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Record W4382862262 · doi:10.2196/43199

Prevalence, Regional Variations, and Predictors of Overweight, Obesity, and Hypertension Among Healthy Reproductive-Age Indian Women: Nationwide Cross-Sectional Polycystic Ovary Syndrome Task Force Study

2023· article· en· W4382862262 on OpenAlexvenueno aff
Mohd Ashraf Ganie, Subhankar Chowdhury, Vanita Suri, Beena Joshi, Prasanta Kumar Bhattacharya, Sarita Agrawal, Neena Malhotra, Rakesh Sahay, Puthiyaveettil Khadar Jabbar, Roya Rozati, Imtiyaz Ahmad Wani, Amlin Shukla, Taruna Arora, Haroon Rashid

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
FundersIndian Council of Medical Research
KeywordsPolycystic ovaryOverweightCross-sectional studyObesityTask forceMedicineBody mass indexReproductive medicineEnvironmental healthDemographyPregnancyInternal medicineInsulin resistanceBiologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: A clear understanding of the anthropometric and sociodemographic risk factors related to BMI and hypertension categories is essential for more effective disease prevention, particularly in India. There is a paucity of nationally representative data on the dynamics of these risk factors, which have not been assessed among healthy reproductive-age Indian women. OBJECTIVE: This cross-sectional polycystic ovary syndrome (PCOS) task force study aimed to assess the anthropometric and sociodemographic characteristics of healthy reproductive-age Indian women and explore the association of these characteristics with various noncommunicable diseases. METHODS: We conducted a nationwide cross-sectional survey from 2018 to 2022 as part of the Indian Council of Medical Research-PCOS National Task Force study, with the primary aim of estimating the national prevalence of PCOS and regional phenotypic variations among women with PCOS. A multistage random sampling technique was adopted, and 7107 healthy women (aged 18-40 years) from 6 representative geographical zones of India were included in the study. The anthropometric indices and sociodemographic characteristics of these women were analyzed. Statistical analysis was performed to assess the association between exposure and outcome variables. RESULTS: Of the 7107 study participants, 3585 (50.44%) were from rural areas and 3522 (49.56%) were from urban areas. The prevalence of obesity increased from 8.1% using World Health Organization criteria to 40% using the revised consensus guidelines for Asian Indian populations. Women from urban areas showed higher proportions of overweight (524/1908, 27.46%), obesity (775/1908, 40.62%), and prehypertension (1008/1908, 52.83%) categories. A rising trend of obesity was observed with an increase in age. Women aged 18 to 23 years were healthy (314/724, 43.4%) and overweight (140/724, 19.3%) compared with women aged 36 to 40 years with obesity (448/911, 49.2%) and overweight (216/911, 23.7%). The proportion of obesity was high among South Indian women, with 49.53% (531/1072) and 66.14% (709/1072), using both World Health Organization criteria and the revised Indian guidelines for BMI, respectively. BMI with waist circumference and waist-to-height ratio had a statistically significant linear relationship (r=0.417; P<.001 and r=0.422; P<.001, respectively). However, the magnitude, or strength, of the association was relatively weak (0.3<|r|<0.5). Statistical analysis showed that the strongest predictors of being overweight or obese were older age, level of education, wealth quintile, and area of residence. CONCLUSIONS: Anthropometric and sociodemographic characteristics are useful predictors of overweight- and obesity-related syndromes, including prehypertension, among healthy Indian women. Increased attention to the health of Indian women from public health experts and policy makers is warranted. The findings of this study can be leveraged to offer valuable insights, informing health decision-making and targeted interventions that mitigate risk factors of overweight, obesity, and hypertension. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23437.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.289
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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