ASSET (Age/Sex Standardised Estimates of Treatment): A Research Model to Improve the Governance of Prescribing Funds in Italy

Favato, Giampiero; Mariani, Paolo; Mills, Roger; Capone, Alessandro; Pelagatti, Matteo; Pieri, Vasco; Marcobelli, Alberico; Trotta, Maria Giovanna; Zucchi, Alberto; Catapano, Alberico L.

doi:10.1371/journal.pone.0000592

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…This observational, cross-sectional study was part of the ASSET (Age and Sex Standardised Estimates of Treatment) project [16], a pharmacoeconomic and pharmacoepidemiological study. This analysis focused on data from the Regional Health Departments of Basilicata (a southern Italian region with almost 600,000 inhabitants) and Marche (a central Italian region with almost 1.5 million inhabitants), with a population of slightly more than two million subjects (ASSET population), 1738 general practitioners (GPs) and 244 family paediatricians (FPs).…”

Section: Methodsmentioning

confidence: 99%

Prevalence of the Prescription of Potentially Interacting Drugs

et al. 2013

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The use of multiple medications is becoming more common, with a correspondingly increased risk of untoward effects and drug-related morbidity and mortality. We aimed at estimating the prevalence of prescription of relevant potentially interacting drugs and at evaluating possible predictors of potentially interacting drug exposure. We retrospectively analyzed data on prescriptions dispensed from January 2004 to August 2005 to individuals of two Italian regions with a population of almost 2.1 million individuals. We identified 27 pairs of potentially interacting drugs by examining clinical relevance, documentation, and volume of use in Italy. Subjects who received at least one prescription of both drugs were selected. Co-prescribing denotes “two prescriptions in the same day”, and concomitant medication “the prescription of two drugs with overlapping coverage”. A logistic regression analysis was conducted to examine the predictors of potential Drug-Drug Interaction (pDDIs). 957,553 subjects (45.3% of study population) were exposed to at least one of the drugs/classes of the 27 pairs. Overall, pDDIs occurred 2,465,819 times. The highest rates of concomitant prescription and of co-prescription were for ACE inhibitors+NSAIDs (6,253 and 4,621/100,000 plan participants). Considering concomitance, the male/female ratio was <1 in 17/27 pairs (from 0.31 for NSAIDs-ASA+SSRI to 0.74 for omeprazole+clopidogrel). The mean age was lowest for methotrexate pairs (+omeprazole, 59.9 years; +NSAIDs-ASA, 59.1 years) and highest for digoxin+verapamil (75.4 years). In 13/27 pairs, the mean ages were ≥70 years. On average, subjects involved in pDDIs received ≥10 drugs. The odds of exposure were more frequently higher for age ≥65 years, males, and those taking a large number of drugs. A substantial number of clinically important pDDIs were observed, particularly among warfarin users. Awareness of the most prevalent pDDIs could help practitioners in preventing concomitant use, resulting in a better quality of drug prescription and potentially avoiding unwanted side effects.

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Section: Methodsmentioning

confidence: 99%

Prevalence of the Prescription of Potentially Interacting Drugs

et al. 2013

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“…Among the factors related to the varying prescription activity, age of patients was most often factored into analyses of variation [ 37 ], although age alone did not account for enough variation to develop an accurate model for predicting prescribing rates [ 38 ]. It was shown that age and gender accounted for approximately 25% of variation [ 39 , 40 ] and additional demographic characteristics (e.g. mortality rates) up to 51% [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Prescriptive variability of drugs by general practitioners

et al. 2018

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Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann–Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and F-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.

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“…The results of some English studies analysing the influence of such variables on drug prescription in primary health care show that they influence prescription patterns [2][3][4][5][6][7] and cost [5][6][7]. GPs' prescribing decisions have come under increased scrutiny, above all in those Italian regions where health care deficits remain high, and there is considerable pressure to contain costs by prescribing cost-effectively [8][9][10][11]. Thus, knowledge of prescription patterns is important in order to improve, evaluate and make adjustments to the pharmaceutical formulary [12].…”

Section: Introductionmentioning

confidence: 99%