Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care.

BACKGROUND: 'High-cost' individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes. AIM: To compare high-cost with lower-cost individuals with multimorbidity and assess whether these populations can be clustered based on similar disease patterns. DESIGN AND SETTING: A cross-sectional study based on 2019/2020 electronic medical records from adults registered to primary care practices (n = 41) in a London borough. METHOD: Multimorbidity is defined as having ≥2 long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High cost was defined as the top 20% of individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns. RESULTS: Of 386 238 individuals, 101 498 (26%) had multimorbidity. The high-cost group (n = 20 304) incurred 53% of total costs and had 6833 unique disease combinations, about three times the diversity of the lower-cost group (n = 81 194). The trio of anxiety, chronic pain, and depression represented the highest share of costs (5%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In three of five clusters, mental health conditions were the most prevalent. CONCLUSION: High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical health care to improve outcomes and reduce costs.

Assessment of ethnic inequalities in diagnostic coding of familial hypercholesterolaemia (FH): A cross-sectional database study in Lambeth, South London.

BACKGROUND AND AIMS: Differences in the perceived prevalence of familial hypercholesterolemia (FH) by ethnicity are unclear. In this study, we aimed to assess the prevalence, determinants and management of diagnostically-coded FH in an ethnically diverse population in South London. METHODS: A cross-sectional analysis of 40 practices in 332,357 adult patients in Lambeth was undertaken. Factors affecting a (clinically coded) diagnosis of FH were investigated by multi-level logistic regression adjusted for socio-demographic and lifestyle factors, co-morbidities, and medications. RESULTS: The age-adjusted FH % prevalence rate (OR, 95%CI) ranged from 0.10 to 1.11, 0.00-1.31. Lower rates of FH coding were associated with age (0.96, 0.96-0.97) and male gender (0.75, 0.65-0.87), p < 0.001. Compared to a White British reference group, a higher likelihood of coded FH was noted in Other Asians (1.33, 1.01-1.76), p = 0.05, with lower rates in Black Africans (0.50, 0.37-0.68), p < 0.001, Indians (0.55, 0.34-0.89) p = 0.02, and in Black Caribbeans (0.60, 0.44-0.81), p = 0.001. The overall prevalence using Simon Broome criteria was 0.1%; we were unable to provide ethnic specific estimates due to low numbers. Lower likelihoods of FH coding (OR, 95%CI) were seen in non-native English speakers (0.66, 0.53-0.81), most deprived income quintile (0.68, 0.52-0.88), smokers (0.68,0.55-0.85), hypertension (0.62, 0.52-0.74), chronic kidney disease (0.64, 0.41-0.99), obesity (0.80, 0.67-0.95), diabetes (0.31, 0.25-0.39) and CVD (0.47, 0.36-0.63). 20% of FH coded patients were not prescribed lipid-lowering medications, p < 0.001. CONCLUSIONS: Inequalities in diagnostic coding of FH patients exist. Lower likelihoods of diagnosed FH were seen in Black African, Black Caribbean and Indian ethnic groups, in contrast to higher diagnoses in White and Other Asian ethnic groups. Hypercholesterolaemia requiring statin therapy was associated with FH diagnosis, however, the presence of cardiovascular disease (CVD) risk factors lowered the diagnosis rate for FH.

Determinants of lipid clinic referral and attendance in a multi-ethnic adult population in south London: a cross-sectional study.

BACKGROUND: Primary dyslipidaemias, including familial hypercholesterolaemia, are underdiagnosed genetic disorders that substantially increase risk for premature coronary artery disease in adults. Early identification of primary dyslipidaemias via lipid clinic referral optimises patient management and enables cascade screening of relatives. Improving the identification of primary dyslipidaemias, and understanding disparities in ascertainment and management, is an NHS priority. We aimed to assess determinants of lipid clinic referral or attendance (LCR) in ethnically diverse adults. METHODS: We did a retrospective cross-sectional study using the Lambeth DataNet containing anonymised data from 41 general practitioner (GP) practices in south London. We looked at referral data for adult patients aged 18 years and older from Jan 1, 1995, until May 14, 2018. LCR was the main outcome. We used sequential multilevel logistic regression models adjusted for practice effects to estimate the odds of LCR assessed across six ethnic groups (reference group White) and patient-level factors (demographic, socioeconomic, lifestyle, comorbidities, total cholesterol [TC] >7·5mmol/L, statin prescription, and practice factors). The study was approved by NHS South East London Clinical Commissioning Group (CCG) and NHS Lambeth CCG. FINDINGS: 780 (0·23%) of 332 357 adult patients were coded as referred (n=538) or seen (n=252) in a lipid clinic. 164 487 (46·49%) were women (appendix). The fully adjusted model for odds of LCR showed the following significant associations for age (odds ratio [OR] 0·96, 95% CI 0·96-0·97, p<0·001); Black, African, Caribbean, or Black-British ethnicity (0·67, 0·53-0·84, p=0·001); ex-smoker status (1·29, 1·05-1·57, p=0·014); TC higher than 7·5 mmol/L (12·18, 9·60-15·45, p<0·001); statin prescription (14·01, 10·85-18·10, p<0·001); diabetes (0·72, 0·58-0·91, p=0·005); high-frequency GP attendance at seven or more GP consultations in the past year (1·49, 1·21-1·84, p<0·001); high GP-density (0·5-0·99 full-time equivalent GPs per 1000 patients; 2·70, 1·23-5·92, p=0·013). Sensitivity analyses for LCR restricted to familial hypercholesterolaemia-coded patients (n=581) found associations with TC higher than 7·5 mmol/L (4·26, 1·89-9·62, p<0·001), statin prescription (16·96, 2·19-131·36, p=0·007), and high GP-density (5·73, 1·27-25·93, p=0·023), with no significant associations with ethnicity. The relative contribution of GP practices to LCR was 6·32% of the total variance. There were no significant interactions between ethnicity and deprivation, age, or obesity. INTERPRETATION: While interpretation is limited by the accuracy and completeness of coded records, the study showed factors associated with a higher likelihood of LCR included individuals recorded as having TC higher than 7·5 mmol/L, statin prescription, ex-smoker status, high-frequency GP attendance, and registration at a GP practice with 0·5-0·99 GP density. Patients with increasing age; Black, African, Caribbean, or Black-British ethnicity patients; and patients with diabetes had lower odds of LCR. Finally, the difference in odds of LCR between Black and White patients highlights potential health inequalities. FUNDING: NHS Race & Health Observatory.

Factors affecting the delivery of community-based salon interventions to prevent cardiovascular disease and breast cancer among ethnically diverse women in South London: a concept-mapping approach.

BACKGROUND: In the UK, women from ethnically diverse and socioeconomically deprived communities are at increased risk of underdiagnosis of cardiovascular disease (CVD) and breast cancer. Promoting CVD prevention and awareness of breast cancer screening via community salons and primary health care partnerships can improve uptake of screening services and early detection. METHODS: Concept mapping is a multistage mixed methods participatory approach comprised of six stages: preparation, brainstorming, structuring of statements, representing statements, interpretation and utilisation of maps using Group wisdom software. A target of 20 salons, excluding male-only salons were approached. Salons included Salons included hairdressing or hairdressing and beauty salons. Purposeful and convenience sampling (online and face to face) among UK salons (hair and beauty) was conducted. Participants were given a focus prompt "What would be some factors that can influence the ability of salons to deliver this service?" and required to generate statements, which were sorted into categories based on similarity and rated for importance and feasibility. Concept maps using multidimensional scaling and hierarchical cluster analyses were produced. FINDINGS: Of 35 participants invited, 25 (71%) consented and agreed to take part in concept mapping. Reported ages were 26-35 years (n=5, 20%), 36-45 years (n=12, 48%), 46-55 years (n=3, 12%), 56-65 years (n=5, 20%), and no age reported (n=10, 40%). Around 36% (n=9) of participants were from non-White ethnic groups, with 12% (n=3) being male and 88% (n=22) female. Seven clusters emerged. Salon staff capabilities and capacities and engaging in health conversations in community salons scored average bridging values of 0·09 and 0·2 respectively, indicating good cluster homogeneity (similar meaning statements were closely sorted). Facilitating health-care access with GP practices was rated highly important to effectively promote the intervention. Engaging in health conversations in community salons and salon incentives for participation were examples of factors that were highly feasible to address. The r correlation coefficient was 0·68 between importance and feasibility to address factors affecting community health interventions. INTERPRETATION: Salons are well positioned to support health promotion interventions. Actionable priorities were identified for a salon-GP surgery partnership to promote CVD prevention through lifestyle changes and health check uptake, raising breast cancer screening awareness and address issue of equity. FUNDING: National Institute of Health and Care Research (NIHR), Research for Patient Benefit (RfPB) Programme NIHR202769.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data.

BACKGROUND: Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data. METHODS: Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes. RESULTS: The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population. CONCLUSIONS: Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Ethnic inequalities in the impact of COVID-19 on primary care consultations: a time series analysis of 460,084 individuals with multimorbidity in South London.

BACKGROUND: The COVID-19 pandemic caused rapid changes in primary care delivery in the UK, with concerns that certain groups of the population may have faced increased barriers to access. This study assesses the impact of the response to the COVID-19 pandemic on primary care consultations for individuals with multimorbidity and identifies ethnic inequalities. METHODS: A longitudinal study based on monthly data from primary care health records of 460,084 patients aged ≥18 years from 41 GP practices in South London, from February 2018 to March 2021. Descriptive analysis and interrupted time series (ITS) models were used to analyse the effect of the pandemic on primary care consultations for people with multimorbidity and to identify if the effect varied by ethnic groups and consultation type. RESULTS: Individuals with multimorbidity experienced a smaller initial fall in trend at the start of the pandemic. Their primary care consultation rates remained stable (879 (95% CI 869-890) per 1000 patients in February to 882 (870-894) March 2020), compared with a 7% decline among people without multimorbidity (223 consultations (95% CI 221-226) to 208 (205-210)). The gap in consultations between the two groups reduced after July 2020. The effect among individuals with multimorbidity varied by ethnic group. Ethnic minority groups experienced a slightly larger fall at the start of the pandemic. Individuals of Black, Asian, and Other ethnic backgrounds also switched from face-to-face to telephone at a higher rate than other ethnic groups. The largest fall in face-to-face consultations was observed among people from Asian backgrounds (their consultation rates declined from 676 (659-693) in February to 348 (338-359) in April 2020), which may have disproportionately affected their quality of care. CONCLUSIONS: The COVID-19 pandemic significantly affected primary care utilisation in patients with multimorbidity. While there is evidence of a successful needs-based prioritisation of multimorbidity patients within primary care at the start of the pandemic, inequalities among ethnic minority groups were found. Strengthening disease management for these groups may be necessary to control widening inequalities in future health outcomes.

What factors influence differential uptake of NHS Health Checks, diabetes and hypertension reviews among women in ethnically diverse South London? Cross-sectional analysis of 63,000 primary care records.

Background: Uptake of health checks among women has not been examined in relation to patient and General Practitioner (GP) practice level factors. We investigated patient and practice level factors associated with differential uptake of health checks. Methods: Primary care records from 44 practices in Lambeth for women aged 40-74 years old (N = 62,967) from 2000-2018 were analysed using multi-level logistic regression models. An odds ratio (OR) >1 indicates increased occurrence of no health check. Findings: The mean age (IQR) of the included female sample (aged 40-74 years) was 52.9 years (45.0-59.0). Adjusted for patient-level factors (age, ethnicity, English as first language, overweight/obesity, smoking, attendance to GP practices, and co-morbidity), the odds of non-uptake of health checks were higher for Other White (OR 1.24, 95% confidence interval 1.17-1.33), and Other ethnicity (1.20, 1.07-1.35) vs. White British. It was also higher for 50-69 year olds (1.55, 1.47-1.62), 70-74 year olds (1.60, 1.49-1.72) vs. 40-49 year olds. These ORs did not change on adjustments for practice level factors (proportion of patients living in deprived areas, proportion of patients with ≥1 chronic condition, ≥3 emergency diabetes admissions annually, GP density/1000 patients, quality outcome framework score of ≥ 95%, and patient satisfaction scores of ≥80%). Non-uptake was lower for Black Caribbeans, Bangladeshis, overweight/obese patients, frequent practice attenders and comorbid patients. Interpretation: Differential uptake in health checks remained after adjustment for patient and practice level factors. Better measures of social determinants of health and of practice context are needed. Funding: NIHR Research for Patient Benefit Programme (NIHR202769).

Cost outcomes of potentially inappropriate prescribing in middle-aged adults: A Delphi consensus and cross-sectional study.

BACKGROUND: Potentially inappropriate prescribing (PIP) is common in older adults and is associated with increased medication costs and costs of associated adverse drug events. PIP also affects almost 1/5 of middle-aged adults (45-64 y), as defined by the PRescribing Optimally in Middle-aged People's Treatments (PROMPT) criteria. However, there has been little research on PIP medication costs within this age group. AIMS: Calculate the medication costs of PIP for middle-aged adults according to the 22 PROMPT criteria and compare with the cost of consensus-validated, evidence-based (adequate) alternative prescribing scenarios. METHODS: Adequate alternatives to the 22 PROMPT criteria were created via literature review. A Delphi consensus panel of experts was recruited (n = 16), supported by a patient and public involvement group, to achieve consensus on the alternatives. A retrospective repeated cross-sectional study from 2014 to 2019 was then conducted utilising pseudonymised primary care data from Lambeth DataNet in South London (41 general practices, n = 1 185 335, using Lambeth DataNet May 2020 extract) to calculate the cost of PIP. RESULTS: The cross-sectional study included 55 880 patients. The total PIP cost was £2.79 million, with adequate alternative prescribing costing £2.74 million (cost savings of £51 278). Duplicate drug classes was the most costly criterion for both PIP and alternative prescribing. CONCLUSION: This study calculated the medication costs of PIP and created alternative prescribing scenarios for the 22 PROMPT criteria. There is no substantial cost difference between adequate prescribing vs. PIP. Future studies should investigate the wider health economic costs of alternative prescribing, such as reducing hospital admissions.

Determinants of long-term opioid prescribing in an urban population: A cross-sectional study.

BACKGROUND: Opioid prescribing has more than doubled in the UK between 1998 and 2016. Potential adverse health implications include dependency, falls and increased health expenditure. AIM: To describe the predictors of long-term opioid prescribing (LTOP) (≥3 opioid prescriptions in a 90-day period). DESIGN AND SETTING: A retrospective cross-sectional study in 41 general practices in South London. METHOD: Multi-level multivariable logistic regression to investigate the determinants of LTOP. RESULTS: Out of 320 639 registered patients ≥18 years, 2679 (0.8%) were identified as having LTOP. Patients were most likely to have LTOP if they had ≥5 long-term conditions (LTCs) (adjusted odds ratio [AOR] 36.5, 95% confidence interval [CI] 30.4-43.8) or 2-4 LTCs (AOR 13.8, CI 11.9-16.1) in comparison to no LTCs, were ≥75 years compared to 18-24 years (AOR 12.31, CI 7.1-21.5), were smokers compared to nonsmokers (AOR 2.2, CI 2.0-2.5), were female rather than male (AOR 1.9, CI 1.7-2.0) and in the most deprived deprivation quintile (AOR 1.6, CI 1.4-1.8) compared to the least deprived. In a separate model examining individual LTCs, the strongest associations for LTOP were noted for sickle cell disease (SCD) (AOR 18.4, CI 12.8-26.4), osteoarthritis (AOR 3.0, CI 2.8-3.3), rheumatoid arthritis (AOR 2.8, CI 2.2-3.4), depression (AOR 2.6, CI 2.3-2.8) and multiple sclerosis (OR 2.5, CI 1.4-4.4). CONCLUSION: LTOP was significantly higher in those aged ≥75 years, with multimorbidity or specific LTCs: SCD, osteoarthritis, rheumatoid arthritis, depression and multiple sclerosis. These characteristics may enable the design of targeted interventions to reduce LTOP.

Identifying multimorbidity clusters with the highest primary care use: 15 years of evidence from a multi-ethnic metropolitan population.

BACKGROUND: People with multimorbidity have complex healthcare needs. Some co-occurring diseases interact with each other to a larger extent than others and may have a different impact on primary care use. AIM: To assess the association between multimorbidity clusters and primary care consultations over time. DESIGN AND SETTING: A retrospective longitudinal (panel) study design was used. Data comprised electronic primary care health records of 826 166 patients registered at GP practices in an ethnically diverse, urban setting in London between 2005 and 2020. METHOD: Primary care consultation rates were modelled using generalised estimating equations. Key controls included the total number of long-term conditions, five multimorbidity clusters, and their interaction effects, ethnic group, and polypharmacy (proxy for disease severity). Models were also calibrated by consultation type and ethnic group. RESULTS: Individuals with multimorbidity used two to three times more primary care services than those without multimorbidity (incidence rate ratio 2.30, 95% confidence interval = 2.29 to 2.32). Patients in the alcohol dependence, substance dependence, and HIV cluster (Dependence+) had the highest rate of increase in primary care consultations as additional long-term conditions accumulated, followed by the mental health cluster (anxiety and depression). Differences by ethnic group were observed, with the largest impact in the chronic liver disease and viral hepatitis cluster for individuals of Black or Asian ethnicity. CONCLUSION: This study identified multimorbidity clusters with the highest primary care demand over time as additional long-term conditions developed, differentiating by consultation type and ethnicity. Targeting clinical practice to prevent multimorbidity progression for these groups may lessen future pressures on primary care demand by improving health outcomes.

Hypertension and cardiovascular risk factor management in a multi-ethnic cohort of adults with CKD: a cross sectional study in general practice.

BACKGROUND: Hypertension, especially if poorly controlled, is a key determinant of chronic kidney disease (CKD) development and progression to end stage renal disease (ESRD). AIM: To assess hypertension and risk factor management, and determinants of systolic blood pressure control in individuals with CKD and hypertension. DESIGN AND SETTING: Cross-sectional survey using primary care electronic health records from 47/49 general practice clinics in South London. METHODS: Known effective interventions, management of hypertension and cardiovascular disease (CVD) risk in patients with CKD Stages 3-5 were investigated. Multivariable logistic regression analysis examined the association of demographic factors, comorbidities, deprivation, and CKD coding, with systolic blood pressure control status as outcome. Individuals with diabetes were excluded. RESULTS: Adults with CKD Stages 3-5 and hypertension represented 4131/286,162 (1.4%) of the total population; 1984 (48%) of these individuals had undiagnosed CKD without a recorded CKD clinical code. Hypertension was undiagnosed in 25% of the total Lambeth population, and in patients with CKD without diagnosed hypertension, 23.0% had systolic blood pressure > 140 mmHg compared with 39.8% hypertensives, p < 0.001. Multivariable logistic regression revealed that factors associated with improved systolic blood pressure control in CKD included diastolic blood pressure control, serious mental illness, history of cardiovascular co-morbidities, CKD diagnostic coding, and age < 60 years. African ethnicity and obesity were associated with poorer systolic blood pressure control. CONCLUSION: We found both underdiagnosed CKD and underdiagnosed hypertension in patients with CKD. The poor systolic blood pressure control in older age groups ≥ 60 years and in Black African or obese individuals is clinically important as these groups are at increased risk of mortality for cardiovascular diseases.

Inequalities in developing multimorbidity over time: A population-based cohort study from an urban, multi-ethnic borough in the United Kingdom.

BACKGROUND: Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. METHODS: We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0-39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. FINDINGS: Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. INTERPRETATION: We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

Applying resolved and remission codes reduced prevalence of multimorbidity in an urban multi-ethnic population.

OBJECTIVE: To estimate the prevalence and determinants of multimorbidity in an urban, multi-ethnic area over 15-years and investigate the effect of applying resolved/remission codes on prevalence estimates. STUDY DESIGN AND SETTING: This is a population-based retrospective cross-sectional study using electronic health records of adults registered between 2005 -2020 in general practices in one inner London borough (n = 826,936). Classification of resolved/remission was based on clinical coding defined by the patient's general practitioner. RESULTS: The crude and age-adjusted prevalence of multimorbidity over the study period were 21.2% (95% CI: 21.1 -21.3) and 30.8% (30.6 -31.0), respectively. Applying resolved/remission codes decreased the crude and age-adjusted prevalence estimates to 18.0% (95% CI: 17.9 -18.1) and 27.5% (27.4 -27.7). Asthma (53.2%) and depression (20.2%) were responsible for most resolved and remission codes. Substance use (Adjusted Odds Ratio 10.62 [95% CI: 10.30 -10.95]), high cholesterol (2.48 [2.44 -2.53]), and moderate obesity (2.19 [2.15 -2.23]) were the strongest risk factor determinants of multimorbidity outside of advanced age. CONCLUSION: Our study highlights the importance of applying resolved/remission codes to obtain an accurate prevalence and the increased burden of multimorbidity in a young, urban, and multi-ethnic population. Understanding modifiable risk factors for multimorbidity can assist policymakers in designing effective interventions to reduce progression to multimorbidity.

Identifying longitudinal clusters of multimorbidity in an urban setting: A population-based cross-sectional study.

BACKGROUND: Globally, there is increasing research on clusters of multimorbidity, but few studies have investigated multimorbidity in urban contexts characterised by a young, multi-ethnic, deprived populations. This study identified clusters of associative multimorbidity in an urban setting. METHODS: This is a population-based retrospective cross-sectional study using electronic health records of all adults aged 18 years and over, registered between April 2005 to May 2020 in general practices in one inner London borough. Multiple correspondence analysis and cluster analysis was used to identify groups of multimorbidity from 32 long-term conditions (LTCs). RESULTS: The population included 41 general practices with 826,936 patients registered between 2005 and 2020, with mean age 40 (SD15·6) years. The prevalence of multimorbidity was 21% (n = 174,881), with the median number of conditions being three and increasing with age. Analysis identified five consistent LTC clusters: 1) anxiety and depression (Ratio of within- to between- sum of squares (WSS/BSS <0·01 to <0·01); 2) heart failure, atrial fibrillation, chronic kidney disease (CKD), chronic heart disease (CHD), stroke/transient ischaemic attack (TIA), peripheral arterial disease (PAD), dementia and osteoporosis (WSS/BSS 0·09 to 0·12); 3) osteoarthritis, cancer, chronic pain, hypertension and diabetes (0·05 to 0·06); 4) chronic liver disease and viral hepatitis (WSS/BSS 0·02 to 0·03); 5) substance dependency, alcohol dependency and HIV (WSS/BSS 0·37 to 0·55). INTERPRETATION: Mental health problems, pain, and at-risk behaviours leading to cardiovascular diseases are the important clusters identified in this young, urban population. FUNDING: Impact on Urban Health, United Kingdom.

Prevalence and predictors of potentially inappropriate prescribing in middle-aged adults: a repeated cross-sectional study.

BACKGROUND: Potentially inappropriate prescribing (PIP) is common in older adults and known to be associated with polypharmacy and multimorbidity. Less is known about the prevalence and causes of PIP in middle-aged adults. AIM: To determine the prevalence and predictors of PIP in middle-aged adults. DESIGN AND SETTING: A repeated cross-sectional study was conducted using primary care data in London. METHOD: PIP was defined using the PRescribing Optimally in Middle-aged People's Treatments (PROMPT) criteria. Prescribing and demographic data were extracted from Lambeth DataNet (LDN), a pseudonymised database of all patients registered at general practices in Lambeth, for those aged 45-64 years prescribed ≥1 medicines in each year from 2014-2019 (n = 46 633-52 582). Prevalence and trends over 6 years were investigated, including the association of PIP with polypharmacy, multimorbidity, deprivation, sex, and age. RESULTS: The prevalence of PIP decreased from 20% in 2014 to 18% in 2019. The most prevalent PROMPT criteria in 2019 were the use of ≥2 drugs from the same pharmacological class (7.6%), use of non-steroidal anti-inflammatory drugs for >3 months (7.1%) and use of proton pump inhibitors above recommended maintenance dosages for >8 weeks (3.1%). Over the study period, the prevalence of multimorbidity increased (47-52%) and polypharmacy remained stable (27%). Polypharmacy, multimorbidity, deprivation, and age were independently associated with PIP. Sex was the only variable not associated with PIP. CONCLUSION: Almost one-fifth of middle-aged adults prescribed medicines are exposed to PIP, as defined by the PROMPT criteria. This is likely to be linked with exposure to avoidable adverse drug events. The PROMPT criteria may provide a useful aid in interventions to optimise prescribing.

Uncoded chronic kidney disease in primary care: a cross-sectional study of inequalities and cardiovascular disease risk management.

BACKGROUND: Uncoded chronic kidney disease (CKD) is associated with poorer quality of care. AIM: To ascertain the proportion and determinants of CKD, which have not been formally recorded (Read coded), and identify differences in management and quality-of-care measures for patients with coded and uncoded CKD. DESIGN AND SETTING: Cross-sectional survey undertaken in an ethnically diverse adult population using primary care electronic health records (EHRs) from GP clinics in Lambeth, South London, UK. METHOD: Multivariable logistic regression analysis examined the association of demographic factors, selected comorbidities, deprivation, and cardiovascular disease risk management in CKD, with coding status as outcome. RESULTS: In total, the survey involved 286 162 adults, of whom 9325 (3.3%) were identified with CKD stage 3-5 (assigned as CKD based on estimated glomerular filtration rate [eGFR] values). Of those identified with CKD, 4239 (45.5%) were Read coded, and 5086 (54.5%) were uncoded. Of those identified with CKD stage 3-5, individuals aged ≥50 years were more likely to be coded for CKD, compared with those aged <50 years. Lower levels of coding were independently associated with deprivation and black Caribbean, black African, South Asian, and non-stated ethnicities, compared with white ethnicity. Prescribed statin and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker medications were associated with increased odds of coded CKD. CONCLUSION: This study found that >50% of CKD was uncoded and, for those patients, quality of care was lower compared with those with coded CKD. Future research and practices should focus on areas of greater deprivation and targeted initiatives for those aged <50 years and of black African, black Caribbean, South Asian, or non-stated ethnic groups. Possible areas for improvement include diagnostic coding support, automated CKD recording, and clinical decision support (based on adjusted eGFR results) in the GP clinical records.

A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19.

BACKGROUND: People of minority ethnic backgrounds may be disproportionately affected by severe COVID-19. Whether this relates to increased infection risk, more severe disease progression, or worse in-hospital survival is unknown. The contribution of comorbidities or socioeconomic deprivation to ethnic patterning of outcomes is also unclear. METHODS: We conducted a case-control and a cohort study in an inner city primary and secondary care setting to examine whether ethnic background affects the risk of hospital admission with severe COVID-19 and/or in-hospital mortality. Inner city adult residents admitted to hospital with confirmed COVID-19 (n = 872 cases) were compared with 3,488 matched controls randomly sampled from a primary healthcare database comprising 344,083 people residing in the same region. For the cohort study, we studied 1827 adults consecutively admitted with COVID-19. The primary exposure variable was self-defined ethnicity. Analyses were adjusted for socio-demographic and clinical variables. FINDINGS: The 872 cases comprised 48.1% Black, 33.7% White, 12.6% Mixed/Other and 5.6% Asian patients. In conditional logistic regression analyses, Black and Mixed/Other ethnicity were associated with higher admission risk than white (OR 3.12 [95% CI 2.63-3.71] and 2.97 [2.30-3.85] respectively). Adjustment for comorbidities and deprivation modestly attenuated the association (OR 2.24 [1.83-2.74] for Black, 2.70 [2.03-3.59] for Mixed/Other). Asian ethnicity was not associated with higher admission risk (adjusted OR 1.01 [0.70-1.46]). In the cohort study of 1827 patients, 455 (28.9%) died over a median (IQR) of 8 (4-16) days. Age and male sex, but not Black (adjusted HR 1.06 [0.82-1.37]) or Mixed/Other ethnicity (adjusted HR 0.72 [0.47-1.10]), were associated with in-hospital mortality. Asian ethnicity was associated with higher in-hospital mortality but with a large confidence interval (adjusted HR 1.71 [1.15-2.56]). INTERPRETATION: Black and Mixed ethnicity are independently associated with greater admission risk with COVID-19 and may be risk factors for development of severe disease, but do not affect in-hospital mortality risk. Comorbidities and socioeconomic factors only partly account for this and additional ethnicity-related factors may play a large role. The impact of COVID-19 may be different in Asians. FUNDING: British Heart Foundation; the National Institute for Health Research; Health Data Research UK.

Demographic variation in fit note receipt and long-term conditions in south London.

OBJECTIVES: Introduced in the UK in 2010, the fit note was designed to address the problem of long-term sickness absence. We explored (1) associations between demographic variables and fit note receipt, 'maybe fit' use and long-term conditions, (2) whether individuals with long-term conditions receive more fit notes and are more likely to have the 'maybe fit' option selected and (3) whether long-term conditions explained associations between demographic variables and fit note receipt. METHODS: Data were extracted from Lambeth DataNet, a database containing electronic medical records of all 45 general practitioner (GP) practices within the borough of Lambeth. Individual-level anonymised data on GP consultations, prescriptions, Quality and Outcomes Framework diagnostic data and demographic information were analysed using survival analysis. RESULTS: In a sample of 326 415 people, 41 502 (12.7%) received a fit note. We found substantial differences in fit note receipt by gender, age, ethnicity and area-level deprivation. Chronic pain (HR 3.7 (95% CI 3.3 to 4.0)) and depression (HR 3.4 (95% CI 3.3 to 3.6)) had the highest rates for first fit note receipt. 'Maybe fit' recommendations were used least often in patients with epilepsy and serious mental illness. The presence of long-term conditions did not explain associations between demographic variables and fit note use. CONCLUSIONS: For the first time, we show the relationships between fit note use and long-term conditions using individual-level primary care data from south London. Further research is required in order to evaluate this relatively new policy and to understand the needs of the population it was designed to support.

Proportion of young people in the general population consulting general practitioners: Potential for mental health screening and prevention.

AIM: One of the main obstacles with prevention in psychiatry is low detection of young subjects at risk for psychosis. The aim of the present work is to test whether general practitioners' (GP) offices are a possible setting for prevention of mental illness. METHODS: We used an Electronic Health Record database (Datanet) representing South-London (Lambeth), where frequency of GP visits were available for each registered subject. RESULTS: We show that in 2018 out of almost 175 000 subjects aged 12 to 35, almost six out of ten people were seen by their General practitioner at least once in 2018, and considering those subjects with at least one medical condition, around nine subjects out of ten did the same. CONCLUSIONS: A high proportion of adolescents and young adults are seen by GPs at least once per year. GP offices should be tested as possible setting for detection of subjects at risk for mental illness, in particular in subjects with risk factors for mental illness.

Journey to multimorbidity: longitudinal analysis exploring cardiovascular risk factors and sociodemographic determinants in an urban setting.

OBJECTIVE: To study the social determinants and cardiovascular risk factors for multimorbidity and the acquisition sequence of multimorbidity. DESIGN: Longitudinal study based on anonymised primary care data. SETTING: General practices in an urban multiethnic borough in London, UK. PARTICIPANTS: 332 353 patients aged ≥18 years. MAIN OUTCOME MEASURES: Clinical and sociodemographic characteristics of patients with multimorbidity, defined as ≥3 of 12 long-term conditions (LTCs) selected according to high predicted healthcare use. Multilevel logistic regression was used to model social determinants and cardiovascular risk factors. Alluvial plots were constructed to illustrate multimorbidity acquisition sequences according to age, ethnicity and social deprivation. RESULTS: 5597 (1.7%) patients had ≥3 selected LTCs, the 'multimorbidity cohort'. The the most common LTCs were diabetes (63.0%) and chronic pain (CP) (42.8%). Social deprivation and ethnicity were independent determinants of multimorbidity: most compared with the least deprived quintile (adjusted OR (AOR) 1.56 (95% CI 1.41 to 1.72)); South Asian compared with white ethnicity (AOR 1.44 (95% CI 1.29 to 1.61)); and black compared with white ethnicity (AOR 0.86 (95% CI 0.80 to 0.92)). The included cardiovascular risk factors were relatively strong determinants of multimorbidity: hypertension (AOR 5.05 (95% CI 4.69 to 5.44)), moderate obesity (AOR 3.41 (95% CI 3.21 to 3.63)) and smoking (AOR 2.30 (95% CI 2.16 to 2.45)). The most common initial onset conditions were diabetes and depression; diabetes particularly in older and black ethnic groups; and depression particularly in younger, more deprived and white ethnicity groups. CP was less common as an initial condition. CONCLUSION: Our findings confirm the importance of age, social deprivation and ethnicity as determinants of multimorbidity. Smoking, obesity and hypertension as cardiovascular risk factors were stronger determinants of multimorbidity than deprivation or ethnicity. The acquisition sequence of multimorbidity is patterned by sociodemographic determinants. Understanding onset conditions of multimorbidity and cardiovascular cardiovascular risk factors may lead to the development of interventions to slow the progression of multimorbidity.