What factors are taken into consideration when determining which medication a patient should take?

As Figure 1 shows, 178 studies were removed from the initial 205—54 on grounds of duplication and 124 because at least one of the four eligibility criteria was not met—and 8 were added, following identification through bibliographies and citations. Table 2 summarises the remaining 35 eligible studies [1, 5–7, 11–41] in terms of location and size of the sample population, type and number of study drugs, methodology, and factors that might influence new drug uptake.

Flow diagram of the search strategy. *The full texts of the 51 potentially relevant studies were assessed by Ágnes Lublóy (100%) and Gábor Benedek (12%). The colleagues agreed on all studies reviewed together.

The 35 eligible studies were all conducted in developed countries—mostly in North America (17 studies) [1, 6, 7, 12–15, 19, 26–30, 33, 35, 38, 39] and North-Western Europe (11 studies) [5, 11, 18, 20–23, 32, 34, 40, 41]—14 studies were conducted in the US [7, 12–15, 19, 26–30, 33, 38, 39], four in Denmark [20–22, 40], three each in Canada [1, 6, 35] and Spain [16, 24, 25], two each in the Netherlands [11, 23] and Taiwan [36, 37], and one each in Australia [17], Finland [32], Germany [41], Ireland [18], Sweden [5], and the UK [34], while one study [31] covered ten developed countries—Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Spain, the UK, and the US.

The sample populations varied greatly, in both unit of observation and size. In one study [31], the unit of observation was the country—in five others [5, 16, 20, 21, 37], the healthcare provider, ranging from 32 healthcare centres [16] to 191 practices [20, 21], with a mean of 146 and one study not disclosing the number [37]. However, doctors were the unit of observation for 29 [1, 6, 7, 11–15, 17–19, 22–30, 32–36, 38–41] of the 35 eligible studies—nine studies observed general practitioners (GPs) [7, 11, 15, 17, 18, 22, 23, 34, 40], three specialists (SPs) [14, 25, 36], and 17 both [1, 6, 12, 13, 19, 24, 26–30, 32, 33, 35, 38, 39, 41]. The sample populations ranged from 68 [22] to 74,075 doctors [39], with a median of 770.

Study drugs

The study drugs covered a wide range—antibiotics, cardiovascular drugs, coxibs, antihypertensives, and antidepressants, for example. Ten studies used the prescription data for one drug/therapeutic class [5, 7, 11–13, 15, 36, 38, 39, 41], eight studies used the prescription data for two to five drugs [6, 14, 17, 22, 23, 32, 35, 40], six for six to ten drugs [18, 25, 26, 30, 33, 37], and 11 for more than ten drugs [1, 16, 19–21, 24, 27–29, 31, 34]—50 new drugs [16] and 72 new compounds of varying therapeutic novelty [27] were the maxima. Twenty two studies assessed highly innovative drugs [5, 6, 11, 13, 14, 17, 20–22, 26, 27, 29–33, 35–38, 40, 41], three me-too drugs [7, 12, 15] and ten both [1, 16, 18, 19, 23–25, 28, 34, 39].

Methods

Eight studies used simple statistical methods [6, 7, 17, 20, 26, 31, 34, 41]—comparison of means or association or correlation analyses, for example. Other eight studies—published mostly in the 2010s—used either discrete-time hazard models or very similar survival analyses [12, 13, 15, 18, 25, 33, 36, 38]. However, the most popular methods of assessing the importance of particular variables were logistic and linear regressions, used in 11 [1, 5, 11, 14, 23, 28–30, 35, 37, 40] and respectively eight studies [16, 19, 21, 22, 24, 27, 32, 39].

Dependent variables

The dependent variables under consideration varied greatly, with some studies focusing on either prescriber [17, 22, 23, 26, 31, 34, 39] or practice [16, 21] characteristics, whilst others assessing two or more categories of variables [1, 5–7, 11–15, 18–20, 24, 25, 27–30, 32, 33, 35–38, 40, 41]—prescriber and practice characteristics were the most common such combination. On average, the studies included nine dependent variables. Table 3 summarises the dependent variables found significant in at least one of the 35 eligible studies, whereas the last column of Table 2 details all the inputted variables—their number highlights the complexity of new drug diffusion.

In both primary and secondary care, new drug diffusion is subject to interacting influences. The idea that early prescribers do not generally exist [20, 35, 40] does not necessarily mean that adoption of new drugs is random. Rather, adoption varies across prescribers, with the prescriber, patient, practice, and drug characteristics. The 35 eligible studies [1, 5–7, 11–41] identified several—partly overlapping—characteristics that proved crucial in the adoption process, and that predicted—seemingly consistently—new drug uptake. This review will clearly indicate the characteristics constant across drug types. However, in a number of cases, there is contradiction within the literature. Whilst some studies found one particular variable significant, others found no evidence for the predictive power of that variable. Also, reported coefficients between one particular variable and new drug uptake were not always consistent in terms of sign. These anomalies will also be clearly indicated in this review.

Prescriber characteristics

Twenty nine of the 35 eligible studies investigate at least one prescriber characteristics [1, 6, 7, 11–15, 18, 19, 22–36, 38–41].

Socio-demographic characteristics

Nineteen of the 35 eligible studies investigate at least one socio-demographic characteristics of prescribers [1, 6, 7, 14, 15, 18, 19, 24, 27–36, 40].

Gender (n = 15) [1, 6, 14, 19, 24, 27–30, 32–36, 40]. Gender played an influential role in the early adoption of new drugs in seven [1, 6, 27, 32–34, 40] of the 15 studies—in the other eight [14, 19, 24, 28–30, 35, 36], the variable was not significant. Where gender was found influential, male prescribers were much more likely to adopt new drugs than female prescribers, a finding consistent across drug types. In a large-scale quantitative study of British doctors, the first of its kind, Inman and Pearce [34] observed that male doctors had much higher rates of new drug utilisation than female doctors—in the group that prescribed new drugs most heavily, women accounted for only 9 per cent. Later studies came to similar conclusions [1, 6, 27, 32, 33, 40]. In contrast, Glass [27] found that women were somewhat more likely than men to embrace new drugs six months after launch, but not after just three.

Age (n = 14) [6, 7, 14, 18, 19, 24, 27–30, 33, 35, 36, 40]. Age was associated with new drug uptake in nine studies [6, 7, 18, 27–30, 33, 35]. Seven of these found that early prescribers were younger than the majority [7, 18, 27–30, 33], with younger doctors’ higher propensity for innovation and older doctors’ more established prescribing practices as the most likely explanations, whereas the other two found that early prescribers were likely to be older [6, 35]. Five other studies found no correlation at all [14, 19, 24, 36, 40].

Professional age (n = 5) [1, 6, 15, 34, 35]. Defined as the number of years since graduation from medical school [15, 34], professional age can be proxied by the number of years spent in practice [6, 35]. Although professional age evidently correlates highly with agea, its impact on new drug uptake is less obvious—one study found no association [35], two studies found significant positive associations [6, 34], whereas two others found significant negative associations [1, 15]. However, positive associations were found in less powerful, bivariate analyses [6, 34]. In more powerful, multivariate analyses, similar to studies assessing the impact of age on new drug uptake, younger professional age was found favourable to early adoption [1, 15].

Training location (n = 5) [1, 6, 33–35]. So far, due to data constraints, only five studies assessed the role of training location in new drug uptake [1, 6, 33–35], and—with one exception [6]—found it influential. From British [34] and Northern American [35] perspectives, more new drugs were prescribed by doctors with overseas qualifications than by doctors with domestic qualifications. At the same time, Huskamp et al. [33] reported that doctors who had graduated from a top-25 medical school were slower to adopt than others, with high-ranking medical schools possibly more conservative than others in adopting new drugs and granting exposure to pharmaceutical marketing. Also, Tamblyn et al. [1] found that doctors who had graduated from the youngest medical schools had higher relative rates of new drug use than others.

Number of current workplaces (n = 2) [24, 36]. The two studies led to ambiguous conclusions—García et al. [24] found that doctors with more than one workplace adopted new drugs earlier than others, whereas Lin et al. [36] found the number of current workplaces irrelevant.

Nationality (n = 1) [31]. The study where the speed of adoption was measured across nationalities found that doctors in Italy, Germany, Spain, and the US were more likely to prescribe products with new active chemical entries than doctors in the other six countries under review, Belgium, Canada, France, Japan, the Netherlands, and the UK [31]. British doctors were found least likely to prescribe new drugs—they relied on progressively fewer active substances for a greater proportion of their prescriptions.

Scientific orientation

Twenty four of the 35 eligible studies access some aspects of prescribers’ scientific orientation [1, 6, 7, 11–15, 19, 22–30, 32, 33, 35, 36, 39, 41].

Speciality (n = 16) [1, 6, 7, 12, 13, 24, 25, 28–30, 32, 33, 35, 36, 39, 41]. Intuitively, doctors are likely to prescribe new drugs in clinical and therapeutic areas where they feel familiar or have special interests. Also intuitively, since a significant amount of general practice prescribing is hospital-initiated or hospital-led [42, 43], SPs are likely to influence new drug uptake among GPs, through advice or example. There is a broad consensus in the literature that new drugs diffuse into general practice in two stages, with SPs as innovators and GPs as followers [44]. In line with this model, ten of the 16 eligible studies reported faster adoption among SPs in secondary care than among GPs in primary care, and found that specialisation in the therapeutic areas of newly introduced drugs associated with adoption rate significantly and positively [1, 7, 13, 25, 28, 29, 32, 33, 36, 41]. For example, Garjón et al. [25] analysed the diffusion of eight new drugs suitable for common disorders in both primary and secondary care concluded that SPs adopted new drugs earlier than GPsb. There is also a broad consensus in the literature that SPs adopt new drugs in their speciality earlier than GPs and other SPs, both of whom tend to rely on norms—low prescribing proportions do not justify high cognition time costs [33].

However, six of the 16 eligible studies came to different conclusions [6, 12, 24, 30, 35, 39]. Groves et al. [6] found that GPs were more likely early prescribers than SPs. Kozyrskyj et al. [35] found mixed evidence—in some cases, GPs adopted new drugs earlier than SPs, whereas in others no significant associations were observed. Four other studies found no or weak associations between speciality and new drug uptake [12, 24, 30, 39].

Hospital affiliation (n = 8) [19, 22, 27–30, 33, 35]. Association with an academic centre such as a university or a research institute—through teaching, publishing, or holding an academic appointment, for example—suggests a professional orientation that may result in early adoption of new drugs. However, hospital-affiliated doctors face formulary restrictions.

In line with the heightened professional orientation argument, two of the eight eligible studies found that doctors in hospital settings adopted some new drugs earlier than doctors with no hospital affiliation—two out of four new drugs, according to Kozyrskyj et al. [35], and two out of nine, according to Huskamp et al. [33]. In contrast, four studies reported that office-based clinical trial investigators were more likely to prescribe newly marketed drugs than other doctors [19, 27–29]. Moreover, the longer doctors worked in hospitals, the less likely they were to prescribe new drugs [19, 29], not least due to hospital formularies [28, 29]. Two studies observed no difference between hospital-affiliated and office-based doctors [22, 30].

Board certification (n = 6) [14, 19, 27–30]. In the US, board certification quantifies on a voluntary basis doctors’ mastery of the core body of knowledge and skills in their chosen specialities at a specific time [45]. Board certification was found consistently associated with adoption in two [19, 28] of the six studies, but not in the others [14, 27, 29, 30].

Clinical trial participation (n = 3) [19, 26, 28]. Due to proximity to research and understanding of the evidence base, clinical trial participation increased early adoption of new drugs and had a significant and positive impact on subsequent prescribing in all eligible studies [19, 26, 28]. Other studies published within the same research framework by Glass and colleagues provided further insight into the new drug uptake of clinical trial investigators [27, 29, 30]. As expected, participation in drug class clinical trials was also associated with increased prescribing of the study drug [27, 29]. Against intuition, the higher the number of clinical studies conducted in total [27], or for the sponsor [27, 29], the lower doctors’ sense of identification with a new drug and the lower the likelihood of prescribing it.

CME and pharmacotherapy audit meetings (PTAMs) (n = 3) [11, 22, 23]. Dybdahl et al. [22] found that CME was significantly and positively associated with new drug uptake in some clinical areas—however, the association was weak and inconsistent across drug classes. Greving et al. [11] found no relationship between CME and new drug uptake and reported negative relationships between doctors involved in PTAMs and new drug uptake. Florentinus et al. [23] did not analyse the differences in new drug uptake between GPs who attended PTAMs voluntarilyc and those who opted out, but they did analyse the effects of PTAMs on early new drug prescribing by GPs. GPs who attended low-quality PTAMs—without structure or concrete outcomes—were more than twice as likely to prescribe new drugs early post-marketing than those who attended high-quality PTAMs—with frequent concrete outcomes and outcome evaluations. The authors argued that high-quality PTAMs may restrict the number of new drugs GPs can prescribe, or may foster a rather negative attitude towards prescribing new drugs, but found no empirical evidence. Moreover, the authors found that increases in the number of GPs—and pharmacists—attending PTAMs led to increases in new drug prescribing by GPs. These findings underscore the social network aspect of such forums, which facilitate interactions among healthcare professionals. Doctors who attend more or larger forums interact and influence one another’s prescribing more than they would otherwise. The section ‘Contagion through social networks’ discusses the role of social networks in new drug uptake in further detail.

Number of professional journals read (n = 3) [7, 11, 15]. A possible measure of scientific orientation, the number of professional journals doctors read is a variable quantifiable only through survey. Peer-reviewed journals are important sources of information on new drugs [8, 43, 46]. Sound research evidence is considered very influential in reaching prescribing decisions [42, 43].

Three studies assessed the impact of professional journals on new drug uptake [7, 11, 15]. Two of these used the same dataset and reported that the higher the number of journals read, pharmaceutical company newsletters and scientific and professional publications included, the earlier doctors adopted new drugs [7, 15]. However, Greving et al. [11] concluded that new drug adoption among GPs was still determined more by promotional than professional information—no significant relation was observed between the use of scientific medical journals and new drug uptaked.

Perceived scientific orientation (n = 3) [7, 15, 22]. Coleman et al. [7] measured doctors’ perceived scientific orientation—through (dis)agreement with the statement ‘it is more important for a physician to keep himself informed of new scientific developments than to devote more time to his patients’—and found that profession-oriented doctors used new drugs earlier than patient-oriented doctors, a conclusion confirmed by Van den Bulte and Lilien’s [15] reanalysis. In contrast, Dybdahl et al. [22] found no clear association between self-rated GP clinical interest and new drug uptake.

Specialist meetings and events (n = 3) [7, 13, 14]. Specialist meetings, congresses, conferences, and symposia contribute to professional development—by providing a highly valued source of information and acting as catalyst for early awareness and positive evaluation, such forums may accelerate the early adoption of new drugs. In their classic study, Coleman et al. [7] found that doctors who attended many out-of-town speciality meetings—or who conscientiously attended conferences in their own hospitals—were more likely to innovate than others. Non-speciality meetings and county medical society meetings had no influence over new drug uptake. In a recent, carefully designed study, Liu and Gupta [13] also reported that attending professional events had a positive impact on new drug uptake. Mark et al.’s [14] bivariate analysis found that doctors who had attended a professional meeting within the past five years were more likely to innovate. Their multivariate analysis, however, found that attending professional events did not influence new drug prescription significantly.

Position (n = 1) [15]. Van den Bulte and Lilien [15] found that hospital doctors in managerial or honorary positions adopted new drugs later than others, due to limited involvement in actual medical practice.

Prescribing characteristics

Fifteen of the 35 eligible studies investigate at least one prescribing characteristics [6, 7, 12–14, 18, 19, 24, 27–30, 33, 34, 36].

Prescribing volume in the therapeutic class of the new drug (n = 11) [6, 7, 12–14, 27–30, 33, 36]. Ten of the 11 studies found that the higher the prescribing volume in the therapeutic class of a new drug, the higher the likelihood of early adoption of that new drug [6, 7, 12–14, 28–30, 33, 36]. Although Iyengar et al. [12] found supporting empirical evidence for only one of two drugs, the variable is likely to remain significant when summing up the prescribing volumes of the two drugs. Mark et al. [14] used stated preference for new medication within the drug class as variable and confirmed the relationship in the bivariate model, but not in the multivariate model—an insufficiently powerful result. Against mainstream research, Glass [27] reported that doctors with higher prescribing volumes in the therapeutic class of the study drug were more reluctant to prescribe the study drug, once on the market, possibly due to strong preferences for other drugs.

Total number of patients/prescriptions (n = 9) [12, 14, 19, 24, 27–30, 34]. To address the unfulfilled medical needs of some of their patients, doctors with high patient flows seem particularly alert to new drugs. Six of the nine studies reported that the higher the total prescribing volume, the higher the likelihood of early adoption of new drugs [19, 24, 27, 28, 30, 34]e. In contrast, the other three studies showed no relationship between the number of patients/prescriptions and new drug uptake [12, 14, 29]. However, Glass and Rosenthal [29] found that once doctors had written at least one prescription for the study drug, the total number of pre-study prescriptions became a significant variable. Also, Mark et al.’s [14] bivariate model found that the more schizophrenia patients doctors were treating, the earlier they adopted atypical medication—the variable became an insignificant determinant of new drug uptake only in the multivariate analysis.

Prescribing volume of drugs by the same pharmaceutical company (n = 4) [27–30]. Unambiguously, all four studies found that the higher the prescribing volume of drugs by the same pharmaceutical company, the higher doctors’ likelihood of early adoption of other drugs by that pharmaceutical company [27–30]. The authors argued that doctors are likely to prescribe new drugs if already writing high numbers or large percentages of prescriptions from the sponsoring company—either due to increased detailing by that pharmaceutical company or to doctors’ confidence and trust in that company or in the company’s sales representatives [27–30]. The high explanatory power of this variable is evidently related to the marketing efforts of pharmaceutical companies discussed in the section ‘Marketing efforts targeted at doctors’.

Portfolio width (n = 1) [18]. Bourke and Roper [18] defined portfolio width as the percentage of drugs prescribed by individual GPs out of the portfolio of drugs prescribed by all the GPs in that practice. Most probable, this variable correlates highly with practice size—the more patients doctors treat, the more likely they are to require a larger portfolio of drugs from which to prescribe. The authors found that the time to adoption was decreasing substantially with increasing portfolio widths, for all six study drugs. A 1 per cent increase in a GP’s portfolio width decreased time to adoption between three and 10.5 months, the variable having very high explanatory power.

Marketing efforts targeted at doctors

Pharmaceutical companies typically direct their marketing efforts at doctors and—of late—patients. Several studies reported that doctors had negative to neutral attitudes towards promotional efforts targeted at them and tried to minimise the importance of sales representatives, to avoid distorted, selective, and overly positive information [47–49]. Nevertheless, detailing (personal selling through sales representatives) is an inexpensive and convenient source of information. Generally, GPs indicated greater preference for commercial information than SPs—time constraints and the broader range of conditions they treated did not allow them to review satisfactorily all relevant professional information [8, 49, 50].

The marketing efforts directed at doctors comprised detailing, sampling (provision of drugs free of charge), doctor meetings and events, and advertisements in medical journals [48, 51]. They aimed to boost profits by incorporating new drugs early in their lifecycles, by raising awareness among top professionals, and by maintaining the new drugs’ first-choice statuses within their respective therapeutic groups [52]. Pharmaceutical marketing not only raises awareness—it evidently influences decision making too. Six of the 35 eligible studies investigate some aspects of the marketing efforts targeted at doctors [7, 12–14, 38, 39]. The empirical evidence presented below shows that pharmaceutical promotion affected prescription behaviour positively, albeit to various degrees.

Detailing (n = 6) [7, 12–14, 38, 39]. Historically, detailing has been the primary promotional instrument of the pharmaceutical industry—one third of total marketing expenditure has been directed at detailing [51]. Four of the six studies reported that doctor-targeted detailing affected early adoption of new drugs significantly and positively—doctors tended to respond to both current and past detailing positively [12, 13, 38, 39]. In contrast, the other two studies found no empirical evidence for the positive impact of detailing [7, 14]. However, Burt [53] used the same dataset as Coleman et al. [7] and found a significant association, possibly due to methodological differences. Studies reporting positive influences used sales call data from pharmaceutical company records, whereas studies reporting no empirical evidence used survey data—norm-conforming doctors with negative attitudes towards sales representatives may have reported less contact than they actually had.

The marketing literature has documented well the impact of pharmaceutical promotions on doctors’ choices, including through extensive reviews [48, 54]. Early studies used aggregate data [55–58], and almost all studies found a strong positive effect of detailing on sales [48, 54]. Recent research used behavioural data [59–61] and came to the broad consensus that detailing affects the number of prescriptions for new drugs positively and significantly. This effect was robust to a wide variety of model specifications and datasets [48, 54]. To provide a more complete picture of sales and prescribing behaviours, several recent models added numerous other marketing variables and found that detailing had a positive and significant effect on sales, even after controlling for other marketing mix instruments [48, 54]. Most studies also found that—relative to other marketing instruments—detailing had the largest effectf.

Sampling (n = 2) [38, 39]. Both studies found that the volume of samples that had been left with doctors in the past had a significant and positive impact on adoption behaviour [38, 39], even if the impact was very small [39]. These findings were in line with the overarching conclusion of the drug marketing literature [59, 60].

Direct-to-consumer advertising (DTCA) (n = 1) [13]. If allowed, DTCA in the mass media may influence early adoption of new drugs through patient requests targeted at doctors. In general, due to time constraints and the desire to avoid conflict and increase patient role in decision making, doctors honour patient requests [49]. The eligible study found that the volume of patient requests for a new drug had a positive and significant impact on doctors’ adoption decisions [13], and the extant marketing literature contributed to a more complete understanding of how DTCA impacted the demand for new drugs. Two rigorous literature reviews concluded that DTCA was associated with increased prescribing of advertised products, due to substantial impact on patients’ requests for specific drugs and doctors’ prescribing confidence [54, 62]. When detailing was controlled for, promotions aimed directly at doctors turned out to affect prescription choice much more than promotions aimed at consumers [54, 63–66]. DTCA had only a short-run effect on choice, whilst the effect of detailing wore out slowly, over the course of the year. There is a general consensus in the literature that DTCA is effective in generating foot traffic into doctors’ offices and increasing the aggregate drug demand per therapeutic class, without affecting doctors’ prescribing choices within the therapeutic class [64, 66, 67]. Once patients get to see their doctors, detailing and sampling have the most significant effect on what drugs doctors choose [64–66]g.

Pharmaceutical companies may facilitate new drug awareness in many other ways, all with a substantial promotional component—for example, funding of clinical trials, sponsoring of conferences and CME, and journal advertisements. Kremer et al. [54] reported that the elasticities of these other direct-to-doctor instruments are significant, but modest in size. This review discusses the impact of these other—partly scientific, partly promotional—efforts in relation with variables measuring doctors’ scientific orientation (see the section ‘Scientific orientation’).

Contagion through social networks

When new drugs are launched, doctors’ behaviours are affected by other doctors’ knowledge, attitudes, and behaviours, thus reducing safety and efficacy uncertainties. Interpersonal communication between opinion-leading doctors and peers seems critical to fast—and wide—acceptance of innovative drugs [8, 50, 68]. Personal contacts may provide real stimuli, since key opinion leaders present reliable, easy-to-digest assessments of new drugs. While other sources of information provide the nurturing groundwork of necessary knowledge, behavioural change requires the legitimising power of personal advice from informed and respected colleagues [69].

The main findings of the six eligible studies [7, 12, 13, 15, 36, 38] are summarised below, in chronological order—five of them found empirical evidence for contagion, even after controlling for a wide variety of factors.

In their path-breaking and highly cited Medical Innovation: A Diffusion Study, Coleman et al.[7] argued that the network of informal relations among doctors was highly effective in transferring information and influencing the diffusion of pharmaceutical innovationsh. Doctors’ adoption decisions were affected by interactions with other doctors—socially integrated doctors introduced new drugs quicker than their more isolated colleagues. The finding was found valid for all three social structures of the medical community studied—advisor, discussion, and friendship networks. However, doctors’ professional interactions had a larger effect on adoption time than social interactions. Moreover, the channels of influence among doctors operated most powerfully during the first few months after the release of a new drug.

Van den Bulte and Lilien[15] found that contagion effects disappeared when journal advertising was controlled for—first entrants’ marketing efforts, not social contagion, were the dominant driver in increasing doctors’ adoption hazard with time. The authors explicitly underscored the importance of controlling for potential confounds, such as marketing efforts, when studying the role of social contagion in new drug diffusion. In a subsequent working paper, deemed ineligible because it did not meet the first eligibility criteria, they used new event history models for a two-stage adoption process—awareness followed by evaluation and adoption [70]. When marketing efforts were allowed to affect awareness and social contagion was allowed to affect evaluation, both effects became statistically significant. In the presence of strong mass media effects, the patterns of social contagion were weak—contagion had a significant effect in the second stage.

In their carefully designed study, Manchanda et al.[38] found that doctors’ decisions to adopt were influenced positively by contagion—that is, doctors’ probabilities to adopt increased significantly with increases in fellow doctor adoption. The authors determined the social networks of doctors on basis of geographic proximity, defined as the area within a 20-mile radius around a doctor’s office. The observed positive and significant contagion effect persisted even after controlling for time trends and marketing efforts. The authors ruled out many alternative explanations, including uniqueness of the social networks under analysis or unobserved common variables that affect all the network members similarly—for example, sales representatives or institutional factors such as working for the same hospital, using the same formularies, and attending the same meetings and events sponsored by pharmaceutical companies.

Interestingly, Manchanda et al. [38] had also calculated the proportion of adoption probability arising from marketing activity relative to that arising from contagion and had found that marketing played a larger role in adoption earlier on. However, consistent with Iyengar et al. [12] and Narayanan et al. [65], contagion dominated from month 4 onwards, asymptoting to around 90 per cent of the effect by month 17.

In a partly similar setting, Liu and Gupta[13] also found that the estimated effect of contagion among doctors in geographic proximity was positive and significant.

In a carefully and insightfully designed study, Iyengar et al.[12] reported that doctors with high indegrees adopted early, and found evidence of social contagion primacy over social network ties even after controlling for targeted marketing efforts and time shocks. Adoption was affected by peers’ prescribing volume within the drug class, rather than by peers’ adoption per se. Due to credibility granted by experience with a newly introduced drug, doctors who prescribed a new drug more often were more reliable sources of information—not only did they follow their own recommendations, but they also had a large experimental base on which to found these recommendations.

Lin et al.[36] also found evidence for contagion. Overall, peers’ and opinion leaders’ adoption ratios were positively associated with the likelihood of early adoption. Of the three age-based groups (younger colleagues, compeers, and older colleagues), compeers’ and older colleagues’ adoption ratios were correlated with doctors’ likelihood of early adoption positively. Of the three tenure-based groups (junior colleagues, compeers, and senior colleagues), compeers’ and senior colleagues’ adoption ratios were associated with doctors’ likelihood of early adoption positively and significantly. The authors concluded that professional authority and informal interaction were equally important in the adoption process. While older doctors with longer tenures were granted professional authorities implicitly, on grounds of clinical experience, compeers in age and tenure exerted similar influences through more direct and frequent interactions. The authors argued that—besides senior colleagues, as almost exclusively advocated in the literature—peers should also be taken into account.

Practice characteristics

Eighteen of the 35 eligible studies investigate at least one practice-related characteristics of prescribers [1, 5–7, 11, 12, 14, 16–18, 20, 21, 24, 33, 35–37, 40].

Location (urban or rural) (n = 7) [1, 5, 6, 11, 16–18]. Rural practice locations may result in late new drug adoption—in contrast with their urban colleagues, rural doctors have fewer opportunities for professional interactions with peers, an important factor in the decision to initiate new treatments. The lower new drug utilisation rates in rural practices might also be explained by the lower number of visits by pharmaceutical sales representatives [5], due to geographic inaccessibility, or by the personal characteristics of doctors who elect to practice in rural communities.

At least partly, three of the seven eligible studies tended to favour the idea that urban practices adopt earlier and rural practices adopt later [1, 6, 18]. Tamblyn et al. [1] found that rural doctors were less likely to prescribe new drugs than their urban colleagues. Albeit only for two of six study drugs, Bourke and Roper [18] also reported an increase in time to adoption for practices in receipt of rural practice allowances. Finally, Groves et al. [6] concluded that the upper quartile of high-relative doctors may be best classified as doctors with urban practices. However, three other eligible studies found no evidence of early adoption of new drugs in urban areas [5, 16, 17], suggesting a reassuring efficiency of information dissemination. At the other extreme, two eligible studies found that rural practices adopted earlier [6, 11]. Greving et al. [11] reported that GPs who worked in rural areas were more likely to prescribe new medication than others, whereas Groves et al. [6] found that doctors classified as high-total new drug prescribers were more likely to operate in rural areas than elsewhere, possibly due to high patient—including elderly patient—loads.

Type (solo or group/partnership) (n = 7) [7, 11, 12, 20, 33, 35, 40]. In group/partnership practices, continuous professional stimulation and other social factors may accelerate the early adoption of new drugs. Joint responsibility for patients may promote the circulation of medical notes and allow for cross-fertilisation of therapeutic information [68], while daily personal contact with colleagues may provide an efficient channel for information transfer and evaluation. (For a discussion on the role of social networks in early adoption of new drugs, see the section ‘Contagion through social networks’.) However, the impact of group/partnership practices on new drug uptake is ambiguous in the literature [7, 11, 12, 20, 33, 35, 40].

On the one hand, Coleman et al. [7] reported that doctors who practised in groups/partnerships introduced new drugs on average 2.3 months earlier than doctors who practised solo—two other studies supported these findings [33, 40]. On the other, four of the seven eligible studies arrived at different conclusions. Iyengar et al. [12] and Kozyrskyj et al. [35] found no significant relationship between type of practice and early adoption of new drugs, whereas Dybdahl et al. [20] argued that the differences between solo and group/partnership practitioners disappeared after adjustment for practice size. Finally, Greving et al. [11] found that higher levels of new drug prescribing were associated with solo practices.

Size (n = 6) [1, 5, 16, 21, 36, 40]. Practice size may be measured in number of doctors, number of patients, prescribing volume, or number of services delivered. Intuitively, the larger the practice, the higher the probability of patients with conditions targeted by new drugs. However, only one of the six eligible studies favoured clearly the idea that larger practices adopt new drugs earlier than smaller practices. Steffensen et al. [40] found that the higher the number of patients, the higher the likelihood of early adoption. Tamblyn et al. [1] also found a modest but significant association between the two variables, but higher numbers of services delivered had different relationships for GPs and SPs, increasing the rate of new drug use for GPs, while reducing that for SPs. The other four studies found no empirical evidence for association between large practice size and early adoption of new drugs—whether size was measured by total prescribing volume [5, 21], by volume of prescribing within the therapeutic class of the new drug [21], by number of doctors [16, 36], or by number of patients [16, 21].

Ownership (public or private), management (reformed or non-reformed), and orientation (for profit or not for profit) (n = 4) [5, 24, 36, 37]. Ohlsson et al. [5] and Lin et al. [36] found that private practices were more likely to adopt new drugs earlier than public practices. In a similar setting, García et al. [24] reported that doctors in reformed institutions—under the management of distinct autonomous communities—adopted earlier than doctors in non-reformed institutions—operated by the National Institute of Health Management. This finding was very country specific, but supported the idea that autonomy may stimulate new drug diffusion. In contrast, Liu et al. [37] found that doctors in not-for-profit institutions were more likely to prescribe new drugs than doctors in for-profit institutions. The authors argued that for-profit hospitals delayed the introduction of new drugs in formularies—even if that hindered quality improvement—due to very strong incentives to reduce costs. However, this argument is at loggerheads with patients’ freedom to choose other hospitals, as well as in need of further evidence—the respective coefficients become insignificant, when standard errors are adjusted.

Region (n = 4) [12, 14, 24, 37]. Four studies examined regional impact on new drug diffusion through city or region dummies. Three of them found no differences across geographic locations [12, 14, 24], whereas Liu et al. [37] found that doctors in Taipei and central Taiwan were more likely to prescribe new drugs than doctors elsewhere in Taiwan. The authors argued that the speed of technological diffusion might depend on market size—most probably, pharmaceutical companies concentrated their marketing efforts on larger cities, with higher population densities and higher family incomes. However, considerable regional differences in Taiwan make generalisation difficult—regions may play an important role in more centralised countries and no role in more decentralised countries.

Accreditation level (n = 2) [12, 37]. Iyengar et al. [12] used a dummy variable to indicate whether doctors worked in—or were affiliated with—a university or teaching hospital, and found no significant association between level of institutional accreditation and new drug uptake. In contrast, Liu et al. [37] reported that accreditation levels—positively associated with practice size—had significant positive effects on the probability of prescribing new drugs. Consequently, academic medical centres had higher probabilities of prescribing new drugs than metropolitan and community hospitals. The authors argued that the higher the accreditation level, the higher the institution valued quality improvement—and the more inclined the institution was to prescribe new drugs. At the same time, higher levels of institutional accreditation—and, thus, higher market sizes—implied more significant marketing efforts from pharmaceutical companies.

Diagnostic and therapeutic activities (n = 2) [16, 40]. The two studies found that high volumes of diagnostic and therapeutic activities were associated positively with early adoption of new drugs [16, 40].

Employee composition (n = 2) [5, 18]. Ohlsson et al. [5] found that practices employing SPs as well as GPs were more likely to adopt new drugs early than practices employing GPs only—this finding supports the two-step model (see the section ‘Scientific orientation’). For two of six study drugs, Bourke and Roper [18] found small but significant lower adoption times for practices employing the assistance of a nurse or secretary—for the other four, no relationships were observed.

Other (n = 2) [1, 37]. Tamblyn et al. [1] found that higher referral rates were associated with significant reductions in SPs’ new drug prescribing rates. Most probably, SPs with higher referral rates treated patients with more comorbidities that required more careful and cautious prescribing. Liu et al. [37] reported that the higher the healthcare market concentration—that is, the higher the market share of certain practices in a given region—the higher the probability of prescribing new drugs. In contrast, the lower the healthcare market concentration—that is, the more competitive the market—the lower the probability of prescribing new drugs. According to Liu et al. [37], thus, high competition among healthcare providers—often advocated by policy makers—hindered new drug diffusion.

Drug characteristics

The majority of drug characteristics—improvement over existing therapies and the safety and efficacy of new drugs, for example—may require randomised controlled trials over long time periods and may not be measured easily. The five drug characteristics measurable quantitatively are the marketing budget of the pharmaceutical company assigned for the new drug, overall acceptance, therapeutic novelty, competition, and cost. Thirteen of the 35 eligible studies investigate at least one of the five quantitatively measurable drug charachteristics [1, 13, 15, 18, 20, 25, 27–30, 33, 37, 38].

Marketing budget of the pharmaceutical company assigned for the new drug (n = 7) [1, 13, 15, 28–30, 38]. The marketing budget of the pharmaceutical company assigned for a new drug affected early adoption in five of the seven studies, exerting significant and consistently signed influence—doctors were more likely to prescribe more heavily marketed drugs [13, 15, 28–30]i. This influence was more pronounced for first entrants than for subsequent ones [15].

The other two studies arrived at different conclusions. Manchanda et al. [38] used monthly DTCA expenditure as proxy for aggregate marketing expenditure, and found no association with doctors’ adoption behaviour—not entirely surprising, since DTCA was zero in the first ten months after launch. Tamblyn et al. [1] also found no empirical evidence of relationships between advertising intensity—measured by detailing minutes and advertising pages—and early adoption of new drugs.

Overall acceptance (n = 6) [13, 18, 27, 29, 30, 38]. Six studies assessed the influence of time on new drug uptake by encompassing many potential influencers—for example, overall acceptance, growth in patient base, and the aggregate diffusion pattern in a geographic area. Manchanda et al. [38] and Liu and Gupta [13] controlled systematically for factors that might influence doctors’ adoption behaviour over time by adding time trend terms to their models—number of months lapsed since launch (for linear effects) and the same variable squared (for non-linear effects). The authors found unequivocally that temporal effects influenced new drug uptake significantly—however, the sign varied with the convexity of the temporal diffusion trend. Similarly, Glass [27] and Glass and Rosenthal [29] controlled for overall acceptance 12 months after launch by adding a market share variable. In line with intuition, the authors found that doctors were more likely to prescribe new drugs that were highly prescribed by other doctors. Bourke and Roper [18] also included an overall acceptance variable—the time-variant percentage of GPs who had adopted the study drug. The authors found a significant effect—for five of six study drugs, time to adoption increased with increases in the number of previous adopters. They explained the counterintuitive sign of the effect through market saturation—the longer the time, the fewer the GPs who had not already adopted. As an exception, Glass and Dalton [30] reported that market share 12 months after launch was not a significant variable in explaining the volume of new drug prescriptions.

Therapeutic novelty (n = 3) [25, 27, 33]. Garjón et al. [17] found that tiotropium—the only drug rated as therapeutic innovation—was adopted the most widely and quickest. Huskamp et al. [33] also reported that the four original antipsychotic formulations were quicker and more widespread adopted than reformulations. In contrast, Glass [27] showed that the prescribing level of the study drug six months after launch was not related to its therapeutic novelty. However, neither Huskamp et al. [33] nor Garjón et al. [25] could affirm that therapeutic novelty was the main reason for rapid new drug diffusion.

Competition (n = 1) [37]. Liu et al. [37] reported that the probability of prescribing new drugs was negatively associated with the level of competition in the pharmaceutical market—when new drugs entered therapeutic subgroups, the prescribing probability decreased by 0.2 percentage points. They argued that the profit margins of pharmaceutical companies increase with increases in the number of products they launch, given the unregulated character of the wholesale market—unlike the reimbursement price, the acquisition price is not determined by the regulator. Hence, profit margins will be larger for older drugs than for new drugs, and the probability that doctors will prescribe new drugs will be smaller.

Cost (n = 1) [20]. Dybdahl et al. [20] found that adoption time adjusted for number of listed patients was only weakly associated with the cost of new drug prescribing. The survey-based literature suggested that cost is less important than both safety and perceived efficacy [47], in general, and does not represent a significant barrier in the early adoption of new drugs [71]. Doctors try to balance efficacy and cost, but are not reluctant to prescribe higher cost, more effective drugs [44, 72]. Jacoby et al. [43] found that the most frequent early adopters of new drugs were the least cost conscious.

Patient characteristics

Patient characteristics such as age, gender, socioeconomic status, and the presence of comorbidities seem to influence new drug uptake. On the one hand, the empirical evidence is vast—on the other, characteristics of early receivers vary from drug to drug, with therapeutic goals and the target audience of the drug. An exhaustive review of the relevant literature is therefore impossible. Nine of the 35 eligible studies investigate at least one patient characteristics [1, 5, 11, 13, 14, 16, 32, 35, 37], their findings are discussed briefly in the following paragraphs.

Age (n = 9) [1, 5, 11, 13, 14, 16, 32, 35, 37]. Doctors’ likelihood of continuing to prescribe particular medications seemed to be influenced by patients’ age. Since elderly patients were more likely to experience side effects, doctors were less likely to prescribe new drugs to older patients [1, 16] and more likely to prescribe new drugs to younger patients [5, 11, 14]. Drugs generally designed for the elderly—to treat Alzheimer’s disease or arthritis, for example—were of course exceptions [32]. At the same time, three of the nine eligible studies found that patients’ age was not an important factor in new drug uptake [13, 35, 37].

Gender (n = 6) [5, 11, 14, 32, 35, 37]. Five of the six studies found that patient gender exerted small influence—if any—on doctors’ prescribing [11, 14, 32, 35, 37]—new drug characteristics and therapeutic goals were usually more important influencers. However, exceptions do occur—Ohlsson et al. [5] reported that more new drugs were prescribed to male than female patients.

Health (n = 4) [11, 14, 35, 37]. Patients’ health statuses—self-reported health, poor response to existing therapies, previous use of certain medications, and presence of comorbidities—evidently played an influential role in new drug uptake [11, 14, 37]. The variables reflecting significant associations included: patient referrals to cardiologists or internists, regardless of number of comorbidities [11]; higher comorbidity indexes; higher severities of diabetes, as measured by number of prescriptions per visit [37]; earlier onsets; and negative rather than positive symptoms in Mark et al.’s [14] multivariate model. However, chronic arthritic conditions made an exception, not playing an influential role in new drug prescribing for the majority of study drugs [35]. Generally, doctors seemed to consider individual contexts seriously, and patient convenience seemed to influence new drug uptake and promote earlier adoption among patients at desperate stages.

Socioeconomic characteristics (income, education, and health insurance) (n = 4) [5, 13, 14, 35]. By definition, socioeconomic statuses reflect patients’ economic and social positions relative to others, based on income, occupation, and education [73]. Three of the four eligible studies suggested that patients’ socioeconomic statuses influenced doctors’ prescribing behaviour irrespective of medical considerations [5, 13, 35]. High-income patients received new drugs earlier than others, not least because of their ability to pay for out-of-pocket treatments [5, 13, 35]. Privately insured patients or patients with a higher insurance index also seemed more likely to receive new drugs earlier than others [13]. However, Mark et al. [14] found no empirical evidence for positive impact of higher level of formal education and better insurance status on new drug uptake.

Marital status (n = 2) [5, 14]. Marital status may influence new drug uptake, but to lesser extent than socioeconomic status. Prescriptions for the treatment of high blood cholesterol were more probable among married or cohabiting patients [5], whereas prescriptions of atypical antipsychotics were equally likely among single patients and married or cohabiting ones [14].

Race/ethnicity (n = 2) [13, 14]. Association between race/ethnicity and socioeconomic status suggests association between race/ethnicity and new drug uptake. For example, non-African Americans were more likely to be treated with new medications than African Americans and Hispanics [13, 14].

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1. In alphabetical order, by keyword. The search strings are available from author on request.