Search

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.

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]

Aims: It is becoming increasingly possible to obtain additional information about health survey participants, though not usually non-participants, via record linkage. We aimed to assess the validity of an assumption underpinning a method developed to mitigate non-participation bias. We use a survey in Finland where it is possible to link both participants and non-participants to administrative registers. Survey-derived alcohol consumption is used as the exemplar outcome. Methods: Data on participants (85.5%) and true non-participants of the Finnish Health 2000 survey (invited survey sample N =7167 aged 30-79 years) and a contemporaneous register-based population sample (N =496,079) were individually linked to alcohol-related hospitalisation and death records. Applying the methodology to create synthetic observations on non-participants, we created 'inferred samples' (participants and inferred non-participants). Relative differences (RDs) between the inferred sample and the invited survey sample were estimated overall and by education. Five per cent limits were used to define acceptable RDs. Results: Average weekly consumption estimates for men were 129 g and 131 g of alcohol in inferred and invited survey samples, respectively (RD –1.6%; 95% confidence interval (CI) –2.2 to –0.04%) and 35 g for women in both samples (RD –1.1%; 95% CI –2.4 to –0.8%). Estimates for men with secondary levels of education had the greatest RD (–2.4%; 95% CI –3.7 to –1.1%). Conclusions: The sufficiently small RDs between inferred and invited survey samples support the assumption validity and use of our methodology for adjusting for non-participation. However, the presence of some significant differences means caution is required. [ABSTRACT FROM AUTHOR]

Background: Telephone triage is increasingly used to manage unscheduled care demand. Younger adults are frequent users, and commonly call with chest pain. We compared pathways of care in younger adults calling with chest pain, and associations of patient characteristics and telephone triage recommendation with hospital admission.Methods: A retrospective study of all triage calls with chest pain to NHS24 advice line by people aged 15-34 years between 1 January 2015 and 31 December 2017 where chest pain was recorded as the call reason. Recommended outcome and subsequent use of services were determined using the continuous urgent care pathways (CUPs) database which records single episodes of care spanning multiple services. We determined the number of services involved, the proportion of patients with inpatient admission, those with an admission for an 'acute-and-serious' diagnosis, and the association between the triage call recommendation and these outcomes.Results: There were 102 822 CUPs identified, with 1251 different combinations of services. The most common pathway was an NHS24 call then attendance at a primary care out-of-hours (PCOOH) centre, accounting for 38 643 (37.6%) CUPs. 9060 (8.8%) CUPs ended with hospital admission, 3030 (3.0%) the result of an 'acute-and-serious' diagnosis. 8453 (8.2%) were given 'self-care' advice and not referred further, while 46.9% ended at PCOOH and 15.2% at ED. 'Asthma, unspecified' was the most frequent 'acute-and-serious' diagnosis. Compared with people given self-care advice, referral to other services had increased odds of inpatient admission (adjusted OR (aOR) for ambulance called 28.7, 95% CI 22.6 to 36.3; for 1-hour in-home general practitioner (GP) visit arranged aOR 36.8, 95% CI 23.2 to 58.5) and for admission with an 'acute-and-serious' diagnosis (aOR ambulance called 23.9, 95% CI 16.2 to 35.4; aOR 1-hour GP visit 48.3, 95% CI 25.5 to 91.6).Conclusion: Chest pain triage by NHS24 appears safe, but care pathways can involve multiple service contacts. While acuity assigned to the call is strongly related to the odds of hospital admission and odds of an 'acute-and-serious' diagnosis, 'overtriage' means few patients are directed to self-care advice. [ABSTRACT FROM AUTHOR]

Background: Clinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code., Objective: This study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. Currently, the process is complex, onerous, and time-consuming, whereas a real-time tool may be used to rapidly inform a guideline developer's judgment about the applicability of evidence., Methods: The National Institute for Health and Care Excellence-particularly the gout guideline development group-and the Scottish Intercollegiate Guidelines Network guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R Shiny (R Foundation for Statistical Computing) tool was designed and developed using electronic primary health care data linked with hospitalization and mortality data built upon an optimized data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality. The tool was deployed within a Trusted Research Environment, allowing only trusted preapproved researchers to conduct analysis., Results: The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables 2 types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce an overview of statistical tables (eg, age, gender) of the index condition population and, within the overview groupings, produce details on, for example, electronic frailty index, comorbidities, and coprescriptions. The disclosure control mechanism is integral to the tool, limiting tabular counts to meet local governance needs. An exemplary result for gout as an index condition is presented to demonstrate the tool's functionality. Guideline developers from the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network provided positive feedback on the tool., Conclusions: The tool is a proof-of-concept, and the user feedback has demonstrated that this is a step toward computer-interpretable guideline development. Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time., (©Shahzad Mumtaz, Megan McMinn, Christian Cole, Chuang Gao, Christopher Hall, Magalie Guignard-Duff, Huayi Huang, David A McAllister, Daniel R Morales, Emily Jefferson, Bruce Guthrie. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.01.2025.)

Objective: To externally evaluate the QFracture risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture., Design: External validation cohort study., Setting: UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016., Participants: 2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year., Main Outcome Measures: Two outcomes were modelled based on the QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture., Results: 2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell's C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell's C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture., Conclusions: The QFracture risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The use of QFracture in its current form needs to be reviewed, particularly in people at high risk of death from other causes., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Institute for Health Research (NIHR) Health Services and Delivery Research Programme for the submitted work; BG reports funding from NIHR, Legal and General, Medical Research Council, and Chief Scientist Office unrelated to this study; CE is supported by a research grant from Legal and General (Advanced Care Research Centre) unrelated to this study; DRM is supported by a Wellcome Trust Clinical Research Development Fellowship (grant 214588/Z/18/Z) and reports funding from NIHR, Chief Scientist Office, and Tenovus, unrelated to this study; PTD reports funding from EU Health FP7 and Chief Scientist Office unrelated to this study; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: Telephone triage is increasingly used to manage unscheduled care demand. Younger adults are frequent users, and commonly call with chest pain. We compared pathways of care in younger adults calling with chest pain, and associations of patient characteristics and telephone triage recommendation with hospital admission., Methods: A retrospective study of all triage calls with chest pain to NHS24 advice line by people aged 15-34 years between 1 January 2015 and 31 December 2017 where chest pain was recorded as the call reason. Recommended outcome and subsequent use of services were determined using the continuous urgent care pathways (CUPs) database which records single episodes of care spanning multiple services. We determined the number of services involved, the proportion of patients with inpatient admission, those with an admission for an 'acute-and-serious' diagnosis, and the association between the triage call recommendation and these outcomes., Results: There were 102 822 CUPs identified, with 1251 different combinations of services. The most common pathway was an NHS24 call then attendance at a primary care out-of-hours (PCOOH) centre, accounting for 38 643 (37.6%) CUPs. 9060 (8.8%) CUPs ended with hospital admission, 3030 (3.0%) the result of an 'acute-and-serious' diagnosis. 8453 (8.2%) were given 'self-care' advice and not referred further, while 46.9% ended at PCOOH and 15.2% at ED. 'Asthma, unspecified' was the most frequent 'acute-and-serious' diagnosis. Compared with people given self-care advice, referral to other services had increased odds of inpatient admission (adjusted OR (aOR) for ambulance called 28.7, 95% CI 22.6 to 36.3; for 1-hour in-home general practitioner (GP) visit arranged aOR 36.8, 95% CI 23.2 to 58.5) and for admission with an 'acute-and-serious' diagnosis (aOR ambulance called 23.9, 95% CI 16.2 to 35.4; aOR 1-hour GP visit 48.3, 95% CI 25.5 to 91.6)., Conclusion: Chest pain triage by NHS24 appears safe, but care pathways can involve multiple service contacts. While acuity assigned to the call is strongly related to the odds of hospital admission and odds of an 'acute-and-serious' diagnosis, 'overtriage' means few patients are directed to self-care advice., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)