Rising premature menopause and variations by education level in India.
Karan BabbarVanita SinghMuthusamy SivakamiPublished in: Scientific reports (2024)
The proportion of women experiencing premature menopause is on the rise in India, particularly in the age groups of 30-39 years. Consequently, there is a need to understand the factors influencing the prevalence of premature menopausal status among women. Our study uses the data from 180,743 women gathered during the latest Indian version of the Demography Health Survey (National Family Health Survey-5). Our results suggest that close to 5% of women in rural areas and 3% of women in urban areas experience premature menopause, and this figure varies across Indian States. The regression results show that surgical menopause, lower levels of education, poorer wealth index, rural residence, female sterilization, and insurance coverage are key drivers of premature menopause. One of the striking factors is that the prevalence of premature menopause among those with the lowest levels of education (6.85%) is around seven times higher than those with the highest level of education (0.94%). We conducted a decomposition analysis to delve into the factors contributing to this inequality. The results show that undergoing a hysterectomy (surgical menopause) account for 73% of the gap in premature menopausal rates between women with the lowest and highest levels of education. This indicates that women with poor education are more likely to undergo hysterectomy at a younger age. This finding warrants further exploration as we would expect that women from lower socio-economic background would have limited access to surgical care, however, our results suggest otherwise. This perhaps indicates a lack of awareness, lack of alternative treatment options, and over-reliance on surgical care while neglecting conservative management. Our results have implications for addressing the diverse needs of the increasing number of women in their post-menopause phase and for focusing on conservative treatment options for these women.
Keyphrases
- polycystic ovary syndrome
- healthcare
- quality improvement
- postmenopausal women
- pregnancy outcomes
- cervical cancer screening
- breast cancer risk
- type diabetes
- palliative care
- risk factors
- insulin resistance
- metabolic syndrome
- pain management
- machine learning
- affordable care act
- skeletal muscle
- deep learning
- electronic health record
- health insurance
- data analysis