Antibiotic prescribing patterns in general medical practices in England: Does area matter?

Trends in urine sampling rates of general practice patients with suspected lower urinary tract infections in England, 2015-2022: a population-based study

OBJECTIVES

Inappropriate prescribing of antibiotics is a key driver of antimicrobial resistance. This study aimed to describe urine sampling rates and antibiotic prescribing for patients with lower urinary tract infections (UTIs) in English general practice.

DESIGN

A retrospective population-based study using administrative data.

SETTING

IQVIA Medical Research Database (IMRD) data from general practices in England, 2015-2022.

PARTICIPANTS

Patients who have consulted with an uncomplicated UTI in England general practices captured in the IMRD.

OUTCOME MEASURES

Trends in UTI episodes (episodes were defined as UTI diagnosis codes occurring within 14 days of each other), testing and antibiotic prescribing on the same day as initial UTI consultation were assessed from January 2015 to December 2022. Associations, using univariate and multivariate logistic regressions, were examined between consultation and demographic factors on the odds of a urine test.

RESULTS

There were 743 350 UTI episodes; 50.8% had a urine test. Testing rates fluctuated with an upward trend and large decline in 2020. Same-day UTI antibiotic prescribing occurred in 78.2% of episodes. In multivariate modelling, factors found to decrease odds of a urine test included age ≥85 years (0.83, 95% CI 0.82 to 0.84), consultation type (remote vs face to face, 0.45, 95% CI 0.45 to 0.46), episodes in London compared with the South (0.74, 95% CI 0.72 to 0.75) and increasing practice size (0.77, 95% CI 0.76 to 0.78). Odds of urine tests increased in males (OR 1.11, 95% CI 1.10 to 1.13), for those episodes without a same-day UTI antibiotic (1.10, 95% CI 1.04 to 1.16) for episodes for those with higher deprivation status (Indices of Multiple Deprivation 8 vs 1, 1.51, 95% CI 1.48 to 1.54). Compared with 2015, 2016-2019 saw increased odds of testing while 2020 and 2021 saw decreases, with 2022 showing increased odds.

CONCLUSION

Urine testing for UTI in general practice in England showed an upward trend, with same-day antibiotic prescribing remaining consistent, suggesting greater alignment to national guidelines. The COVID-19 pandemic impacted testing rates, though as of 2022, they began to recover.

Sociodemographic characterisation of antibiotic heavy users in the Danish elderly population

AIMS

The development of effective interventions to reduce inappropriate use of antibiotics in the elderly population requires knowledge on who can benefit from such interventions. Thus, we aimed to identify and characterise antibiotic heavy users among elderly patients in general practice with respect to sociodemographic variables.

METHODS

We conducted a retrospective nationwide register-based study on all Danish elderly citizens (⩾65 years) who redeemed an antibiotic prescription in 2017. Heavy users were defined as the 10% with the highest excess use, that is, their recorded use minus the average use for their sex, age group and comorbidity level as estimated from a linear regression model. Comparative analyses of sociodemographic characteristics (civil status, employment status, urbanity, educational level and country of origin) of heavy users and non-heavy users were performed using logistic regression models.

RESULTS

The study population consisted of 251,733 elderly individuals, who in total redeemed 573,265 prescriptions of antibiotics. Heavy users accounted for 68% of all excess use of antibiotics. In multivariable analyses, individuals with an educational level above basic schooling, non-retired, residing in an urban municipality and being born in a country outside Scandinavia all had lower odds of being a heavy user. Widowed, divorced or single individuals had higher odds of being a heavy user compared with married individuals. Relative importance analyses showed that civil status and educational level contributed considerably to the explained variance.

CONCLUSIONS

This study found an association between sociodemographic characteristics and risk of being a heavy user, indicating that sociodemographic variation exists with regard to antibiotic prescribing.

Antibiotic prescribing in remote versus face-to-face consultations for acute respiratory infections in primary care in England: an observational study using target maximum likelihood estimation

Background

The COVID-19 pandemic has led to an ongoing increase in the use of remote consultations in general practice in England. Although the evidence is limited, there are concerns that the increase in remote consultations could lead to more antibiotic prescribing.

Methods

In this cohort study, we used patient-level primary care data from the Clinical Practice Research Datalink to estimate the association between consultation mode (remote versus face-to-face) and antibiotic prescribing in England for acute respiratory infections (ARI) between April 2021 and March 2022. Eligibility criteria were applied at both practice-level and patient-level. 400 practices in England were sampled at random and then 600,000 patients were randomly sampled from the eligible patients (whose sex was recorded). Consultations for acute respiratory infections were identified. All antibiotic prescriptions were included, with the exception of antituberculosis drugs and antileprotic drugs, as identified through chapter 5.1 of the British National Formulary. The CPRD Aurum data was linked to the COVID-19 ONS infection survey by region. All analyses were done at the individual level. Repeated consultations from the same patient within 7 days were grouped together. We used targeted maximum likelihood estimation, a causal machine learning method with adjustment for infection type and patient-level, clinician-level and practice-level factors.

Findings

There were 45,997 ARI consultations (34,555 unique patients) within the study period, of which 28,127 were remote and 17,870 were face-to-face. For children, 48% of consultations were remote and, for adults, 66% were remote. For children, 42% of remote and 43% of face-to-face consultations led to an antibiotic prescription; the equivalent values for adults were 52% and 42%, respectively. After adjustment with TMLE, adults with a remote consultation had 23% (odds ratio [OR] 1.23, 95% CI: 1.18-1.29) higher chance of being prescribed antibiotics than if they had been seen face-to-face. We found no significant association between consultation mode and antibiotic prescribing in children (OR 1.04 95% CI: 0.98-1.11).

Interpretation

The higher rates of antibiotic prescribing in remote consultations for adults are cause for concern. We see no significant difference in antibiotic prescribing between consultation mode for children. These findings should inform antimicrobial stewardship activities for health-care professionals and policy makers. Future research should examine differences in guideline-compliance between remote and face-to-face consultations to understand the factors driving antibiotic prescribing in different consultation modes.

Funding

None.

The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing

BACKGROUND

This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19.

METHODS

With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection.

RESULTS

The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found.

CONCLUSIONS

Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

A scoping review and behavioural analysis of factors underlying overuse of antimicrobials

Background

Overuse of antimicrobials is a challenging global issue that contributes to antimicrobial resistance. Despite widespread awareness of the problem among members of the medical community and various attempts to improve prescription practices, existing antimicrobial stewardship programmes are not always effective. In our view, this may reflect limited understanding of factors that influence prescription of antimicrobials as empirical therapy, implying a need to address the psychological mechanisms behind some of the specific behaviours involved.

Objectives

To identify factors that influence the antimicrobials prescription as empirical therapy, and to relate these factors to findings from behavioural science.

Methods

We conducted a scoping review of the literature on the factors underlying antimicrobial prescription decisions, following the protocol designed using PRISMA guidelines.

Results and conclusions

From a final sample of 90 sources, we identified ten factors important in antimicrobial prescription decisions. In the second stage of our analysis, we grouped them into five final categories: (1) nature of the decision, (2) social influences, (3) individual differences, (4) characteristics of the patient, (5) context. We analyse these categories using a behavioural science perspective.

Antibiotic prescribing trends in primary care 2014-2022

Antimicrobial resistance (AMR) is a global healthcare challenge that governments and health systems are tackling primarily through antimicrobial stewardship (AMS). This should, improve antibiotic use, avoid inappropriate prescribing, reduce prescription numbers, aligning with national/international AMS targets. In primary care in the United Kingdom (UK) antibiotics are mainly prescribed for patients with urinary and respiratory symptoms (22.7% and 46% of all antibiotic prescriptions respectively). This study aimed to capture the time-series trends (2014-2022) for commonly prescribed antibiotics for respiratory and urinary tract infections in primary care in England. Trends for Amoxicillin, Amoxicillin sodium, Trimethoprim, Clarithromycin, Erythromycin, Erythromycin ethylsuccinate, Erythromycin stearate, Doxycycline hyclate, Doxycycline monohydrate and Phenoxymethylpenicillin (Penicillin V) were determined. In doing so providing evidence regarding meeting UK antibiotic prescribing rate objectives (a 15% reduction in human antibiotic use 2019-2024). Time series trend analysis of 62,949,272 antibiotic prescriptions from 6,370 General Practices in England extracted from the National Health Service (NHS) Business Services Authority web portal were explored. With additional investigation of prescribing rate trends by quintiles of the Index of Multiple Deprivation (IMD). Overall, there is a downwards trend in antibiotic prescribing for those explored. There is an association between IMD, geographical location, and higher antibiotic prescribing levels (prescribing hot spots). England has a well-documented North-South divide of health inequalities, this is reflected in antibiotic prescribing. The corona virus pandemic (COVID-19) impacted on AMS, with a rise in doxycycline and trimethoprim prescriptions notable in higher IMD areas. Since then, prescribing appears to have returned to pre-pandemic levels in all IMDs and continued to decline. AMS efforts are being adhered to in primary care in England. This study provides further evidence of the link between locality and poorer health outcomes (reflected in higher antibiotic prescribing). Further work is required to address antibiotic use in hot spot areas.

A geospatial approach to identify patterns of antibiotic susceptibility at a neighborhood level in Wisconsin, United States

The global threat of antimicrobial resistance (AMR) varies regionally. This study explores whether geospatial analysis and data visualization methods detect both clinically and statistically significant variations in antibiotic susceptibility rates at a neighborhood level. This observational multicenter geospatial study collected 10 years of patient-level antibiotic susceptibility data and patient addresses from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). We included the initial Escherichia coli isolate per patient per year per sample source with a patient address in Wisconsin (N = 100,176). Isolates from U.S. Census Block Groups with less than 30 isolates were excluded (n = 13,709), resulting in 86,467 E. coli isolates. The primary study outcomes were the results of Moran's I spatial autocorrelation analyses to quantify antibiotic susceptibility as spatially dispersed, randomly distributed, or clustered by a range of - 1 to + 1, and the detection of statistically significant local hot (high susceptibility) and cold spots (low susceptibility) for variations in antibiotic susceptibility by U.S. Census Block Group. UW Health isolates collected represented greater isolate geographic density (n = 36,279 E. coli, 389 = blocks, 2009-2018), compared to Fort HealthCare (n = 5110 isolates, 48 = blocks, 2012-2018) and MCHS (45,078 isolates, 480 blocks, 2009-2018). Choropleth maps enabled a spatial AMR data visualization. A positive spatially-clustered pattern was identified from the UW Health data for ciprofloxacin (Moran's I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole susceptibility (Moran's I = 0.180, p < 0.001). Fort HealthCare and MCHS distributions were likely random. At the local level, we identified hot and cold spots at all three health systems (90%, 95%, and 99% CIs). AMR spatial clustering was observed in urban areas but not rural areas. Unique identification of AMR hot spots at the Block Group level provides a foundation for future analyses and hypotheses. Clinically meaningful differences in AMR could inform clinical decision support tools and warrants further investigation for informing therapy options.

Clinician acceptability of an antibiotic prescribing knowledge support system for primary care: a mixed-method evaluation of features and context

BACKGROUND

Overprescribing of antibiotics is a major concern as it contributes to antimicrobial resistance. Research has found highly variable antibiotic prescribing in (UK) primary care, and to support more effective stewardship, the BRIT Project (Building Rapid Interventions to optimise prescribing) is implementing an eHealth Knowledge Support System. This will provide unique individualised analytics information to clinicians and patients at the point of care. The objective of the current study was to gauge the acceptability of the system to prescribing healthcare professionals and highlight factors to maximise intervention uptake.

METHODS

Two mixed-method co-design workshops were held online with primary care prescribing healthcare professionals (n = 16). Usefulness ratings of example features were collected using online polls and online whiteboards. Verbal discussion and textual comments were analysed thematically using inductive (participant-centred) and deductive perspectives (using the Theoretical Framework of Acceptability).

RESULTS

Hierarchical thematic coding generated three overarching themes relevant to intervention use and development. Clinician concerns (focal issues) were safe prescribing, accessible information, autonomy, avoiding duplication, technical issues and time. Requirements were ease and efficiency of use, integration of systems, patient-centeredness, personalisation, and training. Important features of the system included extraction of pertinent information from patient records (such as antibiotic prescribing history), recommended actions, personalised treatment, risk indicators and electronic patient communication leaflets. Anticipated acceptability and intention to use the knowledge support system was moderate to high. Time was identified as a focal cost/ burden, but this would be outweighed if the system improved patient outcomes and increased prescribing confidence.

CONCLUSION

Clinicians anticipate that an eHealth knowledge support system will be a useful and acceptable way to optimise antibiotic prescribing at the point of care. The mixed method workshop highlighted issues to assist person-centred eHealth intervention development, such as the value of communicating patient outcomes. Important features were identified including the ability to efficiently extract and summarise pertinent information from the patient records, provide explainable and transparent risk information, and personalised information to support patient communication. The Theoretical Framework of Acceptability enabled structured, theoretically sound feedback and creation of a profile to benchmark future evaluations. This may encourage a consistent user-focused approach to guide future eHealth intervention development.

Does poverty increase antibiotic prescribing rates through underlying health conditions? Ecological study using parallel mediation analysis

Objective

Effect of social determinants on antibiotic prescribing rates is poorly studied in modern literature. The objective of this study was to explore the effect of the prevalence of poverty (annual household income <$24,999) in each state on antibiotic prescribing rates in outpatient settings per 1,000 population through chronic health conditions (ie, prevalence of obesity, diabetes, and chronic obstructive pulmonary disease) while also adjusting for confounders (ie, prevalence of population aged ≥65 years and physician density in each state).

Design

Ecological study.

Participants

Entire US population.

Methods

Prevalence estimates from all 51 states were used to calculate direct, indirect, and total effects of poverty on the rates of antibiotic prescribing through parallel mediation analysis using linear regression with chronic health conditions (obesity, diabetes, and chronic obstructive pulmonary disease) as mediators. I obtained these data from point-prevalence estimates of 2020 survey results from the Behavioral Risk Factor Surveillance System for rates on poverty, obesity, diabetes, chronic obstructive pulmonary disease, and population aged ≥ 65 years. I also used the Antibiotic Resistance & Patient Safety Portal for antibiotic prescribing rates per 1,000 population and the Association of American Medical Colleges database for the physician density per 100,000 population.

Results

For every percentage increase in prevalence of poverty in each state, the antibiotic prescribing rate increased by 17.4 courses per 1,000 population (95% bootstrap confidence interval, 9.2-24.9) using indirect effects of poverty through mediators.

Conclusions

Antibiotic stewardship programs should consider targeting social determinants of health along with underlying health conditions of patients being treated with antibiotics.

Comparing antibiotic prescribing between clinicians in UK primary care: an analysis in a cohort study of eight different measures of antibiotic prescribing

BACKGROUND

There is a need to reduce antimicrobial uses in humans. Previous studies have found variations in antibiotic (AB) prescribing between practices in primary care. This study assessed variability of AB prescribing between clinicians.

METHODS

Clinical Practice Research Datalink, which collects electronic health records in primary care, was used to select anonymised clinicians providing 500+ consultations during 2012-2017. Eight measures of AB prescribing were assessed, such as overall and incidental AB prescribing, repeat AB courses and extent of risk-based prescribing. Poisson regression models with random effect for clinicians were fitted.

RESULTS

6111 clinicians from 466 general practices were included. Considerable variability between individual clinicians was found for most AB measures. For example, the rate of AB prescribing varied between 77.4 and 350.3 per 1000 consultations; percentage of repeat AB courses within 30 days ranged from 13.1% to 34.3%; predicted patient risk of hospital admission for infection-related complications in those prescribed AB ranged from 0.03% to 0.32% (5th and 95th percentiles). The adjusted relative rate between clinicians in rates of AB prescribing was 5.23. Weak correlation coefficients (<0.5) were found between most AB measures. There was considerable variability in case mix seen by clinicians. The largest potential impact to reduce AB prescribing could be around encouraging risk-based prescribing and addressing repeat issues of ABs. Reduction of repeat AB courses to prescribing habit of median clinician would save 21 813 AB prescriptions per 1000 clinicians per year.

CONCLUSIONS

The wide variation seen in all measures of AB prescribing and weak correlation between them suggests that a single AB measure, such as prescribing rate, is not sufficient to underpin the optimisation of AB prescribing.

Association of area-level education with the regional growth trajectories of rates of antibacterial dispensing to patients under 3 years in Norway: a longitudinal retrospective study

OBJECTIVE

To examine the association between area-level education and the local growth trajectories in antibacterial dispensing rates in Norwegian municipalities among children under 3 years old.

DESIGN

Retrospective, longitudinal study using individual primary care prescription data from the Norwegian Prescription Database for the period 2006-2016. Data were collected on the date of dispensing, the type and amount of antibiotic, the patient's age, sex and municipality of residence and linked to municipality-level statistics on education available from Statistics Norway. We used multilevel growth curve modelling, with a linear trend variable modelled as a random effect and a cross-level interaction between linear trends and the proportion of the population in the municipality having received a university or college education.

SETTING

The local government level in Norway. The sample includes all municipalities over the study period.

OUTCOME MEASURE

Number of dispensed antibacterial prescriptions per 100 children in individual primary care by municipality and year.

RESULTS

We identified a significant negative linear trend in the square root of the dispensing rate for children under 3 years old during the period. This trend varied between municipalities. A negative cross-level interaction term between population education levels and random trends showed that municipalities with an average level of population education saw a reduction in their square root dispensing rates of -0.053 (95% CI -0.066 to -0.039) prescriptions per 100 children. Each additional percentage point in population education contributed a further -0.0034 (95% CI -0.006 to -0.001) reduction to the square root dispensing rate.

CONCLUSIONS

Municipalities in which a larger proportion of the local population have high educational achievements have been more successful in reducing antibacterial dispensing rates in children under 3 years old. Adopting area-level strategies and addressing local community disadvantages may help to optimise practices and prescribing patterns across local communities.

Factors predicting amoxicillin prescribing in primary care among children: a cohort study

BACKGROUND

Antibiotic prescribing during childhood, most commonly for respiratory tract infections (RTIs), contributes to antimicrobial resistance, which is a major public health concern.

AIM

To identify factors associated with amoxicillin prescribing and RTI consultation attendance in young children in primary care.

DESIGN AND SETTING

Cohort study in Bradford spanning pregnancy to age 24 months, collected 2007-2013, linked to electronic primary care and air pollution data.

METHOD

Amoxicillin prescribing and RTI consultation rates/1000 child-years were calculated. Mixed-effects logistic regression models were fitted with general practice (GP) surgery as the random effect.

RESULTS

The amoxicillin prescribing rate among 2493 children was 710/1000 child-years during year 1 (95% confidence interval [CI] = 677 to 744) and 780/1000 (95% CI = 745 to 816) during year 2. During year 1, odds of amoxicillin prescribing were higher for boys (adjusted odds ratio [aOR] 1.36, 95% CI = 1.14 to 1.61), infants from socioeconomically deprived households (aOR 1.36, 95% CI = 1.00 to 1.86), and infants with a Pakistani ethnic background (with mothers born in the UK [aOR 1.44, 95% CI = 1.06 to 1.94] and outside [aOR 1.42, 95% CI = 1.07 to 1.90]). During year 2, odds of amoxicillin prescribing were higher for infants with a Pakistani ethnic background (with mothers born in the UK [aOR 1.46, 95% CI = 1.10 to 1.94] and outside [aOR 1.56, 95% CI = 1.19 to 2.04]) and those born <39 weeks gestation (aOR 1.20, 95% CI = 1.00 to 1.45). Additional risk factors included caesarean delivery, congenital anomalies, overcrowding, birth season, and childcare attendance, with GP surgery explaining 7%-9% of variation.

CONCLUSION

Socioeconomic status and ethnic background were associated with amoxicillin prescribing during childhood. Efforts to reduce RTI spread in household and childcare settings may reduce antibiotic prescribing in primary care.

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

In 2016, the British government acknowledged the importance of reducing antimicrobial prescriptions to avoid the long-term harmful effects of overprescription. Prescription needs are highly dependent on the factors that have a spatiotemporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyze data by searching for group structures and therefore identifying peer groups of GPs with similar behavior. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatiotemporal clustering approach for modeling the behavior of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a Kernel density estimation-based process. This makes our approach suitable for cases with different local densities, which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behavior within a group of similar peers and detect regions of extreme behaviors.

Prescribing of long-term antibiotics to adolescents in primary care: a retrospective cohort study

Background

Antibiotic overuse is linked to increased risk of antimicrobial resistance. Long-term antibiotics are commonly used for treating acne and prophylaxis of urinary tract infection. Their contribution to the overall burden of antibiotic use is relatively unknown.

Aim

To describe the volume of commonly prescribed long-term (≥28 days) antibiotic prescriptions in adolescents and young adults, trends over time, and comparisons with acute prescriptions.

Design and setting

A retrospective cohort study using UK electronic primary care records.

Method

Patients born between 1979 and 1996 and with data in the Care and Health Information Analytics database were included. The main outcome measures were antibiotic prescription rates per 1000 person-years and antibiotic prescription days per person-year between the ages of 11 and 21.

Results

In total, 320 722 participants received 710 803 antibiotic prescriptions between the ages of 11 and 21 years from 1998 to 2017. Of these 710 803 prescriptions, 191 443 (26.93%) were for long-term antibiotics (≥28 days and ≤6 months in duration). Long-term antibiotics accounted for more than two-thirds (72.48%) of total antibiotic exposure (days per person-year). Total long-term antibiotic prescribing peaked in 2013 at just under 6 days per person-year and declined to around 4 days in 2017.

Conclusion

Among adolescents and young adults, exposure to long-term antibiotics (primarily lymecycline used for acne) was much greater than for acute antibiotics and is likely to make an important contribution to antimicrobial resistance. Urgent action is needed to reduce unnecessary exposure to long-term antibiotics in this group. Increasing the use of, and adherence to, effective non-antibiotic treatments for acne is key to achieving this.

Antibiotic use and deprivation: an analysis of Welsh primary care antibiotic prescribing data by socioeconomic status

OBJECTIVES

To examine the association between socioeconomic status (SES) and antibiotic prescribing, controlling for the presence of common chronic conditions and other potential confounders and variation amongst GP practices and clusters.

METHODS

This was an electronic cohort study using linked GP and Welsh Index of Multiple Deprivation (WIMD) data. The setting was GP practices contributing to the Secure Anonymised Information Linkage (SAIL) Databank 2013-17. The study involved 2.9 million patients nested within 339 GP practices, nested within 67 GP clusters.

RESULTS

Approximately 9 million oral antibiotics were prescribed between 2013 and 2017. Antibiotic prescribing rates were associated with WIMD quintile, with more deprived populations receiving more antibiotics. This association persisted after controlling for patient demographics, smoking, chronic conditions and clustering by GP practice and cluster, with those in the most deprived quintile receiving 18% more antibiotic prescriptions than those in the least deprived quintile (incidence rate ratio = 1.18; 95% CI = 1.181-1.187). We found substantial unexplained variation in antibiotic prescribing rates between GP practices [intra-cluster correlation (ICC) = 47.31%] and GP clusters (ICC = 12.88%) in the null model, which reduced to ICCs of 3.50% and 0.85% for GP practices and GP clusters, respectively, in the final adjusted model.

CONCLUSIONS

Antibiotic prescribing in primary care is increased in areas of greater SES deprivation and this is not explained by differences in the presence of common chronic conditions or smoking status. Substantial unexplained variation in prescribing supports the need for ongoing antimicrobial stewardship initiatives.

Social and Contextual Influences on Antibiotic Prescribing and Antimicrobial Stewardship: A Qualitative Study with Clinical Commissioning Group and General Practice Professionals

Antibiotic prescribing in England varies considerably between Clinical Commissioning Groups (CCGs) and general practices. We aimed to assess social and contextual factors affecting antibiotic prescribing and engagement with antimicrobial stewardship (AMS) initiatives. Semi-structured telephone interviews were conducted with 22 CCG professionals and 19 general practice professionals. Interviews were audio-recorded, transcribed, and analyzed thematically. Social/contextual influences were grouped into the following four categories: (1) Immediate context, i.e., patients' social characteristics (e.g., deprivation and culture), clinical factors, and practice and clinician characteristics (e.g., "struggling" with staff shortage/turnover) were linked to higher prescribing. (2) Wider context, i.e., pressures on the healthcare system, limited resources, and competing priorities were seen to reduce engagement with AMS. (3) Collaborative and whole system approaches, i.e., communication, multidisciplinary networks, leadership, and teamwork facilitated prioritizing AMS, learning, and consistency. (4) Relativity of appropriate prescribing, i.e., "high" or "appropriate" prescribing was perceived as relative, depending on comparators, and disregarding different contexts, but social norms around antibiotic use among professionals and patients seemed to be changing. Further optimization of antibiotic prescribing would benefit from addressing social/contextual factors and addressing wider health inequalities, not only targeting individual clinicians. Tailoring and adapting to local contexts and constraints, ensuring adequate time and resources for AMS, and collaborative, whole system approaches to promote consistency may help promote AMS.

Identifying Antibiotic Prescribing Patterns Through Multi-Level Latent Profile Analyses: A Cross-Sectional Survey of Primary Care Physicians

Background: Overuse of antibiotics significantly fuels the development of Antimicrobial resistance, which threating the global population health. Great variations existed in antibiotic prescribing practices among physicians, indicating improvement potential for rational use of antibiotics. This study aims to identify antibiotic prescribing patterns of primary care physicians and potential determinants. Methods: A cross-sectional survey was conducted on 551 physicians from 67 primary care facilities in Hubei selected through random cluster sampling, tapping into their knowledge, attitudes and prescribing practices toward antibiotics. Prescriptions (n = 501,072) made by the participants from 1 January to March 31, 2018 were extracted from the medical records system. Seven indicators were calculated for each prescriber: average number of medicines per prescription, average number of antibiotics per prescription, percentage of prescriptions containing antibiotics, percentage of antibiotic prescriptions containing broad-spectrum antibiotics, percentage of antibiotic prescriptions containing parenteral administered antibiotics, percentage of antibiotic prescriptions containing restricted antibiotics, and percentage of antibiotic prescriptions containing antibiotics included in the WHO "Watch and Reserve" list. Two-level latent profile analyses were performed to identify the antibiotic prescribing patterns of physicians based on those indicators. Multi-nominal logistic regression models were established to identify determinants with the antibiotic prescribing patterns. Results: On average, each primary care physician issued 909 (ranging from 100 to 11,941 with a median of 474) prescriptions over the study period. The mean percentage of prescriptions containing antibiotics issued by the physicians reached 52.19% (SD = 17.20%). Of those antibiotic prescriptions, an average of 82.29% (SD = 15.83%) contained broad-spectrum antibiotics; 71.92% (SD = 21.42%) contained parenteral administered antibiotics; 23.52% (SD = 19.12%) contained antibiotics restricted by the regional government; and 67.74% (SD = 20.98%) contained antibiotics listed in the WHO "Watch and Reserve" list. About 28.49% of the prescribers were identified as low antibiotic users, compared with 51.18% medium users and 20.33% high users. Higher use of antibiotics was associated with insufficient knowledge, indifference to changes, complacency with satisfied patients, low household income and rural location of the prescribers. Conclusion: Great variation in antibiotic prescribing patterns exists among primary care physicians in Hubei of China. High use of antibiotics is not only associated with knowledge shortfalls but also low socioeconomic status of prescribers.

An examination of trends in antibiotic prescribing in primary care and the association with area-level deprivation in England

Background

Internationally, there are growing concerns about antimicrobial resistance. This has resulted in increased scrutiny of antibiotic prescribing trends – particularly in primary care where the majority of prescribing occurs. In England, antibiotic prescribing targets are set nationally but little is known about the local context of antibiotic prescribing. This study aimed to examine trends in antibiotic prescribing (including broad-spectrum), and the association with area-level deprivation and region in England.

Methods

Antibiotic prescribing data by GP surgery in England were obtained from NHS Business Service Authority for the years 2014–2018. These data were matched with the Index of Multiple Deprivation (IMD) 2015 at the Lower Layer Super Output Area level Lower Layer Super Output Area (LSOA) level. Linear regression methods were employed to explore the relationship between antibiotic use and area-level deprivation as well as region, after controlling for a range of other confounding variables, including health need, rurality, and ethnicity.

Results

Over time, the amount of antibiotic prescribing significantly reduced from 1.11 items per STAR-PU to 0.96 items per STAR-PU – a reduction of 13.6%. The adjusted models found that, at LSOA level, the most deprived areas of England had the highest levels of antibiotic prescribing (0.03 items per STAR-PU higher). However, broad spectrum antibiotic prescribing exceeding 10% of all antibiotic prescribing within a GP practice was higher in more affluent areas. There were also significant regional differences – with the North East and the East of England having the highest levels of antibiotic prescribing (by 0.16 items per STAR-PU).

Conclusion

Although antibiotic prescribing has reduced over time, there remains significant variation in by area-level deprivation and region in England – with higher antibiotic prescribing in more deprived areas. Future prescribing targets should account for local factors to ensure the most deprived communities are not inappropriately penalised.

Developing the infrastructure to support the optimisation of antibiotic prescribing using the learning healthcare system to improve healthcare services in the provision of primary care in England

Introduction

The learning healthcare system (LHS) underpinned by data analysis and feedback to clinical care providers is thought to improve quality of care. The work aimed to implement an LHS for antibiotic prescribing in primary care in England.

Method

Deidentified patient-level data from general practices were processed and analysed at regular intervals (fortnightly increments). A dashboard application was developed and implemented displaying analytical graphics to give periodic feedback to clinicians, tailored to each clinical site. Benchmarking parameters were established by the analysis of two large national primary care datasets allowing peer-to-peer comparisons. To date, the dashboard is available to 70 English practices.

Conclusions

Successful implementation and uptake of the secure technical LHS infrastructure for the analysis and feedback to clinicians of their antibiotic prescribing demonstrate a great appetite for this type of frequent prescribing review in primary care, combining advanced data analytics with tailored feedback.

Development and validation of a multivariable prediction model for infection-related complications in patients with common infections in UK primary care and the extent of risk-based prescribing of antibiotics

BACKGROUND

Antimicrobial resistance is driven by the overuse of antibiotics. This study aimed to develop and validate clinical prediction models for the risk of infection-related hospital admission with upper respiratory infection (URTI), lower respiratory infection (LRTI) and urinary tract infection (UTI). These models were used to investigate whether there is an association between the risk of an infection-related complication and the probability of receiving an antibiotic prescription.

METHODS

The study used electronic health record data from general practices contributing to the Clinical Practice Research Datalink (CPRD GOLD) and Welsh Secure Anonymised Information Linkage (SAIL), both linked to hospital records. Patients who visited their general practitioner with an incidental URTI, LRTI or UTI were included and followed for 30 days for hospitalisation due to infection-related complications. Predictors included age, gender, clinical and medication risk factors, ethnicity and socioeconomic status. Cox proportional hazards regression models were used with predicted risks independently validated in SAIL.

RESULTS

The derivation and validation cohorts included 8.1 and 2.7 million patients in CPRD and SAIL, respectively. A total of 7125 (0.09%) hospital admissions occurred in CPRD and 7685 (0.28%) in SAIL. Important predictors included age and measures of comorbidity. Initial attempts at validating in SAIL (i.e. transporting the models with no adjustment) indicated the need to recalibrate the models for age and underlying incidence of infections; internal bootstrap validation of these updated models yielded C-statistics of 0.63 (LRTI), 0.69 (URTI) and 0.73 (UTI) indicating good calibration. For all three infection types, the rate of antibiotic prescribing was not associated with patients' risk of infection-related hospital admissions.

CONCLUSION

The risk for infection-related hospital admissions varied substantially between patients, but prescribing of antibiotics in primary care was not associated with risk of hospitalisation due to infection-related complications. Our findings highlight the potential role of clinical prediction models to help inform decisions of prescribing of antibiotics in primary care.

General practitioners' accounts of negotiating antibiotic prescribing decisions with patients: a qualitative study on what influences antibiotic prescribing in low, medium and high prescribing practices

BACKGROUND

Antimicrobial resistance (AMR) is high on the UK public health policy agenda, and poses challenges to patient safety and the provision of health services. Widespread prescribing of antibiotics is thought to increase AMR, and mostly takes place in primary medical care. However, prescribing rates vary substantially between general practices. The aim of this study was to understand contextual factors related to general practitioners' (GPs) antibiotic prescribing behaviour in low, high, and around the mean (medium) prescribing primary care practices.

METHODS

Qualitative semi-structured interviews were conducted with 41 GPs working in North-West England. Participants were purposively sampled from practices with low, medium, and high antibiotic prescribing rates adjusted for the number and characteristics of patients registered in a practice. The interviews were analysed thematically.

RESULTS

This study found that optimizing antibiotic prescribing creates tensions for GPs, particularly in doctor-patient communication during a consultation. GPs balanced patient expectations and their own decision-making in their communication. When not prescribing antibiotics, GPs reported the need for supportive mechanisms, such as regular practice meetings, within the practice, and in the wider healthcare system (e.g. longer consultation times). In low prescribing practices, GPs reported that increasing dialogue with colleagues, having consistent patterns of prescribing within the practice, supportive practice policies, and enough resources such as consultation time were important supports when not prescribing antibiotics.

CONCLUSIONS

Insight into GPs' negotiations with patient and public health demands, and consistent and supportive practice-level policies can help support prudent antibiotic prescribing among primary care practices.