Table 1

Systematic review data extraction table

Key characteristics of included studies
	Author, year	Country/city	Study design/type	Population of interest	Exposure of interest	Outcome of interest	Main finding				Conclusion, n/N (%)	Additional notes
Self-reported medication errors
1.	Adam et al, 200972	Australia	Cross-sectional	Analysis of data from 3522 adults participating in stage 2 of the North West Adelaide Health Study aged ≥18 years	Unclear	Self-reported adverse event (medication, diagnosis and others). Using survey.	Of the total 3522 survey participants, 148 (4.2%) reported an adverse event causing harm in the previous 12 months, giving an annual incidence of 4.2% (95% CI 3.4% to 5.0%). Medication error: The main types of adverse events perceived as causing harm were medication error (reported by 46% of the 148 participants reporting adverse events).				Medication error prevalence: 68/3522=1.9%	Subjective data rather than objective
2.	Lu and Roughead, 201120	Australia, Canada, New Zealand, UK, USA, Germany and The Netherlands	Cross-sectional (secondary analysis)	11 910 adult respondents aged ≥18 years. Data from the 2007 Commonwealth Fund International Health Policy Survey.	Prescribed drug	Self-reported medication error and compare factors associated with medication errors across the seven countries. Using survey.	Self-reported medication errors prevalence: 752 respondents had medication error (Australia=7.4%; Canada=5.7%; New Zealand=5.9%; UK=5.2%; USA=7%; Germany=5.2%; The Netherlands=8%). Risk factors across countries included seeing multiple specialists, multiple chronic conditions, hospitalisation and multiple emergency room visits.				Medication error prevalence: 752/11 910=6.3%	Prevalence for medication error alone from table 1, while the risk factors for both medical and medication error.
3.	Sears et al, 201221	Australia, Canada, France, Germany, the Netherlands, New Zealand, UK and USA	Descriptive (secondary/retrospective analysis)	9944 adults aged ≥18 years from the community setting	Taking medication regularly	Patient-related risk factors associated with self-reported medication errors. Using telephone survey.	Medication error prevalence: 570 respondents with medication errors occurring in the community setting. Approximately 4 out of every 5 self-reported medication errors occurred in the community setting.				Medication error prevalence: 570/9944=5.7%	Risk factors for both hospital and community setting
4.	Mira et al, 201373	Alicante, Spain	Cross-sectional	382 elderly aged ≥65 years from primary care. Patients on polypharmacy (five or more drugs) and with comorbidity: cardiovascular (51.6%); diabetes (34.3%).	Prescribed and self-medications	Frequency of mistakes in communication between the physician and the patient and their medication error in the last year. Using semistructured interviews.	Medication error prevalence: 75.1% of the patients reported having made at least one mistake with the medication in the last year. Risk factors: Multiple comorbidities (p=0.006), frequent changes in prescription (p=0.02), not considering the prescriptions of other physicians (p=0.01), inconsistency in the messages (p=0.01), being treated by various different physicians at the same time (p=0.03), a feeling of not being listened to (p<0.001) or loss of trust in the physician (p<0.001). *The error due to drug confusion had very severe consequences, requiring a visit to the emergency service or hospital admission.				Medication error prevalence: 287/382=75%	Consequence*
Risk factors
5.	Sorensen et al, 200676	4 states of Australia	Cross-sectional, prospective	204 general practice patients living in their own home aged 37–99 years	Prescribed drugs	Prevalence and interrelationships of medication-related risk factors for poor patient health outcomes identifiable through ‘in-home’ visit observations.	Risk factors: Prevalence of nominal medication-related risk factors and health outcomes among the sample of 204 patients. 1. Multiple medication storage locations used=17 (8.3%). 2. Expired medication present=40 (19.6%). 3. Discontinued medication repeats retained=43 (21%). 4. Hoarding of medications=43 (21%). 5. Therapeutic duplication present=50 (24.5%). Administration error: 6. No medication administration routine=56 (27.5%). 7. Poor adherence=107 (52.5%). 8. Confused by generic and trade names=114 (55.9%).
6.	Vuong and Marriott, 200625	Melbourne, Australia	Descriptive	142 discharged adults aged ≥55 years who were returning to independent care at home. Patients at risk of medication misadventure.	Discharge prescribed drugs	Unnecessary medicine stored at home as a risk factor. Using home visit within 5 days of discharge.	Unnecessary medicine stored at home prevalence: 85/142=60%. 85 (60%) of 142 patients who received a home visit allowed removal of medicines that had expired or no longer required. Prescribing error: drug duplication prevalence: 32 (27%) patients allowed removal of 82 duplicate packs of the same item that was no longer required. A total of 390 medicines were removed with a mean of 4.6 medicines per patient (range 1–21).				Unnecessary medicine stored at home prevalence: 85/142=60%	No information on how many patients had unnecessary medicine. Information available is on the patient allowed to remove unnecessary medicine.
7.	Pit et al, 200874	New South Wales, Australia.	Cross-sectional study	849 elderly aged ≥65 years from general practice	Self-medications	Prevalence of self-reported risk factors for medication misadventures. Tool used: Medication Risk Assessment Form (patient survey)	Risk factors: 1. Using at least one medication for more than 6 months (95%). 2. More than one doctor involved in their care (59%). 3. Had three or more health conditions (57%). 4. Used five or more medicines (54%). 5. ADRs, in the last month 39% of participants experienced difficulties sleeping, felt drowsy or dizzy (34%), had a skin rash (28%), leaked urine (27%), had stomach problems (22%) or had been constipated (22%).					*ADR as a risk factor for medication misadventure may not be related to the use of medication in all cases.
8.	Mosher et al, 201275	Iowa, USA	Cohort prospective	310 elderly aged ≥65 years who were cognitively intact from a Veterans Administration primary care clinic	Taking five or more non-topical medications	Association of health literacy with medication knowledge, adherence and ADEs. Using interview and chart review.	Total: 310 patients Prevalence of ADEs: ADEs occurred in 51 patients (16.5%) of the patients within the first 3 months of the study, which increased to 119 patients (38.4%) over the full 12-month follow-up period. Risk factor: Association of health literacy with ADEs: The incidence of ADEs at 3 and 12 months appeared higher among patients with low health literacy, but this was not statistically significant.				Low health literacy increased the risk of ADEs.
Medicines’ management process:
9.	Koper et al, 201323	Austria	Descriptive	169 patients from general practice taking five or more medicines. Mean age: 76.4±8.5 SD years. Of the 169 patients, 158 were elderly aged ≥65 years.	Prescribed and OTC drug	Medication errors including non-evidence-based medications, dosing errors and potentially dangerous interactions in all patients. Potential interactions were identified using the Lexi-Interact database. PIMs in subgroup of elderly patients according to the PRISCUS list. Using case report form filled by the GPs.	Prescribing error prevalence: Indication: 158 of the 169 patients (93.5%) had at least one non-evidence-based medication. Dosing error: 74 of the 169 patients (43.8%) had at least one dosing error. DDI prevalence: Category D interactions: 99 patients (58%) had at least one category D interaction. Category X interactions: 4 patients (2.4%) had at least one category X interaction. PIM prevalence: 59 of seniors (37.3%) had at least one medication that was inappropriate.				Medication error prevalence: 1. Non-evidence-based medications: 158/169=93.5%. 2. Dosing error: 74/169=43.8%. 3. Category D drug interaction: 99/168=58%; category X drug interaction: 4/168=2.4%. 4. PIMs: 59/158=37.3%.	A medication was classified as non- evidence-based if the indication for use indicated by the GP was not mentioned in any peer-reviewed chapter of UpToDate.
10.	Mand et al, 201433	Germany	Descriptive retrospective	24 619 elderly aged ≥65 years from family practice with at least one diagnosis named in the Beers list	Prescribed drug	PDDI frequency and whether there are gender-related or age-related differences. Analysis from electronic patient records.	Prescribing error: Contraindication or drug–disease interaction prevalence: 10.4% of elderly were exposed to at least one PDDI. Risk factors: 1. Patients over 75 years (OR 1.10, CI 1.05 to 1.15). 2. Number of drugs prescribed (>4 drugs: OR 1.91, CI 1.83 to 2.00). 3. Blood clotting disorders/receiving anticoagulant therapy (OR 2.38, CI 2.15 to 2.64) showed the strongest association with PDDI.				PDDI prevalence: 2560/24 619=10.4%
11.	Gagne et al, 200836	Regione Emilia-Romagna, Italy	Cohort retrospective	4 222 165 regional Emilia-Romagna residents. Outpatient aged from 0 to ≥85 years.	Prescribed drug	Clinically important potential DDI. Risk factors. Outpatient prescription data from the Regional Emilia-Romagna. DDI screening tool: a list of clinically important potential DDIs included 12 drug pairs that could be captured using the regional Emilia-Romagna database.	Prescribing error: DDI prevalence: exposed to potential DDI adults (19 to ≥85 years)=7893. Unexposed adult=7013. Total=14 906.				DDI prevalence: 7893/14 906=53%	Risk factors for all age groups including paediatrics. All age groups included so results should be considered cautiously.
12.	Dallenbach et al, 200724	Geneva, Switzerland	Descriptive, retrospective file review	591 outpatients, mean age 39 years	Prescription drug and drug currently taking	Clinically significant ADI. Prescription review. DDI screening tool: DDI compendia and (ePocrates RX) with clinical decision support.	Prescribing error: DDI prevalence: In 135 of the consultations, a potentially clinically significant ADI was identified.				DDI prevalence: 135/591=23%
13.	Obreli Neto et al, 201126	Brazil	Cross-sectional	2627 elderly aged 60–88 years from the primary healthcare	Prescribed drug	Potential risks in drug prescriptions: DDI and PIM. Using prescription review. DDI screening tool: (DrugDigest, Medscape and Micromedex). PIM using Beers criteria 2003.	Prescribing error: DDI prevalence: Using DrugDigest showed that 4.7% and 28.4% of the elderly presented at least one potential DDI classified as major and moderate, respectively. Using Medscape showed that 3.4% and 19.3% of the elderly presented at least one potential DDI classified as major and moderate, respectively. Using Micromedex showed that 3.1% and 29.1% of the elderly presented at least one potential DDI classified as major and moderate, respectively. Prescribing error: PIM prevalence: 26.9% of the patients had prescriptions with at least one PIM.				DDI prevalence: 3.1%–29.1% PIM prevalence: 26.9%
14.	Secoli et al, 201030	Sao Paulo, Brazil	Cross-sectional	2143 community-dwelling elderly aged ≥60 years. Data were obtained from the SABE (Health, Well-Being and Ageing) survey.	≥2 prescribed drug use	Potential DDIs and identify associated factors. Using home interview. DDI screening tool: Micromedex Healthcare Series.	Prescribing error: DDI prevalence: 568/2143=26.5%. Risk factors: The use of six or more medications (OR 3.37, 95% CI 2.08to 5.48) or having hypertension (OR 2.56, 95% CI 1.73 to 3.79), diabetes (OR 1.73, 95% CI 1.22to 2.44) or heart problems (OR 3.36, 95% CI 2.11 to 5.34) significantly increased the risk of potential DDI.				DDI prevalence: 568/2143=26.5%
15.	Obreli Neto et al, 201227	5 cities of Brazil	Cross-sectional	12 343 elderly aged ≥60 years from the primary public health system	Prescription for two or more drugs (prescribed both within and across prescriptions)	Potential DDIs (presence of a minimum of 5 days overlap in supply of an interacting drug pair) and predictor of DDI. Using medical prescriptions and patients’ medical records review. DDI screening tool: DDI checker programmes (DrugDigest, Drugs, Micromedex and Medscape).	12 343 patients (5855 exposed; 6488 unexposed). Prescribing error: DDI prevalence: 47.4% Risk factors: Female sex (OR=2.49 (95% CI 2.29 to 2.75)), diagnosis of ≥3 diseases (OR=6.43 (95% CI 3.25 to 12.44)) and diagnosis of hypertension (OR=1.68 (95% CI 1.23 to 2.41)) were associated with potential DDIs. Age was associated with an increasing risk of DDIs. Number of prescribers, number of drugs consumed, ATC codes and drugs that act on CYP450 presented positive associations with potential DDIs in univariate and multivariate analyses of drug therapy characteristics.				DDI prevalence: 5855/12 343=47.4%
16.	Indermitte et al, 200734	Switzerland	Descriptive	434 passer-by customers aged ≥18 years from community pharmacies	Prescription-only medicines and OTC drug	Potential drug interactions. 1. Observation of customer contacts and interviews with passer-by customers purchasing selected OTC drugs. 2. Telephone interviews with regular customers treated with selected prescription-only medicines identified in community pharmacies’ databases. DDI screening tool: Pharmavista database.	Prescribing error: DDI prevalence: Observation of passer-by customers. Of 1183 passer-by customers observed, 164 purchased at least one of the selected OTC drugs. 102 (62.2%) of those subjects were interviewed. 43 (42.2%) mentioned taking prescribed drugs and 3 of them were exposed to potential drug interactions of moderate severity. Telephone interview with regular customers. Out of 592 regular customers selected from the community pharmacy database, 434 (73.3%) could be interviewed. Prevalence of DDI in regular customers: 69 (15.9%) of them were exposed to a potential drug interaction between purchased OTC drug for self-medication and their prescription-only medicines. Furthermore, 116 (26.7%) regular customers were exposed to potential drug interactions within their prescribed drugs and in 28 (6.5%) multiple (>2) potential drug interactions were found.				DDI prevalence: 3/102=3%, 69/434=16%, 116/434=26.7%
17.	Mahmood et al, 200735	USA	Cross-sectional, retrospective	2 795 345 patients who filled prescriptions for medications involved potential DDI from 128 Veterans Affairs medical centres. Ambulatory care clinic.	Prescribed drug	Clinically important DDI. Database analysis of pharmacy records. DDI screening tool: a list of 25 potential DDI.	Prescribing error: DDI prevalence: The overall rate of potential DDIs was 21.54 per 1000 veterans exposed to the object or precipitant medications of interest.				DDI prevalence: 2.15%	Age not mentioned
18.	Lapi et al, 200937	Dicomano, Italy	Cohort, a two-wave, population-based survey	568 community-dwelling elderly aged ≥65 years	Prescription and non-prescription drugs used at least 1 week before enrolment	Suboptimal prescribing: Inappropriate medication=1991 Beers criteria (13 items out of the original 39 (33.3%) Beers list medications were considered). DDI screening tool: Micromedex Drug-Reax system. Using population-based survey.	Prescribing error: Potential DDI prevalence was significantly higher in 1999 compared with 1995 (30.5% vs 20.1%; p<0.001). Inappropriate prescriptions were significantly higher in 1995 compared with 1999 (9.1% vs 5.1%; p=0.004).				Potential DDI prevalence: 30.5%, p<0.001 Inappropriate medication prevalence: 5.1%, p=0.004
								1995	1999	P values
							Inappropriate medication	47 (9.1%)	26 (5.1%)	0.004
							DDI	97 (20.1%)	147 (30.5%)	<0.001
							Major DDI	20 (4.7%)	24 (5.6%)	0.585
							Risk factors: Polypharmacy always predicted a substantial increase in the risk of the PIM and DDI.
19.	Nobili et al, 200938	Lecco, Italy	Cross-sectional, retrospective	58 800 community-dwelling elderly aged ≥65 years registered under the local health authority of Lecco	Receiving at least two coadministered prescriptions	DDIs and associated risk factors (age, sex and number of prescriptions). DDI screening tool: Italian computerised interaction database. Analysed all prescriptions dispensed from 1 January 2003 to 31 December 2003.	Prescribing error: DDI prevalence: 9427 elderly people (16%) were exposed to drug combinations with the potential for 13 932 severe DDIs. Mean number of DDI per patient was 0.2 (range 0–9). Risk factors: Age and number of chronic drugs were associated with an increasing risk of DDIs. The adjusted OR increased from 1.07 (95% CI 1.3 to 1.11) in patients aged 70–74 years to 1.52 (95% CI 1.46 to 1.60) in those aged 85 or older. Elderly taking more than five chronic drugs had a statistically significant higher risk of potentially severe DDIs (OR=5.59, 95% CI 5.39 to 5.80) than those receiving less than 3 (reference category) or 3–5 chronic drugs (OR=2.71, 95% CI 2.63 to 2.80).				Potentially severe DDI prevalence: 9427/58 800=16%	Only the interactions identified as severe were considered in these analyses.
20.	Guthrie et al, 201539	Scotland, UK	Cross-sectional	311 881 residents aged ≥20 years from the community-dispensed prescribing data (general practice). Living in own home: 308 660.	Prescribed drugs	Potentially serious DDI. Patient characteristics associated with the presence of potentially serious DDI. DDI screening tool: analysis of community-dispensed prescribing data using British National Formulary 2010.	Prescribing error: DDI prevalence: 40 689 adults (13%) had potentially serious DDI in 2010 (for both residents living in own home and care home). Number of patient with potentially serous DDI for residence living in their own home in 2010=13 615.				DDI prevalence: 13 615/308 660=4.4%	Resident living in both care home or own home. Risk factors for own home and care home.
21.	Maio et al, 200640	Emilia-Romagna, Italy	Cohort retrospective	849 425 elderly outpatients aged ≥65 years from the Emilia-Romagna outpatient prescription claims database	Prescribed drugs	PIM using the 2002 Beers criteria and factors associated with PIM. Prescription review.	Prescribing error: PIM prevalence: A total of 152 641 (18%) elderly had one or more occurrences of PIM prescribing. Risk factors: 1. Older age (≥85 years) (OR 1.18, 95% CI 1.16 to 1.2, p<0.05). 2. ≥10 drugs prescribed (OR 7.33, 95% CI 7.15 to 7.51, p<0.05). 3. ≥4 chronic conditions (OR 1.76, 95% CI 1.72 to 1.81, p<0.05).				PIM prevalence: 152 641/849 425=18%
22.	de Oliveira Martins et al, 200641	Lisbon, Portugal	Cross-sectional	213 elderly aged ≥65 years from 12 community pharmacies	Prescription and home medications	IDU by 1997 Beers and 2003 Beers explicit criteria. Using survey.	Prescribing error: PIM prevalence: Using the 1997 Beers explicit criteria, 75 occurrences of inappropriate medicines were detected in 59 patients (27.7%). Using the 2003 Beers explicit criteria inappropriate medication was detected in 82 patients (38.5%). Risk factors: The occurrence of inappropriate medicines was significantly associated with the consumption of a high number of drugs.				IDU prevalence: 59/213=27.7% using 1997 Beers IDU prevalence: 82/213=38.5% using 2003 Beers
23.	Pugh et al, 200642	Austin, Texas, USA	Cross-sectional, retrospective	1 096 361 outpatient elderly aged ≥65 years using national data from the Veterans Health Administration	Prescribed drug only	Potentially IP included in the 2006 HEDIS criteria and to determine if patient risk factors are similar to those found using Beers criteria. Using database.	Prescribing error: IP prevalence: Overall, 19.6% of older veterans were exposed to HEDIS 2006 drugs. Risk factors: 1. Patients receiving ≥10 medications were at greatest risk of exposure in men (OR 8.2, 95% CI 8 to 8.4) and women (OR 9.6, 95% CI 8.2 to 11.2). 2. Patients with more outpatient clinic visits (≥10) were at greater risk regardless of gender (OR 1.4, 95% CI 1.3 to 1.6). 3. Diagnosis with other mental illness (eg, depression, anxiety) alone or in combination with serious mental illness was associated with higher risk of potentially IP for women (OR 1.3, 95% CI 1.1 to 1.5).				Potentially IP prevalence: 214 887/1 096 361=19.6%
24.	Saab et al, 200643	Lebanon	Descriptive	277 elderly aged ≥65 years from 10 community pharmacies	Prescription and/or OTC medications	IDU (Beers criteria, missing doses, inappropriate frequency of administration, poor memory, drug–disease interaction, DDI, inappropriate dose, duplicated therapy, discontinuation of therapy, adverse effect and inappropriate indication). Factors that predict potentially inappropriate drug intake. Review patient profile using community pharmacy data and inperson interviews.	Prescribing error: PIM prevalence: The prevalence of elderly outpatient with at least one inappropriate medication: 165/277 (59.6%) (include five patients with ADR). Inappropriate medication use was most frequently identified in terms of Beers criteria (22.4%), missing doses (18.8%) and incorrect frequency of administration (13%). Drug–disease interaction in 28 patients (10.1%), DDI 14 (5.1%), duplicate therapy 12 (4.3%). Risk factors: Female sex (65.7% vs 53.3% for male, p=0.03). There were also significant associations between the likelihood of use of an inappropriate drug and (1) increased number of medical illnesses (p<0.00002); (2) consumption of an OTC drug and/or prescription drug (p=0.048 and p=0.0035, respectively); and (3) consumption of both OTC and prescription drugs (p<0.0002).				IDU prevalence: 62/277=22.4% using Beers criteria	Just extracted the IDU by Beers criteria because the IDU includes 5 cases of ADR and some patients had more than one IDU. Risk factors for all types of IDU.
25.	Zuckerman et al, 200644	USA	Cohort retrospective	487 383 community-dweller elderly aged ≥65 years. Data from MarketScan Medicare Supplemental and Coordination of Benefits database.	Prescribed drug	Inappropriate medication use using Beers criteria	Prescribing error: PIM prevalence: 204 083 elderly used inappropriate medication. Use of inappropriate drugs was associated with a 31% increase in risk of nursing home admission, compared with no use of inappropriate drugs (adjusted relative risk 1.31, 99% CI 1.26 to 1.36).				Inappropriate medication use prevalence: 204 083/487 383=41.9%
26.	Bregnhøj et al, 200745	Copenhagen, Denmark	Cross-sectional	212 elderly aged ≥65 years with polypharmacy (≥5 drugs) patients from primary care	Subsidised and non-subsidised medications prescribed	IP measured by the MAI: 10 criteria are indication, effectiveness, dosage, directions practicality, directions correctness, DDI, drug–disease interaction, duplication, duration and expense). Patients exposed to polypharmacy were identified via the database recording the drug subsidy system of Danish pharmacies and questionnaire.	Prescribing error: IP prevalence: The majority of the patients, namely 94.3%, had one or more inappropriate ratings among their medications.				IP prevalence: 200/212=94.3%
27.	Johnell and Fastbom, 200846	Sweden	Cross-sectional	731 105 people aged ≥75 years from the Swedish Prescribed Drug Register (secondary data analysis)	Prescribed drug only and multidose drug dispensing	Whether the use of multidose drug dispensing is associated with potential IDU (ie, anticholinergic drugs, long-acting benzodiazepines, concurrent use of ≥3 psychotropic drugs and combinations of drugs that may lead to potentially serious DDIs). Information from the Swedish Prescribed Drug Register.	Prescribing error: PIM prevalence: Prevalence of potential IDU in multidose dispensing users: 40.3% (women: 41%, men: 38.5%). Prevalence of potential IDU In prescription users: 13.6% (women: 15%, men: 11.5%). The multidose users had higher prevalence of all indicators of potential inappropriate drug than prescription users. 1. The younger elderly (aged 75–79 years) who used multidose drug dispensing had the highest frequency of all indicators of potential IDU. 2. Most indicators of IDU were more common in women than men. 3. Multidose drug dispensing among those aged 75–79 years old was even more strongly associated with any IDU, anticholinergic drugs, three or more psychotropic drugs in both men and women, and long-acting benzodiazepines among men.				PIM prevalence: Multidose dispensing users: 292 737/731 105=40% Prescription users: 99 430.3/731 105=13.6%	Multidose drug dispensing means that patients get their drugs machine-dispensed into one unit for each dose occasion and packed in disposable bags.
28.	Berdot et al, 200947	Dijon, Bordeaux, Montpellier, France	Cohort prospective	6343 community-dwelling elderly aged ≥65 years	Prescribed drug	PIM using 1997 and 2003 Beers criteria, Fick and Laroche. Face-to-face interview using standardised questionnaire.	Prescribing error: PIM prevalence: One-third (31.6%) of the study participants reported using at least one inappropriate medication at study entry.				PIM prevalence: 2004/6343=31.6%, p<0.001
29.	Haider et al, 200948	Sweden	Cross-sectional, register-based study	626 258 older people aged 75–89 years from the Swedish Prescribed Drug Register (secondary data analysis)	Prescribed drug only	If low education associated with potential IDU (ie, anticholinergic drugs, long-acting benzodiazepines, concurrent use of ≥3 psychotropic drugs and clinically relevant potential DDI). Information from the Swedish Prescribed Drug Register.	Prescribing error: PIM prevalence: The proportion of participants reporting use of at least one potential IDU was 34.6%. Risk factors: Subjects with low education had a higher probability of potential IDU (OR 1.09, 95% CI 1.07 to 1.17). Older age, being a woman and higher CCI were associated with the highest frequencies of potential IDU.				IDU prevalence: 216 685/626 258=34.6%
30.	Lai et al, 200949	Taiwan	Descriptive	2 133 864 patients aged ≥65 years between 2001 and 2004 from ambulatory care National Health Insurance claim database	Prescribed drug	PIM prescribing using updated 2003 Beers criteria and the characteristics of and risk factors for such prescribing	Prescribing error: PIM prevalence: A mean of 63.8% of the older population received a PIM at least once a year in 2001–2004. Details: 2001: 1 974 869 patients of whom 1 297 425 had inappropriate prescription (65.7). 2002: 2 026 737 patients of whom 1 312 147 had inappropriate prescription (64.7). 2003: 2 077 677 patients of whom 1 295 227 had inappropriate prescription (62.3). 2004: 2 133 864 patients of whom 1 333 792 had IP (62.5). Risk factors: The only patient characteristic associated with an increased likelihood of the prescribing of PIM was female sex (male sex: OR 0.982 (95% CI 0.980 to 0.983)) (p<0.001) and when ≥4 drugs were prescribed (p<0.001). The following are physician characteristics associated with a greater likelihood of the prescribing of PIM: 1. Male sex (OR 1.206, 95% CI 1.202 to 1.210, p<0.001). 2. Older age (43–50 years: OR 1.021, 95% CI 1.018 to 1.025, p<0.001; ≥51 years: OR 1.238, 95% CI 1.235 to 1.242, p<0.001). 3. Family medicine/general practice (OR 1.267, 95% CI 1.265 to 1.269, p<0.001).				PIM prevalence: 2001: 65.7% 2002: 64.7% 2003: 62.3% 2004: 1 333 792/2 133 864=62.5%
31.	Ryan et al, 200950	Ireland	Cohort prospective	500 patients aged ≥65 years from primary care	Prescribed drug	IP using 2003 Beers criteria and IPET. Screening patients’ medical records (electronic and paper).	Prescribing error: PIM prevalence: 65 patients (13%) and 52 patients (10.4%) had at least one medicine prescribed inappropriately using 2003 Beers and IPET criteria, respectively.				IP prevalence: Beers 2003: 65/500=13% IPET: 52/500=10.4%
32.	Ryan et al, 200951	Cork, Southern Ireland	Descriptive case record review	1329 elderly aged ≥65 years from primary care	Prescribed drugs	A—1. PIM using 2003 Beers and STOPP criteria. 2. PPO using START criteria. B—Relationship between age and number of prescription drugs and IP. Case record through paper and electronic record review.	Prescribing error: PIM prevalence: IP rate identified by Beers criteria in 18.3% (243) of patients. IP rate identified by STOPP was 21.4% (284). PPO was identified in 22.7% (302) of patients using the START tool. Risk factors: A significant correlation was found between the occurrence of PIM and the following: 1. The number of medicines prescribed when calculated using Beers criteria (r_s=0.270, p<0.01) and STOPP (r_s=0.356, p<0.01) using Spearman’s ρ correlation test. 2. Age using Beers criteria (r_s=0.068, p<0.01) and STOPP (r_s=0.071, p<0.01). 3. Increasing CCI score identified by STOPP (r_s=0.210, p<0.01).				PIM prevalence: Beers: 243/1329=18.3% STOPP: 284/1329=21.4% PPO prevalence: START: 302/1329=22.7%	Spearman’s ρ correlation test
33.	Akazawa et al, 201052	Tokyo, Japan	Cohort retrospective	6628 elderly patients aged ≥65 years from health insurance claim data (secondary data analysis)	Prescribed drugs	PIM using modified Beers criteria in Japan. Drug utilisation review using medical and pharmacy claim from database of Japan Medical Data Center.	Prescribing error: PIM prevalence: 43.6% (2889/6628) were prescribed at least one PIM. Risk factors: Factors positively associated with PIM prescriptions at a significance level of 5% included the following: hospital admission (OR=3.35, 95% CI 2.43 to 4.62); polypharmacy (OR=5.69, 95% CI 5 to 6.48); prescriptions from a hospital (OR=1.19), general medicine practitioner (OR=1.46) or psychiatrist/neurologist (OR=2.33); and comorbid conditions including peptic ulcer disease without bleeding (OR=4.18, 95% CI 3.52 to 4.97), depression (OR=3.69), cardiac arrhythmias (OR=1.93), other neurological disorders (Parkinson’s disease, multiple sclerosis and epilepsy; OR=1.88) and congestive heart failure (OR=1.46). PIM users had significantly higher hospitalisation risk (1.68-fold), more outpatient visit days (1.18-fold) and higher medical costs (33% increase) than did non-users.				PIM prevalence: 2889/6628=43.6%	*Consequence
34.	Zaveri et al, 201053	Ahmedabad city, India	Descriptive prospective	407 geriatric patients aged ≥65 years from medicine outpatient department	Prescribed drug	PIM using 2003 Beers criteria. Using prospective proforma data collection.	Prescribing error: PIM prevalence: Out of 407 patients, 96 patients (23.6%) received at least one drug that was potentially inappropriate. Risk factors: There was highly significant association between the number of drugs prescribed and frequency of use of PIMs (p<0.0002).				PIM prevalence: 96/407=23.6%
35.	Barnett et al, 201154	Tayside, Scotland, UK	Cohort	65 742 elderly aged 66–99 years living in home	Prescribed drug	PIM using 2003 Beers criteria and the association between exposure to PIM and mortality. Using dispensing and prescribing database and medical record.	Prescribing error: PIM prevalence: PIM found in 20 304 (30.9%) patients living at home. Risk factors: After adjustment for age, sex and polypharmacy, 1. Patient at increased risk of receiving at least one PIM if they were younger, female and had higher polypharmacy. 2. Receiving at least one PIM was not associated with increased risk of mortality (adjusted OR 0.98, 95% CI 0.92 to 1.05).				PIM prevalence: 20 304/65 742=30.9%	Risk factors for both care home and home
36.	Chang et al, 201155	Taipei, Taiwan	Cohort	193 outpatient elderly patients aged ≥65 years with polypharmacy (≥8 chronic medications) from Medication Safety Review Clinic in Taiwanese Elders (MSRC-Taiwan) study	Prescribed drugs and dietary supplement excluding herbals	PIM using six different criteria and drug-related problem: the 2003 version of the Beers criteria (from the USA), the Rancourt (from Canada), the Laroche (from France), STOPP (from Ireland), the Winit-Watjana (from Thailand) and the NORGEP criteria (from Norway). Analyse baseline data from the MSRC-Taiwan study. Secondary data analysis.	Prescribing error: PIM prevalence: The proportion of patients who had at least one PIM varied from 24% (the NORGEP criteria) to 73% (the Winit-Watjana criteria). Approximately 31% (the STOPP criteria) to 42% (the NORGEP criteria) of PIMs identified were considered as drug-related problems by the medication review team experts. Risk factors: In the bivariate analysis, the common characteristics associated with having at least one PIM in all criteria were a high number of chronic conditions and a high number of chronic medications.				PIM prevalence: 24%–73%
37.	Leikola et al, 201156	Finland	Cross-sectional	841 509 non-institutionalised elderly patients aged ≥65 years from Finland’s Social Insurance Institution prescription register of all reimbursed drugs for outpatients	Prescribed and OTC medications that are reimbursed	PIM using 2003 Beers criteria	Prescribing error: PIM prevalence: 14.7% (n=123 545) had received PIMs according to the Beers 2003 criteria.				PIM prevalence: 123 545/841 509=14.7%
38.	Lin et al, 201157	Taiwan	Cross-sectional, retrospective analysis	327 elderly patients aged ≥65 years from outpatient clinic of a community health centre	Prescribed drugs	PIM using 2003 Beers criteria and risk factors of PIM use. Using data review.	Prescribing error: PIM prevalence: The prevalence of patients having at least one PIM was 27.5% (90/327). Risk factors: Independent risk factors for PIMs are older age (OR=1.05, 95% CI 1.00 to 1.09, p=0.046), higher number of prescribed medications (OR=1.06, 95% CI 1.39 to 1.98, p<0.001) and diagnosis of acute diseases (OR=8.98, 95% CI 4.71 to 17.1, p<0.001).				PIM prevalence: 90/327=27.5%
39.	Woelfel et al, 201170	California, USA	Cross-sectional	295 elderly aged ≥65 years from ambulatory population of Medicare beneficiaries	Prescribed drug	PIM using 2003 Beers criteria. Using medication review	Prescribing error: PIM prevalence: 54 (18.3% beneficiaries were taking at least one PIM. Risk factors: The number of medications was significantly greater in the PIM than the non-PIM group (p<0.001).				PIM prevalence: 54/295=18.3%
40.	Zhang et al, 201158	USA	Cohort retrospective	3570 elderly community-based respondents aged ≥65 from 2007 MEPS, a nationally representative survey of the US community-dwelling population	Prescribed drug	PIM using Zhan criteria and risk factors for PIM use. Information from MEPS database.	Prescribing error: PIM prevalence: PIM prevalence in 2007: 13.84% (CI 12.52 to 15.17). PIM prevalence in 1996: 21.3% (CI 19.5 to 23.1). Risk factors: Older women, people taking ≥25 prescriptions, people with middle family income, people living in the South census region, and people who said they were in fair or poor health were more likely to have received an inappropriate medication during the year.				PIM prevalence: 13.84%–21.3%
41.	Haasum et al, 201259	Sweden	Cross-sectional, retrospective	1 260 843 home-dwelling elderly aged ≥65 years from the Swedish Prescribed Drug Register	Prescribed drug only	Potentially IDU (use of anticholinergic drugs, long-acting benzodiazepines, concurrent use of ≥3 psychotropics and potentially serious DDIs). Information from the Swedish Prescribed Drug Register.	Prescribing error: PIM prevalence: 11.6% of the home-dwelling elderly were exposed to potentially IDU.				Potentially IDU prevalence: 145 749/1 260 843= 11.6%	Information on both institutionalised and home-dwelling. Extracted home-dwelling information only.
42.	Candela Marroquí et al, 201219	Cáceres, Spain	Descriptive	471 patients aged ≥65 years from health centres	Consumed medications	Potentially IP using STOPP/START criteria. Using patient interview and medical chart review.	Prescribing error: PIM prevalence: 249 patients (52.8%, 95% CI 48.3 to 57.3) had potentially IP according to STOPP/START criteria. STOPP: 162 patients (34.3%, 95% CI 30.2% to 38.8%). START: 114 patients (24.2%, 95% CI 20.5% to 28.2%).				Potentially IP prevalence: 249/471=52.8% (95% CI 48.3 to 57.3)
43.	Nyborg et al, 201260	Norway	Cross-sectional, retrospective	445 900 home-dwelling elderly aged ≥70 years from the Norwegian Prescription Database	Prescribed drug	Prevalence of and predictors for PIM use by the NORGEP criteria. Survey undertaken based on data from the Norwegian Prescription Database.	Prescribing error: PIM prevalence: 34.8% of the study population was exposed to at least one PIM. Risk factors: The odds of receiving potentially harmful prescriptions increased with the number of doctors involved in prescribing (OR 3.52, 99% CI 3.44 to 3.60 for those with ≥5 compared with those with 1 or two prescribers). Women were at higher risk for PIMs (OR 1.6, 99% CI 1.58 to 1.64).				PIM prevalence: 155 341/445 900= 34.8% (99% CI 34.7 to 35)
44.	Yasein et al, 201261	Jordan	Cross-sectional	400 elderly aged ≥65 years from family practice clinic	Prescribed drug	Polypharmacy (≥5 drugs) and IP using 2003 Beers criteria. Using patient file and patient interview.	Prescribing error: PIM prevalence: Inappropriate medications as determined by Beers criteria independent of diagnosis accounted for 118 (29.5%) patients.				IP prevalence: 118/400=29.5%
45.	Blozik et al, 201362	Helsana, Switzerland	Cohort	2008: 1 059 495 2009: 1 047 939 2010: 929 791 Community-dwelling adults aged >18 years from claim data of Helsana	Prescribed drug submitted for reimbursement	Prevalence of polypharmacy and PIM using 2003 Beers criteria or the PRISCUS list. Using analysis of data based on claim data from Switzerland health insurance.	Prescribing error: PIM prevalence: According to 2003 Beers criteria, 10.3% of the community-dwelling population aged >65 years received at least one medication which is PIM, and according to the PRISCUS list 1, 16.0% of persons had a PIM. When using both Beers and PRISCUS criteria, 21.1% of the population received at least one PIM. Of those persons older than 65 years asking for reimbursement of medications, 12.9% received at least one PIM according to 2003 Beers, 20.2% according to PRISCUS and 26.6% of either definition. Risk factors: Women were more likely to receive a PIM: 25.5% of women as compared with 15.4% of men when both Beers and PRISCUS definitions were used.				PIM prevalence: 21.1%	There are huge discrepancies in estimating the prevalence of PIM depending on the definition used.
46.	Cahir et al, 201363	Ireland	Cohort retrospective	931 community-dwelling elderly aged ≥70 years from 15 general practices	Prescribed drug and OTC	The association between potentially IP using STOPP and health-related outcomes (ADEs, HRQOL, and hospital accident and ED). Using patient self-report and medical record.	Prescribing error: PIM prevalence: Prevalence of potentially IP was 40.5% (n=377). ADE prevalence: In total, 674 of 859 participants (78%) were classified as having at least one ADE during the study period. Risk factors: Patients with ≥2 potentially IP indicators were: 1. Twice as likely to have an ADE (adjusted OR 2.21, 95% CI 1.02 to 4.83, p<0.05). 2. Significantly lower mean HRQOL utility (adjusted coefficient −0.09, SE 0.02, p<0.001). 3. A twofold increased risk in the expected rate of ED visits (adjusted incidence rate ratio 1.85, 95% CI 1.32 to 2.58, p<0.001).				Potentially IP prevalence: 377/931=40.5% ADE prevalence: 674/859=78%	*Consequence. Type of ADE was not mentioned.
47.	Weng et al, 201364	Taiwan	Cross-sectional, retrospective	780 older patients aged ≥65 years from the outpatient geriatric clinic	Long-term prescribed drugs (≥28 days) for chronic diseases, not OTC	Impact of number of drugs prescribed on the risk of PIM using STOPP criteria. Patient medical chart review.	Prescribing error: PIM prevalence: 302 patients (39%) had at least one PIM. Risk factors: Multivariate analysis revealed that PIM risk was associated with the number of medications prescribed (p<0.001) and the presence of cardiovascular (p<0.001) or gastrointestinal disease (p=0.003). Patients prescribed ≥5 drugs (adjusted OR=5.4) had significantly higher PIM risk than those prescribed ≤2 drugs.				PIM prevalence: 302/780=39%
48.	Zimmermann et al, 201318	German	Cohort longitudinal analysis	Follow-up 3: n=1942 Baseline: n=3214 1855 elderly aged ≥75 years from primary care. Data from the prospective, multicentre, observational study ‘German Study on Ageing, Cognition and Dementia in Primary Care Patients (AgeCoDe)’.	Prescribed drug	PIM using Beers, PRISCUS list. By checking medications during visits to patients’ homes.	Prescribing error: PIM prevalence: At baseline, PIM prevalence is 29% (848) (according to the PRISCUS list, which decreased to 25.0% (464) 4.5 years later (χ²: 7.87, p=0.004). The Beers list yielded a prevalence of 21% (620) at baseline, decreasing after 4.5 years to 17.1% (317) (χ²: 10.77, p=0.000). Risk factors: By PRISCUS list: The risk for PIM increase with: 1. Increasing age of the patients (OR: 1.06, CI 1.00 to 1.13, p=0.037). 2. The presence of depression (OR: 2.42, CI 1.65 to 3.57, p=0.000). 3. Increasing number of prescription drugs (OR: 1.99, CI 1.80 to 2.18, p=0.000). By contrast, the risk of taking PIM decrease by using PRISCUS list with the number of present illness (OR: 0.88, CI 0.80 to 0.97, p=0.012). As the growing number of ingested prescription drugs increased the risk for the ingestion of PIM from the Beers list (OR: 1.66, CI 1.50 to 1.84; p=0.000).				Prescribing error: PIM prevalence: 17%–29%
49.	Baldoni et al, 201429	Ribeirao Preto, Brazil	Cross-sectional	1000 elderly aged ≥60 years from outpatient pharmacy	Prescribed drug, self-medication (309 users) and OTC (802 users)	Prevalence and factors associated with PIM using 2003 and 2012 Beers criteria. Using structured interview questionnaire.	Prescribing error: PIM prevalence: According to Beers criteria 2003, 480 (48.0%) participants used at least one PIM, the mean being 1.38 (SD=0.65) PIMs/person, ranging from 1 to 5. According to Beers criteria 2012, 592 (59.2%) participants used at least one PIM, the mean being 1.56 (SD=0.81) PIMs/person, ranging from 1 to 6. ADE: During the interview 45.5% of participants reported complaints related to ADEs; 94.5% of these were caused by prescribed medication. Risk factors: Factors that are associated with PIMs use were female gender, self-medication, use of OTC medications, complaints related to ADEs, psychotropic medication and more than five medications. *Ten medications with the highest prevalence of self-reported ADEs complaints are clonidine, amitriptyline, metformin, fluoxetine, dexchlorpheniramine, diclofenac, captopril, acetylsalicylic acid, simvastatin and hydrochlorothiazide. Among them, five were considered PIMs according to Beers criteria, of which clonidine, amitriptyline and dexchlorpheniramine are listed in both criteria, while fluoxetine is listed only in Beers criteria 2003 and diclofenac is listed only in Beers criteria 2012.				PIM prevalence by Beers criteria 2003: 480/1000= 48.0% PIM prevalence by Beers criteria 2012: 592/1000= 59.2%	*Error-related adverse event
50.	Castillo-Páramo et al, 201465	Spain	Cross-sectional	272 electronic records of elderly aged ≥65 years from primary healthcare	Prescribed drugs	PIM using STOPP/START criteria and version adapted to Spanish primary healthcare and factors may modulate PIM onset. Using electronic health record and paper clinical record.	Prescribing error: PIM prevalence: The prevalence of PIM (misprescribing and overprescribing) using the STOPP original criteria was 37.5% (95% CI 31.7 to 43.2), and 50.7% (95% CI 44.7 to 56.6) using the STOPP Spanish AP2012 version. The prevalence of underprescribing was 45.9% (95% CI 40.0 to 51.8) with the START original criteria, and 43.0% (95% CI 37.1 to 48.9) with the START AP2012 version. Risk factors: A significant correlation was found between the number of STOPP PIM and age or number of prescriptions, and between the number of START PIM with age, CCI and number of prescriptions.				PIM prevalence: 102/272 (STOPP)=37.5% (95% CI 31.7 to 43.2), 138/272 (STOPP AP2012)=50.7% (95% CI 44.7 to 56.6), 125/272 (START)=45.9%, 117/272 (START AP2012)=43%
51.	Vezmar Kovačević et al, 201466	Serbia Belgrade	Cross-sectional, prospective	509 elderly aged ≥65 years from five community pharmacies	Prescribed drug	PIM and PPO using STOPP/START criteria. Using patient interview and medical, biomedical record.	Prescribing error: PIM prevalence: There were 164 PIMs identified in 139 patients (27.3%) by STOPP and 439 PPOs identified in 257 patients (50.5%) by START. Risk factors: Patients with more than four prescriptions had a higher risk for PIM (OR 2.85, 95% CI 1.97 to 4.14, p<0.001) and ≥9 medications (OR 7.43, 95% CI 3.20 to 17.23, p<0.001). Patients older than 74 years were more likely to have a PPO (75–84 years: OR 1.47, 95% CI 1.01 to 2.13, p=0.041; and ≥85 years: OR 1.79, 95% CI 1.19 to 2.83, p=0.009).				PIM prevalence: 139/509=27.3% PPO prevalence: 257/509=50.5%
52.	Amos et al, 201567	Emilia-Romagna, Italy	Cohort retrospective	865 354 elderly aged ≥65 years community-dwelling from administrative care data	Prescribed drug only	PIM using updated Maio criteria and patient characteristics related to IP. Using regional Emilia-Romagna administrative healthcare database.	Prescribing error: PIM prevalence: A total of 240 310 (28%) older adults were exposed to at least one PIM. Risk factors: The oldest group (≥85) followed by patients aged 75–84 had 53% and 25% greater odds of receiving PIM than patients 65–75 years old, respectively (OR=1.53, 95% CI 1.50 to 1.55; OR=1.25, 95% CI 1.23 to 1.26, respectively). These odds of exposure to any PIM were slightly lower among men than women (OR=0.98, 95% CI 0.97 to 1.00). An increase in the number of medications prescribed to the patient corresponded with higher odds of PIM exposure. Older GPs (≥56), male GPs and solo practice GPs were more likely to prescribe PIMs to their older patients.				PIM prevalence: 240 310/865 354=28%
53.	Hedna et al, 201568	Sweden	Cohort retrospective	542 elderly aged ≥65 years from the Swedish Total Population Register (primary care)	Prescribed drug	Prevalence of potentially IPs using STOPP criteria and to investigate the association between potentially IPs and occurrence of ADRs. Using the Swedish Prescribed Drug Register, medical records and health administrative data.	Prescribing error: PIM prevalence: 226 patients using primary healthcare had potentially IP. Risk factors: Persons prescribed potentially IP had more than twofold increased odds to experience ADRs (OR 2.47, 95 % CI (1.65 to 3.69), p<0.001), compared with that in persons without potentially IP.				Potentially IP prevalence: 226/542=42%	*Error-related adverse event. The association between PIPs and occurrence of ADRs was for primary care, outpatient or inpatient and hospitalised patients.
54.	Moriarty et al, 201569	Ireland	Cohort prospective	2051 elderly aged ≥65 years from The Irish Longitudinal Study on ageing. Community-dwelling elderly.	Prescribed drug only	PIM and PPO using STOPP, Beers criteria, ACOVE indicators and START. Using face-to-face interview, then follow-up after 1 and 2 years.	Prescribing error: PIM prevalence				PIM: 36.7%–64.8%
								Baseline N (%) (95% CI)	Follow-up N (%) (95% CI)
							Any PIM using STOPP, Beers, ACOVE	1259 (61.4%) (CI 59.3 to 63.5)	1330 (64.8%) (CI 62.8 to 66.9)
							Any PPO using START, ACOVE	1094 (53.3%) (CI 51.2 to 55.5)	1161 (56.6%) (CI 54.5 to 58.8)
							Both PIM and PPO	753 (36.7%)	843 (41.1%)
							Risk factors: Female sex, age and higher number of medicines were significantly associated with change in PIM prevalence. Age and higher numbers of medicines and chronic conditions were found to be significantly associated with change in PPO prevalence.
55.	Ramia and Zeenny, 201471	Lebanon	Cross-sectional	284 outpatients aged ≥18 years visiting community pharmacy	Patients on ≥1 of the chronic medications mentioned in the study	The completion of therapeutic/safety monitoring tests. Patients were subjected to a questionnaire assessing the appropriateness of their laboratory-test monitoring.	Monitoring error: 1. 185 of the patients (65%) were found to complete some, but not all, of the recommended therapeutic/safety monitoring tests. 2. 76 of the patients (27%) completed all recommended therapeutic/safety monitoring. 3. 23 of the patients (8%) did not complete any of the recommended monitoring tests.				Incomplete therapeutic/safety laboratory-test monitoring prevalence: 208/284=73%
Other: discrepancies
56.	Tulner et al, 200931	Amsterdam, The Netherlands	Descriptive prospective	120 elderly aged >65 years from Dutch geriatric outpatient	Using more than one prescribed or OTC medications	1. Frequency and relevancy of discrepancies in drug use. 2. Frequency of MDAPEs. 3. Contributing factors such as increasing age, cognitive status and depressive symptoms, the number of medications used, and the number of physicians visited by the patient. By comparing the medication described by the patient and caregivers with the drugs listed by their GP.	Other: discrepancies prevalence: At least one discrepancy (deletion, addition or difference in dosage) between the medication lists from the patient, the GP or the pharmacy was present in 104 patients (86.7%) involving 386 drugs. MDAPES: MDAPES were identified in 29 patients (24.2%). 7 patients had undertreatment due to deletions. 9 patients had ADR due to additions. 13 patient had DDI. Risk factors: Patients with ≥1 discrepancy reported using a higher mean number of drugs (5.9 vs 4.0; p<0.05) and had more prescribing physicians in addition to their GP (1.1 vs 0.43; p<0.05). Both the presence of discrepancies (Pearson’s r’, 0.293; p=0.05) and MDAPEs (Pearson’s r’, 0.230; p=0.012) were significantly correlated with the number of medications reported by the patient. *The highest rates of discrepancies were seen for acetaminophen (86.7%), laxatives (82.9%) and formulations for dermatological or ophthalmological diseases (81.3%).				Discrepancies prevalence: 104/120=86.7% *Error-related adverse event: MDAPEs: 29/120=24.2%	*Error-related adverse event
57.	Cornu et al, 201232	Brussels, Belgium	Cohort retrospective	189 elderly aged ≥65 years discharged from acute geriatric department of a Belgian university hospital	Prescribed drug	Incidence and type of discrepancies between the discharge letter for the primary care physician and the patient discharge medication and identify possible patient-related determinants for experiencing discrepancies. Discrepancies were categorised as omitted drug, unintended continuation (discontinued home medication documented as home medication), discrepant dose, missing dose, and discrepant brand, omission of a brand name, discrepant frequency, missing frequency or an incorrect pharmaceutical form. By comparing the medications listed in the discharge letter for the primary care physician with those in the patient discharge medication list.	Other: discrepancies prevalence: Almost half of these patients (n=90, 47.6%) (95% CI 40.5 to 54.7) had one or more discrepancies in medication information at discharge. *Two discrepancies (1.2%) were categorised as having the potential to cause severe patient harm. These discrepancies consisted of a wrong dose (doubled the prescribed dose) of digoxin in the patient discharge medication list and the listing of a low-molecular-weight heparin in the patient discharge medication list that was intentionally omitted in the discharge letter because of the development of heparin-induced thrombocytopaenia during hospitalisation. Risk factors: The explorative multivariate model adjusted for age, sex, length of hospital stay and residential situation showed that when the discharge letter contained more than five drugs, the likelihood of experiencing one or more drug discrepancies was 3.22 (95% CI 1.40 to 7.42, p=0.006) times higher than when five or fewer drugs were mentioned. Increasing numbers of drugs in the discharge medication list (OR 1.19, 95% CI 1.07 to 1.32, p=0.001) and discharge letter (OR 1.18, 95% CI 1.07 to 1.32, p=0.001) were associated with a higher risk for discrepancies.				Discrepancies prevalence: 90/189=47.6% (95% CI 40.5 to 54.7)	*Error-related adverse event
Preventable ADEs
58.	Field et al, 200777	USA	Cohort	30 000 elderly ≥65 years from ambulatory care	Prescribed drug	ADE resulting from patients error and risk factors. By electronic tracking of administrative data, review of medical records, reports from clinicians, hospital discharge summaries and ED visit.	Preventable ADE: ADE resulting from patients’ error prevalence: 113 individuals experienced ADE and potential ADE. Risk factor: In a multivariate analysis, there was a dose–response association between patient errors leading to ADEs and potential ADEs and regularly scheduled medications; compared with zero to two medications, the OR for three to four medications was 2.0 (95% CI 0.9 to 4.2); for five to six medications was 3.1 (95% CI 1.5 to 7.0); and for seven or more medications was 3.3 (95% CI 1.5 to 7.0). The strongest association was with the CCI; compared with a score of 0, the OR for a score of 1–2 was 3.8 (95% CI 2.1 to 7.0); for a score of 3–4 was 8.6 (95% CI 4.3 to 17.0); and for a score of 5 or more was 15.0 (95% CI 6.5 to 34.5).				ADE resulting from patients’ error prevalence: 113/30 000=0.38%	*ADE resulting from patients’ error
59.	Gandhi et al, 201022	Boston and Indianapolis, USA	Cross-sectional	68 013 outpatients, mean age 48 and 47 years	Prescribed drug	ADE. Using electronic health record screening, chart review and ADE monitor.	Preventable ADE incidence: The overall rate was 138 ADEs/1000 person-years across the two sites. Preventable ADEs rate 15/1000 person-years across two sites. *Most common drugs associated with preventable ADE were ACE inhibitors and beta-blockers.				Preventable ADEs rate 15/1000 person-years across two sites	*Preventable ADE
60.	Obreli-Neto et al, 201228	Ourinhos microregion, Brazil	Cohort prospective	433 elderly aged ≥60 years from the primary public health system	Prescribed drugs both within and across prescriptions	DDI-related ADR incidence and factors. Using phone or face-to-face structured interview. DDI screening tool: DDI checker programmes (DrugDigest, Drugs, Micromedex and Medscape).	Preventable ADE: DDI-related ADR incidence: occurred in 30 patients (6.9%). Gastrointestinal bleeding occurred in 37% of the DDI-related ADR cases, followed by hyperkalaemia (17%) and myopathy (13%). Seventeen DDI-related ADRs were classified as severity level 2, and hospital admission was necessary in 11 cases. *Warfarin was the most commonly involved drug (37% of cases), followed by acetylsalicylic acid (17%), digoxin (17%) and spironolactone (17%). Risk factors: The multiple logistic regression showed that the following were associated with the occurrence of DDI-related ADRs: 1. Age ≥80 years (OR 4.4, 95% CI 3.0 to 6.1, p<0.01). 2. CCI ≥4 (OR 1.3, 95% CI 1.1 to 1.8, p<0.01). 3. Consumption of five or more drugs (OR 2.7, 95% CI 1.9 to 3.1, p<0.01). 4. Use of warfarin (OR 1.7, 95% CI 1.1 to 1.9, p<0.01).				Incidence of DDI-related ADR: 30/433=6.9%	*Error-related adverse event

ACOVE, Assessing Care of Vulnerable Elders; ADE, adverse drug event; ADI, adverse drug interaction; ADR, adverse drug reaction; CCI, Charlson Comorbidity Index; DDI, drug–drug interaction; ED, emergency department; GP, general practitioners; HEDIS, Health Plan Employer Data and Information Set; HRQOL, health-related quality of life; IDU, inappropriate drug use; IP, inappropriate prescribing; IPET, improved prescribing in the elderly tool; MAI, Medication Appropriate Index; MDAPE, medication discrepancy adverse patient event; MEPS, Medical Expenditure Panel Survey; NORGEP, Norwegian General Practice; OTC, over-the-counter; PDDI, potential drug–disease interaction; PIM, potentially inappropriate medicine; PPO, potential prescribing omissions; START, Screening Tool to Alert doctors to Right Treatment; STOPP, Screening Tool of Older Person’s Prescriptions.