Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

The impact of the COVID-19 pandemic on community prescription of opioid and antineuropathic analgesics for cancer patients in Wales, UK.

PURPOSE: Public health measures instituted at the onset of the COVID-19 pandemic in the UK in 2020 had profound effects on the cancer patient pathway. We hypothesise that this may have affected analgesic prescriptions for cancer patients in primary care. METHODS: A whole-nation retrospective, observational study of opioid and antineuropathic analgesics prescribed in primary care for two cohorts of cancer patients in Wales, using linked anonymised data to evaluate the impact of the pandemic and variation between different demographic backgrounds. RESULTS: We found a significant increase in strong opioid prescriptions during the pandemic for patients within their first 12 months of diagnosis with a common cancer (incidence rate ratio (IRR) 1.15, 95% CI: 1.12-1.18, p < 0.001 for strong opioids) and significant increases in strong opioid and antineuropathic prescriptions for patients in the last 3 months prior to a cancer-related death (IRR = 1.06, 95% CI: 1.04-1.07, p < 0.001 for strong opioids; IRR = 1.11, 95% CI: 1.08-1.14, p < 0.001 for antineuropathics). A spike in opioid prescriptions for patients diagnosed in Q2 2020 and those who died in Q2 2020 was observed and interpreted as stockpiling. More analgesics were prescribed in more deprived quintiles. This differential was less pronounced in patients towards the end of life, which we attribute to closer professional supervision. CONCLUSIONS: We demonstrate significant changes to community analgesic prescriptions for cancer patients related to the UK pandemic and illustrate prescription patterns linked to patients' demographic background.

Impact of the SARS-CoV-2 pandemic on female breast, colorectal and non-small cell lung cancer incidence, stage and healthcare pathway to diagnosis during 2020 in Wales, UK, using a national cancer clinical record system.

BACKGROUND: COVID-19 pandemic responses impacted behaviour and health services. We estimated the impact on incidence, stage and healthcare pathway to diagnosis for female breast, colorectal and non-small cell lung cancers at population level in Wales. METHODS: Cancer e-record and hospital admission data linkage identified adult cases, stage and healthcare pathway to diagnosis (population ~2.5 million). Using multivariate Poisson regressions, we compared 2019 and 2020 counts and estimated incidence rate ratios (IRR). RESULTS: Cases decreased 15.2% (n = -1011) overall. Female breast annual IRR was 0.81 (95% CI: 0.76-0.86, p < 0.001), colorectal 0.80 (95% CI: 0.79-0.81, p < 0.001) and non-small cell lung 0.91 (95% CI: 0.90-0.92, p < 0.001). Decreases were largest in 50-69 year olds for female breast and 80+ year olds for all cancers. Stage I female breast cancer declined 41.6%, but unknown stage increased 55.8%. Colorectal stages I-IV declined (range 26.6-29.9%), while unknown stage increased 803.6%. Colorectal Q2-2020 GP-urgent suspected cancer diagnoses decreased 50.0%, and 53.9% for non-small cell lung cancer. Annual screen-detected female breast and colorectal cancers fell 47.8% and 13.3%, respectively. Non-smal -cell lung cancer emergency presentation diagnoses increased 9.5% (Q2-2020) and 16.3% (Q3-2020). CONCLUSION: Significantly fewer cases of three common cancers were diagnosed in 2020. Detrimental impacts on outcomes varied between cancers. Ongoing surveillance with health service optimisation will be needed to mitigate impacts.

Ranking sets of morbidities using hypergraph centrality.

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.

What do British women know about cervical cancer symptoms and risk factors?

OBJECTIVE: To identify levels of cervical cancer risk factor and symptom awareness, as well as predictors of higher awareness in a United Kingdom (UK) female population. DESIGN: Population based survey. SETTING: Participants' homes in the UK. SAMPLE: UK representative sample of females aged 16 years and over (n=1392). MATERIALS AND METHODS: Respondents completed the Cervical Cancer Awareness Measure which included questions on awareness of cervical cancer symptoms and risk factors (both recalled and recognised). Linear regression analyses were used to identify predictors of higher symptom and risk factor recognition scores. MAIN OUTCOME MEASURES: Awareness of cervical cancer symptoms and risk factors. RESULTS: Sixty-five percent of respondents were unable to recall any risk factors and 75% were unable to recall any symptoms. Awareness was higher when women were prompted (95% recognised at least one risk factor and 93% at least one symptom). Independent predictors of risk factor recognition were older age and higher education. Symptom recognition was associated with older age, White ethnicity, higher education and having a close experience of cervical cancer. CONCLUSIONS: To reduce inequalities in awareness, interventions should target younger women with lower education and those from ethnic minority groups.