In the past two decades, there has been a growing recognition of the need to establish indigenous standards or reference growth charts, particularly following the WHO multicenter growth study in 2006. The availability of accurate and reliable growth charts is crucial for monitoring child health. The choice of an appropriate model for constructing growth charts depends on various data characteristics, including the distribution\'s tails and peak. While Pakistan has reported some reference growth charts, there is a notable absence of indigenous charts for children under two years of age, especially for infants aged 0-6 months who are exclusively breastfed. Additionally, acquiring data poses a significant challenge, particularly for low-income countries, as it demands substantial resources such as finances, time, and expertise. The Multiple Indicator Cluster Survey (MICS) constitutes a large-scale national survey conducted periodically in low-income countries under the auspices of UNICEF. In this study, we propose methods for generating selection variables utilizing the \"Novel Case Selection Method,\" as previously published. Further our approach enables to select and fit appropriate model to the MICS data, selected, and to develop the standard growth charts.
Out of the 11,478 children under 6 months of age included in MICS-6 (Pakistan), 3,655 children (1,831 males and 1,824 females) met the specified criteria and were selected using the \"Novel Case Selection Method\". The sample was distributed across provinces as follows: 841 (23.0%) from KPK, 1,464 (40.1%) from Punjab, 819 (22.4%) from Sindh, and 531 (14.5%) from Balochistan. This sample encompassed both rural (76.4%) and urban (23.6%) populations. Following data cleaning and outlier removal, a total of 3,540 records for weight (1,768 males and 1,772 females) and 3,515 records for height (1,759 males and 1,756 females) were ultimately available for the development of standard charts. The Bayesian Information Criterion (BIC) was employed to determine the optimal degrees of freedom for L, M, and S using RefCurv_0.4.2. Three families within the gamlss class-namely, Box Cox Cole and Green (BCCG), Box Cox T (BCT), and Box Cox Power Exponential (BCPE)-were applied, each with three smoothing techniques: penalized splines (ps), cubic splines (cs), and polynomial splines (poly). The best-fitted model was selected from these nine combinations based on the Akaike Information Criteria.
The Novel Case Selection Method yielded 3655 cases as per criteria. After cleaning the data, this method lead to selection of 3540 children for \"weight for age\" (W/A) and 3515 children for \"height for age\" (H/A). The \"BCPE\" family and \"ps\" as smoothing method proved to be best on AIC for all four curves, i.e. the W/A male, W/A female, H/A male, and H/A female. The optimum selected degrees of freedom for the curve \"W/A\", for both genders were (M = 1, L = 0, S = 0). The optimum degrees of freedom for H/A male were again (M = 1, L = 0, S = 0), but for females the selected degrees of freedom were (M = 1, L = 1, S = 1). The indigenous fitted standard curves for Pakistan were on lower trajectory in comparison to WHO standards.
This study uses the Novel Case Selection Method with introduced algorithms to construct tailored growth charts for lower and middle-income countries. Leveraging extensive MICS data, the methodology ensures representative national samples. The resulting charts hold practical value and await validation from established data sources, offering valuable tools for policy makers and clinicians in diverse global contexts.

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The methods used to study human growth and development (auxology) have not previously been applied within the setting of hominin maturation (ontogeny). Ontogeny is defined here as the pattern of biological change into an adult form, both at the individual and species level. The hominin fossil record has a lack of recovered immature materials, due to such factors as taphonomic processes that destroy pre-adults; the fragility of immature compared to adult bone; and the lower mortality rates of juveniles compared to adults. The recent discovery of pre-adult hominin skeletal material from a single, homogeneous Homo naledi species from the Rising Star cave system in South Africa provides the opportunity for a broader application of auxology methods and thus the need to understand their use in a modern context. Human auxology studies benefit from a robust database, across multiple populations, and with longitudinal studies in order to assess the patterns and variations in typical growth, development and life history stages. Here, we review the approach, vocabulary, and methods of these human studies, investigate commonalities in data with the fossil record, and then advance the reconstruction of ontogeny for the extinct hominin species H. naledi. To this end, we apply an auxology model into the paleontological context to broadly predict H. naledi birthweight of the offspring at 2.06 kg with a range (±1 SD) of 1.89 to 2.24 kg, with a length at birth 45.5 cm. We estimate a H. naledi juvenile partial skeleton DH7 to be a height of 111-125 cm at death.

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OBJECTIVE: To assess the impact of scientific and technical training on midwives\' abilities in collecting and recording the key performance indicators for fetal growth chart development in limited-resource settings.
METHODS: A descriptive design was used to describe midwives\' abilities in timely collecting and recording the minimum data required to estimate fetal weight and develop fetal growth chart. The study was conducted among 19 urban and rural midwives in South Kalimantan, Indonesia, between April 2016 and October 2017. The training provided access to antenatal care information on 4,946 women (retrospective cohort study) and 381 women (prospective cohort study).
RESULTS: The average amount of recorded antenatal care data on the key performance indicators of fetal growth assessment has been significantly improved (from 33.4% to 89.1%, p-value < 0.0005) through scientific and technical training.
CONCLUSIONS: Scientific knowledge and technical abilities have enabled midwives to timely record routine data of the key performance indicators for fetal growth surveillance. Access to this information is vital during different stages of pregnancy. The information can be utilised as evidence-based guidelines to assess fetal risks through fetal weight estimation and to develop fetal growth chart that is currently not available in Indonesian primary healthcare systems.

Most studies aiming to construct reference or standard charts use a cross-sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z-scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness-of-fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age-specific reference or standard centiles for cross-sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH-21st Project, ie, newborn weight (cross-sectional) and fetal head circumference (longitudinal) data as examples.

Low birthweight is more common in infants of indigenous (Aboriginal and/or Torres Strait Islander) than of White Australian mothers. Controversy exists on whether fetal growth is normally different in different populations.
We sought to determine the relationships of birthweight, birthweight percentiles, and smoking with perinatal outcomes in indigenous vs nonindigenous infants to determine whether the White infant growth charts could be applied to indigenous infants.
Data were analyzed for indigenous status, maternal age and smoking, and perinatal outcomes in 45,754 singleton liveborn infants of at least 20 weeks gestation or 400 g birthweight delivered in New South Wales, Australia, between June 2010 and July 2015.
Indigenous infants (n=6372; 14%) had a mean birthweight 67 g lower than nonindigenous infants (P<.0001; with adjustment for infant sex and maternal body mass index). Indigenous mean birthweight percentile was 4.2 units lower (P<.0001). Adjustment for maternal age, smoking, body mass index, and infant sex reduced the difference in birthweight/percentiles to nonsignificance (12 g; P=.07).
Disparities exist between indigenous and non-indigenous Australian infants for birthweight, birthweight percentile, and adverse outcome rates. Adjustment for smoking and maternal age removed any significant difference in birthweights and birthweight percentiles for indigenous infants. Our data indicate that birthweight percentiles should not be adjusted for indigenous ethnicity because this normalizes disadvantage; because White and indigenous Australians have diverged for approximately 50,000 years, it is likely that the same conclusions apply to other ethnic groups. The disparities in birthweight percentiles that are associated with smoking will likely perpetuate indigenous disadvantage into the future because low birthweight is linked to the development of chronic noncommunicable disease and poorer educational attainment; similar problems may affect other indigenous populations.

The study aims to describe our two-dimensional (2D) ultrasound approach to visualize the fetal secondary palate and plot its growth curve and to describe and demonstrate its clinical implementation.
This is a two parts retrospective study. First, we measured the antero-posterior length of the bony secondary palate, from the soft to hard palate interface (SHPI) line to the alveolar ridge, blindly by two operators during routine scans of low-risk fetuses, and plot a longitudinal growth curve. In the second part, we describe four cases of prenatal diagnosis of secondary palate cleft.
Sixty-eight fetuses were included: 14 to 15 weeks (n = 20), 21 to 24 weeks (n = 32), and 29 to 35 weeks (n = 16). The bony secondary palate elongates along gestation from a mean of 5.3 mm (+/-0.46 mm) at 14 to 15 weeks to 15.9 mm (+/-1.7 mm) at 29 to 35 weeks. We found high intraobserver and interobserver correlation between measurements. All four cases diagnosed by this approach were confirmed postnatally.
The SHPI, representing the normally developed secondary bony palate, can be imaged in the fetus by direct 2D ultrasound as early as 14 weeks. A gap within or nonvisualization of the SHPI is highly suggestive for a secondary palate cleft.

Paternal uniparental disomy for chromosome 7 (upd(7)pat) is extremely rare, and only four cases have been previously reported. As these cases were accompanied by autosomal-recessive disorders which are likely to be involved in growth restriction, the relevance of upd(7)pat to the overgrowth phenotype remains unclear. Here we describe one case of upd(7)pat with no additional genetic diseases, which may answer the question.
A 5-year-old Japanese boy presented with a tall stature of unknown causes. To detect the genetic cause of the tall stature, we performed Sanger sequencing, targeted resequencing, comparative genomic hybridisation and single-nucleotide polymorphism (SNP) array analyses, methylation analysis and microsatellite analysis.
We could not detect pathogenic variants in causative genes for overgrowth syndrome or apparent copy number alterations. DNA methylation analysis revealed hypomethylation at the GRB10, PEG1 and PEG10 differentially methylated regions. SNP array and microsatellite analyses suggested paternal uniparental isodisomy for chromosome 7. Furthermore, we could not identify homozygous mutations of known causative genes for inherited disorders on chromosome 7.
We report the first case of upd(7)pat with an overgrowth phenotype.

OBJECTIVE: We evaluate and compare manually collected paper records against electronic records for monitoring the weights of children under the age of 5.
METHODS: Data were collected by 24 community health workers (CHWs) in 2 Rwandan communities, 1 urban and 1 rural.
METHODS: The same CHWs collected paper and electronic records. Paper data contain weight and age for 320 boys and 380 girls. Electronic data contain weight and age for 922 girls and 886 boys. Electronic data were collected over 9 months; most of the data is cross-sectional, with about 330 children with time-series data. Both data sets are compared with the international standard provided by the WHO growth chart.
METHODS: The plan was to collect 2000 individual records for the electronic data set--we finally collected 1878 records. Paper data were collected by the same CHWs, but most data were fragmented and hard to read. We transcribed data only from children for whom we were able to obtain the date of birth, to determine the exact age at the time of measurement.
RESULTS: Mean absolute error (MAE) and mean absolute percentage error (MAPE) provide a way to quantify the magnitude of the error in using a given model. Comparing a model, log(weight)=a+b log(age), shows that electronic records provide considerable improvements over paper records, with 40% reduction in both performance metrics. Electronic data improve performance over the WHO model by 10% in MAPE and 7% in MAE. Results are statistically significant using the Kolmogorov-Smirnov test at p<0.01.
CONCLUSIONS: This study demonstrates that using modern electronic tools for health data collection is allowing better tracking of health indicators. We have demonstrated that electronic records facilitate development of a country-specific model that is more accurate than the international standard provided by the WHO growth chart.