Does community deprivation determine longevity after the age of 75? A cross-national analysis

Ribeiro, Ana Isabel; Krainski, Elias Teixeira; Carvalho, Marília Sá; Launoy, Guy; Pornet, Carole; Pina, Maria de Fátima

doi:10.1007/s00038-018-1081-y

Cited by 8 publications

(15 citation statements)

References 41 publications

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“…Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [25,[43][44][45][46][47][48]. Additionally, the Health Human Development Index [49], healthy life expectancy [50], old-age survival [51], potential years of life lost [52], and disability-adjusted life expectancy [25] were also used.…”

Section: Health Outcomesmentioning

confidence: 99%

“…Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [50], Central and Eastern Europe [48,60], the Visegrad (V4) group [62], or the Asia/ Pacific area [32]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [31,51,56,[62][63][64][65][66]. In two particular cases, a mix of countries and cities was used [35,57].…”

Section: Regions Countries and Populationsmentioning

confidence: 99%

“…In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [31,62,63,66], otherwise defined areas [51] or cities [56], and seven others that were multilevel designs and utilized both country-and region-level data [57], individual-and city-or countrylevel [35], individual-and country-level [44,45,48], individual-and neighborhood-level [64], and city-region-(NUTS3) and country-level data [65]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [25, 33, 43, 45-48, 50, 62, 67, 68, 71, 72], albeit in four of those [43,48,68,72] two separate points in time were taken (thus resulting in a kind of "double cross-section"), while in another the averages across survey waves were used [56].…”

Section: Data Types and Sourcesmentioning

confidence: 99%

“…It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [44], in another-welfare system typology [45], but also gender inequality [33], austerity level [70,78], and deprivation [51]. Most often though, attention went exclusively to GDP [27,29,46,57,65,71].…”

Section: Health Determinantsmentioning

confidence: 99%

“…Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, "unique" within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [39]; difference-in-difference (DiD) analysis was applied in one case [23]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [80]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [70,78]; hierarchical Bayesian spatial models [51] and special autoregressive regression [62] were also implemented. "Map" of determinants connectedness.…”

Section: Plos Onementioning

confidence: 99%

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Recent quantitative research on determinants of health in high income countries: A scoping review

Varbanova

Beutels

2020

PLoS ONE

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Background Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature. Methods We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013-July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis. Results Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, lifestyle , and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health. Conclusions We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It

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Section: Health Outcomesmentioning

confidence: 99%

Section: Regions Countries and Populationsmentioning

confidence: 99%

Section: Data Types and Sourcesmentioning

confidence: 99%

Section: Health Determinantsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Recent quantitative research on determinants of health in high income countries: A scoping review

Varbanova

Beutels

2020

PLoS ONE

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Neighbourhood socioeconomic deprivation and allostatic load: a multi-cohort study

Ribeiro

Fraga

Kelly-Irving

et al. 2019

Sci Rep

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Living in deprived neighbourhoods may have biological consequences, but few studies have assessed this empirically. We examined the association between neighbourhood deprivation and allostatic load, a biological marker of wear and tear, taking into account individual’s socioeconomic position. We analysed data from three cohort studies (CoLaus-Switzerland; EPIPorto-Portugal; Whitehall II-UK) comprising 16,364 participants. We defined allostatic load using ten biomarkers of dysregulated metabolic, cardiovascular, and inflammatory systems (body mass index; waist circumference; total, high and low density lipoprotein cholesterol; triglycerides; glucose; systolic and diastolic blood pressure; C-reactive protein). Mixed Poisson regression models were fitted to examine associations with neighbourhood deprivation (in quintiles, Q1-least deprived as reference). After adjustment for confounding variables, participants living in the most deprived quintile had 1.13 times higher allostatic load than those living in the least deprived quintile (Relative Risk, RR, for Q2 RR = 1.06, 95% CI 1.03-1.09; Q3 = 1.06, 1.03–1.10; Q4 = 1.09, 1.06–1.12; Q5 = 1.13, 1.09–1.16). This association was partially modified by individual’s socioeconomic position, such that the relative risk was higher in participants with low socioeconomic position (Q5 vs Q1 1.16, 1.11–1.22) than those with high socioeconomic position (Q5 vs Q1 1.07, 1.01–1.13). Neighbourhood deprivation is associated with biological wear and tear, suggesting that neighbourhood-level interventions may yield health gains.

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Spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England

Boulieri

Blangiardo

2020

BMJ Open

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ObjectivesTo estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation.DesignWe used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001–2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards.ResultsWe confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level.ConclusionsThere are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies.

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Does community deprivation determine longevity after the age of 75? A cross-national analysis

Cited by 8 publications

References 41 publications

Recent quantitative research on determinants of health in high income countries: A scoping review

Recent quantitative research on determinants of health in high income countries: A scoping review

Neighbourhood socioeconomic deprivation and allostatic load: a multi-cohort study

Spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England

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