What is the carbon footprint of primary care practices? A retrospective life-cycle analysis in Switzerland

Background The medical field causes significant environmental impact. Reduction of the primary care practice carbon footprint could contribute to decreasing global carbon emissions. This study aims to quantify the average carbon footprint of a primary care consultation, describe differences between primary care practices (best, worst and average performing) in western Switzerland and identify opportunities for mitigation. Methods We conducted a retrospective carbon footprint analysis of ten private practices over the year 2018. We used life-cycle analysis to estimate carbon emissions of each sector, from manufacture to disposal, expressing results as CO2 equivalents per average consultation and practice. We then modelled an average and theoretical best- case and worst-case practices. Collected data included invoices, medical and furniture inventories, heating and power supply, staff and patient transport, laboratory analyses (in/out-house) waste quantities and management costs. Results An average medical consultation generated 4.8 kg of CO2eq and overall, an average practice produced 30 tons of CO2eq per year, with 45.7% for staff and patient transport and 29.8% for heating. Medical consumables produced 5.5% of CO2eq emissions, while in-house laboratory and X-rays contributed less than 1% each. Emergency analyses requiring courier transport caused 5.8% of all emissions. Support activities generated 82.6% of the total CO2eq. Simulation of best- and worst-case scenarios resulted in a ten-fold variation in CO2eq emissions. Conclusion Optimizing structural and organisational aspects of practice work could have a major impact on the carbon footprint of primary care practices without large-scale changes in medical activities.

Vertical integration of primary care practices with acute hospitals in England and Wales: why, how and so what? Findings from a qualitative, rapid evaluation

ObjectivesTo understand the rationale, implementation and early impact of vertical integration between primary care medical practices and the organisations running acute hospitals in the National Health Service in England and Wales.Design and settingA qualitative, cross-comparative case study evaluation at two sites in England and one in Wales, consisting of interviews with stakeholders at the sites, alongside observations of strategic meetings and analysis of key documents.ResultsWe interviewed 52 stakeholders across the three sites in the second half of 2019 and observed four meetings from late 2019 to early 2020 (further observation was prevented by the onset of the COVID-19 pandemic). The single most important driver of vertical integration was found to be to maintain primary care local to where patients live and thereby manage demand pressure on acute hospital services, especially emergency care. The opportunities created by maintaining local primary care providers—to develop patient services in primary care settings and better integrate them with secondary care—were exploited to differing degrees across the sites. There were notable differences between sites in operational and management arrangements, and in organisational and clinical integration. Closer organisational integration was attributed to previous good relationships between primary and secondary care locally, and to historical planning and preparation towards integrated working across the local health economy. The net impact of vertical integration on health system costs is argued by local stakeholders to be beneficial.ConclusionsVertical integration is a valuable option when primary care practices are at risk of closing, and may be a route to better integration of patient care. But it is not the only route and vertical integration is not attractive to all primary care physicians. A future evaluation of vertical integration is intended; of patients’ experience and of the impact on secondary care service utilisation.

Associations of primary care structures with polypharmacy and patient-reported indicators in patients with complex multimorbidity: a multicentre cross-sectional study in Japan

ObjectivesEvidence supporting the effects of primary care structures on the quality of care for patients with complex multimorbidity, which is one of the most important challenges facing primary care, is scarce internationally. This study aimed to examine the associations of the types of primary care facilities with polypharmacy and patient-reported indicators in patients with complex multimorbidity, with a focus on differences between community clinics and hospitals.DesignMulticentre cross-sectional study.SettingA total of 25 primary care facilities (19 community clinics and 6 small- and medium-sized hospitals).ParticipantsAdult outpatients with complex multimorbidity, which was defined as the co-occurrence of three or more chronic conditions affecting three or more different body systems within one person.Primary outcome measurePolypharmacy, the Patient-Reported Experience Measure using the Japanese version of Primary Care Assessment Tool Short Form (JPCAT-SF) and the Patient-Reported Outcome Measure using self-rated health status (SRH).ResultsData were analysed for 492 patients with complex multimorbidity. After adjustment for possible confounders and clustering within facilities, clinic-based primary care practices were significantly associated with a lower prevalence of polypharmacy, higher JPCAT-SF scores in coordination and community orientation, and a lower prevalence of poor or fair SRH compared with hospital-based primary care practices. In contrast, the JPCAT-SF score in first contact was significantly lower in clinic-based practices. The associations between the types of primary care facilities and JPCAT-SF scores in longitudinality and comprehensiveness were not statistically significant.ConclusionsClinic-based primary care practices were associated with a lower prevalence of polypharmacy, better patient experience of coordination and community orientation, and better SRH in patients with complex multimorbidity compared with hospital-based primary care practices. In the primary care setting, small and tight teams may improve the quality of care for patients with complex multimorbidity.

Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies.

Background Accurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2). Methods Prospective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model. Findings Data were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of low risk 98.8%. RECAP-O2 included age, degree of breathlessness, fatigue, and SpO2 at rest (AUC: 0.843), Validation NPV of low risk 99.4%. Interpretation Both RECAP models are a valid tool in the assessment of COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored at home and SpO2 is available, RECAP-O2 is useful to assess the need for further treatment escalation.

Smoking history and adherence to cancer-related recommendations in a primary care setting

Public health prevention efforts have led to overall reductions in mortality from screening-preventable cancers. We explored cancer screening behaviors of smokers, former smokers, and nonsmokers among patients of large primary care practices to discover the relationship between smoking status and previous adherence to the United States Preventive Services Task Force breast, cervical, and colorectal cancer screening recommendations. Our descriptive study of electronic medical record data included 6,029 established primary care patients. Multi-predictor log-binomial regression models yielded prevalence ratios (PRs) and 95% confidence intervals (CIs) to determine associations between smoking status and the likelihood of nonadherence. All models were adjusted for race/ethnicity, age, insurance, primary care specialty, number of comorbidities, and sex. Smoking history was obtained from all participants in January 2020. Current smokers accounted for 4.8%, while 22.7% were former smokers, and 72.5% were never smokers. Current smokers (compared to never smokers) were 63% more likely to be mammogram nonadherent (PR: 1.63, 95% CI: 1.31 to 2.02), 26% more likely to be Pap smear nonadherent (PR: 1.26, 95% CI: 1.04 to 1.53), and 39% more likely to be colonoscopy nonadherent (PR: 1.39, 95% CI: 1.16 to 1.66). Current smokers and former Powered by Editorial Manager and ProduXion Manager from Aries Systems Corporation smokers had on average 2.9 comorbidities while never smokers had on average 2.1 comorbidities. Our findings showed that current smokers experienced significantly lower rates of cancer screening compared to never smokers. Further research is needed to investigate and identify best practices for increasing cancer screening uptake in this population.

Pharmacists as Personalized Medicine Experts (PRIME): Experiences Implementing Pharmacist-Led Pharmacogenomic Testing in Primary Care Practices

Research exploring the integration of pharmacogenomics (PGx) testing by pharmacists into their primary care practices (including community pharmacies) has focused on the “external” factors that impact practice implementation. In this study, additional “internal” factors, related to the capabilities, opportunities, and motivations of pharmacists that influence their ability to implement PGx testing, were analyzed. Semi-structured interview data from the Pharmacists as Personalized Medicine Experts (PRIME) study, which examined the barriers and facilitators to implementing PGx testing by pharmacists into primary care practice, were analyzed. Through thematic analysis, using the theoretical domains framework (TDF) domains as deductive codes, the authors identified the most relevant TDF domains and applied the behavioural change wheel (BCW) to generate intervention types to aid in the implementation of PGx testing. Pharmacists described how their professional identities, practice environments, self-confidence, and beliefs in the benefits of PGx impacted their ability to provide a PGx-testing service. Potential interventions to improve the implementation of the PGx service included preparing pharmacists for managing an increased patient load, helping pharmacists navigate the software and technology requirements associated with the PGx service, and streamlining workflows and documentation requirements. As interest in the wide-scale implementation of PGx testing through community pharmacies grows, additional strategies need to address the “internal” factors that influence the ability of pharmacists to integrate testing into their practices.