Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models

In this paper, multilevel logistic regression models are developed for examining the hierarchical effects of contraceptive use and its selected determinants in Oman using the 2008 Oman National Reproductive Health Survey (ONRHS). Comparison between single level and multilevel logistic regression models has been made to examine the plausibility of multilevel effects of contraceptive use. From the multilevel logistic regression model analysis, it was found that there is real multilevel variation among contraceptive users in Oman. The results indicate that a multilevel logistic regression model is the best fit over ordinary multiple logistic regression models. Generally, this study revealed that women’s age, education, number of living children and region of residence are important factors that affect contraceptive use in Oman. The effect of regional variation for age of women, education of women and number of living children further implies that there exists considerable differences in modern contraceptive use among regions, and a model with a random coefficient or slope is more appropriate to explain the regional variation than a model with fixed coefficients or without random effects. The study suggests that researchers should use multilevel models rather than traditional regression methods when their data structure is hierarchal.

Download Full-text

The Association of Sleep Duration With Vision Impairment in Middle-Aged and Elderly Adults: Evidence From the China Health and Retirement Longitudinal Study

Frontiers in Medicine ◽

10.3389/fmed.2021.778117 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mengsha Sun ◽

Qiyu Bo ◽

Bing Lu ◽

Xiaodong Sun ◽

Minwen Zhou

Keyword(s):

Logistic Regression ◽

Longitudinal Study ◽

Sleep Duration ◽

Regression Models ◽

Vision Impairment ◽

Multilevel Logistic Regression ◽

Elderly Adults ◽

Middle Aged ◽

Logistic Regression Models ◽

Long Sleep

Objective: This study aims to investigate the association of sleep duration with vision impairment (VI) in middle-aged and elderly adults.Methods: This cross-sectional study used the data from the baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) 2011–2012, a national survey of adults aged 45 years or older. Weighted multilevel logistic regression models were used to evaluate the association between self-reported sleep duration and VI.Results: Of the 13,959 survey respondents, a total of 4,776 (34.2%) reported VI. The prevalence of short (≤6 h/night) and long (>8 h/night) sleep durations was higher among respondents with VI than those without VI (P < 0.001). Multilevel logistic regression models showed that compared with a sleep duration of 6–8 h/night, a sleep duration of ≤6 h/night was associated with a 1.45-fold [95% confidence interval (CI) = 1.34–1.56] higher VI risk, and a sleep duration of >8 h/night was associated with a 1.18-fold (95% CI = 1.03–1.34) higher VI risk, after adjusting for sociodemographic data, lifestyle factors, and health conditions. Vision impairment was associated with short sleep duration in respondents from all age or gender categories. However, VI was associated with long sleep duration in respondents from the elderly or female categories. The association between VI and long sleep duration disappeared in respondents of middle-aged or male categories.Conclusions: The potential impact of sleep on the risk of visual functions requires further attention. A more comprehensive and integrated health care and rehabilitation system covering vision and sleep is also needed.

Download Full-text

Integral approximations for computing optimum designs in random effects logistic regression models

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2012.05.024 ◽

2014 ◽

Vol 71 ◽

pp. 1208-1220 ◽

Cited By ~ 5

Author(s):

C. Tommasi ◽

J.M. Rodríguez-Díaz ◽

M.T. Santos-Martín

Keyword(s):

Logistic Regression ◽

Random Effects ◽

Regression Models ◽

Logistic Regression Models

Download Full-text

Do Deviations From Historical Precipitation Trends Influence Child Nutrition? An Analysis From Uganda

American Journal of Epidemiology ◽

10.1093/aje/kwz179 ◽

2019 ◽

Vol 188 (11) ◽

pp. 1953-1960 ◽

Cited By ~ 2

Author(s):

Adrienne Epstein ◽

Jacqueline M Torres ◽

M Maria Glymour ◽

David López-Carr ◽

Sheri D Weiser

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Child Nutrition ◽

Multilevel Logistic Regression ◽

Panel Survey ◽

Logistic Regression Models ◽

Child Nutritional Status ◽

Marginal Risk ◽

Precipitation Trends

Abstract Changes in precipitation patterns might have deleterious effects on population health. We used data from the Uganda National Panel Survey from 2009 to 2012 (n = 3,223 children contributing 5,013 assessments) to evaluate the link between rainfall and undernutrition in children under age 5 years. We considered 3 outcomes (underweight, wasting, and stunting) and measured precipitation using household-reported drought and deviations from long-term precipitation trends measured by satellite. We specified multilevel logistic regression models with random effects for the community, village, and individual. Underweight (13%), wasting (4%), and stunting (33%) were common. Reported drought was associated with underweight (marginal risk ratio (RR) = 1.18, 95% confidence interval (CI): 1.04, 1.35) in adjusted analyses. Positive annual deviations (greater rainfall) from long-term precipitation trends were protective against underweight (marginal RR per 50-mm increase = 0.94, 95% CI: 0.92, 0.97) and wasting (marginal RR per 50-mm increase = 0.93, 95% CI: 0.87, 0.98) but not stunting (marginal RR per 50-mm increase = 1.00, 95% CI: 0.98, 1.01). Precipitation was associated with measures of acute but not chronic malnutrition using both objective and subjective measures of exposure. Sudden reductions in rainfall are likely to have acute adverse effects on child nutritional status.

Download Full-text