Vrednotenje stroškovne učinkovitosti posegov v zdravstvu : primer na presejanju za rakom dojke

Rojnik, Klemen (avtor), Mrhar, Aleš (mentor), Primic-Žakelj, Maja (komentor)

Predstavljena je uporaba ekonomskega vrednotenja pri razporejanju sredstev v zdravstvu. S pomocjo Bayesovega analiticnega modeliranja odlocite (angl. decision analytic modeling, DAM) je mogoce opredeliti, katere posege je upraviceno uvesti v standardno prakso, in ali je potrebno sredstva nameniti dodatnim raziskavam. Metodologija je predstavljena na primeru ocenjevanja stroškovne ucinkovitosti amografskega presejanja raka dojke. Postavljen je bil markovski model 36 razlicnih presejalnih programov in programa brez presejanja. S pomocjo probabilisticne analize negotovosti je bila negotovost vhodnih parametrov modela prenesena na negotovost stroškov in ucinkov posameznih programov. Rezultati so pokazali, da imajo programi z enoletnim intervalom med presejanji nižje pricakovane ucinke ob višjih stroških kot manj intenzivna presejanja. Programi z dvoletnim intervalom med presejanji bi bili stroškovno ucinkoviti le ob visokih vrednostih, ki smo jih pripravljeni placati za dodatno enoto ucinka (t.j. za QALY). Tako se je za stroškovno ucinkovit program pri pogosto navajanih mejnih vrednostih za dodaten QALY izkazal program s presejanjem med 40 in 80 letom starosti na vsake tri leta, ki ima inkrementalno razmerje med stroški in ucinki okoli €13.000/QALY. Vendar pa je probabilisticna analiza pokazala tudi veliko mero negotovosti v pricakovane vrednosti stroškov in ucinkov posameznih programov. Tako je bila vrednost dodatnih informacij ocenjena kot precejvisoka, kar je bil prvi pokazatelj morebitne upravicenosti dodatnih raziskav pred samo odlocitvijo. Parametri, katerih vrednosti bi se najbolj izplacalo poznati, so bili parametri o hitrosti napredovanja raka dojke. Nadaljna analiza oportunitetnih stroškov, ki bi nastali zaradi zamika uvedbe stroškovno najucinkovitejšega programa do dostopnosti rezultatov dodatnih raziskav, pa je pokazala, da bi bili le ti višji, kot bi bile koristi dodatnih raziskav. Tako je bilo doloceno, da je stroškovno najucinkovitejša opcija uvedba presejalnega programa med starostjo 40 in 80 let s troletnim intervalom, ob katerem bi se zbirali dodatni podatki v okviru opazovalne raziskave. V skladu z dostopnostjo novih informacij bi se nato periodicno posodabljalo model presejanja raka dojke in iterativno dolocalo stroškovno najucinkovitejši program presejanja. Na dejanskem problemu je bila prikazana teoreticno ustrezna metodologija z veliko prednostmi, vendar prav tako tudi z nekaj slabostmi. Ena izmed vecjih slabosti, racunska zahtevnost, je bila uspešno zaobidena s pomocjo metamodeliranja. Kot najustreznejši pristop so se izkazali metamodeli na osnovi Gaussovih procesov, s pomocjo katerih je bilo moc skrajšati cas analize za vec kot 99%, iz ocenjenih 44 let na 47 dni. Na ta nacin je bila dokazana tudi prakticna vrednost in uporabnost predstavljene metodologije za razvršcanje sredstev v zdravstvu.

• Jezik: Slovenski jezik
• Ključne besede: zdravstvo, stroški, učinkovitost, vrednotenje, dojke, rak (medicina), presejanje, Slovenija
• Vrsta gradiva: Doktorska disertacija
• Tipologija: 2.08 - Doktorska disertacija
• Organizacija: FFA - Fakulteta za farmacijo
• Kraj izida: Ljubljana
• Založnik: [K. Rojnik]
• Leto izida: 2009
• Št. strani: 105 f.
• PID: 20.500.12556/RUL-127052
• UDK: 618.19-073.7:330.4(043.3)
• COBISS.SI-ID: 245242880
• Datum objave v RUL: 14.05.2021

ROJNIK, Klemen, 2009, Vrednotenje stroškovne učinkovitosti posegov v zdravstvu : primer na presejanju za rakom dojke [na spletu]. Doktorska disertacija. Ljubljana : K. Rojnik. [Dostopano 21 april 2025]. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=127052

Sekundarni jezik

• Jezik: Angleški jezik
• Naslov: Assessing cost effectiveness of health care interventions : example on breast cancer screening

Izvleček

The role of economic evaluation in allocation of resources is presented. With the use of Bayesian decision analytical modeling it is possible to determine whether the introduction of the studied intervention into clinical practice is warranted and whether more resources should be allocated to further research in this area. The methodology is applied to the costeffectiveness assessment of breast cancer screening. A Markov model was build for 36 different screening policies and for no screening policy. Uncertainty of the model inputs was transferred to the uncertainty of the model results with probabilistic sensitivity analysis. With the presented analysis, it was shown that a 1-year screening interval in population breast cancer screening would produce less benefits at higher costs than less intensive screening and that a 2-year interval would be cost-effective only at high values of society’s willingness to pay per quality adjusted life-year (QALY). Therefore, based on commonly quoted thresholds of society’s willingness to pay per QALY, the optimal approach in Slovenian population would be screening women aged from 40 to 80 years every 3 years. The incremental cost-effectiveness ratio of this policy is around €13.000/QALY. But the probabilistic analysis revealed relatively high uncertainty of the expected values for costs and effects of different policies. Therefore, the value of additional information was deemed reasonably high, indicating that further research would be warranted. The partial value of information for the groups of parameters indicated that future research would be most valuable if directed toward obtaining more precise estimates of the cancer sojourn times. Further analysis of opportunity loss indicated that the benefits of additional research would be smaller than opportunity loss due to the delay of the decision. Therefore, the best option would be to implement the most cost-effective policy given the existing information (screening women aged 40–80 years, at 3-year intervals) and simultaneously conduct observational studies alongside the implemented policy. The decision analytic model could be in this manner periodically updated with additional information as it became available and the most cost-effective policy chosen iteratively. Theoretically appropriate methodology with many advantages was used on a real world example. But this approach has also some limitations, with computational demand being one of the biggest. This difficulty was successfully resolved with the use of Gaussian process metamodels, which were deemed the most appropriate. With their usage, the time of the analysis was shortened by more than 99%, that is from projected 44 years to 47 days. By doing so, the practical value of the presented methodology for resource allocation in health care was shown.