Evidence mapPaperPMID 42229951Full record

ArticleBMJ open2026

Association between completing at least eight antenatal care contacts and maternal anaemia in Ghana: a cross-sectional study using causal machine learning.

Esther Serwah Agbedinu, Frederick Osei Owusu, Kingsley Boakye, Helena Addai-Manu, Julius Kwabena Karikari, Emmanuel Konadu, Randy Akankpabwen Akanwariwiak, Lydia Asenso, Mercy Addae, Mawuse Kanfra and 1 more

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Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Esther Serwah AgbedinuTwedie Government Hospital, Twedie, Ghana.
Frederick Osei OwusuJuaben Government Hospital, Juaben, Ghana.
Kingsley BoakyeSchool of Public Health, Department of Epidemiology and Biostatistics, Kwame Nkrumah University of Science and Technology, Kumasi, Ashanti Region, Ghana.
Helena Addai-ManuJuaben Government Hospital, Juaben, Ghana.
Julius Kwabena KarikariUniversity Hospital, Kwame Nkrumah Univeristy of Science and Technology, Kumasi, Ashanti Region, Ghana.
Emmanuel KonaduUniversity Hospital, Kwame Nkrumah Univeristy of Science and Technology, Kumasi, Ashanti Region, Ghana.
Randy Akankpabwen AkanwariwiakKomfo Anokye Teaching Hospital, Kumasi, Ashanti Region, Ghana.
Lydia AsensoJuaben Government Hospital, Juaben, Ghana.
Mercy AddaeUniversity Hospital, Kwame Nkrumah Univeristy of Science and Technology, Kumasi, Ashanti Region, Ghana.
Mawuse KanfraSt. Dominic Catholic Hospital, Akwatia, Ghana.
Douglas Aninng OpokuSchool of Public Health, Department of Epidemiology and Biostatistics, Kwame Nkrumah University of Science and Technology, Kumasi, Ashanti Region, Ghana douglasopokuaninng@gmail.com.ORCID http://orcid.org/0000-0003-2321-387X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaternal anaemia remains a pressing global health challenge, with a notable burden in low- and middle-income countries. Existing studies in sub-Saharan Africa have largely relied on average associations, thereby concealing key variation among women and failing to account for heterogeneity.

objectiveTo assess the association between completing at least eight antenatal care (ANC) contacts and maternal anaemia in Ghana and to explore heterogeneity in this association using causal machine learning.

designAn institution-based cross-sectional study was conducted using a retrospective review of medical records and causal machine learning analysis.

settingJuaben Government Hospital.

participantsOf 2326 women who delivered at the hospital, 2114 with complete data on the main exposure and outcome variables were included in the analysis. MAIN EXPOSURE: Completion of at least eight ANC contacts. ANC contact was defined as the in-person visit to the clinic with a healthcare professional for routine ANC services and follow-up. MAIN OUTCOME: Maternal anaemia, defined as a haemoglobin level below 11 g/dL in the last ANC before delivery.

methodsA causal forest model was used to estimate the association between completing at least eight ANC contacts and maternal anaemia. Conditional average treatment effects were used to explore individual-level variation in these associations, providing policy-relevant insights.

resultsCompleting ≥8 ANC contacts was associated with a 6 percentage-point lower probability of maternal anaemia compared with having fewer visits (average treatment effect: -0.06, 95% CI -0.11 to -0.02). Predicted individual-level effects ranged from -0.21 to 0.09. Participants' age, malaria prophylaxis, marital status, parity and educational level were the five most important contributors to the observed heterogeneity.

conclusionsThis study demonstrated that completing ≥8 ANC contacts is associated with a lower probability of maternal anaemia, with heterogeneity across subgroups. We recommend differentiated, context-specific ANC interventions that focus on high-impact subgroups while strengthening the effectiveness and quality of care delivered at each visit.

Indexed as

AnemiaMachine LearningPregnancy Complications, HematologicPrenatal CareAdultCross-Sectional StudiesFemaleGhanaHumansPregnancyRetrospective StudiesYoung AdultAnaemiaAntenatalMaternal medicine

Identifiers

PMID42229951
PMCPMC13239363

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