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Dissertation

Development of an alcohol intervention model for predicting healthcare costs, life years, quality-adjusted life years and using for economic evaluation

TL;DR: An alcohol intervention model that predicts life years, quality adjusted life years (QALYs), and healthcare costs classified by the Alcohol Use Disorder Identification Test (AUDIT) screening tool and other various risk factors related to alcohol consumption was developed and transferred to the Thai setting.
Abstract: Objectives To develop an alcohol intervention model that predicts life years (LYs), quality adjusted life years (QALYs), and healthcare costs classified by the Alcohol Use Disorder Identification Test (AUDIT) screening tool and other various risk factors related to alcohol consumption. Furthermore, the developed model was transferred to the Thai setting. Methods Eight Scottish Health Surveys from 1995-2012 were linked to Scottish morbidity records and death records for the period 1981 to the end of 2013. Parametric survival analysis was used to estimate the hazard risks of first alcohol-related and non-alcohol related hospitalisations and deaths. For men and women, multivariate data analyses were applied separately for each gender in modelling the utility score, risks of subsequent hospitalisation and annual healthcare costs within the follow-up period. Risk profiles were used for the covariates of the models as follows: age, socio-economic status, health condition, alcohol drinking (i.e. AUDIT and binge drinking), smoking, body mass index, and physical activity. According to the under-reporting bias of alcohol consumption among the survey population, this study adjusted the reported alcohol consumption using alcohol sales data. Multiple imputation approach was applied to deal with missing data. A health-state transition model with annual cycle length was developed to predict LYs, QALYs, lifetime costs, and cost-effectiveness. Probabilistic sensitivity analysis was also performed to deal with parameter uncertainty. Moreover, a methodological transferability protocol of the Thai study was detailed. Results The sample size of the cohort was 46,230. The developed model showed the association between drinking and alcohol-related and non-alcohol related hospitalisations and deaths which were calculated as LYs and QALYs. Other risk factors were also taken into account that would likely affect the outcomes of interest. The modelling showed that an increasing AUDIT score and the number of cigarettes per day were associated with an increased risk of first alcohol-attributable hospitalisation. Predicted outcomes for a male aged 30 year with high-risk drinking levels (AUDIT >7) were worse than males with low risk drinking (AUDIT ≤7), with approximately 5 LY gained and 7 QALY gained. The same results for females were obtained for high-risk drinking (AUDIT >4) compared to low-risk drinking (AUDIT ≤4), with approximately 10 LY gained and 12 QALY gained. Furthermore, an economic evaluation was performed to compare the no-intervention situation with a hypothetical health promotion intervention - which aimed to stop drinking (measured by the AUDIT) and smoking (measured by the number of cigarettes per day) behaviours. To compare the costs and benefits of the hypothetical intervention and no intervention over the lifetime period, a within-trial analysis combined with the developed model was able to capture both short- and longer-term consequences (i.e. LYs, QALYs, and healthcare costs) of the intervention. Finally, the model was able to compare cost-effectiveness ratio between risk behaviours without the new intervention and the modified risk behaviours when the new intervention is implemented. Conclusions The study highlights the potential and importance of developing health economic models utilising data from routine national health surveys linked to national hospitalisation and death records. The developed framework can be used for further economic evaluation of alcohol interventions and other health behaviour change interventions. The framework can further be transferred to other country settings.
Citations
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Journal ArticleDOI
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

3,216 citations

01 Jan 2016
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Abstract: Thank you for downloading applied methods of cost effectiveness analysis in healthcare. As you may know, people have search hundreds times for their favorite books like this applied methods of cost effectiveness analysis in healthcare, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their laptop.

186 citations

25 Sep 2018

1 citations

References
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Book ChapterDOI
24 Aug 2005
TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.

4,905 citations

Journal ArticleDOI
TL;DR: The MAST responses of 15 subjects who were found to be alcoholic in the record search were analyzed to determine where the screening failures had occurred and recommendations are made for reducing the number of such "falsė negatives."
Abstract: The Michigan Alcoholism Screening Test (MAST), devised to provide a consistent, quantifiable, structured interview instrument to detect alcoholism, consists of 25 questions that can be rapidly administered. Five groups were given the MAST: hospitalized alcoholics, a control group, persons convicted of drunk driving, persons convicted of drunk and disorderly behavior, and drivers whose licenses were under review. The validity of the MAST was assessed by searching the records of legal, social, and medical agencies and reviewing the subjects' driving and criminal records. The MAST responses of 15 subjects who were found to be alcoholic in the record search were analyzed to determine where the screening failures had occurred. Recommendations are made for reducing the number of such "falsė negatives."

3,422 citations


"Development of an alcohol intervent..." refers methods in this paper

  • ...…Diagnostic Interview (CIDI) (Kessler and Ustun, 2004), the Munich Alcoholism Test (MALT) (Feuerlein et al., 1977), the Severity of Alcohol Dependence Questionnaire (SADQ) (Stockwell et al., 1983) or the Michigan Alcoholism Screening Test Chapter 1: Introduction 23 23 (MAST) (Selzer, 1971)....

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  • ...Signature Printed name Pattara Leelahavarong, 2018 xxii xx ii Abbreviations AAF Alcohol-attributable fraction A&E Accident and emergency AUD Alcohol-use disorder AUDIT Alcohol Use Disorder Identification Test BMI Body mass index BOD Burden of Disease CBA Cost-benefit analysis CCA Cost-consequence analysis CEA Cost-effectiveness analysis CEAC Cost effectiveness acceptability curve CI Confidence interval CIDI Composite International Diagnostic Interview CMA Cost-minimization analysis CUA Cost-utility analysis CVD Cardiovascular disease DALY Disability-adjusted life years eDRIS electronic Data Research and Innovation Service EM Emergency EQ-5D EuroQol 5D GDP Gross domestic product GLM Generalised linear model HRQoL Health related quality of life HTA Health technology assessment ICD International Classification of Diseases ICER Incremental cost-effectiveness ratio ISD Information Services Division Scotland LMIC Low and middle-income countries MALT Munich Alcoholism Test MAST Michigan Alcoholism Screening Test xxiii xx iii MCDM Multi-criteria decision-making NHS National Health Service NICE National Institute of Health and Clinical Excellence NMB Net monetary benefit NRS National Record Scotland NSS National Services Scotland NSSEC National Statistics Socio-economic Classification1 OLS Ordinary least squares PSA Probabilistic sensitivity analysis QALY Quality-adjusted life years RCT Randomized control trial RMSE Root mean square error ROC Receiver operator characteristic curve SADQ Severity of Alcohol Dependence Questionnaire SAPM Sheffield Alcohol Policy Model SBI Screening and brief intervention SD Standard deviation SES Socioeconomic status SF-12 Short Form 12 SF-6D Short Form 6D SHeS Scottish Health Surveys SIMD Scottish Index of Multiple Deprivation2 SMR Scottish Morbidity Record WHO World Health Organisation YLD Years of life lived with disability YLL Years of life lost 1 The National Statistics Socio-economic Classification (NSSEC) is a social classification system that attempts to classify groups on the basis of employment relations, based on characteristics such as career prospects, autonomy, mode of payment, and period of notice....

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  • ...Each setting selected their own validated questionnaire for the alcohol consumption population survey such as the Composite International Diagnostic Interview (CIDI) (Kessler and Ustun, 2004), the Munich Alcoholism Test (MALT) (Feuerlein et al., 1977), the Severity of Alcohol Dependence Questionnaire (SADQ) (Stockwell et al., 1983) or the Michigan Alcoholism Screening Test Chapter 1: Introduction 23 23 (MAST) (Selzer, 1971)....

    [...]

Journal ArticleDOI
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

3,216 citations


"Development of an alcohol intervent..." refers methods in this paper

  • ...Once 100 imputation data sets are created and analysed, the results are combined using standard rules (Rubin, 1987)....

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  • ...Once 100 imputed complete data sets were created and analysed, the results were combined using standard rules (Rubin, 1987)....

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Journal ArticleDOI
TL;DR: This tutorial aims to review statistical methods for the analysis of competing risks and multi-state models, with the emphasis on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities.
Abstract: Standard survival data measure the time span from some time origin until the occurrence of one type of event. If several types of events occur, a model describing progression to each of these competing risks is needed. Multi-state models generalize competing risks models by also describing transitions to intermediate events. Methods to analyze such models have been developed over the last two decades. Fortunately, most of the analyzes can be performed within the standard statistical packages, but may require some extra effort with respect to data preparation and programming. This tutorial aims to review statistical methods for the analysis of competing risks and multi-state models. Although some conceptual issues are covered, the emphasis is on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities. Examples of analysis with standard software are shown.

1,881 citations


Additional excerpts

  • ...3 4 8 76 "brief intervention".tw. (2179) 77 "motivational interviewing".tw. (2759) 78 "motivational enhancement therapy"....

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  • ...3 4 8 76 "brief intervention".tw. (2179) 77 "motivational interviewing"....

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