Family Medicine and Community Health最新文献

Objectives: This study aimed to determine the COVID-19 risk perceptions, vaccination intentions and predictive factors of family physicians and family healthcare staff working in primary care in Üsküdar.

Design: A cross-sectional study was performed using an online questionnaire to determine the demographic and general characteristics of the participants and their willingness to be vaccinated.

Setting: An online questionnaire was applied to family physicians and family health workers working in primary care family health centres in Üsküdar between 25 and 29 December 2020. Multivariate analysis was performed to identify independent predictors of the willingness of individuals to be vaccinated.

Participants: Out of 323 health workers working in 44 family health centres in the district, a total of 276 health workers were reached, including 126 physicians (n=158, 79.7%) and 150 midwives/nurses (n=165, 90.9%) (response rate 85.4%).

Results: 50.4% (n=139) of the healthcare workers were willing to have the COVID-19 vaccine, 29% (n=80) were undecided and 20.7% (n=57) refused the vaccine. The rate of acceptance to be vaccinated was higher in physicians, in men and in those who had not received a seasonal influenza vaccination regularly each year.

Conclusions: Half of the primary healthcare workers, one of the high-risk groups in the pandemic, were hesitant or refused to be vaccinated for COVID-19. Knowing the factors affecting the vaccine acceptance rates of healthcare professionals can be considered one of the most strategic moves in reaching the target of high community vaccination rates. For evidence-based planning in vaccination studies, there is a need to investigate the reasons for COVID-19 vaccine acceptance by healthcare workers at all levels.

Objectives: Journaling is a common non-pharmacological tool in the management of mental illness, however, no clear evidence-based guideline exists informing primary care providers on its use. We seek here to present this synthesis that may begin to inform future research and eventual evidence-based guideline development.

Design: Of the 3797 articles retrieved from MEDLINE, EMBASE, PsycINFO, 20 peer-reviewed randomised control trials (31 outcomes) met inclusion criteria. These studies addressed the impact of a journaling intervention on PTSD, other anxiety disorders, depression or a combination of the aforementioned.

Eligibility criteria: Peer reviewed, randomised control trials on the impact of journaling on mental illness were included.

Information sources: MEDLINE, EMBASE and PsycINFO.

Results: The data are highly heterogeneous (control arm=I² of 71.2%, intervention arm=I² of 83.8%) combined with a B-level Strength of Recommendation Taxonomy recommendation. It was additionally found that there is a significant pre-post psychometric scale difference between control (-0.01, 95% CI -0.03 to 0.00) and intervention arms (-0.06, 95% CI -0.09 to -0.03). This 5% difference between groups indicates that a journaling intervention resulted in a greater reduction in scores on patient health measures. Cohen's d effect size analysis of studies suggests a small to moderate benefit.

Conclusion: Further studies are needed to better define the outcomes. Our review suggests that while there is some randomised control data to support the benefit of journaling, high degrees of heterogeneity and methodological flaws limit our ability to definitively draw conclusions about the benefit and effect size of journaling in a wide array of mental illnesses. Given the low risk of adverse effects, low resource requirement and emphasis on self-efficacy, primary care providers should consider this as an adjunct therapy to complement current evidence-based management.

As the USA becomes more diverse, the inclusion of patients from diverse backgrounds in research becomes ever more important to ensuring a complete understanding of the patient experience in primary care. Language and cultural barriers are important areas in which researchers face substantial challenges. Primary care researchers need tools and approaches to include diverse communities in qualitative interviews. Here, we describe one way primary care researchers can apply an adapted, engaged transcription and interpretation method in qualitative research to improve retention of nuance and meaning across language and cultures, specifically with non-English, non-Spanish-speaking resettled refugees. We also discuss how the approach provided additional information that increased the validity of interpretation and analysis and improved the retention of nuance in a qualitative primary care study. The methodological and practical value, scope of application and potential limitations and improvements of this method through future research are addressed.

Objectives: To evaluate the capacity of general practice (GP) electronic medical record (EMR) data to assess risk factor detection, disease diagnostic testing, diagnosis, monitoring and pharmacotherapy for the interrelated chronic vascular diseases-chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease.

Design: Cross-sectional analysis of data extracted on a single date for each practice between 12 April 2017 and 18 April 2017 incorporating data from any time on or before data extraction, using baseline data from the Chronic Disease early detection and Improved Management in PrimAry Care ProjecT. Deidentified data were extracted from GP EMRs using the Pen Computer Systems Clinical Audit Tool and descriptive statistics used to describe the study population.

Setting: Eight GPs in Victoria, Australia.

Participants: Patients were ≥18 years and attended GP ≥3 times within 24 months. 37 946 patients were included.

Results: Risk factor and disease testing/monitoring/treatment were assessed as per Australian guidelines (or US guidelines if none available), with guidelines simplified due to limitations in data availability where required. Risk factor assessment in those requiring it: 30% of patients had body mass index and 46% blood pressure within guideline recommended timeframes. Diagnostic testing in at-risk population: 17% had diagnostic testing as per recommendations for CKD and 37% for T2D. Possible undiagnosed disease: Pathology tests indicating possible disease with no diagnosis already coded were present in 6.7% for CKD, 1.6% for T2D and 0.33% familial hypercholesterolaemia. Overall prevalence: Coded diagnoses were recorded in 3.8% for CKD, 6.6% for T2D, 4.2% for ischaemic heart disease, 1% for heart failure, 1.7% for ischaemic stroke, 0.46% for peripheral vascular disease, 0.06% for familial hypercholesterolaemia and 2% for atrial fibrillation. Pharmaceutical prescriptions: the proportion of patients prescribed guideline-recommended medications ranged from 44% (beta blockers for patients with ischaemic heart disease) to 78% (antiplatelets or anticoagulants for patients with ischaemic stroke).

Conclusions: Using GP EMR data, this study identified recorded diagnoses of chronic vascular diseases generally similar to, or higher than, reported national prevalence. It suggested low levels of extractable documented risk factor assessments, diagnostic testing in those at risk and prescription of guideline-recommended pharmacotherapy for some conditions. These baseline data highlight the utility of GP EMR data for potential use in epidemiological studies and by individual practices to guide targeted quality improvement. It also highlighted some of the challenges of using GP EMR data.

To describe the methodology of development of a protocol for application of the Brazilian Dietary Guidelines by primary healthcare professionals in individual dietary advice. A five-step approach was followed: (1) format definition; (2) definition of the instrument for assessment of individuals' food consumption; (3) Dietary Guidelines' content extraction; (4) protocol content development; (5) content and face validity. An example from Brazil was displayed with the development of a protocol to guide healthcare professional decision-making when providing nutrition advice based on the Brazilian Dietary Guidelines. The instrument of the Brazilian Nutrition Surveillance System (SISVAN) was chosen to the food consumption assessment, which contains questions about the consumption of seven healthy or unhealthy food groups and one question about eating modes. The Guidelines' content extraction process led to the identification of recommendations related to the food consumption markers assessed by the SISVAN questionnaire. Then, a protocol was developed in a flowchart format, in which the professional's conduct is guided by the answer given to each question of the SISVAN instrument. For each 'non-compliant' answer (unhealthy eating practice), the professional is instructed how to provide recommendations and identify obstacles. Lastly, experts and healthcare professionals highlighted pertinence, clarity and usability of the protocol. This study provides the blueprint for the phase-wise development of protocols of application of the Dietary Guidelines and may contribute to promote healthier eating and ending malnutrition in all its forms.

Objective: To evaluate the effect of a one-time cash transfer of $C1000 in people who are unable to physically distance due to insufficient income.

Design: Open-label, multi-centre, randomised superiority trial.

Setting: Seven primary care sites in Ontario, Canada; six urban sites associated with St. Michael's Hospital in Toronto and one in Manitoulin Island.

Participants: 392 individuals who reported trouble affording basic necessities due to disruptions related to COVID-19.

Intervention: After random allocation, participants either received the cash transfer of $C1000 (n=196) or physical distancing guidelines alone (n=196).

Main outcome measures: The primary outcome was the maximum number of symptoms consistent with COVID-19 over 14 days. Secondary outcomes were meeting clinical criteria for COVID-19, SARS-CoV-2 presence, number of close contacts, general health and ability to afford basic necessities.

Results: The primary outcome of number of symptoms reported by participants did not differ between groups after 2 weeks (cash transfer, mean 1.6 vs 1.9, ratio of means 0.83; 95% CI 0.56 to 1.24). There were no statistically significant effects on secondary outcomes of the meeting COVID-19 clinical criteria (7.9% vs 12.8%; risk difference -0.05; 95% CI -0.11 to 0.01), SARS-CoV-2 presence (0.5% vs 0.6%; risk difference 0.00 95% CI -0.02 to 0.02), mean number of close contacts (3.5 vs 3.7; rate ratio 1.10; 95% CI 0.83 to 1.46), general health very good or excellent (60% vs 63%; risk difference -0.03 95% CI -0.14 to 0.08) and ability to make ends meet (52% vs 51%; risk difference 0.01 95% CI -0.10 to 0.12).

Conclusions: A single cash transfer did not reduce the COVID-19 symptoms or improve the ability to afford necessities. Further studies are needed to determine whether some groups may benefit from financial supports and to determine if a higher level of support is beneficial.

Trial registration number: NCT04359264.

Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. One model fit measure is the count- [Formula: see text], which is the percentage of observations where the model correctly predicted the outcome variable value. Related to the count- [Formula: see text] are model sensitivity-the percentage of those with the outcome who were correctly predicted to have the outcome-and specificity-the percentage of those without the outcome who were correctly predicted to not have the outcome. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.

Objective: To describe common strategies and practice-specific barriers, adaptations and determinants of cancer screening implementation in eight rural primary care practices in the Midwestern United States after joining an accountable care organisation (ACO).

Design: This study used a multiple case study design. Purposive sampling was used to identify a diverse group of practices within the ACO. Data were collected from focus group interviews and workflow mapping. The Consolidated Framework for Implementation Research (CFIR) was used to guide data collection and analysis. Data were cross-analysed by clinic and CFIR domains to identify common themes and practice-specific determinants of cancer screening implementation.

Setting: The study included eight rural primary care practices, defined as Rural-Urban Continuum Codes 5-9, in one ACO in the Midwestern United States.

Participants: Providers, staff and administrators who worked in the primary care practices participated in focus groups. 28 individuals participated including 10 physicians; one doctor of osteopathic medicine; three advanced practice registered nurses; eight registered nurses, quality assurance and licensed practical nurses; one medical assistant; one care coordination manager; and four administrators.

Results: With integration into the ACO, practices adopted four new strategies to support cancer screening: care gap lists, huddle sheets, screening via annual wellness visits and information spread. Cross-case analysis revealed that all practices used both visit-based and population-based cancer screening strategies, although workflows varied widely across practices. Each of the four strategies was adapted for fit to the local context of the practice. Participants shared that joining the ACO provided a strong external incentive for increasing cancer screening rates. Two predominant determinants of cancer screening success at the clinic level were use of the electronic health record (EHR) and fully engaging nurses in the screening process.

Conclusions: Joining an ACO can be a positive driver for increasing cancer screening practices in rural primary care practices. Characteristics of the practice can impact the success of ACO-related cancer screening efforts; engaging nurses to the fullest extent of their education and training and integrating cancer screening into the EHR can optimise the cancer screening workflow.