The Relevance of Maternal Socioeconomic Characteristics for Low Birth Weight - a Case-Control Study

Influence of perinatal factors on full-term low-birth-weight infants and construction of a predictive model

BACKGROUND

Being too light at birth can increase the risk of various diseases during infancy.

AIM

To explore the effect of perinatal factors on term low-birth-weight (LBW) infants and build a predictive model. This model aims to guide the clinical management of pregnant women's healthcare during pregnancy and support the healthy growth of newborns.

METHODS

A retrospective analysis was conducted on data from 1794 single full-term pregnant women who gave birth. Newborns were grouped based on birth weight: Those with birth weight < 2.5 kg were classified as the low-weight group, and those with birth weight between 2.5 kg and 4 kg were included in the normal group. Multiple logistic regression analysis was used to identify the factors influencing the occurrence of full-term LBW. A risk prediction model was established based on the analysis results. The effectiveness of the model was analyzed using the Hosmer-Leme show test and receiver operating characteristic (ROC) curve to verify the accuracy of the predictions.

RESULTS

Among the 1794 pregnant women, there were 62 cases of neonatal weight < 2.5 kg, resulting in an LBW incidence rate of 3.46%. The factors influencing full-term LBW included low maternal education level [odds ratio (OR) = 1.416], fewer prenatal examinations (OR = 2.907), insufficient weight gain during pregnancy (OR = 3.695), irregular calcium supplementation during pregnancy (OR = 1.756), and pregnancy hypertension syndrome (OR = 2.192). The prediction model equation was obtained as follows: Logit (P) = 0.348 × maternal education level + 1.067 × number of prenatal examinations + 1.307 × insufficient weight gain during pregnancy + 0.563 × irregular calcium supplementation during pregnancy + 0.785 × pregnancy hypertension syndrome - 29.164. The area under the ROC curve for this model was 0.853, with a sensitivity of 0.852 and a specificity of 0.821. The Hosmer-Leme show test yielded χ 2 = 2.185, P = 0.449, indicating a good fit. The overall accuracy of the clinical validation model was 81.67%.

CONCLUSION

The occurrence of full-term LBW is related to maternal education, the number of prenatal examinations, weight gain during pregnancy, calcium supplementation during pregnancy, and pregnancy-induced hypertension. The constructed predictive model can effectively predict the risk of full-term LBW.

Enhancing the Follow-up Assessment of Very Preterm Children with Regard to 5-Year IQ Considering Socioeconomic Status

BACKGROUND

Specifying peri- and postnatal factors in children born very preterm (VPT) that affect later outcome helps to improve long-term treatment.

AIM

To enhance the predictability of 5-year cognitive outcome by perinatal, 2-year developmental and socio-economic data.

SUBJECTS AND OUTCOME MEASURES

92 VPT infants, born 2007-2009, gestational age<32 weeks and/or birthweight of 1500 g, were assessed longitudinally including basic neonatal, socio-economic (SES), 2-year Mental Developmental Index (MDI, Bayley Scales II), 5-year Mental Processing Composite (MPC, Kaufman-Assessment Battery for Children), and Language Screening for Preschoolers data. 5-year infants born VPT were compared to 34 term controls.

RESULTS

The IQ of 5-year infants born VPT was 10 points lower than that of term controls and influenced independently by preterm birth and SES. MDI, SES, birth weight and birth complications explained 48% of the variance of the MPC. The MDI proved highly predictive (r=0.6, R2=36%) for MPC but tended to underestimate the cognitive outcome. A total of 61% of the 2-year infants born VPT were already correctly classified (specificity of .93, sensitivity of .54). CHAID decision tree technique identified SES as decisive for the outcome for infants born VPT with medium MDI results (76-91): They benefit from effects associated to a higher SES, while those with a poor MDI outcome and a birth weight≤890 g do not.

CONCLUSION

Developmental follow-up of preterm children enhances the quality of prognosis and later outcome when differentially considering perinatal risks and SES.

Machine learning algorithms for predicting low birth weight in Ethiopia

Background

Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks.

Method

This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.

Results

Data preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score.

Conclusion

The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia’s top four critical predictors of low birth weight in Ethiopia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01981-9.

An Association between Maternal Occupations and Low Birth Weight Infants in Japan from 1995 to 2015

Differences in low birth weight rate depending on maternal socioeconomic characteristics have not yet been demonstrated using the Vital Statistics in Japan; therefore, this study aimed to investigate these differences according to maternal occupations. “Report of Vital Statistics: Occupational and Industrial Aspects” and the Vital Statistics in Japan were used every five years from 1995 to 2015. Nine types of occupations were compared. The low birth weight rate was calculated according to maternal occupations and year. Also, the standardized low birth weight ratio was obtained by dividing the number of low-birth-weight infants for each maternal occupation by an expected number of low birth weight infants. The standardized low birth weight ratio for manual workers was the highest among all occupations from 2000 to 2015, and it was significantly higher than one throughout the years. The ratio for clerical workers was also significantly higher than one from 1995 to 2010. Whereas, the ratio for farmers was significantly lower than one in most of the years. It was suggested that health guidance and prenatal care are particularly needed for manual workers, and a study investigating the differences in prenatal characteristics among maternal occupations is necessary for finding a reason for disparity.

Prevalence of and socioeconomic gradient in low birth weight in Ethiopia: further analysis of the 2016 demographic and health survey data

Background

Evidence suggests appearance of socioeconomic gradient in the probability of low birth weight (LBW). Such evidence, however, is scanty in Ethiopia. The study aimed to examine the prevalence of and socioeconomic gradient in LBW in Ethiopia.

Method

Data for the study were drawn from the Ethiopia Demographic and Health Survey conducted in 2016. The 2016 EDHS is the fourth wave in the series of nationally representative household surveys carried out in the country to deliver up-to-date health and demographic indicators for the Ethiopian population. Women aged 15 to 49 years were the main focus of the survey, with data also gathered from men aged 15 to 59 years and under five children. The study pursued complex sampling strategy to draw samples representative at national as well as at urban and rural levels. The data are available to the public domain and were accessed from the MEASURE DHS following registration. Multivariable logistic regression model and marginal standardization were used to estimate socioeconomic gradient in the probability of LBW. We performed sensitivity analysis to evaluate variation of LBW according to different categories of socioeconomic position. Maternal education and household wealth were used as measures of the socioeconomic position in the study.

Results

13.2% (95% confidence interval = 10.73, 15.65) of births were complicated by LBW. The findings showed that socioeconomic gradient was evident between maternal education and LBW; as education increases from no education to secondary education, the probability of occurrence of LBW consistently declined. However, no gradient in LBW was detected for household wealth.

Conclusions

We have identified education gradient in LBW, with the highest burden of LBW occurring among the non-educated women. To redress the observed education disparity in LBW, targeted interventions need to be implemented with greater emphasis placed on illiterate women.

Demographic surveillance over 12 years helps elicit determinants of low birth weights in India

BACKGROUND

Low birth weight is an important predictor of maternal and child health. Birth weight is likely to be affected by maternal health, socioeconomic status and quality of health care facilities.

OBJECTIVE

To assess trends in the birth weight, the proportion of low birth weight, maternal factors and health care facilities for delivery in villages of Western Maharashtra from the year 2004 to 2016 and to analyze factors associated with low birth weight for total birth data of 2004-2016.

METHODS

Data collected for 19244 births from 22 villages in Vadu Health and Demographic Surveillance System (HDSS), Pune, Maharashtra, India from the year 2004 to 2016 were used for this analysis.

RESULTS

There was an overall increase in the annual mean birth weight from 2640.12 gram [95% CI 2602.21-2686.84] in the year 2004 to 2781.19 gram [95% CI 2749.49-2797.95] in the year 2016. There was no secular trend to show increase or decrease in the proportion of low weight at birth. Increasing maternal age (>18 years) compounded with better education, reduced parity and increasing number of institutional deliveries were significant trends observed during the past decade. Low birth weight was found to be associated with female gender, first birth order, poor maternal education and occupation as cultivation.

CONCLUSION

Changes in maternal age, education, occupation, and increased institutionalized deliveries contributed in to increasing birth weights in rural Maharashtra. Female gender, first birth order, poor maternal education and occupation of cultivation are associated with increased risk of low birth weight.