THE IMPACT OF DATA BIASES ON MEASLES OUTBREAK RISK ESTIMATION IN ZAMBIA: A MULTI-SOURCE DATA ANALYSIS

Measles remains a public health challenge despite the availability of an effective vaccine. To target control and elimination efforts, it is important to identify high-risk areas for measles outbreaks and understand population susceptibility and human mobility, which drive measles transmission. However, current data sources for quantifying mobility are subject to biases. In this dissertation, we addressed limitations of commonly used data sources and explored approaches to leverage multiple datasets. We conducted a travel survey in two districts in Zambia and found that children traveled less than adults. Adjusting mobile phone data to better capture children's travel resulted in a lower proportion of districts with measles introduction events in a transmission model. We also compared travel patterns captured in four different mobility data sources in Zambia and found marked differences, leading to inconsistent estimates of measles introduction events. By using a Bayesian framework to combine information across data sources, we found that mobile phone data dominated the estimates for travel outside the district, emphasizing the need for increased data availability and sharing to reduce misallocation of resources. We then explored a potential source of bias in estimating population immunity. Comparing characteristics of individuals and households excluded from a community-based serosurvey with those included, we found that excluded populations came from smaller, less wealthy households and were more likely to seek healthcare. Adult respondents in the excluded populations had different occupations and were more likely to be male. However, simulations suggested that these differences did not result in biased estimates of measles seroprevalence. Nonetheless, they highlight the threat to validity posed by incomplete sampling frames, even in gold-standard study approaches. We proposed a framework using a missingness threshold to reduce the potential imbalance in key population characteristics. The methods and findings of this dissertation contribute to refining estimates of human mobility and measles seroprevalence. They underscore the need for diverse data sources and careful consideration of potential biases in public health research. By addressing these limitations, we can better target interventions and resources to advance measles control and elimination efforts

The implementation of infection prevention and control measures and health care utilisation in ACF-supported health facilities during the COVID-19 pandemic in Kinshasa, Democratic Republic of the Congo, 2020

Background Infection prevention and control (IPC) was a central component of the Democratic Republic of the Congo’s COVID-19 response in 2020, aiming to prevent infections and ensure safe health service provision. Objectives We aimed to assess the evolution of IPC capacity in 65 health facilities supported by Action Contre la Faim in three health zones in Kinshasa (Binza Meteo (BM), Binza Ozone (BO), and Gombe), investigate how triage and alert validation were implemented, and estimate how health service utilisation changed in these facilities (April–December 2020). Methods We used three datasets: IPC Scorecard data assessing health facilities’ IPC capacity at baseline, monthly and weekly triage data, and monthly routine data on eight health services. We examined factors associated with triage and isolation capacity with a mixed-effects negative binomial model and estimated changes in health service utilisation with a mixed-model with random intercept and long-term trend for each health facility. We reported incidence rate ratios (IRRs) for level change when the pandemic began, for trend change, and for lockdown and post-lockdown periods (Gombe). We estimated cumulative and monthly percent differences with expected consultations. Results IPC capacity reached an average score of 90% by the end of the programme. A one-point increase in the IPC score was associated with +6% and +5% increases in triage capacity in BO and Gombe, respectively, and with +21% and +10% increases in isolation capacity in the same zones. When the pandemic began, decreases were seen in outpatient consultations (IRR: 0.67, 95% confidence interval (CI) [0.48–0.95] BM&BO-combined; IRR: 0.29, 95%CI [0.16–0.53] Gombe), consultations for respiratory tract infections (IRR: 0.48, 95%CI [0.28–0.87] BM&BO-combined), malaria (IRR: 0.60, 95%CI [0.43–0.84] BM&BO-combined, IRR: 0.33, 95%CI [0.18–0.58] Gombe), and vaccinations (IRR: 0.27, 95%CI [0.10–0.71] Gombe). Maternal health services decreased in Gombe (ANC1: IRR: 0.42, 95%CI [0.21–0.85]). Conclusions The effectiveness of the triage and alert validation process was affected by the complexity of implementing a broad clinical definition in limited-resource settings with a pre-pandemic epidemiological profile characterised by infectious diseases with symptoms like COVID-19. Readily available testing capacity remains key for future pandemic response to improve the disease understanding and maintain health services

COVID-19 epidemiology and changes in health service utilization in Azraq and Zaatari refugee camps in Jordan: A retrospective cohort study.

BackgroundThe effects of the Coronavirus Disease 2019 (COVID-19) pandemic in humanitarian contexts are not well understood. Specific vulnerabilities in such settings raised concerns about the ability to respond and maintain essential health services. This study describes the epidemiology of COVID-19 in Azraq and Zaatari refugee camps in Jordan (population: 37,932 and 79,034, respectively) and evaluates changes in routine health services during the COVID-19 pandemic.Methods and findingsWe calculate the descriptive statistics of COVID-19 cases in the United Nations High Commissioner for Refugees (UNHCR)'s linelist and adjusted odds ratios (aORs) for selected outcomes. We evaluate the changes in health services using monthly routine data from UNHCR's health information system (HIS; January 2018 to March 2021) and apply interrupted time series analysis with a generalized additive model and negative binomial (NB) distribution, accounting for long-term trends and seasonality, reporting results as incidence rate ratios (IRRs). COVID-19 cases were first reported on September 8 and September 13, 2020 in Azraq and Zaatari camps, respectively, 6 months after the first case in Jordan. Incidence rates (IRs) were lower in camps than neighboring governorates (by 37.6% in Azraq (IRR: 0.624, 95% confidence interval [CI]: [0.584 to 0.666], p-value: ConclusionsCOVID-19 transmission was lower in camps than outside of camps. Refugees may have been affected from external transmission, rather than driving it. Various types of health services were affected differently, but disruptions appear to have been limited in the 2 camps compared to other noncamp settings. These insights into Jordan's refugee camps during the first year of the COVID-19 pandemic set the stage for follow-up research to investigate how infection susceptibility evolved over time, as well as which mitigation strategies were more successful and accepted

Health care utilisation in Cox’s Bazar district, Bangladesh, during the first year of the COVID-19 pandemic: A mixed-methods study among host communities

ABSTRACTTo respond to the COVID-19 pandemic, countries introduced public health and social measures that had indirect societal, economic consequences. Concerns during epidemics include continuity of routine health services. We investigate how healthcare utilisation and healthcare seeking behaviour changed during the first year of the COVID-19 pandemic among host communities in Cox’s Bazar, Bangladesh. This mixed-methods study combines quantitative analyses of routine health data and population-based findings about healthcare seeking behaviours. Trends in consultations changed according to facility level (higher-level facilities included Upazila Health Complexes and District Hospitals; lower-level facilities included Community Clinics and Union Health and Family Welfare Centers). At the pandemic’s beginning, drops were seen at higher-level health facilities for outpatient department (OPD) consultations, respiratory infections, and antenatal care. Minor reductions or increases were seen at lower-level facilities for the same services. Half of the subdistricts reported a cumulative increase in OPD and respiratory tract infection consultations. Most subdistricts reported a cumulative decrease in antenatal care. Child vaccinations dropped in all subdistricts, half of which did not catch-up, resulting in a cumulative decrease of delivered doses. Fear of contracting COVID-19 and financial constraints were the main reasons for decreased access. Drivers of healthcare seeking behaviours should be better understood to guide preparedness and service delivery modalities at primary and secondary levels

Changes in mobility patterns during the COVID-19 pandemic in Zambia: Implications for the effectiveness of NPIs in Sub-Saharan Africa.

The COVID-19 pandemic has impacted many facets of human behavior, including human mobility partially driven by the implementation of non-pharmaceutical interventions (NPIs) such as stay at home orders, travel restrictions, and workplace and school closures. Given the importance of human mobility in the transmission of SARS-CoV-2, there have been an increase in analyses of mobility data to understand the COVID-19 pandemic to date. However, despite an abundance of these analyses, few have focused on Sub-Saharan Africa (SSA). Here, we use mobile phone calling data to provide a spatially refined analysis of sub-national human mobility patterns during the COVID-19 pandemic from March 2020-July 2021 in Zambia using transmission and mobility models. Overall, among highly trafficked intra-province routes, mobility decreased up to 52% during the time of the strictest NPIs (March-May 2020) compared to baseline. However, despite dips in mobility during the first wave of COVID-19 cases, mobility returned to baseline levels and did not drop again suggesting COVID-19 cases did not influence mobility in subsequent waves

Status of households enrolled in the original community-based measles serological survey and missed populations study, Ndola and Choma Districts, Zambia, 2022.

A. The distribution of household status from listing in the original serosurvey conducted in Choma and Ndola Districts, by cluster. Households classified as “Available” provided consent to participate in the study and reported that they would be available during the data collection; these households comprised the sampling frame for the original study. Households that refused (“Refused”) were excluded from the original study sampling frame and were ineligible for the missed populations study. Households classified as “Non-contact” were households that were locked at the time of listing (and during revisits), or if there was no adult respondent at home, and nobody was available to provide information about the household (e.g. neighbor). Finally, households that were listed but which reported not being available during data collection (“Contact, not available”) were excluded from the sampling frame in the original study. The households in the latter two categories were eligible for the missed populations study. Clusters are arranged in descending order by percentage of households eligible for the missed populations study (“Non-contact” and “Contact, not available” households). “X”‘s indicate clusters selected for the missed population study. B. Distribution of households that the data collection team attempted to reach by status, cluster, and district in the missed populations study. Households classified as “Completed” were successfully located and provided consent to participate in the study. “Household not found” indicates households identified for inclusion in the missed populations study that could not be located during this study. “Non-contact” refers to households which were physically located, but ones in which the data collection team could not contact its occupants. No household refused participation. Clusters are arranged in order of decreasing percent missed in the missed populations study, comprised of “Household Not Found” and “Non-contact” households.</p

Estimates of outcomes of interest using the sampling frame from the original community-based measles serological survey (excluding missed households) in Ndola and Choma Districts, Zambia, 2022, and a mixed sampling frame (including both households enrolled in the missed population study and households enrolled in the original study).

Weighting was done using the estimated population in each age group in each cluster in the missed population study for outcomes of interest including A. Healthcare seeking (actual and theoretical) at facilities of interest (Arthur Davison Children’s Hospital and Choma General Hospital for children 1–4 and 5–14 years old, and Ndola Teaching Hospital and Choma General Hospital for adults 15 years and older), B. MCV2 coverage, children 1–4 years old, and C. Measles seroprevalence, children 1–4 years old.</p

Multivariable mixed-effects model.

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.</div