Development of prediction models for antenatal care attendance in North Shewa Zone, Ethiopia

Abstract

Background. In low-resource settings, coverage of at least four antenatal care (ANC) visits remains low. As a first step towards enhancing ANC attendance, this study aims to develop a series of predictive models to identify women who are at high risk of failing to attend ANC in a rural setting in Ethiopia. Methods. This is a cohort study conducted in the Birhan field site, North Shewa Zone. Using data of a surveillance system and a pregnancy cohort, we developed and internally validated a series of logistic regressions with regularization (LASSO), and ensembles of decision trees. Discrimination was estimated using the area under the receiving operator characteristic curve (AUC). Three prediction time points were considered: conception, and gestational weeks 13 and 24. Results. The study sample size was 2195. Mean age of participants was 26.8 years (Standard Deviation (SD) 6.1) and mean gestational age at enrolment was 25.5 weeks (SD 8.8). A total of 582 women (26.5%) failed to attend ANC during cohort follow-up. We observed AUC in the range of 0.61-0.70, with higher values for models predicting at weeks 13 and 24. All AUC values were similar with slightly higher performance for the ensembles of decision trees. Conclusions. The developed models may be useful to identify women at high risk of missing their ANC visits to target interventions to improve attendance rates. This study calls for new efforts to generate data that enables accurate prediction of ANC access, and it opens the possibility to develop and validate easy-to-use tools to predict health-related behaviours in settings with scarce resources.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by the Bill & Melinda Gates Foundation [INV-010382 and INV-003612 to GJC]. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical clearance was obtained from the Ethics Review Board (IRB) of Saint Paul's Hospital Millennium Medical college, (Addis Ababa, Ethiopia) [PM23/274], and Harvard T.H. Chan School of Public Health (Boston, United states) [IRB19-0991]. Signed informed consent was obtained from all participants. The study was performed under the research standards of the Declaration of Helsinki.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

Data are available upon reasonable request to the authors. Data use is governed by the Birhan Data Access Committee (DAC) and follows Birhan's data sharing policy. All researchers who wish to access Birhan data can complete a Birhan data request form and submit it for decision by the Birhan DAC. Datasets will only be provided with deidentified data to maintain confidentiality of study participants. The analysis code can be found in the Birhan Public Repository: https://github.com/birhan-data/data-public

留言 (0)

沒有登入
gif