Dataset Information

Are Leading Risk Factors for Cancer and Mental Disorders Multimorbidity Shared by These Two Individual Conditions in Community-Dwelling Middle-Aged Adults?

ABSTRACT: Data on the leading shared risk factors of cancer and mental disorders are limited. We included 98,958 participants (54.8% women) aged 45-64 years from the 45 and Up Study who were free of cancer, depression, and anxiety at baseline (2006-2009). The incidence of cancer, mental disorders, and multimorbidity (concurrent cancer and mental disorders) was identified using claim databases during follow-up until 31 December 2016. During a nine-year follow-up, the cumulative incidence of cancer, mental disorders, and multimorbidity was 8.8%, 17.4%, and 2.2%, respectively. Participants with cancer were 3.41 times more likely to develop mental disorders, while individuals with mental disorders were 3.06 times more likely to develop cancer than people without these conditions. The shared risk factors for cancer and mental disorders were older age, female gender, smoking, psychological distress, low fruit intake, poor/fair self-rated health, hypertension, arthritis, asthma, and diabetes. Low education, low income, overweight/obesity, and family history of depression were risk factors for mental disorders and multimorbidity but not cancer. In conclusion, smoking, low fruit intake, and obesity are key modifiable factors for the prevention of cancer and mental disorders. Individuals with poor/fair self-rated health, high psychological distress, asthma, hypertension, arthritis, or diabetes should be targeted for the prevention and screening of cancer and mental disorders.

SUBMITTER: Shang X

PROVIDER: S-EPMC7352661 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Are Leading Risk Factors for Cancer and Mental Disorders Multimorbidity Shared by These Two Individual Conditions in Community-Dwelling Middle-Aged Adults?

Shang Xianwen X Shang Xianwen X Hodge Allison M AM Peng Wei W He Mingguang M Zhang Lei L

Cancers 20200626 6

Data on the leading shared risk factors of cancer and mental disorders are limited. We included 98,958 participants (54.8% women) aged 45-64 years from the 45 and Up Study who were free of cancer, depression, and anxiety at baseline (2006-2009). The incidence of cancer, mental disorders, and multimorbidity (concurrent cancer and mental disorders) was identified using claim databases during follow-up until 31 December 2016. During a nine-year follow-up, the cumulative incidence of cancer, mental ...[more]

PMID: 32604790

Similar Datasets

Project description:Background: Determining the incidence, progression, and patterns of multimorbidity are important for the prevention, management, and treatment of concurrence of multiple conditions. This study aimed to analyze major multimorbidity patterns and the association of the onset of a primary condition or combinations of a primary and a secondary condition with the progression to subsequent conditions. Methods: We included 53,867 participants aged 45-64 years from the 45 and Up Study who were free of 10 predefined chronic conditions at baseline (2006-2009). The incidence of multimorbidity (coexistence of ≥2, ≥3, and ≥4 conditions) was identified using the claims database until December 31, 2016. The primary, secondary, tertiary, and quaternary condition for each participant was defined according to its temporal order of onset. Results: During a mean 9-years follow-up, the cumulative incidence of primary, secondary, tertiary, and quaternary conditions was 49.6, 23.7, 9.0, and 2.9%, respectively. The time to develop a subsequent condition decreased with the accumulation of conditions (P < 0.0001). Two concurrent cardiometabolic disorders (CMDs, 30.4%) and CMDs clustered with musculoskeletal disorders (15.2%), mental disorders (13.5%), asthma (12.0%), or cancer (8.7%) were the five most common multimorbidity patterns. CMDs tended to occur prior to mental or musculoskeletal disorders but after the onset of cancers or asthma. Compared with all participants who developed cancer as a primary condition, individuals who experienced mental disorders/neurodegenerative disorders and a comorbidity as cardiovascular disease, hypertension, dyslipidemia, diabetes, asthma, or osteoarthritis were 3.36-10.87 times more likely to develop cancer as a tertiary condition. Individuals with neurodegenerative disorders and a comorbidity as hypertension, dyslipidemia, osteoarthritis, or asthma were 5.14-14.15 times more likely to develop mental disorders as a tertiary condition. Conclusions: A high incidence of multimorbidity in middle-aged adults was observed and CMDs were most commonly seen in multimorbidity patterns. There may be accelerated aging after a primary condition occurs. Our findings also reveal a potential preventative window to obviate the development of secondary or tertiary conditions.

Project description:BackgroundWe aimed to identify specific patterns of physical multimorbidity, defined as the presence of two or more physical long-term conditions, and to examine the extent to which these specific patterns could predict future incident and persistent common mental health disorders (CMDs) in middle-aged adults enrolled in the UK Biobank.MethodsWe assessed prospective associations between physical multimorbidity status at the baseline assessment (2006-2010) and depression and anxiety 'caseness' according to the Patient Health Questionnaire (PHQ)-9 and the Generalised Anxiety Disorder Assessment (GAD)-7 at the follow-up assessment (2016) in 154,367 middle-aged adults enrolled in the UK Biobank (median age: 57 years, interquartile range = 50-62 years, 56.5% female, mean duration of follow-up: 7.6 years, standard deviation = 0.87). Patterns of physical multimorbidity were identified using exploratory factor analysis. Logistic regression was used to assess prospective associations between physical multimorbidity patterns at baseline and both incident and persistent depression and anxiety at follow-up.FindingsCompared to those with no physical multimorbidity, having two (adjusted odds ratio (aOR) =1.41, 95%CI 1.32 to 1.53), three (aOR = 1.94, 95%CI 1.76 to 2.14), four (aOR = 2.38, 95%CI 2.07 to 2.74), and five or more (aOR = 2.89, 95%CI 2.42 to 3.45) physical conditions was prospectively associated with incident depression at follow-up in a dose response manner. Similar trends emerged for incident anxiety, persistent depression, and persistent anxiety, but associations were strongest for incident CMDs. Regarding specific patterns of physical MM, the respiratory pattern (aOR = 3.23, 95%CI 2.44 to 4.27) and the pain/gastrointestinal pattern (aOR = 2.19, 95%CI 1.92 to 2.50) emerged as the strongest predictors of incident depression. Similar results emerged for incident anxiety.InterpretationThese findings highlight patterns of physical multimorbidity with the poorest prognosis for both emerging and persisting depression and anxiety. These findings might have significant implications for the implementation of integrated mental and physical healthcare and facilitate the development of targeted preventative interventions and treatment for those with physical multimorbidity.FundingAR is supported by Guy's Charity grant number EIC180702; JAT is funded by Medical Research Council (MRC) number MR/SO28188/1; AD is funded by Guy's Charity grant number EIC180702 and MRC grant number MR/SO28188/1. JD is part supported by the ESRC Centre for Society and Mental Health at King's College London (ES/S012567/1), grants from the ESRC (ES/S002715/1), by the Health Foundation working together with the Academy of Medical Sciences, for a Clinician Scientist Fellowship, and by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. The views expressed are those of the author[s] and not necessarily those of the ESRC, NIHR, the Department of Health and Social Care or King's College London.

Project description:Background: Identifying leading determinants for disease-free status may provide evidence for action priorities, which is imperative for public health with an expanding aged population worldwide. This study aimed to identify leading determinants, especially modifiable factors for disease-free status using machine learning methods. Methods: We included 52,036 participants aged 45-64 years from the 45 and Up Study who were free of 13 predefined chronic conditions at baseline (2006-2009). Disease-free status was defined as participants aging from 45-64 years at baseline to 55-75 years at the end of the follow-up (December 31, 2016) without developing any of the 13 chronic conditions. We used machine learning methods to evaluate the importance of 40 potential predictors and analyzed the association between the number of leading modifiable healthy factors and disease-free status. Results: Disease-free status was found in about half of both men and women during a mean 9-year follow-up. The five most common leading predictors were body mass index (6.4-9.5% of total variance), self-rated health (5.2-8.2%), self-rated quality of life (4.1-6.8%), red meat intake (4.5-6.5%), and chicken intake (4.5-5.9%) in both genders. Modifiable behavioral factors including body mass index, diets, smoking, alcohol consumption, and physical activity, contributed to 37.2-40.3% of total variance. Participants having six or more modifiable health factors were 1.63-8.76 times more likely to remain disease-free status and had 0.60-2.49 more disease-free years (out of 9-year follow-up) than those having two or fewer. Non-behavioral factors including low levels of education and income and high relative socioeconomic disadvantage, were leading risk factors for disease-free status. Conclusions: Body mass index, diets, smoking, alcohol consumption, and physical activity are key factors for disease-free status promotion. Individuals with low socioeconomic status are more in need of care.

Project description:BackgroundAdults age ≥ 50 are among the fastest growing populations in correctional supervision and are medically underserved while experiencing unique health disparities. Community-living older adults, referred to as "justice-involved," are people who have been recently arrested, or are on probation or parole. Although medical complexity is common among incarcerated older adults, the occurrence of medical morbidity, substance use disorder (SUD), and mental illness among justice-involved older adults living in US communities is poorly understood.ObjectiveTo estimate the prevalence of medical multimorbidity (≥ 2 chronic medical diseases), SUDs, and mental illness among justice-involved adults age ≥ 50, and the co-occurrence of these conditions.DesignCross-sectional analysis.ParticipantsA total of 34,898 adults age ≥ 50 from the 2015 to 2018 administrations of the US National Survey on Drug Use and Health.Main measuresDemographic characteristics of justice-involved adults age ≥ 50 were compared with those not justice-involved. We estimated prevalence of mental illness, chronic medical diseases, and SUD among adults age ≥ 50 reporting past-year criminal justice system involvement. Logistic regression was used to estimate the odds of these conditions and co-occurrence of conditions, comparing justice-involved to non-justice-involved adults.Key resultsAn estimated 1.2% (95% confidence interval [CI] = 1.1-1.3) of adults age > 50 experienced criminal justice involvement in the past year. Compared with non-justice-involved adults, justice-involved adults were at increased odds for mental illness (adjusted odds ratio [aOR] = 3.04, 95% CI = 2.09-4.41) and SUD (aOR = 8.10, 95% CI = 6.12-10.73), but not medical multimorbidity (aOR = 1.15, 95% CI = 0.85-1.56). Justice-involved adults were also at increased odds for all combinations of the three outcomes, including having all three simultaneously (aOR = 8.56, 95% CI = 4.10-17.86).ConclusionsCommunity-based middle-aged and older adults involved in the criminal justice system are at high risk for experiencing co-occurring medical multimorbidity, mental illness, and SUD. Interventions that address all three social and medical risk factors are needed for this population.

Project description:Sickness absence is a socioeconomic global burden. In Brazil, mental disorders are the third leading cause of social security benefits payments. The aim of the present study was to compare factors associated with long-term sickness absence between workers who claimed social benefits due to mental disorders or by other causes. We investigated individual features and occupational characteristics. In addition, we evaluated psychosocial factors at work assessed by the Demand-Control-Support (DCS) and Effort-Reward Imbalance (ERI) models, and whether they were associated with long-term sickness absence due to mental disorders (LTSA-MD).The present case-control study was conducted in São Paulo, Brazil. The sample (n = 385) included workers on sick leave for more than 15 days. Cases were the participants with disabling psychiatric illnesses, and controls were the ones with other disabling diseases. Interviews were conducted to assess individual features (sociodemographic data, health habits/lifestyle, health conditions) and occupational characteristics. The participants' perception of exposure to dimensions of the DCS and ERI models was also recorded. Multiple logistic regressions were performed to evaluate the association between independent variables and LTSA-MD.All the regression analyses showed that LTSA-MD was associated with female sex, self-reported white skin color, higher education level, high tobacco consumption, high alcohol intake, two or more comorbidities, exposure to violence at work, high job strain and low social support at work, effort-reward imbalance and high overcommitment to work. LTSA-MD was associated with separate and combined DCS and ERI stress models.Individual features and work conditions were associated with LTSA-MD. Combined analysis of stress models showed that psychosocial factors at work were significantly associated with LTSA-MD. Resourceful use of this information may contribute to the implementation of preventive actions and strategies to facilitate return to work targeting the populations most susceptible to mental disorders.

Project description:Sleep misperception has long been a major issue in the field of insomnia research. Most studies of sleep misperception examine sleep underestimation by comparing the results of polysomnography conducted in a laboratory environment with patients' sleep diary entries. We aimed to investigate psychosocial characteristics of adults who underestimated or overestimated sleep time in a nonclinical, middle-aged community-dwelling population. We collected one week of sleep data with wrist-worn accelerometers. We used egocentric social network analysis to analyze the effects of psychosocial factors. Among 4,060 study participants, 922 completed the accelerometer substudy. Underestimation was defined as an accelerometer-measured sleep time ≥ 6 h and a subjective sleep time < 6 h. Overestimation was defined as an objective sleep time < 6 h and a subjective sleep time ≥ 6 h. Psychosocial characteristics of the sleep misperception group were evaluated using multivariate regression analysis. A total of 47 participants underestimated sleep time, and 420 overestimated sleep time. Regression analysis revealed that women, living with spouse, economic satisfaction, and bridging potential had protective effects against sleep underestimation. Blame from a spouse involved a 3.8-times higher risk of underestimation than the control group (p = 0.002). In men, discussing concerns with a spouse had a protective effect against underestimation (p < 0.001). Economic satisfaction, feeling social network-based intimacy, and support from a spouse were associated with overestimation in women. In men, feeling social network-based intimacy was also associated with overestimation (p < 0.001). We found that social relationship quality was related to sleep overestimation and underestimation. This association was marked in women. Good social relationships may have positive effects on sleep misperception via attenuation of negative emotional reactions and effects on emotional regulation.

Dataset Information

Are Leading Risk Factors for Cancer and Mental Disorders Multimorbidity Shared by These Two Individual Conditions in Community-Dwelling Middle-Aged Adults?

Publications

Are Leading Risk Factors for Cancer and Mental Disorders Multimorbidity Shared by These Two Individual Conditions in Community-Dwelling Middle-Aged Adults?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets