Growth Estimation of Under-Five Children Using Statistical Models in Central Region of India.

BACKGROUND AND AIMS: To determine the suitability of 11 basic statistical models for estimating child-growth of under-five children and to bring-forth estimated growth curves for mean height & mean weight by their selected birth-weight categories for Central Region of India. METHODS: The study used fourth round of National Family Health Survey-4 (NFHS-4) data of India, consisting of 75,645 under-five children, belonging to 3 Indian States - Chhattisgarh, Madhya Pradesh & Uttar Pradesh. The children of the Region were first divided into 4 sub categories according to their birth-weight: (i) < 2000 gm, (ii) 2000-2499 gm, (iii) 2500-2999 gm (iv) 3000+gm, growth curve for mean height and mean weight were estimated for two sexes. RESULTS: The significant association of 7 socio-demographic factors studied, namely - age & sex of child, birth-order, BMI, mother's highest level of education, place of residence and wealth index. Further, Cubic Model and Power Model, demonstrated best-fit to height & weight data of under-five children, belonging to different birth-weight categories, for estimating growth of boys & girls separately. These models enabled us to estimate mean height and mean weight, with 95% CI, for boys and girls separately by different birth-weight categories. CONCLUSIONS: Study concluded that 7 socio-demographic factors were significantly associated with birth-weight. Further, Cubic Model and Power Model were most suitable for estimating child growth in terms of mean height & mean weight for boys and girls - considering specific birth-weight categories.

Growth Prediction of Under-5 Children Using Statistical Models for Eastern Region of India.

Background: Percentile curves are often used to assess variances in children's growth pattern. This study is aimed at explaining effect of the respondents' sociodemographic characteristics on under-5 children's birth weight and identifying most suitable models, out of 11 statistical models reviewed, for estimating children's growth in terms of height and weight of a given birth-weight category and obtain estimated growth curves. Material and Methods: The study used National Family Health Survey (NFHS)-4 data from four Eastern States of India, consisting of 54,075 under-5 children. Estimated growth curves were obtained, using best-fit models. Results: Birth weight was found to be associated with children's age, gender, birth order, body mass index, mother's education, living place and wealth index. Two models - Cubic Model and Power Model - showed best fit to the height and weight measurements. We obtained estimated growth curves of boys and girls for a given birth-weight category. Conclusions: All socio-demographic factors studied, except respondent's occupation, were associated with children's birth weight. Cubic and Power Models were most suitable for assessing growth in terms of height and weight of boys and girls, belonging to a given birth-weight category.

Statistical Model for COVID-19 in Different Waves of South Indian States.

Background: COVID-19 has resurfaced in India, where it is rapidly spreading and wreaking havoc in rural areas. An effort has been undertaken to assess the levels and patterns of COVID-19 active cases in the southern states of India. To trace and reason out anomalous trends in the COVID-19 curve so that particular actions such as lockdown, de-lockdown, and healthcare improvisation can be implemented at the appropriate time. Methods: The data has retrieved from the government websites through a platform called Kaggle. The entire duration of COVID - 19 were classified into three compartments: Phase one, Resting phase, and Phase two. The Case Fatality Rate in south Indian states was analysed corresponding to the phases, and a compartmental model for COVID-19 dynamics in the region was proposed. Results: The quadratic regression model was fitted and found to be the best model for the phases except for the resting phase. Phase one was comparatively less fitted when compared to phase two. In most of the south Indian states, the active cases in phase one were almost more than four times that of phase two. The average CFR value in phase one was lower than the subsequent phase in all of the southern Indian states. In phase one, Telangana, Karnataka, and Tamil Nadu had the highest CFR (4.77,4.22, and 3.71, respectively), whereas Lakshadweep and Kerala had the lowest CFR (0.27 and 0.71, respectively). In the resting phase, the CFR stabilized in all states and reached a value between 0.2 to 2. The trend was similar in phase two also, CFR of Lakshadweep, Kerala, Telangana, and Andhra Pradesh (0.143, 0.416,0.553, 0.803) were very low, while the CFR of Andaman and Nicobar Islands, Karnataka, and Tamil Nadu (1.237, 1.306, 1.490) were very high. Conclusion: The first and second phases of the COVID-19 virus in south Indian states had different characteristics. A District-level working group with the autonomy to respond to rapidly changing local situations must be empowered to tackle the next phase. The upcoming phases could be more peaked in less time and could be a hectic situation for the health care system.

Stunting, wasting and underweight as indicators of under-nutrition in under five children from developing Countries: A systematic review.

BACKGROUND AND AIMS: To compute reliable estimates of stunting, wasting and underweight along with their determinants in under 5 children from Developing Countries. METHODS: Out of 190 studies on under-nutrition, accessed from PubMed and Google database, 24 studies meeting the selection criteria were considered for meta-analysis. RESULTS: Overall estimate of prevalence of stunting, wasting and underweight were 43.4%, 17.8% and 35.5% respectively. Mother's education, BMI, height, wealth index, child birth-weight and sex were factors significantly associated with stunting, wasting and underweight. CONCLUSIONS: Prevalence of stunting, wasting and underweight in Developing Countries were quite high. To carry-out differentials of under-nutrition between countries and ways of its reduction, more such studies are required.

Age at menopause in India: A systematic review.

BACKGROUND AND AIMS: Menopause is a physiological process in nature and hence, variations in the age of menopause are not expected. Hence, the study was conducted with an objective to calculate the reliable estimates of age at menopause for India, and understand the differentials in women's age at menopause throughout the country. METHODS: A total of 202 studies of age at menopause, covering the period 2009-2020, were accessed from PubMed database and Google. Of these only ten studies met the selection criteria for this paper, which is that the data for these studies must be collected from house-to-house surveys. RESULTS: The average age at menopause in India, with minimal publication bias, is 46.6 years (95% CI: 44.83, 48.44). In one study slightly above 1.96 Standard Deviation, was observed, as ascertained by Funnel Plot and Egger's test. The mean age ranged from a minimum of 44.69 years (95% CI: 35.01, 54.37) to a maximum of 48.95 (95% CI: 42.29, 55.61) years. Furthermore, the age at menopause did not exhibit any significant variation by age at menarche, although the association was positive. CONCLUSIONS: The age at menopause showed positive association with age at menarche. In India, during the period 2009-2020, it was 46.6 years, which significantly lower than the age in some developed countries. The differences may be methodological since no information was found regarding the distribution of age at menopause in the studies that were considered for meta-analysis.