Multilevel survival analysis of factors associated with under-five mortality in Manipur.

Background: Child mortality is a major public health issue. The studies on under-five mortality that ignore the hierarchical facts mislead the interpretation of the results due to observations in the same cluster sharing common cluster-level random effects. Objectives: The present study uses a multilevel model to analyze under-five mortality and identify the significant factors for under-five mortality in Manipur. Methods: National Family Health Survey-5 (2019-21) data are used in the present study. A multilevel mixed-effect Weibull parameter survival model was fitted to determine the factors affecting under-five mortality. We construct three-level data, individual levels are nested within primary sampling units (PSUs), and PSUs are nested within districts. Results: Out of the 3225 under-five children, 85 (2.64%) died. The three-level mixed-effects Weibull parametric survival model with PSUs nested within the districts, the likelihood-ratio test with Chi-square value = 10.98 and P = 0.004 < 0.05 indicated that the model with random-intercept effects model with PSUs nested within the districts fits the data better than the fixed effect model. The four covariates, namely the number of birth in the last 5 years, age of mother at first birth, use of contraceptive, and size of child at birth, were found as the risk factor for under-five mortality at a 5% level of significance. Conclusions: In the random-intercept effect model, the two estimated variances of the random-intercept effects for district and PSU levels are 0.27 and 0.31, respectively. The values indicate variations (unobserved heterogeneities) in the risk of death of the under-five children between districts and PSUs levels.

Evaluation of seasonal dynamics of the surface water hydrochemistry using multivariate statistical techniques and aquatic macrophyte productivity in a mountainous lake, Northeast India.

The present work elucidates the effective application of multivariate statistics in understanding the probable relations between surface water hydrochemistry and aquatic macrophyte productivity and their underlying seasonal dynamics in a remote mountainous lake of northeast India. The result of hierarchical cluster analysis revealed three distinct clusters corresponding to the pre-monsoon (35.42%), post-monsoon (52.08%), and monsoon (12.50%) seasons. The factor analysis yielded three principal components suggesting the sediment flux, farming discharge, domestic waste, bacterial oxidation of sulfur compounds, and dissolution of plant matters associated with dissolved feldspar minerals as the influential factors. The lake hydrochemistry also varied significantly, both spatially and temporally implying geogenic weathering processes from rock-soil-water interactions. Overall, sixteen aquatic macrophytes were identified, and their monthly and daily net primary productivity varied considerably in different seasons. Regression analysis highlighted the effect of temperature, total dissolved solids, electrical conductivity, and turbidity on the seasonal fluctuations in macrophyte productivity. Overall, the study provides insights into seasonal variation in the lake water chemistry and highlights the role of statistical tools in understanding the fragile aquatic ecosystems over cost-, labor-, and time-intensive inventory studies.