thanks be to solange

thesis/Chapters/Chapter1.qmd

@@ -22,10 +22,10 @@ There are two specific objectives which will help achieve this aim:
 ## Structure of the thesis
 
 @sec-Chapter2 reviews the literature on spatial methods for mapping disease and mortality for small subnational regions, followed by the literature of separating total mortality into different causes of death.
-I will then explore inequalities in UK over the past few decades through to the present.
+I will then explore inequalities in the UK over the past few decades through to the present.
 @sec-Chapter3 presents the data sources, and @sec-Chapter4 the statistical modelling choices common to all objectives of this thesis.
 @sec-Chapter5 concerns the first objective of the thesis - estimating trends in life expectancy for very small areas in England.
-@sec-Chapter6 extends the first objective by focussing on London at a finer scale than the previous chapter as an attempt to gauge whether higher-resolution analyses are possible.
+@sec-Chapter6 extends the first objective by focussing on London at a finer scale than the previous chapter in an attempt to gauge whether higher-resolution analyses are possible.
 @sec-Chapter7 addresses objective two of this thesis, breaking down total mortality in England into specific causes of deaths at a coarser scale, and looking at potential drivers of the observed trends in life expectancy.
-@sec-Chapter8 follows the methods of @sec-Chapter7, but focussing only on deaths from cancers.
+@sec-Chapter8 follows the methods of @sec-Chapter7, but focussing solely on deaths from cancers.
 @sec-Chapter9 concludes with a discussion on the public health implications of the findings and areas for future research building on the work in this thesis.

thesis/Chapters/Chapter3.qmd

@@ -42,7 +42,7 @@ The counts of deaths come from de-identified civil registration data for all dea
 In other words, every death in England from 2002 to 2019.
 
 The data is extracted from the Office for National Statistics (ONS) database and held by SAHSU in a secure environment as individual death records are identifiable data.
-The data are updated every year and are mostly complete for previous years, but a handful of deaths are registered in later extracts if the ONS have been waiting on coroner's report to identify the underlying cause of death.
+The data are updated every year and are mostly complete for previous years, but a handful of deaths are registered in later extracts if the ONS have been waiting on a coroner's report to identify the underlying cause of death.
 
 Each record comes with information on postcode of residence, allowing us to assign each death into a spatial unit for analysis.
 For each analysis, deaths were stratified into the following age groups: 0, 1–4, 5–9, 10–14, then 5-year age groups up to 80–84, and 85 years and older.
@@ -53,7 +53,7 @@ Here, I focus only on the underlying cause of death, which has been assigned usi
 
 The second data sources we require are population counts.
 These are taken from mid-year population estimates of the usual resident population by the ONS [@officefornationalstatisticsMiddleLayerSuper2021; @officefornationalstatisticsLowerLayerSuper2021].
-The ONS estimates inter-censal populations on a rolling basis, updating the previous year's value using the change in the population in the GP patient registration data as an indicator of the true population change.
+The ONS estimates inter-censal populations on a rolling basis, updating the previous year's value using the change in the population in GP patient registration data as an indicator of the true population change.
 The LSOA populations are fully consistent with estimates for higher levels in the nested geographical hierarchical including MSOAs, districts, regions and the national total for England [@officefornationalstatisticsPopulationEstimatesOutput2021].
 
 ### Community deprivation data
@@ -105,4 +105,4 @@ Likewise, life expectancy has improved throughout the study period, but has stal
 @fig-ch-3-district shows the geography of life expectancy after aggregating deaths over the entire study period by district.
 For both sexes, the picture is similar: pockets of low life expectancy in the urban North West, North East, and West Midlands.
 
-Here, I have taken slices across each dimension, but the aim in the following chapters is to calculate death rates for each sex-age-space-time stratum.
+In this section, I have taken slices across each dimension, but the aim in the following chapters is to calculate death rates for each sex-age-space-time stratum.

thesis/Chapters/Chapter4.qmd

@@ -115,4 +115,4 @@ Rewriting the model in `NumPyro` and sampling on a GPU cut the runtime down to a
 
 I have paid a lot of attention to open sourcing code for all analyses during my PhD.
 The code is clean, version-controlled, and follows best practices for scientific software engineering.
-As well as code contributed to open source projects along the way, the code for [statistical models](https://github.com/theorashid/mortality-statsmodel), [plots and analysis](https://github.com/theorashid/thesis-analysis), and the [thesis itself](https://github.com/theorashid/thesis) can be found on GitHub.
+As well having contributed code to open source projects along the way, the code for [statistical models](https://github.com/theorashid/mortality-statsmodel), [plots and analysis](https://github.com/theorashid/thesis-analysis), and the [thesis itself](https://github.com/theorashid/thesis) can be found on GitHub.

thesis/Chapters/Chapter5.qmd

@@ -7,7 +7,7 @@ The figures have been reproduced for the thesis, and the text has been updated b
 There have been declines in female life expectancy in England for the most deprived deciles since the start of the 2010s [@bennettContributionsDiseasesInjuries2018; @marmotMarmotReview102020].
 However, these studies have only broken England down into deciles of deprivation, rather than looking at geographical variation.
 And studies of trends in life expectancy have been limited to the district level [@bennettFutureLifeExpectancy2015], which masks substantial heterogeneity.
-In this chapter, I address the first objective of the thesis – to estimate trends in life expectancy for small areas in England from 2002 to 2019 – choosing a smaller unit of analysis than districts: MSOAs.
+In this chapter, I address the first objective of the thesis – to estimate trends in life expectancy for small areas in England from 2002 to 2019 – by choosing a smaller unit of analysis than districts: MSOAs.
 
 ## Methods
 
@@ -34,7 +34,7 @@ Thus, the MSOAs containing the Isle of Wight, Hayling Island, the Isles of Scill
 The results of the spatial model were virtually identical to the hierarchical random effects model (correlation coefficient 0.999 for female and male sexes; mean difference 0.03 years for women and 0.009 years for men; mean absolute difference 0.07 years for women and 0.09 years for men for life expectancy estimates from the two approaches).
 I present results from the hierarchical model for two reasons.
 First, it allows neighbouring MSOAs that fall in different districts to differ more than those within the same district, reflecting the relevance of district as a unit of resource allocation and policy implementation.
-Second, the hierarchical model was computationally less demanding with run times about 1.4 times faster than the spatial model.
+Second, the hierarchical model was computationally less demanding, with run times about 1.4 times faster than the spatial model.
 
 Inference was performed using MCMC in `NIMBLE` package [@devalpineNIMBLEMCMCParticle2022; @devalpineProgrammingModelsWriting2017].
 I monitored convergence using trace plots and the $\widehat{R}$ diagnostic [@vehtariRanknormalizationFoldingLocalization2021] and thinned post burn-in samples to reduce memory and storage use.

thesis/Chapters/Chapter6.qmd

@@ -133,7 +133,7 @@ Subsequent inspection showed that postcodes coinciding with, or adjacent to, LSO
 Mortality and house prices were estimated using two separate models because a joint model would be extremely complex and would require additional assumptions about how house prices are associated with age- and LSOA-specific mortality.
 It was only possible to indirectly evaluate, through LSOA-level population turnover and population characteristics, whether population change is a potential mechanism for the observed change in life expectancy because routine death registration in England only records place of residence at the time of death.
 Further, there are currently limited time-series data on quality of housing, access to jobs, services and amenities, and other home and neighbourhood characteristics that affect health.
-To understand whether changes in the life expectancy of communities arises from changes in the health of the population, itself due to changes in their economic status and/or local environment and amenities, versus a change in the resident population requires linked datasets which are able to track over time environmental characteristics of areas together with individuals’ place of residence, socioeconomic status and mortality records.
+To understand whether the change in the life expectancy of a community arises from a change in the health of the population, itself due to changes in their economic status and/or local environment and amenities, versus a change in the resident population, requires linked datasets which are able to track over time environmental characteristics of areas, together with individuals’ place of residence, socioeconomic status and mortality records.
 
 ### Comparison with previous literature
 
@@ -158,8 +158,8 @@ Since the turn of the millennium, London's population and economy have grown sub
 The economic growth has been highly polarised with high-pay and high-skilled employment alongside low-pay and insecure jobs [@overmanSpatialDisparitiesLabour2022].
 As a result, despite city-wide growth in income, nearly one half of London's population fall in the bottom two quintiles of national income deprivation [@ministryofhousingcommunities&localgovernmentEnglishIndicesDeprivation2019].
 Together with an uncontrolled property market, this has created house prices that are unfavourable to low-income families, displacing entire subgroups of the population to cheaper parts of the city, with fewer amenities and worse access to jobs, quality education, healthcare and other services and amenities [@bosettiOutNewGeography2015].
-Many are unable to purchase (versus rent) and hence spend an increasing share of income on housing, and/or live in lower quality or smaller accommodation in the more desirable districts [@traversHousingInequalityLondon2016; @bosettiOutNewGeography2015].
-These trends may have contributed to health inequalities both between nearby areas and across the entire city alongside other trends such as differences in the extent of neighbourhood improvement or provisions of health and social care services as council budgets were reduced as a result of austerity policies.
+Many are unable to purchase (versus rent) homes and hence spend an increasing share of income on housing, and/or live in lower quality or smaller accommodation in the more desirable districts [@traversHousingInequalityLondon2016; @bosettiOutNewGeography2015].
+These trends may have contributed to health inequalities both between nearby areas and across the entire city, alongside other trends such as differences in the extent of neighbourhood improvement, or provisions of health and social care services as council budgets were reduced as a result of austerity policies.
 
 The evolution of London, and major cities in other high-income nations and emerging economies, into places where only the well-off can afford to own properties, where the balance of the city is driven by the cost of property and wealth dominates access, poses a gloomy, non-cohesive future for these cities.