edit margins and page numbers

theorashid · Aug 3, 2023 · 1820bcc · 1820bcc
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diff --git a/thesis/_extensions/nmfs-opensci/quarto-thesis/_extension.yml b/thesis/_extensions/nmfs-opensci/quarto-thesis/_extension.yml
@@ -8,7 +8,7 @@ contributes:
       classoption: ["11pt", "english", "doublespacing", "headsepline"]
       format-resources:
         - MastersDoctoralThesis.cls
-      template-partials: 
+      template-partials:
         - "partials/title.tex"
         - "partials/before-body.tex"
       toc: false
@@ -17,11 +17,11 @@ contributes:
         - partials/definitions.tex
       include-in-header: 
         - partials/in-header.tex
-      geometry: 
+      geometry:
         - paper=a4paper
-        - inner=2.5cm
-        - outer=3.8cm
-        - bindingoffset=.5cm
+        - inner=2.8cm
+        - outer=2.8cm
+        - bindingoffset=0cm
         - top=1.5cm
         - bottom=1.5cm
       colorlinks: true

diff --git a/thesis/_extensions/nmfs-opensci/quarto-thesis/partials/before-body.tex b/thesis/_extensions/nmfs-opensci/quarto-thesis/partials/before-body.tex
@@ -161,6 +161,6 @@
 %	THESIS CONTENT - CHAPTERS
 %----------------------------------------------------------------------------------------
 
-\mainmatter % Begin numeric (1,2,3...) page numbering
+% \mainmatter % Begin numeric (1,2,3...) page numbering
 
 \pagestyle{thesis} % Return the page headers back to the "thesis" style
diff --git a/thesis/_quarto.yml b/thesis/_quarto.yml
@@ -13,24 +13,21 @@ book:
   abstract: |
     High-resolution data for how mortality and longevity have changed are scarce.
     Estimating mortality for specific combinations of spatial units, time periods, age groups, and causes of death poses computational challenges and typically compromises are made in the granularity of the results.
-    Here, I developed Bayesian hierarchical models based on patterns of mortality over age, space, and time, to obtain robust yearly estimates of life expectancy and cause-specific mortality for small areas, together with the uncertainty in these estimates.
-    Using civil registration data held by the UK Small Area Health Statistics Unit, I investigated trends in mortality for subnational units in England from 2002 to 2019, a period of substantial change in economic, health, and social care policy.
+    I developed Bayesian hierarchical models based on patterns of mortality over age, space, and time, to obtain robust yearly estimates of life expectancy and cause-specific mortality for small areas.
+    Using civil registration data held by the UK Small Area Health Statistics Unit, I investigated trends in mortality for subnational units in England from 2002 to 2019.
 
     I examined trends in life expectancy in England's 6791 middle-layer super output areas (MSOAs).
-    In 2002–06 and 2006–10, all but a few (0–1%) MSOAs had a life expectancy increase for female and male sexes.
-    In 2010–14, female life expectancy decreased in 351 (5.2%) of 6791 MSOAs.
-    By 2014–19, the number of MSOAs with declining life expectancy was 1270 (18.7%) for women and 784 (11.5%) for men.
+    In the years 2014-19, 1270 (18.7%) MSOAs for women and 784 (11.5%) MSOAs for men saw declines in life expectancy.
     The same analysis was performed for the 4835 lower-layer super output areas which comprise London.
-    At this smaller level, there were issues with the population data in the older age groups that affected the reliability of the life expectancy estimates in the tails of the distribution.
+    At this smaller level, there were issues with the population data in the older age groups that affected the reliability of the life expectancy estimates.
 
     I modelled the cause-specific composition of mortality for 314 districts in England.
-    The inequality in life expectancy progress since 2010 was driven largely by Alzheimer's and other dementias and the residual group of non-communicable diseases, as well as ischaemic heart disease in men.
+    The inequality in life expectancy progress since 2010 was driven largely by dementias and the residual group of non-communicable diseases, as well as ischaemic heart disease in men.
     The analysis was extended to look specifically at the top ten leading cancer causes of death.
-    Preventable cancers were the most unequal in progress from 2002 to 2019, with lung cancer in women and oesophageal cancer in men seeing declines in most districts but increases in other districts.
-    Unlike areas in the rest of the country, mortality in London from lung, colorectal, oesophageal, and all other cancers did not increase in poorer districts, indicating that there is some exceptional effect for cancer mortality in the London region.
+    Preventable cancers were the most unequal in progress from 2002 to 2019.
+    Unlike areas in the rest of the country, mortality in London from several cancers did not increase in poorer districts, suggesting that some features of London weaken the relationship between poverty and mortality.
 
-    For declines in life expectancy to happen before the Covid-19 pandemic in increasing numbers of communities in England is worrying.
-    Small area statistics on total mortality and the cause-composition of mortality are a vital tool for creating targetted initiatives to reduce inequalities and prevent further deteriorations in longevity.
+    Small area statistics on total mortality and the cause-composition of mortality are a vital tool for creating targetted initiatives to reduce inequalities in longevity.
   chapters:
     - index.qmd
     - Chapters/Chapter2.qmd

diff --git a/thesis/_thesis/index.html b/thesis/_thesis/index.html
@@ -251,10 +251,10 @@ <h1 class="title">Spatiotemporal mortality modelling</h1>
 <div>
   <div class="abstract">
     <div class="abstract-title">Abstract</div>
-    <p>High-resolution data for how mortality and longevity have changed are scarce. Estimating mortality for specific combinations of spatial units, time periods, age groups, and causes of death poses computational challenges and typically compromises are made in the granularity of the results. Here, I developed Bayesian hierarchical models based on patterns of mortality over age, space, and time, to obtain robust yearly estimates of life expectancy and cause-specific mortality for small areas, together with the uncertainty in these estimates. Using civil registration data held by the UK Small Area Health Statistics Unit, I investigated trends in mortality for subnational units in England from 2002 to 2019, a period of substantial change in economic, health, and social care policy.</p>
-    <p>I examined trends in life expectancy in England’s 6791 middle-layer super output areas (MSOAs). In 2002–06 and 2006–10, all but a few (0–1%) MSOAs had a life expectancy increase for female and male sexes. In 2010–14, female life expectancy decreased in 351 (5.2%) of 6791 MSOAs. By 2014–19, the number of MSOAs with declining life expectancy was 1270 (18.7%) for women and 784 (11.5%) for men. The same analysis was performed for the 4835 lower-layer super output areas which comprise London. At this smaller level, there were issues with the population data in the older age groups that affected the reliability of the life expectancy estimates in the tails of the distribution.</p>
-    <p>I modelled the cause-specific composition of mortality for 314 districts in England. The inequality in life expectancy progress since 2010 was driven largely by Alzheimer’s and other dementias and the residual group of non-communicable diseases, as well as ischaemic heart disease in men. The analysis was extended to look specifically at the top ten leading cancer causes of death. Preventable cancers were the most unequal in progress from 2002 to 2019, with lung cancer in women and oesophageal cancer in men seeing declines in most districts but increases in other districts. Unlike areas in the rest of the country, mortality in London from lung, colorectal, oesophageal, and all other cancers did not increase in poorer districts, indicating that there is some exceptional effect for cancer mortality in the London region.</p>
-    <p>For declines in life expectancy to happen before the Covid-19 pandemic in increasing numbers of communities in England is worrying. Small area statistics on total mortality and the cause-composition of mortality are a vital tool for creating targetted initiatives to reduce inequalities and prevent further deteriorations in longevity.</p>
+    <p>High-resolution data for how mortality and longevity have changed are scarce. Estimating mortality for specific combinations of spatial units, time periods, age groups, and causes of death poses computational challenges and typically compromises are made in the granularity of the results. I developed Bayesian hierarchical models based on patterns of mortality over age, space, and time, to obtain robust yearly estimates of life expectancy and cause-specific mortality for small areas. Using civil registration data held by the UK Small Area Health Statistics Unit, I investigated trends in mortality for subnational units in England from 2002 to 2019.</p>
+    <p>I examined trends in life expectancy in England’s 6791 middle-layer super output areas (MSOAs). In the years 2014-19, 1270 (18.7%) MSOAs for women and 784 (11.5%) MSOAs for men saw declines in life expectancy. The same analysis was performed for the 4835 lower-layer super output areas which comprise London. At this smaller level, there were issues with the population data in the older age groups that affected the reliability of the life expectancy estimates.</p>
+    <p>I modelled the cause-specific composition of mortality for 314 districts in England. The inequality in life expectancy progress since 2010 was driven largely by dementias and the residual group of non-communicable diseases, as well as ischaemic heart disease in men. The analysis was extended to look specifically at the top ten leading cancer causes of death. Preventable cancers were the most unequal in progress from 2002 to 2019. Unlike areas in the rest of the country, mortality in London from several cancers did not increase in poorer districts, suggesting that some features of London weaken the relationship between poverty and mortality.</p>
+    <p>Small area statistics on total mortality and the cause-composition of mortality are a vital tool for creating targetted initiatives to reduce inequalities in longevity.</p>
   </div>
 </div>
 

diff --git a/thesis/_thesis/sitemap.xml b/thesis/_thesis/sitemap.xml
@@ -2,62 +2,62 @@
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   <url>
     <loc>https://theorashid.github.io/thesis/index.html</loc>
-    <lastmod>2023-08-03T09:37:15.383Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.714Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter2.html</loc>
-    <lastmod>2023-08-03T09:37:15.392Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.724Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter3.html</loc>
-    <lastmod>2023-08-03T09:37:15.395Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.728Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter4.html</loc>
-    <lastmod>2023-08-03T09:37:15.400Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.731Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter5.html</loc>
-    <lastmod>2023-08-03T09:37:15.406Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.737Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter6.html</loc>
-    <lastmod>2023-08-03T09:37:15.411Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.743Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter7.html</loc>
-    <lastmod>2023-08-03T09:37:15.420Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.751Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter8.html</loc>
-    <lastmod>2023-08-03T09:37:15.424Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.755Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Chapters/Chapter9.html</loc>
-    <lastmod>2023-08-03T09:37:15.427Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.759Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/references.html</loc>
-    <lastmod>2023-08-03T09:37:15.434Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.767Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Appendices/AppendixA.html</loc>
-    <lastmod>2023-08-03T09:37:15.438Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.770Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Appendices/AppendixB.html</loc>
-    <lastmod>2023-08-03T09:37:15.444Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.776Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Appendices/AppendixC.html</loc>
-    <lastmod>2023-08-03T09:37:15.445Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.777Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Appendices/AppendixD.html</loc>
-    <lastmod>2023-08-03T09:37:15.448Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.782Z</lastmod>
   </url>
   <url>
     <loc>https://theorashid.github.io/thesis/Appendices/AppendixE.html</loc>
-    <lastmod>2023-08-03T09:37:15.456Z</lastmod>
+    <lastmod>2023-08-03T14:32:51.788Z</lastmod>
   </url>
 </urlset>