Informing Investment to Reduce Inequalities: A Modelling Approach

McAuley, Andrew; Denny, Cheryl; Taulbut, Martin; Mitchell, Rory; Fischbacher, Colin; Graham, Barbara; Grant, Ian; O'Hagan, Paul; McAllister, David; McCartney, Gerry

doi:10.1371/journal.pone.0159256

Cited by 28 publications

(26 citation statements)

References 34 publications

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“…Our model for estimating a person’s EAD can be used to motivate lifestyle changes. Furthermore, decision makers, who might have a possibility to influence working and other relevant environments, can use it to estimate potential need for preventive measures and medical care in the population 43…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

et al. 2020

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ObjectiveTo develop a tool to inform individuals and general practitioners about benefits of lifestyle changes by providing estimates of the expected age of death (EAD) for different risk factor values, and for those who plan and decide on preventive activities and health services at population level, to calculate potential need for these.DesignProspective cohort study to estimate EAD using a model with 27 established risk factors, categorised into four groups: (1) sociodemographic background and medical history, (2) lifestyles, (3) life satisfaction, and (4) biological risk factors. We apply a Poisson regression model on the survival data split into 1-year intervals.ParticipantsTotal of 38 549 participants aged 25–74 years at baseline of the National FINRISK Study between 1987 and 2007.Primary outcome measuresRegister-based comprehensive mortality data from 1987 to 2014 with an average follow-up time of 16 years and 4310 deaths.ResultsAlmost all risk factors included in the model were statistically significantly associated with death. The largest influence on the EAD appeared to be a current heavy smoker versus a never smoker as the EAD for a 30-year-old man decreased from 86.8 years, which corresponds to the reference values of the risk factors, to 80.2 years. Diabetes decreased EAD by >6.6 years. Whole or full milk consumers had 3.4 years lower EAD compared with those consuming skimmed milk. Physically inactive men had 2.4 years lower EAD than those with high activity. Men who found their life almost unbearable due to stress had 2.8 years lower EAD.ConclusionsThe biological risk factors and lifestyles, and the factors connected with life satisfaction were clearly associated with EAD. Our model for estimating a person’s EAD can be used to motivate lifestyle changes.

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Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

et al. 2020

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“…19 To reduce health inequalities, distinguished researchers such as Sir Michael Marmot recommend that policies focus on wider determinants of health, including housing. [20][21][22] However, some housing policy has been implemented despite evidence from housing interventions suggesting small or unclear health effects. 23 Countries in temperate regions with fewer energy-efficient homes, including the UK, Greece, Portugal and Ireland, have more excess winter deaths, indicating that variations in winter mortality are caused by differences in indoor temperatures, health-care spending and socioeconomic circumstances.…”

Section: Background/rationalementioning

confidence: 99%

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

et al. 2018

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BackgroundPoor-quality housing adversely affects residents’ health but there is a paucity of high-quality evidence to support this.ObjectiveThis research investigated the health impact of bringing housing to a national quality standard.DesignA natural experiment of improvements to housing quality analysed using repeated measures of health-care utilisation and economic outcomes at an individual person level.SettingCarmarthenshire, UK.ParticipantsA total of 32,009 residents registered for a minimum of 60 days at 8558 social homes that received housing improvements between January 2005 and March 2015.InterventionsMultiple internal and external housing improvements, including wall and loft insulation, windows and doors, heating system upgrades, new kitchens and bathrooms, garden path safety improvements and electrical system upgrades (adding power sockets, and extractor fans in kitchens and bathrooms).Main outcome measuresEmergency hospital admissions for cardiorespiratory conditions and injuries. Primary health-care utilisation for respiratory and common mental health disorders, emergency department injury attendances and health-care utilisation costs.Data sourcesCarmarthenshire County Council home address and intervention records were anonymously linked within the Secure Anonymous Information Linkage databank to demographic information from the Welsh Demographic Service data set; hospital admission data from the Patient Episode Dataset for Wales; primary care contacts and prescribed medications from general practice data; emergency department attendances from the Emergency Department Data Set; and deaths from the Office for National Statistics mortality register.MethodsThe study used a longitudinal panel design to examine changes in standard of eight housing cointervention from intervention records, and linked to individuals registered at intervention homes. Health outcomes were obtained retrospectively for each individual in a dynamic cohort and were captured for up to 123 consecutive months. An additional local authority region could not be utilised as a comparator owing to different reporting pressures resulting in the recording of a different intervention. The exposure group for each cointervention was compared with an internal reference group of people living in homes that did not receive the cointervention during their tenancy. A multilevel modelling approach was used to account for repeated observations for individuals living in intervention homes. Counts of health outcomes were analysed using negative binomial regression models to determine the effect of each cointervention that reached housing quality standards during an individual’s period of tenancy, compared with those living in properties that did not. We adjusted for potential confounding factors and for background trends in the regional general population. A cost–consequences analysis was conducted as part of the health economic evaluation.ResultsResidents aged ≥ 60 years living in homes in which electrical systems were upgraded were associated with 39% fewer admissions than those living in homes in which they were not [incidence rate ratio (IRR) 0.61, 95% confidence interval (CI) 0.53 to 0.72;p < 0.01]. Reduced admissions were also associated with windows and doors (IRR 0.71, 95% CI 0.63 to 0.81;p < 0.01), wall insulation (IRR 0.75, 95% CI 0.67 to 0.84;p < 0.01) and gardens and estates (IRR 0.73, 95% CI 0.64 to 0.83;p < 0.01) for those living in homes in which these cointervention were upgraded. There were no associations of change in emergency admissions with upgrading heating (IRR 0.91, 95% CI 0.82 to 1.01;p = 0.072), loft insulation (IRR 0.98, 95% CI 0.86 to 1.11;p = 0.695), kitchens (IRR 0.98, 95% CI 0.83 to 1.17;p = 0.843) or bathrooms (IRR 0.93, 95% CI 0.81 to 1.06;p = 0.287).LimitationsThere was no randomisation, there were incomplete data on the scale of the intervention for individual households and there were no estimates for the impact of the whole programme.ConclusionsThis complex interdisciplinary study found that hospital admissions could be avoided through improving housing quality standards.Future workAt their initiation, future non-health projects should have a built-in evaluation to allow intervention exposures to be randomly allocated to residents, with the simultaneous analysis of multiple health outcomes in one statistical model.FundingThe National Institute for Health Research Public Health Research programme.

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“…Despite increasing recognition of inequalities in adolescence, there is little evidence on what works to address these inequalities 10,11 . Interventions can fail to tackle inequalities, there can be 'intervention generated inequalities' 12,13 , and/or under-provision for those most in need 14 . Differential intervention effects can be defined as "differences in intervention effect between groups of lower and higher socioeconomic status" 12 , p.1of 4).…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic inequalities and the equity impact of population-level interventions for adolescent health: an overview of systematic reviews

et al. 2020

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Socioeconomic inequalities and the equity impact of population-level interventions for adolescent health: An overview of systematic reviews ABSTRACTObjectives: Despite robust evidence on health inequalities in adulthood, less attention has been paid to inequalities in adolescence. The aim of this overview was to examine systematic review (SR) evidence on the equity impact of population-level interventions intended to improve health, happiness and wellbeing for adolescents.Study Design: An overview (review of systematic reviews).Methods: Eleven electronic databases were systematically searched to identify SRs of population-level interventions for adolescent health. A secondary data analysis of socioeconomic inequality was conducted to identify whether SRs reported on primary studies in terms of disadvantage, by measures of socioeconomic status (SES) and by differential effects.Results: 35,310 review titles were screened; 566 full texts were retrieved and 140 SRs met the predefined selection criteria. Differential intervention effects were considered in 42/140 (30%) SRs, 18/140 (13%) reported primary studies using an SES measure and 16/140 (11%) explicitly reported differential effects. 15/140 SRs (11%) explicitly focused on socioeconomic inequalities; of these 4/15 reported differential intervention effects in more detail, 7/15 concluded there was insufficient primary evidence to identify the impact of interventions on socioeconomic inequalities and 4/15 planned to examine differential effects by SES, but this was not reported further. Conclusions:Our overview identifies that there is limited SR evidence on the equity impact of population-level interventions for adolescent health. Strengthening the evidence on whether interventions narrow or widen inequalities for adolescents must be a priority for public health research.

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Informing Investment to Reduce Inequalities: A Modelling Approach

Cited by 28 publications

References 34 publications

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

Socioeconomic inequalities and the equity impact of population-level interventions for adolescent health: an overview of systematic reviews

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