The Association between Socioeconomic Status and Race/Ethnicity with Home Evacuation of Lower Manhattan Residents following the 9/11/2001 World Trade Center Disaster.

On 11 September 2001, attacks on the World Trade Center (WTC) killed nearly three thousand people and exposed hundreds of thousands of rescue and recovery workers, passersby, area workers, and residents to varying amounts of dust and smoke. Former New York City Mayor Rudy Giuliani ordered the emergency evacuation of Lower Manhattan below Canal Street, but not all residents evacuated. Previous studies showed that those who did not evacuate had a higher incidence of newly diagnosed asthma. Among the 71,424 who enrolled in the WTC Health Registry in 2003-2004, we evaluated the bivariate association of educational attainment, household income, and race or ethnicity with reported evacuation on or after 9/11/01. We used log binomial regression to assess the relative risks of not evacuating from their home following the 9/11 attacks, adjusting for age, gender, and marital status. Out of a total of 11,871 enrollee residents of Lower Manhattan, 7345 or 61.79% reported evacuating their home on or after 9/11. In a fully adjusted model, the estimated relative risk for not evacuating was elevated for those who identified as non-Hispanic Black, Asian/Pacific Islander, and Hispanic residents compared to non-Hispanic White residents. Residents with a high school diploma/GED had an elevated estimated risk compared to those with at least a bachelor's degree. Those with lower household incomes had an elevated estimated risk compared to those with the highest income category. These significant inequities will need to be prevented in future disasters.

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.

National Hospital-Based Sentinel Surveillance for Cholera in Bangladesh: Epidemiological Results from 2014 to 2021.

Despite focusing on cholera burden, epidemiologic studies in Bangladesh tend to be limited in geographic scope. National-level cholera surveillance data can help inform cholera control strategies and assess the effectiveness of preventive measures. Hospital-based sentinel surveillance among patients with suspected diarrhea in different sites across Bangladesh has been conducted since 2014. We selected an age-stratified sample of 20 suspected cholera cases each week from each sentinel site, tested stool for the presence of Vibrio cholerae O1/O139 by culture, and characterized antibiotic susceptibility in a subset of culture-positive isolates. We estimated the odds of being culture positive among suspected cholera cases according to different potential risk factors. From May 4, 2014 through November 30, 2021, we enrolled 51,414 suspected cases from our sentinel surveillance sites. We confirmed V. cholerae O1 in 5.2% of suspected cases through microbiological culture. The highest proportion of confirmed cholera cases was from Chittagong (9.7%) and the lowest was from Rangpur Division (0.9%). Age, number of purges, duration of diarrhea, occupation, and season were the most relevant factors in distinguishing cholera-positive suspected cases from cholera-negative suspected cases. Nationwide surveillance data show that cholera is circulating in Bangladesh and the southern region is more affected than the northern region. Antimicrobial resistance patterns indicate that multidrug resistance (resistance to three or more classes of antibiotics) of V. cholerae O1 could be a major threat in the future. Alignment of these results with Bangladesh's cholera-control program will be the foundation for future research into the efficacy of cholera-control initiatives.

Estimating the gap between clinical cholera and true community infections: findings from an integrated surveillance study in an endemic region of Bangladesh.

Our understanding of cholera transmission and burden largely rely on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serologic surveillance provides a complementary approach to monitoring infections, though the link between serologically-derived infections and medically-attended disease - 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. We found >50% of the study population had a V. cholerae O1 infection annually, and infection timing was not consistently correlated with reported cases. Four in 2,340 infections resulted in symptoms, only one of which was reported through the surveillance system. These results provide new insights into cholera transmission dynamics and burden in the epicenter of the 7th cholera pandemic and provide a framework to synthesize serological and clinical surveillance data.

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.

Epidemiology of Cholera in Bangladesh: Findings From Nationwide Hospital-based Surveillance, 2014-2018.

BACKGROUND: Despite advances in prevention, detection, and treatment, cholera remains a major public health problem in Bangladesh and little is known about cholera outside of limited historical sentinel surveillance sites. In Bangladesh, a comprehensive national cholera control plan is essential, although national data are needed to better understand the magnitude and geographic distribution of cholera. METHODS: We conducted systematic hospital-based cholera surveillance among diarrhea patients in 22 sites throughout Bangladesh from 2014 to 2018. Stool specimens were collected and tested for Vibrio cholerae by microbiological culture. Participants' socioeconomic status and clinical, sanitation, and food history were recorded. We used generalized estimating equations to identify the factors associated with cholera among diarrhea patients. RESULTS: Among 26 221 diarrhea patients enrolled, 6.2% (n = 1604) cases were V. cholerae O1. The proportion of diarrhea patients positive for cholera in children <5 years was 2.1% and in patients ≥5 years was 9.5%. The proportion of cholera in Dhaka and Chittagong Division was consistently high. We observed biannual seasonal peaks (pre- and postmonsoon) for cholera across the country, with higher cholera positivity during the postmonsoon in western regions and during the pre-monsoon season in eastern regions. Cholera risk increased with age, occupation, and recent history of diarrhea among household members. CONCLUSIONS: Cholera occurs throughout a large part of Bangladesh. Cholera-prone areas should be prioritized to control the disease by implementation of targeted interventions. These findings can help strengthen the cholera-control program and serve as the basis for future studies for tracking the impact of cholera-control interventions in Bangladesh.