Contributing factors for reduction in maternal mortality ratio in India.

Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space-time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019-21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014-2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009-2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

Hidden hunger in patients with type 2 diabetes (T2D) and its effect on glycaemic control: a protocol for systematic review and meta-analysis.

INTRODUCTION: Hidden hunger or micronutrient deficiencies are quite common in many parts of the world, particularly in the countries of sub-Saharan Africa and South Asia. Micronutrient deficiencies may impact insulin signalling pathways and glucose metabolism, potentially accelerating the onset and development of type 2 diabetes (T2D). This review aims to estimate the prevalence of multiple micronutrient deficiencies among patients with T2D and assess the effect of their deficiency on glycaemic control. METHODOLOGY: The review follows the Cochrane Handbook and PRISMA 2020 guidelines. It includes all eligible studies reporting the prevalence of micronutrient deficiencies and their effect on glycaemic control in T2D patients. We would undertake a comprehensive literature search across databases: PubMed, Scopus, EMBASE, LILACS, ProQuest, Google Scholar and grey literature, and identify the studies meeting the inclusion criteria. We would perform data extraction using a prepiloted data extraction sheet and record relevant study characteristics and outcomes. ANALYSIS: Data will be analysed using JBI Sumari software and R software. Pooled prevalence/incidence of micronutrient deficiency will be estimated, and variance will be stabilised using logit transformation and a double-arcsine transformation of the data. The OR and risk ratio of glycaemic control among T2D cases with and without micronutrient deficiency will be estimated using the 'rma' function under the 'meta' and 'metafor' packages.The study findings will have implications for diabetes management strategies and may inform interventions targeting improved glycaemic control through addressing micronutrient deficiencies. ETHICS AND DISSEMINATION: This systematic review will be based on the scientific information available in the public domain; therefore, ethics approval is not required. We will share the study findings at national and international conferences and submit them for publication in relevant scientific journals. PROSPERO REGISTRATION NUMBER: CRD42023439780.

Clinical outcome post treatment of anemia in pregnancy with intravenous versus oral iron therapy: a systematic review and meta-analysis.

Oral iron therapy is often the most common way of treating anaemia; however intravenous iron is considered effective due to rapid iron replenishment. We have dearth of evidence on clinical outcomes post treatment of anaemia. We have searched studies published in English in PubMed, Cochrane, Scopus, ProQuest, and Google Scholar. Our study analysed the clinical outcomes amongst neonates and mother and the adverse events post treatment and assessed the mean change in maternal haemoglobin concentration in both the groups. Forest plots for the clinical outcomes are presented. From a total of 370 studies, 34 Randomized and quasi experimental studies comparing clinical outcomes post-treatment of anaemia in pregnancy were included for quantitative evidence synthesis. Pooled results of maternal clinical outcomes using random effect model [OR: 0.79 (95% CI 0.66; 0.95); 10 outcomes; 17 studies] showed statistically significant difference among both the groups [Moderate quality evidence]; however no significant difference [OR: 0.99 (95% CI 0.86; 1.14); 7 outcomes; 8 studies] have been observed for neonatal complications [Low quality evidence]. The study found that pregnant women receiving IV iron were significantly less likely to experience adverse events as compared with those receiving oral iron [OR 0.39;  (95% CI 0.26-0.60)]; 34 studies; 13,909 women; [Low quality evidence]. Findings from meta-regression analysis showed that IV iron is more likely to reduce maternal complications by 21% compared to oral iron. Increase in odds of adverse maternal outcomes was observed due to increase in gestational age and publication year but no effect for the type of drug used. IV iron increases Hb more and at a higher pace than oral iron. Intravenous iron is more likely to avert adverse maternal outcomes and adverse reactions. However, there is no conclusive evidence on its effectiveness on individual maternal outcome or neonatal outcome/s. Protocol registered with PROSPERO CRD42022368346).

Intersectional Inequalities in Anthropometric Failure among Indian Children: Evidence from the National Family Health Survey (2015-2016).

Increasing body of health planning and policy research focused upon unravelling the fundamental drivers of population health and nutrition inequities, such as wealth status, educational status, caste/ethnicity, gender, place of residence, and geographical context, that often interact to produce health inequalities. However, very few studies have employed intersectional framework to explicitly demonstrate how intersecting dimensions of privilege, power, and resources form the burden of anthropometric failures of children among low-and-middle income countries including India. Data on 2,15,554 sampled children below 5 years of age from the National Family Health Survey 2015-2016 were analysed. This study employed intersectional approach to examine caste group inequalities in the anthropometric failure (i.e. moderate stunting, severe stunting, moderate underweight, severe underweight, moderate wasting, severe wasting) among children in India. Descriptive statistics and multinomial logistic regression models were fitted to investigate the heterogeneities in the burden of anthropometric failure across demographic, socioeconomic and contextual factors. Interaction effects were estimated to model the joint effects of socioeconomic position (household wealth, maternal education, urban/rural residence and geographical region) and caste groups with the likelihood of anthropometric failure among children.More than half of under-5 children suffered from anthropometric failure in India. Net of the demographic and socioeconomic characteristics, children from the disadvantageous caste groups whose mother were illiterate, belonged to economically poor households, resided in the rural areas, and coming from the central and eastern regions experienced disproportionately higher risk of anthropometric failure than their counterparts in India. Concerted policy processes must recognize the existing heterogeneities between and within population groups to improve the precision targeting of the beneficiary and enhance the efficiency of the nutritional program among under-5 children, particularly for the historically marginalized caste groups in India.

Penalized splines model to estimate time-varying reproduction number for Covid-19 in India: A Bayesian semi-parametric approach.

Statistical modelling is pivotal in assessing intensity of a stochastic processes. Novel Corona virus disease demanded proactive measures to understand the severity of disease spread and to plan its control accordingly. We propose estimation of reproduction number as a crucial factor to monitor the random dynamics of Covid-19 in India. In the present paper, semi-parametric regression based on penalized splines embedded under Bayesian formulation is utilised to estimate reproduction number while incorporating effects of underreporting and delay in reporting for the actual number of daily occurrences. Monte Carlo Markov Chain approximations are utilised to perform simulation study and thereby to assess the impact of the reporting probability and misspecification of delay pattern on potential for further substance of the pandemic. For a cycle of reporting on weekly basis, the proposed penalized spline Bayesian framework fits closest to the empirical data drawn for a two-day delay in reporting with approximately half of the actual cases being reported. The present paper is a contribution towards estimation of the true daily reproduction number of Covid-19 incidences in its next generation cycle.

Spatio-temporal modeling for malnutrition in tribal population among states of India a Bayesian approach.

Exploring Bayesian spatio-temporal methods to analyze spatial dependence in malnutrition at the state level for tribal children (less than 3 years) population of India and change over time (three rounds of NFHS-2(1998-99),3(2005-06) and 4(2015-16)). The Bayesian model, fitted by Markov chain Monte Carlo simulation using OpenBUGS, for spatial autocorrelation (through spatial random effects modeling). The model estimated (1) mean time trend and (2) spatial random effects. Results of spatio-temporal modeling for stunting, wasting and underweight exhibited a declining mean trend across the study region from NFHS-2 to NFHS-4. Spatial random effects exhibited spatial dependence for various states in stunting, wasting and underweight tribal children. Future research should analyze spatio-temporal distribution for malnutrition at district level which will require NFHS-5 data. Also, analysis can be done capturing spatio-temporal interaction and identifying hot spots and cold spots at district level.

Do tribal children experience elevated risk of poor nutritional status in India? A multilevel analysis.

Economic progress in India over the past three decades has not been accompanied by a commensurate improvement in the nutritional status of children, and a disproportionate burden of undernutrition is still focused on socioeconomically disadvantaged populations in the poorest regions. This study examined the nutritional status of children under 3 years of age using data from the fourth round of Indian National Family Health Survey conducted in 2015-2016. Child undernutrition was assessed in a sample of 126,431 under-3 children using the anthropometric indices of stunting, underweight and wasting ('anthropometric failure') across 640 districts, 5489 primary sampling units and 35 states/UTs of India. Descriptive statistics were used to examine the regional pattern of childhood undernutrition. Multilevel logistic regression models were fitted to examine the adjusted effect of social group (tribal vs non-tribal) and economic, demographic and contextual factors on the risks of stunting, underweight and wasting accounting for the hierarchical nature of the data. Interaction effects were estimated to model the joint effects of socioeconomic position (household wealth, maternal education, urban/rural residence and geographical region) and social group (tribal vs non-tribal) with the likelihood of anthropometric failure among children. The burden of childhood undernutrition was found to vary starkly across social, economic, demographic and contextual factors. Interaction effects demonstrated that tribal children from economically poorer households, with less-educated mothers, residing in rural areas and living in the Central region of India had elevated odds of anthropometric deprivation than other tribal children. The one-size-fits-all approach to tackling undernutrition in tribal children may not be efficient and could be counterproductive.