From 410d91ffdb929099d8746a11e6c4dfd414f9d205 Mon Sep 17 00:00:00 2001 From: theorashid Date: Fri, 18 Aug 2023 18:02:13 +0100 Subject: [PATCH] add info on limitations of ICAR --- thesis/Chapters/Chapter2.qmd | 5 ++- thesis/Chapters/Chapter7.qmd | 2 +- thesis/Frontmatter/abbreviations.tex | 1 + thesis/Frontmatter/acknowledgements.tex | 32 +++++++++++++------ thesis/_thesis/Chapters/Chapter2.html | 12 ++++---- thesis/_thesis/Chapters/Chapter7.html | 5 ++- thesis/_thesis/references.html | 22 +++++++++---- thesis/_thesis/search.json | 10 +++--- thesis/_thesis/sitemap.xml | 30 +++++++++--------- thesis/thesis.bib | 41 +++++++++++++++++++++++++ 10 files changed, 116 insertions(+), 44 deletions(-) diff --git a/thesis/Chapters/Chapter2.qmd b/thesis/Chapters/Chapter2.qmd index 84da896..e12a10e 100644 --- a/thesis/Chapters/Chapter2.qmd +++ b/thesis/Chapters/Chapter2.qmd @@ -48,7 +48,7 @@ An example in @elliottSpatialEpidemiologyMethods2001 chooses an exponential deca #### Space as discrete units {-} -A more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field, which was first introduced by @besagSpatialInteractionStatistical1974. +A more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field (GMRF), which was first introduced by @besagBayesianImageRestoration1991. These form a joint distribution as in @eq-MVN, but the covariance is usually defined instead in terms of the precision matrix $$ \mathbf{P} = \pmb{\Sigma}^{-1} = \tau(\mathbf{D} - \rho \mathbf{A}), @@ -56,6 +56,7 @@ $$ {#eq-CAR-prec} where $\tau$ controls the overall precision of the effects, $\mathbf{A}$ is the spatial adjacency matrix formed by the small areas, $\mathbf{D}$ is a diagonal matrix with entries equal to the number of neighbours for each spatial unit, and the autocorrelation parameter $\rho$ describes the amount of correlation. This can be seen as tuning the degree of spatial dependence, where $\rho = 0$ implies independence between areas, and $\rho = 1$ full dependence. The case with $\rho = 1$ is called the intrinsic conditional autoregressive (ICAR) model. +There sometimes exists further over-dispersion in the residuals that cannot be modelled by purely spatially-structured random effects. @besagBayesianImageRestoration1991 proposed the model (hereafter called BYM) $$ S_i = U_i + V_i, @@ -73,6 +74,8 @@ Policy is decided at these geographies, so there is reason to believe these boun Note, although these models group by geographical region, these models are not _spatial_ as they do not contain any information on the relative position of the areas. Of the two specifications that are spatial, either as a continuous process or discrete units, the Markov random field priors are often preferred for computational reasons, as we can exploit the sparseness of the adjacency matrix in our inference algorithms rather than computing the covariance between each pair of spatial units as in the general case of @eq-MVN. +There are concerns, however, that the GMRF representation of space as an adjacency matrix, which was originally proposed for a regular lattice of pixels in image analysis, is reductive for more complicated spatial problems. +Despite this, in an epidemiological context, @duncanSpatialSmoothingBayesian2017 found the standard ICAR model with binary, first-order neighbour weights outperformed models with a variety of different weighting schemes, including matrix weights based on higher-order degrees of neighbours, distance between neighbours, and distance between covariate values. In applications to disease mapping, spatial models are the natural choice when the disease exhibits a spatial pattern. This is the case for mortality from infectious diseases, particularly on short timescales like Covid-19 [@konstantinoudisRegionalExcessMortality2022]. diff --git a/thesis/Chapters/Chapter7.qmd b/thesis/Chapters/Chapter7.qmd index ac05855..a307c6f 100644 --- a/thesis/Chapters/Chapter7.qmd +++ b/thesis/Chapters/Chapter7.qmd @@ -272,7 +272,7 @@ Both mortality from ischaemic heart disease and strokes have continued to follow These reflect improvements in reducing and controlling risk factors such as high blood pressure and high blood cholesterol, organisational changes to the NHS such that acute CVD episodes are treated in specific centres, improvements in the treatment of CVD including coronary angiographies and stent insertion, and public health campaigns such as FAST (Face drooping, Arm weakness, Speech difficulties, Time) so the general public know when to seek emergency help for a stroke. Although the management of CVDs has improved over the past decades, the burden of mortality has shifted towards dementias. -This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol). +This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol) [@yuEvidencebasedPreventionAlzheimer2020]. Some part of this trend may also be due to increased diagnosis and coding of deaths as dementias, with doctors increasingly assigning mental and neurological conditions as the underlying cause of death rather than simply "dying of old age". These contrasting trends in mortality from ischaemic heart disease and dementias could explain the finding that these causes of death have largely driven the heterogeneity of the slowdown in life expectancy gains since around 2010 at varying rates across districts, suggesting that CVD risk factors within the population have influenced the inequality in progress in recent years. diff --git a/thesis/Frontmatter/abbreviations.tex b/thesis/Frontmatter/abbreviations.tex index 1d20557..3b1deef 100644 --- a/thesis/Frontmatter/abbreviations.tex +++ b/thesis/Frontmatter/abbreviations.tex @@ -6,6 +6,7 @@ \textbf{CVD} & \textbf{C}ardio\textbf{v}ascular \textbf{D}isease\\ \textbf{GBD} & \textbf{G}lobal \textbf{B}urden of \textbf{D}isease\\ \textbf{GHE} & \textbf{G}lobal \textbf{H}ealth \textbf{E}stimates\\ + \textbf{GMRF} & \textbf{G}aussian \textbf{M}arkov \textbf{R}andom \textbf{F}ield\\ \textbf{ICAR} & \textbf{I}ntrinsic \textbf{C}onditional \textbf{a}uto\textbf{r}egressive\\ \textbf{ICD} & \textbf{I}nternational \textbf{C}lassification of \textbf{D}iseases\\ \textbf{IMD} & \textbf{I}ndex of \textbf{M}ultiple \textbf{D}eprivation\\ diff --git a/thesis/Frontmatter/acknowledgements.tex b/thesis/Frontmatter/acknowledgements.tex index 438db66..2955d34 100644 --- a/thesis/Frontmatter/acknowledgements.tex +++ b/thesis/Frontmatter/acknowledgements.tex @@ -1,11 +1,25 @@ Thanks be to James Bennett. -% Majid Ezzati, Seth Flaxman. -% Eric Johsnon -% Kyle Foreman, Robbie Parks. -% Barbara Metzler, Emily Muller. -% Ricky Nathvani, Honor Bixby, Sierra Clark, Victor Lhoste. -% Sam Acors -% Solange. -% Parents, Ros -% Geoff Hardern. +% Firstly, I would like to thank my supervisor Majid Ezzati for his guidance and mentorship. +% I have learnt so much from you over the past years, and it's inspiring to see the level of care and attention you pay towards your team. +% I am incredibly grateful for the energy you have put into our work. +% It's an honour join the long and distinguished list of your former PhD students. + +% The stalwart of the group, James Bennett, has been far more than just a supervisor, but also a patient sounding board, a guide to the light when no model will converge, and a great friend. +% I hope I didn't bother you too much, but to work so closely with you over the past years has been a pleasure. +% My supervisor, Seth Flaxman, has been a fountain of ideas and energy, and every conversation has stretched my thinking. +% Robbie Parks and Kyle Foreman have looked out for me throughout the past years and I'd like to thank them for pushing me towards new opportunities whenever they can. + +% I am part of an incredibly talented and welcoming group of researchers at Imperial. +% Ricky Nathvani, Sierra Clark, Honor Bixby, Perviz Asaria, Bin Zhou, and the wider group of NCD-RisC collaborators have provided a constant stream of insights and entertainment. +% My fellow PhD students, Barbara Metzler, Emily Muller, and Victor Lhoste, have survived the journey with me and made the experience so much fun. +% Beyond the group, I'd like to thank Adam Howes for helpful, technical conversations and Sam Acors for unhelpful, non-technical conversations. +% There would be no results in this thesis without the dedicated IT support from Eric Johnson and the invaluable probabilistic modelling advice from the oracle-like Chris Paciorek. + +% Thank you to my mother, Claire, and father, Aly, for my incredible upbringing and the foundation you have laid for me. +% Thanks to Rosalind as well. +% Of everyone, my biggest thanks go to Solange, for keeping me sane, happy and loved throughout. +% I am very lucky to have you. + +% This thesis is dedicated to my grandfather, Geoffrey Hardern, who sadly passed during my PhD studies. +% It was his dream for all of his grandchildren to go to university, and I hope he'd be proud of how long I have spent there. \ No newline at end of file diff --git a/thesis/_thesis/Chapters/Chapter2.html b/thesis/_thesis/Chapters/Chapter2.html index d45827a..2e4c697 100644 --- a/thesis/_thesis/Chapters/Chapter2.html +++ b/thesis/_thesis/Chapters/Chapter2.html @@ -276,16 +276,16 @@

S

Space as discrete units

-

A more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field, which was first introduced by Besag (1974). These form a joint distribution as in Equation 2.1, but the covariance is usually defined instead in terms of the precision matrix \[ +

A more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field (GMRF), which was first introduced by Besag et al. (1991). These form a joint distribution as in Equation 2.1, but the covariance is usually defined instead in terms of the precision matrix \[ \mathbf{P} = \pmb{\Sigma}^{-1} = \tau(\mathbf{D} - \rho \mathbf{A}), -\tag{2.2}\] where \(\tau\) controls the overall precision of the effects, \(\mathbf{A}\) is the spatial adjacency matrix formed by the small areas, \(\mathbf{D}\) is a diagonal matrix with entries equal to the number of neighbours for each spatial unit, and the autocorrelation parameter \(\rho\) describes the amount of correlation. This can be seen as tuning the degree of spatial dependence, where \(\rho = 0\) implies independence between areas, and \(\rho = 1\) full dependence. The case with \(\rho = 1\) is called the intrinsic conditional autoregressive (ICAR) model. Besag et al. (1991) proposed the model (hereafter called BYM) \[ +\tag{2.2}\] where \(\tau\) controls the overall precision of the effects, \(\mathbf{A}\) is the spatial adjacency matrix formed by the small areas, \(\mathbf{D}\) is a diagonal matrix with entries equal to the number of neighbours for each spatial unit, and the autocorrelation parameter \(\rho\) describes the amount of correlation. This can be seen as tuning the degree of spatial dependence, where \(\rho = 0\) implies independence between areas, and \(\rho = 1\) full dependence. The case with \(\rho = 1\) is called the intrinsic conditional autoregressive (ICAR) model. There sometimes exists further over-dispersion in the residuals that cannot be modelled by purely spatially-structured random effects. Besag et al. (1991) proposed the model (hereafter called BYM) \[ S_i = U_i + V_i, \tag{2.3}\] where \(U_i\) follow an ICAR distribution, and \(V_i\) are independent and identically distributed random effects. The addition of the spatially-unstructured component \(V\) accounts for any non-spatial heterogeneity.

Space as a nested hierarchy of geographies

The relationships between different levels of a hierarchy of geographical units are often incorporated into models as a nested hierarchy of random effects. These models account for when spatial units lie within common administrative boundaries. This is often a desirable property of the model for certain geographies, like states in the US, which are administrative. Policy is decided at these geographies, so there is reason to believe these boundaries may have a greater effect on health outcomes than spatial structure. Finucane et al. (2014) demonstrate how country-level blood pressure can be modelled by exploiting the hierarchy global, super-region, region and country. Note, although these models group by geographical region, these models are not spatial as they do not contain any information on the relative position of the areas.

-

Of the two specifications that are spatial, either as a continuous process or discrete units, the Markov random field priors are often preferred for computational reasons, as we can exploit the sparseness of the adjacency matrix in our inference algorithms rather than computing the covariance between each pair of spatial units as in the general case of Equation 2.1.

+

Of the two specifications that are spatial, either as a continuous process or discrete units, the Markov random field priors are often preferred for computational reasons, as we can exploit the sparseness of the adjacency matrix in our inference algorithms rather than computing the covariance between each pair of spatial units as in the general case of Equation 2.1. There are concerns, however, that the GMRF representation of space as an adjacency matrix, which was originally proposed for a regular lattice of pixels in image analysis, is reductive for more complicated spatial problems. Despite this, in an epidemiological context, Duncan et al. (2017) found the standard ICAR model with binary, first-order neighbour weights outperformed models with a variety of different weighting schemes, including matrix weights based on higher-order degrees of neighbours, distance between neighbours, and distance between covariate values.

In applications to disease mapping, spatial models are the natural choice when the disease exhibits a spatial pattern. This is the case for mortality from infectious diseases, particularly on short timescales like Covid-19 (Konstantinoudis et al., 2022). Nested hierarchies are a more suitable choice when administrative areas are meaningful and have an effect on the health outcomes of the population. For example, state-specific abortion laws in the USA could affect maternal mortality, and so a model should include an effect for each state.

@@ -398,9 +398,6 @@

Bennett JE, Pearson-Stuttard J, Kontis V, Capewell S, Wolfe I, Ezzati M. 2018. Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: A population-based analysis of vital registration data. The Lancet Public Health 3:e586–e597. doi:10.1016/S2468-2667(18)30214-7 -
-Besag J. 1974. Spatial Interaction and the Statistical Analysis of Lattice Systems. Journal of the Royal Statistical Society: Series B (Methodological) 36:192–225. doi:10.1111/j.2517-6161.1974.tb00999.x -
Besag J, York J, Mollié A. 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43:1–20. doi:10.1007/BF00116466
@@ -419,6 +416,9 @@

Downing A, Forman D, Gilthorpe MS, Edwards KL, Manda SO. 2008. Joint disease mapping using six cancers in the Yorkshire region of England. International Journal of Health Geographics 7:41. doi:10.1186/1476-072X-7-41 +
+Duncan EW, White NM, Mengersen K. 2017. Spatial smoothing in Bayesian models: A comparison of weights matrix specifications and their impact on inference. International Journal of Health Geographics 16:47. doi:10.1186/s12942-017-0120-x +
Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, Mackenbach JP, van Lenthe FJ, Mokdad AH, Murray CJL. 2017a. Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers. JAMA Internal Medicine 177:1003–1011. doi:10.1001/jamainternmed.2017.0918
diff --git a/thesis/_thesis/Chapters/Chapter7.html b/thesis/_thesis/Chapters/Chapter7.html index a9f9449..111968b 100644 --- a/thesis/_thesis/Chapters/Chapter7.html +++ b/thesis/_thesis/Chapters/Chapter7.html @@ -492,7 +492,7 @@

7.3.3 Explaining the variation

Asaria et al. (2012) found CVD mortality followed a persistent downward trend in nearly all wards in England from 1982 to 2006. Both mortality from ischaemic heart disease and strokes have continued to follow this trend through to 2019 at the district level. These reflect improvements in reducing and controlling risk factors such as high blood pressure and high blood cholesterol, organisational changes to the NHS such that acute CVD episodes are treated in specific centres, improvements in the treatment of CVD including coronary angiographies and stent insertion, and public health campaigns such as FAST (Face drooping, Arm weakness, Speech difficulties, Time) so the general public know when to seek emergency help for a stroke.

-

Although the management of CVDs has improved over the past decades, the burden of mortality has shifted towards dementias. This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol). Some part of this trend may also be due to increased diagnosis and coding of deaths as dementias, with doctors increasingly assigning mental and neurological conditions as the underlying cause of death rather than simply “dying of old age”.

+

Although the management of CVDs has improved over the past decades, the burden of mortality has shifted towards dementias. This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol) (Yu et al., 2020). Some part of this trend may also be due to increased diagnosis and coding of deaths as dementias, with doctors increasingly assigning mental and neurological conditions as the underlying cause of death rather than simply “dying of old age”.

These contrasting trends in mortality from ischaemic heart disease and dementias could explain the finding that these causes of death have largely driven the heterogeneity of the slowdown in life expectancy gains since around 2010 at varying rates across districts, suggesting that CVD risk factors within the population have influenced the inequality in progress in recent years. The sizeable contribution to the inequality in life expectancy improvement from all other NCDs is more difficult to explain without further stratifying the cause group.

The heterogeneous trends in mortality from both lung cancer, where the probability of dying declined in all districts for men and saw mixed trends for women, and from COPD, where a larger proportion of districts experienced a decrease in mortality for women than for men, reflected that the peak in female smoking rates and smoking-attributable mortality have lagged behind that in men by about 20-30 years (Thun et al., 2012).

The geography of the change in mortality from liver cirrhosis – an advanced stage of liver damage – is perhaps indicative of the contrasting dynamics of the two main risk factors for liver cirrhosis: alcohol misuse and hepatitis B/C infection. Alcohol is the main cause of liver disease, and has driven a large proportion of increases in liver cirrhosis throughout Europe (Blachier et al., 2013). Alcohol is generally consumed less in the capital, with London having the lowest percentage of adults who abstain from drinking alcohol (23.6%) and the lowest percentage of adults who drink over 14 units per week (20.1%) (Public Health England, 2021). On the other hand, the prevalence of hepatitis B/C has decreased over the past decades. There has been reduced incidence and vaccination for hepatitis B. Although a large number of people acquired hepatitis C in the 1970s and 1980s, the virus has since been identified and transmission has reduced (Blachier et al., 2013).

@@ -572,6 +572,9 @@

World Health Organization. 2020. WHO methods and data sources for country-level causes of death 2000-2019. +
+Yu J-T, Xu W, Tan C-C, Andrieu S, Suckling J, Evangelou E, Pan A, Zhang C, Jia J, Feng L, Kua E-H, Wang Y-J, Wang H-F, Tan M-S, Li J-Q, Hou X-H, Wan Y, Tan L, Mok V, Tan L, Dong Q, Touchon J, Gauthier S, Aisen PS, Vellas B. 2020. Evidence-based prevention of Alzheimer’s disease: Systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials. Journal of Neurology, Neurosurgery & Psychiatry 91:1201–1209. doi:10.1136/jnnp-2019-321913 +

diff --git a/thesis/_thesis/references.html b/thesis/_thesis/references.html index f396c24..af60d97 100644 --- a/thesis/_thesis/references.html +++ b/thesis/_thesis/references.html @@ -318,12 +318,6 @@

References

real estate data. The Lancet Regional Health Europe 27:100580. doi:10.1016/j.lanepe.2022.100580 -
-Besag J. 1974. Spatial Interaction and the -Statistical Analysis of Lattice Systems. -Journal of the Royal Statistical Society: Series B -(Methodological) 36:192–225. doi:10.1111/j.2517-6161.1974.tb00999.x -
Besag J, York J, Mollié A. 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of @@ -439,6 +433,12 @@

References

of England. International Journal of Health Geographics 7:41. doi:10.1186/1476-072X-7-41
+
+Duncan EW, White NM, Mengersen K. 2017. Spatial smoothing in +Bayesian models: A comparison of weights matrix +specifications and their impact on inference. International Journal +of Health Geographics 16:47. doi:10.1186/s12942-017-0120-x +
Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, Mackenbach JP, van Lenthe FJ, Mokdad AH, Murray CJL. 2017a. Inequalities in @@ -1052,6 +1052,16 @@

References

Vancouver, British Columbia, Canada, 1990. Health & Place 72:102692. doi:10.1016/j.healthplace.2021.102692
+
+Yu J-T, Xu W, Tan C-C, Andrieu S, Suckling J, Evangelou E, Pan A, Zhang +C, Jia J, Feng L, Kua E-H, Wang Y-J, Wang H-F, Tan M-S, Li J-Q, Hou X-H, +Wan Y, Tan L, Mok V, Tan L, Dong Q, Touchon J, Gauthier S, Aisen PS, +Vellas B. 2020. Evidence-based prevention of Alzheimer’s +disease: Systematic review and meta-analysis of 243 observational +prospective studies and 153 randomised controlled trials. Journal of +Neurology, Neurosurgery & Psychiatry +91:1201–1209. doi:10.1136/jnnp-2019-321913 +
diff --git a/thesis/_thesis/search.json b/thesis/_thesis/search.json index 94b498a..fb2bc58 100644 --- a/thesis/_thesis/search.json +++ b/thesis/_thesis/search.json @@ -39,7 +39,7 @@ "href": "Chapters/Chapter2.html#mapping-mortality-and-disease-at-small-areas", "title": "2  Background", "section": "2.2 Mapping mortality and disease at small areas", - "text": "2.2 Mapping mortality and disease at small areas\nMany studies compare the prevalence of diseases or mortality in different subgroups of the population by dividing the population geographically into small areas. The number of cases, or number of deaths, in an area are likely to be small numbers. This sparseness issue is even more pertinent when the population is further stratified by age group. When calculating rates of incidence from the observed data, there is an apparent variability between spatial units, which is often larger than the true differences in risk due to the noise in the data. To overcome these issues, we can use statistical smoothing techniques to obtain robust estimates of rates by sharing information between strata.\n\n2.2.1 Disease mapping methods\nIn small-area studies, it is common to smooth data using models with explicit spatial dependence, which are designed to give more weight to nearby areas than those further away. There are three main categories for modelling spatial effects. First, we can treat space as a continuous surface using Gaussian processes or splines. Second, we can use areal models, which make use of the spatial neighbourhood structure of the units. Thirdly, we can explicitly build effects based on a nested hierarchy of geographical units, for example between state, county and census tract in the US. Each of these methods rely on assumptions which may make them more or less appropriate in different applications.\n\nSpace as a continuous process\nIn the context of disease mapping, events are usually aggregated to areas rather than assigned specific geographical coordinates. Wakefield and Elliott (1999) model aggregated counts as realisations of a Poisson process, in which the expected number of cases is calculated by integrating a continuous surface that generates the cases over the area of the spatial unit. The surface was a function of spatially-referenced covariates. Kelsall and Wakefield (2002) describe an alternative model, where the log-transformed risk surface is modelled by a Gaussian process, whose correlation function depends on distance.\nBest et al. (2005) provide a review of the use of hierarchical models with spatial dependence for disease mapping. In particular, the authors focus on Bayesian estimation, and different classes of spatial prior distributions.\nThe first prior proposed for spatial effects \\(\\mathbf{S} = {S_1, ..., S_n}\\) is the multivariate normal \\[\n\\mathbf{S} \\sim \\mathcal{N}(\\pmb{\\mu}, \\pmb{\\Sigma}),\n\\tag{2.1}\\]\nwhere \\(\\pmb{\\mu}\\) is the mean effect vector, \\(\\pmb{\\Sigma} = \\sigma^2 \\pmb{\\Omega}\\) and \\(\\pmb{\\Omega}\\) is a symmetric, positive semi-definite matrix defining the correlation between spatial units. A common choice when specifying the structure of the correlation matrix is to assume a function that decays with the distance between the centroids of the areas, so that places nearby in space share similar disease profiles. Note, this is mathematically equivalent to the practical implementation of a Gaussian process, which uses a finite set of points. An example in Elliott et al. (2001b) chooses an exponential decay function to map cancer risk in northwest England.\n\n\nSpace as discrete units\nA more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field, which was first introduced by Besag (1974). These form a joint distribution as in Equation 2.1, but the covariance is usually defined instead in terms of the precision matrix \\[\n\\mathbf{P} = \\pmb{\\Sigma}^{-1} = \\tau(\\mathbf{D} - \\rho \\mathbf{A}),\n\\tag{2.2}\\] where \\(\\tau\\) controls the overall precision of the effects, \\(\\mathbf{A}\\) is the spatial adjacency matrix formed by the small areas, \\(\\mathbf{D}\\) is a diagonal matrix with entries equal to the number of neighbours for each spatial unit, and the autocorrelation parameter \\(\\rho\\) describes the amount of correlation. This can be seen as tuning the degree of spatial dependence, where \\(\\rho = 0\\) implies independence between areas, and \\(\\rho = 1\\) full dependence. The case with \\(\\rho = 1\\) is called the intrinsic conditional autoregressive (ICAR) model. Besag et al. (1991) proposed the model (hereafter called BYM) \\[\nS_i = U_i + V_i,\n\\tag{2.3}\\] where \\(U_i\\) follow an ICAR distribution, and \\(V_i\\) are independent and identically distributed random effects. The addition of the spatially-unstructured component \\(V\\) accounts for any non-spatial heterogeneity.\n\n\nSpace as a nested hierarchy of geographies\nThe relationships between different levels of a hierarchy of geographical units are often incorporated into models as a nested hierarchy of random effects. These models account for when spatial units lie within common administrative boundaries. This is often a desirable property of the model for certain geographies, like states in the US, which are administrative. Policy is decided at these geographies, so there is reason to believe these boundaries may have a greater effect on health outcomes than spatial structure. Finucane et al. (2014) demonstrate how country-level blood pressure can be modelled by exploiting the hierarchy global, super-region, region and country. Note, although these models group by geographical region, these models are not spatial as they do not contain any information on the relative position of the areas.\nOf the two specifications that are spatial, either as a continuous process or discrete units, the Markov random field priors are often preferred for computational reasons, as we can exploit the sparseness of the adjacency matrix in our inference algorithms rather than computing the covariance between each pair of spatial units as in the general case of Equation 2.1.\nIn applications to disease mapping, spatial models are the natural choice when the disease exhibits a spatial pattern. This is the case for mortality from infectious diseases, particularly on short timescales like Covid-19 (Konstantinoudis et al., 2022). Nested hierarchies are a more suitable choice when administrative areas are meaningful and have an effect on the health outcomes of the population. For example, state-specific abortion laws in the USA could affect maternal mortality, and so a model should include an effect for each state.\n\n\nModelling variation beyond space\nAs computational power has improved, it has become feasible to model patterns over other features of the population, such as time period and age group. Trends over time can be modelled as linear through slopes, or using nonlinear effects which allow neighbouring time points to be alike, the simplest of which is a first-order Gaussian random walk process. All-cause mortality varies smoothly over ages, following a characteristic J-shape with higher mortality in the infant and older age groups (Preston et al., 2001), and therefore can be modelled using a nonlinear process such as a random walk.\nDifficulties arise when considering interactions between the space, age, and time variables. One can imagine situations in which different spatial units will have different age patterns in disease rates, for example, if the certain age groups were vaccinated against disease in that spatial unit before others. After implementing a base model with the main effects, the question is how to model additional terms which account for the interactions between the variables. Space-time interactions could range from fully independent, to each spatial unit having independent temporal patterns, to inseparable space-time variation where interactions borrow strength across neighbouring spatial units and neighbouring time periods (Knorr-Held, 2000).\nHowever, it should be considered that by breaking the population down into smaller and smaller subgroups through space, age and time period, the counts of cases become more sparse and there is a need for stronger smoothing to produce robust estimates, particular for data that is already at the small-area level. Although interaction effects are plausible, modellers should consider whether there evidence for the interaction in the data or whether they can simplify the model if the interaction effect turns out to be negligible.\nIt should be noted that there are situations where statistical smoothing would not be appropriate. There might be true variability in the data which a smoothing model would conceal. For example, the Grenfell Tower fire in 2017 was a localised event that affected mortality. Without accounting for this event, the models described above would either attenuate its effect on mortality, or the spike in mortality would cause estimates of mortality in nearby spatial units or years to be erroneously high.\n\n\n\n2.2.2 Applications of disease mapping methods\n\nSmall-area analyses of mortality\nIn order to compare the health status between areas, health authorities require a measure of mortality that collapses age-specific information into a single number. Indirectly standardised measures such as the standardised mortality ratio – the ratio between total deaths and expected deaths in an area – are easy to calculate, but are not easily understood by laypeople. Directly standardised methods, in contrast, require knowledge of the full age structure of death rates rather than just the total number of deaths. Age-standardised death rates, however, suffer the same interpretability issue as the standardised mortality ratio, and are only comparable between studies if the same reference population is used. An alternative choice is life expectancy. Silcocks et al. (2001) explain that life expectancy is a “more intuitive and immediate measure of the mortality experience of a population, [and] is likely to have greater impact… than other measures that are incomprehensible to most people.”\nThe estimation of death rates requires two data sources: deaths counts and populations. Modern death registration systems are complete and accurate. On the other hand, although usually treated as a known quantity, the population denominator is often problematic. Populations for small geographies are only recorded during a decennial census, and estimates are generated for the years in-between using limited survey data on births, deaths and migration. And although the census is considered the “gold standard”, it is subject to enumeration errors, particularly for areas with special populations such as students or armed forces (Elliott et al., 2001b).\nBeyond the population issue, finer scale studies are restricted by data availability. Where data are available, there is still the need to overcome small number issues before feeding death rates through the life table to calculate life expectancy. Eayres and Williams (2004) recommend a minimum population size of 5000 when using traditional life table methods, below which the calculation of life expectancy is unstable1, or the error estimates become so large that any comparison between subgroups becomes meaningless. One approach, often taken by statistical agencies, is to build larger populations by either aggregating multiple years of data (Bahk et al., 2020; Office for National Statistics, 2015; Public Health England, 2021) or combining spatial units (Ezzati et al., 2008). Here, we focus on studies using Bayesian hierarchical models to generate robust estimates of age-specific death rates by recognising the correlations between spatial units and age groups, which produce more accurate estimates for small population studies of life expectancy (Congdon, 2009; Jonker et al., 2012).\nJonker et al. (2012) demonstrated the advantages of the Bayesian approach for 89 small areas in Rotterdam using a joint model for sex, space and age effects, finding a 8.2 year and 9.2 year gap between the neighbourhoods with the highest and lowest life expectancies for women and men. Stephens et al. (2013) employed the same model for 153 administrative areas in New South Wales, Australia.\nBayesian spatial models for mortality have been scaled to small areas for entire countries, and also consider trends in these regions over time. Bennett et al. (2015) forecasted life expectancy for 375 districts in England and Wales using a spatiotemporal model trained over a 31 year period, and Dwyer-Lindgren et al. (2017a) explored mortality trends 3110 US counties from 1980 to 2014.\nThere have also been studies on specific cities at a finer resolution. In order to improve estimates for disability-free life expectancy, Congdon (2014) considered both ill-health and mortality in a joint likelihood with spatial effects for 625 wards in London, finding more than a two-fold variation in the percent of life spent in disability for men. Bilal et al. (2019) looked at 266 subcity units for six large cities in Latin America. As there is no contiguous boundary in this case, a random effects model for each city was used instead of a spatial model. The largest difference between the top and bottom decile of life expectancy at birth was 17.7 years for women in Santiago, Chile.\nTwo studies in North America have looked below the county level, at census tracts, with wide-ranging population sizes as small as 40. Dwyer-Lindgren et al. (2017b), using a model that relied heavily on sociodemographic covariates, studied trends for life expectancy and many causes of death for 397 tracts in King County, Washington, uncovering an 18.3 year gap in life expectancy for men. Using the same model for Vancouver, Canada, Yu et al. (2021) found widening inequalities over time and a difference of 9.5 years for men.\n\n\nSmall Area Health Statistics Unit\nIn 1983, a documentary on the fallout from a fire at the Sellafield nuclear site in Cumbria claimed that there was a ten-fold increase in cases of childhood leukaemia in the surrounding community. This anomaly had gone undetected by public health authorities, raising concern that routinely collected data were not able to identify local clusters of disease. The subsequent enquiry confirmed the excess, and recommended that a research unit was set up to monitor small-area statistics and respond quickly to ad hoc queries on local health hazards. The Small Area Health Statistics Unit (SAHSU) was established in 1987 (Elliott et al., 1992).\nBeyond producing substantive research on environment and health, a core aim of SAHSU is to develop small-area statistical methodology (Wakefield and Elliott, 1999) for:\n\nPoint source type studies. Is there an increased risk close to an environmental hazard? SAHSU has investigated increased mortality from mesothelioma and asbestosis near Plymouth docks (Elliott et al., 1992); excess respiratory disease mortality near two factories in Barking and Havering (Aylin et al., 1999); kidney disease mortality near chemical plants in Runcorn (Hodgson et al., 2004); possible excess of several morbidities near landfill sites (Elliott et al., 2001a; Jarup et al., 2007, 2002b).\nGeographic correlation studies. Is there a correlation between disease risk and spatially-varying environmental variables? SAHSU have looked at several exposures, including a plume of mercury pollution (Hodgson et al., 2007); mobile phone base stations during pregnancy (Elliott et al., 2010); noise from aircraft near Heathrow (Hansell et al., 2013); road traffic noise in London (Halonen et al., 2015); particulate matter from incinerators during pregnancy (Parkes et al., 2020).\nClustering. Does a disease produce non-random spatial patterns of incidence? If the aetiology is unknown, this could suggest the disease is infectious.\nDisease mapping. Summarising the spatial variation in risk.\n\nSAHSU has been at the forefront of both methodology and applications in disease mapping. Aylin et al. (1999) mapped diseases for wards in Kensington, Chelsea and Westminster using a simple model that smoothed rates towards the mean risk across the region. Thereafter, SAHSU published a plethora of studies for disease mapping models with explicit spatial dependence, including using the BYM model (Equation 2.3) to map spatial variation in the relative risk of testicular (Toledano et al., 2001) and prostate (Jarup et al., 2002a) cancers for small areas in regions of England. In a landmark piece bringing together work on disease mapping and environmental exposures, SAHSU published an environment and health atlas for England and Wales, showing the spatial patterns of 14 health conditions at census ward level over an aggregated 25 year period alongside five environmental exposure surfaces (Hansell, Anna L. et al., 2014).\nFurther disease mapping studies at SAHSU using spatially structured effects have also extended the methodology to look at age patterns and trends over time. Asaria et al. (2012) analysed cardiovascular disease death rates by fitting a spatial model for all wards in England separately for each age group and time period. Bennett et al. (2015) designed a model to jointly forecast all-cause mortality for districts in England, age groups and years. The model used BYM spatial effects and random walk effects over age and time to capture nonlinear relationships." + "text": "2.2 Mapping mortality and disease at small areas\nMany studies compare the prevalence of diseases or mortality in different subgroups of the population by dividing the population geographically into small areas. The number of cases, or number of deaths, in an area are likely to be small numbers. This sparseness issue is even more pertinent when the population is further stratified by age group. When calculating rates of incidence from the observed data, there is an apparent variability between spatial units, which is often larger than the true differences in risk due to the noise in the data. To overcome these issues, we can use statistical smoothing techniques to obtain robust estimates of rates by sharing information between strata.\n\n2.2.1 Disease mapping methods\nIn small-area studies, it is common to smooth data using models with explicit spatial dependence, which are designed to give more weight to nearby areas than those further away. There are three main categories for modelling spatial effects. First, we can treat space as a continuous surface using Gaussian processes or splines. Second, we can use areal models, which make use of the spatial neighbourhood structure of the units. Thirdly, we can explicitly build effects based on a nested hierarchy of geographical units, for example between state, county and census tract in the US. Each of these methods rely on assumptions which may make them more or less appropriate in different applications.\n\nSpace as a continuous process\nIn the context of disease mapping, events are usually aggregated to areas rather than assigned specific geographical coordinates. Wakefield and Elliott (1999) model aggregated counts as realisations of a Poisson process, in which the expected number of cases is calculated by integrating a continuous surface that generates the cases over the area of the spatial unit. The surface was a function of spatially-referenced covariates. Kelsall and Wakefield (2002) describe an alternative model, where the log-transformed risk surface is modelled by a Gaussian process, whose correlation function depends on distance.\nBest et al. (2005) provide a review of the use of hierarchical models with spatial dependence for disease mapping. In particular, the authors focus on Bayesian estimation, and different classes of spatial prior distributions.\nThe first prior proposed for spatial effects \\(\\mathbf{S} = {S_1, ..., S_n}\\) is the multivariate normal \\[\n\\mathbf{S} \\sim \\mathcal{N}(\\pmb{\\mu}, \\pmb{\\Sigma}),\n\\tag{2.1}\\]\nwhere \\(\\pmb{\\mu}\\) is the mean effect vector, \\(\\pmb{\\Sigma} = \\sigma^2 \\pmb{\\Omega}\\) and \\(\\pmb{\\Omega}\\) is a symmetric, positive semi-definite matrix defining the correlation between spatial units. A common choice when specifying the structure of the correlation matrix is to assume a function that decays with the distance between the centroids of the areas, so that places nearby in space share similar disease profiles. Note, this is mathematically equivalent to the practical implementation of a Gaussian process, which uses a finite set of points. An example in Elliott et al. (2001b) chooses an exponential decay function to map cancer risk in northwest England.\n\n\nSpace as discrete units\nA more popular prior is the conditional autoregressive (CAR) prior, also known as a Gaussian Markov random field (GMRF), which was first introduced by Besag et al. (1991). These form a joint distribution as in Equation 2.1, but the covariance is usually defined instead in terms of the precision matrix \\[\n\\mathbf{P} = \\pmb{\\Sigma}^{-1} = \\tau(\\mathbf{D} - \\rho \\mathbf{A}),\n\\tag{2.2}\\] where \\(\\tau\\) controls the overall precision of the effects, \\(\\mathbf{A}\\) is the spatial adjacency matrix formed by the small areas, \\(\\mathbf{D}\\) is a diagonal matrix with entries equal to the number of neighbours for each spatial unit, and the autocorrelation parameter \\(\\rho\\) describes the amount of correlation. This can be seen as tuning the degree of spatial dependence, where \\(\\rho = 0\\) implies independence between areas, and \\(\\rho = 1\\) full dependence. The case with \\(\\rho = 1\\) is called the intrinsic conditional autoregressive (ICAR) model. There sometimes exists further over-dispersion in the residuals that cannot be modelled by purely spatially-structured random effects. Besag et al. (1991) proposed the model (hereafter called BYM) \\[\nS_i = U_i + V_i,\n\\tag{2.3}\\] where \\(U_i\\) follow an ICAR distribution, and \\(V_i\\) are independent and identically distributed random effects. The addition of the spatially-unstructured component \\(V\\) accounts for any non-spatial heterogeneity.\n\n\nSpace as a nested hierarchy of geographies\nThe relationships between different levels of a hierarchy of geographical units are often incorporated into models as a nested hierarchy of random effects. These models account for when spatial units lie within common administrative boundaries. This is often a desirable property of the model for certain geographies, like states in the US, which are administrative. Policy is decided at these geographies, so there is reason to believe these boundaries may have a greater effect on health outcomes than spatial structure. Finucane et al. (2014) demonstrate how country-level blood pressure can be modelled by exploiting the hierarchy global, super-region, region and country. Note, although these models group by geographical region, these models are not spatial as they do not contain any information on the relative position of the areas.\nOf the two specifications that are spatial, either as a continuous process or discrete units, the Markov random field priors are often preferred for computational reasons, as we can exploit the sparseness of the adjacency matrix in our inference algorithms rather than computing the covariance between each pair of spatial units as in the general case of Equation 2.1. There are concerns, however, that the GMRF representation of space as an adjacency matrix, which was originally proposed for a regular lattice of pixels in image analysis, is reductive for more complicated spatial problems. Despite this, in an epidemiological context, Duncan et al. (2017) found the standard ICAR model with binary, first-order neighbour weights outperformed models with a variety of different weighting schemes, including matrix weights based on higher-order degrees of neighbours, distance between neighbours, and distance between covariate values.\nIn applications to disease mapping, spatial models are the natural choice when the disease exhibits a spatial pattern. This is the case for mortality from infectious diseases, particularly on short timescales like Covid-19 (Konstantinoudis et al., 2022). Nested hierarchies are a more suitable choice when administrative areas are meaningful and have an effect on the health outcomes of the population. For example, state-specific abortion laws in the USA could affect maternal mortality, and so a model should include an effect for each state.\n\n\nModelling variation beyond space\nAs computational power has improved, it has become feasible to model patterns over other features of the population, such as time period and age group. Trends over time can be modelled as linear through slopes, or using nonlinear effects which allow neighbouring time points to be alike, the simplest of which is a first-order Gaussian random walk process. All-cause mortality varies smoothly over ages, following a characteristic J-shape with higher mortality in the infant and older age groups (Preston et al., 2001), and therefore can be modelled using a nonlinear process such as a random walk.\nDifficulties arise when considering interactions between the space, age, and time variables. One can imagine situations in which different spatial units will have different age patterns in disease rates, for example, if the certain age groups were vaccinated against disease in that spatial unit before others. After implementing a base model with the main effects, the question is how to model additional terms which account for the interactions between the variables. Space-time interactions could range from fully independent, to each spatial unit having independent temporal patterns, to inseparable space-time variation where interactions borrow strength across neighbouring spatial units and neighbouring time periods (Knorr-Held, 2000).\nHowever, it should be considered that by breaking the population down into smaller and smaller subgroups through space, age and time period, the counts of cases become more sparse and there is a need for stronger smoothing to produce robust estimates, particular for data that is already at the small-area level. Although interaction effects are plausible, modellers should consider whether there evidence for the interaction in the data or whether they can simplify the model if the interaction effect turns out to be negligible.\nIt should be noted that there are situations where statistical smoothing would not be appropriate. There might be true variability in the data which a smoothing model would conceal. For example, the Grenfell Tower fire in 2017 was a localised event that affected mortality. Without accounting for this event, the models described above would either attenuate its effect on mortality, or the spike in mortality would cause estimates of mortality in nearby spatial units or years to be erroneously high.\n\n\n\n2.2.2 Applications of disease mapping methods\n\nSmall-area analyses of mortality\nIn order to compare the health status between areas, health authorities require a measure of mortality that collapses age-specific information into a single number. Indirectly standardised measures such as the standardised mortality ratio – the ratio between total deaths and expected deaths in an area – are easy to calculate, but are not easily understood by laypeople. Directly standardised methods, in contrast, require knowledge of the full age structure of death rates rather than just the total number of deaths. Age-standardised death rates, however, suffer the same interpretability issue as the standardised mortality ratio, and are only comparable between studies if the same reference population is used. An alternative choice is life expectancy. Silcocks et al. (2001) explain that life expectancy is a “more intuitive and immediate measure of the mortality experience of a population, [and] is likely to have greater impact… than other measures that are incomprehensible to most people.”\nThe estimation of death rates requires two data sources: deaths counts and populations. Modern death registration systems are complete and accurate. On the other hand, although usually treated as a known quantity, the population denominator is often problematic. Populations for small geographies are only recorded during a decennial census, and estimates are generated for the years in-between using limited survey data on births, deaths and migration. And although the census is considered the “gold standard”, it is subject to enumeration errors, particularly for areas with special populations such as students or armed forces (Elliott et al., 2001b).\nBeyond the population issue, finer scale studies are restricted by data availability. Where data are available, there is still the need to overcome small number issues before feeding death rates through the life table to calculate life expectancy. Eayres and Williams (2004) recommend a minimum population size of 5000 when using traditional life table methods, below which the calculation of life expectancy is unstable1, or the error estimates become so large that any comparison between subgroups becomes meaningless. One approach, often taken by statistical agencies, is to build larger populations by either aggregating multiple years of data (Bahk et al., 2020; Office for National Statistics, 2015; Public Health England, 2021) or combining spatial units (Ezzati et al., 2008). Here, we focus on studies using Bayesian hierarchical models to generate robust estimates of age-specific death rates by recognising the correlations between spatial units and age groups, which produce more accurate estimates for small population studies of life expectancy (Congdon, 2009; Jonker et al., 2012).\nJonker et al. (2012) demonstrated the advantages of the Bayesian approach for 89 small areas in Rotterdam using a joint model for sex, space and age effects, finding a 8.2 year and 9.2 year gap between the neighbourhoods with the highest and lowest life expectancies for women and men. Stephens et al. (2013) employed the same model for 153 administrative areas in New South Wales, Australia.\nBayesian spatial models for mortality have been scaled to small areas for entire countries, and also consider trends in these regions over time. Bennett et al. (2015) forecasted life expectancy for 375 districts in England and Wales using a spatiotemporal model trained over a 31 year period, and Dwyer-Lindgren et al. (2017a) explored mortality trends 3110 US counties from 1980 to 2014.\nThere have also been studies on specific cities at a finer resolution. In order to improve estimates for disability-free life expectancy, Congdon (2014) considered both ill-health and mortality in a joint likelihood with spatial effects for 625 wards in London, finding more than a two-fold variation in the percent of life spent in disability for men. Bilal et al. (2019) looked at 266 subcity units for six large cities in Latin America. As there is no contiguous boundary in this case, a random effects model for each city was used instead of a spatial model. The largest difference between the top and bottom decile of life expectancy at birth was 17.7 years for women in Santiago, Chile.\nTwo studies in North America have looked below the county level, at census tracts, with wide-ranging population sizes as small as 40. Dwyer-Lindgren et al. (2017b), using a model that relied heavily on sociodemographic covariates, studied trends for life expectancy and many causes of death for 397 tracts in King County, Washington, uncovering an 18.3 year gap in life expectancy for men. Using the same model for Vancouver, Canada, Yu et al. (2021) found widening inequalities over time and a difference of 9.5 years for men.\n\n\nSmall Area Health Statistics Unit\nIn 1983, a documentary on the fallout from a fire at the Sellafield nuclear site in Cumbria claimed that there was a ten-fold increase in cases of childhood leukaemia in the surrounding community. This anomaly had gone undetected by public health authorities, raising concern that routinely collected data were not able to identify local clusters of disease. The subsequent enquiry confirmed the excess, and recommended that a research unit was set up to monitor small-area statistics and respond quickly to ad hoc queries on local health hazards. The Small Area Health Statistics Unit (SAHSU) was established in 1987 (Elliott et al., 1992).\nBeyond producing substantive research on environment and health, a core aim of SAHSU is to develop small-area statistical methodology (Wakefield and Elliott, 1999) for:\n\nPoint source type studies. Is there an increased risk close to an environmental hazard? SAHSU has investigated increased mortality from mesothelioma and asbestosis near Plymouth docks (Elliott et al., 1992); excess respiratory disease mortality near two factories in Barking and Havering (Aylin et al., 1999); kidney disease mortality near chemical plants in Runcorn (Hodgson et al., 2004); possible excess of several morbidities near landfill sites (Elliott et al., 2001a; Jarup et al., 2007, 2002b).\nGeographic correlation studies. Is there a correlation between disease risk and spatially-varying environmental variables? SAHSU have looked at several exposures, including a plume of mercury pollution (Hodgson et al., 2007); mobile phone base stations during pregnancy (Elliott et al., 2010); noise from aircraft near Heathrow (Hansell et al., 2013); road traffic noise in London (Halonen et al., 2015); particulate matter from incinerators during pregnancy (Parkes et al., 2020).\nClustering. Does a disease produce non-random spatial patterns of incidence? If the aetiology is unknown, this could suggest the disease is infectious.\nDisease mapping. Summarising the spatial variation in risk.\n\nSAHSU has been at the forefront of both methodology and applications in disease mapping. Aylin et al. (1999) mapped diseases for wards in Kensington, Chelsea and Westminster using a simple model that smoothed rates towards the mean risk across the region. Thereafter, SAHSU published a plethora of studies for disease mapping models with explicit spatial dependence, including using the BYM model (Equation 2.3) to map spatial variation in the relative risk of testicular (Toledano et al., 2001) and prostate (Jarup et al., 2002a) cancers for small areas in regions of England. In a landmark piece bringing together work on disease mapping and environmental exposures, SAHSU published an environment and health atlas for England and Wales, showing the spatial patterns of 14 health conditions at census ward level over an aggregated 25 year period alongside five environmental exposure surfaces (Hansell, Anna L. et al., 2014).\nFurther disease mapping studies at SAHSU using spatially structured effects have also extended the methodology to look at age patterns and trends over time. Asaria et al. (2012) analysed cardiovascular disease death rates by fitting a spatial model for all wards in England separately for each age group and time period. Bennett et al. (2015) designed a model to jointly forecast all-cause mortality for districts in England, age groups and years. The model used BYM spatial effects and random walk effects over age and time to capture nonlinear relationships." }, { "objectID": "Chapters/Chapter2.html#mortality-from-specific-causes-of-death", @@ -53,7 +53,7 @@ "href": "Chapters/Chapter2.html#health-inequalities-in-the-uk", "title": "2  Background", "section": "2.4 Health inequalities in the UK", - "text": "2.4 Health inequalities in the UK\nWhile the UK is, by global standards, a wealthy nation with relatively high life expectancy, and the breadth of health inequalities are nowhere near the extremes seen in many other countries, the nation suffers still vast, preventable inequalities in mortality and morbidity. Health inequalities can be reduced through, amongst other initiatives, progressive social and economic policies, better nutrition programmes, and improved health care. It is important to estimate and understand differences in health outcomes between population subgroups to aid the design of such policies. There are several ways to stratify the UK population and compare inequalities between subgroups. Here, we focus on class, income, geography, and deprivation.\n\nClass and income inequality\nThe notion of class is prominent in UK society, but health outcomes between classes are difficult to separate from other risk factors such as hazards in manual labour or smoking rates. The Whitehall study of 1967 followed 17,530 men working in the civil service and recorded their mortality over a 10-year period. Marmot et al. (1984) found, by classifying the civil servants into social class according to their employment grade, there was a three-fold difference in mortality between the highest class, administrators, and men in the lowest class, mainly messengers and manual workers. They found, in general, a strong inverse association between grade and mortality – a term Marmot has coined a “social gradient”. The men were working stable, sedentary jobs in the same office building in London, so the gradient could not be explained industrial exposure alone, and the gradient remained even after controlling for smoking. The authors concluded there must be other factors inherent to social class (defined here by employment grade), which explain the mortality differences. A second cohort of Whitehall employees from 1985 to 1988, this time including women as well as men, were screened and asked to answer questions on self-reported ill-health. Marmot et al. (1991) found the social gradient in health had persisted in the 20 years separating the studies. In 2008, Marmot was asked by the Secretary of State to conduct a review into the state of health inequalities in the UK and to use the evidence to design policy for reducing these inequalities. A key plot in the first Marmot Review, released in 2010, depicted the social gradient in mortality for regions in England by socio-economic classification of employment (Marmot et al., 2010).\nIncome is not a routinely collected statistic in the UK. Nevertheless, using a small survey of 7000 people on three measures of morbidity, Wilkinson (1992) showed health improved sharply from the lowest to the middle of the income range.\n\n\nSpatial inequality\nIn 2015, the GBD study released its first subnational estimates of mortality, starting with the UK and Japan. Steel et al. (2018) assessed these data, which divided the UK into 150 regions, finding mortality from all-causes varied twofold across the country, with the highest years of life lost in Blackpool and the lowest in Wokingham. In a study on forecasting subnational life expectancy in England and Wales, Bennett et al. (2015) estimated a 8.2 year range in life expectancy for men and 7.1 year range for women in 2012 between 375 districts. The lowest life expectancies were seen in urban northern England, and the highest in the south and London’s affluent districts. Within London itself, male and female life expectancy showed 5-6 years of variation.\n\n\nDeprivation\nThere have been substantial efforts in the UK to measure the deprivation of an area. Since 2004, the standard deprivation indicator in England has been the Index of Multiple Deprivation (IMD) – a composite indicator for each Lower-layer Super Output Area (LSOA2) covering income, unemployment, health, crime and environmental data sources (Ministry of Housing, Communities & Local Government, 2019). The Marmot Review presented life expectancy and disability-free life expectancy against IMD at the Middle-layer Super Output Area, which exhibit strong social gradients (Marmot et al., 2010). The GBD study found the 15 most deprived UTLAs had consistently raised mortality, especially for all causes, lung cancer and chronic obstructive pulmonary disease. Deprived UTLAs in London, such as Tower Hamlets, Hackney, Barking and Dagenham had lower rates of premature mortality than expected for that level of deprivation (Steel et al., 2018). Bennett et al. (2018) jointly estimated death rates by age, year and deprivation decile. They found since 2011, although national life expectancy has continued to increase, the rise in female life expectancy has reversed in the two most deprived deciles. The second Marmot Review in 2020 also found female life expectancy declined in the most deprived decile between the periods 2010-12 and 2016-18 (Marmot et al., 2020). Digging further into these trends by region, the report found this trend was seen in all regions except London, the West Midlands and the North West, and that male life expectancy in the bottom decile also decreased in the North East, Yorkshire and the Humber, and the East of England.\n\n\nRecent public health strategy and trends in health outcomes\nSince the turn of the millennium, there have been two periods of contrasting public health policy in the UK. The early 2000s saw the implementation of the English health inequalities strategy under New Labour, with explicit goals of reducing geographical inequalities in life expectancy. The strategy saw a large increase in public spending targeting the social determinants of health, with policies on supporting families, tackling deprivation, and preventative healthcare.\nFollowing the change in government in 2010, the strategy came to an end. The Conservative government implemented a widespread series of cuts to public services, collectively known as austerity. This included both tight restrictions on the healthcare budget as well as a sweeping reorganisation of the NHS in hope of improving the organisation’s efficiency, and cuts to the wider determinants of social health, such as housing and education (Ham, 2023).\nAlthough it is difficult to isolate the causal effect of the period of austerity on health outcomes, there is some evidence of differences in health outcomes in these periods. Barr et al. (2017) analysed the trends in life expectancy for different quantiles of deprivation and provided evidence that the English health inequalities strategy achieved its aim of reducing the gap in life expectancy between the 20% most-deprived areas and the rest of the English population, and that the trends were reversing since 2012. These trends have been found across the life course: with rising infant mortality associated with childhood poverty (Taylor-Robinson et al., 2019); increases in “deaths of despair” (drug overdose, suicide, alcoholic liver disease) for those in middle ages (Angus et al., 2023; Hiam et al., 2020); and falls in female life expectancy at 65 and 85 (Hiam et al., 2018). Alexiou et al. (2021) found strong associations between cuts to local government and the change in district-level life expectancy from 2013 to 2017. As written in the The New York Times, “after eight years of budget cutting, Britain is looking less like the rest of Europe and more like the United States, with a shrinking welfare state and spreading poverty” (Goodman, 2018).\nAlthough fiscal policies of austerity were adopted by many countries in response to the global financial crash of 2008, there is evidence that public health has deteriorated more in the UK than in other countries. In an international study comparing mortality trends in England and Wales to 22 industrialised countries, Leon et al. (2019) showed that although there was a general slowdown in improvement of life expectancy across many nations, the slowdown in the most recent period of the study, 2011-16, was more pronounced in England and Wales. More recently, The Economist found the same evidence, comparing the long-run trend from 1980-2011 through to 2022 for 12 European countries: “longer-run slowdowns in life expectancy are observable in other European countries… but none has stalled quite as much as Britain” (The Economist, 2023). The UK has also performed worse as measured by cancer survival rates and infant mortality compared to other industrialised countries (OECD, 2016).\nAfter a decade of cuts, the UK entered the 2020s facing the greatest public health challenge for a generation: the Covid-19 pandemic. Unsurprisingly, England and Wales suffered one of the highest excess deaths tolls relative to other high-income countries (Kontis et al., 2020).\nIt is important to estimate how health inequalities have changed in different areas of the country through this period of substantial change in economic, social, and healthcare policy. Small-area health statistics, and in particular those at high-resolutions, not only reveal the extent of the mortality differences between neighbourhoods, but can also identify the areas at highest risk, allowing public health interventions to target the most disadvantaged groups.\n\n\n\n\nAlexiou A, Fahy K, Mason K, Bennett D, Brown H, Bambra C, Taylor-Robinson D, Barr B. 2021. Local government funding and life expectancy in England: A longitudinal ecological study. The Lancet Public Health 6:e641–e647. doi:10.1016/S2468-2667(21)00110-9\n\n\nAngus C, Buckley C, Tilstra AM, Dowd JB. 2023. Increases in “deaths of despair” during the COVID-19 pandemic in the United States and the United Kingdom. Public Health 218:92–96. doi:10.1016/j.puhe.2023.02.019\n\n\nAsaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P, de Hoogh K, Ezzati M, Elliott P. 2012. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982: Bayesian spatial analysis. International Journal of Epidemiology 41:1737–1749. doi:10.1093/ije/dys151\n\n\nAylin P, Maheswaran R, Wakefield J, Cockings S, Jarup L, Arnold R, Wheeler G, Elliott P. 1999. A national facility for small area disease mapping and rapid initial assessment of apparent disease clusters around a point source: The UK Small Area Health Statistics Unit. Journal of Public Health 21:289–298. doi:10.1093/pubmed/21.3.289\n\n\nBahk J, Kang H-Y, Khang Y-H. 2020. Life expectancy and inequalities therein by income from 2016 to 2018 across the 253 electoral constituencies of the National Assembly of the Republic of Korea. Journal of Preventive Medicine and Public Health 53:143–148. doi:10.3961/jpmph.20.050\n\n\nBarr B, Higgerson J, Whitehead M. 2017. Investigating the impact of the English health inequalities strategy: Time trend analysis. BMJ 358:j3310. doi:10.1136/bmj.j3310\n\n\nBennett JE, Li G, Foreman K, Best N, Kontis V, Pearson C, Hambly P, Ezzati M. 2015. The future of life expectancy and life expectancy inequalities in England and Wales: Bayesian spatiotemporal forecasting. The Lancet 386:163–170. doi:10.1016/S0140-6736(15)60296-3\n\n\nBennett JE, Pearson-Stuttard J, Kontis V, Capewell S, Wolfe I, Ezzati M. 2018. Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: A population-based analysis of vital registration data. 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Health & Place 72:102692. doi:10.1016/j.healthplace.2021.102692" + "text": "2.4 Health inequalities in the UK\nWhile the UK is, by global standards, a wealthy nation with relatively high life expectancy, and the breadth of health inequalities are nowhere near the extremes seen in many other countries, the nation suffers still vast, preventable inequalities in mortality and morbidity. Health inequalities can be reduced through, amongst other initiatives, progressive social and economic policies, better nutrition programmes, and improved health care. It is important to estimate and understand differences in health outcomes between population subgroups to aid the design of such policies. There are several ways to stratify the UK population and compare inequalities between subgroups. Here, we focus on class, income, geography, and deprivation.\n\nClass and income inequality\nThe notion of class is prominent in UK society, but health outcomes between classes are difficult to separate from other risk factors such as hazards in manual labour or smoking rates. The Whitehall study of 1967 followed 17,530 men working in the civil service and recorded their mortality over a 10-year period. Marmot et al. (1984) found, by classifying the civil servants into social class according to their employment grade, there was a three-fold difference in mortality between the highest class, administrators, and men in the lowest class, mainly messengers and manual workers. They found, in general, a strong inverse association between grade and mortality – a term Marmot has coined a “social gradient”. The men were working stable, sedentary jobs in the same office building in London, so the gradient could not be explained industrial exposure alone, and the gradient remained even after controlling for smoking. The authors concluded there must be other factors inherent to social class (defined here by employment grade), which explain the mortality differences. A second cohort of Whitehall employees from 1985 to 1988, this time including women as well as men, were screened and asked to answer questions on self-reported ill-health. Marmot et al. (1991) found the social gradient in health had persisted in the 20 years separating the studies. In 2008, Marmot was asked by the Secretary of State to conduct a review into the state of health inequalities in the UK and to use the evidence to design policy for reducing these inequalities. A key plot in the first Marmot Review, released in 2010, depicted the social gradient in mortality for regions in England by socio-economic classification of employment (Marmot et al., 2010).\nIncome is not a routinely collected statistic in the UK. Nevertheless, using a small survey of 7000 people on three measures of morbidity, Wilkinson (1992) showed health improved sharply from the lowest to the middle of the income range.\n\n\nSpatial inequality\nIn 2015, the GBD study released its first subnational estimates of mortality, starting with the UK and Japan. Steel et al. (2018) assessed these data, which divided the UK into 150 regions, finding mortality from all-causes varied twofold across the country, with the highest years of life lost in Blackpool and the lowest in Wokingham. In a study on forecasting subnational life expectancy in England and Wales, Bennett et al. (2015) estimated a 8.2 year range in life expectancy for men and 7.1 year range for women in 2012 between 375 districts. The lowest life expectancies were seen in urban northern England, and the highest in the south and London’s affluent districts. Within London itself, male and female life expectancy showed 5-6 years of variation.\n\n\nDeprivation\nThere have been substantial efforts in the UK to measure the deprivation of an area. Since 2004, the standard deprivation indicator in England has been the Index of Multiple Deprivation (IMD) – a composite indicator for each Lower-layer Super Output Area (LSOA2) covering income, unemployment, health, crime and environmental data sources (Ministry of Housing, Communities & Local Government, 2019). The Marmot Review presented life expectancy and disability-free life expectancy against IMD at the Middle-layer Super Output Area, which exhibit strong social gradients (Marmot et al., 2010). The GBD study found the 15 most deprived UTLAs had consistently raised mortality, especially for all causes, lung cancer and chronic obstructive pulmonary disease. Deprived UTLAs in London, such as Tower Hamlets, Hackney, Barking and Dagenham had lower rates of premature mortality than expected for that level of deprivation (Steel et al., 2018). Bennett et al. (2018) jointly estimated death rates by age, year and deprivation decile. They found since 2011, although national life expectancy has continued to increase, the rise in female life expectancy has reversed in the two most deprived deciles. The second Marmot Review in 2020 also found female life expectancy declined in the most deprived decile between the periods 2010-12 and 2016-18 (Marmot et al., 2020). Digging further into these trends by region, the report found this trend was seen in all regions except London, the West Midlands and the North West, and that male life expectancy in the bottom decile also decreased in the North East, Yorkshire and the Humber, and the East of England.\n\n\nRecent public health strategy and trends in health outcomes\nSince the turn of the millennium, there have been two periods of contrasting public health policy in the UK. The early 2000s saw the implementation of the English health inequalities strategy under New Labour, with explicit goals of reducing geographical inequalities in life expectancy. The strategy saw a large increase in public spending targeting the social determinants of health, with policies on supporting families, tackling deprivation, and preventative healthcare.\nFollowing the change in government in 2010, the strategy came to an end. The Conservative government implemented a widespread series of cuts to public services, collectively known as austerity. This included both tight restrictions on the healthcare budget as well as a sweeping reorganisation of the NHS in hope of improving the organisation’s efficiency, and cuts to the wider determinants of social health, such as housing and education (Ham, 2023).\nAlthough it is difficult to isolate the causal effect of the period of austerity on health outcomes, there is some evidence of differences in health outcomes in these periods. Barr et al. (2017) analysed the trends in life expectancy for different quantiles of deprivation and provided evidence that the English health inequalities strategy achieved its aim of reducing the gap in life expectancy between the 20% most-deprived areas and the rest of the English population, and that the trends were reversing since 2012. These trends have been found across the life course: with rising infant mortality associated with childhood poverty (Taylor-Robinson et al., 2019); increases in “deaths of despair” (drug overdose, suicide, alcoholic liver disease) for those in middle ages (Angus et al., 2023; Hiam et al., 2020); and falls in female life expectancy at 65 and 85 (Hiam et al., 2018). Alexiou et al. (2021) found strong associations between cuts to local government and the change in district-level life expectancy from 2013 to 2017. As written in the The New York Times, “after eight years of budget cutting, Britain is looking less like the rest of Europe and more like the United States, with a shrinking welfare state and spreading poverty” (Goodman, 2018).\nAlthough fiscal policies of austerity were adopted by many countries in response to the global financial crash of 2008, there is evidence that public health has deteriorated more in the UK than in other countries. In an international study comparing mortality trends in England and Wales to 22 industrialised countries, Leon et al. (2019) showed that although there was a general slowdown in improvement of life expectancy across many nations, the slowdown in the most recent period of the study, 2011-16, was more pronounced in England and Wales. More recently, The Economist found the same evidence, comparing the long-run trend from 1980-2011 through to 2022 for 12 European countries: “longer-run slowdowns in life expectancy are observable in other European countries… but none has stalled quite as much as Britain” (The Economist, 2023). The UK has also performed worse as measured by cancer survival rates and infant mortality compared to other industrialised countries (OECD, 2016).\nAfter a decade of cuts, the UK entered the 2020s facing the greatest public health challenge for a generation: the Covid-19 pandemic. Unsurprisingly, England and Wales suffered one of the highest excess deaths tolls relative to other high-income countries (Kontis et al., 2020).\nIt is important to estimate how health inequalities have changed in different areas of the country through this period of substantial change in economic, social, and healthcare policy. Small-area health statistics, and in particular those at high-resolutions, not only reveal the extent of the mortality differences between neighbourhoods, but can also identify the areas at highest risk, allowing public health interventions to target the most disadvantaged groups.\n\n\n\n\nAlexiou A, Fahy K, Mason K, Bennett D, Brown H, Bambra C, Taylor-Robinson D, Barr B. 2021. Local government funding and life expectancy in England: A longitudinal ecological study. The Lancet Public Health 6:e641–e647. doi:10.1016/S2468-2667(21)00110-9\n\n\nAngus C, Buckley C, Tilstra AM, Dowd JB. 2023. 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Health & Place 72:102692. doi:10.1016/j.healthplace.2021.102692" }, { "objectID": "Chapters/Chapter2.html#footnotes", @@ -207,14 +207,14 @@ "href": "Chapters/Chapter7.html#discussion", "title": "7  Cause-specific mortality at the district level", "section": "7.3 Discussion", - "text": "7.3 Discussion\nThis analysis revealed huge inequalities in mortality for a wide-ranging variety of causes of death in England. In particular, although there were widespread declines in CVDs, there have been huge increases in dementias, which in 2019 became one of the most unequal causes of death, with 4.1- and 3.6-fold differences for women and men between the highest and lowest districts. I found that the slowdown in life expectancy gains since around 2010 has been driven largely by dementias and all other NCDs in women, and by ischaemic heart disease, dementias, and all other NCDs in men.\n\n7.3.1 Strengths and limitations\nThe work presents trends in cause-specific mortality over a period of substantial policy interest at the district level, and uncovers which causes of death have been driving the slowdown in progress since 2010.\nThe study was carried out at the district level rather than for MSOAs, which masks substantial heterogeneity, as shown by the all-cause mortality study in Chapter 5. However, when stratifying further by cause of death, the number of deaths in each age-space-time-cause stratum can be extremely small, even at the district level, which makes the estimation task difficult. Furthermore, running a model that takes a day of GPU time to reach convergence for all causes at the MSOA level for 34 cause-sex combinations is extremely computationally demanding.\nThis study did not look at age- and cause-specific contributions to life expectancy inequality, unlike Bennett et al. (2018), and instead collapsed over age groups. Although this masks some variation, the groups were selected based on the total number of deaths from 2002 to 2019, and are consequently skewed towards older ages, and the age-specific contributions of (e.g.) dementias are not particularly interesting.\nDeath records are subject to issues in the assignment of ICD-10 codes for the cause of death. Although the ONS use selection algorithms to improve consistency between doctors when identifying the underlying cause of death (Office for National Statistics, 2022), the challenge of multimorbidity in older age groups makes the assignment of cause of death increasingly difficult (Meslé and Vallin, 2021). We selected 80 years of age as the upper bound to partially mitigate this effect because it covers a wide age range but does not include the very oldest ages. However, for certain diseases such as dementias where only 96,109 (14.5% of 663,692) of deaths from occur in those under 80 years, the probability of dying between birth and 80 years masks variations in the age groups where the majority of deaths occur. There are also specific cases of problematic cause of death assignment in the data. For example, mortality from all other CVDs showed a downward trend in every district with the exceptions of Hastings and, more noticeably, the Isle of Wight. Looking at the raw data for these districts, there is a step increase in the use of ICD-10 code I51.5 – a garbage CVD code (Murray and Lopez, 1996) – since around 2012 in these districts, suggesting these trends result from poor coding practice.\nThere is always the desire to split the residual groups into more and more causes of death. For example, there are interesting patterns in the change of all other IMPN, and it would be interesting to understand further how the component causes of all other NCDs are driving the inequality in progress. Moreover, there has been a lot of recent attention in the literature on “deaths of despair” – deaths from drug overdose, suicide or alcoholic liver disease – in the UK (Angus et al., 2023) and the US (Case and Deaton, 2015), and it would of interest to separate injuries by whether the deaths were intentional or unintentional. However, the groupings were chosen based on a total mortality rule so that the number of deaths in each cause group allowed robust inference of cause-age-area-year-specific death rates at the district level.\nIt would have been desirable, albeit computationally challenging, to run all cause groups in a single joint model. When calculating contributions using Arriaga’s method, mortality for each cause group was scaled such that the sum across all causes equalled to the estimate for total mortality. A joint model of cause-specific mortality could account for correlations for diseases with common risk factors and anti-correlations for competing causes of death. Foreman et al. (2017) jointly forecasted cause-specific death rates for states in the US, but instead estimated each age group separately.\nThe population in each district can change because of migration, both within the country and from overseas. However, unlike Chapter 5 and Chapter 6, migration was not as much of an issue at the district level compared with the smaller levels of analyses used earlier in the thesis. The majority of moves in the UK are within the same district (van Dijk et al., 2021), and so will have little effect on the change in mortality in a given district.\n\n\n7.3.2 Comparison with previous literature\nThe general trends of decreasing mortality from CVDs and large increases in dementias are consistent with studies on mortality in deciles of deprivation at the national level in England (Bennett et al., 2018). Despite increases in the rates of diabetes over the past decades (NHS Digital, 2023), the observation that mortality from diabetes decreased in every district for both sexes is also consistent with trends at the national level and reflects a diversification of causes of death in individuals with diagnosed diabetes (Pearson-Stuttard et al., 2021).\nThere are two key studies in the literature which break the population of England down into subgroups and look at the cause-composition of mortality: Firstly, from my research group, Bennett et al. (2018) divided the population by deciles of deprivation and looked at largely similar groups of causes to this chapter. The authors found the gap in life expectancy between the most and least deprived areas was caused by the large contributions of respiratory diseases, ischaemic heart disease, and a set of preventable and treatable cancers. Unlike the present work, the authors broke the results down further by age, finding that deaths in children younger than 5 years and dementias in older ages drove the inequality between the top and bottom deciles.\nSecondly, using data from one of the first GBD studies of subnational populations, Steel et al. (2018) looked at trends in mortality of 150 upper tier local authorities, which are larger than the districts used in this chapter, but considered a massive 264 causes of death, relying heavily on covariates in the model to produce estimates for areas with little to no data on more obscure causes. As with Bennett et al. (2018) and the work here, the GBD group found the rate of improvement in life expectancy had slowed since 2010. They found, in general, the slowdown was driven by the disappearance of sustained annual improvements from ischaemic heart disease, strokes, as in the present results, but also to a lesser extent from colorectal cancer, lung cancer, and breast cancer. The authors found the annual reduction in mortality attributable to most major risk factors (tobacco, dietary risks, high blood pressure etc) had declined since 1990, but has slowed since 2010, with the exception of alcohol and drug use, which has seen little change since the 2000s.\nBoth these studies focus on what causes of death have driven the inequality between subgroups of the population. I found the causes with the clearest gradients in contribution from the best- to worst-performing districts were those with modifiable risk factors, such as ischaemic heart disease, lung cancer, COPD, and injuries, and also the residual groups of all other NCDs and all other cancers. There were also contributions from dementias, but these did not follow the direction from best- to worst-performing district as obviously as the aforementioned causes. The work in this chapter extends previous studies by also considering which causes of death have driven the the inequality in progress between subgroups of the population.\n\n\n7.3.3 Explaining the variation\nAsaria et al. (2012) found CVD mortality followed a persistent downward trend in nearly all wards in England from 1982 to 2006. Both mortality from ischaemic heart disease and strokes have continued to follow this trend through to 2019 at the district level. These reflect improvements in reducing and controlling risk factors such as high blood pressure and high blood cholesterol, organisational changes to the NHS such that acute CVD episodes are treated in specific centres, improvements in the treatment of CVD including coronary angiographies and stent insertion, and public health campaigns such as FAST (Face drooping, Arm weakness, Speech difficulties, Time) so the general public know when to seek emergency help for a stroke.\nAlthough the management of CVDs has improved over the past decades, the burden of mortality has shifted towards dementias. This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol). Some part of this trend may also be due to increased diagnosis and coding of deaths as dementias, with doctors increasingly assigning mental and neurological conditions as the underlying cause of death rather than simply “dying of old age”.\nThese contrasting trends in mortality from ischaemic heart disease and dementias could explain the finding that these causes of death have largely driven the heterogeneity of the slowdown in life expectancy gains since around 2010 at varying rates across districts, suggesting that CVD risk factors within the population have influenced the inequality in progress in recent years. The sizeable contribution to the inequality in life expectancy improvement from all other NCDs is more difficult to explain without further stratifying the cause group.\nThe heterogeneous trends in mortality from both lung cancer, where the probability of dying declined in all districts for men and saw mixed trends for women, and from COPD, where a larger proportion of districts experienced a decrease in mortality for women than for men, reflected that the peak in female smoking rates and smoking-attributable mortality have lagged behind that in men by about 20-30 years (Thun et al., 2012).\nThe geography of the change in mortality from liver cirrhosis – an advanced stage of liver damage – is perhaps indicative of the contrasting dynamics of the two main risk factors for liver cirrhosis: alcohol misuse and hepatitis B/C infection. Alcohol is the main cause of liver disease, and has driven a large proportion of increases in liver cirrhosis throughout Europe (Blachier et al., 2013). Alcohol is generally consumed less in the capital, with London having the lowest percentage of adults who abstain from drinking alcohol (23.6%) and the lowest percentage of adults who drink over 14 units per week (20.1%) (Public Health England, 2021). On the other hand, the prevalence of hepatitis B/C has decreased over the past decades. There has been reduced incidence and vaccination for hepatitis B. Although a large number of people acquired hepatitis C in the 1970s and 1980s, the virus has since been identified and transmission has reduced (Blachier et al., 2013).\nAs is the case in most high income countries, which are in the third stage of the epidemiologic transition, there were far fewer deaths from maternal, perinatal, nutritional and infectious causes (GBD Group 1) and injuries (GBD Group 3) compared to CVDs, cancers and other NCDs (GBD Group 2). Although notably, these results consider the period before the Covid-19 pandemic, so the cause-composition will likely have changed to include more deaths from infections in the most recent years. The similar profiles for men and women for the level of mortality from all other IMPN in 2019 reflects that the three main causes by number of deaths are other infectious diseases, diarrhoeal diseases, low birth weight rather than maternal conditions, although without further disaggregating this group, it is difficult to suggest drivers of the observed trends. Mortality from lower respiratory infections, which include influenza and pneumonia, can vary greatly between years depending on the severity of the virus and whether the flu vaccination prevents the prevalent strain. It also depends on the strength of the immune system of the infected. The ability of the lungs to recover from infection can be degraded by years of industrial exposure or smoking, which could influence the high mortality in the urban North West. The highest mortality from injuries were in coastal areas, where there are obvious natural hazards, but also include a number of “left-behind” districts following deindustrialisation and lack of investment (Whitty, 2021). The highest male mortality from injuries was in Blackpool, which agrees with both the literature on mortality from drugs and suicide (Congdon, 2019) and coincides with the MSOA level life expectancy results (Chapter 5)." + "text": "7.3 Discussion\nThis analysis revealed huge inequalities in mortality for a wide-ranging variety of causes of death in England. In particular, although there were widespread declines in CVDs, there have been huge increases in dementias, which in 2019 became one of the most unequal causes of death, with 4.1- and 3.6-fold differences for women and men between the highest and lowest districts. I found that the slowdown in life expectancy gains since around 2010 has been driven largely by dementias and all other NCDs in women, and by ischaemic heart disease, dementias, and all other NCDs in men.\n\n7.3.1 Strengths and limitations\nThe work presents trends in cause-specific mortality over a period of substantial policy interest at the district level, and uncovers which causes of death have been driving the slowdown in progress since 2010.\nThe study was carried out at the district level rather than for MSOAs, which masks substantial heterogeneity, as shown by the all-cause mortality study in Chapter 5. However, when stratifying further by cause of death, the number of deaths in each age-space-time-cause stratum can be extremely small, even at the district level, which makes the estimation task difficult. Furthermore, running a model that takes a day of GPU time to reach convergence for all causes at the MSOA level for 34 cause-sex combinations is extremely computationally demanding.\nThis study did not look at age- and cause-specific contributions to life expectancy inequality, unlike Bennett et al. (2018), and instead collapsed over age groups. Although this masks some variation, the groups were selected based on the total number of deaths from 2002 to 2019, and are consequently skewed towards older ages, and the age-specific contributions of (e.g.) dementias are not particularly interesting.\nDeath records are subject to issues in the assignment of ICD-10 codes for the cause of death. Although the ONS use selection algorithms to improve consistency between doctors when identifying the underlying cause of death (Office for National Statistics, 2022), the challenge of multimorbidity in older age groups makes the assignment of cause of death increasingly difficult (Meslé and Vallin, 2021). We selected 80 years of age as the upper bound to partially mitigate this effect because it covers a wide age range but does not include the very oldest ages. However, for certain diseases such as dementias where only 96,109 (14.5% of 663,692) of deaths from occur in those under 80 years, the probability of dying between birth and 80 years masks variations in the age groups where the majority of deaths occur. There are also specific cases of problematic cause of death assignment in the data. For example, mortality from all other CVDs showed a downward trend in every district with the exceptions of Hastings and, more noticeably, the Isle of Wight. Looking at the raw data for these districts, there is a step increase in the use of ICD-10 code I51.5 – a garbage CVD code (Murray and Lopez, 1996) – since around 2012 in these districts, suggesting these trends result from poor coding practice.\nThere is always the desire to split the residual groups into more and more causes of death. For example, there are interesting patterns in the change of all other IMPN, and it would be interesting to understand further how the component causes of all other NCDs are driving the inequality in progress. Moreover, there has been a lot of recent attention in the literature on “deaths of despair” – deaths from drug overdose, suicide or alcoholic liver disease – in the UK (Angus et al., 2023) and the US (Case and Deaton, 2015), and it would of interest to separate injuries by whether the deaths were intentional or unintentional. However, the groupings were chosen based on a total mortality rule so that the number of deaths in each cause group allowed robust inference of cause-age-area-year-specific death rates at the district level.\nIt would have been desirable, albeit computationally challenging, to run all cause groups in a single joint model. When calculating contributions using Arriaga’s method, mortality for each cause group was scaled such that the sum across all causes equalled to the estimate for total mortality. A joint model of cause-specific mortality could account for correlations for diseases with common risk factors and anti-correlations for competing causes of death. Foreman et al. (2017) jointly forecasted cause-specific death rates for states in the US, but instead estimated each age group separately.\nThe population in each district can change because of migration, both within the country and from overseas. However, unlike Chapter 5 and Chapter 6, migration was not as much of an issue at the district level compared with the smaller levels of analyses used earlier in the thesis. The majority of moves in the UK are within the same district (van Dijk et al., 2021), and so will have little effect on the change in mortality in a given district.\n\n\n7.3.2 Comparison with previous literature\nThe general trends of decreasing mortality from CVDs and large increases in dementias are consistent with studies on mortality in deciles of deprivation at the national level in England (Bennett et al., 2018). Despite increases in the rates of diabetes over the past decades (NHS Digital, 2023), the observation that mortality from diabetes decreased in every district for both sexes is also consistent with trends at the national level and reflects a diversification of causes of death in individuals with diagnosed diabetes (Pearson-Stuttard et al., 2021).\nThere are two key studies in the literature which break the population of England down into subgroups and look at the cause-composition of mortality: Firstly, from my research group, Bennett et al. (2018) divided the population by deciles of deprivation and looked at largely similar groups of causes to this chapter. The authors found the gap in life expectancy between the most and least deprived areas was caused by the large contributions of respiratory diseases, ischaemic heart disease, and a set of preventable and treatable cancers. Unlike the present work, the authors broke the results down further by age, finding that deaths in children younger than 5 years and dementias in older ages drove the inequality between the top and bottom deciles.\nSecondly, using data from one of the first GBD studies of subnational populations, Steel et al. (2018) looked at trends in mortality of 150 upper tier local authorities, which are larger than the districts used in this chapter, but considered a massive 264 causes of death, relying heavily on covariates in the model to produce estimates for areas with little to no data on more obscure causes. As with Bennett et al. (2018) and the work here, the GBD group found the rate of improvement in life expectancy had slowed since 2010. They found, in general, the slowdown was driven by the disappearance of sustained annual improvements from ischaemic heart disease, strokes, as in the present results, but also to a lesser extent from colorectal cancer, lung cancer, and breast cancer. The authors found the annual reduction in mortality attributable to most major risk factors (tobacco, dietary risks, high blood pressure etc) had declined since 1990, but has slowed since 2010, with the exception of alcohol and drug use, which has seen little change since the 2000s.\nBoth these studies focus on what causes of death have driven the inequality between subgroups of the population. I found the causes with the clearest gradients in contribution from the best- to worst-performing districts were those with modifiable risk factors, such as ischaemic heart disease, lung cancer, COPD, and injuries, and also the residual groups of all other NCDs and all other cancers. There were also contributions from dementias, but these did not follow the direction from best- to worst-performing district as obviously as the aforementioned causes. The work in this chapter extends previous studies by also considering which causes of death have driven the the inequality in progress between subgroups of the population.\n\n\n7.3.3 Explaining the variation\nAsaria et al. (2012) found CVD mortality followed a persistent downward trend in nearly all wards in England from 1982 to 2006. Both mortality from ischaemic heart disease and strokes have continued to follow this trend through to 2019 at the district level. These reflect improvements in reducing and controlling risk factors such as high blood pressure and high blood cholesterol, organisational changes to the NHS such that acute CVD episodes are treated in specific centres, improvements in the treatment of CVD including coronary angiographies and stent insertion, and public health campaigns such as FAST (Face drooping, Arm weakness, Speech difficulties, Time) so the general public know when to seek emergency help for a stroke.\nAlthough the management of CVDs has improved over the past decades, the burden of mortality has shifted towards dementias. This reflects that, beyond age and family history, the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol) (Yu et al., 2020). Some part of this trend may also be due to increased diagnosis and coding of deaths as dementias, with doctors increasingly assigning mental and neurological conditions as the underlying cause of death rather than simply “dying of old age”.\nThese contrasting trends in mortality from ischaemic heart disease and dementias could explain the finding that these causes of death have largely driven the heterogeneity of the slowdown in life expectancy gains since around 2010 at varying rates across districts, suggesting that CVD risk factors within the population have influenced the inequality in progress in recent years. The sizeable contribution to the inequality in life expectancy improvement from all other NCDs is more difficult to explain without further stratifying the cause group.\nThe heterogeneous trends in mortality from both lung cancer, where the probability of dying declined in all districts for men and saw mixed trends for women, and from COPD, where a larger proportion of districts experienced a decrease in mortality for women than for men, reflected that the peak in female smoking rates and smoking-attributable mortality have lagged behind that in men by about 20-30 years (Thun et al., 2012).\nThe geography of the change in mortality from liver cirrhosis – an advanced stage of liver damage – is perhaps indicative of the contrasting dynamics of the two main risk factors for liver cirrhosis: alcohol misuse and hepatitis B/C infection. Alcohol is the main cause of liver disease, and has driven a large proportion of increases in liver cirrhosis throughout Europe (Blachier et al., 2013). Alcohol is generally consumed less in the capital, with London having the lowest percentage of adults who abstain from drinking alcohol (23.6%) and the lowest percentage of adults who drink over 14 units per week (20.1%) (Public Health England, 2021). On the other hand, the prevalence of hepatitis B/C has decreased over the past decades. There has been reduced incidence and vaccination for hepatitis B. Although a large number of people acquired hepatitis C in the 1970s and 1980s, the virus has since been identified and transmission has reduced (Blachier et al., 2013).\nAs is the case in most high income countries, which are in the third stage of the epidemiologic transition, there were far fewer deaths from maternal, perinatal, nutritional and infectious causes (GBD Group 1) and injuries (GBD Group 3) compared to CVDs, cancers and other NCDs (GBD Group 2). Although notably, these results consider the period before the Covid-19 pandemic, so the cause-composition will likely have changed to include more deaths from infections in the most recent years. The similar profiles for men and women for the level of mortality from all other IMPN in 2019 reflects that the three main causes by number of deaths are other infectious diseases, diarrhoeal diseases, low birth weight rather than maternal conditions, although without further disaggregating this group, it is difficult to suggest drivers of the observed trends. Mortality from lower respiratory infections, which include influenza and pneumonia, can vary greatly between years depending on the severity of the virus and whether the flu vaccination prevents the prevalent strain. It also depends on the strength of the immune system of the infected. The ability of the lungs to recover from infection can be degraded by years of industrial exposure or smoking, which could influence the high mortality in the urban North West. The highest mortality from injuries were in coastal areas, where there are obvious natural hazards, but also include a number of “left-behind” districts following deindustrialisation and lack of investment (Whitty, 2021). The highest male mortality from injuries was in Blackpool, which agrees with both the literature on mortality from drugs and suicide (Congdon, 2019) and coincides with the MSOA level life expectancy results (Chapter 5)." }, { "objectID": "Chapters/Chapter7.html#summary", "href": "Chapters/Chapter7.html#summary", "title": "7  Cause-specific mortality at the district level", "section": "7.4 Summary", - "text": "7.4 Summary\nI performed a high-resolution spatiotemporal analysis of vital registration data on deaths from the twelve leading causes of death by sex in England from 2002 to 2019. I used life table methods to calculate the probability of dying between birth and 80 years of age by sex, cause of death, district and year, as well as the cause-specific contributions to the inequality in life expectancy between different years of the study for each district.\nThe most unequal causes of death were COPD for women (6.0-fold variation in mortality across districts) and liver cirrhosis for men (6.7-fold). The causes of death with the least geographical variability were lymphomas, multiple myeloma (1.2-fold for both sexes) and leukaemia (1.1-fold for women and 1.2-fold for men). There has been a slowdown in life expectancy gains since 2010, which has been driven largely by dementias and all other NCDs in women, and by ischaemic heart disease, dementias, and all other NCDs in men.\n\n\n\n\nAngus C, Buckley C, Tilstra AM, Dowd JB. 2023. Increases in “deaths of despair” during the COVID-19 pandemic in the United States and the United Kingdom. Public Health 218:92–96. doi:10.1016/j.puhe.2023.02.019\n\n\nArriaga EE. 1984. Measuring and Explaining the Change in Life Expectancies. Demography 21:83–96. doi:10.2307/2061029\n\n\nAsaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P, de Hoogh K, Ezzati M, Elliott P. 2012. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982: Bayesian spatial analysis. International Journal of Epidemiology 41:1737–1749. doi:10.1093/ije/dys151\n\n\nBennett JE, Pearson-Stuttard J, Kontis V, Capewell S, Wolfe I, Ezzati M. 2018. Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: A population-based analysis of vital registration data. The Lancet Public Health 3:e586–e597. doi:10.1016/S2468-2667(18)30214-7\n\n\nBlachier M, Leleu H, Peck-Radosavljevic M, Valla D-C, Roudot-Thoraval F. 2013. The burden of liver disease in Europe: A review of available epidemiological data. Journal of Hepatology 58:593–608. doi:10.1016/j.jhep.2012.12.005\n\n\nCase A, Deaton A. 2015. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences 112:15078–15083. doi:10.1073/pnas.1518393112\n\n\nCongdon P. 2019. Geographical Patterns in Drug-Related Mortality and Suicide: Investigating Commonalities in English Small Areas. International Journal of Environmental Research and Public Health 16:1831. doi:10.3390/ijerph16101831\n\n\nForeman KJ, Li G, Best N, Ezzati M. 2017. Small area forecasts of cause-specific mortality: Application of a Bayesian hierarchical model to US vital registration data. Journal of the Royal Statistical Society Series C (Applied Statistics) 66:121–139. doi:10.1111/rssc.12157\n\n\nMarmot MG, Allen J, Boyce T, Goldblatt P, Morrison J. 2020. Marmot Review: 10 years on. Institute of Health Equity.\n\n\nMeslé F, Vallin J. 2021. Causes of Death at Very Old Ages, Including for Supercentenarians In: Maier H, Jeune B, Vaupel JW, editors. Exceptional Lifespans, Demographic Research Monographs. Cham: Springer International Publishing. pp. 69–84. doi:10.1007/978-3-030-49970-9\\_7\n\n\nMurray CJL, Lopez AD. 1996. The Global Burden of Disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected in 2020. World Health Organization.\n\n\nNHS Digital. 2023. Health Survey for England. NHS Digital.\n\n\nOffice for National Statistics. 2022. User guide to mortality statistics. Office for National Statistics.\n\n\nPearson-Stuttard J, Bennett JE, Cheng YJ, Vamos EP, Cross AJ, Ezzati M, Gregg EW. 2021. Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: An epidemiological analysis of linked primary care records. The Lancet Diabetes & Endocrinology 9:165–173. doi:10.1016/S2213-8587(20)30431-9\n\n\nPhan D, Pradhan N, Jankowiak M. 2019. Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. doi:10.48550/arXiv.1912.11554\n\n\nPreston SH, Heuveline P, Guillot M. 2001. Demography: Measuring and Modeling Population Processes. Blackwell Publishing.\n\n\nPublic Health England. 2021. Local Alcohol Profiles for England. Public Health England.\n\n\nSteel N, Ford JA, Newton JN, Davis ACJ, Vos T, Naghavi M, Glenn S, Hughes A, Dalton AM, Stockton D, Humphreys C, Dallat M, Schmidt J, Flowers J, Fox S, Abubakar I, Aldridge RW, Baker A, Brayne C, Brugha T, Capewell S, Car J, Cooper C, Ezzati M, Fitzpatrick J, Greaves F, Hay R, Hay S, Kee F, Larson HJ, Lyons RA, Majeed A, McKee M, Rawaf S, Rutter H, Saxena S, Sheikh A, Smeeth L, Viner RM, Vollset SE, Williams HC, Wolfe C, Woolf A, Murray CJL. 2018. Changes in health in the countries of the UK and 150 English Local Authority areas 1990: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet 392:1647–1661. doi:10.1016/S0140-6736(18)32207-4\n\n\nThun M, Peto R, Boreham J, Lopez AD. 2012. Stages of the cigarette epidemic on entering its second century. Tobacco Control 21:96–101. doi:10.1136/tobaccocontrol-2011-050294\n\n\nvan Dijk JT, Lansley G, Longley PA. 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society: Series A (Statistics in Society) 184:1452–1474. doi:10.1111/rssa.12713\n\n\nWhitty C. 2021. Chief Medical Officer’s annual report 2021: Health in coastal communities.\n\n\nWorld Health Organization. 2020. WHO methods and data sources for country-level causes of death 2000-2019." + "text": "7.4 Summary\nI performed a high-resolution spatiotemporal analysis of vital registration data on deaths from the twelve leading causes of death by sex in England from 2002 to 2019. I used life table methods to calculate the probability of dying between birth and 80 years of age by sex, cause of death, district and year, as well as the cause-specific contributions to the inequality in life expectancy between different years of the study for each district.\nThe most unequal causes of death were COPD for women (6.0-fold variation in mortality across districts) and liver cirrhosis for men (6.7-fold). The causes of death with the least geographical variability were lymphomas, multiple myeloma (1.2-fold for both sexes) and leukaemia (1.1-fold for women and 1.2-fold for men). There has been a slowdown in life expectancy gains since 2010, which has been driven largely by dementias and all other NCDs in women, and by ischaemic heart disease, dementias, and all other NCDs in men.\n\n\n\n\nAngus C, Buckley C, Tilstra AM, Dowd JB. 2023. Increases in “deaths of despair” during the COVID-19 pandemic in the United States and the United Kingdom. Public Health 218:92–96. doi:10.1016/j.puhe.2023.02.019\n\n\nArriaga EE. 1984. Measuring and Explaining the Change in Life Expectancies. Demography 21:83–96. doi:10.2307/2061029\n\n\nAsaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P, de Hoogh K, Ezzati M, Elliott P. 2012. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982: Bayesian spatial analysis. International Journal of Epidemiology 41:1737–1749. doi:10.1093/ije/dys151\n\n\nBennett JE, Pearson-Stuttard J, Kontis V, Capewell S, Wolfe I, Ezzati M. 2018. Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: A population-based analysis of vital registration data. The Lancet Public Health 3:e586–e597. doi:10.1016/S2468-2667(18)30214-7\n\n\nBlachier M, Leleu H, Peck-Radosavljevic M, Valla D-C, Roudot-Thoraval F. 2013. The burden of liver disease in Europe: A review of available epidemiological data. Journal of Hepatology 58:593–608. doi:10.1016/j.jhep.2012.12.005\n\n\nCase A, Deaton A. 2015. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences 112:15078–15083. doi:10.1073/pnas.1518393112\n\n\nCongdon P. 2019. Geographical Patterns in Drug-Related Mortality and Suicide: Investigating Commonalities in English Small Areas. International Journal of Environmental Research and Public Health 16:1831. doi:10.3390/ijerph16101831\n\n\nForeman KJ, Li G, Best N, Ezzati M. 2017. Small area forecasts of cause-specific mortality: Application of a Bayesian hierarchical model to US vital registration data. Journal of the Royal Statistical Society Series C (Applied Statistics) 66:121–139. doi:10.1111/rssc.12157\n\n\nMarmot MG, Allen J, Boyce T, Goldblatt P, Morrison J. 2020. Marmot Review: 10 years on. Institute of Health Equity.\n\n\nMeslé F, Vallin J. 2021. Causes of Death at Very Old Ages, Including for Supercentenarians In: Maier H, Jeune B, Vaupel JW, editors. Exceptional Lifespans, Demographic Research Monographs. Cham: Springer International Publishing. pp. 69–84. doi:10.1007/978-3-030-49970-9\\_7\n\n\nMurray CJL, Lopez AD. 1996. The Global Burden of Disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected in 2020. World Health Organization.\n\n\nNHS Digital. 2023. Health Survey for England. NHS Digital.\n\n\nOffice for National Statistics. 2022. User guide to mortality statistics. Office for National Statistics.\n\n\nPearson-Stuttard J, Bennett JE, Cheng YJ, Vamos EP, Cross AJ, Ezzati M, Gregg EW. 2021. Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: An epidemiological analysis of linked primary care records. The Lancet Diabetes & Endocrinology 9:165–173. doi:10.1016/S2213-8587(20)30431-9\n\n\nPhan D, Pradhan N, Jankowiak M. 2019. Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. doi:10.48550/arXiv.1912.11554\n\n\nPreston SH, Heuveline P, Guillot M. 2001. Demography: Measuring and Modeling Population Processes. Blackwell Publishing.\n\n\nPublic Health England. 2021. Local Alcohol Profiles for England. Public Health England.\n\n\nSteel N, Ford JA, Newton JN, Davis ACJ, Vos T, Naghavi M, Glenn S, Hughes A, Dalton AM, Stockton D, Humphreys C, Dallat M, Schmidt J, Flowers J, Fox S, Abubakar I, Aldridge RW, Baker A, Brayne C, Brugha T, Capewell S, Car J, Cooper C, Ezzati M, Fitzpatrick J, Greaves F, Hay R, Hay S, Kee F, Larson HJ, Lyons RA, Majeed A, McKee M, Rawaf S, Rutter H, Saxena S, Sheikh A, Smeeth L, Viner RM, Vollset SE, Williams HC, Wolfe C, Woolf A, Murray CJL. 2018. Changes in health in the countries of the UK and 150 English Local Authority areas 1990: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet 392:1647–1661. doi:10.1016/S0140-6736(18)32207-4\n\n\nThun M, Peto R, Boreham J, Lopez AD. 2012. Stages of the cigarette epidemic on entering its second century. Tobacco Control 21:96–101. doi:10.1136/tobaccocontrol-2011-050294\n\n\nvan Dijk JT, Lansley G, Longley PA. 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society: Series A (Statistics in Society) 184:1452–1474. doi:10.1111/rssa.12713\n\n\nWhitty C. 2021. Chief Medical Officer’s annual report 2021: Health in coastal communities.\n\n\nWorld Health Organization. 2020. WHO methods and data sources for country-level causes of death 2000-2019.\n\n\nYu J-T, Xu W, Tan C-C, Andrieu S, Suckling J, Evangelou E, Pan A, Zhang C, Jia J, Feng L, Kua E-H, Wang Y-J, Wang H-F, Tan M-S, Li J-Q, Hou X-H, Wan Y, Tan L, Mok V, Tan L, Dong Q, Touchon J, Gauthier S, Aisen PS, Vellas B. 2020. Evidence-based prevention of Alzheimer’s disease: Systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials. Journal of Neurology, Neurosurgery & Psychiatry 91:1201–1209. doi:10.1136/jnnp-2019-321913" }, { "objectID": "Chapters/Chapter7.html#footnotes", @@ -277,7 +277,7 @@ "href": "references.html", "title": "References", "section": "", - "text": "Alexiou A, Fahy K, Mason K, Bennett D, Brown H, Bambra C,\nTaylor-Robinson D, Barr B. 2021. Local government funding and life\nexpectancy in England: A longitudinal ecological study.\nThe Lancet Public Health 6:e641–e647. doi:10.1016/S2468-2667(21)00110-9\n\n\nAngus C, Buckley C, Tilstra AM, Dowd JB. 2023. Increases in\n“deaths of despair” during the COVID-19\npandemic in the United States and the United\nKingdom. Public Health 218:92–96.\ndoi:10.1016/j.puhe.2023.02.019\n\n\nArık A, Cairns A, Dodd E, Shao A, Streftaris G. 2022. Uneven outcomes:\nFindings on cancer mortality. The Actuary.\n\n\nArık A, Dodd E, Cairns A, Streftaris G. 2021. 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Journal of\nNeurology, Neurosurgery & Psychiatry\n91:1201–1209. doi:10.1136/jnnp-2019-321913" }, { "objectID": "Appendices/AppendixA.html#period-life-tables", diff --git a/thesis/_thesis/sitemap.xml b/thesis/_thesis/sitemap.xml index 25f35ba..a94f247 100644 --- a/thesis/_thesis/sitemap.xml +++ b/thesis/_thesis/sitemap.xml @@ -2,62 +2,62 @@ https://theorashid.github.io/thesis/index.html - 2023-08-11T17:52:38.168Z + 2023-08-18T16:56:30.620Z https://theorashid.github.io/thesis/Chapters/Chapter2.html - 2023-08-11T17:52:38.178Z + 2023-08-18T16:56:30.630Z https://theorashid.github.io/thesis/Chapters/Chapter3.html - 2023-08-11T17:52:38.182Z + 2023-08-18T16:56:30.634Z https://theorashid.github.io/thesis/Chapters/Chapter4.html - 2023-08-11T17:52:38.186Z + 2023-08-18T16:56:30.638Z https://theorashid.github.io/thesis/Chapters/Chapter5.html - 2023-08-11T17:52:38.192Z + 2023-08-18T16:56:30.644Z https://theorashid.github.io/thesis/Chapters/Chapter6.html - 2023-08-11T17:52:38.197Z + 2023-08-18T16:56:30.649Z https://theorashid.github.io/thesis/Chapters/Chapter7.html - 2023-08-11T17:52:38.206Z + 2023-08-18T16:56:30.657Z https://theorashid.github.io/thesis/Chapters/Chapter8.html - 2023-08-11T17:52:38.211Z + 2023-08-18T16:56:30.663Z https://theorashid.github.io/thesis/Chapters/Chapter9.html - 2023-08-11T17:52:38.215Z + 2023-08-18T16:56:30.667Z https://theorashid.github.io/thesis/references.html - 2023-08-11T17:52:38.224Z + 2023-08-18T16:56:30.676Z https://theorashid.github.io/thesis/Appendices/AppendixA.html - 2023-08-11T17:52:38.227Z + 2023-08-18T16:56:30.679Z https://theorashid.github.io/thesis/Appendices/AppendixB.html - 2023-08-11T17:52:38.232Z + 2023-08-18T16:56:30.684Z https://theorashid.github.io/thesis/Appendices/AppendixC.html - 2023-08-11T17:52:38.234Z + 2023-08-18T16:56:30.686Z https://theorashid.github.io/thesis/Appendices/AppendixD.html - 2023-08-11T17:52:38.238Z + 2023-08-18T16:56:30.690Z https://theorashid.github.io/thesis/Appendices/AppendixE.html - 2023-08-11T17:52:38.243Z + 2023-08-18T16:56:30.695Z diff --git a/thesis/thesis.bib b/thesis/thesis.bib index b41e7f1..ca7c8a0 100644 --- a/thesis/thesis.bib +++ b/thesis/thesis.bib @@ -612,6 +612,24 @@ @article{downingJointDiseaseMapping2008 file = {/Users/theorashid/Zotero/storage/TTCU62ST/Downing et al. - 2008 - Joint disease mapping using six cancers in the Yor.pdf;/Users/theorashid/Zotero/storage/2WIGI2Z4/1476-072X-7-41.html} } +@article{duncanSpatialSmoothingBayesian2017, + title = {Spatial Smoothing in {{Bayesian}} Models: A Comparison of Weights Matrix Specifications and Their Impact on Inference}, + shorttitle = {Spatial Smoothing in {{Bayesian}} Models}, + author = {Duncan, Earl W. and White, Nicole M. and Mengersen, Kerrie}, + year = {2017}, + month = dec, + journal = {International Journal of Health Geographics}, + volume = {16}, + number = {1}, + pages = {47}, + issn = {1476-072X}, + doi = {10.1186/s12942-017-0120-x}, + urldate = {2023-08-17}, + abstract = {When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance.}, + keywords = {spatial,statistical model}, + file = {/Users/theorashid/Zotero/storage/TVRRHBSW/Duncan et al. - 2017 - Spatial smoothing in Bayesian models a comparison.pdf;/Users/theorashid/Zotero/storage/Z3R8VR7M/s12942-017-0120-x.html} +} + @article{dwyer-lindgrenInequalitiesLifeExpectancy2017, title = {Inequalities in {{Life Expectancy Among US Counties}}, 1980 to 2014: {{Temporal Trends}} and {{Key Drivers}}}, shorttitle = {Inequalities in {{Life Expectancy Among US Counties}}, 1980 to 2014}, @@ -2387,6 +2405,29 @@ @misc{worldhealthorganizationWHOMethodsData2020 file = {/Users/theorashid/Zotero/storage/XCY2UW3N/ghe-leading-causes-of-death.html} } +@article{yuEvidencebasedPreventionAlzheimer2020, + title = {Evidence-Based Prevention of {{Alzheimer}}'s Disease: Systematic Review and Meta-Analysis of 243 Observational Prospective Studies and 153 Randomised Controlled Trials}, + shorttitle = {Evidence-Based Prevention of {{Alzheimer}}'s Disease}, + author = {Yu, Jin-Tai and Xu, Wei and Tan, Chen-Chen and Andrieu, Sandrine and Suckling, John and Evangelou, Evangelos and Pan, An and Zhang, Can and Jia, Jianping and Feng, Lei and Kua, Ee-Heok and Wang, Yan-Jiang and Wang, Hui-Fu and Tan, Meng-Shan and Li, Jie-Qiong and Hou, Xiao-He and Wan, Yu and Tan, Lin and Mok, Vincent and Tan, Lan and Dong, Qiang and Touchon, Jacques and Gauthier, Serge and Aisen, Paul S. and Vellas, Bruno}, + year = {2020}, + month = nov, + journal = {Journal of Neurology, Neurosurgery \& Psychiatry}, + volume = {91}, + number = {11}, + pages = {1201--1209}, + publisher = {{BMJ Publishing Group Ltd}}, + issn = {0022-3050, 1468-330X}, + doi = {10.1136/jnnp-2019-321913}, + urldate = {2023-08-18}, + abstract = {Background Evidence on preventing Alzheimer's disease (AD) is challenging to interpret due to varying study designs with heterogeneous endpoints and credibility. We completed a systematic review and meta-analysis of current evidence with prospective designs to propose evidence-based suggestions on AD prevention. Methods Electronic databases and relevant websites were searched from inception to 1 March 2019. Both observational prospective studies (OPSs) and randomised controlled trials (RCTs) were included. The multivariable-adjusted effect estimates were pooled by random-effects models, with credibility assessment according to its risk of bias, inconsistency and imprecision. Levels of evidence and classes of suggestions were summarised. Results A total of 44 676 reports were identified, and 243 OPSs and 153 RCTs were eligible for analysis after exclusion based on pre-decided criteria, from which 104 modifiable factors and 11 interventions were included in the meta-analyses. Twenty-one suggestions are proposed based on the consolidated evidence, with Class I suggestions targeting 19 factors: 10 with Level A strong evidence (education, cognitive activity, high body mass index in latelife, hyperhomocysteinaemia, depression, stress, diabetes, head trauma, hypertension in midlife and orthostatic hypotension) and 9 with Level B weaker evidence (obesity in midlife, weight loss in late life, physical exercise, smoking, sleep, cerebrovascular disease, frailty, atrial fibrillation and vitamin C). In contrast, two interventions are not recommended: oestrogen replacement therapy (Level A2) and acetylcholinesterase inhibitors (Level B). Interpretation Evidence-based suggestions are proposed, offering clinicians and stakeholders current guidance for the prevention of AD.}, + chapter = {Cognitive neurology}, + copyright = {\textcopyright{} Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:~http://creativecommons.org/licenses/by-nc/4.0/.}, + langid = {english}, + pmid = {32690803}, + keywords = {cause-specific}, + file = {/Users/theorashid/Zotero/storage/FN5L63GA/Yu et al. - 2020 - Evidence-based prevention of Alzheimer's disease .pdf} +} + @article{yuSpatiotemporalAnalysisInequalities2021a, title = {A Spatiotemporal Analysis of Inequalities in Life Expectancy and 20 Causes of Mortality in Sub-Neighbourhoods of {{Metro Vancouver}}, {{British Columbia}}, {{Canada}}, 1990\textendash 2016}, author = {Yu, Jessica and {Dwyer-Lindgren}, Laura and Bennett, James and Ezzati, Majid and Gustafson, Paul and Tran, Martino and Brauer, Michael},