Table 2A

Systematic review quality assessment: Joanna Briggs Institute Critical Appraisal Checklist for descriptive/case series and cross-sectional

123456789Overall appraised
1Ramia and Zeenny, 201471
Adult
YYNNNANAYYYHighPatients were subjected to a questionnaire assessing the appropriateness of their laboratory-test monitoring, may cause recall bias.
2Sorensen et al, 200676
Adult
YYN, risk factors related to patient not studiedYNANAYYYHigh
3Vuong and Marriott, 200625
Adult
UYNYNANANYY, percentage was used but statistics was not described in the full text.HighUnclear sampling strategy.
4Adams et al, 200972
Adult
YYY (but for all types of adverse event)N (self-reported adverse events)NANANYYHighRisk of recall bias and attribution with self-reported adverse events.
5Gandhi et al, 201022
Adult
UYNYYNANAYYHigh
6Lu and Roughead, 201120
Adult
YYYN (subjective patient-reported medication error)YNANA (secondary analysis)N (telephone survey, self-reported)YHighRisk of recall bias with patient-reported medication error.
7Sears et al, 201221
Adult
YYYN (subjective self-reported medication error)YNANA (secondary analysis)N (telephone survey, self-reported)YHighRisk of recall bias with patient self-reported medication error.
8Koper et al, 201323
Adult
N (convenience)YNYNANANA (100% participants)YYHighSelection bias.
9Dallenbach et al, 200724
Adult-DDI
N (consecutive)NNYNANANA (retrospective)YYModerate
10Indermitte et al, 200734
Adult-DDI
Y (pharmacy choose); N (first 12 customers)YNYNANAYYYHigh
11Mahmood et al, 200735
Adult-DDI
YYNYNANANA (retrospective)YYHighPatients may actually be on other drugs so may not catch all the DDI.
12Guthrie et al, 201539
Adult-DDI
YYY (but for both own home and care home)YYNANA (secondary analysis)YYHighRisk factors for both own home and care home.
13de Oliveira Martins et al, 200641
Elderly-PIM
N (first came to pharmacy carrying prescription for two or more drugs)YY, but not allYYNANYYHighSelf-reported data from elderly concerning drug use may lead to information bias.
14Pugh et al, 200642
Elderly-PIM
YYYYYNANA (secondary data analysis)YYHighMay underestimate the exposure because they do not account for OTC.
15Saab et al, 200643
Elderly-PIM
YYYYNANAYYYHighSelf-reported data from elderly concerning drug use may decrease accuracy.
16Bregnhøj et al, 200745
Elderly-PIM
N (each GP was asked to recruit six patients who were randomly selected)YNYNANAYYYHighSelection bias.
17Johnell and Fastbom, 200846
Elderly-PIM
YYYYYNAYYYHighDid not look for comorbidity as a risk factor because data were from Swedish Prescribing Drug Register.
18Haider et al, 200948
Elderly-PIM
YYYYNANANAYYHigh
19Lai et al, 200949
Elderly-PIM
YYYYNANANA (secondary analysis)YYHighDid not address comorbidity as a risk factor.
20Ryan et al, 200951
Elderly-PIM
YYYYNANANYYHighMay underestimate the outcome because they do not account for OTC.
21Zaveri et al, 201053
Elderly-PIM
UYYYNANANYYHighNot enough information in the article.
22Leikola et al, 201156
Elderly-PIM
YYNYNANANAYYHighMay underestimate the outcome because database lacks diagnostic patient data, therefore used the Beers 2003 criteria independent of diagnoses and the data provide no information on the use of PIMs that are not reimbursable. Nine PIMs that were not reimbursable in Finland in 2007: triazolam, belladonna alkaloids, diphenhydramine, hydroxyzine, ferrous sulfate, bisacodyl, nitrofurantoin and clonidine.
23Lin et al, 201157
Elderly-PIM
UYYYNANANAYYHigh
24Woelfel et al, 201170
Elderly-PIM
YYYYNANANAYYHigh
25Haasum et al, 201259
Elderly-PIM
YYNYYNANA (secondary data analysis)YYHigh
26Nyborg et al, 201260
Elderly-PIM
YYYYYNANA (secondary data analysis)YYHigh
27Yasein et al, 201261
Elderly-PIM
NYNYYNANYYModerate
28Candela Marroquín et al, 201219
Elderly-PIM
N (convenience sample)YNYNANANYYModerateSampling strategy.
Subjective information on socioeconomic and clinical variables may decrease accuracy.
29Weng et al, 201364
Elderly-PIM
YYYYYNANYYHighSampling strategy.
30Baldoni et al, 201429
Elderly-PIM
UYYYYNAYYYHigh
31Castillo-Páramo et al, 201465
Elderly-PIM
YYYYYNAYYYHighElectronic health record use limitations (incomplete record and quality of data).
32Vezmar Kovačević et al, 201466
Elderly-PIM
YYYYNANANYYHigh
33Nobili et al, 200938
Elderly-DDI
YYYYNANANA (administrative database)YYHighThe use of administrative database limits looking for comorbidity as a confounder.
34Secoli et al, 201030
Elderly-DDI
UYYYNANANAYYHighMay underestimate the true DDI prevalence because they do not account for OTC.
35Obreli Neto et al, 201227
Elderly-DDI
YYYYNANANA (data from primary healthcare system)YYHighMay underestimate the DDI prevalence because (1) most instruments available for assessing DDIs consider only pairs of drugs and do not account for interactions involving combinations of three or more drugs so (2) did not account for OTC.
36Pit et al, 200874
Elderly
YYYYNANAYYYHigh
37Tulner et al, 200931
Elderly
N (consecutive)YYYNANAYYYHighInformation on medication described by the patient and caregivers may not always be accurate.
38Obreli Neto et al, 201126
Elderly-DDI
YYNYNANANAYYHigh
39Mira et al, 201373
Elderly
YYYYNANAYYYHighSelf-reported medication error from elderly concerning drug use may have recall bias.
40Mand et al, 201433
Elderly
YYYYNANANAYYHigh
  • 1 Was study based on a random or pseudo-random sample?

  • 2 Were the criteria for inclusion in the sample clearly defined?

  • 3 Were confounding factors identified and strategies to deal with them stated?

  • 4 Were outcomes assessed using objective criteria?

  • 5 If comparisons are being made, was there sufficient descriptions of the groups?

  • 6 Was follow-up carried out over a sufficient time period?

  • 7 Were the outcomes of people who withdrew described and included in the analysis?

  • 8 Were outcomes measured in a reliable way?

  • 9 Was appropriate statistical analysis used?

  • DDI, drug-drug interaction; GP, general practitioner; N, no; NA, not applicable; OTC, over-the-counter; PIM, potentially inappropriate medication; U, unclear; Y, yes.