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Record W4417115226 · doi:10.2196/59874

Impact of the COVID-19 Pandemic on Contraceptive Services at Selected Primary Health Care Facilities in India, Nigeria, and Tanzania: Cross-Sectional Study

2025· article· en· W4417115226 on OpenAlexvenueno aff
Rita Kabra, Beena Joshi, Ester Elisaria, Tanimola M. Akande, Komal Preet Allagh, Adesola Olumide, Deepti Tandon, Ranjan Kumar Prusty, Mary Ramesh, Donat Shamba, Bhavya MK, James Kiarie

Bibliographic record

VenueInteractive Journal of Medical Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsPandemicFamily planningHealth carePrimary careCoronavirus disease 2019 (COVID-19)Primary health careHealth services

Abstract

fetched live from OpenAlex

Background: The COVID-19 pandemic disrupted sexual and reproductive health services, including family planning (FP) and contraceptive services. The World Health Organization conducted a multicountry study in India, Nigeria, and Tanzania to determine the impact of the pandemic on the health system's readiness to provide contraception services and trends in contraceptive uptake. Objective: This study aimed to determine the status, availability, and health facility readiness to provide contraceptive services and to compare trends in contraceptive uptake before and during the pandemic. Methods: This cross-sectional study was conducted by the Indian Council of Medical Research-National Institute of Research in Reproductive and Child Health (India), the University of Ilorin Teaching Hospital (Nigeria), and the Ifakara Health Institute (Tanzania). A total of 50 primary health facilities (11 in India, 6 in Nigeria, and 33 in Tanzania) were evaluated using a standardized facility assessment questionnaire, completed by the most knowledgeable senior health care provider or administrator at the facility. Monthly data on service utilization and contraceptive availability were collected to capture trends before and during the COVID-19 pandemic. Data were collected from May to August 2022. The study received ethical and scientific approval from the World Health Organization Ethics Review Committee and Research Project Review Panel and national regulatory bodies. Key outcomes included availability of FP guidelines and tools, service disruptions including contraceptive and abortion services, stock-outs, reasons for service disruptions, and mitigation measures to sustain service deliveries. Descriptive analysis was used to summarize the key trends and patterns. Results: Health facilities in all three countries reported shortages of various contraceptives. Contraceptive services were partially disrupted in 91% facilities in India, 83% facilities in Nigeria, and 43% facilities in Tanzania. Abortion services were partially disrupted in all surveyed facilities offering these services in India and Nigeria and in 26.7% of facilities in Tanzania. Client visits declined in health facilities in 2020 compared to 2019 in India (30%) and Nigeria (11%), with a gradual recovery thereafter. In contrast, Tanzania experienced a 1% decline in client visits in 2020. Readiness measures such as telemedicine, task shifting, community outreach, triaging, and patient redirection were implemented to minimize service disruptions. Conclusions: This study provides crucial insights into the challenges posed by the COVID-19 pandemic on contraceptive services and the measures taken to alleviate them. The findings can help countries to better prepare to prevent the disruption of FP and contraceptive services in future pandemics or emergencies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.512
Teacher spread0.449 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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