Search

Background: The UK Quality and Outcomes Framework (QOF) offers financial incentives to deliver high-quality care for individual diseases, but the single-disease focus takes no account of multimorbidity., Aim: To examine variation in QOF payments for two indicators incentivised in ≥1 disease domain., Design and Setting: Modelling study using cross-sectional data from 314 general practices in Scotland., Method: Maximum payments that practices could receive under existing financial incentives were calculated for blood pressure (BP) control and influenza immunisation according to the number of coexisting clinical conditions. Payments were recalculated assuming a single new indicator., Results: Payment varied by condition (£4.71-£11.08 for one BP control and £2.09-£5.78 for one influenza immunisation). Practices earned more for delivering the same action in patients with multimorbidity: in patients with 2, 3, and ≥4 conditions mean payments were £13.95, £21.92, and £29.72 for BP control, and £7.48, £11.21, and £15.14 for influenza immunisation, respectively. Practices in deprived areas had more multiple incentivised patients. When recalculated so that each incentivised action was only paid for once, all practices received less for BP control: affluent practices received more and deprived practices received less for influenza immunisation., Conclusion: For patients with single conditions, existing QOF payment methods have more than twofold variation in payment for delivering the same process. Multiple payments were common in patients with multimorbidity. A payment method is required that ensures fairness of rewards while maintaining adequate funding for practices based on actual workload., (© British Journal of General Practice 2016.)

BackgroundWe described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.MethodsWe conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.ResultsWe included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.ConclusionsPatients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.ImpactThis study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Background: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. Methods: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. Results: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1–14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5–0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21–0.40), moderate for 16 (kappa 0.41–0.60), and substantial for 12 (kappa 0.61–0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). Conclusions: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity. [ABSTRACT FROM AUTHOR]

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 ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥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 < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >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. Using a UK wide primary-care dataset and including more than 80 index conditions, Clare MacRae and colleagues report multimorbidity prevalence and how prevalence estimates change when varying the number and type of index conditions. Author summary: Why was this study done?: There is wide variety in the conditions considered by researchers when measuring multimorbidity prevalence. A systematic review of 566 studies, published in 2021, found lack of consensus in the selection of conditions considered. In half of studies only 8 conditions (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure) were consistently considered, and the number of conditions considered varied from 2 to 285 (median 17). A more consistent approach to measuring multimorbidity is needed to facilitate comparability and generalisability across studies. What did the researchers do and find?: We examined the impact of varying the conditions considered when measuring multimorbidity prevalence. We combined different numbers of conditions (from a list of 80) and selections of conditions (using 9 published condition-lists used to define and measure comorbidity, multimorbidity, and its prevalence) to determine how multimorbidity prevalence changed. All conditions were counted in the same way using publicly available code lists. There are large differences in prevalence, a range of 4.6% to 40.5%, when different numbers and selections of conditions are considered. People who are the oldest, living in the most deprived areas, and men require fewer conditions to be considered to reach close to multimorbidity prevalence when considering all 80 conditions (the ceiling effect, where the prevalence approaches the upper limit of prevalence possible in the study). Highest multimorbidity prevalence was found when using the Ho always + usually (derived from a recent Delphi consensus study), Barnett (widely used to measure multimorbidity prevalence), and Fortin (recommended for use in measuring multimorbidity) condition-lists. What do these findings mean?: There is a need for standardisation when measuring multimorbidity prevalence so that results across studies are comparable and population subgroups are accurately represented. To address this, researchers can consider using the Ho always + usually, Barnett, or Fortin condition-lists that report the highest and most stable estimates of multimorbidity prevalence (where adding further conditions to the count had very little impact). [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: The great success of HIV treatments means that, increasingly, people living with HIV (PLHIV) are growing old enough to develop age‐associated comorbid conditions. We investigated the evolution of comorbid conditions and demographics among PLHIV in England. Methods: In a cross‐sectional study linking Clinical Practice Research Datalink (CPRD) primary care, hospitalization, death registry and Index of Multiple Deprivation data, we measured the prevalence of 304 individual health conditions, categorized into 47 condition groups (36 non‐communicable, 11 communicable). Using logistic regression, we calculated odds ratios (ORs) for the likelihood of each condition and condition group in 2015 versus 2008, adjusting for age, sex and deprivation. Results: In 2015, there were 964 CPRD‐registered PLHIV compared with 1987 in 2008; 62% were male and 38% female in both cohorts. The 2015 cohort was older, with 51.1% aged 45–64 years and 7.2% aged 65–84 years compared with 31.8% and 3.2%, respectively, in 2008. Deprivation was higher in 2015, at 23.9% (quintile 4) and 28.7% (quintile 5) compared with 5.8% and 6.6%, respectively, in 2008. Of 36 non‐communicable condition groups, 14 (39%) occurred in ≥ 10% of PLHIV in 2015, of which seven were more likely in 2015 than in 2008: renal‐chronic‐kidney‐disease [odds ratio (OR) = 1.96 (95% CI: 1.33–2.90); endocrine‐obesity [OR = 1.76 (1.12–2.77)]; rheumatology [OR = 1.64 (1.30–2.07)]; dermatology [OR = 1.55(1.29–1.85)]; genito‐urinary‐gynaecological [OR = 1.44(1.18–1.76)]; eyes‐ears/nose/throat [OR = 1.31(1.08–1.59)]; and gastro‐intestinal conditions [OR = 1.28 (1.04–1.58)]. Two condition groups, respiratory‐chronic‐obstructive‐pulmonary‐disease [OR = 0.36 (0.19–0.69)] and endocrine‐diabetes [OR = 0.49 (0.34–0.70)], were less likely in 2015. Ten out of 11 communicable infectious condition groups were less likely in 2015. Conclusions: Although infections in PLHIV have fallen, chronic non‐communicable comorbidity is increasingly prevalent. Alongside the marked increases in deprivation and ageing, this study suggests that socio‐economic measures in addition to healthcare provision are needed to achieve holistic health for PLHIV. [ABSTRACT FROM AUTHOR]

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma. Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions. Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6–8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7–6.8) to 18.5% (95% CI 18.2–18.8) in the diagnosed cohort and 5.2% (95% CI 4.0–6.8) to 20.5% (95% CI 18.6–22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8–2.4) to 16.9% (95% CI 13.8–20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15–30% of hospitalized COVID-19 asthma patients. Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients. KEY MESSAGES Asthma prevalence in COVID-19 patients varied internationally (5.2–20.5%).The prevalence of asthma exacerbation at presentation with COVID-19 in diagnosed and hospitalized patients was low.Comorbidities were common in COVID-19 patients with asthma. Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392. [ABSTRACT FROM AUTHOR]

Objectives to establish and quantify any observable association between the exposure to community prescriptions for quinine and acute kidney injury (AKI) events in a population of older adults. Design two observational studies using the same dataset, a retrospective longitudinal cohort study and a self-controlled case series (SCCS). Setting NHS health board in Scotland. Participants older adults (60+ years) who received quinine prescriptions in Tayside, Scotland, between January 2004 and December 2015. The first study included 12,744 individuals. The SCCS cohort included 5,907 people with quinine exposure and more than or equal to one AKI event. Main outcome measured in the first study, multivariable logistic regression was used to calculate odds ratios (ORs) for AKI comparing between episodes with and without recent quinine exposure after adjustment for demographics, comorbidities and concomitant medications. The SCCS study divided follow-up for each individual into periods 'on' and 'off' quinine, calculating incidence rate ratios (IRRs) for AKI adjusting for age. Results during the study period, 273,596 prescriptions for quinine were dispensed in Tayside. A total of 13,616 AKI events occurred during follow-up (crude incidence 12.5 per 100 person-years). In the first study, exposure to quinine before an episode of care was significantly associated with an increased probability of AKI (adjusted OR = 1.27, 95% confidence interval (CI) 1.21–1.33). In the SCCS study, exposure to quinine was associated with an increased relative incidence of AKI compared to unexposed periods (IRR = 1.20, 95% CI 1.15–1.26), with the greatest risk observed within 30 days following quinine initiation (IRR = 1.48, 95% CI 1.35–1.61). Conclusion community prescriptions for quinine in an older adult population are associated with an increased risk of AKI. [ABSTRACT FROM AUTHOR]