(Un-) healthy ageing: Geographic inequalities in disability-free life expectancy in England and Wales

Norman, Paul; Exeter, Daniel J.; Shelton, Nicola; Head, Jenny; Murray, Emily

doi:10.1016/j.healthplace.2022.102820

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…This study highlights the influence of location on health behaviour and outcomes, supported by previous research indicating different patterns of mortality and long-term illness [ 12 ], self-rated health and functional limitations [ 17 ], and biomarkers of chronic disease and healthy ageing such as HbA1c, systolic blood pressure, and grip strength [ 18 ]. Additionally, geographical factors impact physical activity, and sedentary behaviour [ 15 ], and are intertwined with gender disparities in health, and socio-economic inequalities [ 14 ].…”

Section: Discussionsupporting

confidence: 78%

“…Thus, Scholars devise alternate methods to measure AA, with revised indicators and methods for specific regions [ 8 ]. Advanced techniques like factor analysis and machine learning are used to quantify AA [ 9 , 10 ], healthy ageing [ 11 , 12 ], successful ageing [ 13 ], and the older persons’ index of multiple deprivations [ 14 ]. These approaches allow us to quantify the health matrix and its determinants for older adults by utilising theoretical frameworks and rigorous statistical methods.…”

Section: Introductionmentioning

confidence: 99%

“…Measuring these variations and disparities is crucial for evaluating the impact of policies on health equity. Geographic location significantly influences health-related outcomes, affecting self-rated health [ 15 , 16 ], functional limitations [ 17 ], health behaviours [ 15 ], and healthy ageing [ 12 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand

Keanjoom,

Toyoda,

Nakamura

2024

Public Health in Practice

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

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Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand

Keanjoom,

Toyoda,

Nakamura

2024

Public Health in Practice

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“…Geographically, there has been a shift from a widespread level of deprivation, including in more rural areas in 1971, to being more concentrated in urban areas in the 21st Century. There is a distinctive improvement in deprivation for old industrial and coal eld areas (see Sinnett & Norman, 2024) and although this also appears to be the case for 'Countryside and Coastal' areas, visually this appears to be a mixed picture which is consistent with the case up to 2011 (Norman et al, 2022). London stands out for having relatively high levels of deprivation, including a relatively high proportion of LSOAs with a 'Worsening Deprivation' trajectory; itself consistent with the 'suburbanisation of poverty' (Bailey & Minton, 2018).…”

Section: Discussionsupporting

confidence: 53%

50-year deprivation trajectories: local area change in England and Wales, 1971-2021

Norman,

McLennan,

Ferguson

et al. 2024

Preprint

Self Cite

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Since 1971, the decennial censuses of England and Wales have underpinned the construction of various local level deprivation measures. Many policy-related and academic studies have used deprivation scores calculated cross-sectionally to identify geographical areas in need of regeneration and to explain spatial variations in health outcomes. However, such an approach masks the sometimes very distinct challenges faced in areas with different deprivation histories. There is, therefore, a need to consider the deprivation trajectories of areas over a long time run. This can then enable, for example: monitoring the effects of industry closure; assessing the impacts of area-based planning initiatives; and determining whether a change in the level of deprivation leads to changes in health outcomes. It can also be used to consider what interventions may be linked with positive changes and which could then possibly be implemented elsewhere. Here we extend previous work to cover a 50-year period using input variables relating to employment, housing, and car accessibility, from the six censuses from 1971 to 2021. We identify areas of persistent (dis-)advantage, those areas which have improved their deprivation situation, and those places where the situation has worsened. We cross-classify the changing deprivation measurements with the Office for National Statistics (ONS) Supergroups area classification to thereby determine how different types of area are faring. On average, areas are less deprived in 2021 than in 1971. However, the trajectories of the input variables and of overall deprivation are not linear. The earlier decades are distinctive in rapid falls in non-home ownership and lack of car access but rises and falls in unemployment. The more recent decades have seen rises in non-home ownership and household overcrowding. Geographically, there has been a shift from a widespread level of deprivation, including in more rural areas in 1971, to being more concentrated in urban areas in the 21st Century.

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“…healthy life expectancy is typically measured following the Sullivan methodology, which combines unhealthy status within a life table framework [ 5 ]. With the diversity of health concepts, unhealthy health conditions with different meanings correspond to different healthy life expectancies such as disability-free life expectancy [ 6 ], healthy life expectancy without specific diseases [ 7 ], and self-rated healthy life expectancy [ 8 ]. These metrics are easily calculable, and unhealthy rate data are readily available, with numerous corresponding studies.…”

Section: Introductionmentioning

confidence: 99%

Construction and evaluation of a practical model for measuring health-adjusted life expectancy (HALE) in China

Chen,

Cao,

et al. 2024

BMC Public Health

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Background HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning. Methods First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%). Conclusion We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

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(Un-) healthy ageing: Geographic inequalities in disability-free life expectancy in England and Wales

Cited by 8 publications

References 64 publications

Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand

Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand

50-year deprivation trajectories: local area change in England and Wales, 1971-2021

Construction and evaluation of a practical model for measuring health-adjusted life expectancy (HALE) in China

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