ปัจจัยที่มีผลต่อสัมฤทธิ์ทางการเรียนของนักศึกษาระดับปริญญาตรี

Master of Science (Research Methodology

Factors associated with low birth weight in Nepal using multiple imputation

Abstract Background Survey data from low income countries on birth weight usually pose a persistent problem. The studies conducted on birth weight have acknowledged missing data on birth weight, but they are not included in the analysis. Furthermore, other missing data presented on determinants of birth weight are not addressed. Thus, this study tries to identify determinants that are associated with low birth weight (LBW) using multiple imputation to handle missing data on birth weight and its determinants. Methods The child dataset from Nepal Demographic and Health Survey (NDHS), 2011 was utilized in this study. A total of 5,240 children were born between 2006 and 2011, out of which 87% had at least one measured variable missing and 21% had no recorded birth weight. All the analyses were carried out in R version 3.1.3. Transform-then impute method was applied to check for interaction between explanatory variables and imputed missing data. Survey package was applied to each imputed dataset to account for survey design and sampling method. Survey logistic regression was applied to identify the determinants associated with LBW. Results The prevalence of LBW was 15.4% after imputation. Women with the highest autonomy on their own health compared to those with health decisions involving husband or others (adjusted odds ratio (OR) 1.87, 95% confidence interval (95% CI) = 1.31, 2.67), and husband and women together (adjusted OR 1.57, 95% CI = 1.05, 2.35) were less likely to give birth to LBW infants. Mothers using highly polluting cooking fuels (adjusted OR 1.49, 95% CI = 1.03, 2.22) were more likely to give birth to LBW infants than mothers using non-polluting cooking fuels. Conclusion The findings of this study suggested that obtaining the prevalence of LBW from only the sample of measured birth weight and ignoring missing data results in underestimation

Pregnancy loss in the Philippines

In this cross-sectional study, 8,481 women aged 15-49 who had at least one pregnancy outcome were considered. This study aimed to examine the characteristics of Filipino women having had a pregnancy loss, and to test the association between domestic violence and pregnancy loss. To control for the confounding effect of the number of pregnancies, the sample was divided into seven groups classified by the number of pregnancies. The risk factors considered were demographic characters (age and partner's age, marital status, and place of residence), socioeconomic status (education and partner's education, having a paid helper at home, having a say in how income was spent), domestic violence (physical abuse and forced sex), sexual behavior of partner, whether the pregnancy was wanted, and disease history (tuberculosis, diabetes, hypertension, malaria, hepatitis, kidney disease, heart disease, anemia, goiter and other medical problems). The major risk factors were found to be physical abuse, region, faithfulness of partners, hypertension, hepatitis, kidney disease, anemia, and the other medical problems, respectively. The risk of pregnancy loss for the women suffering domestic violence was 1.59 (95% CI 1.28-1.97) times higher than for the women who did not. Women aged 15-19 years had a much higher risk of pregnancy loss than the other age groups (OR = 1.49, 95% CI 1.22-1.82). There were similar risk for women aged 20-24 years (OR = 1.08, 95% CI 0.94-1.25) and 35-39 years (OR = 1.05, 95% CI 0.92-1.19). No association emerged with marital status, socioeconomic status, forced sex, the number of partners, unwanted pregnancy, tuberculosis, diabetes, malaria, heart disease, and goiter. Although women's age, partner's age, residence, women's education, partner's education, and paid helper at home were significantly associated with pregnancy loss, they were likely to be confounders rather than risk factors.10 page(s

Malaria in North - Western Thailand

This study is based on the individual hospital case records of malaria routinely reported from 1999 to 2004 in the North-western area of Thailand, which included Mae Hong Son and Tak provinces. The objective of this study was to model the patterns of hospital-diagnosed malaria incidences by month, district and age-group for the two North-western border provinces in Thailand. The model used linear regression, Poisson regression and negative binomial regression to forecast the districts and age groups in which epidemics are likely to occur in the near future in order to prevent the disease by using suitable measures. Among the models fitted, the best were chosen based on the analysis of deviance and the negative binomial generalized linear model was clearly preferable. The model contains additive effects associated with the season of the year, district, age group and the malaria incidence rates in previous months, and can be used to provide useful short-term forecasts. Having a model that provides such forecasts of disease outbreaks, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention

Physical Abuse during Pregnancy

5 page(s

Analysis of daily rainfall during 2001-2012 in Thailand

The presented study aims to classify precipitation regions, analyze trends, and fit an appropriate model for daily precipitation in Thailand. Factor analysis and generalized linear model (GLM) with gamma regression are performed on the historical records of daily rainfall amounts from 114 weather stations during 2001 to 2012. The study shows that the factor analysis divides the area of Thailand into seven regions with explanation of 58.9% of the total variance. The conducted gamma models reveal a good fit for the upper part, south-east, and south-west of the examined regions. The deviance residual plots from these models also provide a reasonable fit

The Southern Oscillation Index as a random walk

The Southern Oscillation Index (SOI) has been used as a predictor of variables associated with climatic data, such as rainfall and temperature, and is related to the El Nino and La Nina phenomena, also called the El Nino Southern Oscillation (ENSO). The present study aims to describe the characteristics of the SOI between 1876 and 2014 using statistical methods. The graph of the cumulative monthly SOI in the period 1876 - 2014 shows that the data can be divided into 4 periods. The first period, from 1876 to 1919, shows no trend. An increasing trend is apparent in the second period from 1920 until 1975, while a decreasing trend is apparent in the third period, 1976 to 1995. In the last period, between 1996 and 2014, the SOI appears fairly stable. In order to investigate those trends, the linear regression and autoregressive (AR) model have been fitted. For the linear regression model, the outcome, SOI, is regressed against boxcar function, where the functions model the trends of the SOI. An autoregressive process is used to account for serial correlation in the residuals. The conclusion is that the SOI is quite similar to a random noise process.11 page(s