Reference values for Blood Pressure of healthy schoolchildren in Shiraz [Southern Iran] using Quantile Regression

The Reference values of systolic and diastolic Blood Pressure [BP] levels of school children aged 6-11 years by two different analytic strategies are presented and compared. From the cross-sectional study a total of 2064 children [52.3% boys and 47.7% girls] aged 6-11 years living in Shiraz [southern Iran] and considering their sex and height were used for this analysis. Polynomial Regression [PR] and Quantile Regression [QR] models based on Restricted Cubic Spline [RCS] were performed to calculate age and height specific reference ranges. To assess comparability of the two techniques, a chi-square goodness-of-fit within sex and age groups was preformed for each method. Both statistical methods generated reference values of systolic and diastolic BP using data from apparently healthy children. Analysis of data by two approaches reflected an increase in BP measurements with age and height in both sexes based on a nonlinear manner up to age 11. We found 50th and 95th percentile differences by two methods in BP level between the tallest and the shortest individuals, ranging from 2-7 mmHg. Using the QR model based on RCS offered the most flexible and better fit than PR model. The advantages of the QR led to a better adaptation of reference limits to the original data. This statistical approach might be preferable for the calculation of reference ranges in particular by non-normal distributed variables. Our results might help clinicians reach a consensus on the definition of hypertension in Iranian children living in Shiraz, south of Iran

[Bed occupancy rate modeling in Shiraz hospitals]

The Bed Occupancy Rate [BOR] among Iranian hospitals was reported to be 57.4% in 2001 indicative of a significance difference with standard index of 75%. To evaluate the bed occupancy rate modeling in Shiraz hospitals. This was a cross-sectional study carried out on random samples from different hospitals in Shiraz during the first half of 2004. The Poisson models including the negative binomials and mixture Poisson were used for BOR modeling. Hierarchical mixture Poisson, negative binomial and hierarchical two-component normal distribution were applied for the role of each hospital ward. Our findings showed that the average BOR was 66.4% in Shiraz hospitals. Regarding the relative hemogenity in observation of BOR in different hospitals, the hierarchical mixture Poisson and negative binomial models failed to accurately explain this variable and it was the hierarchical mixture two-component normal model which gave an accurate explanation for BOR found in our study. In this model, one of the components indicates the BOR observation close to mean and the other represents the outlier observations. The hierarchical modeling in which a hospital ward acts as a random component was shown to lead to the highest value for BOR

[ comparison of statistical modeling strategies for analyzing length of stay in Shiraz education hospitals]

The modeling of inpatient length of stay [LOS] has important implications in various aspects of health care management, such as hospital management and planning, the management of health care resources and the performance in hospital care. For many health care management systems, it is important to develop a comprehensive analysis of LOS and to identify hospital- and patient-related characteristics influencing LOS variations. By targeting relevant factors, appropriate policies can be developed to manage the hospital care and the health care resources effectively. Except a specific and limited study, a few studies have carried out on LOS modeling in Iran. This study has been carried out to compare different models for explaining LOS. A sample of 3546 patients was selected using randomized clustered sampling scheme from different wards of Shiraz educational hospitals in 2005 and their hospitalization [LOS] rate registered as response variable and other demographic variables. Advance statistical models such as Poisson regression, ZTP, ZTNB and percentile regression were applied to analyze and modeling the data. Mean +/- S.D of LOS were calculated as 8.4 +/- 13.7days. Over-dispersion for LOS was observed. Therefore, negative binomial, gamma, zero truncated Poisson [ZTP], zero truncated negative binomial [ZTNB] and percentile regression were applied. The results showed that ZTNB is an efficient model for explaining LOS in Shiraz's hospitals. Given heterogeneity in LOS in different wards in Shiraz hospitals, negative binomial model explains adequately the LOS values. Also, percentile regression model seems appropriate for LOS which was applied to our data. Application of the cited models here to other hospitals scattered around the country is suggested

[ effect of missing data in growth curves]

Background and Objective: Applying the growth curve is the most powerful way for monitoring the growth in children and through this method it would be possible to recognize in time the deviation from the natural growth pattern in children. Falling the data and missing values are general problems in analyzing the growth longitudinal data. Therefore, it is important that by computing the missing values, the data should be completed and directed towards the proper path for analysis

Methods: This 2 year longitudinal study was done on 317 infants [153 boys and 164 girls] in Shiraz during 1996. The information related to growth [Weight, height, round the head, round the arm, and round the chest] at the birth time were collected and 11 visits from the infants' living houses were done. In order to influence the missing values on the growth charts, 4 methods [Ignoring the missing values, general and individual models of growth curve and multiple imputation] were considered to study. Mean, 3[rd], 50[th], 97[th] centiles of raw and smooth weight were computed in boys and the smooth growth charts of their weight were determined and compared based on the four methods

Findings: There was no noticeable difference in the boys' mean weight at age under study according to growth curve methods and multiple imputation while missing values were ignored. However, the smooth growth charts showed that applying the individual growth curve model [Second level] and multiple imputation causes the noticeable difference between the values of 3[rd], 97[th] centiles and the traditional analysis [Ignoring missing values]

Conclusion: Regarding the existence of missing values in growth longitudinal studies, ignoring the missing values for analyzing is not acceptable. Applying the growth curve model method could be considered important in making desirable the analysis and the proper growth path