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Storylines of Family Medicine is a 12-part series of thematically linked mini-essays with accompanying illustrations that explore the many dimensions of family medicine as interpreted by individual family physicians and medical educators in the USA and elsewhere around the world. In VIII: clinical approaches, authors address the following themes: Evaluation, diagnosis and management I-toward a working diagnosis, Evaluation, diagnosis and management II-process steps, Interweaving integrative medicine and family medicine, Halfway-the art of clinical judgment, Seamless integration in family medicine-team-based care, Technology-uncovering stories from noise and Caring for patients with multiple long-term conditions. May readers recognise in these essays the uniqueness of a family medicine approach to care.

Author(s): Clare MacRae 1,2,*, Megan McMinn 2, Stewart W. Mercer 1,2, David Henderson 2, David A. McAllister 3, Iris Ho 1,2, Emily Jefferson 4, Daniel R. Morales 4, Jane Lyons [...], Background Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. Methods and findings We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as [greater than or equal to]2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in [greater than or equal to]1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p 99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. Conclusions In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set. Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n = 771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB). Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n = 3, 876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement. Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised. Aim: To examine variation in prevalence using different definitions of multimorbidity. Design and setting: Cross-sectional study of 1 168 620 people in England. Method: Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental–physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions. Results: MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental–physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental–physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental–physical MM at 40–45 years younger, followed by MM2+ at 15–20 years younger, and MM3+ and MM3+ from 3+ at 10–15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental–physical MM. Conclusion: Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies. [ABSTRACT FROM AUTHOR]

Background: Admission rates are rising despite no change to burden of illness, and interventions to reduce unscheduled admission to hospital safely may be justified.Objective: To systematically examine admission prevention strategies and report long-term follow-up of admission prevention initiatives.Data Sources: MEDLINE, Embase, OVID SP, PsychINFO, Science Citation Index Expanded/ISI Web of Science, The Cochrane Library from inception to time of writing. Reference lists were hand searched.Study Eligibility Criteria: Randomised controlled trials and before-and-after studies.Participants: Individuals aged <18 years.Study Appraisal and Synthesis Methods: Studies were independently screened by two reviewers with final screening by a third. Data extraction and the Critical Appraisals Skills Programme checklist completion (for risk of bias assessment) were performed by one reviewer and checked by a second.Results: Twenty-eight studies were included of whom 24 were before-and-after studies and 4 were studies comparing outcomes between non-randomised groups. Interventions included referral pathways, staff reconfiguration, new healthcare facilities and telemedicine. The strongest evidence for admission prevention was seen in asthma-specific referral pathways (n=6) showing 34% (95% CI 28 to 39) reduction, but with evidence of publication bias. Other pathways showed inconsistent results or were insufficient for wider interpretation. Staffing reconfiguration showed reduced admissions in two studies, and shorter length of stay in one. Short stay admission units reduced admissions in three studies.Conclusions and Implications: There is little robust evidence to support interventions aimed at preventing paediatric admissions and further research is needed. [ABSTRACT FROM AUTHOR]

Background: Many drugs should be avoided or require dose-adjustment in chronic kidney disease (CKD). Previous estimates of potentially inappropriate prescribing rates have been based on data on a limited number of drugs, and mainly in secondary care settings.Aim: To determine the prevalence of contraindicated and potentially inappropriate primary care prescribing in a complete population of people with known CKD.Design and Setting: Cross-sectional study of prescribing patterns in a complete geographical population of people with CKD, defined using laboratory data.Method: Drugs were organised by British National Formulary advice - contraindicated drugs: 'avoid'; potentially high-risk (PHR) drugs: 'avoid if possible'; and dose-inappropriate (DI) drugs: 'dose exceeded recommended maximums'. CKD was defined as estimated glomerular filtration rate (eGFR) ≤60 ml/min/1.73 m2 for >3 months.Results: In total, 28 489 people with CKD were included in the analysis, of whom 70.1% had CKD stage 3a, 22.4% CKD stage 3b, 5.9% CKD stage 4, and 1.5% CKD stage 5. A total of 3.9% (95% confidence interval [CI] = 3.7 to 4.1) of people with CKD stages 3a-5 were prescribed ≥1 contraindicated drug, 24.3% (95% CI = 23.8 to 24.8) ≥1 PHR drug, and 15.2% (95% CI = 14.8 to 15.6) ≥1 DI drug. Contraindicated drugs differed in prevalence by CKD stage and were most commonly prescribed in CKD stage 4, with a prevalence of 36.0% (95% CI = 33.7 to 38.2). PHR drugs were commonly prescribed in all CKD stages, ranging from 19.4% (95% CI = 17.6 to 21.3) in CKD stage 4 to 25.1% (95% CI = 24.5 to 25.7) in CKD stage 3a. DI drugs were most commonly prescribed in CKD stage 4 (26.4%, 95% CI = 24.3 to 28.6).Conclusion: Potentially inappropriate prescribing is common at all stages of CKD. Development and evaluation of interventions to improve prescribing safety in this high-risk population are needed. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is commonly comorbid with hypertension, diabetes, and cardiovascular disease (CVD). However, the extent of comorbidity in CKD across a range of concordant (shared pathophysiology and/or treatment) conditions and discordant (unrelated pathophysiology and/or different or contradictory treatment) conditions is not well documented.Aim: To ascertain the prevalence of comorbidity, across 39 physical and mental health comorbidities, in adults with CKD in a large, nationally representative primary care population.Design and Setting: Cross-sectional analysis of a primary care dataset representing 1 274 374 adults in Scotland.Method: This study was a secondary analysis of general practice electronic medical record data using binary logistic regression models adjusted for age, sex, and socioeconomic status. Data of adults aged ≥25 years and 40 long-term conditions were used.Results: A total of 98.2% of adults with CKD had at least one comorbidity, versus 51.8% in controls. After adjustment for age, sex, and deprivation, people with CKD were more likely to have 1 (adjusted odds ratio [aOR] 6.5, 95% confidence interval [CI] = 6.0 to 7.1), 2-3 (aOR 15.2, 95% CI = 14.0 to 16.5), 4-6 (odds ratio [OR] 26.6, 95% CI = 24.4 to 28.9), and ≥7 other conditions (OR 41.9, 95% CI = 38.3 to 45.8). Furthermore, all concordant (seven out of seven), the majority of discordant physical health conditions (17 out of 24), and mental health conditions (six out of eight) had statistically significant positive associations with CKD after adjustment.Conclusion: Chronic kidney disease is associated with extreme comorbidity across a wide range of mental and physical conditions. Routine care for people with CKD should include recognition and management of comorbidities, and clinical guidelines should support clinicians to do this. [ABSTRACT FROM AUTHOR]