Maternal obesity: Perinatal implications

Reed, Julie; Case, Sarah; Rijhsinghani, Asha

doi:10.1177/20503121231176128

Cited by 3 publications

(1 citation statement)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Having one child doubles the 5‐ and 10‐year obesity incidence for women, with many women who gain excessive weight during pregnancy remaining obese permanently 23 . Therefore, excessive gestational weight gain and/or postpartum weight retention should be considered as they significantly contribute to short‐ and long‐term adverse health outcomes for mother, baby, and future pregnancies 24 . Women with a BMI of more than 30 should be referred to a dietician.…”

Section: Preconception Checklist: Key Factors To Be Addressedmentioning

confidence: 99%

FIGO Preconception Checklist: Preconception care for mother and baby

Benedetto,

Borella,

Divakar

et al. 2024

Intl J Gynecology & Obste

View full text Add to dashboard Cite

The preconception period is a unique and opportunistic time in a woman's life when she is motivated to adopt healthy behaviors that will benefit her and her child, making this time period a critical “window of opportunity” to improve short‐ and long‐term health. Improving preconception health can ultimately improve both fetal and maternal outcomes. Promoting health before conception has several beneficial effects, including an increase in seeking antenatal care and a reduction in neonatal mortality. Preconception health is a broad concept that encompasses the management of chronic diseases, including optimal nutrition, adequate consumption of folic acid, control of body weight, adoption of healthy lifestyles, and receipt of appropriate vaccinations. Use of the FIGO Preconception Checklist, which includes the key elements of optimal preconception care, will empower women and their healthcare providers to better prepare women and their families for pregnancy.

show abstract

Section: Preconception Checklist: Key Factors To Be Addressedmentioning

confidence: 99%

FIGO Preconception Checklist: Preconception care for mother and baby

Benedetto,

Borella,

Divakar

et al. 2024

Intl J Gynecology & Obste

View full text Add to dashboard Cite

show abstract

The association between blood groups, Rhesus factors, body mass index and obesity among pregnant women at Gadarif Maternity Hospital, Eastern Sudan

Bashir,

Hassan,

Mahdi

et al. 2023

BMC Pregnancy Childbirth

View full text Add to dashboard Cite

Background The existing evidence regarding the link between blood groups and obesity remains inconclusive, and there is a noticeable lack of data on the potential association between blood groups and obesity during pregnancy. Consequently, this study aimed to investigate the association between blood groups, body mass index (BMI), and obesity among pregnant women receiving care at Gadarif Maternity Hospital in eastern Sudan. Methods This cross-sectional study was conducted in eastern Sudan during the period from April to September 2022. A questionnaire was employed to gather sociodemographic information from pregnant women. BMI was computed based on weight and height. Blood groups determinations were made using the agglutination method which is commonly used in the study’s region. Multinominal and multiple linear regression analyses were performed, and adjusted for covariates in the regression models. Results Eight hundred and thirty-three pregnant women were enrolled with a median (interquartile range, IQR) gestational age of 10.0 (9.3‒11.0) weeks. The median (IQR) BMI of the women was 26.3(24.2‒29.4) kg/m2. Of these women, 11(1.3%) were underweight, 268(32.2%) were of normal weight, 371(44.5%) were overweight, and 183(22.0%) were obese. One hundred eighty-three (22.0%) women had blood group A, 107 (12.8%) had blood group B, 56 (6.7%) had blood group AB, and 487(58.5%) had blood group O. While 798 (95.8%) of the women were Rhesus factor positive, only 35 (4.2%) were Rhesus factor negative. Multinominal regression showed that only urban residency (adjusted odds ratio, AOR = 2.46, 95% confidence interval, CI = 1.47‒4.13) was associated with overweight. Blood groups and Rhesus factors were not associated with overweight. Age (AOR = 1.06, 95% CI = 1.01‒1.11), urban residence (AOR = 2.46, 95%, CI = 1.47‒4.13), and blood group O (AOR = 1.60, 95%, CI = 1.06‒2.40), were associated with obesity. Rhesus factors were not associated with obesity. In the multiple linear regression, age (coefficient = 0.07, P = 0.028), gravidity (coefficient = 0.25, P = 0.014), urban residence (coefficient = 1.33, P = 0.001), and blood group O (coefficient = 0.68, P = 0.035) were associated with BMI. Conclusions Blood group O was associated with obesity and high BMI among pregnant women in eastern Sudan. Rhesus factors were not associated with obesity.

show abstract

Modelling Infant Mortality Rate using Time Series Models

Ogundunmade,

Daniel,

M. Awwal

2023

Int. J. Data. Science.

View full text Add to dashboard Cite

The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

show abstract

Maternal obesity: Perinatal implications

Cited by 3 publications

References 115 publications

FIGO Preconception Checklist: Preconception care for mother and baby

FIGO Preconception Checklist: Preconception care for mother and baby

The association between blood groups, Rhesus factors, body mass index and obesity among pregnant women at Gadarif Maternity Hospital, Eastern Sudan

Modelling Infant Mortality Rate using Time Series Models

Contact Info

Product

Resources

About