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.

Family history of diabetes and risk of SARS-COV-2 in UK Biobank: A prospective cohort study.

INTRODUCTION: The aim of this study was to determine risk of being SARS-CoV-2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. METHODS: We used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS-CoV-2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without. RESULTS: Of 401,268 participants in total, 13,331 tested positive for SARS-CoV-2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS-CoV-2 positive (risk ratio-RR 1.35; 95% confidence interval-CI 1.24-1.47) and severe infection (RR 1.30; 95% CI 1.04-1.59), compared to those without. The excess risk of being tested positive for SARS-CoV-2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes. CONCLUSION: The totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes.

Nonlinear Associations Between Cumulative Dietary Risk Factors and Cardiovascular Diseases, Cancer, and All-Cause Mortality: A Prospective Cohort Study From UK Biobank.

OBJECTIVE: To develop a score from cumulative dietary risk factors and examine its nonlinear associations with cardiovascular disease (CVD) and cancer incidence and mortality, as well as all-cause mortality. PATIENTS AND METHODS: There were 422,702 participants from UK Biobank included in this prospective study. Cumulative dietary risk factors were represented using a score ranging from 0 (healthiest) to 9 (least healthy). This was derived from 9 food items based on current UK guidelines using baseline data. Associations between the cumulative score and health outcomes were investigated using nonlinear penalized cubic splines fitted in Cox proportional hazard models. Follow-up was conducted until June 2020 for mortality, and for incidence, up to June 2020 in England and March 2017 in Wales and Scotland. RESULTS: The median follow-up period was 9.0 years for incidence outcomes and 9.3 years for mortality outcomes. Each 1-point increment in the cumulative dietary risk factors score was associated with higher risk for incidence and mortality of the outcomes studied. The highest risks were identified for mortality due to heart failure (8.0% higher), CVD, and ischemic heart disease (both 7.0% higher). In addition, a higher diet score accounted for 18.8% of all deaths, 4.47% of incident cases of CVD, 25.5% of CVD deaths, 7.7% of incident cancers, and 18.2% of all cancer deaths. CONCLUSION: Our findings show that dietary risk factors contributed to a large proportion of CVD and cancer events, as well as deaths, among those who did not meet most dietary recommendations.

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

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

Understanding the influence of socioeconomic status on the association between combinations of lifestyle factors and adverse health outcomes: a systematic review protocol.

INTRODUCTION: Combinations of unhealthy lifestyle factors are strongly associated with mortality, cardiovascular disease (CVD) and cancer. It is unclear how socioeconomic status (SES) affects those associations. Lower SES groups may be disproportionately vulnerable to the effects of unhealthy lifestyle factors compared with higher SES groups via interactions with other factors associated with low SES (eg, stress) or via accelerated biological ageing. This systematic review aims to synthesise studies that examine how SES moderates the association between lifestyle factor combinations and adverse health outcomes. Greater understanding of how lifestyle risk varies across socioeconomic spectra could reduce adverse health by (1) identifying novel high-risk groups or targets for future interventions and (2) informing research, policy and interventions that aim to support healthy lifestyles in socioeconomically deprived communities. METHODS AND ANALYSIS: Three databases will be searched (PubMed, EMBASE, CINAHL) from inception to March 2020. Reference lists, citations and grey literature will also be searched. Inclusion criteria are: (1) prospective cohort studies; (2) investigations of two key exposures: (a) lifestyle factor combinations of at least three lifestyle factors (eg, smoking, physical activity and diet) and (b) SES (eg, income, education or poverty index); (3) an assessment of the impact of SES on the association between combinations of unhealthy lifestyle factors and health outcomes; (4) at least one outcome from-mortality (all cause, CVD and cancer), CVD or cancer incidence. Two independent reviewers will screen titles, abstracts and full texts of included studies. Data extraction will focus on cohort characteristics, exposures, direction and magnitude of SES effects, methods and quality (via Newcastle-Ottawa Scale). If appropriate, a meta-analysis, pooling the effects of SES, will be performed. Alternatively, a synthesis without meta-analysis will be conducted. ETHICS AND DISSEMINATION: Ethical approval is not required. Results will be disseminated via peer-reviewed publication, professional networks, social media and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020172588.

Is older age associated with COVID-19 mortality in the absence of other risk factors? General population cohort study of 470,034 participants.

INTRODUCTION: Older people have been reported to be at higher risk of COVID-19 mortality. This study explored the factors mediating this association and whether older age was associated with increased mortality risk in the absence of other risk factors. METHODS: In UK Biobank, a population cohort study, baseline data were linked to COVID-19 deaths. Poisson regression was used to study the association between current age and COVID-19 mortality. RESULTS: Among eligible participants, 438 (0.09%) died of COVID-19. Current age was associated exponentially with COVID-19 mortality. Overall, participants aged ≥75 years were at 13-fold (95% CI 9.13-17.85) mortality risk compared with those <65 years. Low forced expiratory volume in 1 second, high systolic blood pressure, low handgrip strength, and multiple long-term conditions were significant mediators, and collectively explained 39.3% of their excess risk. The associations between these risk factors and COVID-19 mortality were stronger among older participants. Participants aged ≥75 without additional risk factors were at 4-fold risk (95% CI 1.57-9.96, P = 0.004) compared with all participants aged <65 years. CONCLUSIONS: Higher COVID-19 mortality among older adults was partially explained by other risk factors. 'Healthy' older adults were at much lower risk. Nonetheless, older age was an independent risk factor for COVID-19 mortality.

Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank.

BACKGROUND: Frailty has been associated with worse prognosis following COVID-19 infection. While several studies have reported the association between frailty and COVID-19 mortality or length of hospital stay, there have been no community-based studies on the association between frailty and risk of severe infection. Considering that different definitions have been identified to assess frailty, this study aimed to compare the association between frailty and severe COVID-19 infection in UK Biobank using two frailty classifications: the frailty phenotype and the frailty index. METHODS: A total of 383,845 UK Biobank participants recruited 2006-2010 in England (211,310 [55.1%] women, baseline age 37-73 years) were included. COVID-19 test data were provided by Public Health England (available up to 28 June 2020). An adapted version of the frailty phenotype derived by Fried et al. was used to define frailty phenotype (robust, pre-frail, or frail). A previously validated frailty index was derived from 49 self-reported questionnaire items related to health, disease and disability, and mental wellbeing (robust, mild frailty, and moderate/severe frailty). Both classifications were derived from baseline data (2006-2010). Poisson regression models with robust standard errors were used to analyse the associations between both frailty classifications and severe COVID-19 infection (resulting in hospital admission or death), adjusted for sociodemographic and lifestyle factors. RESULTS: Of UK Biobank participants included, 802 were admitted to hospital with and/or died from COVID19 (323 deaths and 479 hospitalisations). After analyses were adjusted for sociodemographic and lifestyle factors, a higher risk of COVID-19 was observed for pre-frail (risk ratio (RR) 1.47 [95% CI 1.26; 1.71]) and frail (RR 2.66 [95% CI 2.04; 3.47]) individuals compared to those classified as robust using the frailty phenotype. Similar results were observed when the frailty index was used (RR mildly frail 1.46 [95% CI 1.26; 1.71] and RR moderate/severe frailty 2.43 [95% CI 1.91; 3.10]). CONCLUSIONS: Frailty was associated with a higher risk of severe COVID-19 infection resulting in hospital admission or death, irrespective of how it was measured and independent of sociodemographic and lifestyle factors. Public health strategies need to consider the additional risk that COVID-19 poses in individuals with frailty, including which additional preventive measures might be required.

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.

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.

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.

Associations of fat and carbohydrate intake with cardiovascular disease and mortality: prospective cohort study of UK Biobank participants.

OBJECTIVE: To investigate the association of macronutrient intake with all cause mortality and cardiovascular disease (CVD), and the implications for dietary advice. DESIGN: Prospective population based study. SETTING: UK Biobank. PARTICIPANTS: 195 658 of the 502 536 participants in UK Biobank completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using Oxford WebQ, a web based 24 hour recall questionnaire, and nutrient intakes were estimated using standard methodology. Cox proportional models with penalised cubic splines were used to study non-linear associations. MAIN OUTCOME MEASURES: All cause mortality and incidence of CVD. RESULTS: 4780 (2.4%) participants died over a mean 10.6 (range 9.4-13.9) years of follow-up, and 948 (0.5%) and 9776 (5.0%) experienced fatal and non-fatal CVD events, respectively, over a mean 9.7 (range 8.5-13.0) years of follow-up. Non-linear associations were found for many macronutrients. Carbohydrate intake showed a non-linear association with mortality; no association at 20-50% of total energy intake but a positive association at 50-70% of energy intake (3.14 v 2.75 per 1000 person years, average hazard ratio 1.14, 95% confidence interval 1.03 to 1.28 (60-70% v 50% of energy)). A similar pattern was observed for sugar but not for starch or fibre. A higher intake of monounsaturated fat (2.94 v 3.50 per 1000 person years, average hazard ratio 0.58, 0.51 to 0.66 (20-25% v 5% of energy)) and lower intake of polyunsaturated fat (2.66 v 3.04 per 1000 person years, 0.78, 0.75 to 0.81 (5-7% v 12% of energy)) and saturated fat (2.66 v 3.59 per 1000 person years, 0.67, 0.62 to 0.73 (5-10% v 20% of energy)) were associated with a lower risk of mortality. A dietary risk matrix was developed to illustrate how dietary advice can be given based on current intake. CONCLUSION: Many associations between macronutrient intake and health outcomes are non-linear. Thus dietary advice could be tailored to current intake. Dietary guidelines on macronutrients (eg, carbohydrate) should also take account of differential associations of its components (eg, sugar and starch).

What do we know about demand, use and outcomes in primary care out-of-hours services? A systematic scoping review of international literature.

OBJECTIVE: To synthesise international evidence for demand, use and outcomes of primary care out-of-hours health services (OOHS). DESIGN: Systematic scoping review. DATA SOURCES: CINAHL; Medline; PsyARTICLES; PsycINFO; SocINDEX; and Embase from 1995 to 2019. STUDY SELECTION: English language studies in UK or similar international settings, focused on services in or directly impacting primary care. RESULTS: 105 studies included: 54% from mainland Europe/Republic of Ireland; 37% from UK. Most focused on general practitioner-led out-of-hours cooperatives. Evidence for increasing patient demand over time was weak due to data heterogeneity, infrequent reporting of population denominators and little adjustment for population sociodemographics. There was consistent evidence of higher OOHS use in the evening compared with overnight, at weekends and by certain groups (children aged <5, adults aged >65, women, those from socioeconomically deprived areas, with chronic diseases or mental health problems). Contact with OOHS was driven by problems perceived as urgent by patients. Respiratory, musculoskeletal, skin and abdominal symptoms were the most common reasons for contact in adults; fever and gastrointestinal symptoms were the most common in the under-5s. Frequent users of daytime services were also frequent OOHS users; difficulty accessing daytime services was also associated with OOHS use. There is some evidence to suggest that OOHS colocated in emergency departments (ED) can reduce demand in EDs. CONCLUSIONS: Policy changes have impacted on OOHS over the past two decades. While there are generalisable lessons, a lack of comparable data makes it difficult to judge how demand has changed over time. Agreement on collection of OOHS data would allow robust comparisons within and across countries and across new models of care. Future developments in OOHS should also pay more attention to the relationship with daytime primary care and other services. PROSPERO REGISTRATION NUMBER: CRD42015029741.

No such thing as bad publicity? A quantitative content analysis of print media representations of primary care out-of-hours services.

OBJECTIVE: To explore how out-of-hours primary healthcare services (OOHS) are represented in UK national newspapers, focusing on content and tone of reporting and the use of personal narratives to frame stories. DESIGN: A retrospective cross-sectional quantitative content analysis of articles published in 2005, 2010 and 2015. DATA SOURCES: Nexis database used to search 10 UK national newspapers covering quality, middle-market and tabloid publications. INCLUSION/EXCLUSION CRITERIA: All articles containing the terms 'out-of-hours' (≥3 mentions per article) or ('NHS 24' OR 'NHS 111' OR 'NHS Direct') AND 'out-of-hours' (≥1 mention per article) were included. Letters, duplicate news items, opinion pieces and articles without a substantial portion of the story (>50% of an article's word count, as judged by researchers) concerning OOHS were excluded. RESULTS: 332 newspaper articles were identified: 113 in 2005 (34.1%), 140 in 2010 (42.2%) and 79 in 2015 (23.8%). Of these, 195 (58.7%) were in quality newspapers, 99 (29.8%) in middle-market and 38 (11.3%) in tabloids. The most commonly reported themes were OOHS organisation, personal narratives and telephone triage. Stories about service-level crises and personal tragedy, including unsafe doctors and missed or delayed identification of rare conditions, predominated. The majority of articles (252, 75.9%) were negative in tone. This was observed for all included newspapers and by publication genre; middle-market newspapers had the highest percentage of negative articles (Pearson χ2=35.72, p<0.001). Articles presented little supporting contextual information, such as call rates per annum, or advice on how to access OOHS. CONCLUSION: In this first reported analysis of UK national newspaper coverage of OOHS, media representation is generally negative in tone, with frequent reports of 'negative exemplars' of OOHS crises and fatal individual patient cases with little or no contextualisation. We present recommendations for the future reporting of OOHS, which could apply to the reporting of healthcare services more generally.

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.

Circumstances of child deaths in Mali and Uganda: a community-based confidential enquiry.

BACKGROUND: Interventions to reduce child deaths in Africa have often underachieved, causing the Millennium Development Goal targets to be missed. We assessed whether a community enquiry into the circumstances of death could improve intervention effectiveness by identifying local avoidable factors and explaining implementation failures. METHODS: Deaths of children younger than 5 years were ascertained by community informants in two districts in Mali (762 deaths) and three districts in Uganda (442 deaths) in 2011-15. Deaths were investigated by interviewing parents and health workers. Investigation findings were reviewed by a panel of local health-care workers and community representatives, who formulated recommendations to address avoidable factors and, subsequently, oversaw their implementation. FINDINGS: At least one avoidable factor was identified in 97% (95% CI 96-98, 737 of 756) of deaths in children younger than 5 years in Mali and 95% (93-97, 389 of 409) in Uganda. Suboptimal newborn care was a factor in 76% (146 of 194) of neonatal deaths in Mali and 64% (134 of 194) in Uganda. The most frequent avoidable factor in postneonatal deaths was inadequate child protection (mainly child neglect) in Uganda (29%, 63 of 215) and malnutrition in Mali (22%, 124 of 562). 84% (618 of 736 in Mali, 328 of 391 in Uganda) of families had consulted a health-care provider for the fatal illness, but the quality of care was often inadequate. Even in official primary care clinics, danger signs were often missed (43% of cases in Mali [135 of 396], 39% in Uganda [30 of 78]), essential treatment was not given (39% in Mali [154 of 396], 35% in Uganda [27 of 78]), and patients who were seriously ill were not referred to a hospital in time (51% in Mali [202 of 396], 45% in Uganda [35 of 78]). Local recommendations focused on quality of care in health-care facilities and on community issues influencing treatment-seeking behaviour. INTERPRETATION: Local investigation and review of circumstances of death of children in sub-Saharan Africa is likely to lead to more effective interventions than simple consideration of the biomedical causes of death. This approach discerned local public health priorities and implementable solutions to address the avoidable factors identified. FUNDING: European Union's 7th Framework Programme for research and technological development.

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.