Drug company payments to General Practices in England: Cross-sectional and social network analysis.

Although there has been extensive research on pharmaceutical industry payments to healthcare professionals, healthcare organisations with key roles in health systems have received little attention. We seek to contribute to addressing this gap in research by examining drug company payments to General Practices in England in 2015. We combine a publicly available payments database managed by the pharmaceutical industry with datasets covering key practice characteristics. We find that practices were an important target of company payments, receiving £2,726,018, equivalent to 6.5% of the value of payments to all healthcare organisations in England. Payments to practices were highly concentrated and specific companies were also highly dominant. The top 10 donors and the top 10 recipients amassed 87.9% and 13.6% of the value of payments, respectively. Practices with more patients, a greater proportion of elderly patients, and those in more affluent areas received significantly more payments on average. However, the patterns of payments were similar across England's regions. We also found that company networks-established by making payments to the same practices-were largely dominated by a single company, which was also by far the biggest donor. Greater policy attention is required to the risk of financial dependency and conflicts of interests that might arise from payments to practices and to organisational conflicts of interests more broadly. Our research also demonstrates that the comprehensiveness and quality of payment data disclosed via industry self-regulatory arrangements needs improvement. More interconnectivity between payment data and other datasets is needed to capture company marketing strategies systematically.

Introduction

Drug company payments to the healthcare sector can create conflicts of interest biasing clinical practice [1], research [2,3], and policymaking [4]. A key global trend towards addressing this risk involves payment disclosure via either public regulation (e.g., the US Open Payments or French Transparence Sante databases [5]) or industry self-regulation (e.g., most European countries, including the UK [6], Japan [7], and Australia [5]).

Research on payment disclosures has centered on individual healthcare professionals [8-11], with increasing evidence from the US of even small payments influencing drug prescription [12-14] and healthcare cost [14,15]. However, healthcare organisations (HCOs), including service providers, regulators or medical societies, have received less attention, even though they shape healthcare delivery via resource allocation, regulatory decisions, recommendations and guidelines [16,17]. The limited interest in payments to HCOs in the US [18] seems to reflect the fact that the Sunshine Act only covers payments to hospitals. However, the definition of organisational-level recipients adopted in European countries with self-regulation is conisderably broader, therefore allowing for capturing the unique compositions of HCOs in national healthcare systems [21]. Additionally, pharmaceutical companies and trade groups typically do not interpret payments to HCOs as falling under European data privacy laws, which prevents these recipeints from refusing to have their payments disclosed [17]. This contrasts with payments to healthcare professionals, interpreted by the industry as “personal data”, and therefore characterised by pervasive non-disclosure, precluding comprehensive analysis [11]. For example, in 2015 in the UK, only around 50% of the disclosed payments had any information about the individual recipients [10], and this had increased only to about 60% by 2019 [11].

Despite the relatively greater data availability, the UK is the only European country with patterns of payments to HCOs described at the national level [19] and in relation to organisations commissioning (or procuring) healthcare services for patients [20,21] as well as secondary-care providers [22]. Building on this research, we examine payments to General Practice (GP) surgeries (henceforth, practices), excluding specialist practices providing services related to specific fields of medicine [23]. As healthcare is organised differently across the UK [24], we examine England as its largest part. We focus on practices given their vital role in healthcare delivery in England, with over 60 million patients being registered at practices [25] and over 300 million appointments annually, compared to 23 million accident and emergency service hospital visits [26]. Further, over half of the total National Health Service (NHS) pharmaceutical spending involved prescriptions issued by practices [26].

We anticipate that practices will be a key target of company payments. Consistent with patterns of payments to HCOs in the UK [19] and the US [18], we also expect a few companies and recipients to concentrate most payments. Furthermore, following US research emphasising the importance of relatively small payments in influencing physicians [18,27,28], we predict that most companies will make many relatively small payments rather than a few large payments.

We also consider key practice characteristics–location, size, and some features of the patient population–as potentially affecting company choices about who receives payments. We hypothesise that the proportion of practices receiving payments is roughly equal across the regions of England. However, we expect to see differences in the amount of payments between practices in different parts of England reflecting previously demonstrated regional variation in prescribing patterns [29]. We anticipate more payments to practices with a higher number of registered patients compared to those with a smaller clientele, given the predicted greater “return on investment” for companies. Specifically, we expect that practices with higher shares of patients over the age of 65 will receive more payments compared to those with fewer elderly patients, due to, for example, the greater tendency for polypharmacy in elderly populations [30]. Moreover, we expect practices in more deprived locations to obtain more payments compared to those in more affluent areas [31] as, for example, studies in Northern Ireland [32] and Scotland [33] found greater numbers of prescriptions per patient in the most deprived areas compared with the least deprived ones, making them potentially more attractive as payment recipients.

Given the well-documented “relational” nature of the pharmaceutical industry, including its attempts to develop ties to, and visibility among, actors seen as vital for driving product uptake and profitability [34-37], we use social network analysis (SNA) to explore social structures involved in making payments to practices. Drawing on emerging applications of SNA to study pharmaceutical industry payments and marketing [38], we anticipate that connections established by making payments to practices are not accidental. For example, data analytics companies have offered SNA insights to map Key Opinion Leaders (KOLs) in the medical field in the US [38-41], and it is likely that similar services are also used in European countries [40,41]. As data disclosed within industry self-regulation has no information on products related to payments [6,19], SNA cannot trace product competition among companies. Instead, we examine i) which companies are interested in making payments to the same practices, ii) which companies are dominating the payment networks (if any), and iii) the differential density of connections within such networks. In so doing, we consider two types of networks. We interpret networks based on the value of payments as indicating the “importance” of a practice for a drug company, while networks involving the number of payments as pointing to the intensity of interactions with the practice. More frequent payments may, for example, enhance a company’s visibility, which could be an important goal of marketing efforts [42].

We had two specific objectives. We sought to analyse, first, the distribution of and factors associated with payments across drug companies and practices in England; and, second, the structure of connections between drug companies established by making payments to the same practices.

Methods

Study design

Our study combines cross-sectional and SNA analysis of drug company payments to practices in England. We combined Disclosure UK [43]–an annually published dataset including, among others, non-research payments to named HCOs, disclosed by companies following the Code of Practice of the Association of the British Pharmaceutical Industry (ABPI) [19]–with separately sourced information on practice characteristics. We analysed the distribution of payments across practices and companies and assessed the associations with selected practice characteristics. We then mapped the structure of connections between companies and their shared practices using SNA. Given the exploratory nature of our research, which involved combining datasets which had not been previously used to analyse drug company payments, our study did not follow an a priori protocol.

Data sources and extraction

We extracted data on company payments to practices from the 2015 edition of Disclosure UK as this is the only one for which previous research categorised payment recipients, which enabled isolating payments to practices [19]. The relationship between practices and the previously examined larger category of public sector primary care providers [19] is explained in S1 Appendix. To prevent any payments to practices from being unnecessarily excluded due to companies potentially misidentifying their ultimate recipients [19], we combined payments practices identified in either “Institution name” or “Institution location” columns of the dataset (2,945 payments in total, out of which 2,747 were used for the analysis). The section of Disclosure UK we analysed (Online supplement 1) can be matched with Disclosure UK version 20160630. To allow accurate comparison of payment values between companies we adjusted them for VAT using information from company "methodological notes”, as described elsewhere [19].

We used the GP Friends and Family Test (FFT) dataset [44] to assign unique codes to practices identified in Disclosure UK. The practice names were matched with the practice codes based on comparing practice names and addresses from the two datasets. For each practice with a unique code we obtained the number of registered patients using the Patients Registered at a GP Practice 2015 NHS dataset [25]. We also used this dataset to calculate the share of patients over 65. In addition, we obtained multiple deprivation index (MDI) decile scores for the postcode of each practice from the website of the Ministry of Housing, Communities and Local Government [45]. The MDI is an aggregated score of 37 indicators providing information on income; employment; health and disability; education, skills and training; crime; barriers to housing and services and living environment [46]. We divided practices into 4 quartiles based on their MDI score.

The categorisation and cleaning of drug company payment data is described elsewhere [42]. The extraction of data from the datasets with practice characteristics is described in the protocol available in S2 Appendix.

Analysis

Statistical analysis

We used R [47] version 1.4.1717 to analyse the distribution of payments descriptively and to assess differences across selected practice characteristics. As the distribution was heavily skewed, we examined medians and interquartile ranges. Significance of the difference in the value of payments between different groups was assessed using the Wilcoxon nonparametric statistical test. The reference groups are London in regional comparison; Lowest number of patients (1 quartile) in practice size comparison; Lowest share of elderly patients (1 quartile) in elderly patient population comparison, and Most deprived (1 quartile) in MDI comparison. The threshold for significance (alpha) was set to 0.05.

Social network analysis

We first created company by practice matrices in MS Excel, then converting them into company by company matrices to allow for examining connections between companies established by “shared” practices, i.e., practices to which any pair of companies made payments. We report findings calculated based on “valued” matrices, with the number of shared practices shown at the intersect of companies. We created separate matrices for different thresholds of the number and value of payments involved in establishing connections between companies; quartiles of the overall number and value of payments per company; and practice characteristics (i.e., quartiles of the total number of patients; quartiles of the share of patients over 65; and quartiles of the MDI of the postcodes in which the practices were based) (Online supplement 2).

We analysed the matrices in UCINET version 6.689 [48], visualising them in Gephi version 0.9.2. [49]. We calculated each company’s centrality, which is the number of ties a company has, i.e. the number of connections to other companies established by making payments to the same practices [50]. We also calculated network centralisation, showing, on a scale from 0 to 1, the extent to which a network is dominated by one company [50]. Centralisation score is measured as the ratio of the actual sum of centrality score differences and all possible sum of centrality score differences [51]. Finally, we calculated network density–the strength of existing ties between actors as a share of all possible ties. In our valued networks, density is calculated by dividing the sum of shared practices between all companies in a network by the total of all possible connections [52]. We report findings relating to networks established based on the value of payments made by drug companies but throughout the results we also signpost to web appendices with additional findings relating to networks considering the number of payments. Overall, from the SNA perspective, we expect to be able to detect companies dominating the field of payments to practices and patterns of lower and higher number of shared practices based on different payment sizes and practice characteristics mentioned above.

Results

Descriptive analysis of the distribution of payments to practices

In total, 37 drug companies made 2,945 payments, worth £ 2,726,017.77 to 1,790 practices. In 2015, these companies represented 37.0% of those reporting payments to HCOs in England. Consistent with our expectations regarding the importance of practices as a target of industry payments, payments to practices constituted 6.5% of the value of all payments made to HCOs in England (S3 Appendix) and practices ranked 5th of all HCOs receiving the highest amount of payments after universities, NHS foundation trusts, NHS trusts, and multi-professional organisations.

We excluded from further analysis 198 payments (6.72%), worth £166,351.74 (6.1%) made to 147 (8.21%) practices as we could not link them to practice codes. These practices were randomly distributed across England (S4 Appendix), with the median payment values similar to those in the rest of the dataset. Our final sample, therefore, comprised 2,747 payments worth, £2,559,666.03, made by 34 companies to 1,643 practices. These payments were for donations and grants (76.36%), contributions to costs of events (22.51%), and fees for service and consultancy (1.14%) (S5 Appendix).

As expected, payments were highly concentrated (Table 1). Although three-quarters of practices received no more than two, the top practice received as many as 132. Most companies were “small donors”, with three-quarters making no more than 81 payments, but the maximum number was almost a thousand (i.e., Bayer). The value of payments was similarly concentrated. Although three-quarters of practices received no more than £1,5k, the top one accumulated almost ten times more. Likewise, while three-quarters of companies made payments worth no more than £100k, those made by the top donor were worth more than 7.5 times as much. Three-quarters of companies made payments to no more than 56 practices, but the top donor, Bayer, remarkably, made its 998 payments to 778 practices (2.47% of the value of Bayer’s payments were contributions to costs of events, 96.19% were donations and grants, and 1.34% were fees for service and concultancy). A majority of practices only received payments from one company, while the the top recipient received payments in total from 18 companies.

Table 1

Summary of drug company payments to general practices.

Level of analysis	Minimum	Median [IQR]	Maximum
Single payment value (£)	8.00	320.00 [170.00–869.00]	49,420.80
Value of payments per general practice (£)	9.59	576.00 [217.25–1,520.75]	148,395.20
Number of payments per general practice	1.00	1.00 [1.00–2.00]	132.00
Value of payments per company (£)	80.00	9,036.00 [1,003.00–97,377.00]	765,987.77
Number of payments per company	1.00	14.50 [3.25–80.75]	998.00
Number of practices per company	1.00	8.50 [3.00–56.00]	778.00
Number of companies per practice	1.00	1.00 [1.00–1.00]	18.00

Notes: This table is based on drug company payments reported in Disclosure UK (2015, version 20160630).

Table 2 further evidences the concentration of payments, with those made by the top ten donors constituting, respectively, 93.64% and 83.69% of the total number and value of payments made to practices. Of the 10 companies, Bayer was dominant in the number and value of payments, as well as the number of practices to which payments were made. A similar table including the top 10 recipients is presented in S6 Appendix.

Table 2

Payments made by the top 10 drug company donors to general practices.

Company	Total value of payments (£)	Total number of payments	Number of practices paid	Median value of single payments (£) [IQR]
Bayer	765,987.77	998	773	434.50 [217.20–869.00]
Pfizer	360,556.90	140	105	1,412.10 [236.00–3,907.00]
Eli Lilly	271,139.00	260	185	200.00 [168.00–3,353.00]
Sanofi Aventis	269,965.82	149	126	1,000.00 [240.00–2,400.00]
AstraZeneca	153,865.25	85	16	250.00 [150.00–550.00]
Boehringer Ingelheim	145,070.58	213	146	392.00 [177.60–640.00]
Merck Sharp & Dohme	124,062.80	63	50	800.00 [195.80–4,400.00]
Takeda	112,428.80	94	58	240.00 [180.00–921.40]
Napp	97,743.39	235	212	38.49.00 [28.90–111.73]
Servier	96,162.40	62	61	1,567.00 [576.00–1,567.00]

Notes: This table is based on Disclosure UK (2015, version 20160630).

The evidence of companies’ preference for small payments was mixed. Most payments were indeed relatively small, with three-quarters being no more than £869.0 (Table 1). However, important differences existed among the biggest donors (Table 2). Despite the varying overall size of payments per company, the comparison of median payment values shows that companies such as Pfizer, Merck Sharp & Dohme, and Servier made fewer but more substantive payments, while Bayer, AstraZeneca and Napp made a larger number of smaller payments. However, the comparison of the median values at the payment and practice levels suggests there were two groups of companies prioritising small payments, with one concentrating on a smaller number of practices, while the other dispersing its payments across a larger number of practices. For example, while AstraZeneca made 85 payments to 16 practices, Servier made 62 payments to 61 practices. This reflects the overall payment distribution (Table 1), with three-quarters of practices receiving no more than two payments.

The relationships between payment patterns and practice characteristics were broadly consistent with our expectations (Table 3). In most regions of England, the shares of practices receiving payments ranged between a fifth and a quarter of all practices. The only two regions with markedly lower shares were London (8.88%) and North East England (14.66%). Nevertheless, the number of practices receiving payments varied considerably between the regions, with only 107 located in North East England and 267 in North West England. The median payment values also displayed regional differences, with practices in North East England having the median value almost twice as high as those in London and South East England (London vs. North East England; p <0.001). Moreover, the median value of payments per practice increased together with the practice size and the proportion of patients over 65 (all other quartiles are significantly different from the first quartile). However, unexpectedly, practices in the most deprived areas (1st quartile based on MDI) received significantly smaller payments than other practices.

Table 3

Breakdown of drug company payments according to general practice characteristics.

Classification	Group	Median value of payments (£) [IQR]	P-value	Number of general practices receiving payments (% out of total practices in the region)
	London	434.50 [217.25–2,600.00]	Ref	140 (8.88%)
Regional breakdown	East Midlands	434.50 [217.25–869.00]	0.756	147 (19.57%)
	East of England	600.00 [208.63–1,086.25]	0.994	136 (19.26%)
	North East England	869.00 [434.50–2,909.12]	<0.001	107 (14.66%)
	North West England	665.88 [217.25–3,168.95]	<0.001	261 (19.30%)
	South East England	434.50 [182.04–910.70]	0.086	249 (26.57%)
	South West England	651.75 [320.00–1,104.00]	0.221	168 (23.90%)
	West Midlands	461.42 [164.00–1,344.00]	0.826	220 (23.63%)
	Yorkshire and the Humber	460.00 [217.23–2,422.12]	0.243	215 (no data)
	Lowest number of patients (1st quartile)	217.25 [82.00–245.59]	Ref
Breakdown based on number of registered patients	Lower number of patients (2nd quartile)	434.50 [200.00–651.75]	<0.001
	Higher number of patients (3rd quartile)	869.00 [486.50–2,175.00]	<0.001
	Highest number of patients (4th quartile)	2087.20 [1,012.00–4,400.00]	<0.001
	Lowest share of elderly patients (1st quartile)	320.00 [167.50–863.12]	Ref
Breakdown based on share of patients over 65 years	Lower share of elderly patients (2nd quartile)	585.00 [217.25–1,409.56]	<0.001
	Higher share of elderly patients (3rd quartile)	651.75 [242.50–1,864.25]	<0.001
	Highest share of elderly patients (4th quartile)	869.00 [434.50–2,283.12]	<0.001
	Most deprived (1st quartile)	434.50 [200.00–1,157.68]	Ref
Breakdown based on index of multiple deprivation	More deprived (2nd quartile)	587.70 [217.25–1,470.70]	0.041
	Less deprived (3rd quartile)	651.75 [217.25–1,699.80]	0.001
	Least deprived (4th quartile)	651.75 [325.75–2,056.00]	<0.001

Notes: The share of the number of practices out of the total were only included for the regional breakdown because data could only be extracted for this variable. We did not find data on the number of practices in Yorkshire and the Humber. These practices are possible counted together with practices in North East England. Significance of the difference in the value of payments between different groups was assessed using Wilcoxon nonparametric statistical test. Reference groups are London, Lowest number of patients (1st quartile), Lowest share of elderly patients (1st quartile), and Most deprived (1st quartile). This table is based on Disclosure UK (2015, version 20160630), the GP Friends and Family Test (FFT) dataset, and the Patients Registered at a GP Practice 2015 NHS dataset.

Social network analysis of connections between drug companies

Fig 1 shows valued networks of connections between companies making payments to the same practices. A connection indicates at least one payment made to the same practice and line thickness and darker colour correspond with a greater number of shared practices. Therefore, companies connected with thicker and darker lines can be interpreted as having a shared interest in a greater number of practices. We demonstrate configurations of companies at different level of the value of payments. In Fig 1A, all companies are shown, while in Fig 1B–1D only companies making individual payments worth at least £100, £1000, and £2,500, respectively, are shown.

Fig 1

Networks based on the value of payments.

Notes: 1A) network of all payments; 1B) network of payment over £100 per practice; 1C) network of payment over £1000 per practice; 1D) network of payment over £2500 per practice. Fig 1A–1D shows the visualisation of networks based on the value of payments, created in Gephi. Node label size and darkness corresponds to the centrality of a company, the strength and darkness of the lines corresponds to the number of shared practices between companies. The networks visibly change as the payment number to a single practice increases.

As the value of payments increased, the number of companies decreased from 29 (Fig 1A) to 11 (Fig 1D), suggesting that only a few companies engaged with practices using high-value payments. The configurations of companies also changed, indicating similarities and differences in how they engaged with practices with payments above a certain value.

Fig 1A–1D can also be analysed in terms of their density, where higher density of a graph means stronger connections between a greater proportion of companies. The density of the graphs decreases from A to D, as the value of payments increases (see density scores in S7 Appendix). This suggests that there is an overall lower interest in the same practices as expressed by higher value payments, which means that one practice does not usually receive high value payments from multiple companies.

Moving on to specific companies, considering all payments, Bayer had the greatest shared interest in practices with Napp, Eli Lilly and Boehringer Ingelheim (Fig 1A). But when only accounting for payments worth over £1,000, Bayer made the highest number of payments to the same practices with Servier. In addition, among companies making the highest-value payments, over £2,500, those with the greatest shared interest in practices were Eli Lilly and Pfizer (Fig 1D).

This trend corresponds with companies’ centrality scores, which rose with the increasing number of connections with other companies. In Fig 1A–1C, Bayer is the company with the highest centrality score, while in Fig 1D Eli Lilly is the most central one of the network (see centrality scores in S7 Appendix).

The centralisation level of a graph indicates the extent to which one company dominates a network by being connected to a high number of companies, while other companies have less connections. From the four networks, Fig 1A is the most centralised, with Bayer dominating the network. This means that on many occasions when a company makes payments to a practice, Bayer also makes a payment there. Similarly to density, centralisation decreases as the value of payments increases (see centralisation scores in S7 Appendix). Further graphs of networks based on the number of payments can be found in S8 Appendix.

In S9 Appendix, we present additional results for valued drug company networks associated with making payments to practices with different characteristics. Interesting differences can be observed in terms of centralisation (the extent to which one company dominates a network) and centrality (the number of connections a company has), which we are reporting here, while differences between networks in density are not substantial. Regarding the region in which the practices were located, the highest centralisation was observed in South West of England, while the lowest–in South East England. Across the nine regions of England, Bayer had the highest centrality scores, with only Eli Lilly matching it in London and South East England. In company networks established based on making payments to practices of different sizes, a trend existed of increasing network centralisation as the number of patients increased, with the highest centralisation score in the third quartile of practice size. Bayer was, again, the most central company in all four (quartile 1 –quartile 4) networks showing payments made to practices with different patient numbers. In the networks involving payments made to practices based on the proportion of patients over the age of 65, the centralisation score does not change substantially with the increase in the proportion of elderly patients. Bayer also remained the company with the highest centrality score in all quartiles. A similar trend exists in networks with the MDI index (1st quartile being the most deprived) of the location of the practice. Bayer was, yet again, equally dominant in the most and least deprived areas.

Discussion

As far as we know, this is the first study examining drug company payments to the primary care sector. We find that general practices were a major target of industry payments in England, placing them in the top five and two of organisational recipients based on the value and number of payments, respectively (S3 Appendix). While the value of payments received by general practicioners is unknown given the big gaps in individual-level payment data, they could exceed considerably the organisational-level payments to practices [10]. Notably, the payments made to practices in England (£2.7m) were almost twenty times lower than those made to individual healthcare professionals in UK in 2015 (£50.9m) [10]. Overall, our findings suggest that more attention is needed to drug company payments to organisations and to organisational conflicts of interest [53-55].

Turning to the modes of financial engagement with practices, the high value of “grants and donations” (almost 65%) suggests that companies often provided them with “medical and educational goods and services”, which may bear company names but not product names [56]. Contrastingly, the low value of consultancy payments (less than 2%) suggests limited scope of practices offering, on behalf of their employees, services such as “market research” (defined broadly as “the collection and analysis of information” on medicines) or “chairing and speaking at meetings, assistance with training and participation in advisory boards” [56]. “Contributions to cost of events”, accounting for around a third of payments, covers events, such as conferences, organised by practices or third parties on their behalf.

Payments to practices were highly concentrated, just like in the UK overall [19]. From the industry side, more than a third (37%) of all companies making payments to HCOs in England reported having made payments to practices. Only a few companies were big donors, with the payment landscape largely dominated by one company, Bayer, which, incidentally, was also identified as the second largest source of payments to healthcare professionals in the UK in 2015 [10]. Bayer was dominanant across all regions of England, practice sizes, and patient population profiles. The SNA provided further evidence of concentration of payments among companies.

We also saw concentration of payments among practices, with many receiving only small or occasional payments, yet with a narrow subset being heavily exposed to industry funding. Although the conference or education budgets of the top recipients are unknown, the volume of reported payments suggests that the industry–or, indeed, specific companies–were a major source of such support. This is important as research on drug company funding within the healthcare sector [5], including patient organisations [57], highlights risks associated with dependency on industry funding, especially coming from a few donors.

Not only have we found significant regional differences in payment values received by practices across England, but we have also revealed that practices with the lowest number of patients, the lowest share of elderly patients, and those in the most deprived areas receive significantly lower amount of payments. Why practices in most most deprived areas recive less industry funding, and the consequence of this for general practicioners and their patients, should be investigated further.

We identified some evidence of the high-frequency but low-value payment strategy, which has been highlighted as potentially instrumental in generating networks of obligation with US healthcare professionals [58-62]. Here, we unearthed some divergence within this strategy, with some companies making many small payments to different practices, while others concentrating their small payments on fewer practices.

While these differences may indicate contrasting marketing strategies, our interpretation is constrained by the absence of information on products related to payments. Therefore, unlike with meals and small gifts reported in relation to US physicians [6,19,28,59], we do not know the significance of “small” payments, for example, for establishing extended reciprocity at the organisational level. Investigation of payment strategies would be even less possible in other European countries with self-regulation of payment disclosure. This is because the ABPI is the only European pharmaceutical industry trade group mandating its member companies not to aggregate payments to HCOs annually per recipient, which allows comparing payments of different sizes. While we are not aware of any detailed guidance from the ABPI associated with this requirement, a review of cases from the UK drug industry self-regulatory authority, the PMCPA, has not identified any relevant compliants. Therefore, it is unlikely that companies had difficulties in interpreting how payments to HCOs should be itemised.

While the key issue of company marketing cannot be addressed directly in the European self-regulatory context, previous research on European self-regulatory systems has captured companies’ marketing indirectly by considering the nature and frequency of investigations into unethical marketing for specific products, highlighting heavy marketing of drugs prescribed in general practice—antidepressants in the late 1990s [63], followed by anti-diabetics and urologics (mainly erectile dysfunction drugs) in the next decade [64]. Similarly, we note that between 2012–2018, Bayer was sanctioned by the PMCPA [65,66] on no less than 12 occasions for unethical marketing of Xarelto (rivaroxaban), a direct oral anticoagulant (DOAC) often prescribed by general practicioners as stroke prophylaxis in patients with atrial fibrillation [67], suggesting that Bayer’s payments to practices could be associated with this drug. Indeed, DOACs have been identified as heavily marketed products in the UK [68].

Finally, the prominence of drug company payments does not seem to be matched adequately by governance frameworks available to practices. Although NHS England requires NHS trusts and clinical commissioning group employees to record externally sponsored events and urges NHS staff to decline gifts that may affect their professional judgement [69], less clarity exists regarding organisational conflicts of interests, which might be associated with payments analysed in our study.

Limitations

Our article has some important limitations. While the value of disclosed payments to practices is substantial, it excludes payments for research and development, such as clinical and non-clinical studies, which are not disclosed on a named basis in accordance with self-regulatory rules. Moreover, we did not examine conflict of interest reporting by the practices from our dataset, which might reveal payments underreported by donors or recipients, as indicated by comparison of payments reported separately by drug companies and NHS trusts [22] and clinical commissioning groups [20] in England. Our findings are only part of a bigger picture of payments to primary care organisations. For example, companies also make payments to groups of practices or organisations involved in education of general practitioners (see examples in S3 Appendix). Extensive payments are also made to clinical commissioning groups, which procure primary care services across England [20].

Moreover, the selection of practice characteristics was not theoretically driven and omitted other potentially important ones, such as ratings of quality services. Finally, we did not examine the decision processes behind making payments nor those involved in accepting (or refusing) them. Yet, following a recent study on patient organisations, more qualitative research is needed to explore the different patterns of payments and what they might mean for practices and general practicioners and whether, and, if so, how they can influence treatment decisions [70].

Policy recommendations

The insufficient levels of payment and conflict of interest transparency indentified by our study are concerning, particularly in relation to practices receiving substantial–in the tens of thousands of pounds–annual payments from individual drug companies. Therefore, Disclosure UK should include payment descriptions–similar to those already provided in the self-regulatory arrangements for payments to patient organisations [65]–to illuminate payments’ intended goals. Similarly, without recipient identifiers data users are unable to establish the level of exposure of any practice to drug company payments [11,19]. Consequently, Disclosure UK should introduce identifiers already used by the NHS (i.e., practice codes), which would also allow for linking payment data to other publicly available datasets. Further, information about products associated with payments is necessary to investigate company marketing strategies, as is the case with the government-run US Open Payments Database [71]. In addition, comparing payments made to different HCOs requires the inclusion of recipient categories to avoid the need for checking the nature of and categorising the recipient of each payment [19]. More broadly, payments to HCOs reported in other European countries with self-regulation [6] should be itemised to allow examinining payments of different sizes.

In the long-run, a separate centralised public reporting system by practices is needed, comprising research and non-research payments from pharmaceutical and medical device companies. There are currently voluntary initiatives to make data about the links between doctors and the pharmaceutical industry publicly available, such as the UK’s whopaysthisdoctor.org. However, a central register allowing patients to see the financial interest of all doctors in particular for medicines or medical devices is also being discussed [72]. The establishment of any central payment registers should be coupled with information campaigns directed at medical professionals, patients and members of the public seeking to develop their understanding of conflicts of interests. These steps seems necessary to achieve behavioural change that to-date has not been triggered by the US Open Payments database, as demonstrated by physicians’ continued acceptance of COIs [73] or patients’ and public’s low engagement with payment data [73,74].

Beyond transparency, potential dependency of practices–or some of their activities–on drug company payments requires policy attention. The ABPI has recently acknowledged this problem by prohibiting companies following its Code of Practice from requiring being the sole funders of HCOs [56]. Nevertheless, building on ABPI’s recommendations regarding payments to patient organisations, companies should disclose the share of their payments in relevant organisational budgets [56].

Coding of general practices.

(DOCX)

Click here for additional data file.

Data cleaning protocol.

(DOCX)

Click here for additional data file.

Total value and number of payments to the top 10 healthcare organisations in England in 2015.

(DOCX)

Click here for additional data file.

Distribution of practices across different regions England.

(DOCX)

Click here for additional data file.

Breakdown of payments types received by general practices.

(DOCX)

Click here for additional data file.

Payments to the top ten general practices.

(DOCX)

Click here for additional data file.

Summary of network statistics calculated for valued networks of drug companies.

(DOCX)

Click here for additional data file.

Network visualizations for networks based on the number of payments.

(DOCX)

Click here for additional data file.

Breakdown of network statistics according to general practice characteristics.

(DOCX)

Click here for additional data file.

23 Jul 2021

PONE-D-21-20184

Drug company payments to general practices in England (2015): descriptive and social network analysis

PLOS ONE

Dear Dr. Ozieranski,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In addition to the points raised by the reviewers, I have two additional comments:

In the Introduction I would suggest that you cite the study about the lack of disclose of industry payments to organizations sponsoring guidelines – see: Elder CMAJ 2020 June 8;192:E617-25.

Are all payments to English surgeries entered into the national database that you mention on page 4?

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: I Don't Know

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This article addresses an interesting question and a form of institutional research funding that is unique to UK transparency reports.

General comments:

In terms of the overall framing and interpretation of study results, it would be good to know how this relates to funding of individual GPs who are working within these surgeries. Is a link possible? If not, it would be good to relate this funding to total GP funding for 2015 (if data are available) and to consider this in the overall framing / interpretation of the study results.

I am also unsure what the network analysis adds to understanding of either companies’ payment patterns or surgeries receptivity to payments. Its value to answer clear research questions on funding patterns and purpose wasn't clear. The assumption of competing companies (if they are funding the same surgery) may be incorrect, as without information about which products each company is promoting, they might or might not be competing (e.g. may be promoting in a different treatment area).

If you wanted to provide some information to readers about the pattern of company payments based on the network analysis, I would suggest just including S6(a), the figure with the network of all payments as a figure in your main report, and to explain what it shows about links between companies (ie darker lines and thicker lines mean more shared funding of the same GP surgery). Ideally you would use the concepts of 'centralisation' and 'density' as a guide for yourself for the extent of joint funding and of concentration of funding, but you would leave these jargon terms completely out of the analysis. I would leave out all of the tables and text related to this as well and just keep a very brief section on the results of the network analysis, presented in a way a general medical reader can understand. If you wanted to support this with the details of the network analysis results, this could be included in the supplement.

Detailed comments.

Page 4: please add descriptive info on surgeries - # doctors on average per surgery and extent of variation, # of GP surgeries in total in the UK, if info is available. Also the first time ‘GP surgery’ is used it should likely be translated for North American audiences, where it’s called a GP practice.

Page 5: note about the strategic importance of surgeries needs some qualification. One complication in terms of surgeries being a key target of company payments is that individual GPs working within these surgeries would be a target for payments as well. From previous research on the UK transparency data, can you say anything about the proportion of payments in total going to GPs versus to hospital-based consultants?

Page 6, methods: It’s unclear whether the focus on products that are marketed unethically will provide the information needed, as these are not necessarily the most frequently marketed products; they could be a small subset of marketed products to GPs by each company. If data are available from IQVIA or other market research companies on product-specific advertising spending in the UK over the study time period, this would be much more relevant. IQVIA has provided this type of information to academic researchers free of charge in the past (in other countries). If this information was available then a second step would be to develop a list of the subset of drugs that would be used in primary care, based on a review by a clinician or frequency of use data if available.

Page 7, methods: The decision to focus on 2015 is based on your team’s previous analysis [ref 17]. You had identified five payment categories to healthcare organisations in that study [listed in the Supplement]. Are these 5 types of payments included in the current study or a subset? Additionally eTable1 lists how the various type of health care organisations are classified in that previous article. It would be good to add a reference to how the GP surgeries were classified (for example if combined with other public sector health care providers, state this).

Page 8: You note that specific surgeries were excluded from the analysis because of an inability to match the practice code with the payment data (6.7% of total payments). I’m wondering whether all parts of your analysis required this extra information, or only specific subsets in which you were looking at the characteristics of the GP surgeries. For example, when looking at the amount each company provided to GP surgeries in total, wouldn’t it be more accurate to include the entire set?

Page 8-9: as noted above, I have some concerns about the selection of PMCPA investigations as an information source for which drugs were associated with payments to GP surgeries. You mention that this is likely the tip of the iceberg of payments. I would agree, but an additional concern is that it may not even necessarily represent the specific payment subset that focused on GP surgeries. In my opinion, this is too much of a stretch in terms of a link between this subset of payments and this subset of Disclosure UK payments. If you wanted to obtain a more accurate idea of which drugs were being most heavily promoted in 2015, my recommendation would be to try to obtain information on top drugs by company for advertising spending instead, and assess which are drugs used in primary care, as a more reliable information source on the focus of this funding of GP surgeries.

Page 9: analyses: it is interesting to examine the number of companies providing payments to each surgery and the extent to which specific companies tend to co-fund specific surgeries, which I assume is the main focus of the network analysis. I think this provides an additional very interesting focus on patterns of payments.

If I understand centralisation it measures the extent to which a set of GP surgeries with similar characteristics (regionally or by patient population?) might obtain funding from a single company. I don’t really understand this statement in terms of what it measures of importance concerning industry funding of surgeries: “Centralisation score is measured as the ratio of the actual sum of

centrality score differences and all possible sum of centrality score differences [53].”

Similarly, the measure of density as a proportion of existing ties between actors “as a share of all possible ties” needs to be explained in terms of the meaning to this specific study. It is not clear to me how this measures the “average strength of ties between companies”. It may be a situation in which companies use similar strategies to target specific GP practices, for example selecting those that have high prescribing rates and/or a patient population that is likely to use the promoted drug (e.g. if a drug for older people, a high enough proportion of older patients). Some of these marketing decisions might be directly competitive; others might simply be targeting the same surgeries for shared reasons but in a different treatment area.

P11: top of page – in describing the payments to surgeries and a proportion of total payments to healthcare organisations, it would be helpful to briefly state what types of other healthcare organisations received most of the funds. See below - the fact that they are 5th in payment level among healthcare organisations should be mentioned in results, not discussion.

Without this extra information, the latter seems inaccurate: “Consistent with our expectations, this finding suggests that surgeries were an important target of drug company payments within England’s healthcare system but their significance was reflected more by the monetary value of payments intensity of interactions, indicated by the number of payments.”

I would suggest qualifying as the number of interactions is small (0.11% of total) and monetary value needs to be seen in context, eg. likely much less than payments to individuals.

P11: fees for consultancies are presumably for the surgery to carry out consultancy services, not for individual GPs to act as consultants? Wouldn’t the latter be reported under individual payments?

Table 1: Given the large skew in the distribution of payment numbers to surgeries, I wondered whether some of this data might be better graphed than presented within a table according to quartiles. I missed being able to know actual numbers of surgeries (or proportion of surgeries with the total n provided) with different numbers of payments and/or value of payments. It would also be good to group receipt of payment by surgeries in one section (or figure) and payments by companies in another as each denominator is quite different. Denominators are also needed for numbers of surgeries and numbers of companies and it would be good to have information on the total numbers and total values of payments within the table.

I also missed information on how this subset of surgeries relates to all surgeries in the UK. What proportion have received payments? Is there a regional skew in terms of proportions of surgeries receiving payments?

P12: The following sentence is unclear: “Of the 10 companies, Bayer was clearly dominant both in relation to the number (43.41% of the top 10) and value of payments (31.96% of the top 10) and the number of surgeries to which payments were made (44.63% of the top 10).” What does ’43.41% of the top 10’ refer to exactly? Perhaps instead just say that Table 2 describes the top 10 companies by payment amount and that Bayer provided the largest amount.

Table 2 should be simplified. I would suggest leaving off most of the columns on quartiles etc. and just retaining the first 3 column on total amount paid, number of surgeries and number of payments. You could list the payment value median [IQ range] as a fourth column. Headings could be clearer and I did not understand what the percentages referred to – clearly not a percentage of total value of payments to GP surgeries. Additionally when you say ‘all HCO’ do you mean all surgeries only or all types of health care organisations? I would suggest just focusing on the surgeries in this table.

When you refer to most payments being under 869 pounds, I’d suggest instead stating the proportion under 1000 pounds or another more standard number.

Page 14 describes the proportion of surgeries receiving payments in different regions. It would have been helpful to state at the beginning of the results section what proportion of surgeries the 1790 represents. My recommendation would be to shift this to the beginning of the results section and to include the proportion per region in your table, with total numbers of surgeries per region also stated. Table 3 could easily include this information in the middle column.

An alternative would be to use the table you now call S1 in the text rather than Table 3. It provides key information – text in headings could be cut down.

Table 3: For the three sections on quartiles at the bottom, rather than saying ‘first quartile’ to 4th quartile’ you could use for example ‘Socio-economic index’ as a header row and then say ‘lowest deprivation [1st quartile] to ‘highest deprivation’ and similar for patients over 65. If these are quartiles you do not need to say numbers of surgeries per quartile. A header row ‘region’ at the top would also mean you could organize your columns differently. No need to put ‘breakdown of’ in header rows.

Page 15 – SNA is used without being defined.

Network analysis:

This entire section needs a rethink in terms of what the value to the reader of this network analysis is. I find the concepts of centralisation and density difficult to interpret in terms of the added knowledge on patterns of industry payments to GP surgeries. I can see that this is a way to measure the extent to which surgeries receive funding from many different companies and whether companies tend to have similar patterns of funding but beyond this the density and centralisation levels do not add meaning.

The description and meaning needs to be clearer. What does the high centrality score add for Bayer as compared with the data you have already presented on this company having the highest value of payments?

Additionally, the discussion of competing companies may be overinterpretation, as a company with products treating the same condition would be directly competing; others might just be targeting the same surgeries.

There appear to be different patterns by company of making many small payments versus fewer large payments. This could be a vestige of companies’ reporting patterns. Some may be more likely to break down their reports of payments to surgeries into many smaller subsets; others to amalgamate. In order to understand whether these patterns of payment size are meaningful, you would need to check the Disclosure UK reporting rules to see whether the extent of flexibility.

Our analyses of companies' payments in Australia to patient organisation suggested large differences in reporting patterns per company that were not necessarily reflective of actual differences (e.g. some seemed to amalgamate more than others). This suggested to us that value of payments per organisation was much more reliable as a measure than numbers of payments. I don't know the UK data, but if this is the case in the UK as well it should guide your analysis and interpretation.

P18: exploration of company payments and drug marketing.

I’d suggest leaving this section out as it is not directly relevant to the focus of this paper and it is a stretch to assume that these specific payments are related to the drugs with promotion that has been found to breach the industry code. A reference to the Bayer breaches for this anticoagulant (please use the generic name) might be brought in within the discussion instead, when commenting on Bayer being the company with the highest value payments to GP surgeries.

Discussion

The beginning of the discussion includes information that should be in the results. The fact that GP surgeries are the 5th type of organisation by amount of funding is important. It also refers to a table (Table S2) which states total amount of funding to GP surgeries – this funding amount also should be in the text.

Table S2 is much too long and detailed. You could stick to the top 10 organisations. Please shorten some names e.g. ‘multi-profession associations’ (4th).

P24: As noted above, the network analysis may not reflect direct drug company competition. More information is needed about which drugs are being promoted, to treat which conditions, per company, before that statement can be made. It is also possible that companies are promoting to the same surgeries for different treatment areas.

Table S3 could be simplified (e.g. only include full data rather than having two rows with full and after exclusions), but do state the denominator (# surgeries) for the table. It is useful to know the breakdown of amounts per type.

A general note: there are a number of typos in the text which need to be corrected. Secondly, most tables that include all of the quartiles plus minimum and maximum value and median could be simplified into a single column. This could be ' median [interquartile range]' or 'median [min- max]'. . An example is S4 – but this applies across the board to tables within the text and the supplement. A note for S4 – do not include the practice code unless this would be meaningful to readers.

Reviewer #2: General comments

The study/paper applies much needed scrutiny to industry payments to surgeries in England. I congratulate the authors on investigating this problem.

I have a few general comments and some specific comments below – which have all gone to both authors and editors.

The biggest suggestion I have is that the piece could be reduced in length, and at the same time made clearer. Related to this, the Methods needed to be disentangled from the Introduction and the Results need to be disentangled from the

Methods.

A statement on whether or not there was a pre-existing protocol for the study, is needed.

And an important caveat is that I have no biostats specialty, so I have not reviewed the stats.

Specific Comments

Page 4 (P4), Paragraph 1 (Pa1) You might consider referencing the recent material in BMJ, which summarises all the evidence about the distorting impacts of COIs, and flags the BMJ campaign to push for more independence. ( https://www.bmj.com/content/367/bmj.l6576 )

P5 Large paragraph. This is feeling to me like too much detail. I think you can state hypotheses much more precisely and potentially move some material to the Methods section.

P6 – First main paragraph: this feels like material that should best be in Methods.

P6 Last sentence. This sentence seems to be preempting results – as presumably you discovered this information during study – so again – feels more like methods.

P7 Methods. I would start with an overall description of the study design- before going into specifics. Also, it is important to mention whether or not there was a pre-specified protocol for the study – and if so then later describe any deviations from it.

P8, 1st paragraph. Some of this material seems to be best in results section – forgive me if I am confused, btu it feels like results are being given (in terms of numbers of pounds etc) in the middle of the Methods section. Eg this sentence “Of the total of 1,791 distinct surgeries, receiving 2,944 payments, worth £2,730,261.32, 147 (8.95%) surgeries, receiving 197 (6.69%) payments, worth £170,595.29 (6.24%), could not be linked to a practice code from the FFT dataset”

Table 2. Typo in title of Column 2: “Nnumber”

P14 typo “patters”

P14 Forgive me if I missed something – but this statement on deprived areas seems to contradict the statement below from the abstract? Unless I have misunderstood? “Contrary to our expectation that surgeries in more deprived areas will obtain more payments due to the greater number of prescriptions per patients in these regions, our data shows that surgeries in the most deprived areas (1st quartile based on MDI) received the smallest, while surgeries in the top two quartiles - the largest amount of payments”

From Abstract: “Surgeries with more patients, a greater proportion of elderly patients, and those in more deprived areas received more payments on average”

Table 3. Is there something missing from this line?

“4 th quartile 411 434.50 – 2283.12)”

P15/16/17 The material on SNA could be worded mor simply and clearly. I found the text and Table very difficult (maybe because I am Australian) – though I presume there are some important findings here – which could perhaps be explained more clearly and simply and briefly. I don’t understand at all what is being said about Tekeda on P17, and it having “more competitors”. The whole meaning and importance of “centralisation” needs to be made much clearer.

P19 – I would consider removing the detailed list of the 4 breaches, and try and summarise briefly the nature and importance of the breaches and the issues/dangers associated with the drug and/or its promotion. (I note there is relevant material in Discussion, but I would still summarise rather than list the 4)

P20 – Typo “payment patter” (unless “patter” has a meaning I am unaware of)

P24 Typo “payments”

P26. As mentioned earlier, there seems an opportunity here to be calling for more independence. It is in a way unbelievable that the NHS still allows companies to make payments to people making decisions about those companies’ drugs. The BMJ campaign could be considered, but that is up tto the authors. ( https://www.bmj.com/content/367/bmj.l6576

**********

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19 Oct 2021

PONE-D-21-20184R1

Drug company payments to General Practices in England: cross-sectional and social network analysis

PLOS ONE

Dear Dr. Ozieranski,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

In addition to the comments from the reviewers, I have some additional points:

==============================

Page 3, 7th line from bottom: Delete “a” after “variety”.

Page 4, 7th line from top: Explain the term "healthcare commissioners".

Page 4, 9th line from bottom: Does the term “practices” mean exclusively GP practices or does it also include specialists' practices?

Page 6, last paragraph before Methods: Considering that you devote 3.5 pages in the Results to your network analysis you need to make a stronger case here about why this type of analysis is important.

Page 9, first line: It should be “calculated”.

Page 10, second line: I would suggest moving the sentence beginning "More frequent payments..." and the next paragraph to the end of the Introduction to help make the case why network analysis is important.

Page 10, first line in Results: In the Methods you've already pointed out that your analysis is not based on 1790 practices and you've given the reason. I'd suggest eliminating this sentence and then moving up the first sentence in the next paragraph to start the Results section.

Page 11, 4th line: Insert “in total” after “payments”.

Page 15, 6th line: Is there any way of determining if companies with a shared interest are comarketing certain drugs?

Page 18, 3rd line from bottom: It should be “A similar trend…”

Page 19, 11th line from top: It's unclear what is meant by "recipients' peers".

Page 19, 6th line from bottom: “Medicines” is misspelled.

Page 19, 2nd line from bottom: It should be “covers”.

Page 21, 7th line from bottom: Insert “to” between “payments” and “HCOs”.

Pages 22-23: I think that there is too much space devoted to the issue of unethical marketing of DOACs in general and rivaroxaban in particular, especially given the unknown relationship between rivaroxaban marketing and payments to GP practices.

Page 25, 11th line from bottom: Insert “a” after “Such”.

Page 25, 7th and 6th lines from bottom: Would such a central register make a difference to patients' or doctors' behaviour? Research in the US suggests that it doesn't.

Page 25, 6th line from bottom: Insert “for” between “particular” and “medicines”.

Please submit your revised manuscript by Dec 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for this very clear response to the previous comments and for the clarification of the social network analysis. I have identified a few very minor points of needed clarification:

Abstract:

The reference to the 'top 10 donors' does not flow that well from previous text, and the reader needs to consider this has switched from being about practices to being about companies. Perhaps instead (addition in caps):

Payments to practices were highly concentrated, AND SPECIFIC COMPANIES WERE ALSO HIGHLY DOMINANT. The top 10 donors and the top 10 recipients amassed 87.9% and 13.6% of the value of payments, respectively...

Table 1: 'value of individual payments' could be misread as being about payments to individuals. Perhaps 'value of individual payments to each practice' or 'single payment value'

Also please left justify the row labels, similarly to table 2.

page 17: this line is unclear:

"This suggests that there is an overall lower interest in the same practices as expressed by higher value payments."

Do you mean that if practices received higher value payments from one company, they were less likely to also receive higher value payments from other companies?

Please clarify.

Page 18: This line assumes too much reader recall of newly defined technical terms, especially given the similarity of terms:

"Interesting differences can be observed in terms of centralisation and centrality,"

I'd suggest :

Interesting differences can be observed in terms of centralisation (BRIEFLY REDEFINE) and centrality (BRIEFLY REDEFINE),

This introduces a little bit of repetition, but it is worthwhile for the reader.

In general, I thought that the revisions have made the analysis much clearer, and that this is an interesting article that adds substantially to the research evidence on industry payments.

Reviewer #2: In my original review I had four important main overall comments – and I do not feel that the authors have adequately addressed my comments or concerns. Obviously it is the for editors to decide whether further revision is necessary, but my sense is that it is.

My first main comment in my initial review was that the original paper was too long and could be reduced in length. The authors have responded that they have “shortened wherever possible”. I do not agree, and I think the piece is still too long. The revised Introduction alone seems to be still close to 1000 words long. The Methods section also feels too long in places, such as the data sources and social network analysis sections. It is an important study, but in my view does not warrant an exceptionally lengthy write-up.

My second main comment is that the revision should be clearer. I feel a further revision could make the whole manuscript clearer still.

My third main comment was a suggestion to try and disentangle different sections, and remove overlap between Introduction, Methods and Results. The authors responded in their letter accompanying their revision that “We have also sought to disentangle the different sections.” Yet, if I am not mistaken, in two of the three specific suggestions which I made to disentangle, they have responded that this is not possible. I think authors need to revisit my suggestions in their next revision, particularly in relation to the payment figures currently included in the Methods.

My fourth main comment, and a very important one, pertained to the issue of a pre-existing protocol. I wrote in my initial review “A statement on whether or not there was a pre-existing protocol for the study, is needed.” The authors responded to my comment by writing: “The statement about writing a protocol about the data extraction process has been added to the methods section (p. 8, para 1).” And the new revised manuscript stated “The data extraction process followed a detailed protocol which can be obtained from the authors upon request.” I am concerned because I did not suggest making a statement about a protocol for “data extraction”, I suggested making a statement about a pre-existing protocol for the study. I think any revised manuscript needs to be crystal clear about whether there was a pre-existing protocol for the study, and potentially offer an explanation for why there was not one. Moreover, if there was a pre-existing protocol for part of the study, ie the data extraction, potentially this could be made available as a supplementary file.

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Reviewer #1: Yes: Barbara Mintzes

Reviewer #2: Yes: Ray Moynihan

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22 Nov 2021

Please see the submission file.

Submitted filename: Responding to comments_20.11_ES.docx

Click here for additional data file.

24 Nov 2021

Drug company payments to General Practices in England: cross-sectional and social network analysis

PONE-D-21-20184R2

Dear Dr. Ozieranski,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Joel Lexchin, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

There are still a few minor copyediting changes that need to be made:

Page 7, 9th line from bottom: Delete “using”.

Page 9, 7th line from bottom: Insert “by” between “made” and “drug”.

Page 15, last line: Insert “a” between “above” and “certain”.

Page 16, first line: “Figures” is misspelled.

Page 19, 8th line from bottom: Insert “we have” between “but” and “also”.

Reviewers' comments:

29 Nov 2021

PONE-D-21-20184R2

Drug company payments to General Practices in England: cross-sectional and social network analysis

Dear Dr. Ozieranski:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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PLOS ONE