Reconceptualizing menstrual health and hygiene among young women in India

Profile of the respondents

Table 1 presents the background characteristics of 205,861 sampled women aged 15–24 years included in this study. The majority (64.3%) had never been married and most resided in rural areas (70.7%). Over two-thirds (67.9%) had attained secondary education, and an additional 19.4% higher education. The sample was largely Hindus (82.5%), followed by Muslims (13.4%) and Christians (1.8%). Socially, the largest proportion belonged to the Other Backward Classes (46.1%), while Scheduled Castes and Other categories represented 24.3% and 20%, respectively. In terms of wealth, the richest category represented 20.2% of the sample. As for media exposure, 41.5% of sampled women read newspapers, 14.4% listened to the radio, and 75.3% watched television. Furthermore, a significant proportion reported various barriers to accessing healthcare: obtaining permission to seek medical help (39.1%), obtaining money for treatment (50.9%), distance to health facilities (57.3%), and transportation challenges (55.1%).

Status of different components of MHH among girls and women aged 15–24 years in India, NFHS-5 (2019–21)

In India 78.6% women aged 15–24 years had access to water within premises, and 74.3% had soap available at the handwashing point. While nearly half (49.5%) reported using period products during menstruation, only 27.7% had access to all four essential MHH resources including using appropriate period products, an improved unshared toilet, water within the premises, and soap at the handwashing point. This indicates that a large majority of young women in India lack access to adequate MHH resources.

Adequate MHH by background characteristics among young women, NFHS-5 (2019–21)

Table 2 shows the prevalence of adequate MHH across background characteristics, revealing significant differences in access. Age at menarche showed a small positive association, with women experiencing menarche at 16 years or older having a slightly higher prevalence (31.4%) compared to those with menarche at age 12 or younger (28.4%). Marital status was strongly associated with adequate MHH; widowed, divorced, or separated women had the lowest prevalence (13.1%), followed by married women (21.5%), while never-married women had the highest prevalence (31.5%).

Education level demonstrated a significant role, with prevalence ranging from 5.4% among women with no formal education to 47.4% among those with higher education. Religion affiliation was also significant, with Christian women (38.3%) and women of other religions (50.8%) exhibiting higher prevalence than Hindu (27.2%) or Muslim (26.7%) women.

We observed significant differences across social groups, with women in the “Other” category having the highest prevalence (41.5%), followed by Other Backward Classes (27.6%), Scheduled Tribes (14.7%), and Scheduled Castes (22.3%). In the NFHS-5, the “Other” category within the social group classification refers to individuals who are not included in the three socioeconomically and historically marginalized groups officially designated by the Government of India: Scheduled Castes, Scheduled Tribes, and Other Backward Classes. “Other” is thus a residual category and it predominantly comprises individuals from caste groups occupying the upper levels of the caste-based social hierarchy. Wealth status, as measured by the asset index, was strongly and positively associated with adequate MHH, ranging from 4.7% for women from the poorest category to 59.0% among the richest. Place of residence revealed a substantial gap, with urban women (48%) having a much higher prevalence than rural women (19.4%).

Regional disparities were also evident, with the North (44.8%) and West (40.3%) showing higher prevalence than other regions. Media exposure was positively associated with adequate MHH, while barriers to healthcare access (permission, financial, distance, transportation) were associated with lower prevalence.

Geographical disparities in adequate MHH across the Indian districts

Figure 1 highlights substantial district-level variations in access to adequate MHH across India, ranging from 2.3% in the Karimganj district (Assam) to 89.4% in the Champhai district (Mizoram). 25% of districts had very low prevalence rates (below 16%), concentrated primarily in the states of Uttar Pradesh (31), Madhya Pradesh (18), Odisha (18), Jharkhand (16), Chhattisgarh (14), Assam (11), and parts of Karnataka (7) and West Bengal (6).

District-wise access to adequate MHH in India, NFHS-5, 2019–21.

Determinants of adequate MHH among girls and women aged 15–24 years in India, NFHS-5 (2019–21)

Table 3 presents the odds ratio (OR) and 95% confidence interval (CI) from multivariate logistic regression. Several factors were significantly associated with the odds of having access to adequate MHH. Compared to never-married women, currently married women had significantly lower odds (AOR: 0.82, 95% CI: 0.79, 0.85). Education level showed a strong positive association, with each increase in education level associated with progressively higher odds of adequate MHH. Women with higher education had over three times the odds of adequate access (AOR: 3.23, 95% CI: 2.89, 3.60) compared to women with no formal education. Similarly, wealth as measured by the asset index, demonstrated a strong positive gradient. Women in the richest quintile had nearly ten times the odds of adequate MHH (AOR: 9.92, 95% CI: 9.11, 10.81), compared to those in the poorest quintile.

Religious affiliation, social group, place of residence, and region of residence also showed significant associations. Muslim women (AOR: 0.87, 95% CI: 0.83, 0.92), Scheduled Castes (AOR: 0.71, 95% CI: 0.68, 0.75), Scheduled Tribes (AOR: 0.61, 95% CI: 0.57, 0.65), and Other Backward Classes (AOR: 0.82, 95% CI: 0.79, 0.85) all had lower odds of adequate MHH compared to Hindu women and the “Other” social group, respectively. Regionally, women in the Northern and Western regions had approximately twice the odds of adequate access compared to those in the Central region.

Place of residence played a significant role in adequate MHH, as urban women had higher odds of practicing adequate MHH (AOR: 1.97, 95% CI: 1.90, 2.04) compared to rural women. The region of residence was also associated with varying odds of adequate MHH.

Media exposure, specifically reading newspapers (AOR: 1.45, 95% CI: 1.38,1.52) at least once a week, was positively associated with adequate MHH. Among the barriers to accessing medical help, financial constraints (AOR: 0.78, 95% CI: 0.75, 0.82), distance to health facilities (AOR: 0.93, 95% CI: 0.89, 0.97), and transportation issues (AOR: 0.89, 95% CI: 0.85, 0.93) were all significantly associated with lower odds of adequate MHH.

Results of Fairlie decomposition analysis

We conducted a Fairlie decomposition analysis to quantify the predictors contributing to the disparity in adequate MHH access between the top and bottom quartile of districts. The mean predicted probabilities of adequate access for the bottom and top quartiles were 0.53 and 0.11, respectively, indicating a substantial gap of 0.42.

Our analysis revealed that 72.8% of this gap was explained by the predictors included in our model. Of this explained portion, 76.4% was attributable to differences in the distribution of just three predictors: asset index, area of residence, and region of residence. These findings highlight the crucial role of sociodemographic and geographic factors in shaping disparities in adequate MHH access. However, approximately 27% of the gap remained unexplained by our model, suggesting the potential influence of unmeasured factors not captured in the National Family Health Survey (NFHS)-5 data. These unmeasured factors could include individual-level factors like knowledge and attitudes related to MHH, community-level factors such as social norms and stigma, and programmatic factors like the availability and quality of MHH interventions.

Table 4 provides a more detailed breakdown of decomposition results, expressing each predictor’s contribution as a percentage of the explained gap. The asset index emerged as the most significant contributor, accounting for 36.5% of the explained disparity between high- and low-performing districts. Region of residence (23.6%) and place of residence (16.7%) were the next most influential factors. Education level and exposure to reading newspapers also made notable contributions (6.3% and 4.7%, respectively).