Search

Background: Almost all (99%) neonatal deaths arise in low-income and middle-income countries. Approximately 450 new-born children die every hour, which is mainly from preventable causes. There has been increased recognition of the need for these countries to implement public health interventions that specifically target neonatal deaths. The purpose of this paper is to identify the predictors of neonatal death in Type 4 rural (poorest) counties in Shaanxi Province of northwestern China. Methods: A cross-sectional study was conducted in Shaanxi Province, China. A single-stage survey design was identified to estimate standard errors. Because of concern about the complex sample design, the data were analysed using multivariate logistic regression analysis. Socioeconomic and maternal health service utilization factors were added into the model. Results: During the study period, a total of 4750 women who delivered in the past three years were randomly selected for interview in the five counties. There were 4880 live births and 54 neonatal deaths identified. In the multiple logistic regression, the odds of neonatal death was significantly higher for multiparous women (OR = 2.77; 95% CI: 1.34, 5.70) and women who did not receive antennal health care in the first trimester of pregnancy (OR = 2.49; 95% CI: 1.41, 4.40). Women who gave birth in a county-level hospital (OR = 0.18; 95% CI: 0.04, 0.86) and had junior high school or higher education level (OR = 0.20; 95% CI: 0.05, 0.84) were significantly protected from neonatal death. Conclusions: Public health interventions directed at reducing neonatal death should address the socioeconomic factors and maternal health service utilization, which significantly influence neonatal mortality in rural China. Multipara, low educational level of the women, availability of prenatal visits in the first trimester of pregnancy and hospital delivery should be considered when planning the interventions to reduce the neonatal mortality in rural areas. [ABSTRACT FROM AUTHOR]

Background and Objectives: The effects of muscle meat and vegetable intake on body fat mass remain unclear in the general population. This study aimed to investigate the association of body fat mass and fat distribution with a muscle meat-vegetable intake (MMV) ratio. Methods and Study Design: In total, 29,271 participants aged 18–80 years were recruited from the Shaanxi cohort of the Regional Ethnic Cohort Study in Northwest China. The associations of muscle meat, vegetable and MMV ratio, as the independent variable, with body mass index (BMI), waist circumference, total body fat percentage (TBF) and visceral fat (VF), as dependent variables were evaluated by gender-specific linear regression models. Results: There was 47.9% of men whose MMV ratio was greater than or equal to 1 and this figure was about 35.7% for women. For men, higher muscle meat intake was associated with higher TBF (standardized coefficient [ß], 0.508; 95% CI, 0.187–0.829), higher vegetable intake was associated with lower VF (ß, -0.109; 95% CI, -0.206 – -0.011), and higher MMV ratio was associated with higher BMI (ß, 0.195; 95% CI, 0.039–0.350) and VF (ß, 0.523; 95% CI, 0.209–0.838). For women, both higher muscle meat consumption and MMV ratio were associated with all fat mass markers, but vegetable intake was not correlated with body fat mass markers. The positive association of MMV on body fat mass was more pronounced in higher MMV ratio group, with both men and women. The intake of pork, mutton and beef was associated positively with fat mass markers but no such association was observed for poultry or seafood. Conclusions: An increased intake of muscle meat or a higher MMV ratio was associated with increased body fat, especially among women, and such impact may mainly be attributed to increasing intake of pork, beef and mutton. The dietary MMV ratio could be thus a useful parameter for nutritional intervention. [ABSTRACT FROM AUTHOR]