Non-cancer multimorbidity in people diagnosed and living with and beyond cancer: cross-sectional population-based study of four common cancers

Non-cancer multimorbidity in people diagnosed and living with and beyond cancer: cross-sectional population-based study of four common cancers | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-cancer multimorbidity in people diagnosed and living with and beyond cancer: cross-sectional population-based study of four common cancers Susanne Maxwell, Nazir Lone, Caroline Jackson, Bruce Guthrie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9052348/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Multimorbidity is common at cancer diagnosis, and both cancer and its treatment can lead to new long-term conditions. However, evidence on the prevalence of multimorbidity, particularly mental-physical multimorbidity, at both diagnosis and five years after cancer diagnosis remains limited. This study describes the prevalence of, and factors associated with, non-cancer multimorbidity in people newly diagnosed with cancer and those living with and beyond cancer. Methods Cross-sectional study of people aged 18 years and older diagnosed with, or surviving five years with one of breast, prostate, colorectal or lung cancer in 2018. For 46 long-term conditions, condition burden, multimorbidity (≥ 2 non-cancer conditions) and mental-physical multimorbidity (≥ 1 non-cancer physical and ≥ 1 mental health condition) were measured in incident and survivor cohorts, and examined by age, sex, deprivation and ethnicity. Odds of multimorbidity by cancer type was calculated using logistic regression, stratified by sex and adjusted for age, deprivation and ethnicity. Results Analysis included 33,258 people with incident cancer and 16,807 people living with and beyond cancer (PLWBC). Multimorbidity ranged from 60–86% at cancer diagnosis to 70–90% in PLWBC, dependent on cancer type. Less than 20% had no additional long-term conditions at diagnosis (< 5.3% in lung cancer specifically) and less than 15% in PLWBC (< 2.3% in lung cancer). Mental-physical multimorbidity affected 24–39% at diagnosis and 27–44% in PLWBC. Both types of multimorbidity were higher in those living in the most deprived areas across all age groups. Compared with colorectal cancer, people with lung cancer had higher odds of multimorbidity at both diagnosis (in women adjusted OR 2.15 95%CI 1.86–2.49; in men aOR 1.71 95%CI 1.50–1.94) and in PLWBC (in women aOR 2.11 95%CI 1.45–3.12; in men, aOR 2.20 95%CI 1.51–3.30), whereas women with breast cancer and men with prostate cancer had lower odds. Conclusions Most people with cancer have additional long-term conditions and the burden is highest among those with lung cancer. Clear socioeconomic inequalities in multimorbidity persist into survivorship. Cancer pathways need to ensure coordinated management of non-cancer long-term conditions between primary and secondary care across the whole cancer journey, from diagnosis through treatment and into survivorship. Multimorbidity comorbidity cancer cancer survival Figures Figure 1 Figure 2 Figure 3 Background Cancer is a major public health challenge in the UK and globally, with more than 385,000 new cases diagnosed across the UK each year and close to 20 million new cases worldwide.( 1 , 2 ) Over 60% of new cancer cases occur in individuals over 65, and the increasing number of new cases is driven largely by population ageing.( 3 , 4 ) Since older people have higher rates of almost all long-term conditions, people with cancer increasingly have other long-term conditions at diagnosis. Advances in screening, early detection and treatment have also led to declining mortality, resulting in a growing population of people living with and beyond cancer (PLWBC).( 5 ) Multimorbidity (or multiple long-term conditions) is the coexistence of two or more long-term conditions. Like cancer, multimorbidity is also increasingly common due to population ageing and improved survival from life-threatening diseases. Emerging evidence demonstrates that multimorbidity can influence the entire cancer pathway, from screening uptake, diagnosis, treatment decisions, through to survival, though these effects vary by cancer type alongside the nature and burden of chronic conditions.( 6 , 7 ) Increasing condition burden is associated with higher health service utilisation in patients with cancer including hospital admissions, emergency department visits and primary care consultations.( 8 ) In most high-income countries, primary care plays a central role in cancer detection, with around 70% of people with cancer in the UK for example first presenting in primary care.( 9 ) Primary care is also usually responsible for the ongoing management of chronic conditions during and beyond cancer treatment. Long-term conditions are common in patients with cancer,( 10 , 11 ) but as with the multimorbidity literature more generally,( 12 – 14 ) estimates of how common vary depending on the data sources used and the number and nature of the conditions being counted, and differences between datasets in patient characteristics such as age, sex, ethnicity and socioeconomic status.( 10 , 15 , 16 ) Including primary care data and common mental health conditions is important to truly reflect multimorbidity prevalence in all demographic groups and the actual burden seen within primary care.( 14 ) A recent Delphi consensus study described 59 conditions to ideally include in multimorbidity studies to improve comparability and reproducibility of measurement in this field.( 17 ) The aim of this study is to draw on the Delphi recommendations to compare the prevalence of individual non-cancer conditions and non-cancer multimorbidity in people newly diagnosed with four common cancers (breast, prostate, lung and colorectal), and survivors five years post-diagnosis, and examine how non-cancer multimorbidity is associated with sociodemographic characteristics. Methods Data and study population This cross-sectional study included patients registered with a Clinical Practice Research Datalink (CPRD) Aurum-participating primary care practice in England on 1 January 2018. CPRD Aurum contains anonymised primary care data for approximately 10% of primary care practices in England, accounting for 13% of the English population. Primary care records were linked to Hospital Episode Statistics Admitted Patient Care (HES-APC), Office for National Statistics (ONS) death registrations, and the Index of Multiple Deprivation (IMD). To be included within our study, patients were aged ≥ 18 years, alive and permanently registered on the 1 January 2018 with at least one year of prior registration to ensure consistent coding of morbidities at study entry.( 18 ) Incident cases were defined as people with a diagnosis of breast, prostate, lung or colorectal cancer between 01/01/2018 and 31/12/2018. PLWBC were defined as people diagnosed between 01/01/13 and 31/12/13 who were alive and registered five years after their diagnosis. Patient characteristics were ascertained on an index date defined as the date of cancer diagnosis in 2018 (incident cancer cohort) or the date of cancer diagnosis in 2013 plus five years (PLWBC cohort) Cases were not mutually exclusive since some patients had more than one primary cancer diagnosis. Within the lung cancer cohort, patients with a record in the year before or after their primary lung cancer record of another primary cancer likely to metastasise to the lung were excluded to reduce misclassification from miscoding of metastatic disease as primary lung cancer (1108 cases). Age at index date was determined from year of birth. Socioeconomic status was measured using the English Index of Multiple Deprivation (IMD) of the patient’s area of residence (measured at Lower Super Output Area level) grouped into five categories defined by English quintiles of IMD score (1-least deprived to 5-most deprived). Outcome: Measurement of long-term conditions and multimorbidity This study included 47 of the 56 non-cancer long-term conditions identified by the Delphi consensus on conditions for reporting multimorbidity prevalence. ‘Transient ischaemic attack’ and ‘stroke’ were combined to avoid double counting, so 46 conditions were defined. Anaemia was excluded because it is a common but usually transient consequence of cancer itself (particularly colorectal cancer) or of cancer treatment; and the other conditions were excluded due to likely inaccurate or incomplete coding within primary care records (for example, benign cerebral tumours that cause disability) or because they did not exist in the study period (long COVID). Long-term conditions were defined as present if they were ever coded in GP or inpatient records prior to the index date. Chronic kidney disease was defined as when the two most recent laboratory based eGFR test results were < 60ml/min and at least 90 days apart without a normal eGFR in between. The count of non-cancer morbidities was calculated (ranging from zero to six or more of the 46 long-term conditions examined). Multimorbidity was defined as the presence of ≥ 2 of the 46 chronic physical or mental health conditions in addition to cancer. Mental-physical multimorbidity was defined as the coexistence of at least one physical and at least one mental health condition in addition to cancer. Statistical analysis The prevalence of each outcome was estimated for each of the four cancers in incident and PLWBC cohorts, and differences across socio-demographic characteristics were assessed using the Chi-squared test. Crude and age-sex standardised prevalence of individual conditions were estimated for each of the four cancers, with European standard populations used to calculate age-sex standardised prevalence. Logistic regression models were used to estimate the odds ratio (OR) of people with different cancer types having multimorbidity and of having mental-physical multimorbidity compared to people with colorectal cancer, adjusting for age, deprivation, and ethnicity in both incident and survivor cohorts. Because of collinearity between cancer type and sex, regression analyses were stratified by sex, with breast, lung and colorectal cancer examined in women, and prostate, breast, lung and colorectal cancer examined in men. Colorectal cancer was chosen as the reference cancer because it affects both sexes and is less strongly socially patterned compared with lung cancer. People with synchronous cancers diagnosed in 2018 were excluded from the logistic regression analysis (n = 79). All analysis were conducted in RStudio version 4.4.0 Results Patient characteristics There were 33,258 people newly diagnosed with cancer in 2018, of whom 9452 had breast cancer, 10,230 prostate cancer, 7216 colorectal cancer, and 6360 lung cancer (Table 1 ). There were 16,807 PLWBC included of whom 7199 had breast cancer, 5685 prostate cancer, 3196 colorectal cancer, and 727 lung cancer. Among people newly diagnosed with lung and colorectal cancer, a higher proportion were men (52.1% and 54.8%, respectively). In contrast, among PLWBC, there were more women with lung cancer (53.9%), while men were still a majority in colorectal cancer (55.3%). At diagnosis the age distribution differed between the cancers. Median age was lowest in people with breast cancer (63 years, IQR 52–74) and highest in people with lung cancer (74 years, IQR 66–81). The median age increased in survivors of breast and prostate cancer, however reduced in people with lung cancer and stayed the same in people with colorectal cancer. People with breast, prostate and colorectal cancers more commonly lived in the least deprived quintiles of areas, whereas people with lung cancer more commonly lived in the most deprived quintile. Across all cancers, people were predominantly (≥ 90%) white. Table 1 Patient demographics, condition burden and multimorbidity at diagnosis and in PLWBC Incident Cancers People living with and beyond cancer Breast n (%) Prostate n (%) Colorectal n (%) Lung n (%) Breast n (%) Prostate n (%) Colorectal n (%) Lung n (%) Total N 9452 10230 7216 6360 7199 5685 3196 727 Sex Male 86 (0.9) 10230 (100) 3956 (54.8) 3311 (52.1) 40 (0.6) 5685 (100) 1768 (55.3) 341 (46.9) Female 9366 (99.1) NA 3260 (45.2) 3049 (47.9) 7159 (99.4) NA 1428 (44.7) 386 (53.1) Age Median age (IQR) 63 (52–74) 71 (65–78) 73 (63–81) 74 (66–81) 65 (55–73) 74 (69–80) 73 (65–81) 73 (66–80) Age categories 18–44 761 (8.1) 24 (0.2) 252 (3.5) 70 (1.1) 330 (4.6) 6 (0.1) 98 (3.1) 8 (1.1) 45–59 3183 (33.7) 1221 (11.9) 1087 (15.1) 662 (10.4) 2386 (33.1) 247 (4.3) 386 (12.1) 69 (9.5) 60–74 3320 (35.1) 5195 (50.8) 2664 (36.9) 2659 (41.8) 2889 (40.1) 2651 (46.6) 1231 (38.5) 342 (47.0) ≥ 75 2188 (23.1) 3790 (37.0) 3213 (44.5) 2969 (46.7) 1594 (22.1) 2781 (48.9) 1481 (46.3) 308 (42.4) Deprivation* 1 (least) 2359 (25.0) 2779 (27.2) 1733 (24.0) 1054 (16.6) 1879 (26.1) 1557 (27.4) 822 (25.7) 112 (15.4) 2 2186 (23.1) 2432 (23.8) 1595 (22.1) 1178 (18.5) 1703 (23.7) 1409 (24.8) 739 (23.1) 146 (20.1) 3 1852 (19.6) 1966 (19.2) 1391 (19.3) 1151 (18.1) 1406 (19.5) 1104 (19.4) 635 (19.9) 145 (19.9) 4 1633 (17.3) 1737 (17.0) 1322 (18.3) 1366 (21.5) 1216 (16.9) 868 (15.3) 565 (17.7) 154 (21.2) 5 (most) 1410 (14.9) 1306 (12.8) 1170 (16.2) 1607 (25.3) 990 (13.8) 740 (13.0) 433 (13.5) 169 (23.2) Ethnicity** White 8478 (89.7) 9310 (91.0) 6708 (93.0) 5973 (93.9) 6522 (90.6) 5207 (91.6) 2998 (93.8) 686 (94.4) Asian 399 (4.2) 227 (2.2) 160 (2.2) 140 (2.2) 338 (4.7) 115 (2.0) 83 (2.6) 19 (2.6) Black 246 (2.6) 412 (4.0) 151 (2.1) 105 (1.7) 168 (2.3) 231 (4.1) 51 (1.6) 14 (1.9) Other 213 (2.3) 146 (1.4) 111 (1.5) 77 (1.2) 116 (1.6) 75 (1.3) 47 (1.5) 6 (0.8) Condition count Median (IQR) 2 ( 1 – 4 ) 3 ( 1 – 4 ) 3 ( 1 – 5 ) 4 ( 2 – 7 ) 2 ( 1 – 4 ) 3 ( 2 – 5 ) 3 ( 2 – 6 ) 5 ( 3 – 7 ) 0 1882 (19.9) 1280 (11.5) 825 (11.4) 338 (5.3) 1031 (14.3) 411 (7.2) 230 (7.2) 17 (2.3) 1 1914 (20.2) 1790 (17.5) 1057 (14.6) 561 (8.8) 1355 (18.8) 848 (14.9) 414 (13.0) 54 (7.4) 2 1615 (17.1) 1907 (18.6) 1151 (16.0) 788 (12.4) 1292 (17.9) 954 (16.8) 485 (15.2) 70 (9.6) 3 1273 (13.5) 1603 (15.7) 1047 (14.5) 871 (13.7) 1046 (14.5) 928 (16.3) 508 (15.9) 76 (10.5) 4 900 (9.5) 1227 (12.0) 842 (11.7) 835 (13.1) 800 (11.1) 730 (12.8) 396 (12.4) 106 (14.6) 5 640 (6.8) 841 (8.2) 682 (9.5) 733 (11.5) 553 (7.7) 571 (10.0) 325 (10.2) 89 (12.2) ≥ 6 1228 (13.0) 1682 (16.4) 1612 (22.3) 2234 (35.1) 1122 (15.6) 1243 (21.9) 838 (26.2) 315 (43.3) Multimorbidity Present 5656 (59.8) 7260 (71.0) 5334 (73.9) 5461 (85.9) 4813 (66.9) 4426 (77.9) 2552 (79.8) 656 (90.2) M-P multimorbidity Present 2731 (28.9) 2407 (23.5) 2136 (29.6) 2499 (39.3) 2361 (32.8) 1535 (27.0) 1062 (33.2) 316 (43.5) *46 people had unknown levels of deprivation ** 533 people had unknown ethnicity. Multimorbidity = ≥ 2 non-cancer long-term conditions in addition to cancer. M-P multimorbidity = ≥ 1 non-cancer physical and ≥ 1 mental health condition in addition to cancer Prevalence of multimorbidity and condition burden At the time of diagnosis across the four cancers, the prevalence of multimorbidity ranged from 60–86% and mental-physical multimorbidity ranged from 24–39%, with lung cancer consistently showing the highest prevalence (Table 1 ). People with lung cancer had the highest condition burden with 35.1% having six or more conditions at the time of diagnosis, followed by colorectal cancer (22.3%), prostate cancer (16.4%) and breast cancer (13.0%). Fewer than 20% of people had no other conditions at diagnosis, ranging from 5.3% for lung cancer to 19.9% for breast cancer). In PLWBC, the prevalence of multimorbidity was higher and ranged from 70–90% with the prevalence of mental-physical multimorbidity ranging from 27–44%. Similar to the pattern at diagnosis, condition burden was highest in lung cancer in PLWBC with 43.3% having six or more conditions, followed by colorectal cancer (26.2%), prostate cancer (21.9%) and breast cancer (15.6%). Fewer than 15% of PLWBC had no other condition, ranging from 2.3% for lung cancer to 14.3% for breast cancer. Individual conditions Figure 1 shows the crude prevalence of the nine most common comorbidities of the four cancers (data for all 46 conditions is provided in Additional file: Supplementary Tables S1-S2). Hypertension was the most prevalent long-term condition across all cancer sites, ranging from 38–60% at diagnosis to 42–65% among PLWBC. Osteoarthritis was the second most common condition (30–38% at diagnosis, 33–41% in PLWBC) for all cancers except lung cancer, where chronic obstructive pulmonary disease (COPD) ranked second (39% at diagnosis; 52% in PLWBC). Anxiety and depression were the most common mental health conditions and ranged from 12–23% at diagnosis to 14–26% in PLWBC and were lower in men with prostate cancer. CHD: coronary heart disease; COPD chronic obstructive pulmonary disease * Listed conditions are those which are one of the five most prevalent for one or more of the four cancers. After age-sex standardisation, the higher crude prevalences of hypertension, osteoarthritis and coronary heart disease observed in people with lung cancer were attenuated (Supplementary Figure S1 ). Elevated rates of respiratory conditions persisted, particularly among PLWBC. Lower prevalences of mental health conditions in prostate cancer persisted at diagnosis but reversed in PLWBC, with higher age-sex standardised prevalence of depression in the survivorship cohort. Sociodemographic associations Both at diagnosis and in PLWBC, for all four cancers, there were consistent patterns of association between the number of long-term conditions and age and socioeconomic deprivation (Fig. 2 , Additional file: Supplementary Table S3-S10). The number of non-cancer conditions rose sharply with age in all groups, and there was a clear social gradient with higher counts in people living in more deprived areas (for 6 + conditions, approximately a ten-percentage point difference between those living in least compared to most deprived areas). The effect of ethnicity on condition burden was mixed across cancers and limited by small numbers within specific ethnic groups. Sex differences were observed only at diagnosis for lung cancer (higher condition burden in women) and breast cancer (higher condition burden in men although very small numbers). No sex difference was observed among PLWBC. * <0.05, **<0.01, *** <0.001 (chi-squared test) Figure 3 shows the prevalence of non-cancer multimorbidity by age in people living in the most and least deprived quintiles (Supplementary Table S11). At both diagnosis and among PLWBC, multimorbidity prevalence was higher in those living in the most deprived areas across all age groups. Inequalities were larger in those under 70 years of age: with 13–28 percentage point differences in people aged 18–59 years and 7–25 percentage point differences in people aged 60–69 years. Supplementary Figure S2 shows a similar pattern in mental-physical multimorbidity. Differences between cancers Compared to women with colorectal cancer, women with lung cancer had a significantly higher odds of non-cancer multimorbidity at both diagnosis (adjusted odds ratio [aOR] 2.15, 95% CI 1.86–2.49) and five years after diagnosis (aOR 2.11, 95%CI 1.45–3.12), whereas women with breast cancer had significantly lower odds of non-cancer multimorbidity at diagnosis (aOR 0.83, 95%CI 0.75–0.91 at diagnosis) and five years after diagnosis (aOR 0.74, 95%CI 0.63–0.87 in PLWBC) (Table 2 ). Table 2 Adjusted odds of multimorbidity and mental-physical multimorbidity between cancers stratified by sex Women* Multimorbidity at diagnosis aOR (95% CI) Multimorbidity in PLWBC aOR (95%CI) Mental-Physical Multimorbidity at diagnosis aOR (95%CI) Mental-physical Multimorbidity in PLWBC aOR (95%CI) Colorectal cancer Ref Ref Ref Ref Breast cancer 0.83 (0.75–0.91) 0.74 (0.63–0.87) 0.92 (0.84–1.01) 0.87 (0.77–0.98) Lung cancer 2.15 (1.86–2.49) 2.11 (1.45–3.12) 1.42 (1.28–1.57) 1.39 (1.10–1.74) Men* Colorectal cancer Ref Ref Ref Ref Lung cancer 1.71 (1.50–1.94) 2.20 (1.51–3.30) 1.37 (1.23–1.51) 1.34 (1.05–1.72) Prostate cancer 0.87 (0.79–0.97) 0.78 (0.67–0.90) 0.87 (0.80–0.95) 0.86 (0.76–0.97) Breast cancer 1.33 (0.78–2.37) 0.63 (0.31–1.41) 1.78 (1.12–2.79) 1.03 (0.50-2.00) * Separate models fitted for men and women adjusted for age, socioeconomic status and ethnicity (see supplementary tables S5-S12 for full models). 79 people out of 33258 had incident synchronous cancers (the majority were prostate and colorectal with the remaining breast and colorectal) and were removed from the analysis. aOR = adjusted odds ratio. Compared to men with colorectal cancer, men with lung cancer had significantly higher odds of non-cancer multimorbidity at diagnosis (aOR 1.71, 95%CI 1.50–1.94) and five years after diagnosis (aOR 2.20, 95%CI 1.51–3.30), whereas men with prostate cancer had lower odds at diagnosis (aOR 0.87, 95%CI 0.79–0.95) and five years after diagnosis (aOR 0.78, 95%CI 0.67–0.90). The odds of non-cancer multimorbidity in men with breast cancer were not significantly different from men with colorectal cancer, although numbers are small. Associations between cancer type and mental-physical multimorbidity showed similar patterns, with significantly higher odds both at diagnosis and 5-years after diagnosis in men and women with lung cancer and lower odds in women with breast cancer and men with prostate cancer, compared to people with colorectal cancer. Full logistic regression models are provided in Additional file: Supplementary Tables S12-S19. Discussion In this large cross-sectional study of 33,258 incident cancer cases and 16,807 PLWBC five years after diagnosis, we show non-cancer multimorbidity is common across all four cancer types ranging from 60% (breast cancer) to 86% (lung cancer) at diagnosis and 67% (breast cancer) to 90% (lung cancer) in PLWBC. Mental-physical multimorbidity is also common, present in 24–39% at diagnosis, and 33–44% in PLWBC. Fewer than 20% of people had no other condition at the time of cancer diagnosis, and fewer than 15% in PLWBC. Age, deprivation and cancer type significantly influence the prevalence of both multimorbidity and mental-physical multimorbidity, with marked increases associated with age in particular. After adjusting for age and sex, compared to people with colorectal cancer, both men and women with lung cancer had significantly higher odds of both multimorbidity and mental-physical multimorbidity, whilst women with breast and men with prostate cancer had lower odds of both. The absolute prevalence of any long-term condition at the time of diagnosis in our study is substantially higher than previous studies( 10 , 11 ) and in all four cancers in a recent systematic review: lung (95% in our study vs 47% pooled prevalence), colorectal (89% vs 47%), prostate (89% vs 28%) and breast cancer (80% vs 22%).( 16 ) However, most studies within this recent review quantified comorbidity using the Charlson Comorbidity Index, which includes a smaller number of conditions and does not include mental health conditions. In addition, many studies relied solely on hospital admission data, which is known to under-ascertain long-term conditions compared with linked primary and secondary care data.( 14 , 19 ) Previous evidence on multimorbidity during cancer survivorship is similarly heterogeneous, with mixed prevalence estimates reflecting differences in data sources (with many studies using patient self-reported data), conditions included, and survivorship timeframes studied,( 15 , 20 ) with little evidence examining differences between cancers or associations with patient demographic characteristics. A key finding is the prevalence of mental-physical multimorbidity in over a quarter of people with cancer. In contrast to overall non-cancer multimorbidity, mental-physical multimorbidity shows a weaker association with age but a strong and consistent association with deprivation.( 21 ) We find that mental-physical multimorbidity is more prevalent in people with cancer living in the most deprived areas across all age strata, and that this social gradient persists from diagnosis into survivorship. Consequently, cancer policies aimed at reducing inequalities must account for this distinct multimorbidity pattern and its potential influence on cancer outcomes and survivorship care. Our study demonstrates that between-cancer differences in multimorbidity are attenuated but remain statistically significant after adjustment for age and sex. These persistent differences likely reflect shared risk factors for cancer and other chronic diseases—most notably smoking for lung cancer, COPD and cardiovascular disease—but may also reflect differences in detection pathways and stage at diagnosis. For example, around one fifth of prostate cancers in England are diagnosed following asymptomatic testing, a group less likely to have underlying comorbidity,( 22 ) and lower comorbidity burden is associated with higher breast and bowel cancer screening uptake.( 23 ) Hypertension was the most common long-term condition in the four cancers studied, both at diagnosis and five years after diagnosis. Our reported prevalence of 38–60% exceeds that of studies limited to hospital data but aligns more closely with studies using primary care data.( 10 , 11 , 16 ) Compared to people at diagnosis, PLWBC had higher prevalences of several conditions, including coronary heart disease, venous thromboembolism and mental health conditions, in keeping with known effects of cancer and its treatment as risk factors for these conditions.( 15 , 24 , 25 ) These conditions contributed to the higher condition burden seen in PLWBC, despite effects of survival bias in this group. Strengths and limitations A strength of our study is the use of primary care data linked to hospital records, which is known to improve ascertainment of many conditions compared to using hospital data alone, particularly in women and younger age groups.( 14 , 19 ) Our study included 47 out of 56 non-cancer long-term conditions recommended by a public and professional consensus process for use in multimorbidity research.( 17 ) By using this list of conditions, we incorporated the perspectives of people with lived experience and field experts, capturing a broad spectrum of conditions affecting daily life. Currently no standardised method of reporting comorbidity or multimorbidity burden in people with cancer exists.( 26 ) Developing a standardised measure which reflects the experiences of cancer patients and their consulting clinicians would improve reliability and reproducibility of multimorbidity and cancer research. A limitation of this study is we did not have linked cancer registry data and so there was the potential for under or over recording of cancer diagnosis. However, a recent study comparing cancer diagnosis recording between English datasets found combining CPRD and HES (as in our study) matched over 90% of cancer registry’s recorded diagnoses of lung and colorectal cancer.( 27 ) To reduce concerns about potential miscoding of lung cancer, given that the lung is a common site for metastases, we excluded patients with a record of primary lung cancer who also had a new diagnosis of an additional cancer in the year before or after. A further limitation is that this study included conditions ever coded prior to the cancer diagnosis date, and so there is the potential that some conditions were no longer active. This may be particularly true for mental health conditions such as anxiety and depression, but such conditions commonly remit and recur, and a prior history of depression or anxiety is still important to consider when disclosing a cancer diagnosis or planning treatment. Implications for practice and policy With an increasing number of people having multimorbidity both at the time of their cancer diagnosis and during survivorship, our health care system needs to pivot from a service providing single disease focussed care to a more holistic integrated model of care which takes in to account all the health needs of patients.( 28 ) Although primary care is central to cancer diagnosis, it can become siloed during active cancer treatment, with patients often transitioning back to primary care once treatment concludes. Without effective coordination, this pattern risks fragmented and disjointed care. Identifying and evaluating optimal models for integrating chronic disease management during active treatment and across the longer-term cancer pathway is therefore essential.( 29 , 30 ) Adequate training of primary care professionals on the long-term effects of cancer treatment is also needed for this growing burden of patients. Given the high prevalence of multimorbidity at cancer diagnosis and the central role of primary care in cancer detection across most high-income countries, adequate investment in primary care infrastructure is needed to support cancer diagnosis in increasingly complex patient populations. Robust evidence is needed to identify and evaluate practical, cost-effective approaches to improving early cancer detection in patients with multimorbidity in primary care. Regular contact with services in people with multimorbidity has the potential to improve early diagnosis but may also lead to competing priorities and diagnostic overshadowing.( 6 ) Research is needed to examine whether diagnosis could be improved by embedding cancer risk discussions and systematic symptom assessment into routine chronic disease reviews( 31 ), or digital pre-consultation tools such as a Chatbot to flag potential red-flag symptoms.( 32 ) Integrated electronic safety netting in primary care could further support identification of clinically complex patients whose evolving cancer-related symptoms may be otherwise obscured by competing problems.( 33 ) Finally, evidence is needed on whether optimising chronic disease management—such as improved glycaemic control or blood pressure management—can improve cancer outcomes and reduce future recurrence, to inform and justify appropriate investment. Prehabilitation in cancer patients has been shown to improve quality of life and prepare patients for cancer treatment and its effects.( 34 ) Integrating management of chronic diseases early in the cancer journey may have the potential to improve cancer-outcomes and may also reduce inequalities in cancer care. Conclusion Our findings show that non-cancer multimorbidity is the norm rather than the exception among people with cancer, with clear inequalities between those living in the most and least deprived areas. Cancer pathways- from diagnosis through treatment and into survivorship- must be designed and supported to meet these complex needs effectively. Abbreviations PLWBC People living with and beyond cancer aOR adjusted odds ratio OR odds ratio CPRD Clinical Practice Research Datalink HES-APC Hospital Episode Statistics Admitted Patient Care ONS Office for National Statistics IMD Index of multiple deprivation COPD Chronic obstructive pulmonary disease CHD Coronary artery disease Declarations Acknowledgements Funding information The study was funded by the National Institute for Health Research (NIHR) Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (NIHR202639), with fellowship funding for SM from Wellcome Trust (223499_Z_21_Z) and NHS Education for Scotland (NES).  The funders had no role in conduct of the study, interpretation, or the decision to submit for publication. The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care. Ethics approval and consent to participate Provision of anonymized data for research by the Clinical Practice Research Datalink (CPRD) is approved annually by the NHS Research Ethics Service, and study specific NREC review is not required provided the study has been approved by the CPRD Independent Scientific Advisory Committee (ISAC). This study was reviewed by ISAC and approved (protocol 21_000542). Availability of data and materials The data controllers do not allow sharing of raw data by the research team, but any researcher can access the same raw data subject to data controller approval (https://www.cprd.com/). Competing interests The authors declare that they have no competing interests. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work, SM used OpenAI’s ChatGPT (version 5.2) in order to de-bug R code used for data analysis and figure formatting. After using this tool, the author(s) reviewed and edited the content and take full responsibility for the content of the published article. Authors’ contributions SM and BG were involved in the conception and design of the study, analysis and interpretation of data, drafting the article and final approval of the submitted version. NL and CJ were involved in writing, reviewing and editing. All authors read and approved the final manuscript References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. UK CR. [Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/incidence Ju W, Zheng R, Wang S, Zhang S, Zeng H, Chen R, et al. The occurrence of cancer in ageing populations at global and regional levels, 1990 to 2019. Age Ageing. 2023;52(9):afad043. 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Supplementary Files Additionalfile.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jun, 2026 Reviews received at journal 13 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 06 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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