EVALUATION OF RECURRENT HOSPITALIZATION PROBABILITY IN PATIENTS WITH DUODENAL AND GASTRIC ULCER DEPENDENT UPON REHABILITATION INPUT

Oleksandr Ocheredko, Natalia Kizlova

Abstract


Background: notoriously known worldwide cause of morbidity and disability duodenal (DU) and gastric ulcer (GU) experience their rise in Ukraine, demonstrating formidable increase by 38,4 % in last decade with the prevalence of 2299 per 100 000 population. Every second patient is treated in-patiently, every third experiences disability spell annually. Reduction in related risks confined not so much by absence of effective therapy but rather shortcomings in patient management and patient devotion. By WHO data 50 % of patients fail to follow physician prescriptions, 60 % can’t recollect physician recommendations in first 20 minutes. Ubiquitous belated timing of rehabilitation initiation in post hospital stage appeared to be cardinal obstacle of its efficiency with low (up to 20 %) coverage, and ensuring clinical effect in 8 % cases only.
Aim: to evaluate efficacy of rehabilitation program detailed at first episode of in-patient treatment at gastroenterological department.
Data: organized by cohort design. Control cohort comprised 180 patients with first episode of hospitalization due to DU or GU in gastroenterological Vinnitsa city department in 2009–2010 years. Experimental cohort consisted of 220 alike patients who enter rehabilitation program (RP). RP was administered randomly. Randomness was statistically verified on principal confounders. Cases were traced 4 years.
Methods: we applied three modifications of semi-parametric frailty model to study effect of program on the risk of recurrent hospitalization.
Results: all three modifications coincided in that program secured typically at least 39 days to recurrent hospitalization per patient with drop in risk at least at RR=0,774.


Keywords


rehabilitation; duodenal and gastric ulcer; cohort design; frailty model

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References


Duchateau, L., Janssen, P. (2008). The Frailty Model. Springer, NewYork.

Aalen, O. O., Hjort, N. L. (2002). Frailty models that yield proportional hazards. Statistics & Probability Letters, 58 (4), 335–342. doi: 10.1016/s0167-7152(02)00090-1

Ravishanker, N., Dey, D. (2000). Multivariate survival models with a mixture of positive stable frailties. Methodology and Computing in Applied Probability, 2 (3), 293–308. doi: 10.1023/a:1010033329399

XXIV International Workshop on Helicobacter and related bacteria in chronic digestive inflammation and gastric cancer (2011). Dublin, Ireland.

Aitkin, M., Clayton, D. (1980). The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using GLIM. Applied Statistics, 29 (2), 156. doi: 10.2307/2986301

Orbe, J., Núñez-Antón, V. (2006). Alternative approaches to study lifetime data under different scenarios: from the PH to the modified semiparametric AFT model. Computational Statistics & Data Analysis, 50 (6), 1565–1582. doi: 10.1016/j.csda.2005.01.010

Wooldridge, J. M. (2012). Control Function Methods in Econometrics. East Lansing, 45.

Wooldridge, J. M. (2004). Estimating average partial effects under conditional moment independence assumptions. Institute for fiscal studies, 38.

Therneau, T. M., Grambsch, P. M., Pankratz, V. S. (2003). Penalized Survival Models and Frailty. Journal of Computational and Graphical Statistics, 12 (1), 156–175. doi: 10.1198/1061860031365

Littell, R. C., George, A. M., Walter, W. S., Russell, D. W., Oliver, S. (2006). SAS® for Mixed Models. Cary, NC: SAS Institute Inc., 834.

Congdon, P. D. (2010). Applied Bayesian Hierarchical Methods. Chapman and Hall/CRC, 604. doi: 10.1201/9781584887218

Rosenbaum, P. R., Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70 (1), 41–55. doi: 10.1093/biomet/70.1.41

Lunn, D., Jackson, C., Best, N., Thomas, A., Spiegelhalter, D. (2012). The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman and Hall/CRC, 399.

Torsten, H., Brian, S. E. (2014). A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC Press, Boca Raton, Florida, USA.

Altman, D. G., Bland, J. M. (2009). Parametric v non-parametric methods for data analysis. BMJ, 338, a3167–a3167. doi: 10.1136/bmj.a3167

Ravishanker, N., Dey, D. (2015). Multivariate survival models with a mixture of positive stable frailties. Methodology and Computing in Applied Probability, 2, 293–308.

Rice, K. (2005). Bayesian measures of goodness of fit. Encyclopedia of biostatistics. Chichester: John Wiley, 523. doi: 10.1002/0470011815.b2a09005

Dumont, E., Fortin, B., Jacquemet, N., Shearer, B. (2008). Physicians’ multitasking and incentives: Empirical evidence from a natural experiment. Journal of Health Economics, 27 (6), 1436–1450. doi: 10.1016/j.jhealeco.2008.07.010

Fo Fortin, B., Lacroix, G., Drolet, S. (2004). Welfare benefits and the duration of welfare spells: evidence from a natural experiment in Canada. Journal of Public Economics, 88 (7-8), 1495–1520. doi: 10.1016/s0047-2727(02)00177-9

Damien, É., Bernard, F. (2011). Physician Payment Mechanisms, Hospital Length of Stay and Risk of Readmission: a Natural Experiment. Version 1.




DOI: http://dx.doi.org/10.21303/2504-5679.2016.00098

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