# Exploring Tuberculosis Monitoring Indicators in England; Using Dimension Reduction and Clustering

## Introduction

I recently attended the Public Health Research and Science Conference, run by Public Health England (PHE), at the University of Warwick. I was mainly there to present some work that I have been doing (along with my co-authors) estimating the direct effects of the 2005 change in BCG vaccination policy on Tuberculosis (TB) incidence rates (slides) but it was also a great opportunity to see what research is being done within, and partnered with, PHE. The standout out work for me was the nascent data science work that is being undertaken within PHE, which is currently focused around the fingertipsR R package by Sebastian Fox. fingertipsR provides an easy interface to access the fingertips API, which contains data on a large variety of public health issues, and can be explored interactively online (see previous link).

In this post we will focus on data on Tuberculosis (TB), which is predominately a respiratory disease and if left untreated kills approximately half of those infected. The majority of cases are symptom-less (known as latent TB), with 10% of latent cases progressing to active disease. It is thought that immediately after infection individuals are at a higher risk of proceeding to active disease, with the risk diminishing after several years. However, individuals who have carried the disease for many years can, and do, progress to active TB disease. This makes the control and management of TB on a population scale challenging as cases may either be due to recent transmission or be from the activation of latent cases who have carried the disease for many years. HIV infection is known to drastically increase the likelihood of progression to active TB disease and TB is the leading cause of death among people living with HIV (source). Globally 10.4 million people fell ill with TB in 2016 alone, with 1.7 million deaths (source).

In England, TB incidence rates have declined drastically over the course of the last century, with the introduction of BCG vaccination, effective TB treatments and improved standards of living. However, over the previous two decades incidence rates have remained relatively stable with incidence becoming increasing focused in at risk communities, such as the homeless population and the non-UK born living in high density urban areas (source). Recently it has been recognised that TB interventions in England must be collaborative and consistent across the country (source), as this has proven to be effective in other countries such as the USA.

This post uses dimension reduction (Principal Component Analysis (PCA)) and clustering (Partitioning Around Mediods (PAM)) to explore the Tuberculosis monitoring indicators available from the fingertips API, using the fingertipsR R package, for England. It aims to use hypothesis free techniques to generate clusters of counties with similar characteristics of TB indicators. This may help to identify regional variation in Tuberculosis monitoring indicators and possibly provide a framework for future improvements to TB control efforts. It also seeks to act as an example of dimension reduction and clustering analysis. Therefore, comments to improve this aspect of this post would be greatly appreciated (paper links, package recommendations, methodology improvements etc.)!

## Packages

The first step is to load the packages required for the analysis, we do this using the fantastic pacman package.

if (!require(pacman)) install.packages("pacman"); library(pacman)
p_load_gh("thomasp85/patchwork", dependencies = TRUE)

## Data

As discussed in the introduction we are using fingertipsR as our data source. The first step is to investigate the data profiles provided by the package which mention TB.

profs <- profiles()

sel_profs <- profs[grepl("TB", profs$ProfileName),] kable(sel_profs) ProfileID ProfileName DomainID DomainName 86 TB Strategy Monitoring Indicators 1938132814 Key Indicators We find that there are two profiles: key indicators and LTBI programme monitoring (screening the at-risk population for latent TB). We use the indicators function to explore the variables available in each profile. tb_inds <- indicators(ProfileID = sel_profs$ProfileID)

kable(tb_inds)
IndicatorID IndicatorName DomainID DomainName ProfileID ProfileName
91359 TB incidence in England 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91361 TB incidence (three year average) 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91365 Proportion of pulmonary TB cases that were culture confirmed 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91366 Proportion of culture confirmed TB cases with drug susceptibility testing reported for the four first line agents 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91367 Proportion of drug sensitive TB cases who had completed a full course of treatment by 12 months 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91368 Proportion of drug sensitive TB cases who were lost to follow up at last reported outcome 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91369 Proportion of drug sensitive TB cases who had died at last reported outcome 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91373 Proportion of TB cases offered an HIV test 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91374 Proportion of drug sensitive TB cases with at least one social risk factor who completed treatment within 12 months 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91375 Proportion of culture confirmed TB cases with any first line drug resistance 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91450 Proportion of pulmonary TB cases starting treatment within two months of symptom onset 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators
91451 Proportion of pulmonary TB cases starting treatment within four months of symptom onset 1938132814 Key Indicators 86 TB Strategy Monitoring Indicators

This gives us 12 TB indicators, which we can now extract using the fingertips_data function combined with a call to purrr::map. This results in 12 tibbles, with the first being empty (TB incidence rates).

tb_df <- tb_inds$IndicatorID %>% map(~fingertips_data(IndicatorID = .)) The key indicator, which we will match all of the remaining data too, is the three-year average TB incidence rates (as the annual TB incidence rate is missing and would also be more susceptible to noise). The following code extracts this at the county level, re-codes the value variable as recent incidence rates and pulls the overall incidence of cases. According to the fingertips website, local authorities and CCGs with fewer than 20 TB cases per year have had all data for the indicators (apart from three-year average TB incidence) suppressed to avoid deductive disclosure. We can therefore filter out these counties now to avoid issues with missing data later. We also adjust the time period to represent the final year for each rolling average. tb_inc <- tb_df[[2]] %>% filter(AreaType %in% "County & UA") %>% dplyr::select(AreaName, Sex, Age, Timeperiod, rec_inc_rate = Value, rec_inc = Count) %>% filter(rec_inc >= 20) %>% mutate(Timeperiod = Timeperiod %>% str_split(" - ") %>% map_chr(first) %>% as.numeric %>% {. + 2} %>% as.character) %>% dplyr::select(-rec_inc) Looking through the other tibbles they all have the same structure - we can write a function using this knowledge to speed up data extraction. tb_df_extraction <- function(tb_df, var_name, area_type = "County & UA") { df <- tb_df %>% filter(AreaType %in% area_type) %>% dplyr::select(AreaName, Sex, Age, Value, Timeperiod) %>% rename_at(.vars = vars(Value), funs(paste0(var_name))) return(df) } We now extract data for all remaining indicators, rename variables with meaningful names, join into a single tibble and then left join onto the TB incidence rate tibble. Data is only available aggregated for all ages and genders so we also drop these variables here. Finally we clean up Timeperiod into years. var_names <- c("prop_pul_cc", "prop_cc_ds_front", "prop_ds_treat_com_12", "prop_ds_lost_to_follow", "prop_ds_died", "prop_tb_offered_hiv_test", "prop_ds_rf_treat_com_12", "prop_cc_dr_front", "prop_p_start_treat_2_m_sym", "prop_p_start_treat_4_m_sym" ) extracted_tb <- map2(tb_df[-(1:2)], var_names, ~tb_df_extraction(.x, .y)) %>% reduce(full_join, by = c("AreaName", "Sex", "Age", "Timeperiod")) com_tb_df <- tb_inc %>% left_join(extracted_tb, by = c("AreaName", "Sex", "Age", "Timeperiod")) %>% mutate(year = Timeperiod %>% as.numeric) %>% mutate_if(is.character, as.factor) %>% dplyr::select(-Timeperiod) %>% filter(Sex %in% "Persons", Age %in% "All ages") %>% dplyr::select(-Sex, -Age) com_tb_df ## AreaName rec_inc_rate prop_pul_cc ## 1 Hartlepool 10.001630 50.00000 ## 2 Middlesbrough 16.053904 72.72727 ## 3 Stockton-on-Tees 5.986373 37.50000 ## 4 Blackburn with Darwen 37.723518 61.76471 ## 5 Blackpool 7.953812 NA ## 6 Kingston upon Hull 5.055470 80.00000 ## 7 East Riding of Yorkshire 3.280430 NA ## 8 Derby 19.322306 66.66666 ## 9 Leicester 71.140205 62.62626 ## 10 Nottingham 16.883419 80.00000 ## 11 Stoke-on-Trent 8.859775 77.77778 ## 12 Bath and North East Somerset 5.517611 40.00000 ## 13 Bristol 12.903458 68.88889 ## 14 South Gloucestershire 3.249699 NA ## 15 Plymouth 5.239760 NA ## 16 Torbay 5.913205 NA ## 17 Bournemouth 9.371326 43.75000 ## 18 Poole 7.217715 55.55556 ## 19 Swindon 5.167472 NA ## 20 Peterborough 16.284304 80.00000 ## 21 Luton 39.676056 59.09091 ## 22 Southend-on-Sea 8.486751 63.63636 ## 23 Medway 6.279737 75.00000 ## 24 Reading 23.120800 69.23077 ## 25 Slough 52.049015 72.22222 ## 26 Windsor and Maidenhead 8.482484 NA ## 27 Wokingham 5.119017 83.33334 ## 28 Milton Keynes 8.783349 42.85714 ## 29 Brighton and Hove 6.279376 NA ## 30 Portsmouth 9.037122 40.00000 ## 31 Southampton 9.128654 60.00000 ## 32 County Durham 2.563490 71.42857 ## 33 Cheshire East 2.365887 NA ## 34 Cheshire West and Chester 3.002310 NA ## 35 Cornwall 2.400075 61.53846 ## 36 Wiltshire 2.153606 57.14286 ## 37 Bedford 12.166354 87.50000 ## 38 Central Bedfordshire 3.559702 NA ## 39 Northumberland 3.574489 NA ## 40 Bolton 25.897745 52.77778 ## 41 Bury 5.713096 NA ## 42 Manchester 30.402205 69.23077 ## 43 Oldham 17.401764 65.00000 ## 44 Rochdale 8.559086 82.35294 ## 45 Salford 7.055464 70.00000 ## 46 Stockport 6.921000 57.14286 ## 47 Tameside 11.420579 83.33334 ## 48 Trafford 11.699235 53.84615 ## 49 Liverpool 9.236377 88.88889 ## 50 Sefton 2.833239 NA ## 51 Wirral 4.542780 90.90909 ## 52 Barnsley 3.818310 42.85714 ## 53 Doncaster 5.912804 90.90909 ## 54 Rotherham 15.705373 33.33333 ## 55 Sheffield 13.165780 56.75676 ## 56 Newcastle upon Tyne 12.861110 76.19048 ## 57 North Tyneside 5.560875 66.66666 ## 58 South Tyneside 7.203432 NA ## 59 Sunderland 7.148239 66.66666 ## 60 Birmingham 33.478100 62.82722 ## 61 Coventry 23.700235 43.18182 ## 62 Dudley 8.521228 68.96552 ## 63 Sandwell 28.526901 64.15094 ## 64 Solihull 4.510419 NA ## 65 Walsall 24.523979 61.90476 ## 66 Wolverhampton 22.884573 61.70213 ## 67 Bradford 28.236572 74.24242 ## 68 Calderdale 13.679488 52.94118 ## 69 Kirklees 23.459126 51.02041 ## 70 Leeds 12.212431 63.93443 ## 71 Wakefield 3.911077 85.71429 ## 72 Gateshead 3.480385 NA ## 73 Barking and Dagenham 20.770149 88.88889 ## 74 Barnet 27.934246 84.12698 ## 75 Bexley 8.071935 63.63636 ## 76 Brent 82.045059 58.26087 ## 77 Bromley 7.430734 52.94118 ## 78 Camden 44.631599 72.72727 ## 79 Croydon 29.957165 77.04918 ## 80 Ealing 65.154572 68.00000 ## 81 Enfield 30.369305 54.38596 ## 82 Greenwich 30.304283 76.92308 ## 83 Hackney 64.190529 61.90476 ## 84 Hammersmith and Fulham 44.092407 80.00000 ## 85 Haringey 63.103806 65.11628 ## 86 Harrow 48.456593 71.42857 ## 87 Havering 9.927235 70.00000 ## 88 Hillingdon 36.150009 53.73134 ## 89 Hounslow 49.568016 61.01695 ## 90 Islington 50.246021 64.00000 ## 91 Kensington and Chelsea 24.548199 65.00000 ## 92 Kingston upon Thames 10.089347 66.66666 ## 93 Lambeth 47.768303 68.75000 ## 94 Lewisham 29.469690 67.56757 ## 95 Merton 22.637060 71.87500 ## 96 Newham 88.781654 65.17857 ## 97 Redbridge 36.224197 79.41177 ## 98 Richmond upon Thames 6.887685 63.63636 ## 99 Southwark 37.351101 67.14286 ## 100 Sutton 11.045432 35.00000 ## 101 Tower Hamlets 45.931206 71.64179 ## 102 Waltham Forest 39.275295 63.79310 ## 103 Wandsworth 26.032936 70.17544 ## 104 Westminster 39.973286 51.16279 ## 105 Buckinghamshire 9.128386 78.12500 ## 106 Cambridgeshire 3.963995 55.00000 ## 107 Derbyshire 4.625445 50.00000 ## 108 Devon 3.402015 77.77778 ## 109 Dorset (Cty) 3.399086 57.14286 ## 110 East Sussex 4.454247 52.63158 ## 111 Essex 3.836226 61.53846 ## 112 Gloucestershire 4.068336 73.33334 ## 113 Hampshire 2.820373 57.57576 ## 114 Hertfordshire 5.344966 63.88889 ## 115 Kent 3.754844 75.55556 ## 116 Lancashire 10.505231 71.21212 ## 117 Leicestershire 6.830683 44.00000 ## 118 Lincolnshire 1.595156 50.00000 ## 119 Norfolk 2.504603 58.82353 ## 120 Northamptonshire 11.725757 50.84746 ## 121 North Yorkshire 2.223666 42.85714 ## 122 Nottinghamshire 3.160040 56.25000 ## 123 Oxfordshire 5.208670 58.82353 ## 124 Somerset 2.273065 47.05882 ## 125 Staffordshire 1.899376 46.15385 ## 126 Suffolk 2.974719 70.00000 ## 127 Surrey 3.179450 75.00000 ## 128 Warwickshire 6.060311 48.14815 ## 129 West Sussex 4.857593 62.96296 ## 130 Worcestershire 3.012937 84.61539 ## 131 Hartlepool 7.399761 NA ## 132 Middlesbrough 14.986691 63.63636 ## 133 Stockton-on-Tees 5.952016 37.50000 ## 134 Blackburn with Darwen 32.981216 53.84615 ## 135 Blackpool 8.429097 62.50000 ## 136 Kingston upon Hull 5.066802 100.00000 ## 137 East Riding of Yorkshire 2.721498 NA ## 138 Derby 21.710001 60.00000 ## 139 Leicester 76.689621 70.83334 ## 140 Nottingham 15.681375 77.27273 ## 141 Stoke-on-Trent 13.325782 66.66666 ## 142 Bath and North East Somerset 6.675332 45.45454 ## 143 Bristol 13.148480 59.45946 ## 144 South Gloucestershire 3.768932 71.42857 ## 145 Plymouth 4.952579 57.14286 ## 146 Bournemouth 8.530222 25.00000 ## 147 Poole 5.524504 NA ## 148 Swindon 5.305457 77.77778 ## 149 Peterborough 13.836159 66.66666 ## 150 Luton 41.327065 70.83334 ## 151 Southend-on-Sea 10.352867 72.72727 ## 152 Thurrock 4.608274 NA ## 153 Medway 7.189216 76.92308 ## 154 Reading 25.423592 77.27273 ## 155 Slough 56.571545 55.26316 ## 156 Windsor and Maidenhead 9.467503 50.00000 ## 157 Wokingham 6.019087 77.77778 ## 158 Milton Keynes 7.127749 75.00000 ## 159 Brighton and Hove 4.402712 NA ## 160 Portsmouth 7.586423 66.66666 ## 161 Southampton 11.782709 68.18182 ## 162 County Durham 3.038186 30.76923 ## 163 Cheshire East 2.733320 88.88889 ## 164 Shropshire 2.689845 50.00000 ## 165 Cornwall 2.313040 54.54546 ## 166 Wiltshire 2.738628 81.81818 ## 167 Bedford 15.834571 33.33333 ## 168 Central Bedfordshire 3.385766 NA ## 169 Northumberland 2.809869 57.14286 ## 170 Bolton 22.730650 72.41380 ## 171 Bury 7.916869 42.85714 ## 172 Manchester 30.205076 68.85246 ## 173 Oldham 18.917377 84.00000 ## 174 Rochdale 8.700346 56.25000 ## 175 Salford 7.992622 75.00000 ## 176 Stockport 8.108193 50.00000 ## 177 Tameside 12.035613 50.00000 ## 178 Trafford 12.613025 50.00000 ## 179 Liverpool 7.665703 78.57143 ## 180 Sefton 3.555922 90.00000 ## 181 Wirral 2.961631 NA ## 182 Barnsley 3.652190 77.77778 ## 183 Doncaster 5.891472 81.25000 ## 184 Rotherham 14.422210 52.17391 ## 185 Sheffield 14.974310 64.81481 ## 186 Newcastle upon Tyne 13.623229 82.75862 ## 187 North Tyneside 5.019220 71.42857 ## 188 South Tyneside 6.795504 NA ## 189 Sunderland 7.301281 83.33334 ## 190 Birmingham 33.082249 64.39024 ## 191 Coventry 21.552582 50.00000 ## 192 Dudley 10.477124 68.75000 ## 193 Sandwell 29.625620 60.37736 ## 194 Solihull 5.831992 53.84615 ## 195 Walsall 22.674488 59.45946 ## 196 Wolverhampton 26.779922 79.24529 ## 197 Bradford 28.278658 73.84615 ## 198 Calderdale 13.286216 41.66667 ## 199 Kirklees 21.180962 54.05405 ## 200 Leeds 12.653628 63.04348 ## 201 Wakefield 4.319632 60.00000 ## 202 Barking and Dagenham 21.074986 71.42857 ## 203 Barnet 29.318958 77.35849 ## 204 Bexley 9.727982 66.66666 ## 205 Brent 81.083023 64.54546 ## 206 Bromley 8.313309 60.00000 ## 207 Camden 51.081135 73.77049 ## 208 Croydon 31.596331 84.61539 ## 209 Ealing 61.862850 72.61905 ## 210 Enfield 32.386890 54.54546 ## 211 Greenwich 33.321323 71.11111 ## 212 Hackney 67.236275 60.00000 ## 213 Hammersmith and Fulham 40.165890 80.55556 ## 214 Haringey 61.721767 74.66666 ## 215 Harrow 51.563572 67.92453 ## 216 Havering 7.259055 NA ## 217 Hillingdon 42.120441 44.44444 ## 218 Hounslow 52.667080 72.34042 ## 219 Islington 51.257946 55.55556 ## 220 Kensington and Chelsea 25.035925 72.22222 ## 221 Kingston upon Thames 12.043625 76.92308 ## 222 Lambeth 53.627621 66.27907 ## 223 Lewisham 32.108051 68.18182 ## 224 Merton 22.243044 55.00000 ## 225 Newham 87.657394 71.87500 ## 226 Redbridge 38.986992 70.83334 ## 227 Richmond upon Thames 7.211529 62.50000 ## 228 Southwark 39.311501 83.33334 ## 229 Sutton 14.706072 61.90476 ## 230 Tower Hamlets 54.801449 57.30337 ## 231 Waltham Forest 40.602711 81.48148 ## 232 Wandsworth 29.751532 54.90196 ## 233 Westminster 39.200691 63.63636 ## 234 Buckinghamshire 9.457166 76.66666 ## 235 Cambridgeshire 3.744733 81.81818 ## 236 Derbyshire 4.333031 48.00000 ## 237 Devon 2.535732 72.72727 ## 238 Dorset (Cty) 3.204628 55.55556 ## 239 East Sussex 4.425951 100.00000 ## 240 Essex 3.941561 75.75758 ## 241 Gloucestershire 3.586706 93.33334 ## 242 Hampshire 3.023801 46.42857 ## 243 Hertfordshire 5.158195 77.27273 ## 244 Kent 4.229689 72.54902 ## 245 Lancashire 9.738275 46.15385 ## 246 Leicestershire 6.730673 71.42857 ## 247 Lincolnshire 1.729033 55.55556 ## 248 Norfolk 2.943362 66.66666 ## 249 Northamptonshire 10.782902 60.46511 ## 250 North Yorkshire 2.093741 30.00000 ## 251 Nottinghamshire 3.277503 52.94118 ## 252 Oxfordshire 5.565259 67.85714 ## 253 Somerset 2.054846 NA ## 254 Staffordshire 2.672061 57.14286 ## 255 Suffolk 2.862925 66.66666 ## 256 Surrey 3.739306 79.06977 ## 257 Warwickshire 6.854054 50.00000 ## 258 West Sussex 5.147106 53.33333 ## 259 Worcestershire 2.754015 70.00000 ## 260 Hartlepool 7.765008 NA ## 261 Middlesbrough 15.804938 91.66666 ## 262 Stockton-on-Tees 4.853270 66.66666 ## 263 Blackburn with Darwen 35.834213 65.21739 ## 264 Blackpool 6.542591 100.00000 ## 265 Kingston upon Hull 5.709612 77.77778 ## 266 East Riding of Yorkshire 2.174771 NA ## 267 Derby 20.591814 89.47369 ## 268 Leicester 63.358944 75.32468 ## 269 Nottingham 14.995042 73.07692 ## 270 Telford and Wrekin 4.584747 50.00000 ## 271 Stoke-on-Trent 12.897607 64.70588 ## 272 Bath and North East Somerset 6.265112 42.85714 ## 273 Bristol 16.058702 56.09756 ## 274 South Gloucestershire 3.876192 44.44444 ## 275 Plymouth 4.520690 NA ## 276 Bournemouth 8.726428 55.55556 ## 277 Poole 5.994514 50.00000 ## 278 Swindon 5.607125 NA ## 279 Peterborough 15.319103 66.66666 ## 280 Luton 41.139641 72.22222 ## 281 Southend-on-Sea 12.601119 93.33334 ## 282 Thurrock 7.538360 90.90909 ## 283 Medway 5.578578 83.33334 ## 284 West Berkshire 4.845889 NA ## 285 Reading 26.353811 71.42857 ## 286 Slough 58.461956 50.00000 ## 287 Windsor and Maidenhead 8.190659 NA ## 288 Wokingham 7.378473 50.00000 ## 289 Milton Keynes 6.133286 90.00000 ## 290 Brighton and Hove 3.068974 75.00000 ## 291 Portsmouth 9.436567 50.00000 ## 292 Southampton 14.413613 71.42857 ## 293 County Durham 3.170072 71.42857 ## 294 Cheshire East 2.063343 NA ## 295 Shropshire 3.834427 50.00000 ## 296 Cornwall 2.885184 60.00000 ## 297 Wiltshire 2.788978 42.85714 ## 298 Bedford 16.608316 77.77778 ## 299 Central Bedfordshire 2.935880 85.71429 ## 300 Bolton 19.856026 43.47826 ## 301 Bury 6.991875 NA ## 302 Manchester 31.377089 71.76471 ## 303 Oldham 17.227369 76.92308 ## 304 Rochdale 8.849401 63.63636 ## 305 Salford 8.901945 64.28571 ## 306 Stockport 6.833160 100.00000 ## 307 Tameside 9.682236 62.50000 ## 308 Trafford 12.221453 63.15789 ## 309 Wigan 2.304883 NA ## 310 Liverpool 5.385855 60.00000 ## 311 Sefton 3.453894 100.00000 ## 312 Wirral 2.326686 NA ## 313 Barnsley 4.240863 62.50000 ## 314 Doncaster 6.094700 66.66666 ## 315 Rotherham 13.161078 50.00000 ## 316 Sheffield 15.427517 67.24138 ## 317 Newcastle upon Tyne 13.487406 67.85714 ## 318 North Tyneside 5.354327 71.42857 ## 319 South Tyneside 4.629058 NA ## 320 Sunderland 6.511357 64.28571 ## 321 Birmingham 35.595810 67.38197 ## 322 Coventry 23.663143 65.57377 ## 323 Dudley 12.099399 66.66666 ## 324 Sandwell 30.523861 63.82979 ## 325 Solihull 6.315261 55.55556 ## 326 Walsall 22.574806 65.90909 ## 327 Wolverhampton 31.966377 77.19299 ## 328 Bradford 25.642807 79.16666 ## 329 Calderdale 12.711522 62.50000 ## 330 Kirklees 19.845726 65.78947 ## 331 Leeds 13.537232 77.77778 ## 332 Wakefield 5.463264 75.00000 ## 333 Barking and Dagenham 23.887093 78.26087 ## 334 Barnet 30.846403 79.24529 ## 335 Bexley 11.665677 56.25000 ## 336 Brent 81.708160 68.26923 ## 337 Bromley 9.761812 50.00000 ## 338 Camden 49.222912 67.30769 ## 339 Croydon 33.714504 78.12500 ## 340 Ealing 69.116249 79.83193 ## 341 Enfield 32.788940 57.40741 ## 342 Greenwich 35.811455 68.08511 ## 343 Hackney 71.591515 68.47826 ## 344 Hammersmith and Fulham 40.536713 75.00000 ## 345 Haringey 61.688854 73.25581 ## 346 Harrow 51.686749 67.85714 ## 347 Havering 6.656106 16.66667 ## 348 Hillingdon 45.441833 55.26316 ## 349 Hounslow 51.468540 64.70588 ## 350 Islington 52.597778 61.11111 ## 351 Kensington and Chelsea 26.472830 64.28571 ## 352 Kingston upon Thames 13.766153 75.00000 ## 353 Lambeth 53.691406 76.05634 ## 354 Lewisham 33.374973 55.10204 ## 355 Merton 27.779390 56.75676 ## 356 Newham 92.043762 67.18750 ## 357 Redbridge 42.136028 58.18182 ## 358 Richmond upon Thames 7.331723 88.88889 ## 359 Southwark 43.966450 71.79487 ## 360 Sutton 16.003649 81.81818 ## 361 Tower Hamlets 62.536293 61.66667 ## 362 Waltham Forest 45.326672 67.92453 ## 363 Wandsworth 34.924610 67.21311 ## 364 Westminster 39.768585 54.16667 ## 365 Buckinghamshire 9.021150 83.33334 ## 366 Cambridgeshire 4.053806 85.00000 ## 367 Derbyshire 3.773380 47.36842 ## 368 Devon 2.193369 33.33333 ## 369 Dorset (Cty) 2.928721 70.00000 ## 370 East Sussex 3.858738 69.23077 ## 371 Essex 3.995119 71.42857 ## 372 Gloucestershire 3.921874 84.21053 ## 373 Hampshire 3.572374 61.76471 ## 374 Hertfordshire 5.489788 72.72727 ## 375 Kent 4.790835 64.86487 ## 376 Lancashire 9.468544 56.52174 ## 377 Leicestershire 5.878651 63.15789 ## 378 Lincolnshire 2.310174 85.71429 ## 379 Norfolk 3.457815 72.72727 ## 380 Northamptonshire 11.110753 66.66666 ## 381 North Yorkshire 2.544419 76.47059 ## 382 Nottinghamshire 3.171844 83.33334 ## 383 Oxfordshire 7.205025 84.61539 ## 384 Somerset 2.762102 64.70588 ## 385 Staffordshire 2.946002 61.11111 ## 386 Suffolk 3.276165 94.44444 ## 387 Surrey 4.674674 63.63636 ## 388 Warwickshire 7.640587 74.07407 ## 389 West Sussex 5.910339 64.86487 ## 390 Worcestershire 3.413791 80.00000 ## 391 Middlesbrough 14.182078 75.00000 ## 392 Stockton-on-Tees 5.916778 90.00000 ## 393 Blackburn with Darwen 34.906319 63.15789 ## 394 Blackpool 5.588036 100.00000 ## 395 Kingston upon Hull 4.744258 NA ## 396 East Riding of Yorkshire 2.155495 NA ## 397 Derby 17.902864 100.00000 ## 398 Leicester 69.771278 61.46789 ## 399 Nottingham 16.785614 79.41177 ## 400 Telford and Wrekin 6.431722 50.00000 ## 401 Stoke-on-Trent 13.839107 78.26087 ## 402 Bath and North East Somerset 7.613916 76.47059 ## 403 Bristol 16.099224 72.22222 ## 404 South Gloucestershire 4.511797 71.42857 ## 405 Plymouth 3.537689 NA ## 406 Torbay 5.817586 81.81818 ## 407 Bournemouth 10.118303 47.05882 ## 408 Poole 6.221539 50.00000 ## 409 Swindon 5.888210 NA ## 410 Peterborough 17.091131 57.14286 ## 411 Luton 41.157391 84.00000 ## 412 Southend-on-Sea 13.798217 50.00000 ## 413 Thurrock 9.058197 77.77778 ## 414 Medway 5.697507 70.00000 ## 415 Bracknell Forest 6.094755 NA ## 416 West Berkshire 5.505395 NA ## 417 Reading 30.303030 72.97298 ## 418 Slough 60.069778 65.62500 ## 419 Windsor and Maidenhead 9.612706 81.81818 ## 420 Wokingham 7.375950 NA ## 421 Milton Keynes 8.326016 100.00000 ## 422 Brighton and Hove 4.252175 83.33334 ## 423 Portsmouth 10.170940 84.61539 ## 424 Southampton 14.727036 75.00000 ## 425 County Durham 3.431349 58.33333 ## 426 Cheshire West and Chester 2.661091 54.54546 ## 427 Shropshire 4.842916 85.71429 ## 428 Cornwall 2.865540 69.23077 ## 429 Wiltshire 2.617928 71.42857 ## 430 Bedford 16.308935 86.66666 ## 431 Central Bedfordshire 3.467151 57.14286 ## 432 Bolton 20.924168 68.57143 ## 433 Bury 7.360627 NA ## 434 Manchester 31.254744 69.56522 ## 435 Oldham 18.271347 90.00000 ## 436 Rochdale 10.771461 58.33333 ## 437 Salford 10.538068 53.33333 ## 438 Stockport 6.615515 45.45454 ## 439 Tameside 13.268582 46.42857 ## 440 Trafford 10.737796 40.00000 ## 441 Wigan 2.950991 75.00000 ## 442 Liverpool 9.217076 77.96610 ## 443 Sefton 4.427304 66.66666 ## 444 Wirral 2.221221 NA ## 445 Barnsley 3.463803 NA ## 446 Doncaster 6.184611 87.50000 ## 447 Rotherham 11.518861 60.00000 ## 448 Sheffield 17.043634 68.08511 ## 449 Newcastle upon Tyne 13.568115 68.18182 ## 450 North Tyneside 5.513895 75.00000 ## 451 Sunderland 6.184292 83.33334 ## 452 Birmingham 36.902058 70.64677 ## 453 Coventry 24.965866 40.81633 ## 454 Dudley 10.340023 52.94118 ## 455 Sandwell 34.047718 60.60606 ## 456 Solihull 6.630794 NA ## 457 Walsall 20.513460 58.33333 ## 458 Wolverhampton 34.761414 70.17544 ## 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740 Hackney 60.799927 64.00000 ## 741 Hammersmith and Fulham 45.000465 80.55556 ## 742 Haringey 54.058846 60.00000 ## 743 Harrow 56.176762 61.53846 ## 744 Havering 10.073331 57.14286 ## 745 Hillingdon 50.472076 62.29508 ## 746 Hounslow 63.470665 81.81818 ## 747 Islington 49.336559 61.29032 ## 748 Kensington and Chelsea 26.585941 73.91304 ## 749 Kingston upon Thames 17.801281 68.42105 ## 750 Lambeth 45.308548 69.23077 ## 751 Lewisham 36.429684 70.58823 ## 752 Merton 31.869032 57.14286 ## 753 Newham 102.046455 66.40625 ## 754 Redbridge 52.013062 70.68965 ## 755 Richmond upon Thames 9.698984 66.66666 ## 756 Southwark 45.387100 75.86207 ## 757 Sutton 15.478213 64.28571 ## 758 Tower Hamlets 62.858334 69.56522 ## 759 Waltham Forest 46.887871 67.50000 ## 760 Wandsworth 37.114056 54.54546 ## 761 Westminster 39.605148 66.66666 ## 762 Buckinghamshire 8.036095 77.27273 ## 763 Cambridgeshire 6.055876 64.28571 ## 764 Cumbria 2.004501 72.72727 ## 765 Derbyshire 4.472486 65.00000 ## 766 Devon 2.464023 58.33333 ## 767 Dorset (Cty) 4.184276 50.00000 ## 768 East Sussex 2.799598 75.00000 ## 769 Essex 3.963214 73.33334 ## 770 Gloucestershire 5.163437 80.00000 ## 771 Hampshire 3.639683 65.62500 ## 772 Hertfordshire 8.052289 66.66666 ## 773 Kent 5.679855 73.77049 ## 774 Lancashire 9.524412 73.91304 ## 775 Leicestershire 7.782048 66.66666 ## 776 Lincolnshire 2.625850 66.66666 ## 777 Norfolk 5.066555 69.69697 ## 778 Northamptonshire 10.753147 69.04762 ## 779 North Yorkshire 2.723332 84.61539 ## 780 Nottinghamshire 3.776579 73.91304 ## 781 Oxfordshire 9.875709 82.92683 ## 782 Somerset 2.703155 72.72727 ## 783 Staffordshire 2.698179 57.14286 ## 784 Suffolk 3.696784 59.09091 ## 785 Surrey 6.153352 77.41936 ## 786 Warwickshire 7.434977 62.16216 ## 787 West Sussex 6.816770 78.37838 ## 788 Worcestershire 3.365249 100.00000 ## 789 Middlesbrough 17.863867 62.50000 ## 790 Stockton-on-Tees 8.861383 NA ## 791 Warrington 3.732932 83.33334 ## 792 Blackburn with Darwen 35.883450 76.92308 ## 793 Blackpool 8.366295 100.00000 ## 794 Kingston upon Hull 5.729137 87.50000 ## 795 East Riding of Yorkshire 2.822820 NA ## 796 North Lincolnshire 6.141462 63.63636 ## 797 York 5.794404 75.00000 ## 798 Derby 18.197174 62.50000 ## 799 Leicester 66.322090 72.82609 ## 800 Nottingham 27.963753 76.00000 ## 801 Telford and Wrekin 7.344436 88.88889 ## 802 Stoke-on-Trent 13.109829 80.00000 ## 803 Bristol 18.864090 76.08696 ## 804 North Somerset 3.515356 NA ## 805 South Gloucestershire 4.286449 50.00000 ## 806 Plymouth 5.445589 62.50000 ## 807 Torbay 6.311553 66.66666 ## 808 Bournemouth 10.613479 50.00000 ## 809 Poole 5.837780 NA ## 810 Swindon 9.817064 100.00000 ## 811 Peterborough 21.220896 60.00000 ## 812 Luton 39.884880 55.00000 ## 813 Southend-on-Sea 13.270200 75.00000 ## 814 Thurrock 6.365512 81.81818 ## 815 Medway 7.296170 81.25000 ## 816 Reading 35.375862 81.48148 ## 817 Slough 45.496616 69.69697 ## 818 Windsor and Maidenhead 6.680983 NA ## 819 Wokingham 7.896763 NA ## 820 Milton Keynes 18.014565 72.22222 ## 821 Brighton and Hove 9.410321 81.25000 ## 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851 North Tyneside 4.238732 100.00000 ## 852 South Tyneside 6.051709 83.33334 ## 853 Sunderland 8.043063 73.68421 ## 854 Birmingham 39.432228 63.44538 ## 855 Coventry 36.724808 62.50000 ## 856 Dudley 12.406827 75.00000 ## 857 Sandwell 37.380981 57.14286 ## 858 Solihull 8.848122 42.85714 ## 859 Walsall 21.571888 83.87096 ## 860 Wolverhampton 27.863218 63.15789 ## 861 Bradford 34.965546 59.03614 ## 862 Calderdale 11.371313 57.14286 ## 863 Kirklees 21.404408 54.90196 ## 864 Leeds 18.001930 59.52381 ## 865 Wakefield 5.490771 81.81818 ## 866 Barking and Dagenham 35.388489 60.52632 ## 867 Barnet 33.935413 58.06452 ## 868 Bexley 9.784199 83.33334 ## 869 Brent 96.276146 65.82278 ## 870 Bromley 10.236023 NA ## 871 Camden 42.823845 63.04348 ## 872 Croydon 31.728037 80.39216 ## 873 Ealing 69.622620 59.78261 ## 874 Enfield 31.035845 61.90476 ## 875 Greenwich 47.868919 71.25000 ## 876 Hackney 58.752666 64.06250 ## 877 Hammersmith and Fulham 40.512272 86.48649 ## 878 Haringey 49.270325 62.50000 ## 879 Harrow 54.447475 63.79310 ## 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991 Sunderland 7.221477 72.72727 ## 992 Birmingham 40.858570 69.49807 ## 993 Coventry 35.995308 61.70213 ## 994 Dudley 11.826036 42.85714 ## 995 Sandwell 36.413841 56.71642 ## 996 Solihull 8.640112 77.77778 ## 997 Walsall 20.665689 80.76923 ## 998 Wolverhampton 29.648771 64.44444 ## 999 Bradford 35.895737 68.53933 ## 1000 Calderdale 12.451998 68.75000 ## 1001 Kirklees 23.490036 61.29032 ## 1002 Leeds 16.950600 79.10448 ## 1003 Wakefield 5.776739 84.61539 ## 1004 Gateshead 4.418065 70.00000 ## 1005 Barking and Dagenham 39.108932 67.64706 ## 1006 Barnet 31.572403 71.87500 ## 1007 Bexley 9.419758 50.00000 ## 1008 Brent 100.425087 69.07895 ## 1009 Bromley 9.185177 75.00000 ## 1010 Camden 43.326397 54.83871 ## 1011 Croydon 33.457603 81.03448 ## 1012 Ealing 67.135551 62.88660 ## 1013 Enfield 32.325119 63.33333 ## 1014 Greenwich 50.419819 57.14286 ## 1015 Hackney 54.612991 60.00000 ## 1016 Hammersmith and Fulham 38.787579 75.55556 ## 1017 Haringey 45.003330 51.16279 ## 1018 Harrow 55.409134 66.66666 ## 1019 Havering 9.486725 72.72727 ## 1020 Hillingdon 50.529732 68.75000 ## 1021 Hounslow 61.282139 59.74026 ## 1022 Islington 47.971264 57.89474 ## 1023 Kensington and Chelsea 27.495358 56.25000 ## 1024 Kingston upon Thames 19.024452 72.22222 ## 1025 Lambeth 39.970512 77.46479 ## 1026 Lewisham 31.950323 71.73913 ## 1027 Merton 30.768969 58.33333 ## 1028 Newham 104.798546 77.39726 ## 1029 Redbridge 55.789268 61.11111 ## 1030 Richmond upon Thames 8.549465 90.00000 ## 1031 Southwark 37.918961 79.48718 ## 1032 Sutton 14.331755 73.91304 ## 1033 Tower Hamlets 60.776421 62.12121 ## 1034 Waltham Forest 42.980949 71.69811 ## 1035 Wandsworth 34.911625 68.42105 ## 1036 Westminster 35.808323 69.44444 ## 1037 Buckinghamshire 6.784511 94.44444 ## 1038 Cambridgeshire 6.495006 58.82353 ## 1039 Cumbria 2.529150 50.00000 ## 1040 Derbyshire 3.943651 56.52174 ## 1041 Devon 3.157085 77.77778 ## 1042 Dorset (Cty) 3.816676 87.50000 ## 1043 East Sussex 3.600226 77.77778 ## 1044 Essex 4.154960 76.74419 ## 1045 Gloucestershire 5.046768 64.70588 ## 1046 Hampshire 4.047735 76.92308 ## 1047 Hertfordshire 6.728137 53.57143 ## 1048 Kent 7.641160 65.27778 ## 1049 Lancashire 8.798264 78.12500 ## 1050 Leicestershire 7.254523 69.56522 ## 1051 Lincolnshire 2.666517 42.85714 ## 1052 Norfolk 5.707955 73.07692 ## 1053 Northamptonshire 10.276699 72.54902 ## 1054 North Yorkshire 2.636566 69.23077 ## 1055 Nottinghamshire 3.909764 65.21739 ## 1056 Oxfordshire 9.598466 73.33334 ## 1057 Somerset 2.096023 NA ## 1058 Staffordshire 3.661642 81.81818 ## 1059 Suffolk 5.416321 70.00000 ## 1060 Surrey 6.548875 62.74510 ## 1061 Warwickshire 9.715972 65.51724 ## 1062 West Sussex 6.114789 76.92308 ## 1063 Worcestershire 3.093174 78.57143 ## 1064 Middlesbrough 17.925270 100.00000 ## 1065 Stockton-on-Tees 4.737515 25.00000 ## 1066 Warrington 5.504100 50.00000 ## 1067 Blackburn with Darwen 43.144283 83.33334 ## 1068 Blackpool 9.106788 75.00000 ## 1069 Kingston upon Hull 5.201567 64.28571 ## 1070 North Lincolnshire 11.880072 NA ## 1071 York 4.669374 NA ## 1072 Derby 19.102718 72.22222 ## 1073 Leicester 61.452381 71.91011 ## 1074 Nottingham 23.041891 83.78378 ## 1075 Telford and Wrekin 7.076154 50.00000 ## 1076 Stoke-on-Trent 12.453317 76.47059 ## 1077 Bath and North East Somerset 6.139772 62.50000 ## 1078 Bristol 18.777012 54.54546 ## 1079 North Somerset 5.449762 57.14286 ## 1080 South Gloucestershire 6.930443 55.55556 ## 1081 Plymouth 4.869504 25.00000 ## 1082 Torbay 9.363438 90.00000 ## 1083 Bournemouth 8.943318 54.54546 ## 1084 Poole 5.270394 NA ## 1085 Swindon 8.485210 64.28571 ## 1086 Peterborough 24.219751 80.00000 ## 1087 Luton 44.284264 64.40678 ## 1088 Southend-on-Sea 12.542011 73.68421 ## 1089 Thurrock 10.317677 90.00000 ## 1090 Medway 7.936589 66.66666 ## 1091 Bracknell Forest 8.326791 57.14286 ## 1092 West Berkshire 5.010828 NA ## 1093 Reading 38.421070 60.00000 ## 1094 Slough 47.525341 54.54546 ## 1095 Windsor and Maidenhead 7.714661 NA ## 1096 Wokingham 7.575659 62.50000 ## 1097 Milton Keynes 13.448817 33.33333 ## 1098 Brighton and Hove 10.663683 75.00000 ## 1099 Portsmouth 12.882308 70.58823 ## 1100 Southampton 12.580454 76.92308 ## 1101 County Durham 3.478704 80.00000 ## 1102 Cheshire East 2.082892 NA ## 1103 Cheshire West and Chester 2.327883 44.44444 ## 1104 Shropshire 3.197125 87.50000 ## 1105 Cornwall 1.960621 NA ## 1106 Wiltshire 3.140800 50.00000 ## 1107 Bedford 18.904119 53.84615 ## 1108 Central Bedfordshire 4.389325 54.54546 ## 1109 Northumberland 2.542349 NA ## 1110 Bolton 24.917126 76.66666 ## 1111 Bury 8.338772 NA ## 1112 Manchester 39.345436 74.22681 ## 1113 Oldham 20.044127 71.42857 ## 1114 Rochdale 22.039982 72.72727 ## 1115 Salford 14.398597 84.00000 ## 1116 Stockport 5.671386 66.66666 ## 1117 Tameside 16.856920 47.61905 ## 1118 Trafford 10.907928 NA ## 1119 Wigan 4.243446 70.00000 ## 1120 Liverpool 11.358722 80.00000 ## 1121 Sefton 3.768044 NA ## 1122 Wirral 3.775326 63.63636 ## 1123 Doncaster 4.667336 77.77778 ## 1124 Rotherham 7.807651 35.71429 ## 1125 Sheffield 14.532789 63.63636 ## 1126 Newcastle upon Tyne 15.211402 78.26087 ## 1127 North Tyneside 5.191369 NA ## 1128 South Tyneside 6.283620 NA ## 1129 Sunderland 7.117000 63.63636 ## 1130 Birmingham 39.966656 81.86529 ## 1131 Coventry 30.078344 35.55556 ## 1132 Dudley 10.924619 62.50000 ## 1133 Sandwell 34.735931 66.66666 ## 1134 Solihull 6.811801 NA ## 1135 Walsall 18.876425 69.23077 ## 1136 Wolverhampton 31.528879 72.97298 ## 1137 Bradford 35.637245 72.72727 ## 1138 Calderdale 12.704447 76.92308 ## 1139 Kirklees 24.740328 57.14286 ## 1140 Leeds 17.598688 70.83334 ## 1141 Wakefield 6.267016 70.00000 ## 1142 Gateshead 4.220905 NA ## 1143 Barking and Dagenham 39.409237 58.97436 ## 1144 Barnet 32.128002 66.66666 ## 1145 Bexley 8.460866 77.77778 ## 1146 Brent 100.357574 75.18797 ## 1147 Bromley 9.234593 90.00000 ## 1148 Camden 39.816780 77.50000 ## 1149 Croydon 32.546494 88.88889 ## 1150 Ealing 63.153996 68.26923 ## 1151 Enfield 34.286522 71.42857 ## 1152 Greenwich 51.597687 73.77049 ## 1153 Hackney 46.377090 63.63636 ## 1154 Hammersmith and Fulham 35.870541 80.64516 ## 1155 Haringey 44.979557 64.06250 ## 1156 Harrow 56.815506 66.15385 ## 1157 Havering 8.972391 NA ## 1158 Hillingdon 49.863033 72.22222 ## 1159 Hounslow 68.582314 76.31579 ## 1160 Islington 41.941036 50.00000 ## 1161 Kensington and Chelsea 28.457422 59.25926 ## 1162 Kingston upon Thames 20.551632 80.76923 ## 1163 Lambeth 40.530140 67.69231 ## 1164 Lewisham 28.167240 76.47059 ## 1165 Merton 30.010233 75.86207 ## 1166 Newham 103.584755 75.60976 ## 1167 Redbridge 54.995529 65.75343 ## 1168 Richmond upon Thames 8.850757 NA ## 1169 Southwark 36.466438 82.00000 ## 1170 Sutton 14.378324 46.66667 ## 1171 Tower Hamlets 58.814716 67.69231 ## 1172 Waltham Forest 45.011940 79.10448 ## 1173 Wandsworth 32.802544 72.22222 ## 1174 Westminster 32.473499 71.87500 ## 1175 Buckinghamshire 7.476615 66.66666 ## 1176 Cambridgeshire 6.421709 61.11111 ## 1177 Cumbria 1.731179 71.42857 ## 1178 Derbyshire 3.270107 80.00000 ## 1179 Devon 3.146326 61.53846 ## 1180 Dorset (Cty) 3.322790 42.85714 ## 1181 East Sussex 4.350001 69.23077 ## 1182 Essex 4.442740 68.42105 ## 1183 Gloucestershire 4.288486 50.00000 ## 1184 Hampshire 4.350476 61.53846 ## 1185 Hertfordshire 7.873121 71.42857 ## 1186 Kent 7.980546 75.00000 ## 1187 Lancashire 8.326009 77.55102 ## 1188 Leicestershire 6.691625 88.23529 ## 1189 Lincolnshire 2.879825 93.33334 ## 1190 Norfolk 5.116547 80.95238 ## 1191 Northamptonshire 10.294310 80.95238 ## 1192 North Yorkshire 2.680961 58.33333 ## 1193 Nottinghamshire 3.760972 80.00000 ## 1194 Oxfordshire 8.753905 76.66666 ## 1195 Somerset 2.150716 63.63636 ## 1196 Staffordshire 4.673852 59.09091 ## 1197 Suffolk 5.286326 52.94118 ## 1198 Surrey 7.357095 65.51724 ## 1199 Warwickshire 9.595979 67.85714 ## 1200 West Sussex 5.773645 70.83334 ## 1201 Worcestershire 3.615530 66.66666 ## 1202 Middlesbrough 14.517018 100.00000 ## 1203 Stockton-on-Tees 5.063698 57.14286 ## 1204 Warrington 4.966271 NA ## 1205 Blackburn with Darwen 40.379292 77.77778 ## 1206 Blackpool 12.634210 59.25926 ## 1207 Kingston upon Hull 6.767520 75.00000 ## 1208 East Riding of Yorkshire 2.397554 83.33334 ## 1209 North Lincolnshire 11.008212 40.00000 ## 1210 York 3.417337 NA ## 1211 Derby 18.649708 61.53846 ## 1212 Leicester 61.688667 71.84466 ## 1213 Nottingham 21.258598 88.88889 ## 1214 Telford and Wrekin 5.830775 NA ## 1215 Stoke-on-Trent 14.555864 79.31035 ## 1216 Bath and North East Somerset 5.352040 NA ## 1217 Bristol 19.447165 61.22449 ## 1218 North Somerset 4.771550 83.33334 ## 1219 South Gloucestershire 7.137231 71.42857 ## 1220 Plymouth 5.236068 66.66666 ## 1221 Torbay 9.384265 88.88889 ## 1222 Bournemouth 9.869481 66.66666 ## 1223 Swindon 9.983591 78.57143 ## 1224 Peterborough 24.762373 66.66666 ## 1225 Luton 49.275646 75.40984 ## 1226 Southend-on-Sea 12.586923 92.85714 ## 1227 Thurrock 10.003065 NA ## 1228 Medway 8.631052 71.42857 ## 1229 Bracknell Forest 9.157915 NA ## 1230 West Berkshire 5.204606 NA ## 1231 Reading 36.366707 84.61539 ## 1232 Slough 52.751934 61.53846 ## 1233 Windsor and Maidenhead 7.415819 NA ## 1234 Wokingham 7.762669 100.00000 ## 1235 Milton Keynes 12.915979 90.47619 ## 1236 Brighton and Hove 9.900610 45.45454 ## 1237 Portsmouth 11.529922 80.00000 ## 1238 Southampton 16.309666 64.70588 ## 1239 County Durham 2.678105 NA ## 1240 Cheshire East 2.346973 NA ## 1241 Cheshire West and Chester 2.631060 NA ## 1242 Shropshire 2.845573 62.50000 ## 1243 Cornwall 2.704540 60.00000 ## 1244 Wiltshire 3.046989 76.92308 ## 1245 Bedford 16.198370 50.00000 ## 1246 Central Bedfordshire 4.878479 NA ## 1247 Northumberland 2.219334 NA ## 1248 Bolton 24.469633 61.76471 ## 1249 Bury 10.648628 70.00000 ## 1250 Manchester 41.979767 75.25773 ## 1251 Oldham 19.947155 76.00000 ## 1252 Rochdale 20.539234 85.00000 ## 1253 Salford 12.800746 61.53846 ## 1254 Stockport 6.131135 62.50000 ## 1255 Tameside 17.380220 70.00000 ## 1256 Trafford 11.993071 62.50000 ## 1257 Wigan 4.215936 83.33334 ## 1258 Liverpool 10.689147 75.00000 ## 1259 Sefton 3.163240 NA ## 1260 Wirral 3.762990 NA ## 1261 Barnsley 3.328972 87.50000 ## 1262 Doncaster 6.084401 76.47059 ## 1263 Rotherham 8.431462 54.54546 ## 1264 Sheffield 15.292821 56.14035 ## 1265 Newcastle upon Tyne 15.436659 82.60870 ## 1266 North Tyneside 4.163981 NA ## 1267 South Tyneside 5.391550 NA ## 1268 Sunderland 6.042157 75.00000 ## 1269 Birmingham 38.581932 73.85321 ## 1270 Coventry 31.090342 59.70149 ## 1271 Dudley 10.463881 68.18182 ## 1272 Sandwell 34.222462 78.57143 ## 1273 Solihull 7.273844 81.81818 ## 1274 Walsall 20.221386 77.77778 ## 1275 Wolverhampton 33.220894 80.00000 ## 1276 Bradford 35.403721 60.49383 ## 1277 Calderdale 12.309268 50.00000 ## 1278 Kirklees 26.271919 66.66666 ## 1279 Leeds 15.833096 71.21212 ## 1280 Wakefield 5.939610 66.66666 ## 1281 Gateshead 4.525941 100.00000 ## 1282 Barking and Dagenham 36.898548 73.52941 ## 1283 Barnet 30.147753 75.00000 ## 1284 Bexley 10.410175 53.33333 ## 1285 Brent 98.672562 70.99236 ## 1286 Bromley 11.662588 77.77778 ## 1287 Camden 36.897747 71.11111 ## 1288 Croydon 34.029766 78.57143 ## 1289 Ealing 66.576767 68.68687 ## 1290 Enfield 30.967329 67.50000 ## 1291 Greenwich 46.904690 69.81132 ## 1292 Hackney 40.797031 70.00000 ## 1293 Hammersmith and Fulham 35.700943 78.37838 ## 1294 Haringey 48.279419 68.96552 ## 1295 Harrow 59.878136 73.77049 ## 1296 Havering 8.612316 53.33333 ## 1297 Hillingdon 46.383736 58.82353 ## 1298 Hounslow 73.308189 73.01588 ## 1299 Islington 39.129990 73.33334 ## 1300 Kensington and Chelsea 27.673338 71.42857 ## 1301 Kingston upon Thames 20.571337 64.70588 ## 1302 Lambeth 36.599751 74.57627 ## 1303 Lewisham 30.736168 64.91228 ## 1304 Merton 29.942373 79.31035 ## 1305 Newham 109.365479 74.52229 ## 1306 Redbridge 53.814285 67.85714 ## 1307 Richmond upon Thames 9.315240 63.63636 ## 1308 Southwark 36.081882 86.53846 ## 1309 Sutton 16.707380 81.81818 ## 1310 Tower Hamlets 57.984474 78.84615 ## 1311 Waltham Forest 43.050777 79.03226 ## 1312 Wandsworth 29.790794 87.17949 ## 1313 Westminster 32.122421 78.94736 ## 1314 Buckinghamshire 8.619643 51.85185 ## 1315 Cambridgeshire 5.750091 69.56522 ## 1316 Cumbria 1.932344 62.50000 ## 1317 Derbyshire 3.604068 61.90476 ## 1318 Devon 3.180142 71.42857 ## 1319 Dorset (Cty) 3.477074 66.66666 ## 1320 East Sussex 4.836975 75.00000 ## 1321 Essex 5.090018 63.26530 ## 1322 Gloucestershire 3.982352 75.00000 ## 1323 Hampshire 5.385945 75.00000 ## 1324 Hertfordshire 8.724260 71.42857 ## 1325 Kent 7.510956 67.16418 ## 1326 Lancashire 9.248481 81.81818 ## 1327 Leicestershire 6.237995 73.91304 ## 1328 Lincolnshire 3.001634 71.42857 ## 1329 Norfolk 4.533540 78.26087 ## 1330 Northamptonshire 10.264147 70.00000 ## 1331 North Yorkshire 2.838648 70.00000 ## 1332 Nottinghamshire 3.999071 69.23077 ## 1333 Oxfordshire 9.606622 80.00000 ## 1334 Somerset 2.394784 81.81818 ## 1335 Staffordshire 4.613405 68.75000 ## 1336 Suffolk 4.787570 61.90476 ## 1337 Surrey 8.118913 80.00000 ## 1338 Warwickshire 8.635203 52.17391 ## 1339 West Sussex 7.350156 76.47059 ## 1340 Worcestershire 3.603214 70.00000 ## 1341 Middlesbrough 14.225060 63.63636 ## 1342 Stockton-on-Tees 5.563157 NA ## 1343 Warrington 4.442156 87.50000 ## 1344 Blackburn with Darwen 38.872078 71.42857 ## 1345 Blackpool 14.758592 58.82353 ## 1346 Kingston upon Hull 8.839101 85.71429 ## 1347 East Riding of Yorkshire 2.290442 NA ## 1348 North Lincolnshire 7.762646 86.66666 ## 1349 Derby 17.011358 50.00000 ## 1350 Leicester 58.686039 58.82353 ## 1351 Nottingham 21.488518 89.18919 ## 1352 Telford and Wrekin 6.196493 70.00000 ## 1353 Stoke-on-Trent 15.819984 62.50000 ## 1354 Bath and North East Somerset 5.123330 88.88889 ## 1355 Bristol 19.545835 50.84746 ## 1356 North Somerset 4.094923 66.66666 ## 1357 South Gloucestershire 5.562009 87.50000 ## 1358 Plymouth 6.115333 56.25000 ## 1359 Torbay 7.104507 NA ## 1360 Bournemouth 10.017339 92.30769 ## 1361 Swindon 9.863722 83.33334 ## 1362 Peterborough 28.580990 45.71429 ## 1363 Luton 47.476517 82.05128 ## 1364 Southend-on-Sea 11.697319 88.88889 ## 1365 Thurrock 9.269207 83.33334 ## 1366 Medway 8.545365 75.00000 ## 1367 Bracknell Forest 9.365543 NA ## 1368 West Berkshire 4.753725 NA ## 1369 Reading 33.016743 77.27273 ## 1370 Slough 57.335495 67.64706 ## 1371 Windsor and Maidenhead 7.129256 NA ## 1372 Wokingham 8.579070 85.71429 ## 1373 Milton Keynes 14.168888 90.00000 ## 1374 Brighton and Hove 9.289012 66.66666 ## 1375 Portsmouth 10.249736 78.94736 ## 1376 Southampton 16.820406 71.42857 ## 1377 County Durham 2.210831 54.54546 ## 1378 Cheshire East 2.607515 66.66666 ## 1379 Cheshire West and Chester 2.628113 NA ## 1380 Shropshire 3.478083 75.00000 ## 1381 Cornwall 2.996623 68.75000 ## 1382 Wiltshire 3.095470 80.00000 ## 1383 Bedford 16.886757 62.50000 ## 1384 Central Bedfordshire 4.168322 NA ## 1385 Northumberland 2.425572 NA ## 1386 Bolton 20.924829 80.00000 ## 1387 Bury 10.425523 100.00000 ## 1388 Manchester 39.774212 72.15190 ## 1389 Oldham 21.935740 74.19355 ## 1390 Rochdale 18.593393 42.85714 ## 1391 Salford 11.944203 84.61539 ## 1392 Stockport 6.237988 NA ## 1393 Tameside 15.485000 77.77778 ## 1394 Trafford 13.076063 38.88889 ## 1395 Wigan 3.252347 NA ## 1396 Liverpool 10.806942 86.66666 ## 1397 Sefton 4.138332 92.30769 ## 1398 Wirral 3.856963 NA ## 1399 Barnsley 3.306056 NA ## 1400 Doncaster 7.168957 80.00000 ## 1401 Rotherham 8.925880 68.75000 ## 1402 Sheffield 16.025206 66.66666 ## 1403 Newcastle upon Tyne 17.190622 68.96552 ## 1404 North Tyneside 3.483662 NA ## 1405 Sunderland 6.649949 73.33334 ## 1406 Birmingham 37.446960 72.19731 ## 1407 Coventry 34.802063 46.57534 ## 1408 Dudley 10.436223 76.47059 ## 1409 Sandwell 35.079464 59.64912 ## 1410 Solihull 7.734015 88.88889 ## 1411 Walsall 20.564865 60.71429 ## 1412 Wolverhampton 32.996334 66.66666 ## 1413 Bradford 32.896774 64.78873 ## 1414 Calderdale 10.613648 85.71429 ## 1415 Kirklees 24.710754 54.90196 ## 1416 Leeds 13.919550 70.17544 ## 1417 Wakefield 5.306566 54.54546 ## 1418 Gateshead 4.004324 100.00000 ## 1419 Barking and Dagenham 34.788506 68.57143 ## 1420 Barnet 30.202665 66.66666 ## 1421 Bexley 11.464718 75.00000 ## 1422 Brent 98.108353 68.53147 ## 1423 Bromley 11.252174 86.66666 ## 1424 Camden 30.472044 68.96552 ## 1425 Croydon 33.151550 67.74194 ## 1426 Ealing 68.559433 68.75000 ## 1427 Enfield 26.522091 58.06452 ## 1428 Greenwich 47.211761 83.82353 ## 1429 Hackney 36.430710 72.91666 ## 1430 Hammersmith and Fulham 30.619505 70.37037 ## 1431 Haringey 43.592888 63.46154 ## 1432 Harrow 65.978821 66.66666 ## 1433 Havering 8.124353 86.66666 ## 1434 Hillingdon 47.691502 68.91892 ## 1435 Hounslow 74.474121 78.46154 ## 1436 Islington 34.645378 66.66666 ## 1437 Kensington and Chelsea 24.388910 84.21053 ## 1438 Kingston upon Thames 19.697937 87.50000 ## 1439 Lambeth 33.900276 83.05085 ## 1440 Lewisham 31.680334 90.69768 ## 1441 Merton 31.575834 71.79487 ## 1442 Newham 112.103989 70.66666 ## 1443 Redbridge 53.738640 79.36508 ## 1444 Richmond upon Thames 7.995707 NA ## 1445 Southwark 37.878174 72.30769 ## 1446 Sutton 16.378590 66.66666 ## 1447 Tower Hamlets 53.765121 80.70175 ## 1448 Waltham Forest 46.250549 75.00000 ## 1449 Wandsworth 30.331791 66.66666 ## 1450 Westminster 27.554632 84.21053 ## 1451 Buckinghamshire 10.129227 62.50000 ## 1452 Cambridgeshire 5.582037 94.11765 ## 1453 Cumbria 3.401844 64.70588 ## 1454 Derbyshire 3.589403 71.42857 ## 1455 Devon 3.385725 63.15789 ## 1456 Dorset (Cty) 2.578996 75.00000 ## 1457 East Sussex 5.246696 68.42105 ## 1458 Essex 4.889572 57.89474 ## 1459 Gloucestershire 4.234922 94.11765 ## 1460 Hampshire 5.370842 66.66666 ## 1461 Hertfordshire 8.520131 64.86487 ## 1462 Kent 7.525255 73.97260 ## 1463 Lancashire 9.389652 75.51020 ## 1464 Leicestershire 5.476919 73.91304 ## 1465 Lincolnshire 3.495270 57.89474 ## 1466 Norfolk 4.074191 68.18182 ## 1467 Northamptonshire 10.229030 62.22222 ## 1468 North Yorkshire 2.439373 60.00000 ## 1469 Nottinghamshire 4.066749 70.00000 ## 1470 Oxfordshire 10.236884 72.41380 ## 1471 Somerset 2.945994 100.00000 ## 1472 Staffordshire 4.319049 76.47059 ## 1473 Suffolk 4.252573 61.11111 ## 1474 Surrey 8.340144 75.00000 ## 1475 Warwickshire 8.846642 40.74074 ## 1476 West Sussex 7.166295 79.41177 ## 1477 Worcestershire 4.647228 66.66666 ## 1478 Middlesbrough 13.461376 87.50000 ## 1479 Stockton-on-Tees 5.538785 100.00000 ## 1480 Warrington 4.741127 100.00000 ## 1481 Blackburn with Darwen 35.418274 58.33333 ## 1482 Blackpool 14.798459 100.00000 ## 1483 Kingston upon Hull 8.438019 100.00000 ## 1484 East Riding of Yorkshire 2.483495 NA ## 1485 North Lincolnshire 9.314622 75.00000 ## 1486 York 3.669951 100.00000 ## 1487 Derby 16.115349 80.00000 ## 1488 Leicester 53.195492 65.90909 ## 1489 Nottingham 20.042925 84.37500 ## 1490 Telford and Wrekin 6.556934 33.33333 ## 1491 Stoke-on-Trent 16.161238 57.14286 ## 1492 Bath and North East Somerset 4.509634 50.00000 ## 1493 Bristol 20.546316 56.25000 ## 1494 North Somerset 3.584656 NA ## 1495 South Gloucestershire 6.010812 55.55556 ## 1496 Plymouth 6.210176 37.50000 ## 1497 Torbay 6.584379 100.00000 ## 1498 Bournemouth 9.145455 NA ## 1499 Swindon 11.169581 73.33334 ## 1500 Peterborough 30.030890 63.63636 ## 1501 Luton 41.035255 74.07407 ## 1502 Southend-on-Sea 9.512919 88.88889 ## 1503 Thurrock 7.092051 NA ## 1504 Medway 7.963130 90.00000 ## 1505 Bracknell Forest 7.529074 NA ## 1506 West Berkshire 5.592805 NA ## 1507 Reading 34.200378 90.62500 ## 1508 Slough 58.089626 84.61539 ## 1509 Windsor and Maidenhead 7.091907 57.14286 ## 1510 Wokingham 7.665021 NA ## 1511 Milton Keynes 14.755403 93.33334 ## 1512 Brighton and Hove 8.346771 80.00000 ## 1513 Portsmouth 9.375708 64.28571 ## 1514 Southampton 18.340994 64.70588 ## 1515 County Durham 1.944040 100.00000 ## 1516 Cheshire East 3.763019 81.81818 ## 1517 Cheshire West and Chester 2.725019 NA ## 1518 Shropshire 3.785384 NA ## 1519 Cornwall 3.408226 62.50000 ## 1520 Wiltshire 2.864841 87.50000 ## 1521 Bedford 15.874276 81.25000 ## 1522 Central Bedfordshire 3.465858 75.00000 ## 1523 Northumberland 2.212492 NA ## 1524 Bolton 19.841009 88.46154 ## 1525 Bury 10.751918 87.50000 ## 1526 Manchester 37.129978 72.85714 ## 1527 Oldham 20.505800 81.81818 ## 1528 Rochdale 15.722949 66.66666 ## 1529 Salford 10.984261 77.77778 ## 1530 Stockport 6.925622 66.66666 ## 1531 Tameside 13.475191 80.00000 ## 1532 Trafford 14.149326 52.63158 ## 1533 Wigan 3.135871 50.00000 ## 1534 Liverpool 9.306383 82.60870 ## 1535 Sefton 3.653462 NA ## 1536 Wirral 3.330225 100.00000 ## 1537 Barnsley 2.851285 NA ## 1538 Doncaster 6.820075 64.28571 ## 1539 Rotherham 8.000443 77.77778 ## 1540 Sheffield 16.474651 66.10169 ## 1541 Newcastle upon Tyne 15.115623 90.47619 ## 1542 North Tyneside 3.802803 87.50000 ## 1543 Sunderland 7.495318 77.77778 ## 1544 Birmingham 38.011227 76.00000 ## 1545 Coventry 36.266251 49.01961 ## 1546 Dudley 11.156114 76.00000 ## 1547 Sandwell 36.927345 65.57377 ## 1548 Solihull 9.463531 28.57143 ## 1549 Walsall 20.927635 74.07407 ## 1550 Wolverhampton 32.817989 70.00000 ## 1551 Bradford 31.650457 61.84211 ## 1552 Calderdale 10.073013 90.00000 ## 1553 Kirklees 22.254490 65.71429 ## 1554 Leeds 13.705615 67.64706 ## 1555 Wakefield 5.486857 53.33333 ## 1556 Gateshead 4.994016 85.71429 ## 1557 Barking and Dagenham 34.424431 80.55556 ## 1558 Barnet 25.880678 75.00000 ## 1559 Bexley 13.212142 88.23529 ## 1560 Brent 95.344032 81.81818 ## 1561 Bromley 10.713225 86.66666 ## 1562 Camden 26.207390 73.33334 ## 1563 Croydon 32.592052 68.75000 ## 1564 Ealing 68.607513 80.00000 ## 1565 Enfield 23.331362 71.42857 ## 1566 Greenwich 44.511948 84.61539 ## 1567 Hackney 34.882339 80.00000 ## 1568 Hammersmith and Fulham 29.671253 76.00000 ## 1569 Haringey 41.318527 73.58491 ## 1570 Harrow 67.265717 64.51613 ## 1571 Havering 10.141551 93.75000 ## 1572 Hillingdon 43.907845 52.50000 ## 1573 Hounslow 68.799049 73.33334 ## 1574 Islington 33.785225 55.26316 ## 1575 Kensington and Chelsea 24.348724 70.83334 ## 1576 Kingston upon Thames 16.963251 61.53846 ## 1577 Lambeth 29.248381 82.50000 ## 1578 Lewisham 30.856909 71.42857 ## 1579 Merton 32.331123 72.00000 ## 1580 Newham 112.948471 69.86301 ## 1581 Redbridge 54.394234 80.00000 ## 1582 Richmond upon Thames 7.222939 66.66666 ## 1583 Southwark 36.897438 72.50000 ## 1584 Sutton 14.817725 55.55556 ## 1585 Tower Hamlets 45.382805 80.85107 ## 1586 Waltham Forest 46.201805 80.30303 ## 1587 Wandsworth 26.013170 65.51724 ## 1588 Westminster 26.771358 77.77778 ## 1589 Buckinghamshire 9.849544 82.60870 ## 1590 Cambridgeshire 6.339438 68.18182 ## 1591 Cumbria 3.605959 100.00000 ## 1592 Derbyshire 3.877550 47.36842 ## 1593 Devon 3.674613 55.00000 ## 1594 Dorset (Cty) 2.648894 62.50000 ## 1595 East Sussex 4.958571 75.00000 ## 1596 Essex 5.022174 68.29269 ## 1597 Gloucestershire 5.923174 57.14286 ## 1598 Hampshire 4.982858 75.00000 ## 1599 Hertfordshire 7.758723 73.52941 ## 1600 Kent 7.522978 78.12500 ## 1601 Lancashire 9.417046 68.96552 ## 1602 Leicestershire 5.233001 59.09091 ## 1603 Lincolnshire 3.706321 68.75000 ## 1604 Norfolk 4.123999 84.21053 ## 1605 Northamptonshire 9.095442 82.75862 ## 1606 North Yorkshire 2.376428 78.57143 ## 1607 Nottinghamshire 3.960586 94.44444 ## 1608 Oxfordshire 10.359824 89.65517 ## 1609 Somerset 3.052678 75.00000 ## 1610 Staffordshire 4.025919 59.09091 ## 1611 Suffolk 3.956741 87.50000 ## 1612 Surrey 7.426718 77.77778 ## 1613 Warwickshire 8.574603 53.33333 ## 1614 West Sussex 7.680306 76.92308 ## 1615 Worcestershire 5.560527 42.85714 ## 1616 Middlesbrough 12.481158 NA ## 1617 Stockton-on-Tees 4.997045 NA ## 1618 Darlington 6.307039 83.33334 ## 1619 Warrington 5.197834 71.42857 ## 1620 Blackburn with Darwen 33.631805 52.94118 ## 1621 Blackpool 12.013059 73.33334 ## 1622 Kingston upon Hull 7.646310 90.90909 ## 1623 East Riding of Yorkshire 1.981738 NA ## 1624 North Lincolnshire 7.900766 NA ## 1625 Derby 13.921907 81.81818 ## 1626 Leicester 48.150150 76.31579 ## 1627 Nottingham 17.996691 72.00000 ## 1628 Telford and Wrekin 6.913976 NA ## 1629 Stoke-on-Trent 13.716105 55.00000 ## 1630 Bath and North East Somerset 7.250459 60.00000 ## 1631 Bristol 21.517313 42.62295 ## 1632 North Somerset 3.878343 NA ## 1633 South Gloucestershire 6.326460 77.77778 ## 1634 Plymouth 5.535188 14.28571 ## 1635 Torbay 5.290405 NA ## 1636 Bournemouth 7.094211 66.66666 ## 1637 Swindon 10.281383 44.44444 ## 1638 Peterborough 28.472395 65.00000 ## 1639 Luton 34.987762 88.00000 ## 1640 Southend-on-Sea 7.551530 85.71429 ## 1641 Thurrock 4.948821 NA ## 1642 Medway 6.403697 80.00000 ## 1643 Bracknell Forest 8.580523 87.50000 ## 1644 West Berkshire 5.777032 NA ## 1645 Reading 36.368683 95.83334 ## 1646 Slough 51.302143 83.33334 ## 1647 Windsor and Maidenhead 9.556401 53.33333 ## 1648 Wokingham 9.490928 75.00000 ## 1649 Milton Keynes 12.867604 100.00000 ## 1650 Brighton and Hove 8.149910 75.00000 ## 1651 Portsmouth 8.370572 71.42857 ## 1652 Southampton 15.128740 85.71429 ## 1653 County Durham 2.261337 100.00000 ## 1654 Cheshire East 3.750017 88.88889 ## 1655 Cheshire West and Chester 3.120078 77.77778 ## 1656 Shropshire 3.770434 62.50000 ## 1657 Cornwall 3.013634 70.00000 ## 1658 Wiltshire 2.983242 58.33333 ## 1659 Bedford 16.909971 73.68421 ## 1660 Central Bedfordshire 3.033252 NA ## 1661 Northumberland 2.842973 57.14286 ## 1662 Bolton 19.163404 69.56522 ## 1663 Bury 11.430511 69.23077 ## 1664 Manchester 31.237848 83.82353 ## 1665 Oldham 21.442776 77.77778 ## 1666 Rochdale 15.226195 60.00000 ## 1667 Salford 11.155128 70.00000 ## 1668 Stockport 5.847652 83.33334 ## 1669 Tameside 11.338862 46.15385 ## 1670 Trafford 13.897474 71.42857 ## 1671 Wigan 3.334500 66.66666 ## 1672 Liverpool 8.824263 87.50000 ## 1673 Sefton 3.897562 71.42857 ## 1674 Wirral 2.908904 NA ## 1675 Doncaster 7.683771 76.19048 ## 1676 Rotherham 8.231543 80.00000 ## 1677 Sheffield 16.002970 62.50000 ## 1678 Newcastle upon Tyne 14.835513 80.00000 ## 1679 North Tyneside 4.946675 NA ## 1680 South Tyneside 4.491687 83.33334 ## 1681 Sunderland 7.723546 60.00000 ## 1682 Birmingham 35.194633 70.51282 ## 1683 Coventry 32.659023 58.82353 ## 1684 Dudley 9.750101 66.66666 ## 1685 Sandwell 33.562962 54.34783 ## 1686 Solihull 8.455419 83.33334 ## 1687 Walsall 16.896608 70.00000 ## 1688 Wolverhampton 29.099220 71.87500 ## 1689 Bradford 26.617842 76.08696 ## 1690 Calderdale 9.864516 100.00000 ## 1691 Kirklees 19.171196 70.21277 ## 1692 Leeds 12.741419 84.90566 ## 1693 Wakefield 6.468194 57.14286 ## 1694 Gateshead 6.986598 66.66666 ## 1695 Barking and Dagenham 34.766884 90.32258 ## 1696 Barnet 23.240608 84.37500 ## 1697 Bexley 10.546447 83.33334 ## 1698 Brent 83.315300 77.08334 ## 1699 Bromley 8.072904 63.63636 ## 1700 Camden 21.697716 83.33334 ## 1701 Croydon 27.502701 76.47059 ## 1702 Ealing 65.240479 74.07407 ## 1703 Enfield 22.349667 58.82353 ## 1704 Greenwich 41.995823 71.42857 ## 1705 Hackney 32.092758 75.00000 ## 1706 Hammersmith and Fulham 23.843718 78.94736 ## 1707 Haringey 33.446995 61.36364 ## 1708 Harrow 61.084930 74.54546 ## 1709 Havering 10.852930 71.42857 ## 1710 Hillingdon 42.164459 68.25397 ## 1711 Hounslow 64.300232 81.53846 ## 1712 Islington 29.451084 74.28571 ## 1713 Kensington and Chelsea 22.039362 70.83334 ## 1714 Kingston upon Thames 15.886103 90.90909 ## 1715 Lambeth 26.739021 75.51020 ## 1716 Lewisham 26.053076 67.44186 ## 1717 Merton 29.330704 77.77778 ## 1718 Newham 98.672020 71.30434 ## 1719 Redbridge 50.038738 77.55102 ## 1720 Richmond upon Thames 5.929295 83.33334 ## 1721 Southwark 31.622904 75.00000 ## 1722 Sutton 13.282994 100.00000 ## 1723 Tower Hamlets 38.085457 75.00000 ## 1724 Waltham Forest 41.210846 80.55556 ## 1725 Wandsworth 21.615889 75.86207 ## 1726 Westminster 24.155230 63.33333 ## 1727 Buckinghamshire 8.918093 55.55556 ## 1728 Cambridgeshire 6.349166 70.00000 ## 1729 Cumbria 3.809957 57.14286 ## 1730 Derbyshire 3.518185 71.42857 ## 1731 Devon 3.779001 81.81818 ## 1732 Dorset (Cty) 2.878876 83.33334 ## 1733 East Sussex 4.917538 88.23529 ## 1734 Essex 4.603141 83.87096 ## 1735 Gloucestershire 5.659608 78.57143 ## 1736 Hampshire 4.078383 87.50000 ## 1737 Hertfordshire 6.804700 67.34694 ## 1738 Kent 7.205694 80.76923 ## 1739 Lancashire 7.719346 64.10256 ## 1740 Leicestershire 4.738426 63.63636 ## 1741 Lincolnshire 4.182768 56.52174 ## 1742 Norfolk 3.712875 88.46154 ## 1743 Northamptonshire 8.162930 75.86207 ## 1744 North Yorkshire 2.371809 85.71429 ## 1745 Nottinghamshire 4.480350 70.37037 ## 1746 Oxfordshire 10.434517 86.04651 ## 1747 Somerset 3.156390 55.55556 ## 1748 Staffordshire 3.854021 83.33334 ## 1749 Suffolk 4.070290 90.00000 ## 1750 Surrey 6.700793 88.23529 ## 1751 Warwickshire 9.149923 53.33333 ## 1752 West Sussex 6.155312 79.16666 ## 1753 Worcestershire 5.646406 47.05882 ## 1754 Middlesbrough 9.827138 NA ## 1755 Stockton-on-Tees 4.631345 NA ## 1756 Warrington 5.003010 NA ## 1757 Blackburn with Darwen 28.891623 71.42857 ## 1758 Blackpool 9.463804 50.00000 ## 1759 Kingston upon Hull 6.596059 81.81818 ## 1760 North Lincolnshire 6.105102 100.00000 ## 1761 Derby 13.993400 72.72727 ## 1762 Leicester 41.783085 81.42857 ## 1763 Nottingham 17.055483 82.14286 ## 1764 Telford and Wrekin 5.097170 NA ## 1765 Stoke-on-Trent 12.086662 66.66666 ## 1766 Bath and North East Somerset 7.339611 80.00000 ## 1767 Bristol 20.557961 54.00000 ## 1768 North Somerset 4.004434 NA ## 1769 South Gloucestershire 6.634746 62.50000 ## 1770 Plymouth 5.381235 15.38461 ## 1771 Torbay 6.011181 75.00000 ## 1772 Bournemouth 6.500089 57.14286 ## 1773 Swindon 10.809092 57.14286 ## 1774 Peterborough 23.230589 61.53846 ## 1775 Luton 30.261837 80.00000 ## 1776 Southend-on-Sea 7.118278 92.30769 ## 1777 Thurrock 5.293515 NA ## 1778 Medway 5.616077 100.00000 ## 1779 Bracknell Forest 7.632617 NA ## 1780 West Berkshire 4.892326 NA ## 1781 Reading 34.748955 64.28571 ## 1782 Slough 47.800484 80.00000 ## 1783 Windsor and Maidenhead 8.370454 NA ## 1784 Wokingham 10.027596 62.50000 ## 1785 Milton Keynes 10.002795 80.00000 ## 1786 Brighton and Hove 7.238517 100.00000 ## 1787 Portsmouth 7.357116 63.63636 ## 1788 Southampton 12.499691 76.92308 ## 1789 County Durham 1.867542 62.50000 ## 1790 Cheshire East 4.540057 63.63636 ## 1791 Cheshire West and Chester 3.409239 75.00000 ## 1792 Shropshire 3.004015 66.66666 ## 1793 Cornwall 2.442035 100.00000 ## 1794 Wiltshire 3.097130 66.66666 ## 1795 Bedford 13.416597 66.66666 ## 1796 Central Bedfordshire 3.109221 NA ## 1797 Northumberland 2.527183 NA ## 1798 Bolton 18.626459 84.21053 ## 1799 Bury 10.328684 75.00000 ## 1800 Manchester 27.075327 76.78571 ## 1801 Oldham 21.886787 85.71429 ## 1802 Rochdale 13.762321 68.75000 ## 1803 Salford 12.131254 41.66667 ## 1804 Stockport 5.701341 100.00000 ## 1805 Tameside 8.600554 100.00000 ## 1806 Trafford 11.359715 77.77778 ## 1807 Wigan 4.049138 71.42857 ## 1808 Liverpool 8.267757 86.36364 ## 1809 Sefton 2.556869 NA ## 1810 Wirral 2.801199 100.00000 ## 1811 Barnsley 3.363054 85.71429 ## 1812 Doncaster 7.333730 83.33334 ## 1813 Rotherham 5.512806 55.55556 ## 1814 Sheffield 14.590874 59.09091 ## 1815 Newcastle upon Tyne 14.220886 76.19048 ## 1816 North Tyneside 4.607165 NA ## 1817 South Tyneside 5.612289 NA ## 1818 Sunderland 6.870086 90.90909 ## 1819 Birmingham 28.971825 72.79412 ## 1820 Coventry 27.686014 53.70370 ## 1821 Dudley 10.038665 70.58823 ## 1822 Sandwell 30.967905 70.68965 ## 1823 Solihull 6.664540 50.00000 ## 1824 Walsall 14.724350 76.47059 ## 1825 Wolverhampton 26.445141 67.74194 ## 1826 Bradford 22.230896 66.66666 ## 1827 Calderdale 8.373456 NA ## 1828 Kirklees 17.355139 61.11111 ## 1829 Leeds 12.915644 81.63265 ## 1830 Wakefield 5.624839 77.77778 ## 1831 Gateshead 6.803816 28.57143 ## 1832 Barking and Dagenham 30.184782 85.00000 ## 1833 Barnet 19.628416 83.78378 ## 1834 Bexley 9.593740 77.77778 ## 1835 Brent 67.766190 75.00000 ## 1836 Bromley 7.460583 78.57143 ## 1837 Camden 17.597084 85.00000 ## 1838 Croydon 24.584995 80.55556 ## 1839 Ealing 56.635483 65.15151 ## 1840 Enfield 21.161543 69.23077 ## 1841 Greenwich 36.409031 69.23077 ## 1842 Hackney 28.003963 88.46154 ## 1843 Hammersmith and Fulham 22.740486 79.16666 ## 1844 Haringey 28.474880 71.05264 ## 1845 Harrow 46.940628 87.50000 ## 1846 Havering 10.168843 85.71429 ## 1847 Hillingdon 36.752079 73.91304 ## 1848 Hounslow 53.680798 73.33334 ## 1849 Islington 25.572870 93.33334 ## 1850 Kensington and Chelsea 19.427317 66.66666 ## 1851 Kingston upon Thames 14.237719 100.00000 ## 1852 Lambeth 22.629841 60.00000 ## 1853 Lewisham 23.212013 74.28571 ## 1854 Merton 25.724520 80.00000 ## 1855 Newham 84.602623 67.52137 ## 1856 Redbridge 44.627090 80.00000 ## 1857 Richmond upon Thames 6.049325 50.00000 ## 1858 Southwark 27.145393 76.92308 ## 1859 Sutton 11.960695 100.00000 ## 1860 Tower Hamlets 32.275478 77.77778 ## 1861 Waltham Forest 37.805210 85.10638 ## 1862 Wandsworth 18.333080 69.23077 ## 1863 Westminster 21.348658 73.68421 ## 1864 Buckinghamshire 8.058600 66.66666 ## 1865 Cambridgeshire 6.034756 80.00000 ## 1866 Cumbria 2.407293 57.14286 ## 1867 Derbyshire 2.905855 58.82353 ## 1868 Devon 3.834359 70.83334 ## 1869 Dorset (Cty) 2.387832 NA ## 1870 East Sussex 4.196422 92.30769 ## 1871 Essex 4.213025 75.00000 ## 1872 Gloucestershire 5.612391 88.23529 ## 1873 Hampshire 3.830186 77.77778 ## 1874 Hertfordshire 6.791623 71.42857 ## 1875 Kent 6.606983 77.96610 ## 1876 Lancashire 6.281559 87.87878 ## 1877 Leicestershire 4.047958 100.00000 ## 1878 Lincolnshire 4.376050 65.51724 ## 1879 Norfolk 3.912732 48.48485 ## 1880 Northamptonshire 6.585176 95.45454 ## 1881 North Yorkshire 2.478696 100.00000 ## 1882 Nottinghamshire 3.618583 90.00000 ## 1883 Oxfordshire 9.367378 76.00000 ## 1884 Somerset 2.458454 NA ## 1885 Staffordshire 4.149099 68.18182 ## 1886 Suffolk 3.818377 76.47059 ## 1887 Surrey 5.758301 70.58823 ## 1888 Warwickshire 7.362166 58.33333 ## 1889 West Sussex 5.778544 72.00000 ## 1890 Worcestershire 4.514788 91.66666 ## 1891 Middlesbrough 9.793921 100.00000 ## 1892 Stockton-on-Tees 5.465330 81.81818 ## 1893 Warrington 4.170443 NA ## 1894 Blackburn with Darwen 23.888186 70.83334 ## 1895 Blackpool 9.975228 100.00000 ## 1896 Kingston upon Hull 6.185280 100.00000 ## 1897 North East Lincolnshire 4.168247 100.00000 ## 1898 North Lincolnshire 4.314885 NA ## 1899 Derby 13.116630 69.23077 ## 1900 Leicester 38.467503 81.94444 ## 1901 Nottingham 15.760359 80.00000 ## 1902 Stoke-on-Trent 11.766868 41.17647 ## 1903 Bath and North East Somerset 6.516144 NA ## 1904 Bristol 17.994709 58.69565 ## 1905 North Somerset 3.810292 NA ## 1906 South Gloucestershire 6.694068 50.00000 ## 1907 Plymouth 5.992964 41.66667 ## 1908 Torbay 4.981804 NA ## 1909 Bournemouth 6.087359 100.00000 ## 1910 Swindon 10.733644 90.00000 ## 1911 Peterborough 19.797379 77.27273 ## 1912 Luton 29.077583 87.50000 ## 1913 Southend-on-Sea 5.574063 NA ## 1914 Thurrock 5.418531 NA ## 1915 Medway 5.088781 85.71429 ## 1916 Bracknell Forest 6.723970 NA ## 1917 Reading 26.409721 84.61539 ## 1918 Slough 41.541698 79.16666 ## 1919 Windsor and Maidenhead 8.530852 NA ## 1920 Wokingham 10.750443 75.00000 ## 1921 Milton Keynes 8.991371 80.00000 ## 1922 Brighton and Hove 7.630039 70.00000 ## 1923 Portsmouth 6.013515 83.33334 ## 1924 Southampton 11.644644 80.00000 ## 1925 County Durham 1.732217 83.33334 ## 1926 Cheshire East 4.168030 50.00000 ## 1927 Cheshire West and Chester 2.994176 NA ## 1928 Shropshire 2.666971 NA ## 1929 Cornwall 2.301216 88.88889 ## 1930 Wiltshire 3.002825 100.00000 ## 1931 Bedford 12.019496 72.72727 ## 1932 Central Bedfordshire 2.447986 NA ## 1933 Northumberland 2.419196 NA ## 1934 Bolton 17.609129 95.00000 ## 1935 Bury 9.404200 66.66666 ## 1936 Manchester 24.654676 69.86301 ## 1937 Oldham 21.281895 90.47619 ## 1938 Rochdale 14.915170 78.57143 ## 1939 Salford 11.703133 66.66666 ## 1940 Stockport 5.321746 80.00000 ## 1941 Tameside 8.417104 56.25000 ## 1942 Trafford 10.435966 63.63636 ## 1943 Wigan 4.137001 83.33334 ## 1944 Liverpool 7.692057 81.81818 ## 1945 Sefton 2.795340 NA ## 1946 Wirral 3.003877 75.00000 ## 1947 Barnsley 4.029608 100.00000 ## 1948 Doncaster 6.323125 66.66666 ## 1949 Rotherham 5.106424 66.66666 ## 1950 Sheffield 12.656077 82.92683 ## 1951 Newcastle upon Tyne 14.092301 84.61539 ## 1952 North Tyneside 3.283226 NA ## 1953 South Tyneside 5.826187 NA ## 1954 Sunderland 4.814045 NA ## 1955 Birmingham 25.070694 82.35294 ## 1956 Coventry 25.471638 60.71429 ## 1957 Dudley 7.793231 60.00000 ## 1958 Sandwell 27.139280 73.07692 ## 1959 Solihull 6.474771 42.85714 ## 1960 Walsall 13.756486 60.00000 ## 1961 Wolverhampton 24.400650 73.17073 ## 1962 Bradford 18.113379 87.75510 ## 1963 Calderdale 7.853281 77.77778 ## 1964 Kirklees 16.639845 97.05882 ## 1965 Leeds 11.466852 79.54546 ## 1966 Wakefield 5.085603 70.00000 ## 1967 Gateshead 7.436382 75.00000 ## 1968 Barking and Dagenham 28.527723 79.31035 ## 1969 Barnet 19.432039 81.08108 ## 1970 Bexley 9.072190 50.00000 ## 1971 Brent 57.836781 87.23404 ## 1972 Bromley 6.567572 50.00000 ## 1973 Camden 17.832134 78.12500 ## 1974 Croydon 22.184341 80.55556 ## 1975 Ealing 47.282974 74.07407 ## 1976 Enfield 20.704901 54.28571 ## 1977 Greenwich 30.515455 73.17073 ## 1978 Hackney 25.218111 60.60606 ## 1979 Hammersmith and Fulham 20.159628 75.00000 ## 1980 Haringey 26.468603 86.66666 ## 1981 Harrow 38.614002 73.68421 ## 1982 Havering 9.615693 84.61539 ## 1983 Hillingdon 34.598450 64.28571 ## 1984 Hounslow 47.827000 82.69231 ## 1985 Islington 21.880650 76.19048 ## 1986 Kensington and Chelsea 16.483795 62.50000 ## 1987 Kingston upon Thames 11.289648 75.00000 ## 1988 Lambeth 20.511518 91.66666 ## 1989 Lewisham 22.054184 86.11111 ## 1990 Merton 23.328566 75.00000 ## 1991 Newham 68.196259 76.92308 ## 1992 Redbridge 41.092407 85.00000 ## 1993 Richmond upon Thames 5.835648 77.77778 ## 1994 Southwark 25.354668 83.33334 ## 1995 Sutton 12.004622 66.66666 ## 1996 Tower Hamlets 30.135313 90.32258 ## 1997 Waltham Forest 33.222836 77.08334 ## 1998 Wandsworth 17.023321 87.50000 ## 1999 Westminster 18.030201 87.50000 ## 2000 Buckinghamshire 8.412024 86.36364 ## 2001 Cambridgeshire 5.673286 40.90909 ## 2002 Cumbria 2.072539 NA ## 2003 Derbyshire 2.340228 NA ## 2004 Devon 3.497501 66.66666 ## 2005 Dorset (Cty) 2.218212 75.00000 ## 2006 East Sussex 4.158815 77.77778 ## 2007 Essex 4.127738 79.48718 ## 2008 Gloucestershire 3.832948 90.00000 ## 2009 Hampshire 3.954099 75.86207 ## 2010 Hertfordshire 7.465861 82.75862 ## 2011 Kent 6.232452 77.35849 ## 2012 Lancashire 5.438900 68.75000 ## 2013 Leicestershire 3.315916 62.50000 ## 2014 Lincolnshire 4.516616 63.15789 ## 2015 Norfolk 4.107214 75.00000 ## 2016 Northamptonshire 6.688284 87.87878 ## 2017 North Yorkshire 2.362267 100.00000 ## 2018 Nottinghamshire 3.265386 83.33334 ## 2019 Oxfordshire 8.014046 85.71429 ## 2020 Somerset 1.951083 62.50000 ## 2021 Staffordshire 4.017824 66.66666 ## 2022 Suffolk 4.015333 66.66666 ## 2023 Surrey 5.856544 89.18919 ## 2024 Warwickshire 6.360273 75.00000 ## 2025 West Sussex 4.809364 80.95238 ## 2026 Worcestershire 3.566223 30.00000 ## 2027 Middlesbrough 10.945214 73.33334 ## 2028 Stockton-on-Tees 5.275940 NA ## 2029 Warrington 3.511808 NA ## 2030 Blackburn with Darwen 23.590734 50.00000 ## 2031 Blackpool 8.094949 66.66666 ## 2032 Kingston upon Hull 5.902771 77.77778 ## 2033 North Lincolnshire 4.688013 NA ## 2034 Derby 11.993033 88.23529 ## 2035 Leicester 37.437122 65.15151 ## 2036 Nottingham 14.800745 77.27273 ## 2037 Stoke-on-Trent 10.778369 83.33334 ## 2038 Bath and North East Somerset 3.571996 NA ## 2039 Bristol 15.155309 70.00000 ## 2040 North Somerset 3.467177 NA ## 2041 South Gloucestershire 5.182871 71.42857 ## 2042 Plymouth 7.244433 26.66667 ## 2043 Bournemouth 6.206062 75.00000 ## 2044 Swindon 11.728383 90.90909 ## 2045 Peterborough 19.344790 77.77778 ## 2046 Luton 26.701731 94.28571 ## 2047 Southend-on-Sea 6.461761 100.00000 ## 2048 Thurrock 7.724883 100.00000 ## 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1841 100.00000 87.5000000 3.2967033 ## 1842 100.00000 92.4528275 0.0000000 ## 1843 100.00000 94.2857132 2.5000000 ## 1844 100.00000 83.0188675 3.1250000 ## 1845 95.16129 90.5405426 2.4390244 ## 1846 100.00000 90.0000000 4.3478260 ## 1847 97.91666 83.1325302 7.2164950 ## 1848 98.41270 85.2941208 0.9009009 ## 1849 94.87180 82.9268265 10.4166670 ## 1850 100.00000 75.0000000 14.2857141 ## 1851 100.00000 88.2352905 5.2631578 ## 1852 100.00000 91.6666641 1.6393442 ## 1853 97.50000 91.5254211 0.0000000 ## 1854 95.83334 81.8181839 9.8039217 ## 1855 99.34641 81.2500000 5.6680160 ## 1856 98.48485 87.1287155 2.6548672 ## 1857 100.00000 72.7272720 7.1428571 ## 1858 100.00000 95.3125000 5.1282053 ## 1859 100.00000 100.0000000 0.0000000 ## 1860 100.00000 84.2105255 8.6419754 ## 1861 98.43750 85.7142868 7.0707068 ## 1862 100.00000 86.5384598 4.7619047 ## 1863 100.00000 75.7575760 8.1081085 ## 1864 100.00000 82.0512848 2.4390244 ## 1865 94.73684 71.4285736 6.2500000 ## 1866 NA 87.5000000 12.5000000 ## 1867 100.00000 60.0000000 11.1111107 ## 1868 95.65218 80.0000000 6.2500000 ## 1869 NA 83.3333359 0.0000000 ## 1870 100.00000 88.8888855 0.0000000 ## 1871 100.00000 80.9523773 2.2727273 ## 1872 100.00000 84.6153870 13.7931032 ## 1873 100.00000 69.8113174 6.8965516 ## 1874 100.00000 82.6666641 6.0975609 ## 1875 100.00000 77.7777786 4.3956046 ## 1876 100.00000 80.7692337 1.8181819 ## 1877 100.00000 88.8888855 4.7619047 ## 1878 100.00000 66.6666641 8.3333330 ## 1879 100.00000 62.1621628 5.0000000 ## 1880 93.75000 86.1111145 6.3829789 ## 1881 91.66666 71.4285736 6.6666665 ## 1882 90.90909 72.2222214 11.1111107 ## 1883 96.77419 87.7551041 6.0000000 ## 1884 NA NA NA ## 1885 100.00000 70.0000000 4.6511626 ## 1886 100.00000 73.0769196 0.0000000 ## 1887 100.00000 83.3333359 4.4776120 ## 1888 100.00000 85.7142868 4.1666665 ## 1889 100.00000 82.1428604 0.0000000 ## 1890 93.75000 82.3529434 10.5263157 ## 1891 92.30769 85.7142868 0.0000000 ## 1892 88.88889 84.6153870 0.0000000 ## 1893 NA 100.0000000 0.0000000 ## 1894 100.00000 88.5714264 5.4054055 ## 1895 100.00000 78.5714264 0.0000000 ## 1896 100.00000 76.9230804 7.6923075 ## 1897 87.50000 85.7142868 0.0000000 ## 1898 NA NA 0.0000000 ## 1899 100.00000 85.7142868 7.1428571 ## 1900 92.59259 79.4642868 4.8780489 ## 1901 100.00000 75.0000000 4.2553191 ## 1902 93.33334 93.3333359 0.0000000 ## 1903 NA NA NA ## 1904 94.73684 85.9649124 6.2500000 ## 1905 NA 33.3333321 0.0000000 ## 1906 88.88889 69.2307663 5.5555553 ## 1907 100.00000 66.6666641 12.5000000 ## 1908 NA NA 0.0000000 ## 1909 100.00000 100.0000000 0.0000000 ## 1910 95.00000 85.7142868 3.3333333 ## 1911 95.83334 83.7837830 7.8947368 ## 1912 94.73684 83.3333359 1.8181819 ## 1913 NA NA 0.0000000 ## 1914 100.00000 80.0000000 0.0000000 ## 1915 100.00000 36.3636360 8.3333330 ## 1916 NA NA NA ## 1917 94.11765 95.8333359 0.0000000 ## 1918 94.73684 95.9183655 0.0000000 ## 1919 87.50000 90.0000000 0.0000000 ## 1920 100.00000 100.0000000 0.0000000 ## 1921 100.00000 90.4761887 8.0000000 ## 1922 100.00000 66.6666641 10.5263157 ## 1923 100.00000 90.0000000 9.0909090 ## 1924 88.00000 92.3076935 0.0000000 ## 1925 NA 85.7142868 0.0000000 ## 1926 87.50000 76.4705887 5.8823528 ## 1927 NA NA 0.0000000 ## 1928 NA 71.4285736 0.0000000 ## 1929 100.00000 81.8181839 8.3333330 ## 1930 77.77778 100.0000000 0.0000000 ## 1931 100.00000 82.3529434 11.1111107 ## 1932 NA NA NA ## 1933 NA NA NA ## 1934 100.00000 88.3720932 4.0000000 ## 1935 83.33334 90.9090881 0.0000000 ## 1936 100.00000 84.7457657 8.4615383 ## 1937 100.00000 83.3333359 2.5000000 ## 1938 100.00000 82.7586212 6.4516129 ## 1939 100.00000 90.4761887 0.0000000 ## 1940 100.00000 90.0000000 7.6923075 ## 1941 100.00000 80.9523773 0.0000000 ## 1942 91.66666 86.3636398 8.0000000 ## 1943 100.00000 73.3333359 6.6666665 ## 1944 100.00000 80.0000000 5.8823528 ## 1945 100.00000 87.5000000 0.0000000 ## 1946 100.00000 92.3076935 0.0000000 ## 1947 100.00000 66.6666641 11.1111107 ## 1948 100.00000 87.5000000 0.0000000 ## 1949 100.00000 87.5000000 0.0000000 ## 1950 95.83334 80.3921585 8.0645161 ## 1951 100.00000 90.0000000 5.7142859 ## 1952 NA NA NA ## 1953 NA NA NA ## 1954 100.00000 NA 0.0000000 ## 1955 95.23810 85.2459030 3.0075188 ## 1956 100.00000 84.7222214 5.9523811 ## 1957 100.00000 100.0000000 4.7619047 ## 1958 100.00000 75.9493637 4.6511626 ## 1959 NA 92.3076935 0.0000000 ## 1960 88.88889 86.3636398 4.4444447 ## 1961 90.47619 77.4193573 0.0000000 ## 1962 100.00000 87.6543198 2.2222223 ## 1963 92.30769 94.4444427 5.2631578 ## 1964 100.00000 93.4426193 0.0000000 ## 1965 100.00000 86.3013687 3.7037036 ## 1966 90.00000 80.0000000 0.0000000 ## 1967 100.00000 90.9090881 0.0000000 ## 1968 89.47369 85.9649124 7.6923075 ## 1969 94.44444 90.4761887 4.0540543 ## 1970 100.00000 93.1034470 0.0000000 ## 1971 96.77419 89.6551743 5.8201060 ## 1972 100.00000 95.0000000 0.0000000 ## 1973 97.29729 88.6363602 6.0000000 ## 1974 98.21429 75.3424683 5.9523811 ## 1975 94.28571 87.2549057 6.7796612 ## 1976 100.00000 84.7457657 0.0000000 ## 1977 80.00000 87.2727280 0.0000000 ## 1978 96.77419 94.9152527 0.0000000 ## 1979 95.23810 93.7500000 0.0000000 ## 1980 95.91837 86.5671616 5.4794521 ## 1981 100.00000 88.8888855 4.3478260 ## 1982 93.75000 95.0000000 4.0000000 ## 1983 90.56604 88.3116913 2.2988505 ## 1984 97.29729 81.5533981 4.2735043 ## 1985 96.42857 78.1250000 9.7560978 ## 1986 92.85714 76.4705887 9.5238094 ## 1987 100.00000 90.9090881 0.0000000 ## 1988 100.00000 84.0000000 5.2631578 ## 1989 97.61905 82.0000000 4.8387098 ## 1990 96.55173 95.0000000 0.0000000 ## 1991 97.36842 82.3170700 5.3763442 ## 1992 92.68293 96.1165085 0.0000000 ## 1993 100.00000 44.4444427 0.0000000 ## 1994 100.00000 88.4057999 3.7974684 ## 1995 100.00000 85.0000000 8.0000000 ## 1996 98.00000 81.0126572 5.5555553 ## 1997 100.00000 90.1408463 3.5714285 ## 1998 97.05882 89.5833359 3.8461537 ## 1999 88.88889 80.0000000 10.5263157 ## 2000 91.89189 78.2608719 2.0000000 ## 2001 100.00000 82.3529434 0.0000000 ## 2002 NA 71.4285736 0.0000000 ## 2003 NA 66.6666641 0.0000000 ## 2004 84.61539 63.1578941 5.0000000 ## 2005 100.00000 77.7777786 0.0000000 ## 2006 100.00000 85.0000000 0.0000000 ## 2007 97.67442 84.7457657 5.9701490 ## 2008 100.00000 84.6153870 0.0000000 ## 2009 100.00000 86.2745132 3.3898306 ## 2010 93.93939 84.3373489 5.2083335 ## 2011 90.19608 86.7469864 1.0989012 ## 2012 97.50000 86.1538467 2.9411764 ## 2013 87.50000 68.4210510 0.0000000 ## 2014 94.44444 62.0689659 6.8965516 ## 2015 96.15385 82.3529434 0.0000000 ## 2016 94.59460 73.3333359 2.0408163 ## 2017 92.85714 80.0000000 0.0000000 ## 2018 92.85714 66.6666641 4.3478260 ## 2019 100.00000 83.3333359 7.8947368 ## 2020 NA 42.8571434 0.0000000 ## 2021 89.47369 83.8709641 2.8571429 ## 2022 94.44444 82.7586212 0.0000000 ## 2023 89.13043 71.4285736 4.8387098 ## 2024 100.00000 80.0000000 7.1428571 ## 2025 100.00000 89.1891861 0.0000000 ## 2026 100.00000 83.3333359 11.1111107 ## 2027 100.00000 NA NA ## 2028 NA NA NA ## 2029 NA NA NA ## 2030 100.00000 NA NA ## 2031 100.00000 NA NA ## 2032 100.00000 NA NA ## 2033 NA NA NA ## 2034 95.65218 NA NA ## 2035 100.00000 NA NA ## 2036 100.00000 NA NA ## 2037 100.00000 NA NA ## 2038 NA NA NA ## 2039 94.44444 NA NA ## 2040 NA NA NA ## 2041 NA NA NA ## 2042 77.77778 NA NA ## 2043 100.00000 NA NA ## 2044 94.11765 NA NA ## 2045 93.10345 NA NA ## 2046 91.30434 NA NA ## 2047 91.66666 NA NA ## 2048 92.30769 NA NA ## 2049 100.00000 NA NA ## 2050 92.59259 NA NA ## 2051 93.10345 NA NA ## 2052 87.50000 NA NA ## 2053 NA NA NA ## 2054 95.23810 NA NA ## 2055 90.00000 NA NA ## 2056 100.00000 NA NA ## 2057 100.00000 NA NA ## 2058 100.00000 NA NA ## 2059 83.33334 NA NA ## 2060 NA NA NA ## 2061 88.88889 NA NA ## 2062 NA NA NA ## 2063 NA NA NA ## 2064 100.00000 NA NA ## 2065 100.00000 NA NA ## 2066 98.68421 NA NA ## 2067 88.46154 NA NA ## 2068 100.00000 NA NA ## 2069 93.33334 NA NA ## 2070 91.66666 NA NA ## 2071 100.00000 NA NA ## 2072 100.00000 NA NA ## 2073 NA NA NA ## 2074 96.42857 NA NA ## 2075 NA NA NA ## 2076 NA NA NA ## 2077 81.81818 NA NA ## 2078 100.00000 NA NA ## 2079 100.00000 NA NA ## 2080 100.00000 NA NA ## 2081 100.00000 NA NA ## 2082 96.79487 NA NA ## 2083 100.00000 NA NA ## 2084 100.00000 NA NA ## 2085 97.87234 NA NA ## 2086 NA NA NA ## 2087 96.66666 NA NA ## 2088 94.44444 NA NA ## 2089 98.27586 NA NA ## 2090 NA NA NA ## 2091 100.00000 NA NA ## 2092 97.67442 NA NA ## 2093 100.00000 NA NA ## 2094 100.00000 NA NA ## 2095 94.28571 NA NA ## 2096 100.00000 NA NA ## 2097 90.47619 NA NA ## 2098 98.91304 NA NA ## 2099 100.00000 NA NA ## 2100 100.00000 NA NA ## 2101 95.34884 NA NA ## 2102 95.83334 NA NA ## 2103 100.00000 NA NA ## 2104 97.67442 NA NA ## 2105 92.30769 NA NA ## 2106 95.45454 NA NA ## 2107 100.00000 NA NA ## 2108 95.83334 NA NA ## 2109 100.00000 NA NA ## 2110 100.00000 NA NA ## 2111 98.27586 NA NA ## 2112 100.00000 NA NA ## 2113 95.23810 NA NA ## 2114 NA NA NA ## 2115 96.96970 NA NA ## 2116 91.89189 NA NA ## 2117 94.44444 NA NA ## 2118 96.96970 NA NA ## 2119 92.59259 NA NA ## 2120 100.00000 NA NA ## 2121 92.00000 NA NA ## 2122 100.00000 NA NA ## 2123 100.00000 NA NA ## 2124 98.00000 NA NA ## 2125 100.00000 NA NA ## 2126 100.00000 NA NA ## 2127 95.83334 NA NA ## 2128 95.65218 NA NA ## 2129 NA NA NA ## 2130 100.00000 NA NA ## 2131 90.90909 NA NA ## 2132 NA NA NA ## 2133 100.00000 NA NA ## 2134 95.12195 NA NA ## 2135 83.33334 NA NA ## 2136 96.66666 NA NA ## 2137 94.59460 NA NA ## 2138 100.00000 NA NA ## 2139 97.82609 NA NA ## 2140 100.00000 NA NA ## 2141 94.73684 NA NA ## 2142 100.00000 NA NA ## 2143 96.29630 NA NA ## 2144 100.00000 NA NA ## 2145 100.00000 NA NA ## 2146 90.32258 NA NA ## 2147 88.88889 NA NA ## 2148 100.00000 NA NA ## 2149 100.00000 NA NA ## 2150 97.56097 NA NA ## 2151 86.20689 NA NA ## 2152 96.00000 NA NA ## 2153 100.00000 NA NA ## prop_ds_died prop_tb_offered_hiv_test prop_ds_rf_treat_com_12 ## 1 0.0000000 NA NA ## 2 8.6956520 NA NA ## 3 20.0000000 NA NA ## 4 5.0000000 NA NA ## 5 10.0000000 NA NA ## 6 0.0000000 NA NA ## 7 NA NA NA ## 8 10.8695650 NA NA ## 9 0.5154639 NA NA ## 10 10.0000000 NA NA ## 11 17.6470585 NA NA ## 12 9.0909090 NA NA ## 13 11.2903223 NA NA ## 14 NA NA NA ## 15 0.0000000 NA NA ## 16 33.3333321 NA NA ## 17 11.7647057 NA NA ## 18 10.0000000 NA NA ## 19 25.0000000 NA NA ## 20 8.0000000 NA NA ## 21 4.6511626 NA NA ## 22 25.0000000 NA NA ## 23 0.0000000 NA NA ## 24 10.0000000 NA NA ## 25 1.4705882 NA NA ## 26 0.0000000 NA NA ## 27 11.1111107 NA NA ## 28 0.0000000 NA NA ## 29 16.6666660 NA NA ## 30 0.0000000 NA NA ## 31 11.1111107 NA NA ## 32 10.0000000 NA NA ## 33 0.0000000 NA NA ## 34 0.0000000 NA NA ## 35 30.7692299 NA NA ## 36 0.0000000 NA NA ## 37 4.5454545 NA NA ## 38 0.0000000 NA NA ## 39 0.0000000 NA NA ## 40 10.1694918 NA NA ## 41 16.6666660 NA NA ## 42 7.2992702 NA NA ## 43 2.7027028 NA NA ## 44 5.0000000 NA NA ## 45 10.0000000 NA NA ## 46 8.6956520 NA NA ## 47 6.2500000 NA NA ## 48 7.4074073 NA NA ## 49 0.0000000 NA NA ## 50 33.3333321 NA NA ## 51 7.6923075 NA NA ## 52 33.3333321 NA NA ## 53 14.2857141 NA NA ## 54 13.1578951 NA NA ## 55 7.0175438 NA NA ## 56 18.9189186 NA NA ## 57 40.0000000 NA NA ## 58 16.6666660 NA NA ## 59 4.7619047 NA NA ## 60 5.8103976 NA NA ## 61 1.3157895 NA NA ## 62 0.0000000 NA NA ## 63 11.6279068 NA NA ## 64 11.1111107 NA NA ## 65 8.4745760 NA NA ## 66 1.4492754 NA NA ## 67 4.2372880 NA NA ## 68 6.8965516 NA NA ## 69 3.5714285 NA NA ## 70 5.9405942 NA NA ## 71 10.0000000 NA NA ## 72 0.0000000 NA NA ## 73 11.4285717 NA NA ## 74 7.8431373 NA NA ## 75 0.0000000 NA NA ## 76 1.4084507 NA NA ## 77 3.7037036 NA NA ## 78 10.3448277 NA NA ## 79 9.3457947 NA NA ## 80 2.0000000 NA NA ## 81 3.5714285 NA NA ## 82 6.4102564 NA NA ## 83 5.4794521 NA NA ## 84 9.7222223 NA NA ## 85 6.5217390 NA NA ## 86 5.1724138 NA NA ## 87 15.0000000 NA NA ## 88 4.8076925 NA NA ## 89 5.8823528 NA NA ## 90 4.8543692 NA NA ## 91 3.2258065 NA NA ## 92 5.0000000 NA NA ## 93 4.4303799 NA NA ## 94 7.3684211 NA NA ## 95 9.2592592 NA NA ## 96 2.7522936 NA NA ## 97 3.2608695 NA NA ## 98 6.2500000 NA NA ## 99 5.7142859 NA NA ## 100 15.6250000 NA NA ## 101 7.9365077 NA NA ## 102 4.7619047 NA NA ## 103 6.0000000 NA NA ## 104 3.9473684 NA NA ## 105 4.0000000 NA NA ## 106 4.3478260 NA NA ## 107 17.2413788 NA NA ## 108 15.3846149 NA NA ## 109 37.5000000 NA NA ## 110 20.8333340 NA NA ## 111 15.2542372 NA NA ## 112 10.0000000 NA NA ## 113 12.1951218 NA NA ## 114 7.0175438 NA NA ## 115 12.5000000 NA NA ## 116 6.8181820 NA NA ## 117 0.0000000 NA NA ## 118 7.1428571 NA NA ## 119 18.1818180 NA NA ## 120 14.4578314 NA NA ## 121 10.0000000 NA NA ## 122 10.0000000 NA NA ## 123 7.6923075 NA NA ## 124 10.5263157 NA NA ## 125 0.0000000 NA NA ## 126 5.2631578 NA NA ## 127 0.0000000 NA NA ## 128 7.5000000 NA NA ## 129 5.4054055 NA NA ## 130 5.5555553 NA NA ## 131 0.0000000 NA NA ## 132 16.6666660 NA NA ## 133 11.1111107 NA NA ## 134 2.9411764 NA NA ## 135 0.0000000 NA NA ## 136 0.0000000 NA NA ## 137 0.0000000 NA NA ## 138 3.5087719 NA NA ## 139 0.5076142 NA NA ## 140 11.1111107 NA NA ## 141 18.7500000 NA NA ## 142 16.6666660 NA NA ## 143 4.0000000 NA NA ## 144 0.0000000 NA NA ## 145 11.1111107 NA NA ## 146 0.0000000 NA NA ## 147 NA NA NA ## 148 0.0000000 NA NA ## 149 14.2857141 NA NA ## 150 12.1621618 NA NA ## 151 5.8823528 NA NA ## 152 22.2222214 NA NA ## 153 10.0000000 NA NA ## 154 2.5641026 NA NA ## 155 2.7397261 NA NA ## 156 6.6666665 NA NA ## 157 0.0000000 NA NA ## 158 8.3333330 NA NA ## 159 NA NA NA ## 160 0.0000000 NA NA ## 161 0.0000000 NA NA ## 162 55.5555573 NA NA ## 163 0.0000000 NA NA ## 164 0.0000000 NA NA ## 165 16.6666660 NA NA ## 166 0.0000000 NA NA ## 167 4.0000000 NA NA ## 168 33.3333321 NA NA ## 169 14.2857141 NA NA ## 170 7.5471697 NA NA ## 171 0.0000000 NA NA ## 172 7.7586207 NA NA ## 173 10.8108110 NA NA ## 174 20.0000000 NA NA ## 175 20.0000000 NA NA ## 176 16.0000000 NA NA ## 177 12.1212120 NA NA ## 178 14.2857141 NA NA ## 179 11.7647057 NA NA ## 180 33.3333321 NA NA ## 181 NA NA NA ## 182 33.3333321 NA NA ## 183 9.5238094 NA NA ## 184 15.1515150 NA NA ## 185 4.3956046 NA NA ## 186 9.0909090 NA NA ## 187 10.0000000 NA NA ## 188 12.5000000 NA NA ## 189 13.3333330 NA NA ## 190 4.9853373 NA NA ## 191 0.0000000 NA NA ## 192 3.3333333 NA NA ## 193 10.3448277 NA NA ## 194 6.2500000 NA NA ## 195 11.1111107 NA NA ## 196 13.5135136 NA NA ## 197 3.5714285 NA NA ## 198 7.4074073 NA NA ## 199 7.4074073 NA NA ## 200 8.9743586 NA NA ## 201 6.6666665 NA NA ## 202 7.3170733 NA NA ## 203 2.0000000 NA NA ## 204 8.0000000 NA NA ## 205 3.3018868 NA NA ## 206 9.6774197 NA NA ## 207 2.7777777 NA NA ## 208 4.4642859 NA NA ## 209 6.4864864 NA NA ## 210 8.2474222 NA NA ## 211 4.2253523 NA NA ## 212 5.8441558 NA NA ## 213 0.0000000 NA NA ## 214 5.5999999 NA NA ## 215 3.5398231 NA NA ## 216 23.0769234 NA NA ## 217 2.6785715 NA NA ## 218 5.1546392 NA NA ## 219 6.4516129 NA NA ## 220 3.9215686 NA NA ## 221 5.8823528 NA NA ## 222 6.4935064 NA NA ## 223 2.5000000 NA NA ## 224 12.5000000 NA NA ## 225 2.4896266 NA NA ## 226 2.7272727 NA NA ## 227 9.0909090 NA NA ## 228 4.0816326 NA NA ## 229 0.0000000 NA NA ## 230 5.5172415 NA NA ## 231 3.1914895 NA NA ## 232 5.2083335 NA NA ## 233 3.2967033 NA NA ## 234 2.1276596 NA NA ## 235 5.2631578 NA NA ## 236 6.6666665 NA NA ## 237 7.6923075 NA NA ## 238 23.0769234 NA NA ## 239 0.0000000 NA NA ## 240 11.3636360 NA NA ## 241 13.6363640 NA NA ## 242 9.5238094 NA NA ## 243 10.8695650 NA NA ## 244 8.9552240 NA NA ## 245 5.3191490 NA NA ## 246 3.2258065 NA NA ## 247 0.0000000 NA NA ## 248 15.6250000 NA NA ## 249 8.9285717 NA NA ## 250 0.0000000 NA NA ## 251 6.4516129 NA NA ## 252 2.3255813 NA NA ## 253 0.0000000 NA NA ## 254 17.2413788 NA NA ## 255 0.0000000 NA NA ## 256 5.0000000 NA NA ## 257 6.0606060 NA NA ## 258 9.3023252 NA NA ## 259 16.6666660 NA NA ## 260 NA NA NA ## 261 4.0000000 NA NA ## 262 0.0000000 NA NA ## 263 8.9285717 NA NA ## 264 0.0000000 NA NA ## 265 21.4285717 NA NA ## 266 30.0000000 NA NA ## 267 2.5641026 NA NA ## 268 0.0000000 NA NA ## 269 6.2500000 NA NA ## 270 12.5000000 NA NA ## 271 7.6923075 NA NA ## 272 11.1111107 NA NA ## 273 4.0000000 NA NA ## 274 8.3333330 NA NA ## 275 0.0000000 NA NA ## 276 7.6923075 NA NA ## 277 50.0000000 NA NA ## 278 0.0000000 NA NA ## 279 0.0000000 NA NA ## 280 2.9850745 NA NA ## 281 7.4074073 NA NA ## 282 0.0000000 NA NA ## 283 22.2222214 NA NA ## 284 0.0000000 NA NA ## 285 0.0000000 NA NA ## 286 1.4084507 NA NA ## 287 0.0000000 NA NA ## 288 0.0000000 NA NA ## 289 5.5555553 NA NA ## 290 21.4285717 NA NA ## 291 4.3478260 NA NA ## 292 6.2500000 NA NA ## 293 26.3157902 NA NA ## 294 NA NA NA ## 295 11.1111107 NA NA ## 296 15.7894735 NA NA ## 297 0.0000000 NA NA ## 298 18.5185184 NA NA ## 299 22.2222214 NA NA ## 300 4.4444447 NA NA ## 301 0.0000000 NA NA ## 302 7.0967741 NA NA ## 303 10.8108110 NA NA ## 304 6.6666665 NA NA ## 305 4.5454545 NA NA ## 306 0.0000000 NA NA ## 307 0.0000000 NA NA ## 308 3.5714285 NA NA ## 309 0.0000000 NA NA ## 310 0.0000000 NA NA ## 311 54.5454559 NA NA ## 312 NA NA NA ## 313 0.0000000 NA NA ## 314 5.5555553 NA NA ## 315 17.8571434 NA NA ## 316 0.0000000 NA NA ## 317 7.8947368 NA NA ## 318 9.0909090 NA NA ## 319 16.6666660 NA NA ## 320 5.2631578 NA NA ## 321 6.1538463 NA NA ## 322 5.1948051 NA NA ## 323 6.0606060 NA NA ## 324 5.5555553 NA NA ## 325 0.0000000 NA NA ## 326 15.0000000 NA NA ## 327 6.8965516 NA NA ## 328 11.1111107 NA NA ## 329 11.1111107 NA NA ## 330 2.8985507 NA NA ## 331 4.7169809 NA NA ## 332 0.0000000 NA NA ## 333 7.1428571 NA NA ## 334 3.2967033 NA NA ## 335 0.0000000 NA NA ## 336 0.8849558 NA NA ## 337 6.8965516 NA NA ## 338 4.0000000 NA NA ## 339 6.8376069 NA NA ## 340 5.1587300 NA NA ## 341 5.3191490 NA NA ## 342 5.6818180 NA NA ## 343 4.6357617 NA NA ## 344 5.7142859 NA NA ## 345 2.7397261 NA NA ## 346 2.0202019 NA NA ## 347 16.6666660 NA NA ## 348 4.3859649 NA NA ## 349 7.8947368 NA NA ## 350 2.3255813 NA NA ## 351 12.5000000 NA NA ## 352 0.0000000 NA NA ## 353 3.2258065 NA NA ## 354 2.5974026 NA NA ## 355 6.5573769 NA NA ## 356 5.0209203 NA NA ## 357 15.5963306 NA NA ## 358 8.3333330 NA NA ## 359 2.3076923 NA NA ## 360 0.0000000 NA NA ## 361 5.0847459 NA NA ## 362 5.2083335 NA NA ## 363 2.1739130 NA NA ## 364 4.7058825 NA NA ## 365 3.1250000 NA NA ## 366 18.5185184 NA NA ## 367 8.0000000 NA NA ## 368 0.0000000 NA NA ## 369 7.1428571 NA NA ## 370 10.5263157 NA NA ## 371 7.5471697 NA NA ## 372 4.1666665 NA NA ## 373 1.9607843 NA NA ## 374 12.5000000 NA NA ## 375 11.4754095 NA NA ## 376 5.0505052 NA NA ## 377 0.0000000 NA NA ## 378 5.5555553 NA NA ## 379 16.6666660 NA NA ## 380 9.4594593 NA NA ## 381 15.0000000 NA NA ## 382 0.0000000 NA NA ## 383 0.0000000 NA NA ## 384 23.5294113 NA NA ## 385 14.2857141 NA NA ## 386 7.1428571 NA NA ## 387 6.6666665 NA NA ## 388 8.8888893 NA NA ## 389 7.8431373 NA NA ## 390 11.5384617 NA NA ## 391 6.2500000 NA NA ## 392 6.2500000 NA NA ## 393 5.3571429 NA NA ## 394 0.0000000 NA NA ## 395 14.2857141 NA NA ## 396 NA NA NA ## 397 3.5714285 NA NA ## 398 2.6717558 NA NA ## 399 1.7543859 NA NA ## 400 0.0000000 NA NA ## 401 2.3809524 NA NA ## 402 16.6666660 NA NA ## 403 7.6923075 NA NA ## 404 0.0000000 NA NA ## 405 NA NA NA ## 406 8.3333330 NA NA ## 407 13.0434780 NA NA ## 408 18.1818180 NA NA ## 409 0.0000000 NA NA ## 410 5.5555553 NA NA ## 411 3.4883721 NA NA ## 412 0.0000000 NA NA ## 413 15.3846149 NA NA ## 414 21.4285717 NA NA ## 415 0.0000000 NA NA ## 416 9.0909090 NA NA ## 417 3.3898306 NA NA ## 418 4.0000000 NA NA ## 419 5.8823528 NA NA ## 420 11.1111107 NA NA ## 421 4.1666665 NA NA ## 422 20.0000000 NA NA ## 423 0.0000000 NA NA ## 424 3.3333333 NA NA ## 425 28.5714283 NA NA ## 426 7.1428571 NA NA ## 427 15.3846149 NA NA ## 428 23.0769234 NA NA ## 429 0.0000000 NA NA ## 430 9.5238094 NA NA ## 431 20.0000000 NA NA ## 432 5.9701490 NA NA ## 433 0.0000000 NA NA ## 434 6.2500000 NA NA ## 435 6.6666665 NA NA ## 436 9.3750000 NA NA ## 437 4.7619047 NA NA ## 438 9.5238094 NA NA ## 439 7.6923075 NA NA ## 440 5.5555553 NA NA ## 441 7.6923075 NA NA ## 442 4.7058825 NA NA ## 443 14.2857141 NA NA ## 444 8.3333330 NA NA ## 445 NA NA NA ## 446 6.6666665 NA NA ## 447 11.5384617 NA NA ## 448 2.3809524 NA NA ## 449 10.5263157 NA NA ## 450 9.0909090 NA NA ## 451 16.6666660 NA NA ## 452 5.8666668 NA NA ## 453 2.3255813 NA NA ## 454 9.3750000 NA NA ## 455 8.4745760 NA NA ## 456 0.0000000 NA NA ## 457 9.0909090 NA NA ## 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1450 NA 2012 ## 1451 65.21739 2012 ## 1452 69.23077 2012 ## 1453 66.66666 2012 ## 1454 42.85714 2012 ## 1455 64.28571 2012 ## 1456 75.00000 2012 ## 1457 62.50000 2012 ## 1458 70.96774 2012 ## 1459 45.45454 2012 ## 1460 64.51613 2012 ## 1461 70.83334 2012 ## 1462 66.17647 2012 ## 1463 72.22222 2012 ## 1464 58.82353 2012 ## 1465 76.47059 2012 ## 1466 66.66666 2012 ## 1467 69.23077 2012 ## 1468 57.14286 2012 ## 1469 66.66666 2012 ## 1470 67.85714 2012 ## 1471 100.00000 2012 ## 1472 60.00000 2012 ## 1473 76.92308 2012 ## 1474 76.00000 2012 ## 1475 60.86956 2012 ## 1476 62.06897 2012 ## 1477 NA 2012 ## 1478 62.50000 2013 ## 1479 83.33334 2013 ## 1480 50.00000 2013 ## 1481 54.54546 2013 ## 1482 NA 2013 ## 1483 71.42857 2013 ## 1484 NA 2013 ## 1485 85.71429 2013 ## 1486 NA 2013 ## 1487 73.33334 2013 ## 1488 77.21519 2013 ## 1489 NA 2013 ## 1490 62.50000 2013 ## 1491 78.94736 2013 ## 1492 66.66666 2013 ## 1493 74.41860 2013 ## 1494 NA 2013 ## 1495 71.42857 2013 ## 1496 NA 2013 ## 1497 57.14286 2013 ## 1498 NA 2013 ## 1499 54.54546 2013 ## 1500 66.66666 2013 ## 1501 90.00000 2013 ## 1502 55.55556 2013 ## 1503 NA 2013 ## 1504 50.00000 2013 ## 1505 NA 2013 ## 1506 NA 2013 ## 1507 73.33334 2013 ## 1508 79.16666 2013 ## 1509 57.14286 2013 ## 1510 NA 2013 ## 1511 53.84615 2013 ## 1512 80.00000 2013 ## 1513 92.30769 2013 ## 1514 60.00000 2013 ## 1515 NA 2013 ## 1516 71.42857 2013 ## 1517 NA 2013 ## 1518 NA 2013 ## 1519 NA 2013 ## 1520 50.00000 2013 ## 1521 81.25000 2013 ## 1522 71.42857 2013 ## 1523 NA 2013 ## 1524 71.42857 2013 ## 1525 57.14286 2013 ## 1526 62.00000 2013 ## 1527 70.00000 2013 ## 1528 71.42857 2013 ## 1529 NA 2013 ## 1530 NA 2013 ## 1531 NA 2013 ## 1532 70.00000 2013 ## 1533 NA 2013 ## 1534 42.85714 2013 ## 1535 NA 2013 ## 1536 100.00000 2013 ## 1537 NA 2013 ## 1538 91.66666 2013 ## 1539 83.33334 2013 ## 1540 86.48649 2013 ## 1541 64.70588 2013 ## 1542 87.50000 2013 ## 1543 77.77778 2013 ## 1544 70.68965 2013 ## 1545 68.29269 2013 ## 1546 90.00000 2013 ## 1547 67.30769 2013 ## 1548 100.00000 2013 ## 1549 66.66666 2013 ## 1550 71.42857 2013 ## 1551 67.74194 2013 ## 1552 90.00000 2013 ## 1553 74.07407 2013 ## 1554 71.66666 2013 ## 1555 63.63636 2013 ## 1556 NA 2013 ## 1557 71.42857 2013 ## 1558 73.80952 2013 ## 1559 50.00000 2013 ## 1560 86.15385 2013 ## 1561 76.92308 2013 ## 1562 79.31035 2013 ## 1563 74.41860 2013 ## 1564 83.09859 2013 ## 1565 73.52941 2013 ## 1566 72.54902 2013 ## 1567 77.41936 2013 ## 1568 82.60870 2013 ## 1569 76.08696 2013 ## 1570 75.86207 2013 ## 1571 76.92308 2013 ## 1572 84.37500 2013 ## 1573 74.07407 2013 ## 1574 69.44444 2013 ## 1575 61.90476 2013 ## 1576 81.81818 2013 ## 1577 68.57143 2013 ## 1578 64.51613 2013 ## 1579 72.22222 2013 ## 1580 76.92308 2013 ## 1581 65.21739 2013 ## 1582 NA 2013 ## 1583 73.52941 2013 ## 1584 71.42857 2013 ## 1585 60.00000 2013 ## 1586 70.00000 2013 ## 1587 78.26087 2013 ## 1588 80.00000 2013 ## 1589 81.81818 2013 ## 1590 61.11111 2013 ## 1591 100.00000 2013 ## 1592 61.11111 2013 ## 1593 53.33333 2013 ## 1594 NA 2013 ## 1595 63.63636 2013 ## 1596 51.28205 2013 ## 1597 45.83333 2013 ## 1598 48.14815 2013 ## 1599 60.00000 2013 ## 1600 69.64286 2013 ## 1601 66.66666 2013 ## 1602 80.95238 2013 ## 1603 92.30769 2013 ## 1604 72.22222 2013 ## 1605 64.00000 2013 ## 1606 69.23077 2013 ## 1607 100.00000 2013 ## 1608 51.72414 2013 ## 1609 85.71429 2013 ## 1610 73.33334 2013 ## 1611 64.28571 2013 ## 1612 76.47059 2013 ## 1613 73.07692 2013 ## 1614 67.64706 2013 ## 1615 71.42857 2013 ## 1616 NA 2014 ## 1617 NA 2014 ## 1618 NA 2014 ## 1619 50.00000 2014 ## 1620 56.25000 2014 ## 1621 57.14286 2014 ## 1622 62.50000 2014 ## 1623 NA 2014 ## 1624 NA 2014 ## 1625 40.00000 2014 ## 1626 69.86301 2014 ## 1627 77.27273 2014 ## 1628 NA 2014 ## 1629 65.00000 2014 ## 1630 73.33334 2014 ## 1631 80.00000 2014 ## 1632 NA 2014 ## 1633 87.50000 2014 ## 1634 71.42857 2014 ## 1635 NA 2014 ## 1636 77.77778 2014 ## 1637 71.42857 2014 ## 1638 56.25000 2014 ## 1639 79.16666 2014 ## 1640 42.85714 2014 ## 1641 NA 2014 ## 1642 70.00000 2014 ## 1643 57.14286 2014 ## 1644 NA 2014 ## 1645 76.19048 2014 ## 1646 66.66666 2014 ## 1647 76.92308 2014 ## 1648 62.50000 2014 ## 1649 87.50000 2014 ## 1650 75.00000 2014 ## 1651 71.42857 2014 ## 1652 57.14286 2014 ## 1653 57.14286 2014 ## 1654 75.00000 2014 ## 1655 66.66666 2014 ## 1656 87.50000 2014 ## 1657 60.00000 2014 ## 1658 66.66666 2014 ## 1659 58.82353 2014 ## 1660 NA 2014 ## 1661 71.42857 2014 ## 1662 80.00000 2014 ## 1663 50.00000 2014 ## 1664 70.31250 2014 ## 1665 61.90476 2014 ## 1666 84.21053 2014 ## 1667 88.88889 2014 ## 1668 72.72727 2014 ## 1669 61.53846 2014 ## 1670 71.42857 2014 ## 1671 NA 2014 ## 1672 85.71429 2014 ## 1673 50.00000 2014 ## 1674 NA 2014 ## 1675 92.30769 2014 ## 1676 66.66666 2014 ## 1677 70.73170 2014 ## 1678 83.33334 2014 ## 1679 NA 2014 ## 1680 50.00000 2014 ## 1681 100.00000 2014 ## 1682 71.42857 2014 ## 1683 70.45454 2014 ## 1684 85.71429 2014 ## 1685 61.76471 2014 ## 1686 33.33333 2014 ## 1687 66.66666 2014 ## 1688 84.37500 2014 ## 1689 58.53659 2014 ## 1690 100.00000 2014 ## 1691 70.45454 2014 ## 1692 81.25000 2014 ## 1693 64.70588 2014 ## 1694 66.66666 2014 ## 1695 47.82609 2014 ## 1696 73.33334 2014 ## 1697 75.00000 2014 ## 1698 74.60317 2014 ## 1699 90.00000 2014 ## 1700 58.82353 2014 ## 1701 66.66666 2014 ## 1702 77.17391 2014 ## 1703 64.28571 2014 ## 1704 66.66666 2014 ## 1705 73.07692 2014 ## 1706 75.00000 2014 ## 1707 58.53659 2014 ## 1708 93.10345 2014 ## 1709 83.33334 2014 ## 1710 71.92982 2014 ## 1711 81.03448 2014 ## 1712 59.37500 2014 ## 1713 86.36364 2014 ## 1714 30.00000 2014 ## 1715 72.09303 2014 ## 1716 75.00000 2014 ## 1717 73.91304 2014 ## 1718 78.35052 2014 ## 1719 67.50000 2014 ## 1720 NA 2014 ## 1721 64.51613 2014 ## 1722 63.63636 2014 ## 1723 82.14286 2014 ## 1724 73.68421 2014 ## 1725 87.50000 2014 ## 1726 62.06897 2014 ## 1727 66.66666 2014 ## 1728 69.23077 2014 ## 1729 NA 2014 ## 1730 47.05882 2014 ## 1731 47.61905 2014 ## 1732 NA 2014 ## 1733 80.00000 2014 ## 1734 69.23077 2014 ## 1735 64.28571 2014 ## 1736 59.09091 2014 ## 1737 65.00000 2014 ## 1738 58.33333 2014 ## 1739 85.29412 2014 ## 1740 72.72727 2014 ## 1741 68.42105 2014 ## 1742 75.00000 2014 ## 1743 62.96296 2014 ## 1744 NA 2014 ## 1745 50.00000 2014 ## 1746 59.52381 2014 ## 1747 66.66666 2014 ## 1748 66.66666 2014 ## 1749 68.42105 2014 ## 1750 58.62069 2014 ## 1751 55.55556 2014 ## 1752 68.42105 2014 ## 1753 25.00000 2014 ## 1754 NA 2015 ## 1755 NA 2015 ## 1756 NA 2015 ## 1757 66.66666 2015 ## 1758 NA 2015 ## 1759 88.88889 2015 ## 1760 NA 2015 ## 1761 80.00000 2015 ## 1762 79.10448 2015 ## 1763 69.23077 2015 ## 1764 NA 2015 ## 1765 66.66666 2015 ## 1766 77.77778 2015 ## 1767 68.75000 2015 ## 1768 NA 2015 ## 1769 62.50000 2015 ## 1770 61.53846 2015 ## 1771 87.50000 2015 ## 1772 57.14286 2015 ## 1773 71.42857 2015 ## 1774 80.00000 2015 ## 1775 66.66666 2015 ## 1776 69.23077 2015 ## 1777 NA 2015 ## 1778 75.00000 2015 ## 1779 NA 2015 ## 1780 NA 2015 ## 1781 69.23077 2015 ## 1782 70.00000 2015 ## 1783 NA 2015 ## 1784 85.71429 2015 ## 1785 100.00000 2015 ## 1786 56.25000 2015 ## 1787 72.72727 2015 ## 1788 84.61539 2015 ## 1789 75.00000 2015 ## 1790 72.72727 2015 ## 1791 87.50000 2015 ## 1792 83.33334 2015 ## 1793 75.00000 2015 ## 1794 87.50000 2015 ## 1795 77.77778 2015 ## 1796 NA 2015 ## 1797 NA 2015 ## 1798 89.47369 2015 ## 1799 66.66666 2015 ## 1800 77.08334 2015 ## 1801 63.33333 2015 ## 1802 61.53846 2015 ## 1803 88.88889 2015 ## 1804 NA 2015 ## 1805 50.00000 2015 ## 1806 42.85714 2015 ## 1807 57.14286 2015 ## 1808 75.00000 2015 ## 1809 NA 2015 ## 1810 71.42857 2015 ## 1811 66.66666 2015 ## 1812 58.33333 2015 ## 1813 62.50000 2015 ## 1814 80.00000 2015 ## 1815 94.44444 2015 ## 1816 NA 2015 ## 1817 NA 2015 ## 1818 90.00000 2015 ## 1819 67.69231 2015 ## 1820 62.74510 2015 ## 1821 81.25000 2015 ## 1822 59.18367 2015 ## 1823 66.66666 2015 ## 1824 68.75000 2015 ## 1825 64.51613 2015 ## 1826 78.84615 2015 ## 1827 NA 2015 ## 1828 73.52941 2015 ## 1829 68.88889 2015 ## 1830 75.00000 2015 ## 1831 NA 2015 ## 1832 60.00000 2015 ## 1833 80.55556 2015 ## 1834 77.77778 2015 ## 1835 74.28571 2015 ## 1836 78.57143 2015 ## 1837 80.00000 2015 ## 1838 78.12500 2015 ## 1839 73.33334 2015 ## 1840 64.86487 2015 ## 1841 70.31250 2015 ## 1842 86.36364 2015 ## 1843 77.27273 2015 ## 1844 70.58823 2015 ## 1845 82.75862 2015 ## 1846 76.92308 2015 ## 1847 81.39535 2015 ## 1848 77.77778 2015 ## 1849 82.14286 2015 ## 1850 71.42857 2015 ## 1851 83.33334 2015 ## 1852 70.96774 2015 ## 1853 78.12500 2015 ## 1854 84.21053 2015 ## 1855 76.36364 2015 ## 1856 73.68421 2015 ## 1857 50.00000 2015 ## 1858 80.00000 2015 ## 1859 87.50000 2015 ## 1860 77.77778 2015 ## 1861 83.33334 2015 ## 1862 69.56522 2015 ## 1863 86.66666 2015 ## 1864 40.00000 2015 ## 1865 58.33333 2015 ## 1866 71.42857 2015 ## 1867 57.14286 2015 ## 1868 83.33334 2015 ## 1869 NA 2015 ## 1870 69.23077 2015 ## 1871 78.26087 2015 ## 1872 52.94118 2015 ## 1873 58.33333 2015 ## 1874 67.56757 2015 ## 1875 55.55556 2015 ## 1876 70.00000 2015 ## 1877 66.66666 2015 ## 1878 70.37037 2015 ## 1879 64.28571 2015 ## 1880 76.19048 2015 ## 1881 85.71429 2015 ## 1882 50.00000 2015 ## 1883 70.83334 2015 ## 1884 NA 2015 ## 1885 76.47059 2015 ## 1886 66.66666 2015 ## 1887 68.96552 2015 ## 1888 81.81818 2015 ## 1889 43.47826 2015 ## 1890 66.66666 2015 ## 1891 83.33334 2016 ## 1892 72.72727 2016 ## 1893 NA 2016 ## 1894 60.86956 2016 ## 1895 77.77778 2016 ## 1896 50.00000 2016 ## 1897 71.42857 2016 ## 1898 NA 2016 ## 1899 53.84615 2016 ## 1900 80.28169 2016 ## 1901 79.31035 2016 ## 1902 62.50000 2016 ## 1903 NA 2016 ## 1904 68.18182 2016 ## 1905 NA 2016 ## 1906 66.66666 2016 ## 1907 66.66666 2016 ## 1908 NA 2016 ## 1909 33.33333 2016 ## 1910 42.85714 2016 ## 1911 80.95238 2016 ## 1912 60.86956 2016 ## 1913 NA 2016 ## 1914 NA 2016 ## 1915 50.00000 2016 ## 1916 NA 2016 ## 1917 76.92308 2016 ## 1918 58.33333 2016 ## 1919 NA 2016 ## 1920 37.50000 2016 ## 1921 60.00000 2016 ## 1922 77.77778 2016 ## 1923 50.00000 2016 ## 1924 72.00000 2016 ## 1925 83.33334 2016 ## 1926 36.36364 2016 ## 1927 NA 2016 ## 1928 NA 2016 ## 1929 50.00000 2016 ## 1930 0.00000 2016 ## 1931 40.00000 2016 ## 1932 NA 2016 ## 1933 NA 2016 ## 1934 66.66666 2016 ## 1935 50.00000 2016 ## 1936 75.38461 2016 ## 1937 73.33334 2016 ## 1938 61.53846 2016 ## 1939 84.61539 2016 ## 1940 50.00000 2016 ## 1941 75.00000 2016 ## 1942 55.55556 2016 ## 1943 NA 2016 ## 1944 66.66666 2016 ## 1945 NA 2016 ## 1946 75.00000 2016 ## 1947 71.42857 2016 ## 1948 100.00000 2016 ## 1949 83.33334 2016 ## 1950 85.71429 2016 ## 1951 84.61539 2016 ## 1952 NA 2016 ## 1953 NA 2016 ## 1954 NA 2016 ## 1955 72.13115 2016 ## 1956 75.92593 2016 ## 1957 100.00000 2016 ## 1958 63.63636 2016 ## 1959 85.71429 2016 ## 1960 47.36842 2016 ## 1961 70.00000 2016 ## 1962 75.55556 2016 ## 1963 66.66666 2016 ## 1964 70.58823 2016 ## 1965 70.00000 2016 ## 1966 44.44444 2016 ## 1967 33.33333 2016 ## 1968 70.37037 2016 ## 1969 58.33333 2016 ## 1970 75.00000 2016 ## 1971 79.77528 2016 ## 1972 61.53846 2016 ## 1973 70.96774 2016 ## 1974 51.51515 2016 ## 1975 80.00000 2016 ## 1976 82.35294 2016 ## 1977 73.68421 2016 ## 1978 NA 2016 ## 1979 42.10526 2016 ## 1980 72.09303 2016 ## 1981 72.97298 2016 ## 1982 75.00000 2016 ## 1983 65.00000 2016 ## 1984 82.00000 2016 ## 1985 68.42105 2016 ## 1986 73.33334 2016 ## 1987 87.50000 2016 ## 1988 63.63636 2016 ## 1989 83.33334 2016 ## 1990 70.58823 2016 ## 1991 72.97298 2016 ## 1992 77.58620 2016 ## 1993 66.66666 2016 ## 1994 58.82353 2016 ## 1995 71.42857 2016 ## 1996 75.86207 2016 ## 1997 74.41860 2016 ## 1998 69.56522 2016 ## 1999 68.18182 2016 ## 2000 76.19048 2016 ## 2001 44.44444 2016 ## 2002 NA 2016 ## 2003 NA 2016 ## 2004 66.66666 2016 ## 2005 75.00000 2016 ## 2006 66.66666 2016 ## 2007 57.89474 2016 ## 2008 55.55556 2016 ## 2009 62.06897 2016 ## 2010 58.18182 2016 ## 2011 66.66666 2016 ## 2012 78.04878 2016 ## 2013 42.85714 2016 ## 2014 61.11111 2016 ## 2015 75.00000 2016 ## 2016 62.96296 2016 ## 2017 50.00000 2016 ## 2018 72.72727 2016 ## 2019 76.19048 2016 ## 2020 71.42857 2016 ## 2021 52.94118 2016 ## 2022 50.00000 2016 ## 2023 56.75676 2016 ## 2024 47.05882 2016 ## 2025 73.68421 2016 ## 2026 60.00000 2016 ## 2027 76.92308 2017 ## 2028 NA 2017 ## 2029 NA 2017 ## 2030 35.71429 2017 ## 2031 NA 2017 ## 2032 71.42857 2017 ## 2033 NA 2017 ## 2034 64.70588 2017 ## 2035 71.18644 2017 ## 2036 57.14286 2017 ## 2037 66.66666 2017 ## 2038 NA 2017 ## 2039 43.24324 2017 ## 2040 NA 2017 ## 2041 57.14286 2017 ## 2042 66.66666 2017 ## 2043 62.50000 2017 ## 2044 80.00000 2017 ## 2045 60.86956 2017 ## 2046 53.84615 2017 ## 2047 60.00000 2017 ## 2048 42.85714 2017 ## 2049 50.00000 2017 ## 2050 75.00000 2017 ## 2051 66.66666 2017 ## 2052 NA 2017 ## 2053 NA 2017 ## 2054 53.84615 2017 ## 2055 84.61539 2017 ## 2056 66.66666 2017 ## 2057 61.11111 2017 ## 2058 83.33334 2017 ## 2059 NA 2017 ## 2060 NA 2017 ## 2061 33.33333 2017 ## 2062 42.85714 2017 ## 2063 70.00000 2017 ## 2064 57.14286 2017 ## 2065 87.50000 2017 ## 2066 55.26316 2017 ## 2067 61.53846 2017 ## 2068 62.50000 2017 ## 2069 NA 2017 ## 2070 NA 2017 ## 2071 100.00000 2017 ## 2072 NA 2017 ## 2073 NA 2017 ## 2074 64.70588 2017 ## 2075 83.33334 2017 ## 2076 NA 2017 ## 2077 69.23077 2017 ## 2078 53.33333 2017 ## 2079 73.91304 2017 ## 2080 70.00000 2017 ## 2081 100.00000 2017 ## 2082 74.40000 2017 ## 2083 81.39535 2017 ## 2084 81.81818 2017 ## 2085 54.05405 2017 ## 2086 NA 2017 ## 2087 70.83334 2017 ## 2088 70.96774 2017 ## 2089 72.34042 2017 ## 2090 NA 2017 ## 2091 70.00000 2017 ## 2092 73.07692 2017 ## 2093 44.44444 2017 ## 2094 NA 2017 ## 2095 73.07692 2017 ## 2096 78.78788 2017 ## 2097 60.86956 2017 ## 2098 78.46154 2017 ## 2099 60.00000 2017 ## 2100 66.66666 2017 ## 2101 71.42857 2017 ## 2102 67.85714 2017 ## 2103 60.00000 2017 ## 2104 75.00000 2017 ## 2105 100.00000 2017 ## 2106 40.00000 2017 ## 2107 70.58823 2017 ## 2108 97.14286 2017 ## 2109 68.75000 2017 ## 2110 88.00000 2017 ## 2111 64.86487 2017 ## 2112 83.33334 2017 ## 2113 77.27273 2017 ## 2114 NA 2017 ## 2115 82.60870 2017 ## 2116 60.00000 2017 ## 2117 73.33334 2017 ## 2118 75.60976 2017 ## 2119 68.42105 2017 ## 2120 NA 2017 ## 2121 91.89189 2017 ## 2122 75.00000 2017 ## 2123 56.00000 2017 ## 2124 72.22222 2017 ## 2125 84.61539 2017 ## 2126 50.00000 2017 ## 2127 66.66666 2017 ## 2128 68.42105 2017 ## 2129 NA 2017 ## 2130 NA 2017 ## 2131 70.00000 2017 ## 2132 80.00000 2017 ## 2133 72.72727 2017 ## 2134 60.52632 2017 ## 2135 21.42857 2017 ## 2136 67.85714 2017 ## 2137 66.66666 2017 ## 2138 47.54099 2017 ## 2139 57.14286 2017 ## 2140 79.16666 2017 ## 2141 63.15789 2017 ## 2142 77.27273 2017 ## 2143 70.58823 2017 ## 2144 55.55556 2017 ## 2145 52.63158 2017 ## 2146 88.88889 2017 ## 2147 62.50000 2017 ## 2148 40.00000 2017 ## 2149 69.23077 2017 ## 2150 69.69697 2017 ## 2151 75.86207 2017 ## 2152 85.71429 2017 ## 2153 66.66666 2017 Next, we need to check the completeness of the data. Ideally, we would use the most recent year of data for our clustering analysis but this may not be possible if variables are highly missing. The following function calculates the proportion of missing data in each year for each variable. get_frac_missing <- function(df) { df %>% nest() %>% mutate(missing = map(data,~map_dfr(. ,~sum(is.na(.))/length(.)))) %>% dplyr::select(-data) %>% unnest(missing) } ## Get the proportion missing per variableby year tb_miss_per_year <- com_tb_df %>% group_by(year) %>% get_frac_missing %>% mutate_all(~round(., 2)) %>% arrange(year) tb_miss_per_year %>% mutate(year = as.character(year)) %>% t %>% kable  year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 AreaName 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 rec_inc_rate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 prop_pul_cc 0.13 0.08 0.08 0.08 0.09 0.1 0.12 0.08 0.12 0.12 0.11 0.09 0.09 0.12 0.14 0.13 prop_cc_ds_front 0.17 0.12 0.11 0.05 0.08 0.13 0.09 0.08 0.11 0.11 0.07 0.08 0.08 0.13 0.13 0.14 prop_ds_treat_com_12 0.02 0.02 0.02 0.03 0.04 0.06 0.01 0.01 0.03 0.04 0.02 0.02 0.03 0.06 0.08 1 prop_ds_lost_to_follow 0.02 0.02 0.02 0.02 0.02 0.03 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.04 1 prop_ds_died 0.02 0.02 0.02 0.02 0.02 0.03 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.04 1 prop_tb_offered_hiv_test 1 1 1 1 1 1 1 1 1 1 0.23 0.09 0.04 0.07 0.09 0.06 prop_ds_rf_treat_com_12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 prop_cc_dr_front 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 prop_p_start_treat_2_m_sym 1 1 1 1 1 1 1 1 1 0.23 0.18 0.18 0.13 0.15 0.15 0.17 prop_p_start_treat_4_m_sym 1 1 1 1 1 1 1 1 1 0.23 0.18 0.18 0.13 0.15 0.15 0.17 We see that data completeness increases with time but that some variables are completely missing (e.g. prop_ds_rf_treat_com_12 and prop_cc_dr_front). We therefore drop these variables and then identify which year has the lowest amount of missing data across all remaining variables (by looking at mean missingness). ## Drop full missing variables tb_partial_miss_year <- tb_miss_per_year %>% select_if(~!sum(.) == length(.)) ## Full missing variables com_miss_vars <- setdiff(names(tb_miss_per_year), names(tb_partial_miss_year)) ## Which year has the most complete data tb_complete_years_all_vars <- com_tb_df %>% group_by(year) %>% nest() %>% mutate(missing = map(data,~mean(colSums(is.na(.))/nrow(.)))) %>% dplyr::select(-data) %>% unnest(missing) %>% mutate(missing = round(missing, 2)) %>% arrange(year) kable(tb_complete_years_all_vars) year missing 2002 0.45 2003 0.44 2004 0.44 2005 0.43 2006 0.44 2007 0.45 2008 0.44 2009 0.43 2010 0.44 2011 0.31 2012 0.24 2013 0.22 2014 0.21 2015 0.23 2016 0.24 2017 0.47 The above table indicates that 2016 has a high proportion of missing data. From the previous table we saw that this was partially due to some variables being completely missing. The next best option is 2015 - this has a slightly higher proportion of missing data than previous years but has no variables that are completely missing and is the most relevant after 2016. The final question is to what extent missingness is still related to TB incidence rates. The following table investigates this by looking at what happens as counties are excluded using varying incidence rate cut-offs. com_tb_df %>% filter(year == 2015) %>% dplyr::select(-map_dbl(com_miss_vars, contains)) %>% mutate(inc_rate_lower = list(seq(2, 20, 2))) %>% unnest(inc_rate_lower) %>% group_by(year, inc_rate_lower) %>% filter(rec_inc_rate > inc_rate_lower) %>% nest() %>% mutate(missing = map(data,~mean(colSums(is.na(.))/nrow(.)))) %>% dplyr::select(-data, -year) %>% unnest(missing) %>% kable inc_rate_lower missing 2 0.0750000 4 0.0598291 6 0.0247312 8 0.0183099 10 0.0000000 12 0.0000000 14 0.0000000 16 0.0000000 18 0.0000000 20 0.0000000 The choice of incidence rate cut-off is somewhat arbitrary. However, it appears that a cut-off of at least 10 (per 100,000) is sufficient to deal with the majority of missing data. This is also a sensible cut-off as it represents the World Health Organisations 2035 target for TB eradication (source). This means that our analysis will focus on counties that have relatively high incidence rates in comparison to the median in England. Using everything we have learnt about the quality of the indicator data we now identify the near final analysis dataset. tb_df_2015 <- com_tb_df %>% dplyr::select(-map_dbl(com_miss_vars, contains)) %>% filter(year == 2015) %>% filter(rec_inc_rate > 10) tb_df_2015  ## AreaName rec_inc_rate prop_pul_cc prop_cc_ds_front ## 1 Blackburn with Darwen 28.89162 71.42857 100.00000 ## 2 Derby 13.99340 72.72727 100.00000 ## 3 Leicester 41.78308 81.42857 97.59036 ## 4 Nottingham 17.05548 82.14286 97.14286 ## 5 Stoke-on-Trent 12.08666 66.66666 100.00000 ## 6 Bristol 20.55796 54.00000 95.12195 ## 7 Swindon 10.80909 57.14286 100.00000 ## 8 Peterborough 23.23059 61.53846 100.00000 ## 9 Luton 30.26184 80.00000 100.00000 ## 10 Reading 34.74895 64.28571 95.00000 ## 11 Slough 47.80048 80.00000 100.00000 ## 12 Wokingham 10.02760 62.50000 100.00000 ## 13 Milton Keynes 10.00280 80.00000 100.00000 ## 14 Southampton 12.49969 76.92308 100.00000 ## 15 Bedford 13.41660 66.66666 100.00000 ## 16 Bolton 18.62646 84.21053 100.00000 ## 17 Bury 10.32868 75.00000 100.00000 ## 18 Manchester 27.07533 76.78571 100.00000 ## 19 Oldham 21.88679 85.71429 100.00000 ## 20 Rochdale 13.76232 68.75000 100.00000 ## 21 Salford 12.13125 41.66667 94.11765 ## 22 Trafford 11.35971 77.77778 100.00000 ## 23 Sheffield 14.59087 59.09091 95.00000 ## 24 Newcastle upon Tyne 14.22089 76.19048 100.00000 ## 25 Birmingham 28.97182 72.79412 95.27027 ## 26 Coventry 27.68601 53.70370 95.00000 ## 27 Dudley 10.03866 70.58823 81.25000 ## 28 Sandwell 30.96791 70.68965 98.27586 ## 29 Walsall 14.72435 76.47059 100.00000 ## 30 Wolverhampton 26.44514 67.74194 97.14286 ## 31 Bradford 22.23090 66.66666 100.00000 ## 32 Kirklees 17.35514 61.11111 97.05882 ## 33 Leeds 12.91564 81.63265 96.61017 ## 34 Barking and Dagenham 30.18478 85.00000 100.00000 ## 35 Barnet 19.62842 83.78378 100.00000 ## 36 Brent 67.76619 75.00000 96.26168 ## 37 Camden 17.59708 85.00000 100.00000 ## 38 Croydon 24.58500 80.55556 100.00000 ## 39 Ealing 56.63548 65.15151 97.50000 ## 40 Enfield 21.16154 69.23077 100.00000 ## 41 Greenwich 36.40903 69.23077 100.00000 ## 42 Hackney 28.00396 88.46154 100.00000 ## 43 Hammersmith and Fulham 22.74049 79.16666 100.00000 ## 44 Haringey 28.47488 71.05264 100.00000 ## 45 Harrow 46.94063 87.50000 95.16129 ## 46 Havering 10.16884 85.71429 100.00000 ## 47 Hillingdon 36.75208 73.91304 97.91666 ## 48 Hounslow 53.68080 73.33334 98.41270 ## 49 Islington 25.57287 93.33334 94.87180 ## 50 Kensington and Chelsea 19.42732 66.66666 100.00000 ## 51 Kingston upon Thames 14.23772 100.00000 100.00000 ## 52 Lambeth 22.62984 60.00000 100.00000 ## 53 Lewisham 23.21201 74.28571 97.50000 ## 54 Merton 25.72452 80.00000 95.83334 ## 55 Newham 84.60262 67.52137 99.34641 ## 56 Redbridge 44.62709 80.00000 98.48485 ## 57 Southwark 27.14539 76.92308 100.00000 ## 58 Sutton 11.96070 100.00000 100.00000 ## 59 Tower Hamlets 32.27548 77.77778 100.00000 ## 60 Waltham Forest 37.80521 85.10638 98.43750 ## 61 Wandsworth 18.33308 69.23077 100.00000 ## 62 Westminster 21.34866 73.68421 100.00000 ## prop_ds_treat_com_12 prop_ds_lost_to_follow prop_ds_died ## 1 87.50000 8.8235292 0.000000 ## 2 71.87500 2.8571429 22.857143 ## 3 77.66991 7.1999998 3.200000 ## 4 80.43478 3.7735848 3.773585 ## 5 80.76923 7.1428571 10.714286 ## 6 78.68852 2.5974026 2.597403 ## 7 75.00000 14.2857141 0.000000 ## 8 75.86207 13.3333330 3.333333 ## 9 82.35294 7.0175438 5.263158 ## 10 96.96970 0.0000000 0.000000 ## 11 93.84615 1.4285715 1.428571 ## 12 75.00000 6.2500000 6.250000 ## 13 88.88889 8.6956520 4.347826 ## 14 89.47369 4.3478260 0.000000 ## 15 93.33334 0.0000000 6.250000 ## 16 90.62500 0.0000000 4.651163 ## 17 75.00000 5.8823528 17.647058 ## 18 87.37864 5.8823528 7.563025 ## 19 80.39216 1.8518518 12.962963 ## 20 88.88889 0.0000000 3.846154 ## 21 88.46154 3.1250000 12.500000 ## 22 80.00000 0.0000000 14.285714 ## 23 90.16393 7.4626865 1.492537 ## 24 81.81818 4.3478260 10.869565 ## 25 83.78378 2.0325203 6.097561 ## 26 86.58537 4.5454545 5.681818 ## 27 89.28571 0.0000000 6.250000 ## 28 77.64706 9.3750000 6.250000 ## 29 96.55173 3.0303030 3.030303 ## 30 82.00000 1.8181819 10.909091 ## 31 82.41758 5.0000000 8.000000 ## 32 92.72727 1.5625000 1.562500 ## 33 89.61039 2.2727273 4.545455 ## 34 90.00000 2.4390244 2.439024 ## 35 93.65079 2.7027028 2.702703 ## 36 89.26174 4.2424240 4.242424 ## 37 78.12500 2.8571429 5.714286 ## 38 82.66666 1.1235955 4.494382 ## 39 89.06250 1.8987342 4.430380 ## 40 87.50000 0.0000000 5.797101 ## 41 87.50000 3.2967033 4.395605 ## 42 92.45283 0.0000000 5.084746 ## 43 94.28571 2.5000000 7.500000 ## 44 83.01887 3.1250000 7.812500 ## 45 90.54054 2.4390244 4.878049 ## 46 90.00000 4.3478260 4.347826 ## 47 83.13253 7.2164950 2.061856 ## 48 85.29412 0.9009009 5.405406 ## 49 82.92683 10.4166670 6.250000 ## 50 75.00000 14.2857141 9.523809 ## 51 88.23529 5.2631578 5.263158 ## 52 91.66666 1.6393442 6.557377 ## 53 91.52542 0.0000000 3.174603 ## 54 81.81818 9.8039217 0.000000 ## 55 81.25000 5.6680160 3.238866 ## 56 87.12872 2.6548672 1.769912 ## 57 95.31250 5.1282053 2.564103 ## 58 100.00000 0.0000000 4.545455 ## 59 84.21053 8.6419754 0.000000 ## 60 85.71429 7.0707068 5.050505 ## 61 86.53846 4.7619047 4.761905 ## 62 75.75758 8.1081085 2.702703 ## prop_tb_offered_hiv_test prop_p_start_treat_2_m_sym ## 1 97.05882 33.33333 ## 2 96.77419 45.00000 ## 3 93.85965 47.76119 ## 4 90.69768 42.30769 ## 5 100.00000 46.66667 ## 6 89.83051 43.75000 ## 7 94.44444 50.00000 ## 8 86.95652 70.00000 ## 9 100.00000 36.66667 ## 10 100.00000 53.84615 ## 11 98.52941 40.00000 ## 12 93.75000 14.28571 ## 13 95.00000 77.77778 ## 14 95.23810 46.15385 ## 15 100.00000 22.22222 ## 16 92.10526 68.42105 ## 17 100.00000 50.00000 ## 18 100.00000 39.58333 ## 19 100.00000 30.00000 ## 20 100.00000 53.84615 ## 21 100.00000 44.44444 ## 22 100.00000 28.57143 ## 23 100.00000 60.00000 ## 24 88.09524 83.33334 ## 25 96.18644 41.53846 ## 26 95.18073 29.41176 ## 27 90.32258 31.25000 ## 28 94.93671 38.77551 ## 29 93.10345 56.25000 ## 30 97.95918 32.25806 ## 31 94.56522 42.30769 ## 32 90.16393 52.94118 ## 33 92.50000 35.55556 ## 34 100.00000 20.00000 ## 35 98.61111 52.77778 ## 36 98.77300 52.85714 ## 37 97.05882 55.00000 ## 38 100.00000 34.37500 ## 39 99.34210 50.00000 ## 40 100.00000 27.02703 ## 41 100.00000 42.18750 ## 42 98.14815 68.18182 ## 43 100.00000 45.45454 ## 44 100.00000 41.17647 ## 45 100.00000 55.17241 ## 46 100.00000 38.46154 ## 47 100.00000 37.20930 ## 48 100.00000 42.22222 ## 49 100.00000 46.42857 ## 50 94.44444 50.00000 ## 51 100.00000 16.66667 ## 52 100.00000 51.61290 ## 53 98.33334 43.75000 ## 54 93.75000 52.63158 ## 55 99.17355 42.72727 ## 56 100.00000 47.36842 ## 57 100.00000 57.14286 ## 58 100.00000 50.00000 ## 59 96.29630 55.55556 ## 60 97.80220 54.76191 ## 61 100.00000 39.13044 ## 62 96.66666 53.33333 ## prop_p_start_treat_4_m_sym year ## 1 66.66666 2015 ## 2 80.00000 2015 ## 3 79.10448 2015 ## 4 69.23077 2015 ## 5 66.66666 2015 ## 6 68.75000 2015 ## 7 71.42857 2015 ## 8 80.00000 2015 ## 9 66.66666 2015 ## 10 69.23077 2015 ## 11 70.00000 2015 ## 12 85.71429 2015 ## 13 100.00000 2015 ## 14 84.61539 2015 ## 15 77.77778 2015 ## 16 89.47369 2015 ## 17 66.66666 2015 ## 18 77.08334 2015 ## 19 63.33333 2015 ## 20 61.53846 2015 ## 21 88.88889 2015 ## 22 42.85714 2015 ## 23 80.00000 2015 ## 24 94.44444 2015 ## 25 67.69231 2015 ## 26 62.74510 2015 ## 27 81.25000 2015 ## 28 59.18367 2015 ## 29 68.75000 2015 ## 30 64.51613 2015 ## 31 78.84615 2015 ## 32 73.52941 2015 ## 33 68.88889 2015 ## 34 60.00000 2015 ## 35 80.55556 2015 ## 36 74.28571 2015 ## 37 80.00000 2015 ## 38 78.12500 2015 ## 39 73.33334 2015 ## 40 64.86487 2015 ## 41 70.31250 2015 ## 42 86.36364 2015 ## 43 77.27273 2015 ## 44 70.58823 2015 ## 45 82.75862 2015 ## 46 76.92308 2015 ## 47 81.39535 2015 ## 48 77.77778 2015 ## 49 82.14286 2015 ## 50 71.42857 2015 ## 51 83.33334 2015 ## 52 70.96774 2015 ## 53 78.12500 2015 ## 54 84.21053 2015 ## 55 76.36364 2015 ## 56 73.68421 2015 ## 57 80.00000 2015 ## 58 87.50000 2015 ## 59 77.77778 2015 ## 60 83.33334 2015 ## 61 69.56522 2015 ## 62 86.66666 2015 The final step is to deal with the remaining missing data. As all variables, except incidence rate, are missing for some counties we cannot reliably impute the data. We therefore drop it and make a note of the counties for which data was not available. tb_clean_2015 <- tb_df_2015 %>% drop_na() %>% dplyr::select(-year) missing_regions <- setdiff(tb_df_2015$AreaName %>% as.character, tb_clean_2015$AreaName %>% as.character) missing_regions ## character(0) This leave us with a tidy and complete dataset with data on TB monitoring indicators for 62 counties in 2015. ## Dimension reduction We are now ready to conduct some clustering analysis on this data. The first step is to reduce the dimensionality of the data using principal component analysis (PCA). We use the estim_ncp function (which uses a method outlined in this paper) from the FactoMineR package to estimate the number of principal components required. We then perform PCA (using prcomp) and plot the variance explained by each component as a check on estim_ncp. All of the following analysis is done using nested tibbles and so can be easily generalised to higher dimensional use cases. tb_pca <- tb_clean_2015 %>% nest() %>% mutate( numeric_data = map(data, ~select_if(., is.numeric) %>% as.data.frame()), optimal_pca_no = map(numeric_data, ~estim_ncp(., scale = TRUE, ncp.min = 2, ncp.max = 10)) %>% map_dbl(~.$ncp),
pca = map(numeric_data, ~prcomp(.x,
center = TRUE,
scale = TRUE)),
pca_data = map(pca, ~.$x), pca_aug = map2(pca, data, ~augment(.x, data = .y))) We find that the optimal number of principal components is 2. We can also plot the percentage of variance explained in order to evaluate this choice. ## Variance explained var_exp <- tb_pca %>% dplyr::select(-optimal_pca_no) %>% unnest(pca_aug) %>% summarize_at(.vars = vars(contains("PC")), .funs = funs(var)) %>% gather(key = pc, value = variance) %>% mutate(var_exp = variance/sum(variance) * 100, cum_var_exp = cumsum(var_exp), pc = str_replace(pc, ".fitted", "") %>% str_replace("PC", "")) var_exp %>% rename( Variance Explained = var_exp, Cumulative Variance Explained = cum_var_exp ) %>% gather(key = key, value = value, Variance Explained, Cumulative Variance Explained) %>% mutate(key = key %>% factor(levels = c("Variance Explained", "Cumulative Variance Explained"))) %>% mutate(value = value / 100) %>% mutate(pc = factor(pc, levels = as.character(1:max(var_exp$pc %>% as.numeric)))) %>%
ggplot(aes(pc, value, group = key)) +
geom_point(size = 2, alpha = 0.8) +
geom_line(size = 1.1, alpha = 0.6) +
facet_wrap(~key, scales = "free_y") +
theme_minimal() +
scale_y_continuous(breaks = seq(0, 1, 0.05), lim = c(0, NA),
minor_breaks = NULL, labels = percent) +
labs(
title = "Variance Explained by Principal Component",
subtitle = paste0("The optimal number of principal components suggested by estim_ncp was ",
tb_pca$optimal_pca_no, " which explains ", round(var_exp$cum_var_exp[[2]], 0), "% of the data."),
x = "Principal Component",
y = "Variance Explained (%)",
caption = "@seabbs Source: Public Health England (fingertipsR)"
)

The above plot shows that only 42% of the variance in the data is explained by the first two principle components (PCs) even though the estim_ncp function suggested that this was the optimal number. This indicates that there is large amount of noise in the data with a large amount of non-systematic between county variation. Another approach, using the ‘elbow’ (change from decreasing to stable amount of variance explained), would estimate that 8 PCs are required to explain the variance in the data.

## Clustering of TB Monitoring Indicators

Next, we can now perform clustering using the partitioning around medoids algorithm on the first two principal components. This approach should be more stable than K means and also has the benefit of producing a metric (the average silhouette width) which can be used to estimate the number of clusters that provides the best fitting model. The outline of the PAM algorithm is as follows:

1. Randomly select k observations as the initial medoid.
2. Assign each observation to the closest medoid.
3. Swap each medoid and non-medoid observation, computing the dissimilarity cost.
4. Select the configuration that minimizes the total dissimilarity.
5. Repeat steps 2 through 4 until there is no change in the medoids.

Again, we use an approach that makes use of nested tibbles, this should be easier to generalise to other use cases.

## Perform pam on pca data 1 to 10 groups
tb_pca_pam <- tb_pca %>%
mutate(centers = list(2:10)) %>%
unnest(centers, .preserve = everything()) %>%
dplyr::select(-centers, centers = centers1) %>%
group_by(centers) %>%
mutate(
pam = map(pca_data,
~ pam(x = .x[, 1:optimal_pca_no], k = centers, stand = TRUE)),
clusters = map(pam, ~.$clustering), avg_silhouette_width = map(pam, ~.$silinfo$avg.width), data_with_clusters = map2(.x = data, .y = clusters, ~mutate(.x, cluster = factor(.y, ordered = TRUE))) ) %>% ungroup tb_pca_pam ## # A tibble: 9 x 11 ## optimal_pca_no data numeric_data pca pca_data pca_aug centers pam ## <dbl> <lis> <list> <lis> <list> <list> <int> <lis> ## 1 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 2 <pam> ## 2 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 3 <pam> ## 3 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 4 <pam> ## 4 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 5 <pam> ## 5 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 6 <pam> ## 6 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 7 <pam> ## 7 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 8 <pam> ## 8 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 9 <pam> ## 9 2 <tib… <df[,9] [62… <prc… <dbl[,9… <tibbl… 10 <pam> ## # … with 3 more variables: clusters <list>, avg_silhouette_width <list>, ## # data_with_clusters <list> To assess the optimal number of clusters we can plot the average silhouette width. This indicates that two clusters are optimal, although this estimate may not be that robust as the average silhouette width is low (0.38) with 6, 7, and 8 clusters also having average silhouette widths that are comparable. In general, we prefer the parsimonious model and therefore we will only investigate two clusters for the remainder of this post. Note that this does not test for no clustering (i.e a single cluster). ## Get max silhouette width max_silhouette_width <- tb_pca_pam %>% dplyr::select(centers, avg_silhouette_width) %>% unnest(avg_silhouette_width) %>% arrange(desc(avg_silhouette_width)) %>% slice(1) ## Plot average silhouette width tb_pca_pam %>% dplyr::select(centers, avg_silhouette_width) %>% unnest(avg_silhouette_width) %>% ggplot(aes(x = centers, y = avg_silhouette_width)) + geom_line(size = 2, alpha = 0.4) + geom_point(size = 3, alpha = 0.8) + theme_minimal() + scale_x_continuous(breaks = seq(1, 10, 1), minor_breaks = NULL) + scale_y_continuous(limits = c(NA, NA), breaks = seq(0, 1, 0.01), minor_breaks = NULL) + labs(title = "Average Silhouette Width by Number of PAM Clusters", subtitle = paste0("The optimal number of clusters identifed by avg. silhouette width was ", max_silhouette_width$centers,
" with an avg. silhouette width of ",
round(max_silhouette_width$avg_silhouette_width, 2) ), x = "Clusters", y = "Avg. Silhouette Width", caption = "@seabbs Source: Public Health England (fingertipsR)") ## Results We can now explore the clusters we have identified. A useful way to do this is to visual the first two principal components overlaid with the original variable loadings, and the clusters we have identified. ## Plot clusters pca_plot <- tb_pca_pam %>% filter(centers == max_silhouette_width$centers) %>%
dplyr::select(data_with_clusters, pca) %>%
mutate(pca_graph = map2(.x = pca,
.y = data_with_clusters,
~ autoplot(.x, x = 1, y = 2,
label = TRUE, label.label = "AreaName",
label.size = 1.5, label.vjust = -1,
alpha = 0.3, frame = TRUE,
frame.type = 'convex', frame.alpha= 0.05,
colour = "cluster", size = "rec_inc_rate") +
theme_minimal() +
labs(x = paste0("Principal Component 1 (Variance Explained: ",
round(var_exp$var_exp[[1]], 1), "%)"), y = paste0("Principal Component 2 (Variance Explained: ", round(var_exp$var_exp[[2]], 1), "%)")) +
guides(colour=guide_legend(title = "Cluster", ncol = 2),
fill=guide_legend(title= "Cluster", ncol = 2),
size = guide_legend(title = "TB Incidence Rate (per 100,000 population)",
ncol = 2)) +
scale_colour_viridis(option = "viridis",
discrete = TRUE, end = 0.5) +
scale_fill_viridis(option = "viridis",
discrete = TRUE, end = 0.5) +
theme(legend.position = "bottom",
legend.box = "horizontal") +
labs(
title = "Tuberculosis Data in England; First Two Principal Components",
subtitle = "The arrows are variable loadings and points are counties coloured by cluster membership",
caption = "@seabbs Source: Public Health England (fingertipsR)"
)
)) %>%
pull(pca_graph) %>%
first

pca_plot

From this we see that the clusters are generally split by incidence rates with lower incidence rate counties also having a higher proportion that either die or are lost to follow up. The higher incidence counties have a higher proportion of cultured confirmed pulmonary cases and more cases that complete treatment within 12 months. It appears that the proportion of cases that start treatment within 2 and 4 months varies over both clusters. We can also see that the proportion lost to follow up is inversely related to the proportion that are offered HIV tests, with a higher proportion that are lost to follow up corresponding to a reduced proportion of cases being offered HIV tests.

Another way of summarising the between cluster differences is to summarise the data by cluster, which is presented in the following plot. This approach to exploring differences between clusters may not be applicable to data with a large number of clusters, for this a faceted ridge plot (ggridges) would probably offer a better solution.

sum_tb_df <- tb_pca_pam %>%
filter(centers == max_silhouette_width$centers) %>% pull(data_with_clusters) %>% map(~ gather(., key = "Variable", value = "value", -AreaName, -cluster)) %>% first %>% rename(Cluster = cluster) plot_cluster_diff <- sum_tb_df %>% ggplot(aes(x = Variable, y = value, col = Cluster, fill = Cluster)) + geom_violin(draw_quantiles = c(0.025, 0.5, 0.975), alpha = 0.2, scale = "width") + geom_jitter(position = position_jitterdodge(), alpha = 0.3) + coord_flip() + theme_minimal() + theme(legend.position = "bottom") + scale_y_continuous(breaks = seq(0, 100, 5), minor_breaks = NULL) + scale_colour_viridis(option = "viridis", discrete = TRUE, end = 0.5) + scale_fill_viridis(option = "viridis", discrete = TRUE, end = 0.5) + labs( title = "Tuberculosis in England; Summarised by Cluster", subtitle = "Violin plots are scaled by width, with the 2.5%, 50% and 97.5% quantiles shown.", x = "Variable", y = "Incidence rate (per 100,000) for rec_int_rate, otherwise proportion (0-100%)", caption = "@seabbs Source: Public Health England (fingertipsR)") plot_cluster_diff To explore this further we can calculate the percentage difference between clusters for several of the variables’ summary statistics. The following table does this for the mean, the median, the 2.5% quantile, and the 97.5% quantile. sum_tb_df %>% group_by(Cluster, Variable) %>% summarise(mean = mean(value), median = median(value), lll = quantile(value, 0.025), hhh = quantile(value, 0.975)) %>% group_by(Variable) %>% mutate_if(is.numeric, .funs = funs((lag(.) - .)/ .)) %>% drop_na %>% mutate_if(is.numeric, .funs = funs(paste0(round(. * 100, 1), "%"))) %>% ungroup %>% mutate(Variable = factor(Variable, levels = rev(.$Variable))) %>%
arrange(Variable) %>%
rename(Mean = mean, Median = median, 2.5% Quantile = lll, 97.5% Quantile = hhh) %>%
dplyr::select(-Cluster) %>%
kable
## mutate_if() ignored the following grouping variables:
## Column Variable
## mutate_if() ignored the following grouping variables:
## Column Variable
Variable Mean Median 2.5% Quantile 97.5% Quantile
rec_inc_rate 53.6% 37.5% -0.8% 123.2%
prop_tb_offered_hiv_test 2.2% 4.9% 2.4% 0%
prop_pul_cc 15.8% 15.3% 30.2% 17.5%
prop_p_start_treat_4_m_sym 10.1% 12.7% 26.5% 7%
prop_p_start_treat_2_m_sym 13.9% 9% 3.2% 22.6%
prop_ds_treat_com_12 11.3% 12% 6.4% 10.4%
prop_ds_died -55.7% -49.6% -100% -62.8%
prop_cc_ds_front -0.1% 0% 0.5% 0%

From the plot and table above, we see that, cluster 1 contains the counties with higher incidence rates and therefore has a higher median incidence rate. At the same time, it has a large interquartile range but a similar lower quartile to cluster 2. We also see that in cluster 1 a greater number of counties are not offering HIV tests to all cases, although it appears the majority of counties are offering HIV tests to 100% of TB cases in both cases. In cluster 2 there is a large reduction in the proportion of pulmonary cases that were culture confirmed. In addition, in pulmonary cases, fewer start treatment within 2 or 4 months of developing symptoms in comparison to cluster 1. There is also a moderate increase in the proportion of cases that are lost to follow up and a large increase in the proportion of cases that died in cluster 2 compared to cluster 1. There is little difference between clusters for the proportion of cultured confirmed cases which had drug susceptibility reported, with the majority of clusters reporting on 100% of cases.

A more visual way to understand the clustering of TB in England based on the data we have extracted using the fingertipsR package is to plot the cluster membership for each county on a map. We do this using data on the outlines of the counties in England from the UK data service (Darren L Dahly kindly pointed me in the direction of this data - thank you!)

## Make the plot into a function as ggplot2 object is very large and cause git issues (i.e therefore easier to remake the plot than it is to transfer between code chunks)
tb_cluster_map <- function(tb_pca_pam) {
## Some issues here with extracting code from the sp file
## Solved using the folling maptools functions - improvements appreciated!
gpclibPermit()

england_counties <- shapefile("../../static/data/shapefiles/england-2011-ct-shape/england_ct_2011.shp") %>%
fortify(region = "code") %>%
as_tibble

england_urban_areas <- shapefile("../../static/data/shapefiles/england-urb-2001-shape/england_urb_2001.shp") %>%
fortify(region = "name") %>%
as_tibble %>%
filter(id %in% c("Greater London Urban Area",
"Greater Manchester Urban Area",
"Bristol Urban Area",
"West Midlands Urban Area",
"Milton Keynes Urban Area"))

## Make custom positions for urban area labels
urban_area_labels <- england_urban_areas %>%
group_by(id) %>%
slice(100) %>%
ungroup() %>%
mutate(long = long - 200000,
lat = lat + 20000)

tb_cluster_results <- tb_pca_pam %>%
filter(centers == max_silhouette_width\$centers) %>%
pull(data_with_clusters) %>%
first

tb_cluster_results <- tb_df[[2]] %>%
dplyr::select(AreaName, AreaCode, AreaType) %>%
filter(AreaType %in% "County & UA") %>%
unique %>%
left_join(tb_cluster_results,
by = "AreaName") %>%
left_join(england_counties, by = c("AreaCode" = "id"))

tb_cluster_results %>%
rename(Cluster = cluster) %>%
drop_na(Cluster) %>%
dplyr::select(long, lat, Cluster, group) %>%
ggplot(
aes(x = long,
y = lat,
fill = Cluster)) +
geom_polygon(data = england_urban_areas,
aes(group = group, fill = NULL),
alpha = 0.4) +
geom_path(data = tb_cluster_results,
aes(group = group, fill = NULL),
alpha = 0.4) +
geom_polygon(data = tb_cluster_results,
aes(group = group, fill = NULL),
alpha = 0.1) +
geom_polygon(aes(group = group), alpha = 0.6) +
geom_line(data = urban_area_labels %>%
bind_rows(urban_area_labels %>%
mutate(long = long + 200000,
lat = lat - 20000)),
aes(fill = NA, group = id), alpha = 0.8) +
geom_label(data = urban_area_labels,
aes(label = id), fill = "grey") +
scale_fill_viridis(option = "viridis", discrete = TRUE,
end = 0.5) +
coord_equal() +
theme_map() +
theme(legend.position = "bottom") +
labs(title = "Tuberculosis Monitoriing Indicators; Map of County Level Clusters in England",
subtitle = "Using data from 2015 - only counties with incidence rates above 10 per 100,000 population and complete data are shown",
caption = "Selected urban areas are shown (dark grey) and labelled.
@seabbs Source: Public Health England (fingertipsR)
}

plot_tb_cluster_map <- tb_cluster_map(tb_pca_pam)

ggsave("../../static/img/fingertips/map-tb-clust.png",
plot_tb_cluster_map, width = 8, height = 8, dpi = 330)

plot_tb_cluster_map

## Remove objects to reduce stored chunk size
rm("plot_tb_cluster_map")

In the map above we see that cluster 1 is mainly made up of counties in London, Birmingham, and in the North West of England. Cluster 2 accounts for the majority of counties that are not within these large urban areas, as well the remaining as several counties in Birmingham and the North West and a single county in London. As expected we see that the majority of counties in England have been excluded from our analysis due to low incidence rates or missing data.

## Summary and Wrap-up

In this post we have explored the fingertipsR R package and the data on TB monitoring indicators for counties in England that is provided through the fingertips API. We found that there was a large amount of non-random missing data, much of which was due to censoring to prevent deductive disclosure. However, several variables were entirely missing and even once counties with low incidence rates were removed there was still several counties with little TB monitoring data available. Substantial improvements could be made here to improve any future analysis or monitoring using this dataset.

Once counties and variables with missing data had been removed we found that there was substantial non-systematic variation between counties with only 42% of variation explained by the optimal number of principal components. The large amount of variation between counties we observed reinforces the need for the collaborative TB strategy in England which was launched in 2015. Hopefully once data becomes available for 2016, and 2017 this will show a decrease in variation between counties.

We found that, after dimension reduction, the data best supported two clusters. One cluster contained the majority of high incidence counties, which also had a higher proportion of culture confirmed pulmonary TB cases and more cases completing treatment in the first 12 months. This cluster was mainly centred around the greater London area but also included counties in Birmingham and the North West. The second cluster contained mainly counties not in these large urban areas but did contain the remaining counties in the North West and Birmingham area, as well as a single county in London. This cluster had a lower proportion of cases that started treatment within 2 and 4 months of developing symptoms, as well as having a greater proportion of cases that were lost to follow up and that died.

This analysis was limited by the TB monitoring indicators available, the large amount of missing data for the variables that were available, and the lack of high quality up to date data. There was a large amount of non-systematic variation present that was exluded from our clustering analysis and the clusters that we did identify cannot be considered to be highly robust. We did not test for a single cluster, which may have produced a model more consistent with the date. A single clustering algorithm was used, and although it is considered a robust approach, further validation of these results is necessary using another (or multiple) clustering methods.

Hopefully this post proved useful for informing you about the present state of TB in England, but also as an example of dimension reduction and clustering analysis. This analysis serves as a framework for a similar analysis that I am carrying out as part of my PhD so any comments, suggested improvements etc. would be greatly appreciated.

To wrap-up this post we use the patchwork package to quickly compose a storyboard of the results from the clustering analysis carried out above. See here for a full size version.

plot_tb_cluster_map <- tb_cluster_map(tb_pca_pam)

storyboard <- plot_tb_cluster_map | ( pca_plot / plot_cluster_diff)

ggsave("../../static/img/fingertips/storyboard-fingertips-tb-clust.png",
storyboard, width = 20, height = 16, dpi = 330)

storyboard

## Remove to reduce chunk size as over 100mb git limit
rm("storyboard", "plot_tb_cluster_map")