Who is missed in a community-based survey: Assessment and implications of biases due to incomplete sampling frame in a community-based serosurvey, Choma and Ndola Districts, Zambia, 2022.

Community-based serological studies are increasingly relied upon to measure disease burden, identify population immunity gaps, and guide control and elimination strategies; however, there is little understanding of the potential for and impact of sampling biases on outcomes of interest. As part of efforts to quantify measles immunity gaps in Zambia, a community-based serological survey using stratified multi-stage cluster sampling approach was conducted in Ndola and Choma districts in May-June 2022, enrolling 1245 individuals. We carried out a follow-up study among individuals missed from the sampling frame of the serosurvey in July-August 2022, enrolling 672 individuals. We assessed the potential for and impact of biases in the community-based serosurvey by i) estimating differences in characteristics of households and individuals included and excluded (77% vs 23% of households) from the sampling frame of the serosurvey and ii) evaluating the magnitude these differences make on healthcare-seeking behavior, vaccination coverage, and measles seroprevalence. We found that missed households were 20% smaller and 25% less likely to have children. Missed individuals resided in less wealthy households, had different distributions of sex and occupation, and were more likely to seek care at health facilities. Despite these differences, simulating a survey in which missed households were included in the sampling frame resulted in less than a 5% estimated bias in these outcomes. Although community-based studies are upheld as the gold standard study design in assessing immunity gaps and underlying community health characteristics, these findings underscore the fact that sampling biases can impact the results of even well-conducted community-based surveys. Results from these studies should be interpreted in the context of the study methodology and challenges faced during implementation, which include shortcomings in establishing accurate and up-to-date sampling frames. Failure to account for these shortcomings may result in biased estimates and detrimental effects on decision-making.

A Systematic Review of Coronavirus Disease 2019 Vaccine Efficacy and Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Disease.

Billions of doses of coronavirus disease 2019 (COVID-19) vaccines have been administered globally, dramatically reducing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence and severity in some settings. Many studies suggest vaccines provide a high degree of protection against infection and disease, but precise estimates vary and studies differ in design, outcomes measured, dosing regime, location, and circulating virus strains. In this study, we conduct a systematic review of COVID-19 vaccines through February 2022. We included efficacy data from Phase 3 clinical trials for 15 vaccines undergoing World Health Organization Emergency Use Listing evaluation and real-world effectiveness for 8 vaccines with observational studies meeting inclusion criteria. Vaccine metrics collected include protection against asymptomatic infection, any infection, symptomatic COVID-19, and severe outcomes including hospitalization and death, for partial or complete vaccination, and against variants of concern Alpha, Beta, Gamma, Delta, and Omicron. We additionally review the epidemiological principles behind the design and interpretation of vaccine efficacy and effectiveness studies, including important sources of heterogeneity.

COVID-19 vaccine hesitancy in Zambia: a glimpse at the possible challenges ahead for COVID-19 vaccination rollout in sub-Saharan Africa.

With unprecedented speed, multiple vaccines against SARS-CoV-2 are available 1 year after the COVID-19 pandemic was first identified. As we push to achieve global control through these new vaccines, old challenges present themselves, including cold-chain storage, the logistics of mass vaccination, and vaccine hesitancy. Understanding how much hesitancy toward COVID-19 vaccines might occur and what factors may be driving these concerns can improve the ability of public health workers and communicators to maximize vaccine uptake. We nested a survey within a measles-rubella mass vaccination campaign in Zambia in November 2020 and asked about sentiments and beliefs toward COVID-19 and COVID-19 vaccines. Among parents bringing their children to receive a measles-rubella vaccine, we found high acceptability of COVID-19 vaccination of their children, but substantial uncertainty and hesitancy about receiving the vaccine themselves. COVID-19 vaccination hesitancy was correlated with beliefs around COVID-19 severity and risk, as well as vaccine safety and effectiveness.

Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world.

Background: Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae, and yellow fever over the years 2000-2030 across 112 countries. Methods: Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios. Results: We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases. Conclusions: This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future. Funding: VIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

A scenario modeling pipeline for COVID-19 emergency planning.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

Politicizing public health: the powder keg of rushing COVID-19 vaccines.

Vaccine hesitancy is on the rise, as more individuals are delaying or refusing vaccines. This rise in hesitancy has been primarily driven by vaccine safety concerns, even though the vaccine development process is regulated by a robust and rigorous scientific system. Recent data suggest that many individuals would be unwilling to take a COVID-19 vaccine, once one is available. The Trump administration's Operation Warp Speed aims to deliver a vaccine in the near future, even though no American or European COVID-19 vaccine has yet completed Phase 3 trials. The administration has used the emergency use authorization mechanism to fast track therapeutic products through the Food and Drug Administration and has not ruled out using the mechanism to fast track a COVID-19 vaccine. Perceived political pressure to push a COVID-19 vaccine will have a multitude of negative consequences. Not only will it lead to sub-optimal levels of vaccine acceptance toward a COVID-19 vaccine, it will reverse progress made in controlling vaccine preventable disease for years to come.

Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study.

BACKGROUND: Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. METHODS: From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. FINDINGS: Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20-22). Cases were isolated on average 4·6 days (95% CI 4·1-5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1-2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49-26·33] for household contacts and 7·06 [1·43-34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1-13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3-0·5), with a mean serial interval of 6·3 days (95% CI 5·2-7·6). INTERPRETATION: Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control. FUNDING: Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.

Clinical and Epidemiological Aspects of Diphtheria: A Systematic Review and Pooled Analysis.

BACKGROUND: Diphtheria, once a major cause of childhood morbidity and mortality, all but disappeared following introduction of diphtheria vaccine. Recent outbreaks highlight the risk diphtheria poses when civil unrest interrupts vaccination and healthcare access. Lack of interest over the last century resulted in knowledge gaps about diphtheria's epidemiology, transmission, and control. METHODS: We conducted 9 distinct systematic reviews on PubMed and Scopus (March-May 2018). We pooled and analyzed extracted data to fill in these key knowledge gaps. RESULTS: We identified 6934 articles, reviewed 781 full texts, and included 266. From this, we estimate that the median incubation period is 1.4 days. On average, untreated cases are colonized for 18.5 days (95% credible interval [CrI], 17.7-19.4 days), and 95% clear Corynebacterium diphtheriae within 48 days (95% CrI, 46-51 days). Asymptomatic carriers cause 76% (95% confidence interval, 59%-87%) fewer cases over the course of infection than symptomatic cases. The basic reproductive number is 1.7-4.3. Receipt of 3 doses of diphtheria toxoid vaccine is 87% (95% CrI, 68%-97%) effective against symptomatic disease and reduces transmission by 60% (95% CrI, 51%-68%). Vaccinated individuals can become colonized and transmit; consequently, vaccination alone can only interrupt transmission in 28% of outbreak settings, making isolation and antibiotics essential. While antibiotics reduce the duration of infection, they must be paired with diphtheria antitoxin to limit morbidity. CONCLUSIONS: Appropriate tools to confront diphtheria exist; however, accurate understanding of the unique characteristics is crucial and lifesaving treatments must be made widely available. This comprehensive update provides clinical and public health guidance for diphtheria-specific preparedness and response.

Characterizing the impact of spatial clustering of susceptibility for measles elimination.

Measles elimination efforts are primarily focused on achieving and maintaining national vaccination coverage goals, based on estimates of the critical vaccination threshold (Vc): the proportion of the population that must be immune to prevent sustained epidemics. Traditionally, Vc estimates assume evenly mixing populations, an invalid assumption. If susceptible individuals preferentially contact one another, communities may remain vulnerable to epidemics even when vaccination coverage targets are met at the national level. Here we present a simple method to estimate Vc and the effective reproductive number, R, while accounting for spatial clustering of susceptibility. For measles, assuming R0 = 15 and 95% population immunity, adjustment for high clustering of susceptibility increases R from 0.75 to 1.29, Vc from 93% to 96%, and outbreak probability after a single introduction from <1% to 23%. The impact of clustering remains minimal until vaccination coverage nears elimination levels. We illustrate our approach using Demographic and Health Survey data from Tanzania and show how non-vaccination clustering potentially contributed to continued endemic transmission of measles virus during the last two decades. Our approach demonstrates why high national vaccination coverage sometimes fails to achieve measles elimination, and that a shift from national to subnational focus is needed as countries approach elimination.

Mitigating measles outbreaks in West Africa post-Ebola.

The Ebola outbreak in 2014-2015 devastated the populations, economies and healthcare systems of Guinea, Liberia and Sierra Leone. With this devastation comes the impending threat of outbreaks of other infectious diseases like measles. Strategies for mitigating these risks must include both prevention, through vaccination, and case detection and management, focused on surveillance, diagnosis and appropriate clinical care and case management. With the high transmissibility of measles virus, small-scale reactive vaccinations will be essential to extinguish focal outbreaks, while national vaccination campaigns are needed to guarantee vaccination coverage targets are reached in the long term. Rapid and multifaceted strategies should carefully navigate challenges present in the wake of Ebola, while also taking advantage of current Ebola-related activities and international attention. Above all, resources and focus currently aimed at these countries must be utilized to build up the deficit in infrastructure and healthcare systems that contributed to the extent of the Ebola outbreak.

Reduced vaccination and the risk of measles and other childhood infections post-Ebola.

The Ebola epidemic in West Africa has caused substantial morbidity and mortality. The outbreak has also disrupted health care services, including childhood vaccinations, creating a second public health crisis. We project that after 6 to 18 months of disruptions, a large connected cluster of children unvaccinated for measles will accumulate across Guinea, Liberia, and Sierra Leone. This pool of susceptibility increases the expected size of a regional measles outbreak from 127,000 to 227,000 cases after 18 months, resulting in 2000 to 16,000 additional deaths (comparable to the numbers of Ebola deaths reported thus far). There is a clear path to avoiding outbreaks of childhood vaccine-preventable diseases once the threat of Ebola begins to recede: an aggressive regional vaccination campaign aimed at age groups left unprotected because of health care disruptions.

Comparison of patients hospitalized with pandemic 2009 influenza A (H1N1) virus infection during the first two pandemic waves in Wisconsin.

BACKGROUND: Wisconsin was severely affected by pandemic waves of 2009 influenza A H1N1 infection during the period 15 April through 30 August 2009 (wave 1) and 31 August 2009 through 2 January 2010 (wave 2). METHODS: To evaluate differences in epidemiologic features and outcomes during these pandemic waves, we examined prospective surveillance data on Wisconsin residents who were hospitalized ≥ 24 h with or died of pandemic H1N1 infection. RESULTS: Rates of hospitalizations and deaths from pandemic H1N1 infection in Wisconsin increased 4- and 5-fold, respectively, from wave 1 to wave 2; outside Milwaukee, hospitalization and death rates increased 10- and 8-fold, respectively. Hospitalization rates were highest among racial and ethnic minorities and children during wave 1 and increased most during wave 2 among non-Hispanic whites and adults. Times to hospital admission and antiviral treatment improved between waves, but the overall hospital course remained similar, with no change in hospitalization duration, intensive care unit admission, requirement for mechanical ventilation, or mortality. CONCLUSIONS: We report broader geographic spread and marked demographic differences during pandemic wave 2, compared with wave 1, although clinical outcomes were similar. Our findings emphasize the importance of using comprehensive surveillance data to detect changing characteristics and impacts during an influenza pandemic and of vigorously promoting influenza vaccination and other prevention efforts.

Epidemiologic and clinical features among patients hospitalized in Wisconsin with 2009 H1N1 influenza A virus infections, April to August 2009.

BACKGROUND: During April 15 through July 23, 2009, Wisconsin reported the most confirmed and probable cases of 2009 influenza A (H1N1) virus (2009 H1N1) infection in the United States. Preliminary reports suggest that 2009 H1N1 infection disproportionately affected minority populations. METHODS: Prospective surveillance among all acute care hospitals in Wisconsin to detect patients hospitalized at least 24 hours with confirmed 2009 H1N1 infection during April 23 through August 15, 2009. RESULTS: During the study interval, 252 patients were hospitalized and 11 (4%) died. Statewide hospitalization rates by age, sex, and race/ethnicity categories were highest among patients aged <1 year (21.6/100,000), females (4.9/100,000), and African Americans (36.3/100,000). The median age was 28 years: Hispanics (median age=16 years) and African Americans (24 years) were younger than non-Hispanic whites (37 years) and Asians (38 years). African Americans were more likely to have a hematologic condition and be morbidly obese (BMI > or = 40 kg/m2), and less likely to be admitted to an intensive care unit compared to other race/ethnicity groups (P<0.05). Hispanics and non-Hispanic whites were more likely to have cancer, be non-morbidly obese (BMI 30-39.9 kg/m2 or BMI percentile > or = 95%), and be hospitalized for >5 days compared to African Americans and Asians (P<0.05). There were no significant racial/ethnic differences in time from illness onset to admission or receipt of antiviral therapy, need for mechanical ventilation, acute respiratory distress syndrome, or death. CONCLUSIONS: The first wave of the 2009 H1N1 pandemic in Wisconsin disproportionately affected hospitalized patients who were African Americans, Asians, and Hispanics compared to non-Hispanic whites. Preventive measures focused on these populations may reduce morbidity associated with 2009 H1N1 infection.