Descriptive statistics of the indicators included in the Women’s Health Index are provided in Supplementary Table S1. On average, 72% of women had attended school; 12% of women used tobacco; and 18%, 23%, and 56% of women were identified to be low BMI, obese, and anemic, respectively. The overall prevalence of high blood sugar and blood pressure was 6% and 13%, respectively. Around 79% of the women received post-natal care; most of the mothers had four antenatal care visits (61%); most births (90%) were attended by skilled health personnel; and a small proportion of deliveries (23%) were through a C-section. Approximately, 66% women had used a method of family planning; early pregnancy was reported by 7%.

Correlation coefficients between 17 indicators included in the Women’s Health Index are shown in Supplementary Table S2. Most indicators exhibited moderate to low correlation, with several indicators showing negative correlations. As expected, obesity was positively correlated with the prevalence of blood sugar (r2 = 0.66) and blood pressure (r2 = 0.35). Antenatal care visits were positively correlated with the consumption of iron tablets during pregnancy (r2 = 0.69), accessing post-natal care (r2 = 0.69), undergoing C-sections (r2 = 0.50), and birth conducted by skilled personnel (r2 = 0.56).

We conducted PCA using all 17 indicators to determine weights for each indicator and to summarize them into a single score, enabling us to compute the rank of the districts. Supplementary Table S3 shows the eigenvalues from the principal component analysis. By examining the scree plot (Supplementary Fig. S1), we determined the point where the slope of the curve clearly leveled off (the ‘elbow’), and using the variance explained (ensuring that at least 5% of variance was accounted for) (Supplementary Table S3), we determined the number of components that should be retained by the analysis. After examining the scree plot, we retained only six components, these six principal components collectively explained nearly 71% of the total variation in the data (Supplementary Table S3). For example, the first principal component explained nearly 29% proportion of the variation, while the second principal component explained nearly 13% of the variance. The third component explained nearly nine percent of the variation, followed by the fourth and fifth components, which accounted for seven and six percent of the variance, respectively. Lastly, the sixth component explained five percent of the variance. The first principal component assigns large (> 0.30 based on the values of eigenvectors in Supplementary Table S3 positive weights to the following indicators: ANC visits, consumption of iron tablets for 180 days during pregnancy, post-natal care, and birth attended by skilled birth personnel. The second principal component allocates large negative weights to low BMI, anaemia and positive weights to health workers ever talked about family planning. The third to sixth principal components highlight positive weights for indicators, including sex ratio, early pregnancy, blood sugar prevalence, anaemia, family planning, tobacco use, and neonatal tetanus injection. We then grouped all districts were into three categories according to the composite score generated for each district with a lower rank corresponding to better women’s health outcomes (see section “Data and methods”).

The computed Omega Hierarchical was 0.86, which suggested modest internal consistency and appropriateness of the PCA methodology; the value of Omega was higher than the suggested value of 0.70. Table 2 presents the Women’s Health Index ranking, MMR, and the coefficient of variation of the WHI for each state. The WHI correlation with MMR was 0.573, indicating a positive relationship between the two and providing evidence of construct validity. To better illustrate this moderately strong positive relationship between WHI and MMR, refer to the scatter plot in Supplementary Fig. S2. We also correlated the Women’s Health Index with the life expectancy and SHDI. The WHI showed a moderate correlation with life expectancy at 0.67 and with SHDI at 0.59, indicating a moderate relation with this widely recognized measure of overall development.

The state-level WHI rank showed wide differences between states (Supplementary Table S5 and Fig. S3). Of all the states and union territories (UT), Goa from the West and Puducherry, Lakshadweep, Kerala, and Tamil Nadu from the South depict the best women’s health as indicated by their lower rank and lower score. Bihar, Jharkhand, and Uttar Pradesh from the East and Tripura Arunachal Pradesh, Mizoram, Nagaland, and Meghalaya from the Northeast depict the worst women’s health. Tamil Nadu showed low WHI rankings (ranging from 2 to 153), indicating better health outcomes and a low coefficient of variation CV of 70, indicating low inter-district variation. Kerala exhibited low WHI ranking (ranging from 4 to 170), with a moderate coefficient of variation (CV) of 116. Districts in states such as Bihar and Jharkhand have high WHI rankings, signifying poorer women’s health and low CV, suggesting that all the districts within these states cluster within the lower spectrum of health (Supplementary Table S5 and Fig. S4).

The distribution for all districts of India classified by ranking of Women’s Health Index is shown in Fig. 1. In states like Kerala and Tamil Nadu, all districts are positioned in the lead category, signifying that their WHI values fall below 234. Conversely, in Meghalaya, every district is classified in the lagged category, indicating WHI values exceeding 470. In Ladakh, all districts fall under the intermediate category. Districts in the lead category are primarily distributed across states, with a majority (60%) located in Tamil Nadu (14%), Haryana, and Karnataka (each approximately 7%). Other states such as Gujarat, Maharashtra, Punjab, Kerala, and Telangana also contribute 6% each to this category. A significant portion (60%) of the lagged category comprises districts from Uttar Pradesh (18%), Bihar (14%), Assam (11%), and Jharkhand and Arunachal Pradesh (each with 8% representation).

Map for all districts of India classified by ranking of Women’s Health Index

The correlation coefficients presented in Table 3 shows a positive association between key indicators and WHI. Moderate or strong positive correlations with WHI were observed across various indicators in each domain. Specifically, in the health system and policy domain, lower rates of antenatal care, prompt post-natal medical care, and the rate of C-section exhibited strong positive correlations, with respective values of r2 = 0.83, r2 = 0.73, and r2 = 0.62. Conversely, in the socio-cultural domain, women’s schooling demonstrated a negative weak correlation with WHI (r2 = − 0.34). Health status indicators such as the prevalence of blood sugar and blood pressure showed moderate and weak negative correlations (r2 = − 0.52 and − 0.39, respectively). Additionally, in the health-determining risk factor domain, obesity displayed a moderate negative correlation with WHI (r2 = − 0.60).