Conectar
To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches.
Cross-sectional analysis of survey data.
Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021.
270 000 adults aged 18–85+ years who participated in the Global Mind Project.
We used 120+ demographic and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor.
Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within ±15% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18–24 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18–24 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45–54 years.
Social determinants of traumas, adversities and lifestyle can account for 60%–90% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.
by Priyanka Dixit, Thiagarajan Sundararaman, Shiva Halli
BackgroundThe role of place of delivery on the neonatal health outcomes are very crucial. Although the quality of care is being improved, there is no consensus about who is the better healthcare provider in low and middle-income countries (LMICs), public or private facilities. The aim of this study is to assess the differentials in neonatal mortality by the type of healthcare providers in India and its states.
MethodsWe used the data from the fourth wave of the National Family Health Survey 2015–16 (NFHS-4). Information on 259,627 live births to women within the five years preceding the survey was examined. Neonatal mortality rates for state and national levels were calculated using DHS methodology. Multi-variate logistics regression was performed to find the effect of birthplace on neonatal deaths. Propensity score matching (PSM) was used to evaluate the relationship between place of delivery and neonatal deaths to account for the bias attributable to observable covariates.
ResultsThe rise in parity of the women and purchasing power influences the choice of healthcare providers. Increased neonatal mortality was found in private hospital delivery compared to public hospitals in Punjab, Rajasthan, Chhattisgarh, Madhya Pradesh, Bihar, Jharkhand, Odisha, Goa, Maharashtra, Andhra Pradesh and Karnataka states using propensity score matching analysis. However, analysis on the standard of pre-natal and post-natal care indicates that private hospitals generally outperformed public hospitals.
ConclusionsThe study observed a significant variation in neonatal mortality among public and private health care systems in India. Findings of the study urges that more attention be paid to the improve care at the place of delivery to improve neonatal health. There is a need of strengthened national health policy and public-private partnerships in order to improve maternal and child health care in both private and public health facilities.
FreshRSS 