Evidence map›Paper›PMID 36203476›Full record

ArticleSSM - population health2022

Using machine learning to understand determinants of IUD use in India: Analyses of the National Family Health Surveys (NFHS-4).

Arnab K Dey, Nabamallika Dehingia, Nandita Bhan, Edwin Elizabeth Thomas, Lotus McDougal, Sarah Averbach, Julian McAuley, Abhishek Singh, Anita Raj

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in SSM - population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. 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.

Arnab K DeyCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Nabamallika DehingiaCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Nandita BhanCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Edwin Elizabeth ThomasCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Lotus McDougalCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Sarah AverbachCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.
Julian McAuleyDepartment of Computer Science, School of Engineering, University of California San Diego, San Diego, CA, USA.
Abhishek SinghDepartment of Public Health & Mortality Studies, International Institute for Population Sciences, Mumbai, India.
Anita RajCenter on Gender Equity and Health, Department of Medicine, University of California San Diego, San Diego, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intra-uterine devices (IUDs) are a safe and effective method to delay or space pregnancies and are available for free or at low cost in the Indian public health system; yet, IUD uptake in India remains low. Limited quantitative research using national data has explored factors that may affect IUD use. Machine Learning (ML) techniques allow us to explore determinants of low prevalence behaviors in survey research, such as IUD use. We applied ML to explore the determinants of IUD use in India among married women in the 4th National Family Health Survey (NFHS-4; N = 499,627), which collects data on demographic and health indicators among women of childbearing age. We conducted ML logistic regression (lasso and ridge) and neural network approaches to assess significant determinants and used iterative thematic analysis (ITA) to offer insight into related variable constructs generated from a series of regularized models. We found that couples' shared family planning (FP) goals were the strongest determinants of IUD use, followed by receipt of FP services and desire for no more children, higher wealth and education, and receipt of maternal and child health services. Findings highlight the importance of male engagement and family planning services for IUD uptake and the need for more targeted efforts to support awareness of IUD as an option for spacing, especially for those of lower SES and with lower access to care.

Indexed as

Couple dynamicsFamily planningIndiaIntra-uterine devicesIUDMachine learningMale engagementNFHSReproductive health

Identifiers

PMID36203476
PMCPMC9529578

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.