Inequalities in demand satisfied with modern methods of family planning among women aged 15–49 years: a secondary data analysis of Demographic and Health Surveys of six South Asian countries

Abstract

ObjectiveTo estimate educational and wealth inequalities in demand satisfied with modern methods of family planning (mDFPS).DesignA secondary data analyses of Demographic and Health Surveys.SettingSix South Asian countries, Afghanistan (2015), Bangladesh (2014), India (2015–2016), Maldives (2016–2017), Nepal (2016) and Pakistan (2017–2018).ParticipantsWomen aged 15–49 years. Primary and secondary outcome measures mDFPS was defined as married women aged 15–49 years or their partners, who desired no child, no additional children or to postpone the next pregnancy and who are currently using any modern contraceptive method. We estimated weighted and age-standardised estimates of mDFPS. We calculated the slope index of inequality (SII) and relative index of inequality (RII) as the measures of socioeconomic inequalities.ResultsA total of 782 639 women were surveyed. The response rate was 84.0% and above. The prevalence of mDFPS was below 50% in Maldives (22.8%, 95% CI 20.7 to 25.0), Pakistan (42.0%, 95% CI 39.9 to 44.0) and Afghanistan (39.1%, 95% CI 36.9 to 41.3), whereas Bangladesh had achieved 76% (75.8%, 95% CI 74.2 to 77.3). Both wealth and educational inequalities varied in magnitude and direction between the countries. Except in Nepal and Bangladesh, mDFPS wealth inequalities showed a trend of increasing mDFPS as we moved towards richer, and richest wealth quintiles that is, pro-poor (RII (0.5 to 0.9); SII (−4.9 to −23.0)). In India and Nepal, higher versus no education was in favour of no education (higher mDFPS among not educated women) (RII 1.1 and 1.4; SII 4.1 and 15.3, respectively) and reverse in other countries ((RII (0.4 to 0.8); SII (−10.5 to −30.3)). Afghanistan, Maldives and Pakistan fared badly in both educational and wealth inequalities among the countries.ConclusionsSouth Asia region still has a long way ahead towards achieving universal access to mDFPS. Diverse patterns of socioeconomic inequalities between the countries call for national governments and international development agencies to target the population subgroups for improving the mDFPS coverage.

Equity trends for the UHC service coverage sub-index for reproductive, maternal, newborn and child health in Pakistan: Evidence from demographic health surveys

“…However, the pace of improvement varies among subgroups and provinces. While progress has been made in closing the coverage gap, the trajectory of improvement appeared to have slowed down for demand satis ed by modern methods and care-seeking for pneumonia (21)(22)(23). Despite the improvements, persistent socioeconomic and urban-rural inequalities persist across the RMNCH service indicators, posing challenges to achieving universal health coverage in Pakistan.…”

Equity trends for the UHC service coverage sub-index for reproductive, maternal, newborn and child health in Pakistan: evidence from demographic health surveys

“…However, the pace of improvement varies among subgroups and provinces. While progress has been made in closing the coverage gap, the trajectory of improvement appeared to have slowed down for demand satisfied by modern methods and care-seeking for pneumonia [ 22 – 24 ]. Despite the improvements, persistent socioeconomic and urban‒rural inequalities persist across the RMNCH service indicators, posing challenges to achieving universal health coverage in Pakistan.…”

Socioeconomic Inequalities and Factors Associated with the Use of Modern Contraceptive Methods in Women of Childbearing Age in Ecuador, 2018

“…The following study covariates were used and selected based on previous studies, the PROGRESS framework [ 18 , 19 ], and data available in the ENSANUT-2018. For the assessment of inequalities, the variables used were age group (15 to 19 years, 20 to 29 years, 30 to 39, years, and 40 to 49 years) [ 7 , 9 , 20 ]; educational level (up to primary, secondary, higher) [ 6 , 7 ]; currently working (no, yes) [ 21 ]; ethnicity (“non-indigenous” for individuals who self-reported as afro-ecuatorian, white, mestizo, montubio, or others, and “indigenous” for those who self-reported as indigenous) [ 22 , 23 ]; and, area of residence (rural, urban). The following variables were used for the analysis of associated factors: age group (15 to 19 years, 20 to 29 years, 30 to 39, years, and 40 to 49 years) [ 7 , 9 , 20 ]; educational level (up to primary, secondary, higher) [ 21 ]; marital status (married, cohabiting) [ 10 ]; currently working (no, yes); parity (0 to 1, 2, 3 or more children) [ 24 , 25 ]; ethnicity (indigenous, non-indigenous) [ 23 ]; health insurance (yes, no) [ 26 ]; area of residence (rural, urban) [ 7 ]; region of residence (Highlands, Coast, Amazon, Island) [ 13 ]; and wealth quintile (“very poor”, “poor”, “medium”, “rich”, and “very rich”) [ 6 , 7 ].…”

“…Several studies have reported the presence of inequalities in the use of MCM according to certain population characteristics [ 6 , 7 , 8 , 9 , 10 , 11 ]. Thus, a study that included 11 low- and middle-income countries in the African and Asian regions identified inequalities in the satisfied demand for MCM in favor of women within the highest wealth quintile, those who were older, and women with higher levels of education [ 11 ].…”