Determinants of family planning use among married women of reproductive age in south Asia: a systematic review and meta-analysis

Summary

Background

Family Planning (FP) is central to reproductive autonomy; however, structural barriers and restrictive gender norms limit equitable access to modern contraceptive methods in South Asia. We aimed to synthesize the current evidence on determinants of modern contraceptive use among married women of reproductive age in South Asia.

Methods

We searched Medline, Scopus, Embase, and CINAHL from ^1st January, 2015 to 23rd April, 2026. Observational studies from South Asian countries using multivariate analyses to examine determinants of modern contraceptive use among married women of reproductive age (15–49 years) were included. Risk of bias was assessed using the Joanna Briggs Institute (JBI) tool. Generic inverse variance method was used to estimate pooled odds ratios and 95% confidence intervals in RevMan. The review was registered with PROSPERO(CRD42024542764).

Findings

There were 78 studies with 2,212,823 study participants that met the inclusion criteria and 70 studies were included in the meta-analysis. Women’s formal education and employment (OR: 1.35, 95% CI: 1.08–1.70, I² = 74; OR: 1.20, 95% CI: 1.01–1.42, I² = 0%), spousal involvement in decision-making (OR: 2.79, 95% CI: 1.17–6.65, I² = 66%), access to health facility with FP services (OR: 2.13, 95% CI: 1.12–4.05, I² = 0%), media exposure (OR: 1.26, 95% CI: 1.10–1.45, I² = 0%), and counselling from healthcare workers (OR: 1.57, 95% CI: 1.05–2.35, I² = 0%) were associated with higher modern contraceptive use, but some estimates were based on limited studies hence should be interpreted with caution.

Interpretation

Strengthening women’s empowerment, increasing male engagement, and improving access of FP services are essential for equitable access of modern contraceptive methods across South Asia.

Funding

This study was not funded by any individual or agency.

Research in context.

Evidence before this study

Modern contraceptive prevalence rate (mCPR) in South Asia remains below the global average, with persistent unmet needs and substantial disparities across and within countries. Previous country-based analyses, largely based on Demographic and Health Survey data, have identified associations between modern contraceptive use and age, education, wealth, residence, employment, media exposure, and access to services. However, existing evidence is dispersed across settings, varies in methodological rigour, and lacks a region-specific synthesis. To address this gap, we systematically searched Medline, Embase, Scopus, and CINAHL for observational studies published between 1st January, 2015 and 23rd April, 2026, using terms related to determinants, family planning, modern contraceptive use, reproductive-age women and South Asia.

Added value of this study

This review provides an updated region-specific synthesis of sociodemographic, reproductive health seeking behavior, and FP information and access related determinants of modern contraceptive use among married women of South Asia. By systematically integrating evidence from multiple countries, it identifies patterns that are consistent across diverse sociocultural and health system contexts. In addition to modern contraceptive use, it examines determinants of overall contraceptive use and unmet need, offering a broader assessment of gaps in access and reproductive autonomy.

Implications of all the available evidence

The combined evidence indicates that modern contraceptive use in South Asia is consistently influenced by socioeconomic position, women’s autonomy, partner involvement and access to FP information and services. The consistency of these associations across different settings suggests that improving FP use requires coordinated and multi level strategies that address structural inequities, strengthen women’s decision-making power, and improve access to quality service and accurate information. Addressing these interconnected determinants is essential to reduce unmet need and close persistent gaps in FP use across the region.

Introduction

Family Planning (FP), defined by the World Health Organization (WHO) as the ability to control the number and determine the spacing of pregnancies, constitutes a core component of women’s rights by promoting healthcare autonomy and improving reproductive health outcomes.¹ The absence of effective family planning services is reflected in persistently high levels of maternal mortality (MMR) across the world. In 2023, globally, approximately one woman died every 2 min, primarily as a result of pregnancy or childbirth related complications.² Access to contraceptive methods has been associated with a 44% reduction of global MMR through the prevention of unintended and high-risk pregnancies, underscoring its essential role in public health and gender equality.³

South Asia, including countries such as India, Bangladesh, Pakistan, Nepal, and Afghanistan, is home to nearly one-quarter of the world’s population.⁴ Although fertility has declined, South Asia still bears the second-highest burden of maternal mortality worldwide, with an estimated ratio of approximately 120 deaths per 100,000 live births.^5,6 Despite the widespread availability of FP services through clinics, pharmacies, community health workers, and self-care modalities, the region continues to experience substantial gaps in contraceptive access and uptake.⁷ The region’s modern contraceptive prevalence rate (mCPR) remains approximately 42% which is markedly lower than the global average of 49%, and contributes to an unmet need for FP of nearly 17%.^8,9

Modern contraceptive use in South Asia remains lower and more inequitable compared to other world regions, particularly Latin America and the Caribbean, where mCPR exceeds 70% in many countries.¹⁰

Within South Asia, considerable variations also exist with mCPR ranging from 16% in Afghanistan and 34% in Pakistan, up to 55% in Bangladesh reflecting a complex interplay of structural and sociocultural barriers.11, 12, 13 Restricted availability of contraceptive methods, fragmented service delivery, entrenched gender norms and misconceptions regarding side effects are among the key factors that impede sustained use of modern contraception, particularly in rural and low-resource communities.¹⁴ Similar patterns are observed across other low- and middle-income countries (LMICs), where an estimated 259 million women wishing to avoid pregnancy remain unable to use safe and effective methods due to inadequate information, limited access, and insufficient partner and community support.¹⁵

The utilization of FP methods remains influenced by a range of underlying determinants. In Pakistan, a pooled analysis of four Demographic and Health Survey (DHS) datasets reported that age, wealth index, level of education, employment, and exposure to FP messages significantly predicted contraceptive uptake.¹⁶ In Bangladesh, women’s employment and media exposure increased the likelihood of using modern contraception while rural residence and limited autonomy significantly reduced it.¹⁷ The findings from Sreeramareddy et al. from six South Asian countries further demonstrated significant wealth-related inequalities in demand for FP satisfied with modern methods (mDFPS), with a general trend of increasing mDFPS across richer wealth quintiles in most countries.¹⁸ While the magnitude of each determinant may differ across settings, the recurring influence of socio-cultural factors across South Asian countries highlights the necessity of understanding these context-specific determinants to inform targeted, effective FP strategies in the region.

Understanding these determinants is critical for improving reproductive health outcomes; however evidence from South Asia remains dispersed across studies that vary in methodology, populations and analytical rigor. Although previous reviews have evaluated modern contraceptive use and FP-related factors in South Asia and other LMICs, they have mostly focused on selected determinants, specific countries or broadrer reproductive health outcomes.^7,19,20 There is a lack of systematic assessment of the influence of demographic and socioeconomic, reproductive health seeking behavior related, and FP information and access related determinants on FP use within a single regional framework in South Asia. Therefore, the primary objective of this meta-analysis was to identify the updated evidence on determinants of modern contraceptive use among married women of reproductive age in South Asia. The secondary objectives included examining factors associated with overall contraceptive use and unmet need of FP among married women of reproductive age in this region.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for this meta-analysis (Supplement S1) was followed for review reporting.²¹ The review protocol was prospectively registered on PROSPERO under the ID: CRD-42024542764.²²

We searched Medline, Embase, Scopus, and CINAHL for studies published from Jan 1st, 2015, to April 25th, 2024 and then updated the search to include studies published up to 23rd April 2026. We chose 2015 as the starting date to ensure that the findings reflect the recent sociocultural contexts in South Asia, which has evolved substantially over time, although the studies did use data collected prior to 2015. Eligible studies were conducted in South Asian countries, namely India, Bangladesh, Pakistan, Nepal, Afghanistan, Bhutan, Sri Lanka and Maldives and employed multivariate analysis to assess determinants of modern contraceptive use among married women of reproductive age. Modern contraceptive methods included sterilization, oral contraceptive pills, intrauterine devices, injectables, implants, condoms, emergency contraception, and other clinically recognized modern FP methods as defined by individual studies. Included study designs were cross-sectional, prospective and retrospective cohort, and case-control studies published in English.

We excluded commentaries, randomised trials, reviews, case reports, case series, and studies reporting only univariate associations.

We searched four databases including Medline/PUBMED, Embase, Scopus, and CINAHL for articles between 1st April 2015 till 23rd April 2026. Two authors (SK, HM) independently conducted the initial comprehensive search for studies published up to 25th April, 2024 using predefined search strategy, while the updated search for studies published between 26th April, 2024 and 23rd April, 2026 was conducted independently by KN and VM. The strategy included combinations of terms and synonyms related to determinants (“social determinants “or “precipitating factor “or “prevalence”), AND family planning (“family planning” or “modern contraceptive use” or “birth control”), AND population (“women” or “reproductive age women”), and geographic scope (“South Asia” or “South Asian Countries”) used as keywords in the title/abstract. We adapted the search strings for each database accordingly. The detailed search strategy for each database can be found in Supplement S2.

Based on the predefined inclusion and exclusion criteria, we exported all search results into End Note version 20 for initial management and removal of duplicate articles. We subsequently uploaded all the articles on Rayyan. ai platform for screening.²³ After de-duplication, four authors (SK, HM, VM, KF) independently screened the title and abstracts of the remaining articles, with each study assessed independently by two atleast reviewers. Screening of studies identified through the updated search was conducted independently by KN and VM. Two reviewers independently assessed the full texts for eligibility. The authors resolved any disagreements through discussion and consultation with a third author (ZH).

Risk of bias was assessed independently in duplicate using the Joanna Briggs Institute (JBI) critical appraisal tool for observational studies.²⁴ The JBI assessment tool consists of eight key domains which included inclusion criteria, study setting, exposure validity, standardized measurement, outcome validity, confounding identification, confounding control, and statistical analysis. Each domain was rated as “yes”, “no” or “unclear”. Disagreements were resolved by discussion and, when needed, adjudication by a third reviewer.

The primary outcome of this review was the demographic and socioeconomic, reproductive health seeking behavior, and FP information and access related determinants of modern contraceptive use among married women of reproductive age in South Asia. Secondary outcomes included overall contraceptive use and unmet need for family planning.

Data was independently extracted in duplicate by two reviewers (SK, HM, IM, PK, KF, VM) from studies meeting the inclusion criteria. Data extraction for studies identified through the updated search was conducted independently and in duplicate by KN and LH. Extracted information included study characteristics (author, year of publication, region, setting, design, sample size), demographic and socioeconomic determinants (current age, age at marriage, residence, education, employment status, husband’s education, husband’s employment status, economic status, number of children, number of abortions, place of delivery), reproductive health-seeking behavior related determinants (woman’s fertility intentions, decision maker to use FP, decision-making autonomy, participation in decision making, spousal involvement in decision making, intimate partner violence (IPV)) and FP information and access related determinants (knowledge about FP methods, knowledge about source of contraceptives, presence of FP providing health facility, easy availability of FP services, prior contraceptive use, information from healthcare workers (HCW), media exposure). For each determinant, we extracted adjusted/crude odds ratios (AORs/CORs) with 95% confidence intervals (CIs) to assess associations between reported determinants and FP use. Any discrepancies were resolved through discussion with a third reviewer.

Statistical analysis

We performed data synthesis and statistical analyses using Cochrane Review Manager (RevMan) version 5.4.1.²⁵ For each included study, we extracted ORs and their corresponding 95% CIs for the association between reported determinants and the outcome and transformed estimates to the log scale, and derived standard errors from reported 95% CIs using standard inverse-variance methods. Where studies reported multiple exposure categories sharing a common reference group and raw frequencies were unavailable, subgroup estimates were combined using fixed-effect inverse variance methods to avoid double counting. Pooled effect estimates were calculated with the generic inverse-variance method and statistical heterogeneity was assessed with Cochran’s Q, τ², and Higgins’ I².²⁶ We used fixed-effect models when heterogeneity was low to moderate and random-effects models when heterogeneity was substantial when I² exceeded 75%. Funnel plots and Egger’s regression tests were used to assess publication bias with at least ten studies using STATA version 15.²⁷ We did not do meta-regression because only few studies contributed to a particular determinant and exposure definitions were not sufficiently comparable across studies to support stable estimates. Wherever possible, we conducted sub-group analysis based on each of the countries included in the meta-analysis. But most subgroup estimates were based on a few studies, so estimates should be interpreted with caution.

Studies reporting effect measures that could not be harmonised with odds ratios, including prevalence ratios or relative risk ratios, were retained for narrative synthesis but excluded from quantitative pooling due to lack of adjusted baseline risk in reference groups for them to be converted to ORs.

For studies using the same data set within an individual forest plot, such as the DHS data sets, only the study with the larger sample size was retained to avoid duplication of participants. Sensitivity analysis was performed for analyzing the effect of older datasets (2006–2011) to account for any temporal bias.

Results

The database search yielded a total of 6855 articles. Following the de-duplication process, 3502 studies were screened and 261 articles were included for full text review. Following a detailed assessment, 78 studies28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50^,51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75^,76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95^,96, 97, 98, 99, 100, 101, 102, 103, 104, 105 were included in the systematic review of which 7028, 29, 30, 31^,33, 34, 35^,37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48^,51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63^,65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75^,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87^,89, 90, 91, 92, 93, 94, 95, 96^,98, 99, 100, 101, 102, 103, 104, 105 were included in the meta-analysis. The overall study selection process is illustrated in Fig. 1 below.

Fig. 1.

PRISMA flowchart for study selection process. Two studies, Mukherjee 2021 and Khan 2022 were included in the meta-analysis of more than 1 outcome.

Of the 78 included studies, 25 (37%) were conducted in India,28, 29, 30, 31, 32, 33^{,48,49,52,53,}73, 74, 75, 76^,95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105 22 (28%) in Bangladesh,35, 36, 37, 38, 39, 40, 41, 42, 43, 44^,51,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89 15 (15%) in Pakistan,^45,47,50,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70^,77 8 (11%) in Nepal,54, 55, 56, 57, 58, 59^,71,72 and 6 (8%) in Afghanistan.^34,90, 91, 92, 93, 94 Two studies were multi-country analyses conducted across different South Asian settings. The evidence base included 42 studies that utilized nationally representative survey analyses, predominantly DHS- and NFHS-derived studies, and 36 studies that were community-based primary surveys, with only one case-control study identified.⁶⁶ Although our inclusion criteria was studies published between 1st January, 2015 till 23rd April, 2026, the datasets used within the included studies spanned a broader period from 2005 to 2023. The sample size in these studies ranged from 151 women in the smallest study³³ to 649,775 women in the largest study.⁹⁷ The unique total sample size for all included studies was 22, 212, 823.

Participants were primarily married women aged 15–49 years. Across studies, contraceptive prevalence varied widely, from less than 5% to more than 80%, reflecting substantial heterogeneity in regional access and utilization. The characteristics of all studies included in the review are summarized in Table 1. Since the primary aim of this review was to assess the determinants of modern contraceptive use, analyses pertaining to this are included in the main manuscript, whereas results on overall contraceptive use and unmet need are provided in the Supplement Figs. S22–S53.

Table 1.

Description of study characteristics of all included studies.

S#	Study name	Data collection period	Country	Participants/population (age in years)	Sample size	Determinants of family planning			Contraceptive use
						Demographic and socioeconomic	Reproductive health seeking behavior	FP information and access
1.	Ahmed 2021	2014	Bangladesh	15–49	17,592	Age, Residence, Education, Employment Status, Husband’s education, Husband’s Employment Status, Economic status, Number of children, Number of abortions		Knowledge about FP methods	mCPR, CPR
2.	Alam 2025	2022	Bangladesh	15–49	17,848	Residence, Employment, Husband’s Employment, Socioeconomic status	Decision-making autonomy	Knowledge of FP, knowledge of source of contraceptives, access of contraceptives	mCPR
3.	Asif 2024	2017–2018	Pakistan	15–49	12,735	Age, Education, Employment, Husband’s Education, Socioeconomic status	Participation in Decision-making		Unmet need
4.	Azmat 2015	2006–2007	Pakistan	15–49	3998	Age, Age at marriage, Education, Husband’s education, Economic Status, Number of children	Decision-making autonomy, Spousal involvement in decision-making	Information from HCW	CPR
5.	Bhandari 2019	2016	Nepal	15–49	9875	Age, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions	Media exposure	mCPR
6.	Bhusal 2018	Oct 2015–April 2016	Nepal	15–49	650	Age, Age at Marriage, Education, Employment Status, Husband’s Education, Number of children		Knowledge about FP methods, Previous contraceptive use, Availability of contraceptive, Information from HCW	Unmet Need
7.	Bibia 2023	April–July 2021	Pakistan	NA	Hormonal contraceptives: 141, No hormonal contraceptives: 282	Age, Residence, Education	Decision Maker to use Family Planning	Presence of FP providing health facility, counselling from HCWs	CPR
8.	Dadras 2022	2015	Afghanistan	18–49	20,593	Age, Residence, Education, Employment Status, Economic Status, Husband’s Education, Number of children			Unmet Need
9.	Das 2026	2019–2021	India	15–49	181,848	Age, Residence, Education, Socioeconomic status, Number of Children		Media exposure	mCPR, CPR
10.	Dasgupta 2016	Dec 2015–Mar 2016	India	15–49	151	Age, Education, Employment Status, Husband’s education, Husband’s Employment Status, Economic Status	Decision making autonomy		CPR
11.	Dehingia 2019	Aug–Oct 2016	India	15–49	1398	Age, Age at marriage, Education, Husband’s education, Economic Status, Number of children		Previous contraceptive use, Information from HCW	CPR
12.	Ghosh 2021	2015–2016	India	15–49	499,627	Age, Residence, Education, Employment, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children		Knowledge about FP methods, Media exposure	CPR
13.	Haq 2017	2014	Bangladesh	15–35	17,863	Age, Age at marriage, Residence, Education, Employment Status, Economic Status	Woman’s fertility intentions	Media exposure	mCPR, CPR
14.	Hoo 2023	2014, 2017, 2018	Malaysia and Pakistan	15–49	14,502	Age, Age at marriage, Residence, Education, Employment Status, Number of children		Knowledge about FP	mCPR, CPR
15.	Hossain 2018	Jun–Nov 2014	Bangladesh	15–49	16,858	Age, Residence, Education, Employment, Husband’s education, Husband’s employment Status, Economic Status, Number of children	Woman’s fertility intentions	Media exposure	CPR
16.	Ibrahimi 2022	2015–2016	Afghanistan	15–49	18,985	Age, Residence, Education, Economic Status, Number of children	Women’s fertility intentions, IPV	Previous contraceptive use	CPR
17.	Ishaque 2021	Not mentioned	Pakistan	15–49	1682	Age, Number of children			CPR
18.	Islam 2015	June–Dec 2010	Bangladesh	16–65	200	Age, Age at marriage, Residence, Education, Employment, Husband’s education, Economic Status, Number of children	Decision-making autonomy, Spousal involvement in decision-making		CPR
19.	Islam 2016	2011	Bangladesh	13–24	4982	Age, Residence, Husband’s Education, Number of children		Interaction with HCW, Media Exposure	Unmet Need
20.	Islam 2017	2011	Bangladesh	13–25	3744	Age, Age at marriage, Residence, Husband’s education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions, Decision making autonomy	Interaction with HCW, Media exposure	CPR
21.	Islam 2018	Jul–Dec 2011	Bangladesh	Fecund women aged ≤ 25	3507	Age, Age at marriage, Residence, Education, Husband’s Employment Status, Economic Status, Number of children		Woman’s fertility intentions, Decision making autonomy, Information from HCW	mCPR
22.	Jamali 2024	2017–2018	Pakistan	15–49	10,282	Age, Residence, Education, Employment Status, Economic status, Husband Education, Number of children		Knowledge about FP methods, Information from HCW, Media Exposure	mCPR, CPR
23.	Jana 2023	2005–2006, 2015–2016, 2019–2021	India	15–49	NFHS 3–13,682, NFHS 4–58,859, NFHS 5–68,720	Age, Residence, Education, Economic Status	Decision maker to use FP	Knowledge about FP methods, Media Exposure	mCPR, CPR
24.	Jay 2026	March 2023–December 2023	India	18–49	565	Age, Place of delivery		Knowledge about FP
25.	Joshi 2020	Sep–Oct 2013	Nepal	Women who delivered a child in last 1 year	427	Age, education, employment, Husband’s education, Husband’s employment status, Number of children	Woman’s fertility intentions, Decision maker to use FP, Knowledge about FP methods	Previous contraceptive use, Information from HCW, Media exposure	CPR
26.	Kamal 2015	2006	Bangladesh	13–49	5160	Age, Residence, Education, Employment Status, Economic Status, Number of children		Media exposure	mCPR, CPR
27.	Kamal 2026	1990–2018	Pakistan	15–49	35,239	Age, Age at Marriage, Residence, Education, Employment, Husband’s Education, Socioeconomic status, Number of Children, Number of Abortions			CPR
28.	Khan 2015	Not mentioned	Pakistan	15–49	851	Age, Residence, Education, Employment Status, Number of children	Woman’s fertility intentions, Decision maker to use FP, Spousal involvement in decision-making	Knowledge about FP methods, Previous contraceptive use, Media exposure	mCPR, CPR
29.	Khan 2020	2014	Bangladesh	15–49 aged women who had given live births within 3 years	4493	Age, Residence, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions, Decision making autonomy	Knowledge about FP methods, Previous contraceptive use	mCPR
30.	Khan 2021	Nov 2016	Bangladesh	Women who gave birth in the previous two years	493	Age, Age at marriage, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions	Presence of FP providing health facility	CPR
31.	Khan 2022	2017–2018	Bangladesh	15–49/did not desire a child within 2 years of survey	10,384	Residence, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children		Presence of FP providing health facility, Availability of contraceptive	mCPR, CPR
32.	Khan 2022	2017–2018	Pakistan	15–49	NA	Age, Residence, Education, Employment Status, Economic Status, Number of children			CPR
33.	Khan 2026	2015–2018	Pakistan, Bangladesh, Afghanistan	15–49	PDHS = 14,000, BDHS = 20,127, AFDHS = 20,000, Total = 54,127	Age, Residence, Education, Socioeconomic status	Decision-making autonomy		mCPR
34.	Khatri 2019	Jan–Mar 2017	Nepal	15–49	273	Age, Age at marriage, Education, Husband’s education, Number of children, Number of abortions		Knowledge about FP methods, Presence of FP providing health facility, Information from HCW	mCPR
35.	Khatun 2023	2017–2018	Bangladesh	15–49	14,493	Age, Residence, Education, Employment Status, Economic Status, Husband’s Education, Number of children		Media Exposure	CPR
36.	Khurrum 2025	April–July 2024	Pakistan	18–49	524	Education, Husband’s Education, Number of Children, Place of Delivery	Spousal involvement in decision-making, Decision-making autonomy		CPR
37.	Kibria 2017	2014	Bangladesh	15–49	1147	Age, Residence, Education, Employment Status, Husband’s education, Economic Status, Number of children		Information from HCW	CPR
38.	Kumar 2020	2015–2016	India	15–49	35,373	Age, Residence, Education, Economic Status		Previous contraceptive use, Information from HCW	CPR
39.	Kundu 2022	2017–2018	Bangladesh	15–49	11,523	Age, Residence, Education, Employment Status, Husband’s Education, Economic Status, Number of children, Number of abortions	Woman fertility intentions, Decision maker to use FP, Decision making autonomy, Spousal involvement in decision making	Knowledge about FP methods	mCPR, CPR
40.	MacQuarrie 2021	2017–2018	Pakistan	15–49	12,676	Age, Age at marriage, Residence, Education, Employment Status, Husband’s Education, Economic Status, Number of children	Woman’s fertility intentions, Decision maker to use FP, Decision making autonomy, Spousal involvement in decision-making		mCPR, CPR
41.	Mahfuzur 2022	2017–2018	Bangladesh	15–49	906	Age, Residence, Education, Economic Status		Decision making autonomy, Media exposure, Information from HCW
42.	Mehata 2019	Jul 2014–Jun 2017	Nepal	≤24 who received induction abortion or post abortion care services	20,307	Age, Education, Number of children, Number of abortions		Presence of FP providing health facility, Previous Contraceptive use	mCPR
43.	Memon 2024	Oct–Dec 2020	Pakistan	15–49	1684	Age, Residence, Education, Economic Status, Husband’s Education, Number of children		Knowledge about FP methods, Information from Health Workers, Previous Contraceptive Use	mCPR, CPR
44.	Mudi 2023	2020–2021	India	15–49	360	Age, Age at Marriage, Education, Employment Status, Economic status, Number of children	Decision making autonomy, Spousal involvement in decision-making	Media Exposure	CPR
45.	Mukherjee 2021	May 2019–April 2020	India	15–49	530	Age, Age at marriage, Education, Employment Status, Economic Status, Number of children	Spousal involvement in decision making, Decision maker to use FP Decision making autonomy	Availability of contraceptive, Previous contraceptive use, Availability of contraceptive	Unmet Need, CPR
46.	Noormal 2022	Jul 2015–Feb 2016	Afghanistan	15–49	22,947	Age, Residence, Education, Employment Status, Economic Status	Spousal involvement in decision making, Decision maker to use FP, Decision making autonomy	Media Exposure	mCPR
47.	Osmani 2015	July–Dec 2012	Afghanistan	12–49	13,654	Age, Residence, Education, Economic Status, Number of children		Media exposure	CPR
48.	Pal 2022	Jun 2020–July 2021	India	18–49	331	Age, Age at Marriage, Education, Employment Status, Husband’s education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions	Knowledge about FP methods, Previous contraceptive use	CPR
49.	Panda 2023	2019–2021	India	15–49	91,976	Age, Residence, Education, Employment Status, Economic Status, Husband’s Education, Number of children		Media Exposure	mCPR
50.	Pandey 2015	2005–2006	India	15–34	54,918	Age, Age at marriage, Residence, Education		Media exposure	CPR
51.	Pickard 2024	2019	Bangladesh	15–24	1665	Age, Residence, Education, Economic Status, Number of children		Media Exposure	CPR
52.	Pratap 2024	Jan–June 2022	Nepal	15–49	11,180	Age, Residence, Education, Employment Status, Economic Status, Husband’s Education, Number of children			Unmet Need
53.	Rahaman 2022	2015–2016	India	15–49	56,742	Age, Residence, Education, Employment Status, Economic Status, Number of children	Decision making autonomy	Media exposure	Unmet Need
54.	Raj 2015	Bangladesh-2007 India–2005–2006 Nepal–2011	Bangladesh, India, Nepal	15–49	4738	Age, Age at marriage, Residence, Education, Husband’s education, Economic Status, Number of children	IPV	Previous contraceptive use	mCPR, CPR
55.	Rana 2023	2011, 2014, 2017, 2018	Bangladesh	35–49	17,736	Age, Residence, Education, Employment, Economic Status, Husband’s Education, Husband’s Employment Status, Number of children		Media Exposure	mCPR, CPR
56.	Rana 2024	2005 and 2012	India	15–49	38,634	Age, Residence, Education, Employment Status, Economic status	Decision making autonomy	Previous contraceptive use	CPR
57.	Rasooly 2015	2010	Afghanistan	15–49	25,743	Age, Age at marriage, Residence, Education, Economic Status, Number of children		Media exposure	CPR
58.	Roy 2021	Oct 2020–Dec 2020	Bangladesh	15–49	1990	Age, Residence, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children		Information from HCW, Media Exposure	CPR
59.	Saheem 2021	Sep–Oct 2019	Afghanistan	15–49	325	Age, Residence, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children		Presence of FP providing health facility, Previous contraceptive use, Information from HCW, Media exposure	mCPR
60.	Samanta 2023	2018–2019	India	15–35	2716	Age, Education, Employment Status	Decision making Autonomy, Women’s fertility intentions	Knowledge about FP methods, Availability of contraceptive	mCPR, CPR
61.	Saya 2021	2016–2017	India	18–49	1924	Age, Residence, Education, Employment Status, Economic status, Number of children, Number of abortions		Knowledge about FP methods, Previous contraceptive use	CPR
62.	Shahbuz 2022	2017–2018	Bangladesh	15–49	13,031	Age, Residence, Education, Employment Status, Economic Status, Husband’s Education, Number of children			Unmet Need
63.	Sharma 2021	2015–2016	India	15–24	94,034	Age, Residence, Education, Economic Status, Number of children		Knowledge about FP methods, Media Exposure	Unmet Need
64.	Singh 2017	2011	India	Women with atleast one child	47,069	Education, Husband’s education, Economic Status, Number of children	Decision making autonomy, IPV	Media exposure	NA
65.	Singh 2018	2015–2016	India	15–49	649,775	Age, Residence, Education, Employment Status, Economic Status	Decision making autonomy	Previous contraceptive use, Media exposure	NA
66.	Singh 2020	2015–2016	India	15–34	279,896	Age, Age at marriage, Residence, Education, Economic Status		Previous contraceptive use, Media Exposure	CPR
67.	Singh 2021	2015–2016	India	15–19	13,232	Residence, Education, Economic Status, Number of children		Information from HCW, Media Exposure	mCPR, CPR
68.	Singh 2023	2015,2019–2021	India	15–49	521,352	Age, Residence, Education, Employment Status, Number of children			Unmet Need
69.	Smith 2017	2011	Nepal	15–49	8068	Age, Residence, Education, Employment Status, Economic Status, Number of children			mCPR
70.	Speizer 2015	Jan–Aug 2010	India	15–49	14,633	Age, Residence, Education, Economic Status, Number of children			mCPR
71.	Tappis 2015	June–July 2014	Pakistan	15–49	6200	Residence, Education, Economic Status, Number of children	Woman’s fertility intentions	Media exposure	CPR
72.	Tariq 2025	August 2023–August 2024	Pakistan	18–49	209	Age, Employment		Knowledge about FP, Easy availability of FP services	Unmet need
73.	Thakuri 2022	Jun 2019–Sep 2019	Nepal	15–49	400	Age, Residence, Education, Employment Status, Husband’s Employment Status, Number of children		Knowledge about FP methods	mCPR
74.	Thompson 2024	NA	Pakistan	15–49	444	Age, Education, Employment Status	Woman fertility intentions	Knowledge of FP methods	CPR
75.	Uddin 2016	2007	Bangladesh	not specified	3336	Age, Residence, Education, Employment Status, Husband’s Education, Husband’s Employment Status, Economic Status, Number of children	Woman’s fertility intentions, Spousal involvement in decision-making	Media Exposure	Unmet Need
76.	Uddin 2017	2007	Bangladesh	Married couples	3336 couples	Age, Residence, Education, Employment Status, Economic Status, Number of children	Spousal involvement in decision-making	media exposure
77.	Verma 2015	Not mentioned	India	18–45	410	Age, Residence, Education, Economic Status, Number of children, Number of abortions		Knowledge about FP methods	mCPR, CPR
78.	Yadav 2020	Aug 2015–Jul 2016	India	15–24	535	Age, Age at Marriage, Education, Employment Status, Economic Status, Husband’s Education	Decision making autonomy	Knowledge about FP methods, Information from HCW, Media Exposure	Unmet Need

CPR: Contraceptive Prevalence Rate; mCPR: Modern Contraceptive Rate.

^aStudy design for this study was-case control, the rest of the studies are cross-sectional surveys.

Using the JBI critical appraisal tool, most studies demonstrated low risk of bias across the majority of domains including inclusion criteria, setting description, confounding identification and control, and statistical analysis (refer to Fig. 2). However, unclear risk of bias was observed in standardized measurement and outcome validity domains, as most studies relied on self-reported FP use that were not independently verified through facility registers. Overall, the included studies were considered to have acceptable methodological quality. The complete results for the risk of bias assessment can be found in Supplement Fig. S3 of the Supplement.

Fig. 2.

Summary of the risk of bias assessment using the JBI tool.

Within 70 studies included in the meta-analysis, data was across three domains: demographic and socioeconomic determinants, reproductive health seeking behavior related determinants, and FP information and access related determinants for modern contraceptive use among women of reproductive age in South Asia.

Among demographic and socioeconomic determinants, women with formal education had higher odds of modern contraceptive use than women without formal education (OR 1.35, 95% CI 1.08–1.70; I² = 74%, Fig. 3(a)). By contrast, husband’s education was not significantly associated with contraceptive use (OR 0.90, 95% CI 0.75–1.07; I² = 0%; Fig. 3(b)). Women’s employment and women’s age was also positively associated with modern contraceptive use (Supplement Figs. S4 and S5 in Supplement) while husband’s employment was not significantly associated with modern contraceptive use (OR 0.93, 95% CI 0.80–1.07; I² = 0%, Supplement Fig. S6 in Supplement).

Fig. 3.

Association between education and modern contraceptive use. (a) Association between women’s education and modern contraceptive use. (b) Association between husband’s education and modern contraceptive use.

Women aged ≥ 25 years had higher odds of modern contraceptive use overall, but with high heterogeneity (OR 1.70, 95% CI 0.99–2.90; I² = 93%, Supplement Fig. S54 in Supplement), the association was statistically significant for countries of India, Pakistan, and Afghanistan, while estimates for Bangladesh and Nepal were not significant. Formal education and women employment was associated with higher modern contraceptive use overall with the country-specific association observed in the former for Pakistan only (Supplement Figs. S56 and S57 in supplement). Husband’s education and employment (Supplement Figs. S60 and S61 in supplement) were not clearly associated with modern contraceptive use overall or by country.

Discussion with spouses regarding family planning was significantly associated with higher odds of modern contraceptive use compared with those without such discussions (OR 2.79, 95% CI 1.17–6.65; I² = 66%, Supplement Fig. S8 in Supplement). Women whose husbands were the primary decision-makers for family planning demonstrated greater odds of non-use compared with women who independently made FP decisions (OR 3.96, 95% CI: 0.75–20.85; I² = 0%, Supplement Fig. S9 in Supplement), although the association was not statistically significant. Women with greater decision-making autonomy had slightly higher odds of modern contraceptive use compared to women with no autonomy, although the overall pooled association was not statistically significant (OR 1.13, 95% CI 0.98–1.31; I² = 0%, Supplement Fig. S10 in Supplement).

Women who desired no additional children did not show significantly different odds of modern contraceptive use compared with women who desired children (OR 0.88, 95% CI: 0.14–5.44; I² = 85%, Supplement Fig. S11 in Supplement), Women who were undecided regarding future childbearing demonstrated lower odds of modern contraceptive use compared to women reporting no desire for children (OR 0.49, 95% CI 0.23–1.03, Supplement Fig. S11 in Supplement).

Women with access to FP-providing health facilities had significantly higher odds of modern contraceptive use compared with women without access (OR 2.13, 95% CI 1.12–4.05; I² = 0%, Supplement Fig. S20 in Supplement). Similarly, exposure to FP messages through media was significantly associated with increased modern contraceptive uptake (OR 1.26, 95% CI 1.10–1.45; I² = 0%, Fig. 4(a)), with the association being statistically significant for India only (OR 1.29, 95% CI 1.09–1.54, I² = 0%, Supplement Fig. S55 in Supplement). Counselling and interaction with healthcare workers also demonstrated a positive association with modern contraceptive use (OR 1.57, 95% CI 1.05–2.35; I² = 0%, Fig. 4 (b)).

Fig. 4.

Role of information access in modern contraceptive use. (a) Association between media exposure and modern contraceptive use. (b) Association between information from HCWs and modern contraceptive use.

Women with previous contraceptive use had higher odds of modern contraceptive uptake compared with women without prior use (OR 10.22, 95% CI 0.61–169.95; I² = 0%, Supplement Fig. S12 in Supplement), while women with good knowledge regarding FP methods similarly demonstrated increased odds of use (OR 3.40, 95% CI 0.58–20.11; I² = 0%, Supplement Fig. S21 in Supplement). However, both analyses were based on a limited number of studies and demonstrated wide confidence intervals, indicating substantial uncertainty in the pooled estimates.

Discussion

This review aimed to synthesize the available evidence on the demographic and socioeconomic, reproductive health seeking behaviour, and FP information and access related determinants that influence modern contraceptive use among women of reproductive age in South Asia. Overall, the meta-analysis demonstrated that the modern contraceptive use in South Asia is less strongly driven by sociodemographic characteristics (such as age, parity, place of residence) and more strongly determined by women’s socioeconomic position, particularly education and employment. Moreover, access to FP services and information, media exposure from TV or radio, and spousal involvement in decision making were significant positive indicators of use. A summary of the findings of our meta-analysis can be found in Table 2.

Table 2.

Summary statistics.

Determinant	Overall	India	Pakistan	Afghanistan	Nepal	Bangladesh
Women’s education for modern contraceptive	(OR 1.35, 95% CI 1.08–1.70) N = 13, n = 259,582	Formal education (Ref: No formal education) (OR 0.93, 95% CI 0.78–1.12) N = 1, n = 117,300	Formal education (Ref: No formal education) (OR 1.83, 95% CI 1.09–3.08) N = 4, n = 52,592	Formal education (Ref: No formal education) (OR 1.22, 95% CI 0.74–2.00) N = 1, n = 22,947	Formal education (Ref: No formal education) (OR 1.19, 95% CI 0.95–1.48) N = 3, n = 36,679	Formal education (Ref: No formal education) (OR 1.15, 95% CI 0.88–1.51) N = 3, n = 25,326
Women’s employment for modern contraceptive	(OR 1.20, 95% CI 1.01–1.42) N = 10, n = 20,281	Employed (Ref: Unemployed) (OR 1.61, 95% CI 0.59–4.41) N = 1, n = 2704	Employed (Ref: Unemployed) (OR 1.20, 95% CI 0.83–1.75) N = 2, n = 24,781	Employed (Ref: Unemployed) (OR 1.20, 95% CI 0.61–2.34) N = 1, n = 22,928	Employed (Ref: Unemployed) (OR 0.94, 95% CI 0.47–1.90) N = 2, n = 10,274	Employed (Ref: Unemployed) (OR 1.21, 95% CI 1.97–1.50) N = 4, n = 32,780
Husband’s education for modern contraceptive	(OR 0.90, 95% CI 0.75–1.07) N = 8, n = 17,592	Literate (Ref: Illiterate) (OR 0.82, 95% CI 0.65–1.03) N = 1, n = 17,736	Literate (Ref: Illiterate) (OR 1.01, 95% CI 0.56–1.81) N = 2, n = 6809	Literate (Ref: Illiterate) (OR 2.82, 95% CI 0.31–25.38) N = 1, n = 325	Literate (Ref: Illiterate) (OR 1.79, 95% CI 0.52–6.18) N = 2, n = 10,463	Literate (Ref: Illiterate) (OR 2.09, 95% CI 0.70–6.21) N = 1, n = 17,592
Husband’s employment for modern contraceptive	(OR 0.93, 95% CI 0.80–1.07) N = 5, n = 42,904	Others (Ref: Agriculture) (OR 0.89, 95% CI 0.71–1.10) N = 1, n = 17,736	–	–	Others (Ref: Agriculture) (OR 1.43, 95% CI 0.78–2.62) N = 2, n = 10,274	Others (Ref: Agriculture) (OR 0.92, 95% CI 0.75–1.13) N = 2, n = 14,894
Household socioeconomic status for modern contraceptive	(OR 2.06, 95% CI 0.42–10.12) N = 15, n = 247,730
Women’s age for modern contraceptive	(OR 1.63, 95% CI 1.06–2.49) N = 13, na = 184,827	>25 (ref: <25) (OR 2.62, 95% CI 1.93–3.56) N = 4, n = 91,985	>25 (ref: <25) (OR 4.33, 95% CI 2.34–7.99) N = 1, n = 14,502	>25 (ref: <25) (OR 4.86, 95% CI 3.38–7.00) N = 1, n = not available	>25 (ref: <25) (OR 1.40, 95% CI 0.40–4.97) N = 3, n = 6355	>25 (ref: <25) (OR 0.78, 95% CI 0.52–1.17) N = 3, n = 37,298
Age at marriage for contraceptive use	(OR 1.01 95% CI 0.91–1.13) N = 3, `n = 21,029
Parity for modern contraceptive use	(OR 1.47 95% CI 0.48–4.48) N = 11, n = 143,375	>1 (Ref: ≤1) (OR 14.72, 95% CI 10.16–21.33) N = 2, n = 76,477	>1 (Ref: ≤1) (OR 2.97, 95% CI 0.95–9.25) N = 2, n = 2068	–	>1 (Ref: ≤1) (OR 3.68, 95% CI 0.01–1987.2) N = 1, n = 273	>1 (Ref: ≤1) (OR 2.28, 95% CI 1.18–4.40) N = 1, n = 5160
Residence for modern contraceptive use	(OR 0.85, 95% CI 0.73–0.99) N = 12, na = 194,594	Rural (Ref: Urban) (OR 0.93, 95% CI 0.74–1.18) N = 3, n = 122,944	Rural (Ref: Urban) (OR 0.80, 95% CI 0.06–1.07) N = 2, n = 24,784	Rural (Ref: Urban) (OR 0.98, 95% CI 0.55–1.75) N = 2, n = 23,272	Rural (Ref: Urban) (OR 0.84, 95% CI 0.36–1.98) N = 1, n = 8068	Rural (Ref: Urban) (OR 0.74, 95% CI 0.54–1.03) N = 3, n = 10,788
Decision maker to use family planning	(OR 1.13, 95% CI 0.98–1.31) N = 2, n = N/A
Discussion with spouse for modern contraceptive use	(OR 2.79, 95% CI 1.17–6.65) N = 2, n = 14,114
Woman desire for children	(OR 2.04, 95% CI 0.94–4.44) N = 5, na = 29,613
Decision making autonomy for modern contraceptive	(OR 3.96, 95% CI: 0.75–20.85) N = 4, na = 56,208
Access to FP providing health facility	(OR 2.13, 95% CI 1.12–4) N = 2, na = 273
Media exposure	(OR 1.26, 95% CI 1.10–1.45) N = 8, n = 165,517	Media exposure (Ref: No Media Exposure) (OR 1.29, 95% CI 1.09–1.54) N = 2, n = 69,128	Media exposure (Ref: No Media Exposure) (OR 1.11, 95% CI 0.61–2.02) N = 1, n = 10,282	Media exposure (Ref: No Media Exposure) (OR 1.43, 95% CI 0.95–2.15) N = 1, n = 22,862	–	Media exposure (Ref: No Media Exposure) (OR 1.16, 95% CI 0.88–1.53) N = 4, n = 27,165
Counselling from healthcare worker	(OR 1.57, 95% CI 1.05–2.35) N = 6, na = 6771
Previous contraceptive use	(OR 10.22, 95% CI 0.61–169.95) N = 2, n = 598
Knowledge regarding FP methods	(OR 3.40, 95% CI 0.58–20.11) N = 3, n = 1083

^an not available for one or more studies.

Among the demographic and socioeconomic determinants, educational attainment and employment emerged as the strongest predictors of modern contraceptive use. A meta-analysis by Biswas et al., pooling data from 40 DHSs across multiple Asian countries reported that educated women had approximately 13% higher odds of using modern contraceptive methods compared to women with primary or no education.¹⁰⁶ Similar trends have also been observed in sub-Saharan Africa where higher uptake of modern contraceptives has been reported among women with secondary or higher education and among those who were employed.¹⁰⁷ Higher education attainment may be associated with greater access to, comprehension of, and effective use of FP information, including available method, benefits, and potential side effects.¹⁰⁸ Similarly, employment has been shown to be associated with greater decision-making autonomy, improved affordability of transportation and FP services, and a stronger negotiating position within households, together underscoring the central role of women’s empowerment in shaping modern contraceptive use.¹⁰⁹

This review also highlighted the importance of reproductive health seeking behavior such as spousal involvement in FP related decision making as an exploratory outcome. Similar patterns have been observed in studies from South Asia and other LMICs where joint partner discussion and decision-making are associated with higher uptake of contraceptive methods.¹¹⁰ These findings suggest that open communication between couples has been shown to enhance a woman’s decision-making autonomy, particularly in contexts where male approval is often more influential.¹¹¹ Such discussions may also facilitate practical support including assistance with transportation or financial resources, thereby lowering barriers to contraceptive use. However, our findings should be interpreted cautiously, as the pooled estimate for this factor was derived from only two studies indicating a need to further explore this aspect.

FP information and access related determinants also played a key role in shaping modern contraceptive use. A study from Ethiopia reported that participation in community conversational programs and home visits by community health workers were associated with greater contraceptive uptake, reinforcing the importance of community engagement and counselling.¹¹² Within South Asia, community-based efforts in Pakistan, most notably the Sukh Initiative, showed that door-to-door counselling and assistance in navigating nearby health facilities effectively increased the adoption of modern contraceptive methods.¹¹³ Mass media exposure was also identified as an important facilitator of contraceptive use across multiple Asian countries, with woman exposed to FP messages through television and radio consistently showing higher uptake.¹¹⁴ These associations may reflect the influence of accurate FP information delivered through reliable sources on reducing misconceptions, enhancing method-related knowledge, and normalizing discussions about contraceptive use among couples over time.¹¹⁵ However, findings related to access to FP providing health facilities within this meta-analysis should be interpreted cautiously due to small study sample size and limited number of studies.

An important finding of this review was that several traditionally recognized demographic and sociodemographic factors such as women’s age, age at marriage, husband’s education, husband’s employment status, socioeconomic status and parity did not demonstrate significant associations with modern contraceptive use across included studies. The non-significant finding of woman’s age should also be interpreted in the light of broader global evidence from Haakenstad et al. in the Global Burden of Disease study, which showed substantial age-related differences and reported that mCPR and demand satisfied among women aged 15–19 was lower than older age group.¹¹⁶ However, such trends were less apparent in our results, likely due to heterogeneity in study characteristics, inconsistent categorization of variables and varying age cut-offs. Similarly, the non significant association of partner characteristics such as husband’s education and employment status is consistent with a multicountry south Asian analysis, which reported that increasing husband’s education was not associated with higher mCPR especially in Pakistan.¹¹⁷ These findings may suggest that the woman’s own education and employment are more consistently associated with FP-related decision-making in South Asia rather than partner-level characteristics highlighting the importance of maintaining focus on determinants related to female herself in future FP programs.

Despite the consistency in direction of most associations, moderate to high heterogeneity was observed across a number of forest plots particularly for age, education, spousal involvement in decision-making, number of children and women’s desire for children (I² > 50%). The heterogeneity may reflect variations in study populations and their characteristics, sample sizes, national and subnational settings, inconsistent categories and measurement approaches. Conducting subgroup analyses and meta regression may have helped further explore potential sources of heterogeneity, however the limited number of studies within several forest plots reduced its feasibility.

This study has several important methodological strengths. To our knowledge, it is one of the first meta-analyses to comprehensively examine the demographic and socioeconomic, reproductive health seeking behaviour related, and FP information and access related determinants associated with modern contraceptive use in South Asia. The overall methodological quality of the included studies, assessed using the JBI tool, was robust which supports the credibility of our findings. In addition, all included studies used multivariable analyses and adjusted for key confounders, which strengthened the internal validity of the findings.

Almost all included studies were cross-sectional, limiting the ability to infer causal relationships between identified determinants and modern contraceptive uptake and raising the possibility of reverse causality. In addition, while the review focuses on South Asia, most evidence originated from the countries of Pakistan, India, Bangladesh, Afghanistan, and Nepal, with limited or no representation from countries such as Bhutan, and Maldives, which may affect regional generalizability given the sociocultural and health system differences across the region. Additionally, the findings may not completely reflect important country-specific and subnational heterogeneity. FP use in South Asia is also shaped by intersecting social, cultural, geographic and structural factors that may not have been fully captured in the included studies. Furthermore, we did not perform subgroup analyses, meta regression or intersectional analyses to explore sources of heterogeneity and interaction between determinants due to limitations in the data. Some determinants including the number of abortions, previous contraceptive use, and decision-maker to use FP, were only represented by a small number of studies, and in some cases yielded wide confidence intervals reducing the precision of the pooled estimates. Moreover, this review was limited to married women and unmarried sexually active women, widowed women, adolescents and those in informal unions were beyond the scope of this review. Therefore, the findings may not be generalizable to these populations who may experience different barriers and determinants influencing FP use.

In conclusion, these findings highlight important sociodemographic and health system factors associated with modern contraceptive use in South Asia. These findings underscore the importance of considering both individual and health-system level factors in FP programs across the region. The observed association of spousal involvement in decision-making with modern contraceptive use further highlights the importance of engaging men and encouraging communication between partners regarding FP. Collectively, the evidence suggests that context-specific and equity oriented approaches may be important for improving modern contraceptive use in the South Asian context.

Contributors

KN: Conceptualization, Methodology, Screening, Extraction, Data Curation, Formal Analysis, writing (review and editing). LH: Methodology, Data Curation, Extraction, Formal Analysis, Writing (original draft), writing (review and editing). SK: Conceptualization, Methodology, Screening, Extraction, Data Curation. VM: Screening, Extraction, Methodology, Data Curation. HM: Methodology, Screening, Extraction, Data Curation. IM: Screening, Extraction, writing (original draft). PK: Screening, Extraction, writing (original draft). AM: Data Curation, Writing (original draft). KF: Screening, Extraction, Writing (reviewing and editing). JKD: Conceptualization, Methodology, Data Curation, Formal Analysis, Validation, Writing (review and editing). Supervision. ZH: Conceptualization, Methodology, Data Curation, Formal Analysis, Validation, Writing (review and editing), Supervision.

All authors read, edited and approved the final manuscript and had access to the raw data. KN, LH, JD, ZH accessed and verified all the data and had final responsibility for publication.

Data sharing statement

All manuscript related data is available in the tables provided and in Supplementary material. If any further information is needed, the corresponding author may be contacted. Requests to access this data should be directed to Zahra Hoodbhoy at zahra.hoodbhoy@aku.edu.

Declaration of interests

The authors declare no conflict of interest for the current study.