PREVALENCE AND RISK FACTORS ASSOCIATED WITH GESTATIONAL DIABETES MELLITUS AMONG PREGNANT WOMEN IN EMOHUA LOCAL GOVERNMENT OF RIVERS STATE.
DOI:
https://doi.org/10.5281/zenodo.17224260Keywords:
Gestational Diabetes Mellitus, Prevalence, Risk Factors, Pregnant Women, Rivers StateAbstract
This study aimed to assess the prevalence and risk factors associated with gestational diabetes mellitus (GDM) among pregnant women in the Emohua local government of Rivers State. The study adopted a descriptive cross-sectional design. The study population comprised of pregnant women attending an antenatal clinic in the Emohua local government area. The sample size for this study was 400, and data were obtainedusing a simple random sampling method by balloting without replacement. The instrument for data collection was a proforma data collection form titled “Prevalence and Risk Factors associated with Gestational Diabetes among Pregnant women in Emohua Local Government, Rivers State”. The data collected from this study was collated and analyzed using the Statistical Products for Service Solution (SPSS), the descriptive statistics of mean and standard deviation was used to analyze the demographic data, age-adjusted incidence of GDM was calculated using logistic regression, and multivariate logistic regression analysis was used to identify risk factors associated with GDM. The results showed that the prevalence of gestational diabetes mellitus among pregnant women in the Emohua local government area of Rivers State was low (16.6%), but there was high relationship between age, obesity, family history of diabetes, polycystic ovarian syndrome, history of GDM, prior large-for-gestation age birth and gestational diabetes mellitus. The results also showed a significant relationship between age and GDM [f(1,289) = 289.57, p<0.05], obesity and GDM [f(1,289) = 194.66, p<0.05], family history of diabetes and GDM [f(1,289) = 201.92, p<0.05], polycystic ovarian syndrome and GDM [f(1,289) = 198.34, p<0.05], history of GDM and GDM [f(1,289) = 198.25, p<0.05] and between prior large-for-gestation age and GDM [f(1,289) = 247.94, p<0.05]. The government should support ongoing research to better understand the risk factors and prevalence of GDM, establish robust surveillance systems to monitor trends, and evaluate the impact of prevention strategies
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