Double burden of malnutrition among women of reproductive age: Trends and determinants over the last 15 years in India.

INTRODUCTION: Double burden of malnutrition (DBM) has been recognized by the World Health Organisation (WHO) as an emerging Global Syndemic characterized by the simultaneous occurrence of both undernutrition and overnutrition. Women of the reproductive age group (15 to 49 years) are disproportionately affected by DBM and are at high risk of continuing the intergenerational cycle of malnutrition. This study aims to assess the changing trends and determinants of DBM among women of the reproductive age group in India. MATERIALS AND METHODS: We used data from three rounds of National Family Health Surveys (NFHS-3,4,5) conducted in years 2005-06, 2015-16, and 2019-2021. Descriptive statistics and Poisson regression analysis were done using weights with log link function. RESULTS: The prevalence of anaemia, underweight and overweight/obesity was 57.2%, 18.6% and 24% respectively. The combined burden of underweight and anaemia has declined by 46% (21.6% to 11.7%), whereas the combined burden of overweight/obesity and anaemia has increased by 130% (5.4% to 12.4%) in the past 15 years. The prevalence of DBM, which includes both underweight and overweight/obesity with anaemia was 24.1% in 2021, a decline of 11% in 15 years. Women who were younger, rural, less educated, poor and middle class, and women living in the eastern, western and southern regions of India had higher risk for being underweight with anaemia and lower risk for developing overweight/obesity with anaemia. CONCLUSION: The significant decrease in underweight yet enormous increase in overweight/obesity over the past 15 years with the persistence of anaemia in both ends of the nutritional spectrum is characteristic of the new nutritional reality emphasizing the need to address malnutrition in all its forms. It is critical to consider geography and a population specific, double-duty targeted intervention to holistically address the risk factors associated with DBM and accomplish India's commitment to the global agenda of Sustainable Development Goals-2030.

Modeling Zero-inflated Count Data Using Generalized Poisson and Ordinal Logistic Regression Models in Medical Research.

Objectives: In medical research, the study's design and statistical methods are pivotal, as they guide interpretation and conclusion. Selecting appropriate statistical models hinges on the distribution of the outcome measure. Count data, frequently used in medical research, often exhibit over-dispersion or zero inflation. Occasionally, count data are considered ordinal (with a maximum outcome value of 5), and this calls for the application of ordinal regression models. Various models exist for analyzing over-dispersed data such as negative binomial, generalized Poisson (GP), and ordinal regression model. This study aims to examine whether the GP model is a superior alternative to the ordinal logistic regression (OLR) model, specifically in the context of zero-inflated Poisson models using both simulated and real-time data. Methods: Simulated data were generated with varied estimates of regression coefficients, sample sizes, and various proportions of zeros. The GP and OLR models were compared using fit statistics. Additionally, comparisons were made using real-time datasets. Results: The simulated results consistently revealed lower bias and mean squared error values in the GP model compared to the OLR model. The same trend was observed in real-time datasets, with the GP model consistently demonstrating lower standard errors. Except when the sample size was 1000 and the proportions of zeros were 30% and 40%, the Bayesian information criterion consistently favored the GP model over the OLR model. Conclusions: This study establishes that the proposed GP model offers a more advantageous alternative to the OLR model. Moreover, the GP model facilitates easier modeling and interpretation when compared to the OLR model.

Cytogenetic profile of 1791 adult acute myeloid leukemia in India.

BACKGROUND: Cytogenetic analysis continues to have an important role in the management of acute myeloid leukemia (AML) because it is essential for prognostication. It is also necessary to diagnose specific categories of AML and to determine the most effective form of treatment. Reports from South Asia are few because the availability of cytogenetic services is relatively limited. METHODS: We performed a retrospective analysis of the cytogenetic findings in adults with AML seen consecutively in a single centre in India. The results were categorised according to the 2022 World Health Organisation (WHO), International Consensus Classification (ICC) and European LeukemiaNet (ELN) classifications. RESULTS: There were 1791 patients aged 18-85 years (median age 42, 1086 males). Normal karyotypes were seen in 646 (36%) patients. The 1145 (64%) abnormal karyotypes comprised 585 (32.7%) with recurrent genetic abnormalities (RGA), 403 (22.5%) with myelodysplasia-related cytogenetic abnormalities (MRC), and 157 (8.8%) with other abnormalities. There were 567 (31.7%) patients with solitary abnormalities and 299 (16.7%) with two abnormalities. Among the 279 (15.6%) patients with ≥ 3 abnormalities, 200 (11.2%) had complex karyotypes (CK) as per the WHO/ICC and 184 (10.3%), as per the ELN definition. There were 158 (8.8%) monosomal karyotypes (MK). Patients with normal karyotypes had a higher median age (45 years) than those with abnormal karyotypes (40 years, p < 0.001), and those with ≥ 3 abnormalities (43 years), than those with fewer abnormalities (39 years, p = 0.005). Patients with CK (WHO/ICC) and monosomal karyotypes had a median age of 48 years. Those with RGA had a lower median age (35 years, p < 0.001) than MRC (46 years) or other abnormalities (44 years). The t(15;17) was the most common abnormality (16.7%),followed by trisomy 8 (11.6%), monosomy 7/del 7q (9.3%), t(8;21) (7.2%), monosomy 5/del 5q (6.7%) and monosomy 17/del 17p (5.2%). CONCLUSION: Our findings confirm the lower age profile of AML in India and show similarities and differences with respect to the frequencies of individual abnormalities compared to the literature. The frequencies of the t(15;17), trisomy 8 and the high-risk abnormalities monosomy 7 and monosomy 5/del 5q were higher, and that of the inv(16), lower than in most reports.

Trends in prevalence and determinants of severe and moderate anaemia among women of reproductive age during the last 15 years in India.

BACKGROUND: Anaemia is a serious global public health problem that disproportionally affects children, adolescent girls, and women of reproductive age, especially pregnant women. Women of reproductive age are more vulnerable to anaemia, particularly severe and moderate anaemia leads to adverse outcomes among pregnant women. Despite continuous Government efforts, anaemia burden still poses a serious challenge in India. The objective of this study is to assess the trends in prevalence and determinants of severe and moderate anaemia among women of reproductive age between 15 and 49 years. METHOD: We used three rounds of the large-scale National Family Health Survey (NFHS) India, conducted on a representative sample of households using a cross-sectional design across the country in 2005-06, 2015-16 and 2019-2021. We included all the women aged 15 to 49 years in our analysis. We used the same haemoglobin (Hb) cut-off values for all the three rounds of surveys to ensure comparability. Generalized linear regression analyses with log link were done. Survey weights were incorporated in the analysis. RESULTS: The prevalence of severe or moderate Anaemia (SMA) in non-pregnant women was 14.20%, 12.43% and 13.98%; it was 31.11%, 25.98% and 26.66% for pregnant women in 2006, 2016 and 2021 respectively. The decline in SMA prevalence was 1.54% in non-pregnant women, whereas it was 14.30% in pregnant women in 15 years. Women who were poor, and without any formal education had a higher risk for severe and moderate Anaemia. CONCLUSION: Despite the intensive anaemia control program in India, SMA has not declined appreciably in non-pregnant women during the last two decades. Despite the decline, the prevalence of SMA was about 26% in pregnant women which calls for a comprehensive review of the existing anaemia control programmes and there must be targeted programmes for the most vulnerable and high-risk women such as rural, poor and illiterate women of reproductive age to reduce the burden of anaemia among them.

Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables.

In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion.