Table 2 Key characteristics of the eligible studies
From: Factors affecting the uptake of new medicines: a systematic literature review
Authors | Population | Drugs | Methods | Variables |
|---|---|---|---|---|
Álvárez and Hernández 2005 [16] | 32 healthcare centres, 313321 inhabitants, Spain | 50 new drugs | multiple linear regressions | Practice characteristics: size (number of doctors and number of patients), location (urban or rural), number of years functioning as a primary care centre, pharmaceutical expense per secured patients, number of consultations per doctor |
Patient characteristics: proportion of patients in pension | ||||
Behan et al. 2005 [17] | 126991 inhabitants, 134 full-time equivalent GPs, Australia | 2 new drugs (celecoxib and rofecoxib) | comparison of means (Student’s t-test) | Practice characteristics: location (urban or rural) |
Bourke and Roper 2012 [18] | 616 GPs and all their prescriptions, Ireland | 6 new drugs, from 6 therapeutic classes | survival analysis | Prescriber characteristics: age, portfolio width, savings made from meeting prescribing targets, GP being an early adopter in at least one of the other five study drugs |
Practice characteristics: employee composition* (practice with nurse, practice with secretary), practice with in-house dispensary, location (urban or rural) | ||||
Drug/market characteristics: time-variant percentage of GPs who have adopted the study drug | ||||
Coleman et al. 1966 [7] | 125 GPs (prescriptions and interviews) and 103 SPs (interviews), four small cities in Illinois, US | 1 new drug (tetracycline, a broad-spectrum antibiotic) | elementary statistics | Prescriber characteristics: age, prescribing volume in the therapeutic class of the new drug, speciality, number of contacts with drug representatives, number of professional journals read, number of pharmaceutical house organs read, number of speciality meetings attended, number of non-speciality meetings attended, number of hospital meetings attended, number of county medical society meetings attended, perceived scientific orientation, social position in advisor, discussion, and friendship networks |
Practice characteristics: type (solo or group/partnership) | ||||
Note: The survey questionnaire resulted in a very large number of variables—only those most frequently discussed in the relevant literature are reported here | ||||
Corrigan and Glass 2005 [19] | 4216 doctors, US | 38 new compounds | analysis of covariance (ANCOVA) model | Prescriber characteristics: gender, age, board certification, hospital affiliation, type of doctor (trialist or control), prescribing volume |
Dybdahl et al. 2004 [20] | 191 practices, 470000 inhabitants, Denmark | 14 new drugs, grouped in 4 categories | Pearson’s correlation coefficient | Practice characteristics: type (solo or group/partnership) |
Drug characteristics: prescription cost* | ||||
Dybdahl et al. 2005 [21] | 191 practices, 470000 inhabitants, Denmark | 14 new drugs, grouped in 4 categories | multiple linear regressions | Practice characteristics: prescribing volume within the therapeutic class of the new drug, number of patients eligible for the new drug, prescribing volume of all other drugs, number of all other patients |
Dybdahl et al. 2011 [22] | 68 GPs, Denmark | 2 new drug groups (COX-2 and AT-II) | univariate and multivariate linear regressions | Prescriber characteristics: perceived scientific orientation, perceived need for continuing medical education (CME), current CME activities*, previous hospital employment |
Florentinus el al 2007 [23] | 86 GPs, 13997 patients, the Netherlands | 5 new drugs, from 5 therapeutic classes | logistic multilevel model | Prescriber characteristics: quality of pharmacotherapy audit meetings (PTAMs), PTAM composition (number of pharmacists and pharmacies, total number of participants, number of GPs) |
García et al. 2000 [24] | 74 GPs and SPs (paediatrics), Spain | 28 new drugs, 10 with therapeutic novelty and 18 without | univariate and multivariate linear regressions | Prescriber characteristics: age, gender, speciality, type of contract (permanent or temporary), number of workplaces, drug expenditure captured by the deviation from the district’s mean |
Practice characteristics: management (reformed or non-reformed), region | ||||
Garjón et al. 2012 [25] | 1248 doctors, Spain | 8 new drugs, suitable for both primary and secondary care | survival analysis | Prescriber characteristics: speciality |
Drug characteristics: therapeutic novelty | ||||
Glass 2003 [26] | 1876 doctors, US | new drugs for the outpatient treatment of 8 disorders or diseases | comparison of means (Fischer’s least significant difference method) | Prescriber characteristics: investigator type (phase IIIb investigator, phase IV investigator, non-investigator) |
Glass 2004 [27] | 2108 clinical trial investigators, US | 72 new compounds | multiple linear regressions | Prescriber characteristics: gender, age, board certification, hospital affiliation, per cent of working hours spent in a hospital setting, clinical research experience (total number of clinical studies, number of clinical studies conducted for the sponsoring company, number of clinical studies conducted in the therapeutic class of the new drug, number of clinical studies with the compound), relative grant amount received by investigator, pre-launch prescribing volume, pre-launch prescribing volume in the therapeutic class of the new drug, pre-launch prescribing of the sponsoring company’s products as a percentage of total prescribing (company loyalty), market share of the sponsoring company 3 and 6 months post launch as percentage of total prescribing volume |
Drug/market characteristics: therapeutic novelty, number of drugs tested for the same indication, participation of a contract research organisation, market share 1 year post launch | ||||
Glass and Rosenthal 2004 [28] | 3646 doctors, US | 32 new drugs | binomial logistic regression | Prescriber characteristics: gender, age, board certification, speciality, clinical investigation experience, hospital affiliation, pre-launch prescribing volume, pre- launch prescribing volume in the same therapeutic class as the new drug, pre-launch company loyalty |
Drug characteristics: therapeutic novelty, marketing budget of the pharmaceutical company assigned for the new drug | ||||
Glass and Rosenthal 2005 [29] | 2287 clinical trial investigators, US | 38 new drugs | ordinary least squares (OLS) and binomial logistic regression | Prescriber characteristics: gender, age, board certification, speciality, hospital affiliation, percentage of working hours spent in a hospital setting, clinical research experience (total number of clinical trials, total number of clinical studies conducted, total number of clinical studies conducted for the sponsoring company, total number of clinical studies conducted in the therapeutic class of the new drug, total number of clinical studies conducted with the compound), total pre-launch prescribing volume, pre-launch prescribing volume in the therapeutic class of the new drug, pre-launch prescribing volume of sponsoring company’s products, pre-launch prescribing volume of sponsoring company’s products as a percentage of total prescribing (company loyalty) |
Drug characteristics: market share of the new drug 12 months post launch, marketing budget of the pharmaceutical company assigned for the new drug | ||||
Glass and Dalton 2006 [30] | 484 phase IV clinical trial investigators, US | new drugs for the outpatient treatment of 8 disorders or diseases | binomial logistic regression | Prescriber characteristics: gender, age, board certification, speciality, practice type (office, hospital, academic medical centre, other), per cent of working hours spent in a hospital setting, clinical research experience (number of clinical trials conducted, number of clinical studies conducted for the sponsoring company, number of clinical studies conducted in the same therapeutic class as the new drug, number of clinical studies conducted with the compound), relative grant amount received, pre-study prescribing volume, pre-study prescribing volume in the same therapeutic class as the new drug, pre-study prescribing volume of the new drug, pre-study prescribing volume of sponsoring company’s products, pre-study prescribing volume of sponsoring company’s products as a percentage of total prescribing (company loyalty) |
Drug characteristics: market share of the new drug in its therapeutic class 12 months post launch, revenue of the pharmaceutical company | ||||
Greving et al. 2006 [11] | 70 GPs, 9470 hypertensive patients, the Netherlands | 1 new drug (angiotensin II receptor blocker (ARB)) | multilevel logistic regressions | Prescriber characteristics: use of commercial information, use of medical journals, CME, use of other professional information, use of a prescribing decision support system, personal involvement in PTAMs |
Practice characteristics: type (solo or group/partnership), location (urban or rural) | ||||
Patient characteristics: age, gender, number and type of comorbidities, referrals to internist/cardiologist | ||||
Drug characteristics: perceived benefits (survey data) | ||||
JP Griffin and TD Griffin 1993 [31] | 10 developed countries | drugs introduced in the last 5 years | descriptive statistics | Prescriber characteristics: nationality |
Groves et al. 2010 [6] | 925 doctors and all their prescriptions, Canada | 4 new drugs (COX-2 inhibitors) | correlation analysis with t-tests | Prescriber characteristics: gender, age, birthplace, speciality, training location (domestic or overseas qualification), professional age, prescribing volume of four related drugs |
Practice characteristics: location (urban or rural) | ||||
Helin-Salmivaara et al. 2005 [32] | 2558 doctors, 507262 prescriptions from the same therapeutic class, Finland | 2 new drugs (celecoxib and rofecoxib) | general linear mixed model | Prescriber characteristics: gender, professional age, speciality |
Patient characteristics: age, gender | ||||
Huskamp et al. 2013 [33] | 30369 doctors, US | 9 new drugs (second-generation antipsychotics) | Cox’s proportional hazard model | Prescriber characteristics: gender, age, speciality, hospital affiliation*, prescribing volume in the same therapeutic class as the new drug, training location (domestic or overseas qualification, top- or non-top-25 medical school) |
Practice characteristics: type (solo or group/partnership) | ||||
Drug characteristics: degree of innovation (original formulation or reformulation) | ||||
Inman and Pearce 1993 [34] | 3346 GPs, England | 27 new drugs | descriptive statistics | Prescriber characteristics: gender, number of treatments, professional age, training location (domestic or overseas qualification) |
Iyengar et al. 2011 [12] | 185 doctors, US | 1 new drug, third entry in the category (for treatment of viral infections) | discrete-time hazard model | Prescriber characteristics: speciality (GP or SP), number of patients, referral to other doctors, prescribing volume in the same therapeutic class as the new drug, self-reported leadership*, detailing, indegree centrality (referral, discussion, and total network), outdegree centrality (referral, discussion, and total network), peer influence through adoption and usage* |
Practice characteristics: type (solo or group/partnership), type of hospital (university/teaching), location (city dummies) | ||||
Kozyrskyj et al. 2007 [35] | 12 million patients and 2000 doctors, Canada | 4 new drugs, from 4 therapeutic classes | polytomous logistic regression | Prescriber characteristics: age, gender, speciality*, professional age, training location, hospital affiliation* |
Practice characteristics: type (solo or group/partnership) | ||||
Patient characteristics: age, gender, neighbourhood income quintile*, prescription reimbursement status, presence of chronic conditions | ||||
Lin et al. 2011 [36] | 155 SPs (psychiatry) affiliated with 12 healthcare centres, Taiwan | 1 new drug (antidepressant, in the selective norepinephrine reuptake inhibitor (SNRI) family) | Cox’s proportional hazard model | Prescriber characteristics: age, gender, speciality (in depressive disorder), hospital experience (number of current workplaces), outpatient services (number of visits), peers’ adoption ratio, opinion leaders’ adoption ratio, (dis)similarities with adopting peers’ age, gender, and tenure, SNRI proportion (past experience and preference for SNRI) |
Practice characteristics: size (number of SPs), ownership (public or private), location | ||||
Liu et al. 2011 [37] | 41488 patients, 4429681 prescriptions, Taiwan | 7 new drugs (oral hypo-glycemic agents, for treatment of diabetes) | logit model | Practice characteristics: market share (number of outpatient visits), ownership* (public or private not-for-profit or private for-profit), accreditation level (clinic, academic medical centre, metropolitan hospital, or local community hospital), location |
Patient characteristics: age, gender, disease severity, number of prescriptions per visit | ||||
Drug characteristics: prescribing volume in the same therapeutic class as the new drug | ||||
Liu and Gupta 2012 [13] | 2129 doctors, US | 1 new drug (for treatment of a chronic condition) | discrete-time hazard model | Prescriber characteristics: speciality, prescribing volume in the same therapeutic class as the new drug, number of detailing visits, number of medical meetings and events, number of patient requests made to doctors, number of peers in geographic proximity who had already adopted the new drug |
Drug characteristics: journal advertising, linear time trend | ||||
Patient characteristics: median age of people in the community, average household income, health insurance index, percentage of white population | ||||
Manchanda et al. 2008 [38] | 466 doctors, Manhattan (New York City), US | 1 new drug (for treatment of a chronic condition) | discrete-time hazard model | Prescriber characteristics: contemporaneous effect of detailing, accumulated stock of detailing, accumulated stock of sampling, contagion measure (number of adopting doctors in geographic proximity) |
Drug characteristics: aggregate marketing expenditure (direct-to-consumer advertising (DTCA)), time since launch | ||||
Mark et al. 2002 [14] | 187 doctors, 752 patients, prescriptions from medical records, US | 4 new drugs (antipsychotics—(clozaril, risperidone, olanzapine, and quetiapine) | bivariate and multivariate probit regression analysis | Prescriber characteristics: age, gender, board certification*, number of patients*, number of contacts with pharmaceutical representatives*, attendance on professional meetings*, preference for atypical initial treatments, preference for atypical treatments for nine different conditions*, percentage of patients influenced by individual medication costs |
Practice characteristics: location | ||||
Patient characteristics: age, gender, race/ethnicity, education, marital status, insurance status, first onset of the disorder, diagnosis*, symptoms, hospitalisation in the past 12 months* | ||||
Mizik and Jacobson 2004 [39] | 74075 doctors, US | 1 new drug, within a well-established therapeutic area, and 2 older drugs | dynamic fixed effects distributed lag regression | Prescriber characteristics: speciality*, detailing volume, sampling volume, prescribing volume in the same therapeutic class as the new drug (competitor prescribing) |
Ohlsson et al. 2009 [5] | 73547 doctors, 32011 patients, Sweden | 1 new drug (rosuvastatin, for treatment of high blood cholesterol) | generalised estimation equations and alternating logistic regression | Practice characteristics: ownership (public or private), proximity to SPs, location (urban or rural), size (prescribing volume) |
Patient characteristics: age, gender, income, marital status, birthplace, length of residence in Sweden | ||||
Steffensen et al. 1999 [40] | 319 GPs, 193876 prescriptions, Denmark | 5 generically new compounds | multiple logistic regression | Prescriber characteristics: gender, age |
Practice characteristics: type (solo or group/partnership), size (number of patients), number of consultations per patient, number of telephone consultations per patient, number of home visits per patient, number of procedures performed per patient, number of laboratory tests performed per patient | ||||
Ruof et al. 2002 [41] | 72 GPs, 28 SPs (neurology), Germany | 1 new drug class (for treatment of Alzheimer’s disease) | Sperman’s rank correlation coefficient | Prescriber characteristics: speciality (GP or neurologist) |
Drug characteristics (perceived): safety, efficacy, life quality improvement, nursing home admission delayed, budgetary impact | ||||
Tamblyn et al. 2003 [1] | 1661 doctors, 669867 elderly patients, Canada | 20 new drugs, from 6 therapeutic classes | multivariate logistic and conditional Poisson regressions | Prescriber characteristics: gender, speciality, professional age*, training location |
Practice characteristics: location (urban or rural), referral rate to SPs who prescribe drugs in the same therapeutic class as the new drug*, size | ||||
Drug characteristics: detailing (minutes), advertising (pages) | ||||
Patient characteristics: proportion of elderly patients in the practice population | ||||
Van den Bulte and Lilien 2001 [15] | 121 GPs, four small cities in Illinois, US | 1 new drug (tetracycline, a broad-spectrum antibiotic) | discrete-time hazard model | Prescriber characteristics: professional age, number of journals read, position, scientific orientation, status (number of nominations received as advisor or discussant—2 contagion variables to capture word of mouth operating over direct ties and 2 contagion variables to capture competition for status between structurally equivalent doctors |
Drug characteristics: seasonal effect, depreciation-adjusted stock of marketing effort by the first entrant, depreciation-adjusted stock of marketing effort by the two subsequent entrants |