Evidence map›Paper›PMID 33228585›Full record

ArticleBMC pregnancy and childbirth2020

Mapping of anaemia prevalence among pregnant women in Kenya (2016-2019).

Julius Nyerere Odhiambo, Benn Sartorius

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Article in BMC pregnancy and childbirth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

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11citing papers in PubMed, 1 pooled it
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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.

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

2 authors.

Julius Nyerere OdhiamboDiscipline of Public Health Medicine, College of Health Sciences, University of KwaZulu-Natal, Howard College Campus, 2nd Floor George Campbell Building, Durban, 4001, South Africa. nyererejulius7@gmail.com.ORCID http://orcid.org/0000-0002-9817-1849
Benn SartoriusDiscipline of Public Health Medicine, College of Health Sciences, University of KwaZulu-Natal, Howard College Campus, 2nd Floor George Campbell Building, Durban, 4001, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReducing the burden of anaemia is a critical global health priority that could improve maternal outcomes amongst pregnant women and their neonates. As more counties in Kenya commit to universal health coverage, there is a growing need for optimal allocation of the limited resources to sustain the gains achieved with the devolution of healthcare services. This study aimed to describe the spatio-temporal patterns of maternal anaemia prevalence in Kenya from 2016 to 2019.

methodsQuarterly reported sub-county level maternal anaemia cases from January 2016 - December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical negative binomial spatio-temporal conditional autoregressive (CAR) model was used to estimate maternal anaemia prevalence by sub-county and quarter. Spatial and temporal correlations were considered by assuming a conditional autoregressive and a first-order autoregressive process on sub-county and seasonal specific random effects, respectively.

resultsThe overall estimated number of pregnant women with anaemia increased by 90.1% (95% uncertainty interval [95% UI], 89.9-90.2) from 155,539 cases in 2016 to 295,642 cases 2019. Based on the WHO classification criteria, the proportion of sub-counties with normal prevalence decreased from 28.0% (95% UI, 25.4-30.7) in 2016 to 5.4% (95% UI, 4.1-6.7) in 2019, whereas moderate anaemia prevalence increased from 16.8% (95% UI, 14.7-19.1) in 2016 to 30.1% (95% UI, 27.5-32.8) in 2019 and severe anaemia prevalence increased from 7.0% (95% UI, 5.6-8.6) in 2016 to 16.6% (95% UI, 14.5-18.9) in 2019. Overall, 45.1% (95% UI: 45.0-45.2) of the estimated cases were in malaria-endemic sub-counties, with the coastal endemic zone having the highest proportion 72.8% (95% UI: 68.3-77.4) of sub-counties with severe prevalence.

conclusionAs the number of women of reproductive age continues to grow in Kenya, the use of routinely collected data for accurate mapping of poor maternal outcomes remains an integral component of a functional maternal health strategy. By unmasking the sub-county disparities often concealed by national and county estimates, our study findings reiterate the importance of maternal anaemia prevalence as a metric for estimating malaria burden and offers compelling policy implications for achieving national nutritional targets.

Indexed as

AnemiaBayes TheoremCost of IllnessFemaleHumansKenyaMalariaModels, StatisticalPregnancyPregnancy Complications, HematologicPrevalencePublic HealthSpatio-Temporal AnalysisBayesian inferenceConditional autoregressive modelKenyaMaternal anaemiaPolicyPrevalenceSub-county

Identifiers

PMID33228585
PMCPMC7685542

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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.