Published in PLOS One on 2025-11-04, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Eva Lorenz, John Amuasi, Tiana Randrianarisoa, Tahinamandranto Rasamoelina, Leonard Gunga, Dominik Benke, Jonathan Ströbele, Jenny Kettenbeil, Wibke Loag, Haja Andriamahandry, Landry Razanakolona, Jean Rolland Randrianirina, Hosea Randrianasolo, Jean Christian Ratombotsoa, Fitahina Nahita, Daniel Eibach, Daniela Fusco, Jürgen May, Rivo A Rakotoarivelo, Aurélia Souares, Emmanuel Bonnet, Nicole S Struck
Source: PLOS One • DOI 10.1371/journal.pone.0334627
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Implementing population-based surveys in resource-constrained settings presents logistical challenges when detailed population enumeration is unavailable. We developed a field mapping system integrated into a cluster sampling approach to eliminate pre-enumeration requirements for a SARS-CoV-2 seroprevalence survey in Madagascar. We conducted a cross-sectional observational study in urban Fianarantsoa, Madagascar, between February and June 2021. Using probability proportional to size sampling, we selected clusters from administrative areas (fokontany) and randomly generated GPS coordinates within these clusters. Field teams navigated to coordinates using OpenStreetMap software on tablets, identified eligible households, and conducted health surveys with blood sampling. We employed a mobile-compatible system for real-time household mapping and data collection, functioning without continuous network connectivity. Sample size calculation targeted 650 households (SARS-CoV-2 seroprevalence 30%, precision ±5%, design effect 2.0). Our specific objectives were to develop and implement a geographic cluster sampling method that did not require pre-enumeration; to assess the feasibility of this method through participation rates; and to evaluate potential selection biases related to socioeconomic factors. We identified households at 95.3% (696/730) of randomly generated GPS coordinates. Of contacted households, 96.8% (674/696) participated, representing 1,121 individuals across 57 clusters. Participation rates varied geographically, with a modest inverse correlation with household wealth (participation decreased by 0.85% per wealth quintile increase, 95% CI: −3.54% to 1.84%). Demographic characteristics of our sample matched census data for urban Fianarantsoa, supporting the representativeness of our approach. This integrated field mapping system created a virtual household map simultaneously with survey implementation, enabling cost-effective two-stage cluster sampling without pre-enumeration. The approach enabled evaluation of selection bias, simplified logistics, and provided a permanent geo-referenced database of surveyed households. This methodology offers a practical solution for population-based surveys in resource-constrained settings with incomplete enumeration data and has applications beyond COVID-19 research for various public health surveillance activities.
Introduction
Estimates of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) seroprevalence are important for decision-makers at global, national and subnational levels to prioritise public health interventions, vaccine introduction and resource allocation. However, accurate population-level prevalence estimates are scarce, especially in resource-limited settings [1]. The SeroCoV project is a multi-country study (Burkina Faso, Ghana and Madagascar) that aimed to help close this gap [2]. Common approaches to assessing SARS-CoV-2 seroprevalence include facility-based and population-based studies. While facility-based studies are more commonly conducted due to their operational simplicity, they have inherent limitations in population representativeness as they primarily capture patients actively seeking care at study facilities, potentially overrepresenting those with severe symptoms and healthcare access [3]. In contrast, population-based studies provide direct measurements of disease prevalence in the general population, but remain uncommon due to the substantial resources requirements and complex field implementation demands [4]. They can be further complicated in areas with rapidly changing or hard-to-reach populations or incomplete enumeration coverage of the underlying population [5–7]. A systematic review by Bobrovitz et al. [8] found that only 22% of SARS-CoV-2 seroprevalence studies worldwide used population-based probability sampling approaches, while the majority relied on selective convenience samples from blood donors, healthcare workers, or outpatients.
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider financing & policy desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2025 Eva Lorenz, John Amuasi, Tiana Randrianarisoa, Tahinamandranto Rasamoelina, Leonard Gunga, Dominik Benke, Jonathan Ströbele, Jenny Kettenbeil, Wibke Loag, Haja Andriamahandry, Landry Razanakolona, Jean Rolland Randrianirina, Hosea Randrianasolo, Jean Christian Ratombotsoa, Fitahina Nahita, Daniel Eibach, Daniela Fusco, Jürgen May, Rivo A Rakotoarivelo, Aurélia Souares, Emmanuel Bonnet, Nicole S Struck. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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