A systematic approach to developing comparative health outcome indicators

Author(s):  
Alastair Mason ◽  
Andrew Garratt ◽  
Edel Daly ◽  
Michael Goldacre
2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S603-S603
Author(s):  
Huashuai Chen ◽  
Yi Zeng ◽  
Huashuai Chen ◽  
Yao Yao

Abstract This paper reviews and compares demographic, socioeconomic, behavioral (including diet) characteristics and heath phenotypes of centenarians in China and Italy. The results revealed that the interactions between familial longevity and any one of the three environmental factors (receipt of adequate medical care when ill as a child, number of living children, and household economic conditions) were significantly associated with the three health outcome indicators (IADL, self-rated life satisfaction, and anxiety-loneness) at old ages. We discovered that the effects of these environmental factors on the health outcome indicators were substantially stronger among elders who had no family history of longevity compared to centenarians’ children who likely carry genes and/or inherited healthy behavior and better lifestyle from long-lived parents.


2017 ◽  
Vol 33 (S1) ◽  
pp. 62-63
Author(s):  
Songul Cinaroglu ◽  
Onur Baser

INTRODUCTION:In Turkey, there is a scarcity of knowledge about the predictors of health outcomes at a national level, and it is well known that there is a gap between rural and urban parts of developing countries in terms of the level of health outcomes. This study aims to find out predictor factors of the public health outcomes at a province level in Turkey.METHODS:Life expectancy at birth and mortality are used as public health outcome indicators. Logistic regression and Random Forest classification generated by using 50, 100, and 150 trees were used to compare prediction performance of health outcomes. The results of different prediction methods were recorded changing the “k” parameter from 3 to 20 in k-fold cross validation. The Area Under the ROC Curve (AUC) was used as a measure of prediction accuracy. Prediction performance differences were tested using Kruskall-Wallis analysis and visualized on a heatmap. Finally, predictor variables of public health outcomes were shown on a decision tree.RESULTS:Study results revealed that Logistic regression outperformed Random Forest classification. The difference between all prediction methods to predict public health outcome indicators was statistically significant (p<.000). The heatmap shows that AUC values to predict mortality have superior performance when compared with life expectancy at birth. Decision tree graphs present that the most important predictor variables were total number of beds for mortality and percentage of higher education graduates for life expectancy at birth.CONCLUSIONS:The results of this study represent a preliminary attempt to determine public health outcome indicators. It is hoped that the results of this study serve as a basis to understand the determinants of health care outcomes at province level with focus on a developing country. This study illustrates that there is a need to spend extra effort for future studies to analyze public health outcomes to improve social welfare functions in health systems.


2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Ying-Ying Meng ◽  
Christina Lombardi ◽  
Michelle Wilhelm

1996 ◽  
Vol 20 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Mark A. Smith ◽  
Stephen R. Leeder ◽  
Bin Jalaludin ◽  
Wayne T. Smith

2016 ◽  
Vol 6 (6) ◽  
pp. 682-687
Author(s):  
Jinwon Cho ◽  
Young-Jin Jeong ◽  
Junyong Lee ◽  
Tae-Hee Jeon ◽  
Nayeon Moon ◽  
...  

1998 ◽  
Vol 7 (2) ◽  
pp. 90-97 ◽  
Author(s):  
A. McColl ◽  
P. Roderick ◽  
J. Gabbay ◽  
G. Ferris

2017 ◽  
Vol 2 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Lindsay Heggie ◽  
Lesly Wade-Woolley

Students with persistent reading difficulties are often especially challenged by multisyllabic words; they tend to have neither a systematic approach for reading these words nor the confidence to persevere (Archer, Gleason, & Vachon, 2003; Carlisle & Katz, 2006; Moats, 1998). This challenge is magnified by the fact that the vast majority of English words are multisyllabic and constitute an increasingly large proportion of the words in elementary school texts beginning as early as grade 3 (Hiebert, Martin, & Menon, 2005; Kerns et al., 2016). Multisyllabic words are more difficult to read simply because they are long, posing challenges for working memory capacity. In addition, syllable boundaries, word stress, vowel pronunciation ambiguities, less predictable grapheme-phoneme correspondences, and morphological complexity all contribute to long words' difficulty. Research suggests that explicit instruction in both syllabification and morphological knowledge improve poor readers' multisyllabic word reading accuracy; several examples of instructional programs involving one or both of these elements are provided.


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