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Discrepancies between personal income and neighbourhood status: effects on physical and mental health.

ABSTRACT: During their life course, older persons' income level may become discrepant with the socio-economic status of their neighbourhood. This study examines whether and how such discrepancies affect older persons' physical and mental health. Using baseline data from the Longitudinal Aging Study Amsterdam, 2,540 non-institutionalised persons aged 55-85 years were classified based on self-reported income and neighbourhood status. Two categories defined discrepancies: discrepant-low (DL, low income in high-status neighbourhood), and discrepant-high (DH, high income in low-status neighbourhood). Both categories were compared with the same reference category: matched-high (MH, high personal and high neighbourhood income status). A range of health indicators were examined, as well as mediating effects of neighbourhood and individual characteristics. Among the 504 persons who reported a high income, 16% lived in a low-status neighbourhood (DH). Conversely, among the 757 persons living in a high-status neighbourhood, 24% had a low income (DL). The DL category mainly lived in rural areas, and the DH category predominantly in large cities. The data show discrepant income effects (DL vs. MH) on physical and cognitive ability, self-rated health, and loneliness, and discrepant neighbourhood effects (DH vs. MH) on physical and cognitive ability, depressive symptoms, and loneliness. Personal income effects were partly mediated by other personal characteristics, and neighbourhood effects were fully mediated by socio-economic neighbourhood characteristics as well as by older persons' perceptions of their neighbourhood and their income. It is concluded that discrepancies between personal income and neighbourhood status, accrued throughout the life course, are associated with poor health.

SUBMITTER: Deeg DJH

PROVIDER: S-EPMC5547679 | biostudies-other | 2005 Jun

REPOSITORIES: biostudies-other

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Project description:Background: Health-related quality of life (HRQoL) is multidimensional and is composed of, at a minimum, self-perceived health status, physical functioning, and psychological well-being. HRQoL measures reflect the extent of disability and dysfunction associated with a chronic disease such as cancer. The objective of this study is to examine factors associated with HRQoL among cancer survivors. Methods: Data from the 2009 Behavioral Risk Factor Surveillance System survey was used to examine factors associated with HRQoL among participants who reported having ever been diagnosed with cancer. Four questions associated with HRQoL included self-perceived health status, number of bad physical health days, and number of bad mental health days per month. Least square regression and logistic regression models, adjusted for confounding variables, were used for an ordinal and dichotomous [5 (bad) vs. 1-4 (excellent, very good, good, fair)] scale of HRQoL, respectively. Results: Fifty nine thousand one hundred seventy three participants reported having ever been diagnosed with cancer. Adjusted mean self-perceived health status (5-point scale) among survivors of thyroid, colon, lung, cervical, breast, prostate, and ovarian cancer was 3.83 (0.05), 4.02 (0.04), 4.36 (0.06), 3.77 (0.03), 3.88 (0.03), 3.78 (0.04), and 3.96 (0.05), respectively. After adjusting for confounders, a positive dose-response effect was observed between income range and all three HRQoL measures across all seven cancer sites. Income was consistently and inversely associated with a higher chance for reporting poorer HRQoL [OR: 0.64, 95% CI: 0.57-0.71], [OR: 0.63, 95% CI: 0.48-0.82], [OR: 0.67, 95% CI: 0.56-0.80], [OR: 0.69, 95% CI: 0.56-0.86], [OR: 0.55, 95% CI: 0.49-0.62], [OR:0.55, 95% CI: 0.44-0.69], [OR: 0.75, 95% CI: 0.62-0.91] among those with thyroid, colon, lung, cervical, breast, prostate, and ovarian cancer, respectively. Discussion: This study found that income range was associated with HRQoL among cancer survivors. It is plausible that financial resources may lessen the overall burden of cancer survivors, which could improve health-related quality of life among cancer survivors.

Project description:BackgroundHow neighbourhood characteristics affect the physical safety of people with mental illness is unclear.AimsTo examine neighbourhood effects on physical victimisation towards people using mental health services.MethodWe developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation. This was applied to records on all patients attending National Health Service mental health services in Southeast London. Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital Episode Statistics, linked to Clinical Record Interactive Search), were collected in this group, defined as 'cases' and concurrently sampled controls. Multilevel logistic regression models estimated associations (odds ratios, ORs) between neighbourhood-level fragmentation, crime, income deprivation, and population density and physical victimisation.ResultsBased on a human-rated gold standard, the NLP algorithm had a positive predictive value of 0.92 and sensitivity of 0.98 for (clinically recorded) physical victimisation. A 1 s.d. increase in neighbourhood crime was accompanied by a 7% increase in odds of physical victimisation in women and an 13% increase in men (adjusted OR (aOR) for women: 1.07, 95% CI 1.01-1.14, aOR for men: 1.13, 95% CI 1.06-1.21, P for gender interaction, 0.218). Although small, adjusted associations for neighbourhood fragmentation appeared greater in magnitude for women (aOR = 1.05, 95% CI 1.01-1.11) than men, where this association was not statistically significant (aOR = 1.00, 95% CI 0.95-1.04, P for gender interaction, 0.096). Neighbourhood income deprivation was associated with victimisation in men and women with similar magnitudes of association.ConclusionsNeighbourhood factors influencing safety, as well as individual characteristics including gender, may be relevant to understanding pathways to physical victimisation towards people with mental illness.

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Discrepancies between personal income and neighbourhood status: effects on physical and mental health.

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