JL was paid for work with a Canadian generic company, Apotex Inc., in 2007. GS, PRM, BM, JL, NO, and AV are members of Healthy Skepticism Inc. Healthy Skepticism is an international nonprofit research, education, and advocacy association with the main aim of reducing harm from misleading health information. PRM is the director of Healthy Skepticism Inc., and is mostly unpaid. PRM's wife and two of his daughters are part-time employees of Healthy Skepticism Inc. JL is a member of the management committee of Healthy Skepticism.
Geoff Spurling and colleagues report findings of a systematic review looking at the relationship between exposure to promotional material from pharmaceutical companies and the quality, quantity, and cost of prescribing. They fail to find evidence of improvements in prescribing after exposure, and find some evidence of an association with higher prescribing frequency, higher costs, or lower prescribing quality.
Pharmaceutical companies spent $57.5 billion on pharmaceutical promotion in the United States in 2004. The industry claims that promotion provides scientific and educational information to physicians. While some evidence indicates that promotion may adversely influence prescribing, physicians hold a wide range of views about pharmaceutical promotion. The objective of this review is to examine the relationship between exposure to information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.
We searched for studies of physicians with prescribing rights who were exposed to information from pharmaceutical companies (promotional or otherwise). Exposures included pharmaceutical sales representative visits, journal advertisements, attendance at pharmaceutical sponsored meetings, mailed information, prescribing software, and participation in sponsored clinical trials. The outcomes measured were quality, quantity, and cost of physicians' prescribing. We searched Medline (1966 to February 2008), International Pharmaceutical Abstracts (1970 to February 2008), Embase (1997 to February 2008), Current Contents (2001 to 2008), and Central (The Cochrane Library Issue 3, 2007) using the search terms developed with an expert librarian. Additionally, we reviewed reference lists and contacted experts and pharmaceutical companies for information. Randomized and observational studies evaluating information from pharmaceutical companies and measures of physicians' prescribing were independently appraised for methodological quality by two authors. Studies were excluded where insufficient study information precluded appraisal. The full text of 255 articles was retrieved from electronic databases (7,185 studies) and other sources (138 studies). Articles were then excluded because they did not fulfil inclusion criteria (179) or quality appraisal criteria (18), leaving 58 included studies with 87 distinct analyses. Data were extracted independently by two authors and a narrative synthesis performed following the MOOSE guidelines. Of the set of studies examining prescribing quality outcomes, five found associations between exposure to pharmaceutical company information and lower quality prescribing, four did not detect an association, and one found associations with lower and higher quality prescribing. 38 included studies found associations between exposure and higher frequency of prescribing and 13 did not detect an association. Five included studies found evidence for association with higher costs, four found no association, and one found an association with lower costs. The narrative synthesis finding of variable results was supported by a meta-analysis of studies of prescribing frequency that found significant heterogeneity. The observational nature of most included studies is the main limitation of this review.
With rare exceptions, studies of exposure to information provided directly by pharmaceutical companies have found associations with higher prescribing frequency, higher costs, or lower prescribing quality or have not found significant associations. We did not find evidence of net improvements in prescribing, but the available literature does not exclude the possibility that prescribing may sometimes be improved. Still, we recommend that practitioners follow the precautionary principle and thus avoid exposure to information from pharmaceutical companies.
A prescription drug is a medication that can be supplied only with a written instruction (“prescription”) from a physician or other licensed healthcare professional. In 2009, 3.9 billion drug prescriptions were dispensed in the US alone and US pharmaceutical companies made US$300 billion in sales revenue. Every year, a large proportion of this revenue is spent on drug promotion. In 2004, for example, a quarter of US drug revenue was spent on pharmaceutical promotion. The pharmaceutical industry claims that drug promotion—visits from pharmaceutical sales representatives, advertisements in journals and prescribing software, sponsorship of meetings, mailed information—helps to inform and educate healthcare professionals about the risks and benefits of their products and thereby ensures that patients receive the best possible care. Physicians, however, hold a wide range of views about pharmaceutical promotion. Some see it as a useful and convenient source of information. Others deny that they are influenced by pharmaceutical company promotion but claim that it influences other physicians. Meanwhile, several professional organizations have called for tighter control of promotional activities because of fears that pharmaceutical promotion might encourage physicians to prescribe inappropriate or needlessly expensive drugs.
But is there any evidence that pharmaceutical promotion adversely influences prescribing? Reviews of the research literature undertaken in 2000 and 2005 provide some evidence that drug promotion influences prescribing behavior. However, these reviews only partly assessed the relationship between information from pharmaceutical companies and prescribing costs and quality and are now out of date. In this study, therefore, the researchers undertake a systematic review (a study that uses predefined criteria to identify all the research on a given topic) to reexamine the relationship between exposure to information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.
The researchers searched the literature for studies of licensed physicians who were exposed to promotional and other information from pharmaceutical companies. They identified 58 studies that included a measure of exposure to any type of information directly provided by pharmaceutical companies and a measure of physicians' prescribing behavior. They then undertook a “narrative synthesis,” a descriptive analysis of the data in these studies. Ten of the studies, they report, examined the relationship between exposure to pharmaceutical company information and prescribing quality (as judged, for example, by physician drug choices in response to clinical vignettes). All but one of these studies suggested that exposure to drug company information was associated with lower prescribing quality or no association was detected. In the 51 studies that examined the relationship between exposure to drug company information and prescribing frequency, exposure to information was associated with more frequent prescribing or no association was detected. Thus, for example, 17 out of 29 studies of the effect of pharmaceutical sales representatives' visits found an association between visits and increased prescribing; none found an association with less frequent prescribing. Finally, eight studies examined the relationship between exposure to pharmaceutical company information and prescribing costs. With one exception, these studies indicated that exposure to information was associated with a higher cost of prescribing or no association was detected. So, for example, one study found that physicians with low prescribing costs were more likely to have rarely or never read promotional mail or journal advertisements from pharmaceutical companies than physicians with high prescribing costs.
With rare exceptions, these findings suggest that exposure to pharmaceutical company information is associated with either no effect on physicians' prescribing behavior or with adverse affects (reduced quality, increased frequency, or increased costs). Because most of the studies included in the review were observational studies—the physicians in the studies were not randomly selected to receive or not receive drug company information—it is not possible to conclude that exposure to information actually causes any changes in physician behavior. Furthermore, although these findings provide no evidence for any net improvement in prescribing after exposure to pharmaceutical company information, the researchers note that it would be wrong to conclude that improvements do not sometimes happen. The findings support the case for reforms to reduce negative influence to prescribing from pharmaceutical promotion.
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Pharmaceutical companies in the United States spent about US$57.5 billion, or 24.4% of their revenue, on promotion in 2004
There is a wide range of views amongst health professionals about pharmaceutical promotion. Qualitative studies suggest that many perceive pharmaceutical promotion to be a useful and convenient source of information
The evidence base illuminating these conflicting views is growing. In 2000, Wazana identified eight studies linking pharmaceutical promotion to increased prescribing, “nonrational prescribing,” and increased prescribing costs
Those previous reviews are now out of date, narrowly focused, or only partially assessed the relationship between information (promotional or otherwise) from pharmaceutical companies and prescribing costs and quality. The objective of this review is to examine the relationship between exposure to information directly provided by pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.
Randomized controlled trials, time series analyses, before–after studies, cohort studies, case-control studies, ecological studies, and cross-sectional studies were eligible for inclusion. Studies were included if they had both a measure of exposure to any type of information directly provided by pharmaceutical companies and a measure of physicians' prescribing. We excluded studies that looked at the indirect provision of information, for example, through continuing medical education courses that were funded by unrestricted grants from pharmaceutical companies. Case series, case reports, abstracts, news items, and short reports were excluded.
Exposure to information directly provided by pharmaceutical companies was defined as including pharmaceutical sales representative visits, advertisements in journals or prescribing software, presentations from pharmaceutical companies to groups, meetings sponsored by pharmaceutical companies, mailed information including advertisements, and participation in sponsored clinical trials. We did not include studies of other forms of promotion such as gifts or samples or studies of indirect forms of information provision such as sponsored education.
The outcome measures were the quality, frequency, and costs of prescribing.
We searched Medline (1966 to February 2008), International Pharmaceutical Abstracts (1970 to February 2008), Embase (1997 to February 2008), Current Contents (2001 to 2008), and Central (The Cochrane Library Issue 3, 2007). The search strategy below was devised for Medline by an expert librarian at the University of Queensland and adapted for the other databases: (exp Drug Industry OR exp Advertising OR exp Gift Giving OR exp “Conflict of Interest”) AND (exp Prescriptions, Drug/OR (prescribing or prescription$).mp.))
We looked for additional articles in the references of each retrieved article including review articles in an iterative, exhaustive process. Efforts to find additional studies included placement of messages on email drug discussion groups, contacting experts in the field, and asking Australian subsidiaries of international pharmaceutical companies for information. All languages were considered.
The title and abstract, if available, of all articles detected by the database searches were reviewed by two authors. Articles that possibly met the inclusion criteria were retrieved and subjected to a formal inclusion process independently by two different authors. Differences of opinion were resolved by consensus and if necessary a third author was involved.
Articles meeting inclusion criteria were appraised for methodological quality independently by two authors. Randomized studies were assessed for adequacy of randomization method, allocation concealment, blinding, follow-up, and use of intention to treat analyses
For included studies, two authors independently extracted data on study site, dates of data collection or publication, types of participants (primary care providers, specialists, and residents), study medication(s), exposure to information from pharmaceutical companies, and prescribing outcomes.
For quality of prescribing we accepted the original authors' definitions of what constituted more (or less) appropriate prescribing.
We divided studies into two groups on the basis of whether the information was delivered with or without conventional promotional techniques. This distinction was made because information delivered with versus without conventional promotion may produce different effects on prescribing.
Conventional promotional techniques were defined as advertisements (in journals and software), representatives' visits, attendance at pharmaceutical sponsored meetings, and mailed information from pharmaceutical companies. In addition, we included in this group studies looking at total promotional investment/summated scores of commercial information use/general use of commercial sources. The other group of studies included warning letters, participation in company sponsored trials, and representatives' visits for nonpromotional purposes.
A narrative synthesis of results was undertaken following the MOOSE guidelines and meta-analysis performed where appropriate data were available (
We reported standardized effect measures (Pearson correlation coefficients, odds ratios [ORs], or beta coefficients) where study reports provided them or the data needed to calculate them. For econometric studies, we also reported
Meta-analysis was not appropriate for the outcomes of quality of prescribing and cost of prescribing because in both cases the studies examined different exposures or outcome measures and/or lacked control groups. We undertook a meta-analysis for one component—studies of frequency of prescribing with identifiable control groups where the information exposure was delivered with conventional promotional techniques. We used ORs for change in prescribing frequency as the outcome measure. Where studies had suitable designs for inclusion in the meta-analysis but ORs and standard errors were not published we contacted corresponding authors. Out of ten studies
Heterogeneity was assessed using the tau squared test with a sensitivity analysis to investigate likely sources of heterogeneity. Factors identified a priori as possible explanations for heterogeneity were study design, study quality indicators, year of publication, type of exposure to pharmaceutical company information (active versus passive), and physician characteristics (level of experience and also primary care provider versus specialist). We defined active exposure as information presented to physicians at meetings or during pharmaceutical sales representatives' visits. We defined passive exposure as journal advertisements, mailed information, advertisements on clinical software, and participation in sponsored clinical trials. Studies reporting more than one unit of analysis were subjected to sensitivity analysis. Meta-analysis was performed using RevMan (version 5.0.24) with further analysis conducted using Stata version 10.0 (Stata Corporation).
Our search found 7,185 studies from electronic databases and 138 studies were retrieved from reference lists, experts in the field and email lists. The full text of 255 articles was retrieved. 18 studies were excluded, all because inadequate reporting precluded quality assessment. Quality appraisal results for included studies are presented in
Randomised Controlled Study (First Author Name) | Satisfactory Randomization | Allocation Concealment | Blinding | Adequate Follow-up | Appropriate Statistical Measures |
Appropriate cluster randomization | No | No | Yes | Yes | |
Appropriate cluster randomization | No | No | Yes | Yes |
Received research funding from a pharmaceutical company.
Study Type | Study (First Author Name) | Prospective Design | Comparability of Cases and Controls | Selection Bias Minimized | Response Rate >80% | Confounders Controlled | Appropriate Statistical Measures | Adequate Follow-Up |
Controlled Cohort | Andersen |
No | Yes | Yes | Yes | Yes | Yes | Yes |
Case-Control | Spingarn |
No | Yes | No | Yes (100%) | Yes | Yes | Yes |
Chren |
No | Yes | Yes | Yes (88%) | Yes | Yes | Yes |
Received research funding from a pharmaceutical company.
Time-Series Analysis | Study (First Author Name) | Prospective Design | Control Group | Confounders Controlled | Selection Bias Minimized | Appropriate Statistical Measures |
Ching |
No | No | Yes | Yes | Yes | |
Venkataraman |
No | No | Yes | Yes | Yes | |
Windmeijer |
No | No | Yes | Yes | Yes | |
Chintagunta |
No | No | Yes | Yes | Yes | |
Narayanan |
No | No | Yes | Yes | Yes | |
Donohue |
No | No | Yes | Yes | Yes | |
Mizik |
No | No | Yes | Yes | Yes | |
Manchanda |
No | No | Yes | Yes | Yes | |
Manchanda and Chintagunta |
No | No | Yes | Yes | Yes | |
Berndt |
No | No | Yes | Yes | Yes | |
Rosenthal |
No | No | Yes | No | Yes | |
Azoulay |
No | No | Yes | Yes | Yes | |
Rizzo |
No | No | Yes | No | Yes | |
Hurwitz |
No | No | Yes | Yes | Yes | |
Mackowiak |
No | No | No | Yes | Yes | |
Leffler |
No | No | Yes | Yes | Yes | |
Telser |
No | No | Yes | Yes | Yes | |
Spurling |
Yes | No | No | No | Yes | |
Stafford |
Yes | No | No | Yes | No | |
Charbit |
No | No | No | Yes | No | |
Auvray |
No | No | No | No | No | |
Cleary |
Yes | Yes | Yes | No | Yes | |
Soumerai |
No | Yes | No | Yes | No |
Before–After Study (First Author Name) | Prospective Design | Control Group | Response Rate >80% | Confounders Controlled | Selection Bias Minimized |
Hemminki |
No | Yes | No (68%) | No | Yes |
Schwartz |
No | Yes | Unsure | No | Unsure |
Kazmierczak |
No | No | NA | No | Yes |
Orlowski |
No | No | Yes (100%) | Yes | No |
Bowman |
Yes | No | No (43%–77%) | No | No |
Cross-Sectional Study (First Author Name) | Prospective Design | Response Rate >80% | Confounders Controlled | Selection Bias Minimized | Appropriate Statistical Measures |
Henderson |
No | Yes | Yes | Yes | Yes |
Greving |
No | Yes (96%) | Yes | Yes | Yes |
Kreyenbuhl |
Yes | No (58%) | No | Yes | Yes |
de Bakker |
No | Unsure | Yes | Yes | Yes |
Steinman |
No | Yes | Yes | No | Yes |
Canli |
Yes | No (79%) | No | Yes | Yes |
Verdoux |
Yes | No (24%) | Yes | No | Yes |
Muijrers |
Yes | No (71%) | Yes | Yes | Yes |
Huang |
No | NA | No | No | Yes |
Watkins |
Yes | No (64%) | Yes | Yes | Yes |
Prosser |
Yes | No (73%) | No | Yes | No |
Caamano |
Yes | No (75%) | Yes | Yes | Yes |
Gonul |
Yes | NA | Yes | Unsure | Yes |
Mansfield |
Yes | No (6%) | No | No | Yes |
Jones |
Yes | NA | No | No | No |
Caudill |
Yes | No (28%) | Yes | Yes | Yes |
Berings |
Yes | No (28%) | Yes | No | Yes |
Lurie |
Yes | No (75–78%) | Yes | Yes | Yes |
Health Care Communications 1989 |
No | Unsure | No | No | No |
Peay |
No | No (52%–70%) | Yes | Yes | Yes |
Blondeel |
Yes | No (30%) | Yes | Yes | Yes |
Haayer |
Yes | Yes (90%) | Yes | No | Yes |
Walton |
Yes | Unsure | No | Yes | Yes |
Dajda |
No | NA | No | Yes | Yes |
Becker |
Yes | Yes (84%) | Yes | Yes | Yes |
Received research funding from a pharmaceutical company.
The most common study design was cross-sectional (24/58 studies, 41%). There were also two cluster randomized controlled trials, one controlled-cohort study, two case-control studies, 24 time-series analyses, and five before–after studies. Over half (55%) of the studies were conducted in the United States. Characteristics of included studies are outlined in
Study Design | Study (First Author Name) | Study Site, Year | Participants ( |
Medication | Intervention/Exposure | Outcome Measure(s) |
Freemantle |
UK 2000 | PCPs (79: 40 intervention, 39 control) | Lansoprazole versus omeprazole | PSR visits: PSRs instructed by local health authority (one visit); controls: normal detailing | Switch from omeprazole to less costly lansoprazole | |
Dolovich |
Canada 1999 | PCPs and pediatric specialists (641 in intervention group and 574 in control group) | Antibiotics for otitis media | PSR visits, PSRs trained in evidence-based education by academic department of a university; Control group: no detailing | Market share of antibiotics for otitis media | |
Andersen |
Denmark 1999–2003 | 297 PCPs (26 intevention/271 controls) | Asthma medications | Participation in a RCT funded by a pharmaceutical company | Prescribing trial drug; Adherence to prescribing guidelines | |
Spingarn |
US° 1990 | Hospital residents (75) | Medications for Lyme disease | Intervention: presentation by academic who was also a pharmaceutical executive; Controls: did not attend | Appropriateness of intention to prescribe for mild versus severe Lyme disease | |
Chren |
US 1989–1990 | Physicians (40 cases, 80 controls) | Addition to hospital formulary | PSR visits; cases added to formulary, controls did not | Addition of detailed drug to hospital formulary | |
Ching |
Canada 1993–1999 | Physician's prescribing antihypertensives in Canada | Antihypertensive medications (angiotensin converting enzyme inhibitors and diuretics) | PSR visits ( |
Market share; Elasticity of demand | |
Venkataraman |
Not stated 2002–2003 | Physicians (2,774) | Statins, coagulation drugs, erectile dysfunction drugs, gastrointestinal drugs, placebo | PSR visits (total number); attendance at pharmaceutical; sponsored meetings; (total number attended) | ||
Windmeijer |
Netherlands 1995–1999 | PCPs and psychiatrists |
11 therapeutic markets (over 50% of the Dutch drug market) | PSR visits (expenditure); Journal advertisements (expenditure); Mail (expenditure) | ||
Chintagunta |
US, UK, Germany, France, Italy 1989–1999 | Prescribers of antidepressant medications | Fluoxetine, sertraline, paroxetine | PSR visits (expenditure) | Market share (sales) | |
Narayanan |
US 1993–2001 | All prescribers of antihistamines in US |
2nd generation antihistamines: loratidine cetirizine, fexofenadine | PSR visits (total expenditure) | New prescriptions per month | |
Donohue |
US 1997–2000 | 11,000 office and hospital physicians | First prescriptions of 6 antidepressants | Monthly spending on PSR detailing | New prescriptions | |
Mizik |
US 2004 | Physicians (74,075) | 3 unknown drugs | PSR visits | ||
Manchanda |
US 1999–2001 | Physicians (1,000), 18.5% specialists (for study drug), 60.1% PCPs, 21.4% other specialists, controls (1,000) | Drug unknown | PSR visits | Numbers of prescriptions | |
Manchanda and Chintagunta |
US 1996–1998 | Physicians (1,000), 11% specialists (for study drug), 59% PCPs, 30% other specialists | Drug unknown | PSR visits | ||
Berndt |
US 1977–1993 | All US physicians | H2 antagonist antiulcer drugs (cimetidine, ranitidine, famotidine, nizatidine) | PSR visits (min) | Sales volume (units of average daily dose) and market share; Elasticity of demand | |
Rosenthal |
US 1996–1999 | Large sample of office and hospital physicians |
Medications prescribed in primary care | PSR visits (expenditure) | Sales of detailed medications per month | |
Azoulay |
US 1977–1993 | All prescribing physicians | H2 antagonist antiulcer drugs (cimetidine, ranitidine, famotidine, nizatidine) | PSR visits; Journal advertisements | Market share for the four H2 antagonists (patient days of therapy) | |
Gonul |
US 1989–1994 | Physicians |
One medication for a particular indication “relatively more common among the elderly” | PSR visits (min) | ||
Rizzo |
US 1988–1993 | All prescribers of antihypertensives in the US |
Antihypertensive medications | PSR visits (expenditure) | Sales of detailed medication; Price elasticity; Quadratic term for sales | |
Hurwitz |
US 1978–1983 | Specialists and PCPs prescribing promoted drugs |
Brand and generic drugs | Total promotional investment in PSR visits, journal advertising, direct mail advertising | Market share held by original brand; Market share held by generic competitors (measured in |
|
Mackowiak |
US 1977–1981 | Office based physicians across the US |
Benzodiazepines for anxiety; Diuretics for hypertension | PSR visits (expenditure); Journal advertisements (expenditure) | Expenditure on prescriptions; Market size and market share | |
Leffler |
US 1968–1977 | Not stated |
51 new products | Total promotional outlay (PSR visits, journal advertising) | Market share 2 y after market entry; Market share in 1977 for drugs introduced since 1968 expressed | |
Telser |
US 1963–1972 | All prescribing physicians |
Prescription medications in: the hospital market and drugstore market | Promotional intensity: ratio of total promotional outlays/total sales (includes PSR visits, journal advertising, direct mail) | Proportion of sales for entrant drugs | |
Spurling |
Australia 2004–2005 | PCPs (7) | Medications prescribed in primary care | PSR visits; Promotional items in PCP surgeries | Generic prescribing (% of total) | |
Stafford |
US 1996–2002 | Physicians (3,500) | Alpha-blockers | PSR visits (expenditure) | Prescriptions | |
Charbit |
France 1991–2001 | Prescribing physicians in France |
6 classes of antihypertensive medications | Journal advertising ( |
Drug sales for each of the six classes of antihypertensive medications | |
Auvray |
France 1992–1998 | PCPs, ear nose throat surgeons, chest physicians, psychiatrists-1,600 | Macrolide antibiotics and psychoanaleptic antidepressants | Total promotional investment | ||
Cleary |
US 1988 | Physicians prescribing 3rd generation cephalosporins |
Ceftazidime, cefriaxone, cefotaxime | PSR visits | New prescriptions; |
|
Soumerai |
US 1974–1983 | All propoxyphene prescribers in USA |
Propoxyphene | PSR visits (to warn about risks of propoxyphene) | Sales of propoxyphene; No-refill rates of prescriptions | |
Hemminki |
Estonia 2000 | Gynecologists and PCPs (342) | Hormone replacement therapy | Journal advertisements; Pharmaceutical company-sponsored medical education | Probability of detailed drug being prescribed | |
Schwartz |
US 1999–2000 | Psychiatry residents |
Psychiatric medications | PSR detailing (12 wk period when residents were detailed versus 12 wk with no detailing) | New prescriptions | |
Kazmierczak |
US 1996 | Physicians (60) | Tramadol | Drug company letter to physicians warning about tramadol seizure risk | Prescriptions for tramadol in high risk patients | |
Orlowski |
US 1992 | Hospital physicians (20) | Intravenous hospital medications called A (antibiotic) and B (cardiovascular drug) | Attendance at pharmaceutical sponsored meetings (all expenses paid trips to vacation site) | ||
Bowman |
US date not stated | Physicians (374) | Calcium channel blockers and beta-blockers | PSR sponsored continuing medical education course | Self-reported new prescriptions | |
Henderson |
Australia 2003–2005 | PCPs (1,336) | 7 advertised pharmaceutical products | Advertising on clinical software | ||
Kreyenbuhl |
US 2003–2004 | Psychiatrists |
Antipsychotic medication | PSR visits; Attendance at pharmaceutical sponsored meetings | Use of “switch” or “add” strategies in treatment of refractory schizophrenia | |
de Bakker |
Netherlands 2001 | PCPs (138) | Medications prescribed in primary care | PSR visits; Reliance on commercial sources of information | ||
Steinman |
US 1995–1990 | Physicians (97) | Gabapentin | PSR visits | Intention to prescribe gabapentin | |
Greving |
Netherlands 2003 | PCPs (70) | Angiotensin II receptor blockers | PSR visits; Journal advertisements; Attendance at pharmaceutical sponsored meetings | New prescriptions of this drug | |
Canli |
Turkey 2001 | PCPs (316) | Antibiotics for acute tonsillopharyngitis | PSR visits | Intention to prescribe antibiotics | |
Verdoux |
France 2004 | PCPs (848) | Antipsychotic medication | PSR visits | Initiation of antipsychotic medication in a 1-mo period | |
Muijrers |
Netherlands 2000–2001 | PCPs (1,434) | Medications prescribed in primary care | PSR visits | Quality of prescribing (determined by panel of experts) | |
Huang |
US 2001–2003 | Resident physicians |
Antidepressants | Sponsorship of resident conferences | Prescription of antidepressants from sponsoring companies | |
Watkins |
UK 1995–1996 | PCPs (1,714) | Medications prescribed in primary care | PSR visits (at least once per week); Journal advertisements; Reading written material from pharmaceutical companies | Cost of prescriptions | |
Prosser |
UK 1999–2000 | PCPs (107) | New medications prescribed in primary care | PSR visits; Journal advertisements/mailings (considered together) | New drug prescriptions (high/medium/low prescribers) | |
Caamano |
Spain 1993 | Physicians (234) | All prescribing | PSR visits | ||
Mansfield |
Australia 1999 | PCPs (1,174) | Medications used in primary care | PSR visits (self-report); Attendance at pharmaceutical sponsored meetings (self-report) | Quality use of medicine score | |
Jones |
UK 1995–1997 | PCPs |
Nine new drugs | Journal advertisements | Prescribing data for the advertised drugs | |
Caudill |
US 1996 | PCPs (446) | Medications for acute bronchitis, hypertension and urinary tract infection | PSR visits (frequency of use) | Cost of prescribing | |
Berings |
Belgium date not stated | PCPs (128) | Benzodiazepines | PSR visits ( |
Prescription of benzodiazepines | |
Lurie |
US 1987–1988 | Hospital physicians (240 faculty staff and 131 residents) | Hospital medications | PSR visits (<5 min and >5 min) | Change in prescribing habit Addition to hospital formulary | |
Healthcare Communications |
US 1987–1988 | Physicians (1184) | Newly promoted medications | Journal advertisements (awareness of) | Market share | |
Peay |
Australia 1981 | PCPs (74) and specialists (50) | Temazepam | PSR visits (contact versus no contact); Direct mailing; Attendance at PSR-sponsored function | Temazepam prescription | |
Blondeel |
Belgium 1987 | PCPs (358) | Medications prescribed by PCPs | PSR visits | Response to 8 simulated patients where prescribing was not advisable. Quality index compiled based on GP medication choices by expert panel (range 1–100) Proneness to prescribe (proxy for prescribing frequency) | |
Haayer |
Netherlands 1979 | PCPs (116) | Medications that would result from 8 case-histories devised by a panel | PSR visits; Journal advertisements; Companies' mailings | Prescribing rationality based on a composite scale (drug choice, duration, dose and use of combination products) | |
Walton |
US 1976–77 | PCPs (29%) and specialists (71%) (1,000 total) | 186 different medications | Journal advertisements | Prescriptions of advertised drugs (intention to prescribe) | |
Dajda |
UK 1975 | PCPs in UK |
Branded advertised drugs in the UK | Mailed advertisements (number in 1 y) | ||
Becker |
US 1970 | PCPs (29), internists (3). osteopathic physicians (5) | Chloramphenicol, equagesic, vitamin B12, methylphenidate, oral contraceptives | Use of journal advertisements PSR visits (frequency) | Proportion of chloramphenicol scripts. Physicians' self-reported prescribing behaviour. |
Experimental partnerships between pharmaceutical company and health authority or academic department.
Data from pharmaceutical company.
Information from a market research company.
Total number unknown.
Using national prescribing data.
PCP, primary care provider; RCT, randomized controlled trial.
Prescribing quality was measured by ten studies with 14 units of analysis
Exposure to Information from Drug Company | Study (First Author Name) | Result in Exposed Group Versus Controls (Where Applicable) | Change in Prescribing Quality Result |
de Bakker |
Wider prescribing range was associated with more visits from PSRs in the last 4 wk | Beta coefficient +0.18 ( |
|
Muijrers |
More frequent visits from PSRs was associated with less adherence to prescription guidelines | Multiple linear regression: | |
Beta −0.23 (95% CI −0.32 to −0.15) |
|||
Mansfield |
Frequency of visits from PSRs was not associated with a difference in quality score | Pearson coefficient of 0.0363; |
|
Blondeel |
Based on responses to 8 case histories: | Multivariate regression analysis: | |
First contact with a drug from the pharmaceutical industry was not associated with quality index; | |||
Based on prescriptions for actual patients: First contact with a drug from the pharmaceutical industry was associated with reduced quality of prescribing; | |||
Becker |
Fewer visits from PSRs/month were not associated with a change in the appropriateness of prescribing | Gamma statistic; 0.04, not statistically significant | |
Mansfield |
Attendance at pharmaceutical sponsored meetings was associated with lower quality scores | Pearson correlation coefficient of 0.0635; |
|
Spingarn |
Attendees at a sponsored talk about Lyme disease were less likely to choose appropriate oral antibiotics for mild Lyme disease than nonattendees | 0% of attendees ( |
|
For attendees and nonattendees of a sponsored talk about Lyme disease there was no difference in choice of acceptable treatment for Lyme disease with central nervous system signs | OR = 3.2 (95% CI 0.8–19.2) | ||
Attendees of a sponsored talk about Lyme disease were more likely to appropriately choose the sponsoring company's treatment for Lyme disease complicated by 2nd degree heart block | OR = 7.9 (95% CI 2.4–29.3) | ||
Becker |
Infrequent use of journal ads as a source of prescribing information by doctors was not associated with a change in the appropriateness of prescribing | Gamma statistic 0.373, not statistically significant | |
de Bakker |
There was a positive correlation for how frequently doctors used the pharmaceutical industry as a source of information and the range of drugs they prescribed | Beta coefficient +0.15 ( |
|
Haayer |
Frequency of use of information from the pharmaceutical industry was associated with less rational prescribing | Beta coefficient +0.134 ; |
|
Andersen |
Participation in a randomized controlled trial was not associated with a change in guideline adherence at 2 y for trial sponsor's medication | OR 1.00 (95% CI 0.84–1.19) | |
Kazmierczak |
Mailed warning letters regarding tramadol for those with a seizure risk were not associated with a change in prescription rates for tramadol | 9 (10%) prescriptions before and 7 (9%) after warning letters were sent out no association detected | |
Soumerai |
PSR visits: Propoxyphene use continued a preexisting decline of about 8% a year during the time when warnings from the manufacturing pharmaceutical company were conveyed by PSRs after which time this decline halted, however a statistical association was not shown. Refill rates and rates of overdose did not change following the warnings | No association detected | |
Mailed Information: Propoxyphene use continued a preexisting decline of about 8% a year during the time when warnings from the manufacturing pharmaceutical company were expressed by PSRs after which time this decline halted, however a statistical association was not shown. Refill rates and rates of overdose did not change following the warnings | No association detected |
Assumes a wide prescribing range is lower quality prescribing than a narrow prescribing range.
Residents attending a sponsored meeting were more likely than nonattending residents at the same hospital to prescribe the sponsoring company's medication, both when it was appropriate according to the authors and when it was not
Primary care providers who saw more pharmaceutical sales representatives and those who used the pharmaceutical industry in general as a source of information prescribed a wider range of drugs
Two studies measured guideline adherence. One found less adherence by primary care providers who received more frequent visits from pharmaceutical sales representatives
One study of warnings conveyed by pharmaceutical sales representatives and mailed information
51 studies
Exposure to Information from Drug Company | Study (First Author Name) | Results | Change in Prescribing Frequency Results |
Ching |
Higher levels of detailing for enalapril/hydrochlorothiazide and lisinopril/hydrochlorothiazide was associated with higher levels of demand (prescriptions) | Detailing elasticity 0.1–0.27 ( |
|
Kreyenbuhl |
Meeting PSRs >4 times in the preceding month was not associated with the “add” rather than “switch” strategy for antipsychotic medication prescribing | OR 1.22 (95% CI 0.68–2.20) | |
Steinman |
PSR visits of ≤5 min versus >5 min were not associated with intention to prescribe | No association detected | |
PSR visits to doctors in a small group were associated with increase in more frequent intention to prescribe |
OR 12.9 (95% CI 1.2–138.8) |
||
PSR visits were associated with increased gabapentin prescribing if physician's previous gabapentin prescribing was nil |
OR 15.1 (95% CI 3.9–58.2) |
||
PSR visits were associated with increased gabapentin prescribing if physician's previous gabapentin prescribing was low |
OR 8.6 (95% CI 2.4–31.4) |
||
Venkataraman |
PSR visits were associated with increased |
Beta coefficient: +0.944 (significant with a 95% CI) | |
Canli |
PSR visits were associated with increased antibiotic prescribing |
||
Chintagunta |
Higher levels of detailing were associated with higher market share for that brand in the three of the countries studied and no significant difference in two others | Detailing related change in market share; US; beta coefficient +0.06; |
|
Narayanan |
PSR visits were associated with an increase in market share | 1% increase in expenditure on detailing was associated with increases in market shares for promoted drugs ranging from 0.11% to 0.14% ( |
|
Verdoux |
PSR visits were associated with general practitioners initiating a newer antipsychotic medication | OR 2.80 (95% CI 2.09–3.76); |
|
Mizik |
PSR visits were associated with increased prescribing of Drugs A, B. and C | Drug A: 1 PSR visit generates 1.56 new prescriptions (95% CI 0.8–2.23) or 0.64 visits to induce one prescription | |
Drug B: 1 PSR visit generates 0.32 new prescriptions (95% CI 0.22–0.43) or 3.11 visits to induce one prescription | |||
Drug C: 1 PSR visit generates 0.153 new prescriptions (95% CI 0.11–0.2) or 6.54 visits to induce one prescription | |||
Donohue |
Expenditure on PSR visits is associated with higher probability that the detailed antidepressant is prescribed | Beta coefficient +0.703 ( |
|
Stafford |
Decreasing promotional expenditure was associated with a decrease in prescribing for alpha blockers |
Decreased with decreased promotion | |
Manchanda |
PSR visits were associated with more new prescriptions | 1.8 detailing visits results in 5 new prescriptions (average result) |
|
Manchanda and Chintagunta |
PSR visits were not associated with a significant change in mean prescriptions | Beta coefficient +0.83 detailing |
|
More frequent PSR visits were associated with diminishing increases in prescribing | Quadratic term for PSR visits: −0.49; |
||
Berndt |
PSR detailing were associated with increased cumulative days of therapy | Beta coefficient +0.7414; |
|
Rosenthal |
PSR visits were associated with increased frequency of prescription | Beta coefficient +0.017; |
|
Prosser |
PSR visits were more likely to be cited as a prescribing influence by high prescribers than by low prescribers | OR 7.32 (95% 1.64–32.61); Fisher exact test; |
|
Azoulay |
PSR detailing is associated with diffusion of product information and performance on the product market with marketing activities having a more pronounced effect than scientific information in the form of clinical trial reports |
Beta coefficient +0.654; |
|
Gonul |
PSR visits in minutes were a positive predictor of medication prescription | Beta coefficient +0.1085; |
|
Caamano |
PSR visits were not associated with the |
Adjusted regression coefficient −0.490.001; |
|
Schwartz |
PSR visits to residents were associated with increased initiation of prescriptions for 12 drugs |
||
PSR visits were not associated with increased prescription of one medication however for this medication unlike the others there had been more PSR visits in the control group | No association detected ( |
||
Rizzo |
PSR visits were associated with increased prescription sales | Beta coefficient +0.28; |
|
PSR visits may result in diminishing returns given the quadratic beta coefficient is statistically significant and negative | Quadratic sales coefficient for PSR visits: −0.490.01 ( |
||
Chren |
PSR meetings were associated with a formulary request | Multivariate result: OR = 3.4 (95% CI 1.8–6.6); |
|
Berings |
PSR visits were not significantly associated with benzodiazepine prescribing |
Linear regression analysis: beta 0.16 ( |
|
Cleary |
PSR visits were associated with an increase in prescribing of promoted medications; prescribing of them decreased when they were not promoted | Ceftriaxone 24.2% and 27.8% increase in promoted periods; |
|
Cefotaxime 14.6% and 26.2% increase in promoted periods; |
|||
Ceftazidime (promoted in period I but not promoted in period II): 27.7% decrease when not promoted in period II ( |
|||
Lurie |
PSR visits for faculty staff for less than 5 min were associated with more prescribing | Logistic regression coefficient 0.016; |
|
PSR visits for faculty staff for more than 5 min were not associated with a change in prescribing | |||
PSR visits for faculty staff for less than 5 min were not associated with an addition to the hospital formulary | Logistic regression coefficient 0.014; |
||
PSR visits for faculty staff for more than 5 min were not associated with an addition to the hospital formulary | |||
PSR visits for residents for less than 5 min were associated with more prescribing | Logistic regression coefficient 0.049; |
||
PSR visits for residents for more than 5 min were not associated with a change in prescribing | |||
PSR visits for residents for less than 5 min were not associated with an addition to the hospital formulary | |||
PSR visits for residents for more than 5 min were not associated with an addition to the hospital formulary | |||
Peay |
PSR visits were associated with temazepam prescription | Multivariate regression: −0.35 ( |
|
Blondeel |
Based on responses to 8 case-histories: | Multivariate regression: | |
First contact with a drug from the pharmaceutical industry was not associated with proneness to prescribe | |||
Number of PSRs received was not associated with proneness to prescribe | |||
Based on prescriptions for actual patients: | |||
First contact with a drug from the pharmaceutical industry was not associated with proneness to prescribe | |||
Number of PSRs received was associated with proneness to prescribe | |||
Mackowiak |
PSR visit expenditure was not associated with a change market size nor market share for benzodiazepines or diuretics | No association detected | |
Becker |
PSR visits per month were not associated with chloramphenicol prescribing | Gamma statistic 0.236; not significant | |
Hemminki |
Journal advertisements were associated with a trend for increased hormone replacement therapy (HRT) prescribing in Estonia | Increased prescriptions | |
Charbit |
Journal advertising was associated with increased prescriptions of ARA. When journal advertisements for ACE inhibitors and CCB decreased, their market share also decreased | 10.5% decrease in mean annual advertising of ACE inhibitors associated with 19.3% decrease in market share 11% decrease in mean annual advertising for CCBs associated with 19.3% decrease in market share. 20.5% increase in mean annual advertising rate for ARAs associated with 22.9% increase in market share | |
Prosser |
Journal advertisements were no more likely to be cited as a prescribing influence by high prescribers than by low prescribers | 9% high prescribers versus 0% of low prescribers; Fisher exact test; |
|
Azoulay |
Journal advertisements were associated with diffusion of product information and performance on the product market with marketing activities having a more pronounced effect than scientific information in the form of clinical trial reports |
Beta coefficient +0.112; |
|
Jones |
Journal advertisements were not associated with PCP prescribing | No association detected | |
Healthcare Communications |
Journal advertisement recognition was associated with increased market share for the advertised medication | 14.5% difference in market share between those physicians not recognising advertisements (19.6%) and those associating the advertisement message with the product (34.1%) | |
Walton |
Journal advertisement recognition was associated with medication prescription | OR 1.68 (95% CI 1.21–2.35) |
|
Becker |
Infrequent use of journal advertisement use was not associated with chloramphenicol prescribing | Gamma statistic −0.186 not statistically significant | |
Infrequent use of journal advertisements to learn about the usefulness of new medications was associated with reduced chloramphicol prescribing |
Gamma statistic +0.51; |
||
Kreyenbuhl |
Attendance at pharmaceutical sponsored CME meetings more than once in the preceding month was associated with the “add” rather than “switch” strategy for antipsychotic medication prescribing |
OR 2.32 (95% CI 1.29–4.18); |
|
Venkataraman |
Attendance at pharmaceutical sponsored meetings was not significantly associated with prescriptions for 7 out of 12 brands | Beta coefficient −0.659 (significant with a 90% CI) | |
Narayanan |
Attendance at pharmaceutical company-sponsored meetings was associated with an increase in promoted medication market share | A 1% increase in expenditure on “other marketing activities” (including meetings) was associated with increases in market shares for promoted drugs ranging from 0.02% to 0.04% ( |
|
Huang |
Attendance at pharmaceutical sponsored conferences was associated with more prescriptions of the corresponding sponsored antidepressant |
Pearson correlation coefficient; 2001–2002: 0.87; |
|
Spingarn |
Attendance at a pharmaceutical sponsored meeting was not associated with the intention to prescribe the promoted medication where it was indicated | OR 2.51 (95% CI 0.91–6.95) | |
Orlowski |
Attendance at pharmaceutical sponsored meeting was associated with more prescriptions of medications being discussed | Drug A: 81 (±44) prescriptions before, 272 (±117) prescriptions after; |
|
Drug B: 34 (±30) prescriptions before, 87 (±24) prescriptions after; |
|||
Bowman |
Attendance at pharmaceutical sponsored courses was associated with more prescriptions of medication made by sponsoring company | Before and 6 mo after 3 sponsored course involving sponsoring company's drugs: | |
Course I: Nifedipine, increase in prescriptions 5.6%; |
|||
Course II: Metoprolol, increase in prescriptions 12.4%; |
|||
Course III: Diltiazem, increase in prescriptions 18.7%; |
|||
Peay |
Attendance at pharmaceutical sponsored meeting was not associated with prescription of temazepam | No association detected | |
Prosser |
Mailed information was no more likely to be cited as an influence by high prescribers than low prescribers |
9% for high prescribers, 0% for low prescribers; Fisher exact test; |
|
Peay |
Mailed information was not associated with a change in temazepam prescribing frequency | No association detected | |
Dajda |
Mailed advertisements to general practitioners was associated with an increase in prescriptions | Correlation coefficient 0.08 | |
Henderson |
Advertisements on clinical software were not associated with a difference in prescribing for all advertised medications combined | Adjusted OR 0.96 (95% CI 0.87–1.06); |
|
Greving |
Commercial information sources of information were associated with an increase in rates of prescribing of angiotensin receptor blocking medications | OR 2.0 (95% CI 1.5–2.6) | |
Commercial information sources of information were not associated with an increase in the |
OR 12.8 (95% CI 0.20–816.58) | ||
Windmeijer |
Expenditure on pharmaceutical promotion was associated with more prescribing | Beta coefficient +0.0137; |
|
Auvray |
Total promotional investment was associated with an increase in the |
No statistical measures presented | |
Peay |
Commercial information sources were associated with a preference for temazepam prescribing |
||
Commercial information sources were associated with earlier temazepam prescribing |
|||
Hurwitz |
Promotion of the branded leading drug was associated with increased market share especially for acute or sporadic conditions | Beta coefficient +0.295; |
|
Promotion of “following generic drugs” was associated with reduced the market share for the leading drug | Beta coefficient −0.150; |
||
Mackowiak |
Expenditure on PSRs and journal advertisements was not associated with a change in market size nor market share for benzodiazepines or diuretics | No association detected | |
Leffler |
The promotional intensity for new products was not associated with increased market share for the entrant product 2 y post introduction | Beta coefficient +0.88; |
|
The promotional intensity for new products introduced over a 9-y period was associated with increased market share for the entrant products | Beta coefficient +1.25; |
||
Telser |
Overall promotional intensity was associated with the market share of entrant drugs in the hospital and drug store market in the period 1964–1968 | Drug store: beta coefficient +1.28; |
|
Hospital: beta coefficient +1.45; |
|||
Overall promotional intensity was not associated with the market share of entrant drugs in the hospital and drug market in the period 1968–1972 | Drug store: beta coefficient +1.19; |
||
Hospital: beta coefficient +0.608; |
|||
Andersen |
Participation in pharmaceutical funded research was associated with increase in the sponsoring company's share of asthma drug in practices conducting the trial compared to control practices | 6.7% increase (95% CI 3.0%–11.7%) |
|
Freemantle |
PSR visits were not associated with an increase in the prescription of the detailed medication | OR = 1.04 (95% CI 0.83–1.31); |
|
Dolovich |
PSR visits were not associated with a change in the market share of amoxicillin | Intervention group: +0.63% market share, control group: −0.72% market share; |
|
Kazmierczak |
Mailed warning letters regarding tramadol for those with a seizure risk were not associated with a change in prescription rates for tramadol |
Before mailing: 10% prescribing rate, after mailing 9% prescribing rate. | |
Soumerai |
PSR visits: Propoxyphene use continued a preexisting decline of about 8% a year during the time when warnings from the manufacturing pharmaceutical company were expressed by PSRs after which time this decline halted, however a statistical association was not shown. Refill rates and rates of overdose did not change following the warnings |
No association detected | |
Mailed information: Propoxyphene use continued a preexisting decline of about 8% a year during the time when warnings from the manufacturing pharmaceutical company were expressed by PSRs after which time this decline halted, however a statistical association was not shown. Refill rates and rates of overdose did not change following the | No association detected |
Study authors reported that exposure to information from drug companies was associated with decreased quality of prescribing.
Reported by study authors as statistically significant.
Study authors reported that exposure to information from drug companies was associated with increased quality of prescribing.
*Chi-squared statistic.
ACE, angiotensin converting enzyme; ARA, angiotensin receptor antagonist; CCB, calcium channel blocker; CME, continuing medical education.
Of the 29 studies of pharmaceutical sales representative visits, 17 found only an association with increased prescribing of the promoted drug
Nine of these studies with either positive or mixed results provided insights into features of pharmaceutical sales representative visits that modified the impact of these visits on prescribing
In addition to increasing the promoted drug's market share, pharmaceutical sales representative visits were associated with a decrease in the market share of competitor products
Four out of the eight studies measuring the effects of journal advertisements presented data but did not include statistical tests
Of the four studies that included statistical tests, one found that journal advertisements have a more pronounced effect on market share for the advertised drug than does positive scientific information published in medical journals
There were eight studies of pharmaceutical company-sponsored meetings. Five found positive associations with prescribing frequency
One of the three studies of mailed promotional material found an association with increased prescribing
A single study examined the effect of advertising in clinical practice software and found no association with prescribing frequency for six medications and less prescribing of one medication
Eight studies combined the outcome measures for various exposures to pharmaceutical company information or measured overall promotional investment, a proxy for the amount of exposure to information from pharmaceutical companies. Three studies found that total promotional investment was positively associated with prescribing frequency
We pooled results from a total of seven studies using a random effects model to examine whether exposure to promotion was associated with prescribing of the promoted medication. The seven study results included in the meta-analysis showed significant heterogeneity (
Five studies looked for associations between information delivered without conventional promotion techniques and the frequency of physicians' prescribing
Eight studies (
Exposure to Information from Drug Company | Study (First Author Name) | Results | Change in Prescribing Costs |
Watkins |
High cost prescribers were more likely to see PSRs at least once a week than low cost prescribers | OR 3.11 (95% CI 2.48–3.89); |
|
Caamano |
There was no association between PSR visits and the cost of prescriptions | Adjusted regression coefficient: 21.0; |
|
Gonul |
PSR visits were associated with increased physicians' price sensitivity | Maximum likelihood estimate, 0.0012; |
|
Rizzo |
PSR visits were associated with reduced price elasticity for the promoted drug | Sales estimate +0.14; |
|
Caudill |
Frequency of PSR visits was associated with higher prescribing costs | Multivariate regression beta +0.155; |
|
Watkins |
High cost prescribers were less likely to “rarely or never” read journal advertisements than low cost prescribers | OR 0.79 (95% CI 0.64–0.98); |
|
High cost prescribers were less likely to “rarely or never” read mailed information than low cost prescribers | OR 0.49 (95% CI 0.38–0.64); |
||
Spurling |
Reduced |
3 mo: OR 2.28 (95% CI 1.31–3.86); |
|
9 mo: OR 2.07 (95% CI 1.13–3.82); |
|||
Windmeijer |
Promotional outlay (PSR visits, journal advertisements, direct mail) was associated with reduced price elasticity for promoted drugs | ln regression coefficient −0.0102 (se 0.0055) |
|
Freemantle |
There was no significant difference in costs between the group that was detailed by PSRs instructed by a local health authority and the control group | Mean difference: £122.32 (95% CI −£94.91 to £342.91) |
Chi-squared statistic.
We found that the reported relationship between exposure to information provided directly by pharmaceutical companies and the quality, frequency, and cost of prescribing varied from case to case. However, with only one exception
This review has supported, updated, and extended the findings of previous reviews regarding the effects of exposure to information from pharmaceutical companies. 38 of the 58 included studies (66%) were not included in previous systematic reviews on this topic
Most of the included studies measured the frequency of prescribing. Amongst these, the studies of informational exposure where physicians are active participants, such as representatives' visits, sponsored meetings, or sponsored trials, more consistently found associations with higher prescribing frequency than studies of more passive exposures, such as journal advertisements and mailed information. Poor study quality precludes confident conclusions about journal advertising. However, one higher quality econometric analysis found that advertisements in journals were associated with a more pronounced effect on market share than positive scientific findings published in journals
All of the included studies had design limitations (
All other included studies were observational and thus able to measure associations but not prove causation. There is a risk that reported associations may be false positives, and that statistically significant findings may not necessarily be clinically significant. One example is the study by Mizik et al. that reports only a small increase in prescriptions associated with visits from pharmaceutical sales representatives
To the extent that the measured associations are real, causality may be bidirectional. The influence of information from pharmaceutical companies on prescribing is a likely explanation for the associations given that the major purpose of pharmaceutical promotion is to influence prescribing
Some studies found no association between exposure to information from pharmaceutical companies and prescribing outcomes or small effect sizes that seem unlikely to be clinically significant. Some of these may be false negatives or underestimations caused by study flaws, but it is likely that information from companies sometimes has little or no effect, especially when the information is not designed to increase sales, e.g., letters warning about safety problems. Most of the studies included in this review examined single components of promotional campaigns that may have little or no effect alone but have a synergistic effect in combination with other components. Promotion may be less effective if it is used beyond the point of diminishing returns or is up against similarly effective promotion for another similar product.
Given the controversial nature of this topic, there are many reasons why the studies could be biased overall in either direction. Authors may have produced results consistent with their ideological bias. Also reciprocal obligation to funders who preferred certain results may have lead to bias with or without conscious awareness. Publication and outcome reporting bias may have led to underrepresentation of negative, positive, uninteresting, or unwanted findings.
The strengths of this review include use of a comprehensive search strategy over multiple databases without any language exclusions. We consulted widely with experts in the field and we used validated instruments to assess quality of the studies. However, only one of the included studies was conducted in a low-income economy, as defined by the World Bank, so the effects of promotion there are less certain
Our efforts to access data that was not in the databases we searched had mixed results. Messages on e-mail discussion groups and contact with experts yielded five additional studies subsequent to the initial search
Given the wide range of knowledge and experience among the sources that we consulted and the expertise in our group, we are confident that we exhausted all reasonable avenues in our attempt to obtain additional literature.
Of the 58 studies included in this review, 38 studies reported a single unit of analysis with 25 (66%) finding significant associations between exposure to information from pharmaceutical companies and the quality, frequency, and cost of prescribing and eight (21%) finding no associations. The remaining five (13%) had multiple measures and found significant associations on some measures but not on others. The 20 studies with more than one unit of analysis reported 49 units of analysis of which 21 (43%) found significant associations, 24 (49%) found no associations, and four (8%) found mixed results. The difference between the results of the single versus multiple unit of analysis studies is significant (
Interpretation of our meta-analysis requires caution because many studies included in the narrative synthesis could not be included in the meta-analysis. Where a sufficient number of studies could be combined, there was significant heterogeneity. The summary result has not been presented because it is unlikely to accurately reflect the true effect size of most promotional campaigns for two main reasons. First, effect sizes varied widely so it is likely that promotional campaigns often have effect sizes far from average. Second, single promotional techniques are likely to be less effective individually than campaigns employing multiple promotional methods.
A sensitivity analysis found the difference between passive and active promotion is one possible cause of heterogeneity. Other possible explanations for variation in the effectiveness of promotion include variation from campaign to campaign in the relative benefits of the drug being promoted, the promoter's skills and budget, and the target group's level of resistance to promotion.
The limitations of studies reported in the literature mentioned above mean that we are unable to reach any definitive conclusions about the degree to which information from pharmaceutical companies increases, decreases, or has no effect on the frequency, cost, or quality of prescribing. In theory, advertising may be beneficial in several ways: by distributing information and thus improving the quality of prescribing
Some argue that prescribers have an ethical duty to avoid exposure to pharmaceutical promotion
Malaysian translation of the abstract by NO.
(0.04 MB DOC)
French translation of the abstract by AIV.
(0.05 MB DOC)
Spanish translation of the abstract by Diana L. Matallana.
(0.05 MB DOC)
MOOSE checklist.
(1.72 MB PDF)
The authors acknowledge Chris Del Mar for advice regarding the conduct of the systematic review, Suhail Doi for advice regarding meta-analysis, Lars Eriksson for advice regarding literature searching, and Kim Nguyen for advice regarding the interpretation of econometric studies.
confidence interval
odds ratio
pharmaceutical sales representative