Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia
Importance: Antenatal care prevents maternal and neonatal deaths and improves birth outcomes. There is a lack of predictive models to identify pregnant women who are at high risk of failing to attend antenatal care in low-resource settings.
Objective: To develop a series of predictive models to identify women who are at high risk of failing to attend antenatal care in a rural setting in Ethiopia.
Design, Setting, and Participants: This prognostic study used data from the Birhan Health and Demographic Surveillance System and its associated pregnancy and child cohort. The study was conducted at the Birhan field site, North Shewa zone, Ethiopia, a platform for community- and facility-based research and training, with a focus on maternal and child health. Participants included women enrolled during pregnancy in the pregnancy and child cohort between December 2018 and March 2020, who were followed-up in home and facility visits. Data were analyzed from April to December 2022.
Exposures: A wide range of sociodemographic, economic, medical, environmental, and pregnancy-related factors were considered as potential predictors. The selection of potential predictors was guided by literature review and expert knowledge.
Main Outcomes and Measures: The outcome of interest was failing to attend at least 1 antenatal care visit during pregnancy. Prediction models were developed using logistic regression with regularization via the least absolute shrinkage and selection operator and ensemble decision trees and assessed using the area under the receiving operator characteristic curve (AUC).
Results: The study sample included 2195 participants (mean [SD] age, 26.8 [6.1] years; mean [SD] gestational age at enrolment, 25.5 [8.8] weeks). A total of 582 women (26.5%) failed to attend antenatal care during cohort follow-up. The AUC was 0.61 (95% CI, 0.58-0.64) for the regularized logistic regression model at conception, with higher values for models predicting at weeks 13 (AUC, 0.68; 95% CI, 0.66-0.71) and 24 (AUC, 0.66; 95% CI, 0.64-0.69). AUC values were similar with slightly higher performance for the ensembles of decision trees (conception: AUC, 0.62; 95% CI, 0.59-0.65; 13 weeks: AUC, 0.70; 95% CI, 0.67-0.72; 24 weeks: AUC, 0.67; 95% CI, 0.64-0.69).
Conclusions and Relevance: This prognostic study presents a series of prediction models for antenatal care attendance with modest performance. The developed models may be useful to identify women at high risk of missing their antenatal care visits to target interventions to improve attendance rates. This study opens the possibility to develop and validate easy-to-use tools to project health-related behaviors in settings with scarce resources.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
JAMA network open - 6(2023), 5 vom: 01. Mai, Seite e2315985 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wilder, Bryan [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 02.06.2023 Date Revised 09.06.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1001/jamanetworkopen.2023.15985 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM357587502 |
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| 100 | 1 | |a Wilder, Bryan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia |
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| 500 | |a Date Completed 02.06.2023 | ||
| 500 | |a Date Revised 09.06.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Importance: Antenatal care prevents maternal and neonatal deaths and improves birth outcomes. There is a lack of predictive models to identify pregnant women who are at high risk of failing to attend antenatal care in low-resource settings | ||
| 520 | |a Objective: To develop a series of predictive models to identify women who are at high risk of failing to attend antenatal care in a rural setting in Ethiopia | ||
| 520 | |a Design, Setting, and Participants: This prognostic study used data from the Birhan Health and Demographic Surveillance System and its associated pregnancy and child cohort. The study was conducted at the Birhan field site, North Shewa zone, Ethiopia, a platform for community- and facility-based research and training, with a focus on maternal and child health. Participants included women enrolled during pregnancy in the pregnancy and child cohort between December 2018 and March 2020, who were followed-up in home and facility visits. Data were analyzed from April to December 2022 | ||
| 520 | |a Exposures: A wide range of sociodemographic, economic, medical, environmental, and pregnancy-related factors were considered as potential predictors. The selection of potential predictors was guided by literature review and expert knowledge | ||
| 520 | |a Main Outcomes and Measures: The outcome of interest was failing to attend at least 1 antenatal care visit during pregnancy. Prediction models were developed using logistic regression with regularization via the least absolute shrinkage and selection operator and ensemble decision trees and assessed using the area under the receiving operator characteristic curve (AUC) | ||
| 520 | |a Results: The study sample included 2195 participants (mean [SD] age, 26.8 [6.1] years; mean [SD] gestational age at enrolment, 25.5 [8.8] weeks). A total of 582 women (26.5%) failed to attend antenatal care during cohort follow-up. The AUC was 0.61 (95% CI, 0.58-0.64) for the regularized logistic regression model at conception, with higher values for models predicting at weeks 13 (AUC, 0.68; 95% CI, 0.66-0.71) and 24 (AUC, 0.66; 95% CI, 0.64-0.69). AUC values were similar with slightly higher performance for the ensembles of decision trees (conception: AUC, 0.62; 95% CI, 0.59-0.65; 13 weeks: AUC, 0.70; 95% CI, 0.67-0.72; 24 weeks: AUC, 0.67; 95% CI, 0.64-0.69) | ||
| 520 | |a Conclusions and Relevance: This prognostic study presents a series of prediction models for antenatal care attendance with modest performance. The developed models may be useful to identify women at high risk of missing their antenatal care visits to target interventions to improve attendance rates. This study opens the possibility to develop and validate easy-to-use tools to project health-related behaviors in settings with scarce resources | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Pons-Duran, Clara |e verfasserin |4 aut | |
| 700 | 1 | |a Goddard, Frederick G B |e verfasserin |4 aut | |
| 700 | 1 | |a Hunegnaw, Bezawit Mesfin |e verfasserin |4 aut | |
| 700 | 1 | |a Haneuse, Sebastien |e verfasserin |4 aut | |
| 700 | 1 | |a Bekele, Delayehu |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Grace J |e verfasserin |4 aut | |
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