Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

BACKGROUND: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. METHODS AND FINDINGS: We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. CONCLUSIONS: Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Ethnic inequalities in age-related patterns of multiple long-term conditions in England: Analysis of primary care and nationally representative survey data.

Little is known about the patterning of multiple long-term conditions (MLTCs) by age, ethnicity and across conceptualisations of MLTCs (e.g. MLTCs with/without mental health conditions [MHCs]). We examined ethnic inequalities in age-related patterns of MLTCs, and combinations of physical and MHCs using the English GP Patient Survey and Clinical Practice Research Datalink. We described the association between MLTCs and age using multilevel regression models adjusting for sex and area-level deprivation with patients nested within GP practices. Similar analyses were repeated for MLTCs that include MHCs. We observed ethnic inequalities from middle-age onwards such as older Pakistani, Indian, Black Caribbean and Other ethnic people had increased risk of MLTCs compared to white British people, even after adjusting for area-level deprivation. Compared to white British people, Gypsy and Irish Travellers had higher levels of MLTCs across the age groups, and Chinese people had lower levels. Pakistani and Bangladeshi people aged 50-74 years were more likely than white people to report MLTCs that included MHCs. We find clear evidence of ethnic inequalities in MLTCs. The lower prevalence of MLTCs that include MHCs among some minoritised ethnic groups may be an underestimation due to underdiagnosis and/or inadequate primary care and requires further scrutiny.

The current state of genetic risk models for the development of kidney cancer: a review and validation.

OBJECTIVE: To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. METHODS: Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). RESULTS: A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. CONCLUSIONS: Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.

Development and validation of the Cambridge Multimorbidity Score.

BACKGROUND: Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. METHODS: We modelled the association between 37 morbidities and 3 key outcomes (primary care consultations, unplanned hospital admission, death) at 1 and 5 years. We extracted development (n = 300 000) and validation (n = 150 000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. RESULTS: Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731-0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.737-0.747) and death at 1 year (C-index 0.912, 95% CI 0.905-0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725-0.728; 0.738, 95% CI 0.732-0.743; and 0.910, 95% CI 0.904-0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.734-0.736, and 0.889, 95% CI 0.885-0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705-0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690-0.693) and admissions (C-index 0.703, 95% CI 0.697-0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900-0.914). INTERPRETATION: The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity.

Using Ancillary Sociodemographic Data to Identify Sexual Minority Adults Among Those Responding "Something Else" or "Don't Know" to Sexual Orientation Questions.

BACKGROUND: General population surveys are increasingly offering broader response options for questions on sexual orientation-for example, not only gay or lesbian, but also "something else" (SE) and "don't know" (DK). However, these additional response options are potentially confusing for those who may not know what the terms mean. Researchers studying sexual orientation-based disparities face difficult methodological trade-offs regarding how best to classify respondents identifying with the SE and DK categories. OBJECTIVES: Develop respondent-level probabilities of sexual minority orientation without excluding or misclassifying the potentially ambiguous SE and DK responses. Compare 3 increasingly inclusive analytic approaches for estimating health disparities using a single item: (a) omitting SE and DK respondents; (b) classifying SE as sexual minority and omitting DK; and (c) a new approach classifying only SE and DK respondents with >50% predicted probabilities of being sexual minorities as sexual minority. MATERIALS AND METHODS: We used the sociodemographic information and follow-up questions for SE and DK respondents in the 2013-2014 National Health Interview Survey to generate predicted probabilities of identifying as a sexual minority adult. RESULTS: About 94% of the 144 SE respondents and 20% of the 310 DK respondents were predicted to identify as a sexual minority adult, with higher probabilities for younger, wealthier, non-Hispanic white, and urban-dwelling respondents. Using a more specific definition of sexual minority orientation improved the precision of health and health care disparity estimates. CONCLUSIONS: Predicted probabilities of sexual minority orientation may be used in this and other surveys to improve representation and categorization of those who identify as a sexual minority adult.

Current and future cardiovascular disease risk assessment in the European Union: an international comparative study.

Background: Risk assessment is central to primary prevention of cardiovascular disease (CVD), but there remains a need to better understand the use of evidence-based interventions in practice. This study examines: (i) the policies and guidelines for risk assessment in Europe, (ii) the use of risk assessment tools in clinical practice and (iii) the barriers to, and facilitators of, risk assessment. Methods: Data were collected from academics, clinicians and policymakers in an online questionnaire targeted at experts from all European Union member states, and in 8 in-depth country case studies that were developed from a targeted literature review and 36 interviews. Results: The European Society of Cardiology (ESC) produces European guidelines for CVD risk assessment and recommends the Systematic COronary Risk Evaluation tool, which is the most widely used risk assessment tool in Europe. The use of risk assessment tools is variable. Lack of time and resources are important barriers. Integrating risk assessment tools into clinical systems and providing financial incentives to carry out risk assessments could increase implementation. Novel biomarkers would need to be supported by evidence of their clinical effectiveness and cost-effectiveness to be introduced in clinical practice. These findings were consistent across Europe. Conclusions: Efforts to improve the assessment of CVD risk in clinical practice should be carried out by or in collaboration with, the ESC. Increasing the use of existing risk assessment tools is likely to offer greater gains in primary prevention than the development of novel biomarkers.

Do Differential Response Rates to Patient Surveys Between Organizations Lead to Unfair Performance Comparisons?: Evidence From the English Cancer Patient Experience Survey.

BACKGROUND: Patient surveys typically have variable response rates between organizations, leading to concerns that such differences may affect the validity of performance comparisons. OBJECTIVE: To explore the size and likely sources of associations between hospital-level survey response rates and patient experience. RESEARCH DESIGN, SUBJECTS, AND MEASURES: Cross-sectional mail survey including 60 patient experience items sent to 101,771 cancer survivors recently treated by 158 English NHS hospitals. Age, sex, race/ethnicity, socioeconomic status, clinical diagnosis, hospital type, and region were available for respondents and nonrespondents. RESULTS: The overall response rate was 67% (range, 39% to 77% between hospitals). Hospitals with higher response rates had higher scores for all items (Spearman correlation range, 0.03-0.44), particularly questions regarding hospital-level administrative processes, for example, procedure cancellations or medical note availability.From multivariable analysis, associations between individual patient experience and hospital-level response rates were statistically significant (P<0.05) for 53/59 analyzed questions, decreasing to 37/59 after adjusting for case-mix, and 25/59 after further adjusting for hospital-level characteristics.Predicting responses of nonrespondents, and re-estimating hypothetical hospital scores assuming a 100% response rate, we found that currently low performing hospitals would have attained even lower scores. Overall nationwide attainment would have decreased slightly to that currently observed. CONCLUSIONS: Higher response rate hospitals have more positive experience scores, and this is only partly explained by patient case-mix. High response rates may be a marker of efficient hospital administration, and higher quality that should not, therefore, be adjusted away in public reporting. Although nonresponse may result in slightly overestimating overall national levels of performance, it does not appear to meaningfully bias comparisons of case-mix-adjusted hospital results.

Informal carers' health-related quality of life and patient experience in primary care: evidence from 195,364 carers in England responding to a national survey.

BACKGROUND: We aim to describe the health-related quality of life of informal carers and their experiences of primary care. METHODS: Responses from the 2011-12 English General Practice Patient Survey, including 195,364 informal carers, were analysed using mixed effect logistic regressions controlling for age, gender, ethnicity and social deprivation to describe carer health-related quality of life (mobility, self-care, usual activities, pain, and anxiety/depression, measured using EQ-5D) and primary care experience (access, continuity and communication). RESULTS: Informal carers reported poorer health-related quality of life than non-carers of similar age, gender, ethnicity and social deprivation. Increasing caring commitment was associated with worse EQ-5D scores, with carers of 50+ hours a week scoring 0.05 points lower than non-carers (95 % CI 0.05 to 0.04), equivalent to 18 fewer days of full health annually. Considering each domain of EQ-5D separately, carers of 50+ hours/week were more likely to report pain OR = 1.53 (1.50-1.57), p < 0.0001, and anxiety/depression OR = 1.69 (1.66-1.73), p < 0.0001, than non-carers. Younger carers scored lower on EQ-5D than non-carer peers but the converse was true among over-85s. In the most deprived areas carers reported the equivalent of 37 fewer days of full health annually than carers in the most affluent areas. On average, carers reported poorer patient experiences in all areas of primary care than non-carers (odds ratios 0.84-0.97), with this difference being most marked in the domain of access. CONCLUSIONS: Informal carers experience a double disadvantage of poorer health-related quality of life and poorer patient experience in primary care. We find no evidence for health benefits of caregiving. We recommend physicians identify and treat carer health problems, including pain and anxiety/depression, particularly among young, deprived and high time-commitment carers. Improving patient experience for carers, including access to primary care, should be a priority.

Variation in promptness of presentation among 10,297 patients subsequently diagnosed with one of 18 cancers: evidence from a National Audit of Cancer Diagnosis in Primary Care.

Cancer awareness public campaigns aim to shorten the interval between symptom onset and presentation to a doctor (the 'patient interval'). Appreciating variation in promptness of presentation can help to better target awareness campaigns. We explored variation in patient intervals recorded in consultations with general practitioners among 10,297 English patients subsequently diagnosed with one of 18 cancers (bladder, brain, breast, colorectal, endometrial, leukaemia, lung, lymphoma, melanoma, multiple myeloma, oesophageal, oro-pharyngeal, ovarian, pancreatic, prostate, renal, stomach, and unknown primary) using data from of the National Audit of Cancer Diagnosis in Primary Care (2009-2010). Proportions of patients with 'prompt'/'non-prompt' presentation (0-14 or 15+ days from symptom onset, respectively) were described and respective odds ratios were calculated by multivariable logistic regression. The overall median recorded patient interval was 10 days (IQR 0-38). Of all patients, 56% presented promptly. Prompt presentation was more frequent among older or housebound patients (p < 0.001). Prompt presentation was most frequent for bladder and renal cancer (74% and 70%, respectively); and least frequent for oro-pharyngeal and oesophageal cancer (34% and 39%, respectively, p <.001). Using lung cancer as reference, the adjusted odds ratios of non-prompt presentation were 2.26 (95% confidence interval 1.57-3.25) and 0.42 (0.34-0.52) for oro-pharyngeal and bladder cancer, respectively. Sensitivity analyses produced similar findings. Routinely recorded patient interval data reveal considerable variation in the promptness of presentation. These findings can help to prioritise public awareness initiatives and research focusing on symptoms of cancers associated with greater risk of non-prompt presentation, such as oro-pharyngeal and oesophageal cancer.

Cancer patient experience, hospital performance and case mix: evidence from England.

UNLABELLED:   AIMS: This study aims to explore differences between crude and case mix-adjusted estimates of hospital performance with respect to the experience of cancer patients. MATERIALS & METHODS: This study analyzed the English 2011/2012 Cancer Patient Experience Survey covering all English National Health Service hospitals providing cancer treatment (n = 160). Logistic regression analysis was used to predict hospital performance for each of the 64 evaluative questions, adjusting for age, gender, ethnic group and cancer diagnosis. The degree of reclassification was explored across three categories (bottom 20%, middle 60% and top 20% of hospitals). RESULTS: There was high concordance between crude and adjusted ranks of hospitals (median Kendall's τ = 0.84; interquartile range: 0.82-0.88). Across all questions, a median of 5.0% (eight) of hospitals (interquartile range: 3.8-6.4%; six to ten hospitals) moved out of the extreme performance categories after case mix adjustment. CONCLUSION: In this context, patient case mix has only a small impact on measured hospital performance for cancer patient experience.