods miss most infections, skewing disease burden estimates. Triangulating serosurvey data, clinical cases, and risk factors could reveal India's true cholera risk. METHODS: We synthesized data from a nationally representative serosurvey, outbreak reports and risk factors like water, sanitation and the Multidimensional Poverty Index, to create a composite vulnerability index for assessing state-wise cholera risk in India. We tested 7,882 stored sera samples collected during 2017-18 from individuals aged 9-45 years, for vibriocidal antibodies to Vibrio cholerae O1 using a cut-off titre ≥320 defining as elevated titre. We also extracted data from the 2015-19 Integrated Disease Surveillance Programme and published cholera reports. RESULTS: Overall, 11.7% (CI: 10.4-13.3%) of the sampled population had an elevated titre of cholera vibriocidal antibodies (≥320). The Southern region experienced the highest incidence (16.8%, CI: 12.1-22.8), followed by the West (13.2%, CI: 10.0-17.3) and North (10.7%, CI: 9.3-12.3). Proportion of samples with an elevated vibriocidal titre (≥320) was significantly higher among individuals aged 18-45 years (13.0% CI: 11.2-15.1) compared to children 9-17 years (8.6%, CI 7.3-10.0, p<0.05); we found no differences between sex or urbanicity. Between 2015-2019, the Integrated Disease Surveillance Program (IDSP) reported 29,400 cases of cholera across the country. Using the composite vulnerability index, we found Karnataka, Madhya Pradesh, and West Bengal were the most vulnerable states in India in terms of risk of cholera. CONCLUSION: The present study showed that cholera infection is present in all five regions across India. The states with high cholera vulnerability could be prioritized for targeted prevention interventions.

Annual risk of hepatitis E virus infection and seroreversion: Insights from a serological cohort in Sitakunda, Bangladesh.

Hepatitis E virus (HEV) is a major cause of acute jaundice in South Asia. Gaps in our understanding of transmission are driven by non-specific symptoms and scarcity of diagnostics, impeding rational control strategies. In this context, serological data can provide important proxy measures of infection. We enrolled a population-representative serological cohort of 2,337 individuals in Sitakunda, Bangladesh. We estimated the annual risks of HEV infection and seroreversion both using serostatus changes between paired serum samples collected 9 months apart, and by fitting catalytic models to the age-stratified cross-sectional seroprevalence. At baseline, 15% (95 CI: 14-17%) of people were seropositive, with seroprevalence highest in the relatively urban south. During the study, 27 individuals seroreverted (annual seroreversion risk: 15%, 95 CI: 10-21%), and 38 seroconverted (annual infection risk: 3%, 95CI: 2-5%). Relying on cross-sectional seroprevalence data alone, and ignoring seroreversion, underestimated the annual infection risk five-fold (0.6%, 95 CrI: 0.5-0.6%). When we accounted for the observed seroreversion in a reversible catalytic model, infection risk was more consistent with measured seroincidence. Our results quantify HEV infection risk in Sitakunda and highlight the importance of accounting for seroreversion when estimating infection incidence from cross-sectional seroprevalence data.

State variation in neighborhood COVID-19 burden across the United States.

BACKGROUND: A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on. METHODS: We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment. RESULTS: We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state. CONCLUSIONS: Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

A lack of data on the geographic location of COVID-19 cases in the U.S has limited our ability to examine how COVID-19 burden has been distributed across neighborhoods within U.S. states. It may be that certain neighborhoods have borne a disproportionate burden of COVID-19 and are more likely to experience greater long-term social and economic consequences from the pandemic. We used data from 21 states to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We find that the distribution of COVID-19 varies widely both within neighborhoods in a state, and between states. We also find that the features of the neighborhood social environment that are correlated with neighborhood COVID-19 burden vary by state. Our findings show that the local neighborhood may play an important role in addressing long-term social and economic consequences from COVID-19.

Clinical surveillance systems obscure the true cholera infection burden in an endemic region.

Our understanding of cholera transmission and burden largely relies on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serological surveillance provides a complementary approach to monitoring infections, although the link between serologically derived infections and medically attended disease incidence-shaped by immunological, behavioral and clinical factors-remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare-seeking and longitudinal serological data through statistical modeling. Combining the serological trajectories with a reconstructed incidence timeline of symptomatic cholera, we estimated an annual Vibrio cholerae O1 infection incidence rate of 535 per 1,000 population (95% credible interval 514-556), with incidence increasing by age group. Clinic-based surveillance alone underestimated the number of infections and reported cases were not consistently correlated with infection timing. Of the infections, 4 in 3,280 resulted in symptoms, only 1 of which was reported through the surveillance system. These results impart insights into cholera transmission dynamics and burden in the epicenter of the seventh cholera pandemic, where >50% of our study population had an annual V. cholerae O1 infection, and emphasize the potential for a biased view of disease burden and infection risk when depending solely on clinical surveillance data.

Potential for Person-to-Person Transmission of Henipaviruses: A Systematic Review of the Literature.

Nipah virus Bangladesh (NiVB) is a bat-borne zoonosis transmitted between people through the respiratory route. The risk posed by related henipaviruses, including Hendra virus (HeV) and Nipah virus Malaysia (NiVM), is less clear. We conducted a broad search of the literature encompassing both human infections and animal models to synthesize evidence about potential for person-to-person spread. More than 600 human infections have been reported in the literature, but information on viral shedding was only available for 40 case-patients. There is substantial evidence demonstrating person-to-person transmission of NiVB, and some evidence for NiVM. Less direct evidence is available about the risk for person-to-person transmission of HeV, but animals infected with HeV shed more virus in the respiratory tract than those infected with NiVM, suggesting potential for transmission. As the group of known henipaviruses continues to grow, shared protocols for conducting and reporting from human investigations and animal experiments are urgently needed.

Water, Sanitation, and Cholera in Sub-Saharan Africa.

Improvements in water and sanitation should reduce cholera risk though the associations between cholera and specific water and sanitation access measures remain unclear. We estimated the association between eight water and sanitation measures and annual cholera incidence access across sub-Saharan Africa (2010-2016) for data aggregated at the country and district levels. We fit random forest regression and classification models to understand how well these measures combined might be able to predict cholera incidence rates and identify high cholera incidence areas. Across spatial scales, piped or "other improved" water access was inversely associated with cholera incidence. Access to piped water, septic or sewer sanitation, and septic, sewer, or "other improved" sanitation were associated with decreased district-level cholera incidence. The classification model had moderate performance in identifying high cholera incidence areas (cross-validated-AUC 0.81, 95% CI 0.78-0.83) with high negative predictive values (93-100%) indicating the utility of water and sanitation measures for screening out areas that are unlikely to be at high cholera risk. While comprehensive cholera risk assessments must incorporate other data sources (e.g., historical incidence), our results suggest that water and sanitation measures could alone be useful in narrowing the geographic focus for detailed risk assessments.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project.

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

Exposure, Susceptibility, and Recovery: A Framework for Examining the Intersection of the Social and Physical Environments and Infectious Disease Risk.

Despite well-documented evidence that structurally disadvantaged populations are disproportionately affected by infectious diseases, our understanding of the pathways that connect structural disadvantage to the burden of infectious diseases is limited. We propose a conceptual framework to facilitate more rigorous examination and testing of hypothesized mechanisms through which social and environmental factors shape the burden of infectious diseases and lead to persistent inequities. Drawing upon the principles laid out by Link and Phelan in their landmark paper on social conditions (J Health Soc Behav. 1995;(spec no.):80-94), we offer an explication of potential pathways through which structural disadvantage (e.g., racism, sexism, and economic deprivation) operates to produce infectious disease inequities. Specifically, we describe how the social environment affects an individual's risk of infectious disease by 1) increasing exposure to infectious pathogens and 2) increasing susceptibility to infection. This framework will facilitate both the systematic examination of the ways in which structural disadvantage shapes the burden of infectious disease and the design of interventions that can disrupt these pathways.

The importance of community during rapid development: The influence of social networks on acute gastrointestinal illness in rural Ecuador.

Social networks are often measured as conduits of infection. Our prior cross-sectional analyses found that denser social ties among individuals reduces transmission of acute gastrointestinal illness (AGI) in coastal Ecuador; social networks can describe both risk and protection. We extend findings to examine how social connectedness influences AGI longitudinally in Ecuador from 2007 to 2013, a time of rapid development, using a two-stage Bayesian hierarchical model to estimate multiple network effects. A larger community network of people to discuss important matters with was consistently protective against AGI over time, and a network defined by people passing time together became a stronger measure of risk, due to increasing population density and travel. These networks were interdependent: the joint effect of having a small passing time network and large important matters network reduced the odds of AGI over time (2007: OR 1.16 (95% CI: 0.94, 1.44), 2013: OR 0.56 (95% CI: 0.45, 0.71)); and synergistic: the people an individual passed time with became the people they discussed important matters with. Focus groups emphasized that with greater remoteness came greater community cohesion resulting in safer WASH practices. Social networks can enhance and reduce health differently as social infrastructure evolves, highlighting the importance of community-level factors in a period of rapid development.

SARS-CoV-2 Seroprevalence before Delta Variant Surge, Chattogram, Bangladesh, March-June 2021.

A March-June 2021 representative serosurvey among Sitakunda subdistrict (Chattogram, Bangladesh) residents found an adjusted prevalence of severe acute respiratory syndrome coronavirus 2 antibodies of 64.1% (95% credible interval 60.0%-68.1%). Before the Delta variant surge, most residents had been infected, although cumulative confirmed coronavirus disease incidence was low.

Clinical Cholera Surveillance Sensitivity in Bangladesh and Implications for Large-Scale Disease Control.

BACKGROUND: A surveillance system that is sensitive to detecting high burden areas is critical for achieving widespread disease control. In 2014, Bangladesh established a nationwide, facility-based cholera surveillance system for Vibrio cholerae infection. We sought to measure the sensitivity of this surveillance system to detect cases to assess whether cholera elimination targets outlined by the Bangladesh national control plan can be adequately measured. METHODS: We overlaid maps of nationally representative annual V cholerae seroincidence onto maps of the catchment areas of facilities where confirmatory laboratory testing for cholera was conducted, and we identified its spatial complement as surveillance greyspots, areas where cases likely occur but go undetected. We assessed surveillance system sensitivity and changes to sensitivity given alternate surveillance site selection strategies. RESULTS: We estimated that 69% of Bangladeshis (111.7 million individuals) live in surveillance greyspots and that 23% (25.5 million) of these individuals live in areas with the highest V cholerae infection rates. CONCLUSIONS: The cholera surveillance system in Bangladesh has the ability to monitor progress towards cholera elimination goals among 31% of the country's population, which may be insufficient for accurately measuring progress. Increasing surveillance coverage, particularly in the highest risk areas, should be considered.

Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.

Vibrio cholerae O1 transmission in Bangladesh: insights from a nationally representative serosurvey.

BACKGROUND: Pandemic Vibrio cholerae from cholera-endemic countries around the Bay of Bengal regularly seed epidemics globally. Without reducing cholera in these countries, including Bangladesh, global cholera control might never be achieved. Little is known about the geographical distribution and magnitude of V cholerae O1 transmission nationally. We aimed to describe infection risk across Bangladesh, making use of advances in cholera seroepidemiology, therefore overcoming many of the limitations of current clinic-based surveillance. METHODS: We tested serum samples from a nationally representative serosurvey in Bangladesh with eight V cholerae-specific assays. Using these data with a machine-learning model previously validated within a cohort of confirmed cholera cases and their household contacts, we estimated the proportion of the population with evidence of infection by V cholerae O1 in the previous year (annual seroincidence) and used Bayesian geostatistical models to create high-resolution national maps of infection risk. FINDINGS: Between Oct 16, 2015, and Jan 24, 2016, we obtained and tested serum samples from 2930 participants (707 households) in 70 communities across Bangladesh. We estimated national annual seroincidence of V cholerae O1 infection of 17·3% (95% CI 10·5-24·1). Our high-resolution maps showed large heterogeneity of infection risk, with community-level annual infection risk within the sampled population ranging from 4·3% to 62·9%. Across Bangladesh, we estimated that 28·1 (95% CI 17·1-39·2) million infections occurred in the year before the survey. Despite having an annual seroincidence of V cholerae O1 infection lower than much of Bangladesh, Dhaka (the capital of Bangladesh and largest city in the country) had 2·0 (95% CI 0·6-3·9) million infections during the same year, primarily because of its large population. INTERPRETATION: Serosurveillance provides an avenue for identifying areas with high V cholerae O1 transmission and investigating key risk factors for infection across geographical scales. Serosurveillance could serve as an important method for countries to plan and monitor progress towards 2030 cholera elimination goals. FUNDING: The Bill & Melinda Gates Foundation, National Institutes of Health, and US Centers for Disease Control and Prevention.

Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh.

BACKGROUND: Understanding the true burden of emergent diseases is critical for assessing public-health impact. However, surveillance often relies on hospital systems that only capture a minority of cases. We use the example of Nipah-virus infection in Bangladesh, which has a high case-fatality ratio and frequent person-to-person transmission, to demonstrate how healthcare-seeking data can estimate true burden. METHODS: We fit logistic-regression models to data from a population-based, healthcare-seeking study of encephalitis cases to characterize the impact of distance and mortality on attending one of three surveillance hospital sites. The resulting estimates of detection probabilities, as a function of distance and outcome, are applied to all observed Nipah outbreaks between 2007 and 2014 to estimate the true burden. RESULTS: The probability of attending a surveillance hospital fell from 82% for people with fatal encephalitis living 10 km away from a surveillance hospital to 54% at 50 km away. The odds of attending a surveillance hospital are 3.2 (95% confidence interval: 1.6, 6.6) times greater for patients who eventually died (i.e. who were more severely ill) compared with those who survived. Using these probabilities, we estimated that 119 Nipah outbreaks (95% confidence interval: 103, 140)-an average of 15 outbreaks per Nipah season-occurred during 2007-14; 62 (52%) were detected. CONCLUSIONS: Our findings suggest hospital-based surveillance missed nearly half of all Nipah outbreaks. This analytical method allowed us to estimate the underlying burden of disease, which is important for emerging diseases where healthcare access may be limited.