Estimated Growth Curve Research Articles

Child development lags and learning trajectories in primary school in Uruguay: compensation or divergence? / Rezagos en el desarrollo infantil y trayectorias de aprendizaje en la enseñanza primaria de Uruguay: ¿compensación o divergencia?

We evaluate two rival hypotheses of: (a) divergent trajectories — or cumulative learning gaps — between children with severe and moderate developmental lags and developmentally on-track children; and (b) convergent trajectories — which predict the inverse pattern. We estimate growth curves using panel data from a cohort of students first assessed by the Early Years Evaluation (Uruguay) at the end of kindergarten (age five), assessed up to three times in language and mathematics on an annual basis during years three to five since their transition to first grade. By the third year in school, substantial differences are found in reading and maths skills between children that did and did not show early developmental lags (effects range between .31 and .46 sd). We find a prevalent pattern of divergence in learning gains between years three to five in school (gaps increase by .10 sd per year). Our results suggest that tackling academic gaps will require timely interventions based on early identification of children who are academically at risk. Uruguay has developed early developmental assessment tools and learning monitoring systems, both of which should serve as the basis of an effective early warning system to deal with learning disparities from early ages.

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.

Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

Longitudinal Meta-Analysis of Peak Height Velocity in Young Female Athletes.

Growth patterns and biological milestones in youth sports are key to interpreting the development of young athletes. However, there is no analysis of longitudinal meta-analysis describing the growth of young female athletes. This longitudinal meta-analysis estimated growth curves and age at peak height velocity (PHV) in young female athletes based on anthropometric data from longitudinal studies found in the literature. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies with repeated measurements in young female athletes were identified from searches of four databases (MEDLINE, Web of Science, SCOPUS, and SPORTDiscus) without date restrictions through August 2023. We adapted our bias assessment criteria using the Cochrane risk of bias tool for randomized controlled trials as a reference. Bayesian multilevel modeling was used to perform a longitudinal meta-analysis to extract stature growth curves and age at PHV. Fourteen studies met our eligibility criteria. Twenty-one independent samples could be included in the analysis. Conditional on the data and models, the predicted mean age at PHV for female athletes was 11.18 years (90% CI: 8.62; 12.94). When studies were aggregated by sport in the models, the models could not capture sport-specific growth curves for stature and estimate a corresponding age at PHV. We provide the first longitudinal meta-analytic summary of pubertal growth and derive age at PHV in young female athletes. The meta-analysis predicted that age at PHV occurs at similar ages to those in the general pediatric population. The data pool was limited in sports and geographic distribution, emphasizing the need to promote longitudinal research in females across different youth sports contexts.

Objectively measured daytime sleepiness predicts weight change among adults: Findings from the Wisconsin Sleep Cohort Study

ObjectiveBody mass index (BMI) trajectories are associated with night-time sleep, but it is not clear how they relate to daytime sleepiness in population data. This study aimed to examine longitudinal associations between levels and changes in daytime sleepiness and BMI trajectories among men and women. MethodsWe estimated growth curve models among 827 participants in the Wisconsin Sleep Cohort Study (mean [sd] age = 55.2 [8.0] years at baseline). The outcome variable was BMI (kg/m2) and the key predictor was daytime sleepiness measured by Multiple Sleep Latency Test (MSLT) scores. Covariates included demographics, health behaviors, retirement status, stimulant use, and depressive symptoms. In sensitivity analyses, we evaluated the potential effects of cardiovascular disease, shift work status, and sleep apnea on the robustness of sleepiness and BMI associations. ResultsAt the between-person level, men who were sleepier had higher BMI levels. At the within-person level, age moderated the positive association between sleepiness and BMI among women. Specifically, young women who became sleepier over time gained more BMI than older women with comparable increases in sleepiness. Furthermore, while BMI tended to increase with age among women, BMI trajectories were steeper among sleepy women than among well-rested women, who experienced less increase in BMI over time. ConclusionThe study suggested that levels and changes in daytime sleepiness as objectively measured by MSLT scores are associated with body mass among adults.

Genome-wide association study of growth curve parameters reveals novel genomic regions and candidate genes associated with metatarsal bone traits in chickens

The growth and development of chicken bones have an enormous impact on the health and production performance of chickens. However, the development pattern and genetic regulation of the chicken skeleton are poorly understood. This study aimed to evaluate metatarsal bone growth and development patterns in chickens via non-linear models, and to identify the genetic determinants of metatarsal bone traits using a genome-wide association study (GWAS) based on growth curve parameters. Data on metatarsal length (MeL) and metatarsal circumference (MeC) were obtained from 471 F2 chickens (generated by crossing broiler sires, derived from a line selected for high abdominal fat, with Baier layer dams) at 4, 6, 8, 10, and 12 weeks of age. Four non-linear models (Gompertz, Logistic, von Bertalanffy, and Brody) were used to fit the MeL and MeC growth curves. Subsequently, the estimated growth curve parameters of the mature MeL or MeC (A), time-scale parameter (b), and maturity rate (K) from the non-linear models were utilized as substitutes for the original bone data in GWAS. The Logistic and Brody models displayed the best goodness-of-fit for MeL and MeC, respectively. Single-trait and multi-trait GWASs based on the growth curve parameters of the Logistic and Brody models revealed 4 618 significant single nucleotide polymorphisms (SNPs), annotated to 332 genes, associated with metatarsal bone traits. The majority of these significant SNPs were located on Gallus gallus chromosome (GGA) 1 (167.433–176.318 Mb), GGA2 (96.791–103.543 Mb), GGA4 (65.003–83.104 Mb) and GGA6 (64.685–95.285 Mb). Notably, we identified 12 novel GWAS loci associated with chicken metatarsal bone traits, encompassing 35 candidate genes. In summary, the combination of single-trait and multi-trait GWASs based on growth curve parameters uncovered numerous genomic regions and candidate genes associated with chicken bone traits. The findings benefit an in-depth understanding of the genetic architecture underlying metatarsal growth and development in chickens.

Genetic analysis of body weight and growth curve parameters in Muzaffarnagari sheep of India.

The objective of this study was to estimate genetic effects on parameters of the Brody and Richards growth curves using body weight records from birth to 12 months of age on 2287 Muzaffarnagari lamb for a period of 29 years (1976-2004). Estimated growth curve parameters were analysed using six univariate animal models, and genetic correlations among and between the parameters of each function and between parameters of the functions and observed birth and yearling weights were estimated using bivariate analyses. Significant environmental factors including birth year, sex, season, birth status and dam parity were included as fixed effects in all models. Likelihood ratio tests indicated that maternal genetic effects were significant only for birth weight (BW) and degree of maturity at birth (u0 ) for the Brody and Richards functions. For these traits, direct heritabilities were similar (0.21, 0.19 and 0.17, respectively), but the estimated maternal heritability for BW (0.18) was twice that of u0 for both functions. Heritabilites for yearling weight and asymptotic final body weights for the Brody and Richards functions were 0.28, 0.17 and 0.21, respectively. The remaining growth curve parameters were lowly heritable, ranging from zero for the predicted degree of maturity at the age of maximum growth rate for the Richards function to 0.08 for the maturing rate parameter of the Brody function. Genetic correlations between corresponding parameters for different growth functions exceeded 0.88. Our results showed that the Brody and Richards functions had similar genetic architecture, but the Richards function had no apparent advantages over the more easily interpreted Brody function. Failure to identify maternal genetic effects on maturing rate parameters suggested that both functions failed to identify potentially important maternal genetic effects. Therefore, there is no usefulness of estimated growth curve parameters in selection compared to the simple multi-trait genetic evaluations of individual body weights.

Introducing exceptional growth mining-Analyzing the impact of soil characteristics on on-farm crop growth and yield variability.

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods.

Heteroscedastic Growth Curve Modeling with Shape-Restricted Splines

Growth curve analysis (GCA) has a wide range of applications in various fields where growth trajectories need to be modeled. Heteroscedasticity is often present in the error term, which can not be handled with sufficient flexibility by standard linear fixed or mixed-effects models. One situation that has been addressed is where the error variance is characterized by a linear predictor with certain covariates. A frequently encountered scenario in GCA, however, is one in which the variance is a smooth function of the mean with known shape restrictions. A naive application of standard linear mixed-effects models would underestimate the variance of the fixed effects estimators and, consequently, the uncertainty of the estimated growth curve. We propose to model the variance of the response variable as a shape-restricted (increasing/decreasing; convex/concave) function of the marginal or conditional mean using shape-restricted splines. A simple iteratively reweighted fitting algorithm that takes advantage of existing software for linear mixed-effects models is developed. For inference, a parametric bootstrap procedure is recommended. Our simulation study shows that the proposed method gives satisfactory inference with moderate sample sizes. The utility of the method is demonstrated using two real-world applications.

Stochastic differential equations mixed model for individual growth with the inclusion of genetic characteristics

In early work we have studied a class of stochastic differential equation (SDE) models, for which the Gompertz and the Bertalanffy-Richards stochastic models are particular cases, to describe individual growth in random environments, and applied it to model cattle weight evolution using real data. We have started to work on these type of models considering that the model parameters are fixed, i.e. the same for all the animals. Aiming to incorporate variability among individuals, we consider that the model parameters can be random variables, resulting in SDE mixed models. In additon, here we consider SDE mixed models, allowing the parameters to be random and propose to incorporate each animal's genetic characteristics considering the transformed animal's weight at maturity to be a function of its genetic values. The main objective is to extend the SDE mixed model to the more realistic case where the individual genetic value becomes an important component in the estimated growth curve. For the estimation of the model parameters we have used maximum likelihood estimation theory. Estimates and asymptotic confidence intervals of the parameters are presented. A comparison with SDE non-mixed model and SDE mixed model without the inclusion of genetic characteristics is shown with the conclusion that the incorporation of some genetic characteristics in the model parameters improves the estimation of the animal's growth curve.

Analysis of Red Meranti Wood Color Change Due to Weather Exposure in Samarinda

Red Meranti is a high-quality commercial wood that can be used outdoors. Outdoor timber will be exposed to the weather so that, in the long period, it will cause discoloration. This study aims to determine the colour change of meranti wood due to weather exposure for five months. Observation of macroscopic and colorimeter changes in the colour of red meranti wood refers to the ASTM D2244 standard. The results showed that red meranti wood at the beginning of the study was brownish red with L* 64.67, a* 16.13, and b* 17.00. After 150 days, it turned greyish black with L* 50.74, a* 1.25, b* 3.41, and ΔE 24.50. Colour changes in L*, a*, and b* parameters for 150 days followed the estimated growth curve pattern.

Determination of the effect of the dam age, birth type, and sex on growth curve traits of hair goat kids.

This study was conducted to investigate the best-fit growth curve and dam age, sex, and birth type effect on growth curve traits of hair goat kids. Monthly 3858 test day body weight (BW) records of 643 hair goat kids from birth to 150days of age were used to determine the best-fit growth curve and estimate growth curve parameters with Gompertz and Von Bertalanffy models. The BW records were assigned to three groups: dam age (3, 4, 5, 6, 7years), sex (female, male), and birth type (single, twin). The Gompertz model gave more consistent results than the Von Bertalanffy model according to the goodness of fit criteria. Dam age had no significant effect on any of the growth curve traits. Sex of kids showed a significant effect on maturity index (parameter K) (P < 0.001), estimated mature body weight (parameter A), and weight at point of inflection (IPW) (P < 0.01). Also, birth type had a significant effect on initial/birth weight (parameter B) and parameter K (P < 0.001). Age at point of inflection (IPT) was not affected by any of the factors. Twin kids had a higher maturity index than singles while females higher than males. In conclusion, the Gompertz model was the most suitable model for hair goat kids for selection strategies. For proper selection, the effect of sex and birth type on growth curve traits should be considered by hair goat breeders.

Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates

In January 2020, the world was taken by surprise as a novel disease, COVID-19, emerged, attributed to the new SARS-CoV-2 virus. Initial cases were reported in China, and the virus rapidly disseminated globally, leading the World Health Organization (WHO) to declare it a pandemic on 11 March 2020. Given the novelty of this pathogen, limited information was available regarding its infection rate and symptoms. Consequently, the necessity of employing mathematical models to enable researchers to describe the progression of the epidemic and make accurate forecasts became evident. This study focuses on the analysis of several dynamic growth models, including the logistics, Gompertz, and Richards growth models, which are commonly employed to depict the spread of infectious diseases. These models are integrated to harness their predictive capabilities, utilizing an ensemble modeling approach. The resulting ensemble algorithm was trained using COVID-19 data from the Brazilian state of Paraíba. The proposed ensemble model approach effectively reduced forecasting errors, showcasing itself as a promising methodology for estimating COVID-19 growth curves, improving data forecasting accuracy, and providing rapid responses in the early stages of the pandemic.

Gray divorce and parent–child disconnectedness: Implications for depressive symptoms

AbstractObjectiveDrawing on the divorce‐stress‐adjustment framework, the authors assessed whether parent–adult child relationship dynamics, including disconnectedness from an adult child, exacerbates the negative impact of gray divorce on parental well‐being.BackgroundDivorce after age 50 is increasingly common but the role of parent–child relationships in parents' adjustment to gray divorce is largely unknown.MethodUsing panel data from 1998 to 2018 Health and Retirement Study in the US, the authors estimated growth curve models to track depressive symptoms prior to, during, and after gray divorce among 930 gray divorced individuals. The authors examined whether the lack of any contact with an adult child in the past 12 months (i.e., parent–child disconnectedness) was associated with depressive symptoms trajectories surrounding divorce and subsequent repartnering.ResultsHaving no contact with at least one adult child aggravated the negative effect of divorce on parent's mental health. This association was robust for mothers and fathers. Having no contact with at least one child, however, did not diminish the temporarily positive effect of subsequent repartnering on mental health.ConclusionThe study contributes to the literature by showing that parent–child disconnectedness adds another blow to parents who are convalescing from divorce.

Modelling the growth curve and estimation of associated genetic parameters in Munjal sheep.

The study of growth curves in sheep is an effective approach to monitor the animal development for predicting the growth rate and improving overall flock performance. The purpose of the present work was to study the growth curve traits of Munjal sheep using different non-linear models and to estimate the genetic parameters of those traits for the possibility of inclusion of them under selection strategy. Total 2285 weight records at birth, 3, 6 and 12months of age of 706 lambs born to 48 sires and 149 dams were collected from the registers maintained from 2004 to 2019. Various non-linear growth models viz., Brody, Gompertz, logistic, Bertalanffy and negative exponential were fitted to targeted growth curve traits and then evaluated using goodness of fit criteria such as adjusted R2, root means square error (RMSE), Akaike's information criterion (AIC) and Bayesian information criterion (BIC). The genetic parameters of growth curve traits were estimated using animal model. The results suggested that Brody model was best fitted to the data than other models. The growth curve estimates for mature weight (A), inflexion point (B) and rate of maturation (k) under Brody model were 25.82 ± 1.72, 0.84 ± 0.04, and 0.21 ± 0.04, respectively for female lambs, and 29.55 ± 2.04, 0.86 ± 0.03 and 0.19 ± 0.04, respectively for male lambs. The males showed superiority for mature weights whereas female lambs had higher maturation rates. The estimates of direct heritability for A, B and k were 0.33, 0.41 and 0.10, respectively. The moderate estimate of direct heritability of A and its negative genetic correlation with k indicated the scope of genetic improvement through selection based on mature weights. Therefore, it was concluded from the present findings that Brody model was the one that best describes the growth curve in the Munjal sheep and the selection based on mature weights can be employed for genetic improvement of Munjal flock.

Effects of Aging Uncertainty on the Estimation of Growth Functions of Major Tuna Species

Fishery stock assessment requires accurate specification of the growth function of target species, and aging uncertainty is an important factor that affects the estimation of growth parameters. In this study, we used simulations to study the effects of two types of aging uncertainty, aging error and sampled age range, on the parameter estimation of the Von Bertalanffy growth function, including asymptotic length (L∞), growth coefficient (k), and theoretical age in the year at zero length (t0) of five important tuna species. We found that the uncertainty of the estimated growth curves increased with increasing aging errors. When aging errors were fixed among ages, the effects of age range on estimation error of growth parameters were different among species and growth parameters. When the aging error increased with age, the estimation uncertainty of L∞ and k was the greatest when only young age groups were sampled, while the estimation uncertainty of t0 was the greatest when only old age groups were sampled. Therefore, reducing the aging error and sampling individuals with a wider age range are important for increasing the accuracy and decreasing the uncertainty of the estimated growth function, which will further reduce the uncertainty in fishery stock assessment.

Growth as a measure for environmental conditions and relatedness of North Sea fish stocks

Information about the ecological connections between species is needed to make the shift from fisheries management strategies centred around single species to ecosystem-based fisheries management. Growth rates of fish strongly depend on the environment. Individual growth curves could therefore contain valuable information about the environmental conditions experienced by an individual throughout its life. In this study, we explored whether estimated growth curves contain information about the competition within and between species. We estimated growth curves for 10 species in 10 North Sea roundfish areas based on length-at-age data from the North Sea International Bottom Trawl Survey (NS-IBTS) fish survey by fitting a von Bertalanffy growth model with varying asymptotic length. From these model fits, we extracted a relative measure for the temporal variation in growth in body size of a stock, which can be used as an indicator of the experienced environmental conditions. We found signs of density-dependent growth in at least 6 out of the 10 species for some specific areas as well as all North Sea areas combined. In addition, we used cluster analysis to explore the relatedness in ecological growth conditions between species and areas. Ecologically related species tended to show more similar temporal variation in ecological growth conditions than ecologically more distant species. Likewise, areas that were geographically closer showed more similarity in ecological growth conditions compared to geographically more distant areas. This suggests that information on ecological conditions derived from growth curves provide a useful way to distinguish ecological groups or regions without the need for additional stomach sampling or ecological studies.

A United States national reference for foetal growth for Asian American ethnicities

The purpose of this study was to calculate 10th, 50th, and 90th percentiles of birth weight for gestational age for the total US population and the Asian-American ethnicities. Using the US Natality data files for the years 1992–2020, estimated growth curves were determined across gestational ages and for each Asian American ethnic group—Chinese, Asian Indians, Japanese, Koreans, Filipino, and Vietnamese. Average weight at the 10th, 50th and 90th foetal growth percentiles by race/ethnicity and sex were calculated. Overall, for the 10th, 50th and 90th percentiles, Asian American average birth weight was 18, 90 and 144 g lower than the national average, respectively. We also found that Asian Indians consistently had the lowest birthweight, while Koreans had the highest birthweight among Asian Americans. Updated racial/ethnic-specific weight percentiles by gestational age can be a useful reference for accurate small and large-for-gestational age classifications among different Asian American sub-populations. Impact statement What is already known on this subject? Foetal growth is an important determinant of infants' immediate and long-term health outcomes and foetal growth reference curves have been developed to provide average birth weights for each week of gestation and identify growth-restricted as well as excess-weighing foetuses. What the results of this study add? Using the U.S. Natality data files for the years 1992–2020, estimated growth curves were determined across gestational ages and for each Asian American ethnic group—Chinese, Asian Indians, Japanese, Koreans, Filipino, and Vietnamese. Average weight at the 10th, 50th and 90th foetal growth percentiles by race/ethnicity and sex were calculated. What the implications are of these findings for clinical practice and/or further research? Updated racial/ethnic-specific weight percentiles by gestational age can be a useful reference for accurate small and large-for-gestational age classifications among different Asian American sub-groups.

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

Background and aimsTo 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. MethodsThe 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. ResultsThe 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. ConclusionsStudy 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.

Estimation of Growth Curves of Awassi Sheep by Isotonic Regression Analysis

This study used isotonic regression analysis to classify non-linear regression models that were used to describe the growth curve in relation to different quality fit criteria. The best model selection was made by using the weight–age data obtained from Awassi sheep. Ten non-linear models measured by the fit quality determination coefficient, Akaike information criterion, Bayesian information criterion, mean quadratic estimation error, and estimated coefficient of determination were used. As a result of different non-linear models that were used to predict the growth curves of Awassi sheep and the isotonic regression analysis applied to these models, considering the Mean Square Error and R2 values, the Von Bertalanffy model turned out to be the most appropriate model. As a result, it was revealed that the percentage of predictability and goodness of fit of the models increased significantly with isotonic regression analysis, and as a result, more consistent adult weight estimations could be made.

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

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. 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. 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. 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.