Evidence map›Paper›PMID 40254889›Full record

ArticleTropical medicine & international health : TM & IH2025

Mobile phone survey estimates of perinatal mortality in Malawi: A comparison of data from truncated and full pregnancy histories.

Georges Reniers, Julio Romero-Prieto, Michael Chasukwa, Funny Muthema, Sarah Walters, Bruno Masquelier, Jethro Banda, Emmanuel Souza, Boniface Dulani

Abstract readComparative Study
In one paragraph

Article in Tropical medicine & international health : TM & IH, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Measuring childhood mortality through mobile phone interviews in Mozambique.Tropical medicine & international health : TM & IH · 2025
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Georges ReniersDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0001-6582-1692
Julio Romero-PrietoDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0002-8131-7821
Michael ChasukwaInstitute of Public Opinion and Research, Zomba, Malawi.ORCID https://orcid.org/0000-0003-4819-7028
Funny MuthemaInstitute of Public Opinion and Research, Zomba, Malawi.ORCID https://orcid.org/0000-0001-8717-6655
Sarah WaltersDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0001-9929-1917
Bruno MasquelierCenter for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium.ORCID https://orcid.org/0000-0002-9585-891X
Jethro BandaMalawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi.ORCID https://orcid.org/0000-0001-8075-8564
Emmanuel SouzaDepartment of Population Studies, University of Malawi, Zomba, Malawi.ORCID https://orcid.org/0000-0002-7380-1349
Boniface DulaniInstitute of Public Opinion and Research, Zomba, Malawi.ORCID https://orcid.org/0000-0002-0281-3000

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn many low- and middle-income countries, perinatal mortality estimates are derived retrospectively from periodically conducted household surveys. Mobile phone surveys offer advantages in terms of cost and ease of implementation. However, their suitability for monitoring perinatal mortality has not been established.

methodsWe use data from the Malawi Rapid Mortality Mobile Phone Survey (RaMMPS) to estimate perinatal mortality rates from two versions of the survey instrument: a full pregnancy history and a shorter truncated pregnancy history. Female respondents of reproductive age were randomly allocated to either of these instruments. The sample was generated through random digit dialling with active strata monitoring. Post-stratification weighting was used to correct for sample selection bias, and estimates are reported with bootstrap confidence intervals. We estimated the stillbirth rate as the synthetic cohort probability of a foetal death with 28+ weeks of gestation over all pregnancies reaching the same gestational age. The perinatal and extended perinatal mortality rates were defined as the probabilities of dying between 28 weeks and 7 or 28 days of life, respectively. RaMMPS estimates are compared to the 2015-2016 Malawi Demographic and Health Survey and estimates published by the United Nations Inter-agency Group for Child Mortality Estimation.

resultsTruncated and full pregnancy histories were administered for 2093 and 2067 women, respectively. Weighted point estimates of the stillbirth (19.81 deaths per 1000 pregnancies, 95%-confidence interval (CI): 14.11-25.62), perinatal (42.41, 95%-CI: 33.91-50.92), and extended perinatal mortality rates (50.11, 95%-CI: 41.56-58.84) from the full pregnancy history instrument are in line with Demographic and Health Survey and United Nations Inter-agency Group for Child Mortality Estimation estimates. In comparison, the mortality estimates from the truncated pregnancy history instrument are higher, but this difference only approaches statistical significance in the case of the stillbirth rate. Post-stratification weighting produces a small upwards adjustment in the estimates.

conclusionMobile phone surveys are a promising method for collecting perinatal mortality data. The full pregnancy history instrument produces more plausible results than the shorter truncated pregnancy history questionnaire where the window of retrospection is restricted.

Indexed as

Cell PhonePerinatal MortalityStillbirthAdolescentAdultFemaleHealth SurveysHumansInfant, NewbornMalawiPregnancySurveys and QuestionnairesYoung AdultMalawiMobile phone surveysperinatal mortality ratestillbirth rate

Identifiers

PMID40254889
PMCPMC12136939

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.