Socioeconomic circumstances, health behaviours and functional limitations in older persons in four Central and Eastern European populations

Doryńska, Agnieszka; Pająk, Andrzej; Kubínová, Růžena; Malyutina, Sofia; Tamošiūnas, Abdonas; Pikhart, Hynek; Peasey, Anne; Nikitin, Yu. P.; Marmot, Michael; Bobák, Martin

doi:10.1093/ageing/afs114

Cited by 13 publications

(19 citation statements)

References 25 publications

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“…The results of the study show that women have lower values in all quality of life indicators. These results are consistent with other studies in the literature showing that men receive higher average quality of life than women [15,28,36,37]. Women are likely to experience multiple roles, many at the same time.…”

Section: Social Status Using Individual's Characteristics and Qualitysupporting

confidence: 92%

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Ellina

Middleton

Lambrinou

et al. 2020

Preprint

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Background Social inequalities in health threaten social cohesion and, therefore, their investigation is an important research field. The reasons behind this phenomenon remain unclear. Monitoring the health of the population is necessary to identify health needs, design programs focused in people’s needs and to evaluate the effectiveness of health policies. Methods A descriptive correlation study with cross-sectional comparisons using primary data was applied. The study investigates the size and the extent of social inequalities in quality of life and health behaviours in Limassol, Cyprus. Data collection was done by door-to-door survey, in the form of interviews. The sample consisted of 450 residents aged 45-64 across 45 randomly selected neighbourhoods, that met the selection criteria. The tools used were: Demographic questionnaire, SF 36 Questionnaire - Quality of Life, IPAQ- International Physical Activity Questionnaire short form. Results The social gradient seems to be appeared in all social indicators. As for the physical dimension of health, it seems to show strong relationship between quality of life with the education index. Regarding mental health dimension, the pattern of social gradient seems to be not so strong. Conclusions Exploring social inequalities in quality of life, is a complex state influencing social physical and psychological state of health. It seems that being male, young, highly educated, with high income, working full time and having a mild physical activity, there is significant higher level of quality life in relation to others. Many studies show that gender affects the patterns of risk factors and that this has a different impact on quality of life.

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Section: Social Status Using Individual's Characteristics and Qualitysupporting

confidence: 92%

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Ellina

Middleton

Lambrinou

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The results of the study show that women have lower values in all health-related quality of life indicators. These results are consistent with other studies in the literature showing that men receive higher average health-related quality of life than women [16,35,43,44]. Women are likely to experience multiple roles, many at the same time.…”

Section: Social Status Using Individual's Characteristics and Health-supporting

confidence: 92%

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Ellina

Middleton

Lambrinou

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Social inequalities in health threaten social cohesion and, therefore, their investigation is an important research field. Monitoring the health of the population is necessary to identify health needs, design programs focused in people’s needs and to evaluate the effectiveness of health policies.Methods: A cross-sectional survey using primary data was applied. The study investigates the size and the extent of social inequalities in quality of life and health behaviours in Limassol, Cyprus. Data collection was done by door-to-door survey, in the form of interviews. The sample consisted of 450 residents aged 45-64 across 45 randomly selected neighbourhoods, that met the selection criteria. The tools used were: Demographic questionnaire, SF 36 Questionnaire, IPAQ- International Physical Activity Questionnaire short form.Results: The social gradient appears in all social indicators. As for the physical dimension of health has a strong relationship between health-related quality of life with the education index. Specifically, the difference between the two poles is 12 points for men and 14 points for women (p = 0.16). Profession systematically appears to have a stronger relationship with men than with women, and is present in both physical and mental dimensions. Specifically, the difference between the two poles is 13 points for men and 10 points for women (p = 0.31).Conclusions: Exploring social inequalities in health-related quality of life, is a complex situation influencing social, physical and psychological health state. It seems that young male individuals, who are highly educated, employed full time, earning high incomes and engaging in mild physical activity, have significantly higher level of health-related life quality, compared to other middle age adult groups, living in Limassol. This finding is in agreement with other studies that show correlations between gender and the patterns of risk factors.

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“…The standardised summary score for the Physical Component Score (PCS) of the Short Form 36 (SF-36) Health Survey is population normalised (0 is worst physical health and 100 is best physical health) and was classified by tertiles (≤33, 34–66, >66) as has been used previously 25 26 with a 6-month poor outcome defined using the most severe tertile (≤33). Outcome was defined as poor physical health at 6-month follow-up using the SF-36 PCS because this was the most appropriate physical function outcome score available in both studies, and it has demonstrated good validity and responsiveness in this population.…”

Section: Methodsmentioning

confidence: 99%

Does a modified STarT Back Tool predict outcome with a broader group of musculoskeletal patients than back pain? A secondary analysis of cohort data

et al. 2016

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ObjectivesThe STarT Back Tool has good predictive performance for non-specific low back pain in primary care. We therefore aimed to investigate whether a modified STarT Back Tool predicted outcome with a broader group of musculoskeletal patients, and assessed the consequences of using existing risk-group cut-points across different pain regions.SettingSecondary analysis of prospective data from 2 cohorts: (1) outpatient musculoskeletal physiotherapy services (PhysioDirect trial n=1887) and (2) musculoskeletal primary–secondary care interface services (SAMBA study n=1082).ParticipantsPatients with back, neck, upper limb, lower limb or multisite pain with a completed modified STarT Back Tool (baseline) and 6-month physical health outcome (Short Form 36 (SF-36)).OutcomesArea under the receiving operator curve (AUCs) tested discriminative abilities of the tool's baseline score for identifying poor 6-month outcome (SF-36 lower tertile Physical Component Score). Risk-group cut-points were tested using sensitivity and specificity for identifying poor outcome using (1) Youden's J statistic and (2) a clinically determined rule that specificity should not fall below 0.7 (false-positive rate <30%).ResultsIn PhysioDirect and SAMBA, poor 6-month physical health was 18.5% and 28.2%, respectively. Modified STarT Back Tool score AUCs for predicting outcome in back pain were 0.72 and 0.79, neck 0.82 and 0.88, upper limb 0.79 and 0.86, lower limb 0.77 and 0.83, and multisite pain 0.83 and 0.82 in PhysioDirect and SAMBA, respectively. Differences between pain region AUCs were non-significant. Optimal cut-points to discriminate low-risk and medium-risk/high-risk groups depended on pain region and clinical services.ConclusionsA modified STarT Back Tool similarly predicts 6-month physical health outcome across 5 musculoskeletal pain regions. However, the use of consistent risk-group cut-points was not possible and resulted in poor sensitivity (too many with long-term disability being missed) or specificity (too many with good outcome inaccurately classified as ‘at risk’) for some pain regions. The draft tool is now being refined and validated within a new programme of research for a broader musculoskeletal population.Trial registration numberISRCTN55666618; Post results.

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Socioeconomic circumstances, health behaviours and functional limitations in older persons in four Central and Eastern European populations

Cited by 13 publications

References 25 publications

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Social gradient in health-related quality of life among urban residents in Limassol, Cyprus: Research article

Does a modified STarT Back Tool predict outcome with a broader group of musculoskeletal patients than back pain? A secondary analysis of cohort data

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