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Factors associated with perinatal mortality in Nepal: evidence from Nepal demographic and health survey 2001-2016.

ABSTRACT: BACKGROUND:Perinatal mortality is a devastating pregnancy outcome affecting millions of families in many low and middle-income countries including Nepal. This paper examined the more distant factors associated with perinatal mortality in Nepal. METHODS:A sample of 23,335 pregnancies >?28?weeks' gestation from the Nepal Demographic and Health Survey datasets for the period (2001-2016) was analysed. Perinatal Mortality (PM) is defined as the sum of stillbirth (fetal deaths in pregnancies >?28?weeks' gestation) and early neonatal mortality (deaths within the first week of life), while Extended Perinatal Mortality (EPM) is denoted as the sum of stillbirth and neonatal mortality (deaths within the first 28?days of life). Rates of PM and EPM were calculated. Logistic regression generalized linear latent and mixed models (GLLAMM) that adjusted for clustering and sampling weight was used to examine the factor associated with perinatal mortality. RESULTS:Over the study period, the PMR was 42 [95% Confidence Interval (CI): 39, 44] per 1000 births for the five-year before each survey; while corresponding EPMR was 49 (95% CI, 46, 51) per 1000 births. Multivariable analyses revealed that women residing in the mountains, who did not use contraceptives, women aged 15-18?years or 19-24?years, and women having no education were associated with increased PM and EPM. The study also identified households using biomass as cooking fuel, and households who reported unimproved sanitation or open defecation were significantly more likely to experience PM and EPM. CONCLUSIONS:Interventions aimed to improve use of contraceptives, and reduce biomass as a source of cooking fuel are needed to achieve the recommended target of

SUBMITTER: Ghimire PR

PROVIDER: S-EPMC6417106 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe perinatal mortality rate in Ethiopia is among the highest in Sub Saharan Africa. The aim of this study was to identify the spatial patterns and determinants of perinatal mortality in the country using a national representative 2016 Ethiopia Demographic and Health Survey (EDHS) data.MethodsThe analysis was completed utilizing data from 2016 Ethiopian Demographic and Health Survey. This data captured the information of 5 years preceding the survey period. A total of 7230 women who at delivered at seven or more months gestational age nested within 622 enumeration areas (EAs) were used. Statistical analysis was performed by using STATA version 14.1, by considering the hierarchical nature of the data. Multilevel logistic regression models were fitted to identify community and individual-level factors associated with perinatal mortality. ArcGIS version 10.1 was used for spatial analysis. Moran's, I statistics fitted to identify global autocorrelation and local autocorrelation was identified using SatSCan version 9.6.ResultsThe spatial distribution of perinatal mortality in Ethiopia revealed a clustering pattern. The global Moran's I value was 0.047 with p-value <0.001. Perinatal mortality was positively associated with the maternal age, being from rural residence, history of terminating a pregnancy, and place of delivery, while negatively associated with partners' educational level, higher wealth index, longer birth interval, female being head of household and the number of antenatal care (ANC) follow up.ConclusionsIn Ethiopia, the perinatal mortality is high and had spatial variations across the country. Strengthening partner's education, family planning for longer birth interval, ANC, and delivery services are essential to reduce perinatal mortality and achieve sustainable development goals in Ethiopia. Disparities in perinatal mortality rates should be addressed alongside efforts to address inequities in maternal and neonatal healthcare services all over the country.

Project description:IntroductionUnderstanding socio-economic correlates and inequality of underweight and overweight is crucial to develop interventions to prevent adverse health outcomes.Materials and methodsWe analysed Nepal Demographic and Health Survey 2016 data from 6,069 women aged 15-49 years. WHO cut-offs for Body Mass Index categorised as: underweight (<18.5 kg/m2), normal weight (18.5kg/m2 to 24.9kg/m2) and overweight/ obesity (> = 25.0 kg/m2) were used. We used multinomial logistic regression to explore associations of factors with Body Mass Index and concentration indices to estimate socio-economic inequalities.ResultsHigher risk of underweight was found in age group 15-19 (RRR 3.08, 95% CI: 2.29-4.15), 20-29 (RRR 1.64, 95% CI: 1.29-2.08) and in lowest (RRR 1.60, 95% CI: 1.03-2.47) and second wealth quintiles (RRR 1.77 (95% CI: 1.18-2.64). Education, occupation, urban/rural residence and food security were not associated with underweight (p>0.05). Lower risk of overweight/obesity was found in age group 15-19 (RRR 0.07, 95% CI: 0.05-0.10), 20-29 (RRR 0.40, 95% CI: 0.32-0.51), in manual occupation (RRR 0.58, 95% CI: 0.46-0.74) and in lower quintiles. Women with primary (RRR 1.91, 95% CI: 1.36-2.67), secondary education (RRR 1.42, 95% CI 1.00, 2.01) were at increased risk of overweight/obesity. Household food security and urban/rural residence were not associated with overweight/obesity (p>0.05). Socioeconomic inequalities were detected, with overweight/obesity strongly concentrated (concentration index: 0.380) amongst the higher quintiles and underweight concentrated (concentration index: -0.052) amongst the poorest.ConclusionNutrition programmes should target younger and poor women to address undernutrition and higher wealth group women to address overnutrition. Equity based nutrition interventions improving socio-economic status of poor households may benefit undernourished women. Interventions to encourage physical activity as women age and among wealthier women as well as healthy eating for prevention of under- and over-nutrition are needed.

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Factors associated with perinatal mortality in Nepal: evidence from Nepal demographic and health survey 2001-2016.

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