Differences In Multimorbidity Research Articles

Analysis of sex-specific disease patterns associated with human lifespan.

Disability and multimorbidity increase with aging and constitute a challenge for the health system. However, different individuals display different aging trajectories, and understanding the underlying relationships between lifespan and disease is fundamental for identifying the different patterns in human lifespan. A previous study explored associations between lifespan and age of onset of diseases of different organ systems, prevalence of escapers, and percentage of life free of disease (health span), comparing them between genders in Catalonian population. In this retrospective study, we analyzed a cohort of 41,063 old individuals (20,722 men, with a mean lifespan of 79years [50-109], and 20,341 women, with a mean lifespan of 84years [50-109]) deceased between 2014 and 2019 in Gipuzkoa (Basque Country, Spain) taken from the Basque Health Service electronic health records data lake and analyzed and contrasted the above cited characteristics. Longevity, defined by age above the mean lifespan of the population, showed a protective effect against developing diseases, with higher lifespans delaying the onset of all diseases both at single and multisystemic levels. Furthermore, individuals with the lowest and highest lifespans were the ones presenting the healthier profiles, with fewer systems altered. There were specific differences according to sex, with women exhibiting decreased mortality risk despite having greater multimorbidity and men having less multimorbidity. In addition, men reached the highest lifespans with a smaller number of comorbidities. These results define the aging trajectories of the Basque population, extend the characterization of the human aging, and provide insight into sex-specific differences in multimorbidity and survival risk.

Disparities in multimorbidity and comorbidities in rheumatoid arthritis by sex across the lifespan.

Multimorbidity is burdensome for people with RA. We investigated differences in multimorbidity and comorbidities by sex and age in the RA population. This cross-sectional analysis used national administrative claims (OptumLabs® Data Warehouse) from people with RA and non-RA comparators (matched on age, sex, race, census region, index year and length of baseline insurance coverage) from 2010-2019. RA was determined using a validated algorithm. Multimorbidity was defined as ≥2 (MM2+) or ≥5 (MM5+) comorbidities from a validated set of 44 chronic conditions. We used logistic regression to assess associations between characteristics and multimorbidity. The sample included 154 391 RA patients and 154 391 non-RA comparators. For people aged 18-50 years, RA women (vs RA men) had 7.5 and 4.4 (vs 3.2 and 0.9 in non-RA women vs non-RA men) percentage point increases for MM2+ and MM5+, respectively. For people aged 51+ years, RA women (vs RA men) had 2.1 and 2.5 (vs 1.2 and 0.3 in non-RA women vs non-RA men) percentage point increases for MM2+ and MM5+, respectively. Interactions revealed that differences in multimorbidity between women and men were exacerbated by RA (vs non-RA) (P < 0.05), with more pronounced effects in people aged 18-50. Men had more cardiovascular-related conditions, whereas RA women had more psychological, neurological and general musculoskeletal conditions. Other comorbidities varied by sex and age. Multimorbidity disproportionately impacts women with RA. Research, clinical and policy agendas for rheumatic diseases should acknowledge and support the variation in care needs by sex and gender across the lifespan.

Educational inequality in multimorbidity: causality and causal pathways. A mendelian randomisation study in UK Biobank

BackgroundMultimorbidity, typically defined as having two or more long-term health conditions, is associated with reduced wellbeing and life expectancy. Understanding the determinants of multimorbidity, including whether they are causal, may help with the design and prioritisation of prevention interventions. This study seeks to assess the causality of education, BMI, smoking and alcohol as determinants of multimorbidity, and the degree to which BMI, smoking and alcohol mediate differences in multimorbidity by level of education.MethodsParticipants were 181,214 females and 155,677 males, mean ages 56.7 and 57.1 years respectively, from UK Biobank. We used a Mendelian randomization design; an approach that uses genetic variants as instrumental variables to interrogate causality.ResultsThe prevalence of multimorbidity was 55.1%. Mendelian randomization suggests that lower education, higher BMI and higher levels of smoking causally increase the risk of multimorbidity. For example, one standard deviation (equivalent to 5.1 years) increase in genetically-predicted years of education decreases the risk of multimorbidity by 9.0% (95% CI: 6.5 to 11.4%). A 5 kg/m2 increase in genetically-predicted BMI increases the risk of multimorbidity by 9.2% (95% CI: 8.1 to 10.3%) and a one SD higher lifetime smoking index increases the risk of multimorbidity by 6.8% (95% CI: 3.3 to 10.4%). Evidence for a causal effect of genetically-predicted alcohol consumption on multimorbidity was less strong; an increase of 5 units of alcohol per week increases the risk of multimorbidity by 1.3% (95% CI: 0.2 to 2.5%). The proportions of the association between education and multimorbidity explained by BMI and smoking are 20.4% and 17.6% respectively. Collectively, BMI and smoking account for 31.8% of the educational inequality in multimorbidity.ConclusionsEducation, BMI, smoking and alcohol consumption are intervenable causal risk factors for multimorbidity. Furthermore, BMI and lifetime smoking make a considerable contribution to the generation of educational inequalities in multimorbidity. Public health interventions that improve population-wide levels of these risk factors are likely to reduce multimorbidity and inequalities in its occurrence.

Ethnic differences in multimorbidity after accounting for social-economic factors, findings from The Health Survey for England.

Social-economic factors and health behaviours may be driving variation in ethnic health inequalities in multimorbidity including among distinct ethnic groups. Using the cross-sectional nationally representative Health Surveys for England 2011-18 (N = 54438, aged 16+), we performed multivariable logistic regression on the odds of having general multimorbidity (≥2 longstanding conditions) by ethnicity [British White (reference group), White Irish, Other White, Indian, Pakistani, Bangladeshi, Chinese, African, Caribbean, White mixed, Other Mixed], adjusting for age, sex, education, area deprivation, obesity, smoking status and survey year. This was repeated for cardiovascular multimorbidity (N = 37148, aged 40+: having ≥2 of the following: self-reported diabetes, hypertension, heart attack or stroke) and multiple cardiometabolic risk biomarkers (HbA1c ≥6.5%, raised blood pressure, total cholesterol ≥5mmol/L). Twenty percent of adults had general multimorbidity. In fully adjusted models, compared with the White British majority, Other White [odds ratio (OR) = 0.63; 95% confidence interval (CI) 0.53-0.74], Chinese (OR = 0.58, 95% CI 0.36-0.93) and African adults (OR = 0.54, 95% CI 0.42-0.69), had lower odds of general multimorbidity. Among adults aged 40+, Pakistani (OR = 1.27, 95% CI 0.97-1.66; P = 0.080) and Bangladeshi (OR = 1.75, 95% CI 1.16-2.65) had increased odds, and African adults had decreased odds (OR = 0.63, 95% CI 0.47-0.83) of general multimorbidity. Risk of cardiovascular multimorbidity was higher among Indian (OR = 3.31, 95% CI 2.56-4.28), Pakistani (OR = 3.48, 95% CI 2.52-4.80), Bangladeshi (OR = 3.67, 95% CI 1.98-6.78), African (OR = 1.61, 95% CI 1.05-2.47), Caribbean (OR = 2.18, 95% CI 1.59-2.99) and White mixed (OR = 1.98, 95% CI 1.14-3.44) adults. Indian adults were also at risk of having multiple cardiometabolic risk biomarkers. Ethnic inequalities in multimorbidity are independent of social-economic factors. Ethnic minority groups are particularly at risk of cardiovascular multimorbidity, which may be exacerbated by poorer management of cardiometabolic risk requiring further investigation.

Assessing Representativeness of Randomised Controlled Trials using Serious Adverse Events.

Context: The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. Objectives: We explore an approach assessing trial representativeness by comparing rates of trial Serious Adverse Events (SAEs: most of which reflect hospitalisations/deaths) to rates of hospitalisation/death in routine care (which, in a trial setting, would be SAEs be definition). Study design: Secondary analysis of trial and routine healthcare data. Dataset and population: 483 trials (n=636,267) from clinicaltrials.gov across 21 index conditions. A routine care comparison was identified from SAIL databank (n=2.3M). Instrument: SAIL data were used to derive the expected rate of hospitalisations/deaths by age, sex and index condition. Outcomes: We calculated the expected number of SAEs for each trial compared to the observed number of SAEs (observed/expected SAE ratio). We then re-calculated the observed/expected SAE ratio additionally accounting for comorbidity count in 125 trials for which we accessed individual participant data. Results: For 12/21 index conditions the observed/expected SAE ratio was <1, indicating fewer SAEs in trials than expected given community rates of hospitalisations and deaths. A further 6/21 had point estimates <1 but the 95% CI included the null. The median observed/expected SAE ratio was 0.60 (95% CI 0.56-0.65; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.88 (0.59-1.33; IBD). Higher comorbidity count was associated with SAEs/hospitalisations and deaths across index conditions. For most trials, the observed/expected ratio was attenuated but remained <1 after additionally accounting for comorbidity count. Conclusion: Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Racial Differences in Multimorbidity and Comorbidities in Rheumatoid Arthritis.

To identify differences in multimorbidity and individual comorbidities among individuals with rheumatoid arthritis (RA), separated by race and ethnicity. This case-control study within OptumLabs Data Warehouse from 2010 to 2019 matched RA cases (defined by 2 codes plus prescription of an RA drug) to non-RA controls 1:1 on age, sex, race and ethnicity, region, index date of RA, and insurance coverage duration. We defined multimorbidity as the presence of ≥2 or ≥5 validated comorbidities. Logistic regression models calculated adjusted odds of multimorbidity with 95% confidence intervals (95% CIs) within each race and ethnicity. We identified 154,391 RA cases and 154,391 controls (mean age 59.6, 76% female). Black enrollees had the most multimorbidity ≥2/≥5 (73.1%, 34.3%); Asian enrollees had the least (52.4%, 17.3%). Adjusted odds of multimorbidity ≥2 and ≥5 in RA cases versus controls was 2.19 (95% CI 2.16-2.23) and 2.06 (95% CI 2.02-2.09), respectively. This increase was similar across race and ethnicity. However, we observed elevated occurrence of certain comorbidities by race and ethnicity versus controls (P < 0.001), including renal disease in White enrollees (4.7% versus 3.2%) and valvular heart disease in Black and White enrollees (3.2% and 2.8% versus 2.6% and 2.2%). Multimorbidity is a problem for all RA patients. Targeted identification of certain comorbidities by race and ethnicity may be a helpful approach to mitigate multimorbidity.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

BackgroundThe applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care.MethodsThis was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov. Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity.ResultsFor 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55–0.64; COPD) and the interquartile range was 0.44 (0.34–0.55; Parkinson’s disease) to 0.87 (0.58–1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most.ConclusionsTrial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Age, sex, and socioeconomic differences in multimorbidity measured in four ways: UK primary care cross-sectional analysis.

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. To examine variation in prevalence using different definitions of multimorbidity. Cross-sectional study of 1 168 620 people in England. 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. 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. 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.

Epidemiology of physical–mental multimorbidity and its impact among Aboriginal and Torres Strait Islander in Australia: a cross-sectional analysis of a nationally representative sample

ObjectivesThis study aimed to examine the differences in multimorbidity between Aboriginal and Torres Strait Islander people and non-Indigenous Australians, and the effect of multimorbidity on health service use and work...

Life-course socioeconomic conditions, multimorbidity and polypharmacy in older adults: A retrospective cohort study.

Socioeconomic conditions across the life course may contribute to differences in multimorbidity and polypharmacy in old age. However, whether the risk of multimorbidity changes during ageing and whether life-course socioeconomic conditions are associated with polypharmacy remain unclear. We investigated whether disadvantaged childhood socioeconomic conditions (CSCs) predict increased odds of multimorbidity and polypharmacy in older adults, whether CSCs remain associated when adjusting for adulthood socioeconomic conditions (ACSs), and whether CSCs and ACSs are associated cumulatively over the life course. We used data for 31,432 participants (multimorbidity cohort, mean [SD] age 66·2[9] years), and 21,794 participants (polypharmacy cohort, mean age 69·0[8.9] years) from the Survey of Health, Ageing, and Retirement in Europe (age range 50–96 years). We used mixed-effects logistic regression to assess the associations of CSCs, ASCs, and a life-course socioeconomic conditions score (0–8; 8, most advantaged) with multimorbidity (≥2 chronic conditions) and polypharmacy (≥5 drugs taken daily). We found an association between CSCs and multimorbidity (reference: most disadvantaged; disadvantaged: odds ratio (OR) = 0·79, 95% confidence interval (CI) 0·70–0·90; middle: OR = 0·60; 95%CI 0·53–0·68; advantaged: OR = 0·52, 95%CI 0·45–0·60, most advantaged: OR = 0·40, 95%CI 0·34–0·48) but not polypharmacy. This multimorbidity association was attenuated but remained significant after adjusting for ASCs. The life-course socioeconomic conditions score was associated with multimorbidity and polypharmacy. We did not find an association between CSCs, life-course socioeconomic conditions, and change in odds of multimorbidity and polypharmacy with ageing. Exposure to disadvantaged socioeconomic conditions in childhood or over the entire life-course could predict multimorbidity in older age.

Racial inequalities in multimorbidity: baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

BackgroundEvidence of multimorbidity has come mainly from high-income regions, while disparities among racial groups have been less explored. This study examined racial differences in multimorbidity in the multiracial cohort of the Longitudinal Study of Adult Health (Estudo Longitudinal de Saúde do Adulto), ELSA-Brasil.MethodsThe study examined baseline (2008–2010) data for 14 099 ELSA-Brasil participants who self-reported being white, mixed-race, or black. A list of 16 morbidities was used to evaluate multimorbidity, operationalised by simple count into ≥ 2, ≥ 3, ≥ 4, ≥ 5 and ≥ 6 morbidities, in addition to evaluating the number of coexisting conditions. Prevalence ratios (PR) were estimated from logistic models and a quantile model was used to examine racial differences graphically in the distribution quantiles for the number of morbidities.ResultsOverall prevalence of multimorbidity (≥ 2 morbidities) was 70% and, after controlling for age and sex, was greater among mixed-race and black participants – by 6% (PR: 1.06; 95% CI: 1.03–1.08) and 9% (PR: 1.09; 95% CI: 1.06–1.12), respectively – than among white participants. As the cutoff value for defining multimorbidity was raised, so the strength of the association increased, especially among blacks: if set at ≥ 6 morbidities, the prevalence was 27% greater for those of mixed-race (PR: 1.27; 95% CI: 1.07–1.50) and 47% greater for blacks (PR: 1.47; 95% CI: 1.22–1.76) than for whites. The disparities were smaller in the lower morbidity distribution quantiles and larger in the upper quantiles, indicating a heavier burden of disease, particularly on blacks.ConclusionsMultimorbidity was common among adults and older adults in a Brazilian cohort, but important racial inequalities were found. Raising the cutoff point for defining multimorbidity revealed stronger associations between race/skin colour and multimorbidity, indicating a higher prevalence of multimorbidity among mixed-race and black individuals than among whites and that the former groups coexisted more often with more complex health situations (with more coexisting morbidities). Interventions to prevent and manage the condition of multimorbidity that consider the social determinants of health and historically discriminated populations in low- and middle-income regions are necessary.

Ethnic inequalities in mental and physical multimorbidity in women of reproductive age: a data linkage cohort study

ObjectivesExplore inequalities in risk factors, mental and physical health morbidity in non-pregnant women of reproductive age in contact with mental health services and how these vary per ethnicity.DesignRetrospective cohort study.SettingData...

955 ANALYSIS OF RANDOMISED CONTROLLED TRIAL SERIOUS ADVERSE EVENT RATES AS A MARKER OF TRIAL REPRESENTATIVENESS

Abstract Introduction The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial Serious Adverse Events (SAEs: most of which reflect hospitalisations/deaths) to rates of hospitalisation/death in routine care (which, in a trial setting, would be SAEs be definition). Method We identified 483 trials (n = 636,267) from clinicaltrials.gov across 21 index conditions. A routine care comparison was identified from SAIL databank (n = 2.3 M). SAIL data were used to derive the expected rate of hospitalisations/deaths by age, sex and index condition. We then calculated the expected number of SAEs for each trial compared to the observed number of SAEs (observed/expected SAE ratio). We then re-calculated the observed/expected SAE ratio additionally accounting for comorbidity count in 125 trials for which we accessed individual participant data. Results For 12/21 index conditions the observed/expected SAE ratio was &amp;lt;1, indicating fewer SAEs in trials than expected given community rates of hospitalisations and deaths. A further 6/21 had point estimates &amp;lt;1 but the 95% CI included the null. The median observed/expected SAE ratio was 0.60 (95% CI 0.56–0.65; COPD) and the interquartile range was 0.44 (0.34–0.55; Parkinson’s disease) to 0.88 (0.59–1.33; IBD). Higher comorbidity count was associated with SAEs and hospitalisations/deaths across index conditions. For most trials, the observed/expected ratio was attenuated but remained &amp;lt;1 after additionally accounting for comorbidity count. Conclusion Trial participants experience fewer SAEs than expected based on age/sex/condition specific hospitalisation/death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Multimorbidity as assessed by reporting of multiple causes of death: variations by period, sociodemographic characteristics and place of death among older decedents in England and Wales, 2001–2017

BackgroundMultimorbidity is common at older ages and is associated with disability, frailty and poor quality of life. Research using clinical databases and surveys has shown associations between multimorbidity and indicators...

Cohort Trends in the Burden of Multiple Chronic Conditions Among Aging U.S. Adults.

Multimorbidity, also referred to as multiple chronic conditions (MCCs), is the concurrent presence of 2 or more chronic health conditions. Increasing multimorbidity represents a substantial threat to the health of aging populations. Recent trends suggest greater risk of poor health and mortality among later-born cohorts, yet we are unaware of work examining cohort differences in multimorbidity among aging U.S. adults. We examine intercohort variation in MCC burden in adults aged 51 years and older using 20 years (n = 33,598; 1998-2018) of repeated assessment drawn from the Health and Retirement Study. The index of MCCs included 9 chronic conditions (heart disease, hypertension, stroke, diabetes, arthritis, lung disease, cancer excluding skin cancer, high depressive symptoms, and cognitive impairment). We used linear mixed models with various approaches to estimate age/period/cohort effects to model intercohort patterns in MCC burden. We also explored variation in the specific conditions driving cohort differences in multimorbidity. More recent cohorts had greater MCC burden and developed multimorbidity at earlier ages than those born to prior generations. The burden of chronic conditions was patterned by life-course sociodemographic factors and childhood health for all cohorts. Among adults with multimorbidity, arthritis and hypertension were the most prevalent conditions for all cohorts, and there was evidence that high depressive symptoms and diabetes contributed to the observed cohort differences in multimorbidity risk. Our results suggest increasing multimorbidity burden among more recently born cohorts of aging U.S. adults and should inform policy to address diminishing health in aging populations.

Decomposing urban-rural differences in multimorbidity among older adults in India: a study based on LASI data

BackgroundMultimorbidity is defined as the co-occurrence of two or more than two diseases in the same person. With rising longevity, multimorbidity has become a prominent concern among the older population. Evidence from both developed and developing countries shows that older people are at much higher risk of multimorbidity; however, urban-rural differential remained scarce. Therefore, this study examines urban-rural differential in multimorbidity among older adults by decomposing the risk factors of multimorbidity and identifying the covariates that contributed to the change in multimorbidity.MethodsThe study utilized information from 31,464 older adults (rural-20,725 and urban-10,739) aged 60 years and above from the recent release cross-sectional data of the Longitudinal Ageing Study in India (LASI). Descriptive, bivariate, and multivariate decomposition analysis techniques were used.ResultsOverall, significant urban-rural differences were found in the prevalence of multimorbidity among older adults (difference: 16.3; p < 0.001). The multivariate decomposition analysis revealed that about 51% of the overall differences (urban-rural) in the prevalence of multimorbidity among older adults was due to compositional characteristics (endowments). In contrast, the remaining 49% was due to the difference in the effect of characteristics (Coefficient). Moreover, obese/overweight and high-risk waist circumference were found to narrow the difference in the prevalence of multimorbidity among older adults between urban and rural areas by 8% and 9.1%, respectively. Work status and education were found to reduce the urban-rural gap in the prevalence of multimorbidity among older adults by 8% and 6%, respectively.ConclusionsThere is a need to substantially increase the public sector investment in healthcare to address the multimorbidity among older adults, more so in urban areas, without compromising the needs of older adults in rural areas.

Sociodemographic Differences in Multimorbidity: A Closer Look from Indonesian Family and Life Survey

The prevalence of multimorbidity, the coexistence of two or more chronic diseases, is expected to increase, including in Indonesia. This phenomenon is associated with increased life expectancy and incidence of non-communicable diseases. Therefore, this study assesses the burden of multimorbidity in Indonesia by sociodemographic factors. The researchers analyzed cross-sectional data from the latest wave of IFLS conducted in 2014, the IFLS wave 5. The researchers included individuals aged 15 and above with blood pressure measurements (n= 32.256) from 13,536 households. Meanwhile, the researchers excluded individuals with missing data on BMI (183 individuals) or who had biologically implausible or extreme values (n=6). The analyses were then conducted on 32,067 individuals. The analyses comprised the ten most common self-reported chronic diseases diagnosis in IFLS-5. The findings revealed that the prevalence of self-reported multimorbidity in Indonesia was relatively high, at 9.32% (n= 2.989), with the highest proportion of multimorbidity reported among the elderly. Approximately 2.76% of the respondents (n- 885) reported having three or more chronic diseases. The most common combinations were hypertension and digestive problem (2.15%, n= 689), followed by hypertension and arthritis (1.79%, n= 574), and hypertension and high cholesterol (1.68%, n= 539). When stratified by sociodemographic factors, the researchers found a higher proportion of multimorbidity among females (11.01%, n= 3,530) compared to males (7.41%, n= 2.376), elderly (21.54%, n= 3.530) compared to younger adults, and previous smokers (20.90%, n= 6.072). People who were obese and overweight also reported a higher prevalence of multimorbidity (13.73%, n= 4.403 and 9.3%, n= 2.998, respectively). In addition, those living in urban areas had a higher proportion of multimorbidity (10.33%, n= 3.313) compared to rural areas (7.2%, n= 2.527). In conclusion, this study uncovered a relatively high prevalence of multimorbidity. People living in urban areas were overweight/obese and those who had low SES had a higher proportion of multimorbidity. With the nature of self-reported data and previously reported underdiagnosis of chronic diseases, screening to examine multimorbidity is needed.

Racial and ethnic disparities in multimorbidity in non-pregnant women of reproductive age in Lambeth, UK: a data linkage study

Racial and ethnic disparities in multimorbidity in non-pregnant women of reproductive age in Lambeth, UK: a data linkage study

Urban-Rural Differences in Patterns and Associated Factors of Multimorbidity Among Older Adults in China: A Cross-Sectional Study Based on Apriori Algorithm and Multinomial Logistic Regression.

Introduction: Multimorbidity has become one of the key issues in the public health sector. This study aimed to explore the urban–rural differences in patterns and associated factors of multimorbidity in China and to provide scientific reference for the development of health management strategies to reduce health inequality between urban and rural areas.Methods: A cross-sectional study, which used a multi-stage random sampling method, was conducted effectively among 3,250 participants in the Shanxi province of China. The chi-square test was used to compare the prevalence of chronic diseases among older adults with different demographic characteristics. The Apriori algorithm and multinomial logistic regression were used to explore the patterns and associated factors of multimorbidity among older adults, respectively.Results: The findings showed that 30.3% of older adults reported multimorbidity, with significantly higher proportions in rural areas. Among urban older adults, 10 binary chronic disease combinations with strong association strength were obtained. In addition, 11 binary chronic disease combinations and three ternary chronic disease combinations with strong association strength were obtained among rural older adults. In rural and urban areas, there is a large gap in patterns and factors associated with multimorbidity.Conclusions: Multimorbidity was prevalent among older adults, which patterns mainly consisted of two or three chronic diseases. The patterns and associated factors of multimorbidity varied from urban to rural regions. Expanding the study of urban–rural differences in multimorbidity will help the country formulate more reasonable public health policies to maximize the benefits of medical services for all.

POS0023 RACIAL/ETHNIC AND REGIONAL DIFFERENCES IN MULTIMORBIDITY BETWEEN PATIENTS WITH RHEUMATOID ARTHRITIS AND COMPARATORS IN A LARGE NATIONWIDE US STUDY

POS0023 RACIAL/ETHNIC AND REGIONAL DIFFERENCES IN MULTIMORBIDITY BETWEEN PATIENTS WITH RHEUMATOID ARTHRITIS AND COMPARATORS IN A LARGE NATIONWIDE US STUDY