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

Covariates for predictive model for measles seropositivity.

Covariates for predictive model for measles seropositivity.</p

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

S1 Checklist -

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

Number of households selected in each cluster in bootstrapping, by age group and district.

Number of households selected in each cluster in bootstrapping, by age group and district.</p

Results of unweighted and weighted bootstrapping.

We used bootstrapping procedure to simulate inclusion of missed households in the sampling frame of a serosurvey carried out in Ndola and Choma districts, Zambia, in April—June 2022. (DOCX)</p

Estimates of outcomes of interest using the original sampling frame (excluding missed households) and a mixed sampling frame (including both missed households and households enrolled in the parent study).

Weighted and unweighted estimates. Weighting was done using the estimated population in each age group in each cluster in the missed population study. A. Health care 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. C. Measles seroprevalence, children 1–4 years old. (PDF)</p

Individual demographic characteristics of individuals enrolled in the original study and missed population study, adults 15 years and older.

The original serosurvey was carried out in April—June 2022 in Ndola and Choma districts, Zambia, using stratified multi-stage clustering design. The follow-up missed population study was carried out in a subset of clusters of the original survey between July—August 2022. This study was carried out in a subsample of clusters from the original survey; in each selected cluster, a sample of households not available during listing of the original serosurvey, and hence excluded from its sampling frame, were randomly selected. (DOCX)</p

Healthcare-seeking and characteristics reported by caregivers of children 1–4 years old and 5–14 years old.

Results presented are for univariable analysis, by district and age group, and multivariable analysis, by age group only. “Original” refers to the serosurvey carried out in April—June 2022 in Ndola and Choma districts, Zambia, using stratified multi-stage clustering design. “Missed” refers to the study sample from the follow-up missed population study, carried out in a subset of clusters of the original survey between July—August 2022. This study was carried out in a subsample of clusters from the original survey; in each selected cluster, a sample of households not available during listing of the original serosurvey, and hence excluded from its sampling frame, were randomly selected.</p

Selection probabilities of households in bootstrapping.

Selection probabilities of households in bootstrapping.</p