Estimating birth weight from observed postnatal weights in a Guatemalan highland community

Abstract

Objective: Low birth weight is one of the leading contributors to global perinatal deaths. Detecting this problem close to birth enables the initiation of early intervention, thus reducing the long-term impact on the fetus. However, in low-and middle-income countries, sometimes newborns are weighted days or months after birth, thus challenging the identification of low birth weight. This study aims to estimate birth weight from observed postnatal weights recorded in a Guatemalan highland community. Approach: With 918 newborns recorded in postpartum visits at a Guatemalan highland community, we fitted traditional infant weight models (Count’s and Reeds models). The model that fitted the observed data best was selected based on typical newborn weight patterns reported in the medical literature and previous longitudinal studies. Then, estimated birth weights were determined using the weight gain percentage derived from the fitted weight curve. Main results: The best model for both genders was the Reeds2 model, with a mean square error of 0.30 kg2 and 0.23 kg2 for male and female newborns, respectively. The fitted weight curves exhibited similar behavior to those reported in the literature, with a maximum weight loss around three to five days after birth, and birth weight recovery, on average, by day ten. Moreover, the estimated birth weight was consistent with the 2015 Guatemalan National Survey, no having a statistically significant difference between the estimated birth weight and the reported survey birth weights (two-sided Wilcoxon rank-sum test; ). Significance: By estimating birth weight at an opportune time, several days after birth, it may be possible to identify low birth weight more accurately, thus providing timely treatment when is required.

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

“…These post-partum visits could occur days or months after birth since sometimes it was difficult to follow up on the patients. The birth weight was then estimated using a Reed2 second-order model (Berkey and Reed, 1987 ) fitted on 917 observed post-natal weights using an approach we have previously described in Valderrama et al ( 2020 ). A weight threshold was used to classify the estimated birth weights as low or normal.…”

“…After applying the inclusion criteria, the final dataset comprised 167 visits from 153 non-preeclampsia women who were pregnant with singleton fetuses. From these patients, 142 gave birth to normal weight singletons, whereas 24 gave birth to LBW newborns, based on our thresholds defined for the study population (Valderrama et al, 2020) (see subsection 3.1.1). Table 1 shows demographics of the patients.…”

Deep Sequence Learning for Assessing Hypertension in Pregnancy from Doppler Signals

“…Association of maternal blood pressure and birth weight. Normal Birth Weight is defined by setting weight threshold to 2.64 kg for male newborns, and for females 2.57 kg (30). Different colors indicate gestational age at the time of visit based on last menstrual period (LMP) in month.…”

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

“…In the event a risk factor was identified, the app connected the TBA to appropriate (local or remote) medical care through a voice call, to provide decisional support and onward referral to appropriate healthcare, if needed. The Doppler signal and maternal blood pressure recorded with the mHealth system has allowed the development of different modules for providing estimates of fHRV, gestational age, and hypertension (Valderrama et al 2017, Valderrama et al 2018, Valderrama et al 2019, Valderrama et al 2020a, Valderrama et al 2020b, Katebi et al 2020.…”