Pearson-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
+
+Pell JP, Haw S, Cobbe S, Newby DE, Pell ACH, Fischbacher C, McConnachie A, Pringle S, Murdoch D, Dunn F, Oldroyd K, MacIntyre P, O’Rourke B, Borland W. 2008. Smoke-free
Legislation and
Hospitalizations for
Acute Coronary Syndrome.
New England Journal of Medicine 359:482–491. doi:
10.1056/NEJMsa0706740
+
Phan D, Pradhan N, Jankowiak M. 2019. Composable
Effects for
Flexible and
Accelerated Probabilistic Programming in
NumPyro. doi:
10.48550/arXiv.1912.11554
References
Change in Life Expectancies.
Demography 21:83–96. doi:10.2307/2061029
+
+Asaria P, Elliott P, Douglass M, Obermeyer Z, Soljak M, Majeed A, Ezzati
+M. 2017. Acute myocardial infarction hospital admissions and deaths in
+
England: A national follow-back and follow-forward
+record-linkage study.
The Lancet Public Health
+
2:e191–e201. doi:
10.1016/S2468-2667(17)30032-4
+
Asaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P, de
Hoogh K, Ezzati M, Elliott P. 2012. Trends and inequalities in
@@ -496,6 +503,12 @@
References
Disparities in the
United States.
PLOS
Medicine 5:e66. doi:
10.1371/journal.pmed.0050066
+Ezzati M, Obermeyer Z, Tzoulaki I, Mayosi BM, Elliott P, Leon DA. 2015.
+Contributions of risk factors and medical care to cardiovascular
+mortality trends.
Nature Reviews Cardiology
+
12:508–530. doi:
10.1038/nrcardio.2015.82
+
Finucane MM, Paciorek CJ, Danaei G, Ezzati M. 2014. Bayesian
Estimation of Population-Level Trends in
@@ -559,6 +572,12 @@
References
England and Wales. Oxford University
Press.
+
+Hayat S, Luben R, Khaw K-T, Wareham N, Brayne C. 2022. Evaluation of
+routinely collected records for dementia outcomes in
UK: A
+prospective cohort study.
BMJ Open 12:e060931.
+doi:
10.1136/bmjopen-2022-060931
+
Held L, Natário I, Fenton SE, Rue H, Becker N. 2005. Towards joint
disease mapping.
Statistical Methods in Medical Research
@@ -738,6 +757,12 @@
References
Population.
Public Health Reports (1896-1970)
63:537–545. doi:
10.2307/4586527
+Mukadam N, Livingston G, Rantell K, Rickman S. 2014. Diagnostic rates
+and treatment of dementia before and after launch of a national dementia
+policy: An observational study using
English national
+databases.
BMJ Open 4:e004119. doi:
10.1136/bmjopen-2013-004119
+
Murray CJL, Lopez AD. 1996. The
Global Burden of
Disease: A comprehensive assessment of mortality and
@@ -856,6 +881,14 @@
References
epidemiological analysis of linked primary care records.
The Lancet
Diabetes & Endocrinology 9:165–173. doi:
10.1016/S2213-8587(20)30431-9
+Pell JP, Haw S, Cobbe S, Newby DE, Pell ACH, Fischbacher C, McConnachie
+A, Pringle S, Murdoch D, Dunn F, Oldroyd K, MacIntyre P, O’Rourke B,
+Borland W. 2008. Smoke-free
Legislation and
+
Hospitalizations for
Acute Coronary Syndrome.
+
New England Journal of Medicine 359:482–491.
+doi:
10.1056/NEJMsa0706740
+
Phan D, Pradhan N, Jankowiak M. 2019. Composable Effects
for Flexible and Accelerated Probabilistic
diff --git a/thesis/_thesis/search.json b/thesis/_thesis/search.json
index e095b63..3bb7451 100644
--- a/thesis/_thesis/search.json
+++ b/thesis/_thesis/search.json
@@ -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) (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)."
+ "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.\nIn England, about 51% of all mortality from myocardial infarction, an acute manifestation of ischaemic heart disease, is attributable to out-of-hospital deaths (Asaria et al., 2017). Reductions in out-of-hospital mortality reflects improvements in reducing and controlling risk factors for CVD (smoking, blood glucose and diabetes, raised blood pressure, and high blood cholesterol) (Ezzati et al., 2015). Unlike cancers and COPD where the increased risk following smoking spans decades, the risk of CVD returns to the level of non-smokers within ten years after smoking cessation (Ezzati et al., 2015). Declines in CVD mortality have thus gained greatly from reductions in smoking rates, and more recently, the ban on smoking in public places, which came into force in the UK in 2007 and has had a rapid effect on hospitalisation rates for acute CVD cases (Pell et al., 2008). As well as improvements in these risk factors, reductions in in-hospital CVD mortality partially reflect organisational changes to the NHS such that patients with acute myocardial infarctions are taken directly to centres with capacity to re-open arteries, bypassing local accident and emergency services. Furthermore, strategies to shorten pre-hospital delays between symptom onset and a call for help, for example, the FAST (Face drooping, Arm weakness, Speech difficulties, Time) public health campaign for stroke events, can improve the efficacy of treatments such as stent insertions.\nThe burden of mortality has shifted towards dementias. A number of the main risk factors for dementias are the same as for CVDs (smoking, obesity, diabetes, high blood pressure, high cholesterol) (Yu et al., 2020). However dementia diagnoses are also heavily influenced by age, family history and education. Some part of this trend may also be due to increased efforts in improving diagnosis and cause of death coding of dementias in the UK (Hayat et al., 2022; Mukadam et al., 2014).\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 heterogeneous dynamics of 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)."
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"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.\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"
+ "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, Elliott P, Douglass M, Obermeyer Z, Soljak M, Majeed A, Ezzati M. 2017. Acute myocardial infarction hospital admissions and deaths in England: A national follow-back and follow-forward record-linkage study. The Lancet Public Health 2:e191–e201. doi:10.1016/S2468-2667(17)30032-4\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\nEzzati M, Obermeyer Z, Tzoulaki I, Mayosi BM, Elliott P, Leon DA. 2015. Contributions of risk factors and medical care to cardiovascular mortality trends. Nature Reviews Cardiology 12:508–530. doi:10.1038/nrcardio.2015.82\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\nHayat S, Luben R, Khaw K-T, Wareham N, Brayne C. 2022. Evaluation of routinely collected records for dementia outcomes in UK: A prospective cohort study. BMJ Open 12:e060931. doi:10.1136/bmjopen-2022-060931\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\nMukadam N, Livingston G, Rantell K, Rickman S. 2014. Diagnostic rates and treatment of dementia before and after launch of a national dementia policy: An observational study using English national databases. BMJ Open 4:e004119. doi:10.1136/bmjopen-2013-004119\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\nPell JP, Haw S, Cobbe S, Newby DE, Pell ACH, Fischbacher C, McConnachie A, Pringle S, Murdoch D, Dunn F, Oldroyd K, MacIntyre P, O’Rourke B, Borland W. 2008. Smoke-free Legislation and Hospitalizations for Acute Coronary Syndrome. New England Journal of Medicine 359:482–491. doi:10.1056/NEJMsa0706740\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"
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Life expectancy and risk of death in 6791 communities in\nEngland from 2002 to 2019: High-resolution spatiotemporal\nanalysis of civil registration data. The Lancet Public Health\n6:e805–e816. doi:10.1016/S2468-2667(21)00205-X\n\n\nRasmussen CE, Williams CKI. 2006. Gaussian Processes for\nMachine Learning. MIT Press.\n\n\nRoberts GO, Rosenthal JS. 2004. General state space Markov\nchains and MCMC algorithms. Probability Surveys\n1:20–71. doi:10.1214/154957804100000024\n\n\nSalomon JA, Murray CJL. 2002. The Epidemiologic Transition\nRevisited: Compositional Models for\nCauses of Death by Age and\nSex. Population and Development Review\n28:205–228. doi:10.1111/j.1728-4457.2002.00205.x\n\n\nSemenova E, Xu Y, Howes A, Rashid T, Bhatt S, Mishra S, Flaxman S. 2022.\nPriorVAE: Encoding spatial priors with variational\nautoencoders for small-area estimation. Journal of The Royal Society\nInterface 19:20220094. doi:10.1098/rsif.2022.0094\n\n\nSilcocks PBS, Jenner DA, Reza R. 2001. Life expectancy as a summary of\nmortality in a population: Statistical considerations and suitability\nfor use by health authorities. Journal of Epidemiology &\nCommunity Health 55:38–43. doi:10.1136/jech.55.1.38\n\n\nStan development team. 2020. Prior Choice Recommendations.\nGitHub.\n\n\nSteel N, Ford JA, Newton JN, Davis ACJ, Vos T, Naghavi M, Glenn S,\nHughes A, Dalton AM, Stockton D, Humphreys C, Dallat M, Schmidt J,\nFlowers J, Fox S, Abubakar I, Aldridge RW, Baker A, Brayne C, Brugha T,\nCapewell S, Car J, Cooper C, Ezzati M, Fitzpatrick J, Greaves F, Hay R,\nHay S, Kee F, Larson HJ, Lyons RA, Majeed A, McKee M, Rawaf S, Rutter H,\nSaxena S, Sheikh A, Smeeth L, Viner RM, Vollset SE, Williams HC, Wolfe\nC, Woolf A, Murray CJL. 2018. Changes in health in the countries of the\nUK and 150 English Local Authority areas 1990:\nA systematic analysis for the Global Burden of\nDisease Study 2016. The Lancet\n392:1647–1661. doi:10.1016/S0140-6736(18)32207-4\n\n\nStephens AS, Purdie S, Yang B, Moore H. 2013. Life expectancy estimation\nin small administrative areas with non-uniform population sizes:\nApplication to Australian New South Wales local government\nareas. BMJ Open 3:e003710. doi:10.1136/bmjopen-2013-003710\n\n\nTaylor-Robinson D, Lai ETC, Wickham S, Rose T, Norman P, Bambra C,\nWhitehead M, Barr B. 2019. Assessing the impact of rising child poverty\non the unprecedented rise in infant mortality in England,\n2000: Time trend analysis. BMJ Open 9:e029424.\ndoi:10.1136/bmjopen-2019-029424\n\n\nThatcher AR, Kannisto V, Andreev KF. 2002. The Survivor Ratio\nMethod for Estimating Numbers at High\nAges. Demographic Research 6:1–18.\ndoi:10.4054/DemRes.2002.6.1\n\n\nThe Economist. 2023. Britain has endured a decade of early deaths.\nWhy? The Economist 19–21.\n\n\nThun M, Peto R, Boreham J, Lopez AD. 2012. Stages of the cigarette\nepidemic on entering its second century. Tobacco Control\n21:96–101. doi:10.1136/tobaccocontrol-2011-050294\n\n\nToledano MB, Jarup L, Best N, Wakefield J, Elliott P. 2001. Spatial\nvariation and temporal trends of testicular cancer in Great\nBritain. British Journal of Cancer\n84:1482–1487. doi:10.1054/bjoc.2001.1739\n\n\nTravers T, Sims S, Bosetti N. 2016. Housing and inequality in\nLondon. Centre for London.\n\n\nTsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA. 2015. Type\n2 diabetes and cancer: Umbrella review of meta-analyses of observational\nstudies. BMJ 350:g7607. doi:10.1136/bmj.g7607\n\n\nvan Dijk JT, Lansley G, Longley PA. 2021. Using linked consumer\nregisters to estimate residential moves in the United\nKingdom. Journal of the Royal Statistical Society: Series A\n(Statistics in Society) 184:1452–1474. doi:10.1111/rssa.12713\n\n\nVehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. 2021.\nRank-normalization, folding, and localization: An improved\nR-hat for assessing convergence of\nMCMC. Bayesian Analysis\n16:667–718. doi:10.1214/20-BA1221\n\n\nWakefield J, Elliott P. 1999. Issues in the statistical analysis of\nsmall area health data. Statistics in Medicine\n18:2377–2399. doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18{$<$}2377::AID-SIM263{$>$}3.0.CO;2-G\n\n\nWalters S, Benitez-Majano S, Muller P, Coleman MP, Allemani C, Butler J,\nPeake M, Guren MG, Glimelius B, Bergström S, Påhlman L, Rachet B. 2015.\nIs England closing the international gap in cancer\nsurvival? British Journal of Cancer\n113:848–860. doi:10.1038/bjc.2015.265\n\n\nWalters S, Quaresma M, Coleman MP, Gordon E, Forman D, Rachet B. 2011.\nGeographical variation in cancer survival in England, 1991:\nAn analysis by Cancer Network. J Epidemiol Community\nHealth 65:1044–1052. doi:10.1136/jech.2010.126656\n\n\nWhitty C. 2021. Chief Medical Officer’s annual report 2021:\nHealth in coastal communities.\n\n\nWilkinson RG. 1992. Income distribution and life expectancy. British\nMedical Journal 304:165–168. doi:10.1136/bmj.304.6820.165\n\n\nWilmoth JR. 1995. Are mortality projections always more pessimistic when\ndisaggregated by cause of death? Mathematical Population\nStudies 5:293–319. doi:10.1080/08898489509525409\n\n\nWorld Bank. 2022. Life expectancy at birth, data. World Bank.\n\n\nWorld Health Organization. 2020. WHO methods and data\nsources for country-level causes of death 2000-2019.\n\n\nYu J, Dwyer-Lindgren L, Bennett J, Ezzati M, Gustafson P, Tran M, Brauer\nM. 2021. A spatiotemporal analysis of inequalities in life expectancy\nand 20 causes of mortality in sub-neighbourhoods of Metro\nVancouver, British Columbia, Canada,\n1990. Health & Place 72:102692. doi:10.1016/j.healthplace.2021.102692\n\n\nYu J-T, Xu W, Tan C-C, Andrieu S, Suckling J, Evangelou E, Pan A, Zhang\nC, Jia J, Feng L, Kua E-H, Wang Y-J, Wang H-F, Tan M-S, Li J-Q, Hou X-H,\nWan Y, Tan L, Mok V, Tan L, Dong Q, Touchon J, Gauthier S, Aisen PS,\nVellas B. 2020. Evidence-based prevention of Alzheimer’s\ndisease: Systematic review and meta-analysis of 243 observational\nprospective studies and 153 randomised controlled trials. Journal of\nNeurology, Neurosurgery & Psychiatry\n91:1201–1209. doi:10.1136/jnnp-2019-321913"
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https://theorashid.github.io/thesis/Chapters/Chapter2.html
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https://theorashid.github.io/thesis/Chapters/Chapter3.html
- 2023-08-21T13:07:17.453Z
+ 2023-08-23T11:31:54.521Z
https://theorashid.github.io/thesis/Chapters/Chapter4.html
- 2023-08-21T13:07:17.457Z
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https://theorashid.github.io/thesis/Chapters/Chapter5.html
- 2023-08-21T13:07:17.463Z
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https://theorashid.github.io/thesis/Chapters/Chapter6.html
- 2023-08-21T13:07:17.468Z
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https://theorashid.github.io/thesis/Chapters/Chapter7.html
- 2023-08-21T13:07:17.478Z
+ 2023-08-23T11:31:54.545Z
https://theorashid.github.io/thesis/Chapters/Chapter8.html
- 2023-08-21T13:07:17.484Z
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https://theorashid.github.io/thesis/Chapters/Chapter9.html
- 2023-08-21T13:07:17.488Z
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https://theorashid.github.io/thesis/references.html
- 2023-08-21T13:07:17.497Z
+ 2023-08-23T11:31:54.564Z
https://theorashid.github.io/thesis/Appendices/AppendixA.html
- 2023-08-21T13:07:17.500Z
+ 2023-08-23T11:31:54.567Z
https://theorashid.github.io/thesis/Appendices/AppendixB.html
- 2023-08-21T13:07:17.506Z
+ 2023-08-23T11:31:54.572Z
https://theorashid.github.io/thesis/Appendices/AppendixC.html
- 2023-08-21T13:07:17.507Z
+ 2023-08-23T11:31:54.574Z
https://theorashid.github.io/thesis/Appendices/AppendixD.html
- 2023-08-21T13:07:17.510Z
+ 2023-08-23T11:31:54.578Z
https://theorashid.github.io/thesis/Appendices/AppendixE.html
- 2023-08-21T13:07:17.517Z
+ 2023-08-23T11:31:54.583Z
diff --git a/thesis/thesis.bib b/thesis/thesis.bib
index ca7c8a0..197c9b6 100644
--- a/thesis/thesis.bib
+++ b/thesis/thesis.bib
@@ -88,6 +88,26 @@ @article{arriagaMeasuringExplainingChange1984
file = {/Users/theorashid/Zotero/storage/YRH3WN83/Arriaga - 1984 - Measuring and Explaining the Change in Life Expect.pdf}
}
+@article{asariaAcuteMyocardialInfarction2017,
+ title = {Acute Myocardial Infarction Hospital Admissions and Deaths in {{England}}: A National Follow-Back and Follow-Forward Record-Linkage Study},
+ shorttitle = {Acute Myocardial Infarction Hospital Admissions and Deaths in {{England}}},
+ author = {Asaria, Perviz and Elliott, Paul and Douglass, Margaret and Obermeyer, Ziad and Soljak, Michael and Majeed, Azeem and Ezzati, Majid},
+ year = {2017},
+ month = apr,
+ journal = {The Lancet Public Health},
+ volume = {2},
+ number = {4},
+ pages = {e191-e201},
+ publisher = {{Elsevier}},
+ issn = {2468-2667},
+ doi = {10.1016/S2468-2667(17)30032-4},
+ urldate = {2023-08-23},
+ langid = {english},
+ pmid = {29253451},
+ keywords = {cause-specific,cvd},
+ file = {/Users/theorashid/Zotero/storage/VE9H3KFA/Asaria et al. - 2017 - Acute myocardial infarction hospital admissions an.pdf}
+}
+
@article{asariaTrendsInequalitiesCardiovascular2012,
title = {Trends and Inequalities in Cardiovascular Disease Mortality across 7932 {{English}} Electoral Wards, 1982\textendash 2006: {{Bayesian}} Spatial Analysis},
shorttitle = {Trends and Inequalities in Cardiovascular Disease Mortality across 7932 {{English}} Electoral Wards, 1982\textendash 2006},
@@ -786,6 +806,26 @@ @article{exarchakouImpactNationalCancer2018
file = {/Users/theorashid/Zotero/storage/4UA2ICMC/Exarchakou et al. - 2018 - Impact of national cancer policies on cancer survi.pdf}
}
+@article{ezzatiContributionsRiskFactors2015,
+ title = {Contributions of Risk Factors and Medical Care to Cardiovascular Mortality Trends},
+ author = {Ezzati, Majid and Obermeyer, Ziad and Tzoulaki, Ioanna and Mayosi, Bongani M. and Elliott, Paul and Leon, David A.},
+ year = {2015},
+ month = sep,
+ journal = {Nature Reviews Cardiology},
+ volume = {12},
+ number = {9},
+ pages = {508--530},
+ publisher = {{Nature Publishing Group}},
+ issn = {1759-5010},
+ doi = {10.1038/nrcardio.2015.82},
+ urldate = {2023-08-23},
+ abstract = {Death rates from ischaemic heart disease (IHD), stroke, and other cardiovascular diseases (CVDs) are decreasing in high-income and many Latin American countries, and this trend shows no signs of slowingDeclines in some behavioural risk factors, including smoking, and physiological risk factors, such as blood pressure and serum cholesterol, are likely to have helped to reduce CVDsBy contrast, the nearly universal increase in adiposity seems not to have modified the long-term declining trend in CVD mortality, although it might have had some slowing effectImproved medical care, including effective treatment of physiological risk factors, diagnosis, treatment of acute CVDs, and post-hospital care, has also contributed to declining CVD events and mortalityMeasured risk factor and treatment variables, while important, explain neither why the decline began when it did nor many of the similarities and differences between countries or between men and womenSubstantial fluctuations in CVDs, and in alcohol intake, in former communist countries of Europe have followed times of massive political and social changes since the early 1990s},
+ copyright = {2015 Springer Nature Limited},
+ langid = {english},
+ keywords = {cause-specific,cvd},
+ file = {/Users/theorashid/Zotero/storage/HC237D5K/Ezzati et al. - 2015 - Contributions of risk factors and medical care to .pdf}
+}
+
@article{ezzatiReversalFortunesTrends2008,
title = {The {{Reversal}} of {{Fortunes}}: {{Trends}} in {{County Mortality}} and {{Cross-County Mortality Disparities}} in the {{United States}}},
shorttitle = {The {{Reversal}} of {{Fortunes}}},
@@ -977,6 +1017,29 @@ @book{hansellannal.EnvironmentHealthAtlas2014
keywords = {mortality,SAHSU,spatial}
}
+@article{hayatEvaluationRoutinelyCollected2022,
+ title = {Evaluation of Routinely Collected Records for Dementia Outcomes in {{UK}}: A Prospective Cohort Study},
+ shorttitle = {Evaluation of Routinely Collected Records for Dementia Outcomes in {{UK}}},
+ author = {Hayat, Shabina and Luben, Robert and Khaw, Kay-Tee and Wareham, Nicholas and Brayne, Carol},
+ year = {2022},
+ month = jun,
+ journal = {BMJ Open},
+ volume = {12},
+ number = {6},
+ pages = {e060931},
+ publisher = {{British Medical Journal Publishing Group}},
+ issn = {2044-6055, 2044-6055},
+ doi = {10.1136/bmjopen-2022-060931},
+ urldate = {2023-08-23},
+ abstract = {Objectives To evaluate the characteristics of individuals recorded as having a dementia diagnosis in different routinely collected records and to examine the extent of overlap of dementia coding across data sources. Also, to present comparisons of secondary and primary care records providing value for researchers using routinely collected records for dementia outcome capture. Study design A prospective cohort study. Setting and participants A cohort of 25 639 men and women in Norfolk, aged 40\textendash 79 years at recruitment (1993\textendash 1997) followed until 2018 linked to routinely collected to identify dementia cases. Data sources include mortality from death certification and National Health Service (NHS) hospital or secondary care records. Primary care records for a subset of the cohort were also reviewed. Primary outcome measure Diagnosis of dementia (any-cause). Results Over 2000 participants (n=2635 individuals) were found to have a dementia diagnosis recorded in one or more of the data sources examined. Limited concordance was observed across the secondary care data sources. We also observed discrepancies with primary care records for the subset and report on potential linkage-related selection bias. Conclusions Use of different types of record linkage from varying parts of the UK's health system reveals differences in recorded dementia diagnosis, indicating that dementia can be identified to varying extents in different parts of the NHS system. However, there is considerable variation, and limited overlap in those identified. We present potential selection biases that might occur depending on whether cause of death, or primary and secondary care data sources are used. With the expansion of using routinely collected health data, researchers must be aware of these potential biases and inaccuracies, reporting carefully on the likely extent of limitations and challenges of the data sources they use.},
+ chapter = {Epidemiology},
+ copyright = {\textcopyright{} Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:~https://creativecommons.org/licenses/by/4.0/.},
+ langid = {english},
+ pmid = {35705339},
+ keywords = {dementia},
+ file = {/Users/theorashid/Zotero/storage/2C469MW2/Hayat et al. - 2022 - Evaluation of routinely collected records for deme.pdf}
+}
+
@article{heldJointDiseaseMapping2005,
title = {Towards Joint Disease Mapping},
author = {Held, Leonhard and Nat{\'a}rio, Isabel and Fenton, Sarah Elaine and Rue, H{\aa}vard and Becker, Nikolaus},
@@ -1485,6 +1548,29 @@ @article{moriyamaStatisticalStudiesHeart1948
file = {/Users/theorashid/Zotero/storage/ASQB8Y5Z/Moriyama and Gover - 1948 - Statistical Studies of Heart Diseases I. Heart Di.pdf}
}
+@article{mukadamDiagnosticRatesTreatment2014,
+ title = {Diagnostic Rates and Treatment of Dementia before and after Launch of a National Dementia Policy: An Observational Study Using {{English}} National Databases},
+ shorttitle = {Diagnostic Rates and Treatment of Dementia before and after Launch of a National Dementia Policy},
+ author = {Mukadam, Naaheed and Livingston, Gill and Rantell, Khadija and Rickman, Sam},
+ year = {2014},
+ month = jan,
+ journal = {BMJ Open},
+ volume = {4},
+ number = {1},
+ pages = {e004119},
+ publisher = {{British Medical Journal Publishing Group}},
+ issn = {2044-6055, 2044-6055},
+ doi = {10.1136/bmjopen-2013-004119},
+ urldate = {2023-08-23},
+ abstract = {Objectives To assess the 2009 National Dementia Strategy's (NDS) impact on dementia diagnosis and treatment. Setting and participants Primary care data for England before and after launch of the NDS. Primary outcome measures We used nationally available data to estimate the trends over time in rates of dementia diagnoses recorded on the Quality Outcomes Framework (QOF) in Primary Care Trusts (PCT) and antidementia medication prescriptions from 2006/2007 (the first available figures) and the associated increase in cost relative to all other prescriptions. To establish PCT general practitioner (GP) QOF dementia recording validity, we correlated it with medication prescription using the NIC (net ingredient cost). Results Regression analysis showed that dementia diagnosis rate was lower prior to launch of the NDS and increased significantly after it was launched. The number of antidementia prescriptions and the cost of antidementia drugs relative to total PCT prescribing costs increased significantly after 2009. GP recording of dementia diagnosis correlated highly with prescription of cholinesterase inhibitors and memantine in the same area (p{$<$}0.001 each year). Conclusions The launch of the NDS was associated with a significant increase in dementia diagnosis rates and prescriptions of antidementia drugs. We cannot establish the causality but this was a change from the prelaunch pattern. Further assessment of any intervention to increase the diagnoses should include an assessment of harm as well as potential benefit.},
+ chapter = {Health policy},
+ copyright = {Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) 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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/},
+ langid = {english},
+ pmid = {24413352},
+ keywords = {cause-specific,dementia},
+ file = {/Users/theorashid/Zotero/storage/29V79WS5/Mukadam et al. - 2014 - Diagnostic rates and treatment of dementia before .pdf}
+}
+
@book{murrayGlobalBurdenDisease1996,
title = {The {{Global Burden}} of {{Disease}}: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected in 2020},
author = {Murray, Christopher J. L. and Lopez, Alan D.},
@@ -1815,6 +1901,25 @@ @article{pearson-stuttardTrendsPredominantCauses2021
file = {/Users/theorashid/Zotero/storage/ULYM2JE5/Pearson-Stuttard et al. - 2021 - Trends in predominant causes of death in individua.pdf}
}
+@article{pellSmokefreeLegislationHospitalizations2008,
+ title = {Smoke-Free {{Legislation}} and {{Hospitalizations}} for {{Acute Coronary Syndrome}}},
+ author = {Pell, Jill P. and Haw, Sally and Cobbe, Stuart and Newby, David E. and Pell, Alastair C.H. and Fischbacher, Colin and McConnachie, Alex and Pringle, Stuart and Murdoch, David and Dunn, Frank and Oldroyd, Keith and MacIntyre, Paul and O'Rourke, Brian and Borland, William},
+ year = {2008},
+ month = jul,
+ journal = {New England Journal of Medicine},
+ volume = {359},
+ number = {5},
+ pages = {482--491},
+ publisher = {{Massachusetts Medical Society}},
+ issn = {0028-4793},
+ doi = {10.1056/NEJMsa0706740},
+ urldate = {2023-08-23},
+ abstract = {The Smoking, Health and Social Care Act, which was passed in 2005, prohibited smoking in all enclosed public places and workplaces in Scotland after the end of March 2006. Smoke-free legislation aims to protect nonsmokers from secondhand smoke, but it may also reduce the risk among smokers because of reduced smoking or increased smoking cessation.1\textendash 4 Eight studies have shown reduced numbers of hospital admissions for acute coronary syndrome after the enactment of such legislation.5\textendash 12 These studies were limited by retrospective data collection,5\textendash 12 the use of clinical diagnostic labels,5\textendash 12 confounding by seasonal variations,7 and small numbers of . . .},
+ pmid = {18669427},
+ keywords = {cause-specific,cvd},
+ file = {/Users/theorashid/Zotero/storage/YZHPQ8S9/Pell et al. - 2008 - Smoke-free Legislation and Hospitalizations for Ac.pdf}
+}
+
@misc{phanComposableEffectsFlexible2019,
title = {Composable {{Effects}} for {{Flexible}} and {{Accelerated Probabilistic Programming}} in {{NumPyro}}},
author = {Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},