Exploring Public, Practitioner and Policymaker Perspectives of Unhealthy Lifestyle Factors in the Context of Socioeconomic Deprivation: A Qualitative Study.

INTRODUCTION: Unhealthy lifestyle factors, such as smoking, high alcohol intake, poor diet and physical inactivity, are key risk factors for premature mortality. How unhealthy lifestyle factors are viewed in the wider context of socioeconomic deprivation is rarely considered. Understanding key stakeholder views on lifestyle factors in the context of deprivation is critical to intervention development and reducing harm in more deprived populations. The aim of this study was to explore public, healthcare professional and policymaker views around unhealthy lifestyle factors in the context of deprivation. The aim was broad to facilitate iterative development of ideas, as the views of this wide range of stakeholders are rarely captured. METHODS: Twenty-five adult members of the public in Scotland took part in four focus groups between August 2022 and June 2023. Eighteen semi-structured interviews were conducted with professionals: 12 primary-care practitioners and 6 public-health practitioners and policymakers. Reflexive thematic analysis was undertaken. RESULTS: Four main themes were developed: (1) Evolving complexity of lifestyle factors - the number of lifestyle factors that adversely impact health has grown, with increasingly complex interactions, (2) Social determinants of lifestyle - numerous links were made between socioeconomic conditions and unhealthy lifestyle factors by all participants, (3) Poverty as a fundamental social determinant - poverty was identified as a core factor for unhealthy lifestyle factors, and (4) Agency versus structure in relation to lifestyle - individual agency to address lifestyle factors was limited by structural constraints. Among professionals, understanding the challenging social determinants of unhealthy lifestyle factors was countered by a desire to support healthy change in those affected by deprivation. CONCLUSION: Public and professional views around lifestyle highlight an evolving understanding of the new and growing number of lifestyle factors as well as the increasingly complex interplay between lifestyle factors. Views of the social determinants of lifestyle and structural limits to agency strengthen arguments for reduced emphasis on individual-level responsibility for unhealthy lifestyle factors and for deeper integration of social determinants into lifestyle interventions. In addition to addressing poverty and socioeconomic inequalities directly, innovative policy, planning and legislation that incorporate wider approaches could tackle upstream determinants of numerous unhealthy lifestyle factors simultaneously. PATIENT OR PUBLIC CONTRIBUTION: Members of the public who participated in this study have made contributions by sharing their views and perspectives. The National Health Service Research Scotland Primary Care Patient and Public Involvement (NRS PPI) Group contributed to the development of this work. The NRS PPI Group was consulted as part of the preparatory work for H. M. E. F.'s doctoral thesis funding application. The findings of the qualitative work were presented to them, and they informed the interpretation of those findings and related work presented at conferences and public engagement events.

Social connection and mortality in UK Biobank: a prospective cohort analysis.

BACKGROUND: Components of social connection are associated with mortality, but research examining their independent and combined effects in the same dataset is lacking. This study aimed to examine the independent and combined associations between functional and structural components of social connection and mortality. METHODS: Analysis of 458,146 participants with full data from the UK Biobank cohort linked to mortality registers. Social connection was assessed using two functional (frequency of ability to confide in someone close and often feeling lonely) and three structural (frequency of friends/family visits, weekly group activities, and living alone) component measures. Cox proportional hazard models were used to examine the associations with all-cause and cardiovascular disease (CVD) mortality. RESULTS: Over a median of 12.6 years (IQR 11.9-13.3) follow-up, 33,135 (7.2%) participants died, including 5112 (1.1%) CVD deaths. All social connection measures were independently associated with both outcomes. Friends/family visit frequencies < monthly were associated with a higher risk of mortality indicating a threshold effect. There were interactions between living alone and friends/family visits and between living alone and weekly group activity. For example, compared with daily friends/family visits-not living alone, there was higher all-cause mortality for daily visits-living alone (HR 1.19 [95% CI 1.12-1.26]), for never having visits-not living alone (1.33 [1.22-1.46]), and for never having visits-living alone (1.77 [1.61-1.95]). Never having friends/family visits whilst living alone potentially counteracted benefits from other components as mortality risks were highest for those reporting both never having visits and living alone regardless of weekly group activity or functional components. When all measures were combined into overall functional and structural components, there was an interaction between components: compared with participants defined as not isolated by both components, those considered isolated by both components had higher CVD mortality (HR 1.63 [1.51-1.76]) than each component alone (functional isolation 1.17 [1.06-1.29]; structural isolation 1.27 [1.18-1.36]). CONCLUSIONS: This work suggests (1) a potential threshold effect for friends/family visits, (2) that those who live alone with additional concurrent markers of structural isolation may represent a high-risk population, (3) that beneficial associations for some types of social connection might not be felt when other types of social connection are absent, and (4) considering both functional and structural components of social connection may help to identify the most isolated in society.

Chronic pain and COVID-19 hospitalisation and mortality: a UK Biobank cohort study.

ABSTRACT: The risk of COVID-19 in those with chronic pain is unknown. We investigated whether self-reported chronic pain was associated with COVID-19 hospitalisation or mortality. UK Biobank recruited 502,624 participants aged 37 to 73 years between 2006 and 2010. Baseline exposure data, including chronic pain (>3 months, in at least 1 of 7 prespecified body sites) and chronic widespread pain (>3 months, all over body), were linked to COVID-19 hospitalisations or mortality. Univariable or multivariable Poisson regression analyses were performed on the association between chronic pain and COVID-19 hospitalisation and Cox regression analyses of the associations with COVID-19 mortality. Multivariable analyses adjusted incrementally for sociodemographic confounders, then lifestyle risk factors, and finally long-term condition count. Of 441,403 UK Biobank participants with complete data, 3180 (0.7%) were hospitalised for COVID-19 and 1040 (0.2%) died from COVID-19. Chronic pain was associated with hospital admission for COVID-19 even after adjustment for all covariates (incidence rate ratio 1.16; 95% confidence interval [CI] 1.08-1.24; P < 0.001), as was chronic widespread pain (incidence rate ratio 1.33; 95% CI 1.06-1.66; P = 0.012). There was clear evidence of a dose-response relationship with number of pain sites (fully adjusted global P -value < 0.001). After adjustment for all covariates, there was no association between chronic pain (HR 1.01; 95% CI 0.89-1.15; P = 0.834) but attenuated association with chronic widespread pain (HR 1.50, 95% CI 1.04-2.16, P -value = 0.032) and COVID-19 mortality. Chronic pain is associated with higher risk of hospitalisation for COVID-19, but the association with mortality is unclear. Future research is required to investigate these findings further and determine whether pain is associated with long COVID.

The influence of socioeconomic status on the association between unhealthy lifestyle factors and adverse health outcomes: a systematic review.

Background: Combinations of lifestyle factors (LFs) and socioeconomic status (SES) are independently associated with cardiovascular disease (CVD), cancer, and mortality. Less advantaged SES groups may be disproportionately vulnerable to unhealthy LFs but interactions between LFs and SES remain poorly understood. This review aimed to synthesise the available evidence for whether and how SES modifies associations between combinations of LFs and adverse health outcomes. Methods: Systematic review of studies that examine associations between combinations of >3 LFs (eg.smoking/physical activity/diet) and health outcomes and report data on SES (eg.income/education/poverty-index) influences on associations. Databases (PubMed/EMBASE/CINAHL), references, forward citations, and grey-literature were searched from inception to December 2021. Eligibility criteria were analyses of prospective adult cohorts that examined all-cause mortality or CVD/cancer mortality/incidence. Results: Six studies (n=42,467-399,537; 46.5-56.8 years old; 54.6-59.3% women) of five cohorts were included. All examined all-cause mortality; three assessed CVD/cancer outcomes. Four studies observed multiplicative interactions between LFs and SES, but in opposing directions. Two studies tested for additive interactions; interactions were observed in one cohort (UK Biobank) and not in another (National Health and Nutrition Examination Survey (NHANES)). All-cause mortality HRs (95% confidence intervals) for unhealthy LFs (versus healthy LFs) from the most advantaged SES groups ranged from 0.68 (0.32-1.45) to 4.17 (2.27-7.69). Equivalent estimates from the least advantaged ranged from 1.30 (1.13-1.50) to 4.00 (2.22-7.14). In 19 analyses (including sensitivity analyses) of joint associations between LFs, SES, and all-cause mortality, highest all-cause mortality was observed in the unhealthiest LF-least advantaged suggesting an additive effect. Conclusions: Limited and heterogenous literature suggests that the influence of SES on associations between combinations of unhealthy LFs and adverse health could be additive but remains unclear. Additional prospective analyses would help clarify whether SES modifies associations between combinations of unhealthy LFs and health outcomes. Registration: Protocol is registered with PROSPERO (CRD42020172588;25 June 2020).

The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort.

BACKGROUND: Infection with SARS-CoV-2 virus (COVID-19) impacts disadvantaged groups most. Lifestyle factors are also associated with adverse COVID-19 outcomes. To inform COVID-19 policy and interventions, we explored effect modification of socioeconomic-status (SES) on associations between lifestyle and COVID-19 outcomes. METHODS: Using data from UK-Biobank, a large prospective cohort of 502,536 participants aged 37-73 years recruited between 2006 and 2010, we assigned participants a lifestyle score comprising nine factors. Poisson regression models with penalised splines were used to analyse associations between lifestyle score, deprivation (Townsend), and COVID-19 mortality and severe COVID-19. Associations between each exposure and outcome were examined independently before participants were dichotomised by deprivation to examine exposures jointly. Models were adjusted for sociodemographic/health factors. RESULTS: Of 343,850 participants (mean age > 60 years) with complete data, 707 (0.21%) died from COVID-19 and 2506 (0.76%) had severe COVID-19. There was evidence of a nonlinear association between lifestyle score and COVID-19 mortality but limited evidence for nonlinearity between lifestyle score and severe COVID-19 and between deprivation and COVID-19 outcomes. Compared with low deprivation, participants in the high deprivation group had higher risk of COVID-19 outcomes across the lifestyle score. There was evidence for an additive interaction between lifestyle score and deprivation. Compared with participants with the healthiest lifestyle score in the low deprivation group, COVID-19 mortality risk ratios (95% CIs) for those with less healthy scores in low versus high deprivation groups were 5.09 (1.39-25.20) and 9.60 (4.70-21.44), respectively. Equivalent figures for severe COVID-19 were 5.17 (2.46-12.01) and 6.02 (4.72-7.71). Alternative SES measures produced similar results. CONCLUSIONS: Unhealthy lifestyles are associated with higher risk of adverse COVID-19, but risks are highest in the most disadvantaged, suggesting an additive influence between SES and lifestyle. COVID-19 policy and interventions should consider both lifestyle and SES. The greatest public health benefit from lifestyle focussed COVID-19 policy and interventions is likely to be seen when greatest support for healthy living is provided to the most disadvantaged groups.

Correction: Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort.

[This corrects the article DOI: 10.1371/journal.pone.0238091.].

Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort.

BACKGROUND: It is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors. METHODS AND FINDINGS: We studied data from UK Biobank (428,199 participants; aged 37-73; recruited 2006-2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96-1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28-1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12-1.46) and 1.77 (1.46-2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09-3.78); 2.79 (2.00-3.90); 2.66 (1.88-3.76); 2.13 (1.46-3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population. CONCLUSIONS: Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.

Does the association between physical capability and mortality differ by deprivation? Findings from the UK Biobank population-based cohort study.

BACKGROUND: To investigate whether the excess risk of adverse health outcomes associated with a lower physical capability in adulthood differs by deprivation levels. METHODS: 279,030 participants from the UK Biobank were included. Handgrip strength and walking pace were the exposures. All-cause mortality, CVD mortality and incidence were the outcomes. Townsend deprivation index was treated as a potential effect modifier. The associations were investigated using Cox-regression models with years of follow-up as the time-varying covariate. RESULTS: A significant interaction between deprivation and handgrip strength was found for all-cause mortality (p = 0.024), CVD mortality (p = 0.006) and CVD incidence (p = 0.001). The hazard ratio for all-cause mortality was 1.18 [1.09; 1.29] per 1-tertile higher level of grip strength in the least deprived group, whereas it was 1.30 [1.18; 1.43] in the most deprived individuals. Similar results were found for CVD mortality and incidence per tertile increment in handgrip strength in the least and most deprived quintiles, respectively. No significant interactions between deprivation and walking pace were found for any of the outcomes. CONCLUSION: Low handgrip strength is a stronger predictor of morbidity and mortality in individuals living in more deprived areas.

Understanding How Much TV is Too Much: A Nonlinear Analysis of the Association Between Television Viewing Time and Adverse Health Outcomes.

OBJECTIVE: To inform potential guideline development, we investigated nonlinear associations between television viewing time (TV time) and adverse health outcomes. METHODS: From 2006 to 2010, 490,966 UK Biobank participants, aged 37 to 73 years, were recruited. They were followed from 2006 to 2018. Nonlinear associations between self-reported TV time (hours per day) and outcomes explored using penalized cubic splines in Cox proportional hazards adjusted for demographics and lifestyle. Population-attributable and potential impact fractions were calculated to contextualize population-level health outcomes associated with different TV time levels. Nonlinear isotemporal substitution analyses were used to investigate substituting TV time with alternative activities. Primary outcomes were mortality: all-cause, cardiovascular disease (CVD) and cancer; incidence: CVD and cancer; secondary outcomes were incident myocardial infarction, stroke, and heart failure and colon, lung, breast, and prostate cancer. RESULTS: Those with noncommunicable disease (109,867 [22.4%]), CVD (32,243 [6.6%]), and cancer (37,81 [7.7%]) at baseline were excluded from all-cause mortality, CVD, and cancer analyses, respectively. After 7.0 years (mortality) and 6.2 years (disease incidence) mean follow-up, there were 10,306 (2.7%) deaths, 24,388 (5.3%) CVD events, and 39,121 (8.7%) cancer events. Associations between TV time and all-cause and CVD mortality were curvilinear (Pnon-linear ≤.003), with lowest risk observed <2 hours per day. Theoretically, 8.64% (95% confidence interval [CI], 6.60-10.73) of CVD mortality is attributable to TV time. Limiting TV time to 2 hours per day might have prevented, or at least delayed, 7.97% (95% CI, 5.54-10.70) of CVD deaths. Substituting TV time with sleeping, walking, or moderate or vigorous physical activity was associated with reduced risk for all outcomes when baseline levels of substitute activities were low. CONCLUSION: TV time is associated with numerous adverse health outcomes. Future guidelines could suggest limiting TV time to less than 2 hours per day to reduce most of the associated adverse health events.

The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort.

BACKGROUND: Combinations of lifestyle factors interact to increase mortality. Combinations of traditional factors such as smoking and alcohol are well described, but the additional effects of emerging factors such as television viewing time are not. The effect of socioeconomic deprivation on these extended lifestyle risks also remains unclear. We aimed to examine whether deprivation modifies the association between an extended score of lifestyle-related risk factors and health outcomes. METHODS: Data for this prospective analysis were sourced from the UK Biobank, a prospective population-based cohort study. We assigned all participants an extended lifestyle score, with 1 point for each unhealthy lifestyle factor (incorporating sleep duration and high television viewing time, in addition to smoking, excessive alcohol, poor diet [low intake of oily fish or fruits and vegetables, and high intake of red meat or processed meats], and low physical activity), categorised as most healthy (score 0-2), moderately healthy (score 3-5), or least healthy (score 6-9). Cox proportional hazards models were used to examine the association between lifestyle score and health outcomes (all-cause mortality and cardiovascular disease mortality and incidence), and whether this association was modified by deprivation. All analyses were landmark analyses, in which participants were excluded if they had an event (death or cardiovascular disease event) within 2 years of recruitment. Participants with non-communicable diseases (except hypertension) and missing covariate data were excluded from analyses. Participants were also excluded if they reported implausible values for physical activity, sleep duration, and total screen time. All analyses were adjusted for age, sex, ethnicity, month of assessment, history of hypertension, systolic blood pressure, medication for hypercholesterolaemia or hypertension, and body-mass index categories. FINDINGS: 328 594 participants aged 40-69 years were included in the study, with a mean follow-up period of 4·9 years (SD 0·83) after the landmark period for all-cause and cardiovascular disease mortality, and 4·1 years (0·81) for cardiovascular disease incidence. In the least deprived quintile, the adjusted hazard ratio (HR) in the least healthy lifestyle category, compared with the most healthy category, was 1·65 (95% CI 1·25-2·19) for all-cause mortality, 1·93 (1·16-3·20) for cardiovascular disease mortality, and 1·29 (1·10-1·52) for cardiovascular disease incidence. Equivalent HRs in the most deprived quintile were 2·47 (95% CI 2·04-3·00), 3·36 (2·36-4·76), and 1·41 (1·25-1·60), respectively. The HR for trend for one increment change towards least healthy in the least deprived quintile compared with that in the most deprived quintile was 1·25 (95% CI 1·12-1·39) versus 1·55 (1·40-1·70) for all-cause mortality, 1·30 (1·05-1·61) versus 1·83 (1·54-2·18) for cardiovascular disease mortality, and 1·10 (1·04-1·17) versus 1·16 (1·09-1·23) for cardiovascular disease incidence. A significant interaction was found between lifestyle and deprivation for all-cause and cardiovascular disease mortality (both pinteraction<0·0001), but not for cardiovascular disease incidence (pinteraction=0·11). INTERPRETATION: Wide combinations of lifestyle factors are associated with disproportionate harm in deprived populations. Social and fiscal policies that reduce poverty are needed alongside public health and individual-level interventions that address a wider range of lifestyle factors in areas of deprivation. FUNDING: None.

Evaluation of the diagnostic accuracy of the Haemoglobin Colour Scale to detect anaemia in young children attending primary healthcare clinics in Zanzibar.

OBJECTIVES: This study evaluates the diagnostic accuracy of Haemoglobin Colour Scale (HCS), compared with clinical diagnosis, to detect anaemia and severe anaemia in preschool-age children attending primary healthcare clinics in rural Zanzibar. METHODS: In all participants, haemoglobin (Hb) concentration was independently estimated by clinical examination for palmar pallor, HCS and HemoCue™. HemoCue was considered the reference method. Data collection was integrated into the usual health services and performed by local healthcare workers (HCWs). Sensitivity, specificity, positive and negative predictive values were calculated for HCS and clinical examination for palmar pallor. The limits of agreement between HCS and HemoCue, and inter-observer variability for HCS, were also defined. RESULTS: A total of 799 children age 2-59 months were recruited to the study. The prevalence of anaemia (Hb<11 g/dl) and severe anaemia (<5 g/dl) were 71% and 0.8% respectively. The sensitivity of HCS to detect anaemia was 33% [95% confidence interval (CI) 29-36] and specificity was 87% (83-91). The sensitivity of HCS to detect severe anaemia was 14% (95% CI 0-58) and specificity was 100% (99-100). The sensitivity of palmar pallor to detect anaemia was low, but superior to HCS (58% vs. 33%, P<0.001); specificity was inferior to HCS (55% vs. 87%, P<0.001). There was no evidence of a difference in either sensitivity (P>0.1) or specificity (P>0.1) between HCS and palmar pallor to detect severe anaemia. CONCLUSIONS: Haemoglobin Colour Scale does not improve the capacity of HCWs to diagnose anaemia in this population. Accuracy is limited by considerable variability in the performances of test operators. However, optimizing the training protocol for those using the test may improve performance.