Estimating the Population Standard Deviation with Confidence Interval: A Simulation Study under Skewed and Symmetric Conditions
Pages 356-367
Shipra Banik, Ahmed N. Albatineh, Moustafa Omar Ahmed Abu-Shawiesh and B.M. Golam Kibria
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.04.4
Published: 06 November 2014

Abstract: This paper investigates the performance of ten methods for constructing a confidence interval estimator for the population standard deviation by a simulation study. Since a theoretical comparison among the interval estimators is not possible, a simulation study has been conducted to compare the performance of the selected interval estimators. Data were randomly generated from several distributions with a range of sample sizes. Various evaluation criterions are considered for performance comparison. Two health related data have been analyzed to illustrate the application of the proposed confidence intervals. Based on simulation results, some intervals with the best performance have been recommended for practitioners.

Determinants of Wasting Among Under-Five Children in Ethiopia: (A Multilevel Logistic Regression Model Approach)
Pages 368-377
Gutu Adugna Dabale and M.K Sharma
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.04.5
Published: 06 November 2014

Abstract: Child malnutrition in Ethiopia is one of the most serious public health problems and the highest in the world. Wasting refers to low weight-for-height and measures the body’s mass in relation to body length. The objective of this study was to identify determinants of wasting among under-five children in Ethiopia. The study used data collected in the Ethiopian Demographic and Health Survey in 2010/2011. A total of 9611 under-five age children were included in the present study. To analyze the data descriptive statistics and multilevel binary logistic regression techniques were employed. The descriptive statistics results indicate that about 11.7 % of under-five children in Ethiopia were wasted. The results of study indicated that the risk of wasting was highest among male children, small size at birth, children whose parents resided in rural areas, children’s of illiterate mothers, children whose mother’s body mass index was low, children from poor families and children who had diarrhea and fever two weeks before the date of the survey. The multilevel model also showed the existence of significant variations in the prevalence of wasting among the regions in Ethiopia.

Imputation of Missing Data for a Continuous Variable with an Ordinal form of Risk Function: When to Apply the Transformation?
Pages 378-383
Mohammad Reza Baneshi, Behshid Garrusi and Saiedeh Haji-Maghsoudi
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.04.6
Published: 06 November 2014

Abstract: Introduction:Imputation of missing data and selection of appropriate risk function are of importance . Sometimes a variable with continuous nature will be offered to the regression model as an ordinal variable. Our aim is to investigate whether to offer the continuous form of the variable to the imputation phase and its ordinal from to the modeling phase, or whether to offer the ordinal version to both phases.

Material and Methods:The outcome and main variable of interest was use of diet as a body change approach, and Body Mass Index (BMI). We randomly deleted 10%, 20%, and 40% of BMI values. In strategies 1 and 2, BMI was offered to the imputation phase as a continuous (BMIC) and ordinal variable (BMIO). Missing data were imputed using linear and polytomous regression respectively. In strategy 1, after imputation, BMIC was categorized (named BMICO) and offered to the modeling phase. In strategy 2, after imputation of BMIO values, this variable was offered to the logistic model (named BMIOO). We compared two strategies at Event Per Variables (EPV) of 75, 10, and 5.

Result:At EPVs of 75 and 10 no remarkable difference was seen. However, at EPV of 5, strategy 2 was superior. At 20% and 40% missing rates, strategy 1 was 2.21 and 3.67 times more likely to produce Severe Relative Bias. At high missing rate, power was higher in strategy2 (90% versus 83%).

Conclusions:When EPV is low and missing rate is high, categorizing of variable before imputation of missing data produces less SRB and leads to higher power.

Avoiding Inferential Errors in Public Health Research: The Statistical Modelling of Physical Activity Behavior
Pages 384-391
Ann O. Amuta and Dudley Poston Jr
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.04.7
Published: 06 November 2014

Abstract:  Background: A review of the health behavior literature on the statistical modeling of days of physical activity (PA) indicates that in many instances linear regression models have been used. It is inappropriate statistically to model a count dependent variable such as days of physical activity with Ordinary Least Squares (OLS). Many count variables have skewed distributions, and, also, have a preponderance of zeroes. Count variables should not be treated as continuous and unbounded. If OLS is used, estimations of the regression will frequently turn out to be inefficient, inconsistent and biased, and such outcomes could well have incorrect impacts on health programs and policies.

Methods: We considered three statistical methods for modelling the distribution of days of PA data for respondents in the 2013 Health Information Trends Survey (HINTS). The three regression models analyzed were: Ordinary Least Squares (OLS), Negative Binomial (NBRM), and Zero-inflated Negative Binomial (ZINB). We used the exact same predictor variables in the three models. Our results illustrate the differences in the results.

Results: Our analyses of the PA data demonstrated that the ZINB model fits the observed PA data better than either the OLS or the NBRM models. The coefficients and standard errors differed in the zero-inflated count models from the other models. For instance, the ZINB coefficient for the association between income and PA behavior was not statistically significant (p>0.05), whereas in the NBRM and in the OLS models, it was statistically significant (p<0.05).

Conclusions: The inappropriate use of regression models could well lead to wrong statistical inferences. Our analyses of the number of days of moderate PA demonstrated that the ZINB count model fits the observed PA data much better than the OLS model and the NBRM.

LQAS in Health Monitoring – Insights from a Bayesian Perspective
Pages 392-403
David KwamenaMensah and Paul Hewson
DOI: http://dx.doi.org/10.6000/1929-6029.2014.03.04.8
Published: 06 November 2014

Abstract: Lot Quality Assurance Sampling (LQAS) is strongly advocated for use in monitoring the health status of populations, largely in the developing world. It is advocated both for the monitoring of small areas as well as for making global assessments of the health status of a larger region. This paper contrasts the interpretation offered by LQAS methods to that offered by Bayesian hierarchical models. It considers applications to previously reported local area data and presents a reanalysis of published data on vaccine coverage in Peru as well as HTLV-1 prevalence in Benin. The desirability of using Bayesian methods in the field may be challenged; nevertheless this work amplifies previously expressed concerns about the way the LQAS method can be used. It raises questions about the ability of the LQAS approach to make, sufficiently often, the correct decisions in order to be useful in monitoring health programmes at the local level.