Abstract
Objectives
Primary care practices in England differ in antibiotic prescribing rates, and, anecdotally, prescribers justify high prescribing rates based on their individual case mix. The aim of this paper was to explore to what extent factors such as patient comorbidities explain this variation in antibiotic prescribing.
Methods
Primary care consultation and prescribing data recorded in The Health Improvement Network (THIN) database in 2013 were used. Boosted regression trees (BRTs) and negative binomial regression (NBR) models were used to evaluate associations between predictors and antibiotic prescribing rates. The following variables were considered as potential predictors: various infection-related consultation rates, proportions of patients with comorbidities, proportion of patients with inhaled/systemic corticosteroids or immunosuppressive drugs, and demographic traits.
Results
The median antibiotic prescribing rate was 65.6 (IQR 57.4–74.0) per 100 registered patients among 348 English practices. In the BRT model, consultation rates had the largest total relative influence on antibiotic prescribing rate (53.5%), followed by steroid and immunosuppressive drugs (31.6%) and comorbidities (12.2%). Only 21% of the deviance could be explained by an NBR model considering only comorbidities and age and gender, whereas 57% of the deviance could be explained by the model considering all variables.
Conclusions
The majority of practice-level variation in antibiotic prescribing cannot be explained by variation in prevalence of comorbidities. Factors such as high consultation rates for respiratory tract infections and high prescribing rates for corticosteroids could explain much of the variation, and as such may be considered in determining a practice’s potential to reduce prescribing.
Introduction
There is substantial variation in antibiotic prescribing rates between general practices.1 Part of this variation may be due to medically legitimate reasons, such as differences in the prevalence of comorbidities or in the age and gender distributions of practices’ catchment populations. For example, English guidelines recommend avoiding antibiotic treatment for self-limiting respiratory tract infections (RTIs), except if the patient is at high risk of serious complications because of pre-existing comorbidity.2 Hence, one would expect higher prescribing rates in practices with a relatively high number of patients with pre-existing comorbidities compared with practices with mainly healthy patients without comorbidities. Similarly, a practice with a high proportion of young children or elderly patients would be expected to have higher prescribing rates than a practice with mainly working-age adults.1
On the other hand, a substantial fraction of antibiotic prescriptions in primary care are likely to be inappropriate (defined here as clinically unnecessary).3,4 Variation in the percentage of antibiotics that are prescribed unnecessarily may also explain part of the between-practice variation in antibiotic prescribing rates. To date, it is unclear to what extent observed variation in prescribing between practices is due to legitimate medical reasons and how much can be explained by differences in the amount of inappropriate antibiotic prescribing.
In England, to account for differences in the age and gender profiles of patients that may explain legitimate variation between practices, comparisons of antibiotic prescribing rates are typically performed by evaluating antibiotic use per Specific Therapeutic group Age-sex weighting Related Prescribing Unit (STAR-PU).5–7 In the case of antibiotics, STAR-PU weightings are based on the number of antibiotic prescriptions in 16 different age and gender categories (Table 1).6 Using STAR-PU as the denominator instead of the number of registered patients is intended to result in fairer comparisons between practices. However, it is at least questionable whether it is fair to make comparisons and judge practices based on STAR-PU while ignoring other differences in case mix. Patient populations with equal STAR-PU denominators might differ in the prevalence of comorbidities and consultation rates for various infections. These remaining differences might legitimately explain at least some between-practice variation in antibiotic prescribing.
Table 1.
2013 Item-based age–sex weighting for oral antibacterials (British National Formulary, chapter 5.1)
| Age band (years) | Male | Female |
|---|---|---|
| 0–4 | 0.8 | 0.8 |
| 5–14 | 0.3 | 0.4 |
| 15–24 | 0.3 | 0.6 |
| 25–34 | 0.2 | 0.6 |
| 35–44 | 0.3 | 0.6 |
| 45–54 | 0.3 | 0.6 |
| 55–64 | 0.4 | 0.7 |
| 65–74 | 0.7 | 1.0 |
| 75+ | 1.0 | 1.3 |
In this study, we evaluated the extent to which differences in comorbidity prevalence, the use of certain drugs, demographics and consultation rates could explain variation in antibiotic prescribing, beyond differences already explained by STAR-PU. Better insight into the importance of these variables in determining antibiotic prescribing rates is needed to better inform policies around inappropriate antibiotic prescribing in primary care.8,9 If variation in antibiotic prescribing per STAR-PU cannot be explained by differences in the prevalence of comorbidities or markers of frailty, such as consultation rates, then one can more comfortably set a single prescribing reduction target for all practices. By contrast, if these factors do play an important role, one may avoid using the same target for all practices or develop an alternative way of expressing antibiotic use that accounts for additional predictors of antibiotic prescribing.
Methods
Ethics
Data from The Health Improvement Network (THIN) were used for this work. The data collection scheme for THIN is approved by the UK Multicentre Research Ethics Committee (reference number 07H1102103). In accordance with this approval, the study protocol was reviewed and approved by an independent Scientific Review Committee (SRC) (reference numbers 16THIN071 and 16THIN071-A1).
Data
This cross-sectional study used data from the UK’s THIN, a large primary care electronic medical record database covering >3.7 million active patients (∼7% of the general UK population).10–13 We extracted THIN data from English practices meeting an acceptable standard for research data collection and with complete data for the whole period between January 2013 and December 2013.
We identified all systemic antibiotic prescriptions [British National Formulary chapter 5.1, except antituberculosis drugs (5.1.9) and antileprotic drugs (5.1.10)14] among permanently registered patients. The number of patients registered in each gender and age category (Table 1) at each practice was determined by counting the number of permanently registered patients in each category of interest at 1 July 2013, thereby assuming a relatively stable number of patients throughout the year. The number of STAR-PUs per practice was subsequently estimated by multiplying the number of patients in each category by the relevant STAR-PU weights.
We considered overall consultation rate as well as consultation rates for specific conditions, comorbidities, the use of certain prescription drugs and demographics as potential predictors of antibiotic prescribing rates. Consultation rates for the following common infection-related conditions were considered: upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), urinary tract infection (UTI), skin condition and acute otitis media (AOM).1 URTI included sinusitis, common cold/nasopharyngitis, sore throat, laryngitis/tracheitis and unspecific upper respiratory tract infections. LRTI included cough, exacerbations of COPD, acute bronchitis, pneumonia and unspecific LRTI. UTI included both lower and upper urinary tract infections. Skin conditions included impetigo, cellulitis, boil/cyst/abscess and acne. Consultation rates were expressed as the number of consultations per 1000 registered patients.
Relevant comorbidities were based on the Read codes that indicate high-risk patients who qualify for the free seasonal influenza vaccination programme.15,16 The Read code classification represents a terminology used to code primary care electronic health records in the UK.17 The selected comorbidities were asthma, chronic kidney disease, chronic respiratory disease, chronic heart disease, diabetes, chronic liver disease, immunosuppression and chronic neurological disease.15,16 Of these considered comorbidities, general practice-specific prevalences are publicly available at the national level for asthma, chronic kidney disease, chronic respiratory disease, chronic heart disease and diabetes via the Quality Outcomes Framework (QOF) indicators.18 Ideally one would use a model with variables that are all also publicly available on a general practice level. This would facilitate fair comparisons of antibiotic prescribing levels between practices not captured within THIN, accounting for these publicly available variables. The proportion of patients with the relevant comorbidities per practice was measured on 1 July 2013.
Besides these comorbidities, we also identified the proportion of patients within each practice that received at least two prescriptions of one of the following drugs in the 365 days before 1 July 2013: immunosuppressive drugs, inhaled corticosteroids and systemic corticosteroids. These drugs are considered as indicators for patients at risk of complications after (respiratory tract) infections.15,16
Statistical analyses
The association between the potential predictor variables listed above and the number of antibiotic prescriptions per STAR-PU was analysed by general practice. We used two different methods: a conventional negative binomial regression (NBR) model19 and a stochastic Poisson boosted regression tree (BRT) model.20 The number of antibiotic prescriptions was modelled as the outcome with the natural logarithm of the number of STAR-PUs per practice as an offset.
Boosted regression trees
An advantage of the BRT model is that it can handle complex non-linear relationships with the outcome—almost all considered predictors were on a continuous scale—and its results can be intuitively understood, with results presented as the relative influence of each variable (i.e. predictor) and using partial dependence plots. The relative importance is a measure based on the number of times a variable is selected for splitting, weighted by the squared improvement to the model as a result of each split, and averaged over all trees.20 The relative importances of all variables included in the model sum to 100.20–22 The partial dependence plots show the effect of a variable on antibiotic prescribing rate per STAR-PU after accounting for the average effects of all other variables in the model. For this BRT model, all potential predictor variables were considered at once.
We used a bagging fraction of 0.5, making the model stochastic, and fixed the tree complexity to 1, because we were only interested in main effects and not in interactions between the predictor variables. We ran the stochastic BRT model 1000 times and averaged results over these runs. All BRT analyses were performed using the ‘gbm’ and ‘dismo’ packages in R version 3.2.2.21,22
Negative binomial regression models
We also evaluated associations between the predictor variables listed previously and the number of antibiotics per STAR-PU using NBR models. We built six different models, each with a different set of potential predictor variables. For each model, variables were selected for inclusion in the final model based on the Akaike information criterion (AIC). For model 1 we did not consider any potential predictors. For model 2 we considered comorbidities that are captured by the QOF indicators, i.e. asthma, chronic kidney disease, chronic respiratory disease, chronic heart disease and diabetes, and demographics (the proportion of patients being male and the proportions aged <19 and >64 years).18 For model 3, we additionally considered the proportion of patients having received at least two prescriptions of immunosuppressive drugs, inhaled corticosteroids or systemic corticosteroids.23 For model 4, we also considered the proportion of patients with chronic liver disease, immunosuppressive diseases and chronic neurological disease (no QOF indicators).
For model 5, we also considered the practice’s consultation rate. Model 6 considered the same variables as model 5, but without any comorbidity.
Comparing countries within the UK
It has been suggested that variation in antibiotic prescribing rates in England could be mainly explained by geographical location of the practice, independent of practice and patient population characteristics.24,25 Although we had insufficient data to explore geographical variation within England, the THIN data allowed us to evaluate whether the country (England, Scotland, Wales or Northern Ireland) would explain much of the variation between practices. Since the STAR-PU weighting is based on data from England only,5 for this analysis we expressed the antibiotic prescribing rate as the number of antibiotics per mid-year population. The analysis was performed in the same way as the previously described BRT model, except that the natural logarithm of the number of registered patients at 1 July 2013 was used as an offset.
Results
In total, 552 practices were included in the analyses. Of these, 348 were located in England, 61 in Wales, 110 in Scotland and 33 in Northern Ireland. For the primary analysis, we focused on general practices from England. The characteristics of the 348 practices in England are shown in Table 2. There was considerable variation in antibiotic prescribing rates, consultation rates and the percentage of registered patients with relevant comorbidities.
Table 2.
Characteristics of English general practices included for analysis
| Variable | Median (IQR) |
|---|---|
| Antibiotic prescriptions per 100 registered patients | 65.6 (57.4–74.0) |
| Practice size, number of registered patients | 7879 (5156–11 070) |
| Patient characteristics | |
| asthma (%) | 9.3 (7.7–10.8) |
| chronic kidney disease (%) | 3.9 (2.6–4.9) |
| diabetes (%) | 4.9 (4.1–5.6) |
| chronic respiratory disease (%) | 3.0 (2.1–4.8) |
| chronic heart disease (%) | 4.0 (3.2–4.8) |
| immunosuppressive disease (%) | 0.9 (0.7–1.0) |
| liver disease (%) | 0.2 (0.1–0.2) |
| neurological disease (%) | 2.0 (1.6–2.4) |
| ≥2 prescriptions of immunosuppressive drugs (%) | 0.2 (0.2–0.3) |
| ≥2 prescriptions of systemic steroids (%) | 2.0 (1.4–2.5) |
| ≥2 prescriptions of inhaled steroids (%) | 5.2 (4.6–6.1) |
| male (%) | 49.4 (48.6–50.3) |
| aged <18 years (%) | 20.2 (18.2–21.8) |
| aged >64 years (%) | 18.2 (14.6–21.8) |
| URTI consultations/1000 patients | 139.0 (111.7–174.1) |
| LRTI consultations/1000 patients | 171.3 (140.7–213.2) |
| AOM consultations/1000 patients | 15.8 (11.2–21.5) |
| UTI consultations/1000 patients | 48.0 (32.2–63.1) |
| Skin consultations/1000 patients | 54.4 (43.8–63.7) |
| Overall consultations/patient | 6.4 (5.3–7.5) |
Boosted regression trees
We used BRT to evaluate the relative importance of predictor variables in explaining between-practice variation in the number of antibiotic prescriptions per STAR-PU. The relative importance of each variable is shown in Figure 1. The cross-validation deviance of the full BRT model was 114.
Figure 1.
Relative influence of the variables in the model predicting antibiotic prescriptions per STAR-PU in England. Variables are ranked from most important at the top to least important at the bottom, and the sum of the relative influence of all variables is 100.
After averaging over 1000 runs, the variables with the largest relative influence were URTI consultation rates (18.7%), LRTI consultation rates (18.2%), percentage of patients receiving at least two prescriptions of systemic steroids (13.7%) and the percentage of patients receiving at least two prescriptions of inhaled steroids (12.6%). When summing the relative influences of all consultation rates, drugs and comorbidities, consultation rates had the largest total relative influence (49.9%), followed by steroid and immunosuppressive drugs (27.6%) and comorbidities (16.8%). The effects of the six predictor variables with the largest relative influences were plotted using partial dependence plots (Figure 2). As can be seen from these plots, the most important variables have a positive association with the number of antibiotics per STAR-PU. The skin consultation rates and percentage of patients with liver disease seem to have a negative association with the number of antibiotics per STAR-PU.
Figure 2.
Partial dependence plots for the six most influential variables in the generalized boosted regression model assessing the association between predictors and antibiotic prescribing rates per STAR-PU in England. The y-axes are centred to have zero mean over the data distribution.
Negative binomial regression models
We also evaluated associations between the predictor variables and the number of antibiotics per STAR-PU using NBR models. The variables included in the final six models and their fit compared with the null model (model 1) are shown in Table 3. As indicated by lower AICs and more explained deviance, models 5 and 6 (which both include consultation rate) provide the best fit to the data. The small difference in percentage reduction in deviance between these models indicates that, accounting for other variables, the importance of comorbidities in explaining differences in antibiotic prescribing rates per STAR-PU is limited. This is in line with the results of the BRT model, where antibiotic prescribing rates per STAR-PU were mainly explained by consultation rates and the percentage of patients receiving at least two prescriptions of steroids or immunosuppressive drugs. The BRT model gave similar predictions to the full NBR model (model 5) as shown in Figure 3. While model 2, allowing publicly available comorbidities and demographics into the model, explained 17% of the deviance, only 11% of the deviance was explained by a model considering only publicly available comorbidities.
Table 3.
Goodness-of-fit statistics and included variables in negative binomial regression modelsa
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
|---|---|---|---|---|---|---|
| Comorbidities | NA | diabetes; CHD | CHD; asthma | diabetes; asthma; liver disease; (liver disease)2; neurological disease | asthma; liver disease; (liver disease)2; immunosuppression | NA |
| Drugs | NA | NA | inhaled steroids; systemic steroids (rcs, 3 df) | inhaled steroids; systemic steroids (rcs, 3 df); immunosuppressives | inhaled steroids; systemic steroids (rcs, 3 df); immunosuppressives | inhaled steroids; systemic steroids (rcs, 3 df); immunosuppressives |
| Demographics | NA | aged <18 years; aged >64 years; (aged >64 years)2 | aged <18 years | aged <18 years; aged >64 years | aged >64 years | – |
| Consultation rates | NA | NA | NA | NA | URTI; LRTI (rcs, 3 df); AOM; AOM2; skin; any (rcs, 3 df) | URTI; LRTI (rcs, 3df); AOM; AOM2; UTI; skin; any |
| Akaike information criterion | 5879 | 5761 | 5666 | 5653 | 5530 | 5539 |
| Deviance | 741 | 613 | 513 | 491 | 350 | 372 |
| Reduction in deviance compared with null model (%) | 0 | 17 | 31 | 34 | 53 | 50 |
CHD, chronic kidney disease; df, degrees of freedom; NA, not applicable; rcs, restricted cubic splines.
Variables with ()2 stand for quadratic terms.
Figure 3.
Observed versus expected antibiotic prescriptions per STAR-PU. Each dot represents an individual general practice. The red dots represent the boosted regression trees model and the blue dots represent the full negative binomial regression model (model 5).
Comparing countries within the UK
Noticeable differences in the crude median antibiotic prescribing rates per 100 registered patients were observed between countries in the UK: 65.6 (IQR 57.4–74.0) in England; 70.0 (IQR 58.0–79.1) in Scotland; 77.1 (IQR 68.5–86.5) in Wales; and 90.2 (IQR 76.1–103.9) in Northern Ireland. Country was an important predictor of antibiotic prescribing rates in the BRT model (Figures 4 and 5). Variables that had an even stronger influence than country were the percentage of patients receiving at least two prescriptions of inhaled steroids or systemic steroids and the URTI consultation rate. The LRTI consultation rate and the percentage of patients with coronary heart disease were also among the six most influential predictors (Figure 4).
Figure 4.
Relative influence of the variables in the model predicting antibiotic prescriptions per registered population in the UK. The sum of the relative influence of those variables is 100.
Figure 5.
Partial dependence plots for the six most influential variables in the generalized boosted regression model assessing the association between predictors and antibiotic prescribing rates per registered population in the UK. The y-axes are centred to have zero mean over the data distribution. E, England; NI, Northern Ireland; S, Scotland; W, Wales.
Discussion
Between-practice variation in age- and gender-weighted antibiotic prescribing rates could be partly explained by differences in consultation rates for various infectious conditions and the percentage of patients receiving inhaled and systemic steroids, as well as other factors to a lesser degree. Although patients with comorbidities are more likely to receive antibiotics,26 both the BRT model and the more traditional NBR model indicated that comorbidities had much lower explanatory power. Even the most extensive NBR model, considering consultation rates, comorbidities, steroid and immunosuppressive use and demographics, could not explain 47% of the total deviance, suggesting that a considerable amount of the between-practice variation is caused by other factors, such as inappropriate prescribing.3
It is important to consider whether differences in consultation rates and prescribing rates for inhaled and systemic steroids reflect legitimate medical reasons for variation in antibiotic prescribing rates. If they do, policies to reduce prescribing should take into account these factors. However, if these variables do not represent legitimate medical reasons for variation in antibiotic prescribing, they can safely be ignored. Apparent differences in consultation rates can have several causes.
First, incidences of infection are known to vary by region, partly due to variation in behavioural, demographic, socioeconomic and health characteristics of the population in different areas.27–32 Variation in the incidence of infections can be considered as a legitimate reason for variation in antibiotic prescribing. Second, differences in healthcare-seeking behaviour may affect consultation rates. Some prescribers might attract patients who seek care for even mild cases of disease. If a proportion of these patients, who may be less frequently if ever seen in other practices, still receive an antibiotic because of diagnostic uncertainty and/or to meet patients’ needs within a short consultation,33,34 higher prescribing rates would be observed at practices with a patient population with higher healthcare-seeking behaviour. This type of variation in healthcare-seeking behaviour may not be considered a legitimate reason for variation in antibiotic prescribing. In fact, high antibiotic prescribing rates might actually result in higher consultation rates and medicalization of self-limiting infections.35,36 Third, differences in diagnostic coding behaviour might contribute to apparent differences in consultation rates. It is well known that there is variation in coding behaviour of practices, with a substantial proportion of visits having either no Read code at all, or only uninformative Read codes like ‘had a chat with patient’.1,37–39 While overall consultation rates are not influenced by poor coding, some general practitioners may be more likely to document a relevant Read code when prescribing an antibiotic. Hence, infection-related consultation rates may be artificially high in high-prescribing practices. This type of bias, if present, is clearly not a legitimate reason for variation between antibiotic prescribing rates.
Likewise, differences in the percentage of patients receiving inhaled and systemic steroids may be explained by different underlying causes. First, some practices may truly have a higher number of severely ill patients that require more systemic and/or inhaled steroid prescriptions than other practices. Second, higher use of inhaled and/or systemic steroids may reflect that certain practices are more liberal with prescribing medication in general, be it antibiotics or steroids. Among adults presenting in primary care with sore throat and lower respiratory tract infection, both not requiring immediate antibiotics, oral corticosteroids appeared to be ineffective in two recent randomized controlled trials.40,41 Hence, liberal use of corticosteroids to treat respiratory tract infections does not represent best practice. The first but not the second of these causes can be considered as a legitimate reason for varying antibiotic prescribing rates.
This study has several strengths. First, it uses data from a large, representative sample of UK general practices.11 To our knowledge, this is the first study evaluating whether variation in antibiotic prescribing rates between general practices can be explained by differences in consultation rates, the percentage of patients receiving immunosuppressive drugs, inhaled or systemic steroids, and the percentage of patients with comorbidities. Moreover, this is the first study showing substantial differences in antibiotic prescribing rates between countries within the UK. We used two different methodologies: boosted regression trees20 and negative binomial regression19 Both resulted in similar conclusions, thereby strengthening confidence in our results.
This study has also some limitations. As described above, some of the predictor variables may be markers of both legitimate as well as non-legitimate reasons for variation in antibiotic prescribing rates. In addition, variation in prescribing may be further explained by factors that were not readily available to us, such as markers of the severity of infections.42–45 We analysed only antibiotic items prescribed by the practice, which may artificially create differences between practices that tend to prescribe multiple shorter courses compared with practices that tend to prescribe one longer course for the same condition. Finally, ideally one would obtain a parsimonious model with a good fit using only variables that are publicly available for all practices. Although between-practice variations in prescribing of inhaled and systemic steroids are readily available to identify practices that may legitimately have higher prescribing rates per STAR-PU—if assumed to be markers of more severely ill patients—this is unfortunately not possible for consultation rates using publicly available data.
Conclusions
The proportion of patients with comorbidities in a practice’s patient population does not explain a substantial proportion of the variance in antibiotic prescribing rates, suggesting that practice-level prescribing targets do not necessarily have to take into account the different levels of comorbidities.
Although we cannot exclude the possibility that consultation rates and use of inhaled and systemic steroids may be markers of (i) poor coding practice, (ii) a high propensity to prescribe drugs in general, or (iii) stronger health-care seeking behaviour, the predictive power of these variables indicates that one should be careful in setting the same practice-level antibiotic prescribing target for all practices, and that differences in these variables between practices may need to be taken into account. Further studies are needed to evaluate whether the explanatory power of consultation rates is mainly due to true differences in the incidence of infection or severity of infections, or e.g. due to differences in healthcare-seeking behaviour.
Acknowledgements
The authors are grateful for the support of a group of subject matter experts who formed a Modelling Oversight Group (MOG) that convened bimonthly to discuss and review this work. Group members are listed in another paper of this supplement.3 J. V. R. is affiliated with the National Institute for Health Research Health Protection Research Units (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at both Imperial College London and University of Oxford in partnership with PHE.
Funding
This paper was published as part of a Supplement supported and resourced by Public Health England (PHE). Only internal resources were used for this paper.
Transparency declarations
J. V. R. is a co-opted member of the UK Government Advisory Committee on Antimicrobial Prescribing, Resistance and Healthcare Associated Infection (APRHAI). All other authors: none to declare.
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