Determinants of Quality Of Life of recent Ghanaian stroke survivors

Author

Dr. Ayisi-Boateng

Published

October 20, 2025

Import data

Show the code 
df_paper_02 <- dget("df_for_papers")

Summary table: All data

Show the code 
gtsummary::theme_gtsummary_compact()
Setting theme "Compact"
Show the code 
table_0 <- 
    df_paper_02 %>%
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
            a_domicile, income, d_st_type, d_stroke_ct,d_stroke_loc, bmi,
            mobility, selfcare,  usual_act, pain_disc, anxiety, vas, vas_cat, 
            barthels_index),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing"
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("**Table 1**: ") %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "****")

Tables

Table1

Show the code 
gtsummary::reset_gtsummary_theme()
gtsummary::theme_gtsummary_compact()
Setting theme "Compact"
Show the code 
df_paper_02 %>%
    mutate(a_livingsit = droplevels(a_livingsit)) %>% 
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
            a_domicile, income, d_st_type, d_stroke_ct,d_stroke_loc, bmi),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = list(
            gtsummary::all_categorical() ~ "{n} ({p})",
            gtsummary::all_continuous() ~ "{mean} ({sd})"
            ),
        missing = "no",
        by = a_gender,
        label = a_livingsit ~ "Living Status"
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Socio-demographic and clinical characteristics of the study participants"
        ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Sex of study participant**") %>% 
    gtsummary::add_overall(last = TRUE)%>% 
    gtsummary::add_p(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_p()
Table 1: Socio-demographic and clinical characteristics of the study participants
Characteristic
Sex of study participant
Overall
N = 5001		 p-value2
Male
N = 2811		 Female
N = 2191
Age in years 58 (11) 59 (11) 58 (11) 0.083
Marital Status


<0.001
    Currently Married 235 (83.6) 98 (44.7) 333 (66.6)
    Previously Married 32 (11.4) 112 (51.1) 144 (28.8)
    Never Married 14 (5.0) 9 (4.1) 23 (4.6)
Educational Status


<0.001
    None 11 (3.9) 38 (17.4) 49 (9.8)
    Primary 89 (31.7) 114 (52.1) 203 (40.6)
    Secondary 114 (40.6) 51 (23.3) 165 (33.0)
    Tertiary 67 (23.8) 16 (7.3) 83 (16.6)
Living Status


<0.001
    Lives Alone 16 (5.7) 13 (5.9) 29 (5.8)
    Lives With Spouse and Children 199 (70.8) 74 (33.8) 273 (54.6)
    Lives With Spouse 18 (6.4) 12 (5.5) 30 (6.0)
    Lives With Extended Family 30 (10.7) 42 (19.2) 72 (14.4)
    Lives With Children 18 (6.4) 78 (35.6) 96 (19.2)
Religion


0.901
    Christianity 250 (89.0) 198 (90.4) 448 (89.6)
    Islam 29 (10.3) 20 (9.1) 49 (9.8)
    Other 2 (0.7) 1 (0.5) 3 (0.6)
Domicile


0.421
    Rural 16 (5.7) 17 (7.8) 33 (6.6)
    Semi-Urban 99 (35.2) 67 (30.6) 166 (33.2)
    Urban 166 (59.1) 135 (61.6) 301 (60.2)
Income in GHC


0.026
    0-100 83 (29.5) 91 (42.1) 174 (35.0)
    101-250 89 (31.7) 61 (28.2) 150 (30.2)
    251-500 67 (23.8) 42 (19.4) 109 (21.9)
    >500 42 (14.9) 22 (10.2) 64 (12.9)
Stroke Type (Choose One)


0.004
    Ischemic Stroke 170 (67.5) 162 (81.8) 332 (73.8)
    Intracerebral Hemorrhagic Stroke 71 (28.2) 32 (16.2) 103 (22.9)
    Ischemic With Hemorrhagic Transformation 8 (3.2) 2 (1.0) 10 (2.2)
    Untyped Stroke (no CT scan available) 3 (1.2) 2 (1.0) 5 (1.1)
Stroke Subtype ( with results of Brain CT scan)


0.004
    Ischaemic 161 (67.4) 152 (81.7) 313 (73.6)
    Haemorrhage infarct 13 (5.4) 3 (1.6) 16 (3.8)
    Haemorrhagic 55 (23.0) 29 (15.6) 84 (19.8)
    Ischaemic and Haemorrhagic 10 (4.2) 2 (1.1) 12 (2.8)
Stroke Location


0.041
    Anterior Circulation 149 (71.3) 132 (80.5) 281 (75.3)
    Posterior Circulation 60 (28.7) 32 (19.5) 92 (24.7)
Body Mass Index 25.5 (4.8) 28.0 (6.1) 26.6 (5.5) <0.001
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 2

Show the code 
# gtsummary::reset_gtsummary_theme()
# gtsummary::theme_gtsummary_compact()
df_paper_02 %>%
    gtsummary::tbl_summary(
        include = c(
            a_gender, mobility, selfcare,  usual_act, pain_disc, anxiety, vas, 
            vas_cat, barthels_index),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = list(
            gtsummary::all_categorical() ~ "{n} ({p})",
            gtsummary::all_continuous() ~ "{mean} ({sd})"
            ),
        type = list(
            c(mobility, selfcare,  usual_act, pain_disc, anxiety)~ 
                "continuous"),
        missing = "no",
        by = a_gender
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 2**: Descriptive statistics of EQ-5D, VAS and Barthel index scales"
        ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Sex of study participant**") %>% 
    gtsummary::add_overall(last = TRUE) %>% 
    gtsummary::add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_p()
Table 2: Descriptive statistics of EQ-5D, VAS and Barthel index scales
Characteristic
Sex of study participant
Overall
N = 5001		 p-value2
Male
N = 2811		 Female
N = 2191
Mobility 1.71 (0.67) 1.80 (0.66) 1.75 (0.66) 0.131
Self Care 1.67 (0.72) 1.70 (0.76) 1.69 (0.74) 0.734
Usual Activity 1.84 (0.72) 1.90 (0.68) 1.87 (0.71) 0.306
Pain Discomfort 1.59 (0.60) 1.71 (0.61) 1.64 (0.61) 0.024
Anxiety 1.44 (0.60) 1.44 (0.57) 1.44 (0.58) 0.787
Your Health Today 64 (23) 68 (22) 65 (23) 0.040
Categorised VAS


0.076
    Below Median 132 (47.1) 85 (39.2) 217 (43.7)
    Median & above 148 (52.9) 132 (60.8) 280 (56.3)
Barthels Index 68 (26) 65 (29) 66 (27) 0.458
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

Table 3: Logistic regression

Show the code 
table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas_cat)) %>% 
    mutate(vas_cat = factor(vas_cat)) %>% 
    select(
        a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
        a_domicile, income, d_st_type, d_stroke_ct,d_stroke_loc, bmi, 
        barthels_index, vas_cat) %>% 
     tbl_uvregression(
        y = vas_cat, 
        method = glm,
        method.args = family(binomial),
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3),
        exponentiate = T) %>% 
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>% 
    select(
        vas_cat, a_agebase, a_gender, a_livingsit, a_domicile, income, 
        d_st_type, bmi, barthels_index, d_stroke_ct) %>% 
    mutate(vas_cat = factor(vas_cat)) %>%
    glm(vas_cat ~ ., data = ., family = "binomial") %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3),
        exponentiate = T) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3a**:Univartiate and multivariate logistic 
        regression for Quality of Life of study participants")
Table 3a:Univartiate and multivariate logistic regression for Quality of Life of study participants
Characteristic
Univariate
Multivariate
N OR 95% CI p-value OR 95% CI p-value
Age in years 497 0.97 0.96, 0.99 0.002 0.99 0.97, 1.01 0.203
Gender 497





    Male
— —
— —
    Female
1.39 0.97, 1.99 0.076 2.23 1.30, 3.88 0.004
Marital Status 497





    Currently Married
— —



    Previously Married
0.83 0.56, 1.24 0.368


    Never Married
0.55 0.23, 1.29 0.170


Educational Status 497





    None
— —



    Primary
1.63 0.87, 3.08 0.132


    Secondary
1.71 0.90, 3.29 0.104


    Tertiary
1.33 0.65, 2.74 0.429


Living Status 497





    Lives Alone
— —



    Lives With Spouse and Children
0.67 0.28, 1.48 0.338


    Lives With Spouse
0.34 0.11, 0.98 0.050


    Lives With Extended Family
0.58 0.22, 1.42 0.245


    Lives With Children
0.40 0.16, 0.94 0.040


Religion 497





    Christianity
— —



    Islam
0.65 0.36, 1.18 0.158


    Other
0.37 0.02, 3.88 0.417


Domicile 497





    Rural
— —
— —
    Semi-Urban
2.40 1.12, 5.34 0.026 2.40 0.94, 6.34 0.069
    Urban
2.39 1.15, 5.18 0.022 2.62 1.07, 6.66 0.037
Income in GHC 494





    0-100
— —
— —
    101-250
2.50 1.60, 3.95 <0.001 2.32 1.32, 4.13 0.004
    251-500
2.69 1.64, 4.48 <0.001 2.35 1.24, 4.53 0.009
    >500
1.21 0.67, 2.16 0.522 1.31 0.59, 2.94 0.510
Stroke Type (Choose One) 448





    Ischemic Stroke
— —
— —
    Intracerebral Hemorrhagic Stroke
1.37 0.88, 2.18 0.171 0.26 0.01, 3.11 0.351
    Ischemic With Hemorrhagic Transformation
0.20 0.03, 0.82 0.044 0.26 0.01, 3.65 0.332
    Untyped Stroke (no CT scan available)
0.00
0.981 0.00
0.979
Stroke Subtype ( with results of Brain CT scan) 423





    Ischaemic
— —
— —
    Haemorrhage infarct
0.36 0.11, 1.01 0.062 1.61 0.11, 49.6 0.749
    Haemorrhagic
2.09 1.25, 3.60 0.006 4.94 0.42, 150 0.267
    Ischaemic and Haemorrhagic
0.07 0.00, 0.37 0.012 0.20 0.01, 2.53 0.226
Stroke Location 371





    Anterior Circulation
— —



    Posterior Circulation
0.79 0.49, 1.27 0.323


Body Mass Index 478 1.05 1.01, 1.08 0.011 1.03 0.99, 1.08 0.195
Barthels Index 483 1.02 1.01, 1.03 <0.001 1.01 1.01, 1.02 <0.001
a_livingsit






    Lives Alone



— —
    Lives With Spouse and Children



0.94 0.31, 2.56 0.900
    Lives With Spouse



0.47 0.12, 1.77 0.275
    Lives With Extended Family



0.56 0.17, 1.73 0.325
    Lives With Children



0.40 0.12, 1.19 0.108
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 3 - Linear regression version

Show the code 
table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas)) %>% 
    select(
        a_agebase, a_gender, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, d_stroke_ct, d_stroke_loc, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>% 
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>% 
    select(
        vas, a_agebase, a_gender, a_domicile, d_st_type, d_stroke_ct, income,
        barthels_index, bmi) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3b**:Univartiate and multivariate linear 
        regression for quality of life of study participants")
Table 3b:Univartiate and multivariate linear regression for quality of life of study participants
Variable
Univariate
Multivariate
N Estimate 95% CI p-value Beta 95% CI p-value
Age in years 497 -0.26 -0.44, -0.09 0.003 -0.16 -0.35, 0.03 0.099
Gender 497





    Male
— —
— —
    Female
3.9 -0.09, 8.0 0.056 6.3 1.9, 11 0.005
Marital Status 497





    Currently Married
— —



    Previously Married
-2.5 -7.0, 1.9 0.264


    Never Married
-5.3 -15, 4.3 0.279


Educational Status 497





    None
— —



    Primary
3.6 -3.6, 11 0.324


    Secondary
5.1 -2.2, 12 0.173


    Tertiary
4.8 -3.3, 13 0.244


Living Status 497





    Lives Alone
— —



    Lives With Spouse and Children
-5.8 -14, 2.9 0.191


    Lives With Spouse
-11 -23, 0.16 0.053


    Lives With Extended Family
-8.3 -18, 1.5 0.097


    Lives With Children
-11 -20, -1.4 0.024


Religion 497





    Christianity
— —



    Islam
-4.6 -11, 2.1 0.179


    Other
-2.6 -28, 23 0.846


Domicile 497





    Rural
— —
— —
    Semi-Urban
11 2.4, 19 0.012 11 2.0, 19 0.016
    Urban
12 3.4, 20 0.006 12 3.4, 20 0.006
Income in GHC 494





    0-100
— —
— —
    101-250
10 5.2, 15 <0.001 7.2 2.1, 12 0.006
    251-500
9.8 4.5, 15 <0.001 8.1 2.4, 14 0.006
    >500
1.4 -5.0, 7.9 0.661 1.6 -5.7, 9.0 0.664
Stroke Type (Choose One) 448





    Ischemic Stroke
— —
— —
    Intracerebral Hemorrhagic Stroke
4.0 -0.92, 9.0 0.110 -6.0 -24, 12 0.513
    Ischemic With Hemorrhagic Transformation
-16 -30, -2.2 0.024 -5.8 -26, 14 0.572
    Untyped Stroke (no CT scan available)
-24 -43, -3.8 0.020 -27 -71, 16 0.220
Stroke Subtype ( with results of Brain CT scan) 423





    Ischaemic
— —
— —
    Haemorrhage infarct
-10 -21, 0.66 0.065 -2.7 -23, 17 0.793
    Haemorrhagic
8.6 3.3, 14 0.002 9.3 -8.8, 27 0.315
    Ischaemic and Haemorrhagic
-18 -30, -4.9 0.007 -6.0 -25, 13 0.530
Stroke Location 371





    Anterior Circulation
— —



    Posterior Circulation
-1.5 -6.6, 3.7 0.572


Body Mass Index 478 0.34 -0.03, 0.71 0.073 0.13 -0.25, 0.51 0.502
Barthels Index 483 0.25 0.18, 0.32 <0.001 0.18 0.11, 0.26 <0.001
Abbreviation: CI = Confidence Interval

Table 4a - Linear regression for males

Show the code 
table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Male") %>% 
    select(
        a_agebase, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, d_stroke_ct, d_stroke_loc, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>%
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p(t = 0.1)

table_vas_adj <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Male") %>% 
    select(
        vas, a_agebase, a_domicile, d_stroke_ct, income, d_st_type,
        barthels_index) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 4a**:Univartiate and multivariate linear 
        regression for quality of life of study participants (Males)")
Table 4a:Univartiate and multivariate linear regression for quality of life of study participants (Males)
Variable
Univariate
Multivariate
N Estimate 95% CI p-value Beta 95% CI p-value
Age in years 280 -0.30 -0.53, -0.06 0.014 -0.14 -0.40, 0.12 0.281
Marital Status 280





    Currently Married
— —



    Previously Married
-0.27 -8.8, 8.3 0.950


    Never Married
-8.8 -21, 3.7 0.166


Educational Status 280





    None
— —



    Primary
-0.54 -15, 14 0.942


    Secondary
3.1 -11, 18 0.668


    Tertiary
2.5 -12, 17 0.742


Living Status 280





    Lives Alone
— —



    Lives With Spouse and Children
-7.7 -19, 4.1 0.200


    Lives With Spouse
-16 -32, -0.87 0.039


    Lives With Extended Family
-9.3 -23, 4.7 0.191


    Lives With Children
-16 -31, -0.14 0.048


Religion 280





    Christianity
— —



    Islam
-3.0 -12, 5.9 0.504


    Other
6.0 -26, 38 0.714


Domicile 280





    Rural
— —
— —
    Semi-Urban
20 8.2, 32 0.001 19 7.9, 31 0.001
    Urban
16 3.8, 27 0.010 17 6.1, 28 0.003
Income in GHC 280





    0-100
— —
— —
    101-250
11 4.2, 18 0.002 8.1 1.0, 15 0.025
    251-500
13 5.4, 20 <0.001 8.5 0.80, 16 0.031
    >500
7.6 -0.92, 16 0.080 2.5 -6.9, 12 0.603
Stroke Type (Choose One) 252





    Ischemic Stroke
— —
— —
    Intracerebral Hemorrhagic Stroke
5.3 -1.1, 12 0.104 -0.44 -24, 24 0.971
    Ischemic With Hemorrhagic Transformation
-11 -28, 5.1 0.177 -5.2 -29, 19 0.664
    Untyped Stroke (no CT scan available)
-17 -44, 9.0 0.195 -24 -72, 24 0.318
Stroke Subtype ( with results of Brain CT scan) 239





    Ischaemic
— —
— —
    Haemorrhage infarct
-10 -23, 2.7 0.123 -7.0 -33, 19 0.596
    Haemorrhagic
9.5 2.6, 16 0.007 4.3 -20, 29 0.729
    Ischaemic and Haemorrhagic
-11 -26, 3.2 0.126 -8.8 -31, 13 0.434
Stroke Location 209





    Anterior Circulation
— —



    Posterior Circulation
-3.5 -10, 3.1 0.294


Body Mass Index 273 -0.23 -0.81, 0.35 0.439


Barthels Index 269 0.28 0.17, 0.38 <0.001 0.19 0.08, 0.30 <0.001
Abbreviation: CI = Confidence Interval

Table 4b - Linear regression for Females

Show the code 
table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Female") %>% 
    select(
        a_agebase, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, d_stroke_ct, d_stroke_loc, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>%
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Female") %>% 
    select(
        vas, a_agebase, a_domicile, d_st_type, d_stroke_ct, income,
        barthels_index,  d_stroke_loc, maristat, a_livingsit) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3),
        label = d_st_type ~ "Stroke Type (Choose One)") %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 4a**:Univartiate and multivariate linear 
        regression for quality of life of study participants (Females)")
Table 4a:Univartiate and multivariate linear regression for quality of life of study participants (Females)
Variable
Univariate
Multivariate
N Estimate 95% CI p-value Beta 95% CI p-value
Age in years 217 -0.26 -0.52, 0.00 0.052 -0.03 -0.35, 0.28 0.825
Marital Status 217





    Currently Married
— —
— —
    Previously Married
-7.9 -14, -1.9 0.010 -3.2 -15, 8.9 0.607
    Never Married
-1.8 -17, 13 0.817 5.0 -14, 24 0.608
Educational Status 217





    None
— —



    Primary
6.6 -1.6, 15 0.115


    Secondary
8.4 -1.0, 18 0.080


    Tertiary
12 -0.55, 25 0.060


Living Status 217





    Lives Alone
— —



    Lives With Spouse and Children
-0.18 -13, 13 0.978


    Lives With Spouse
-3.8 -21, 13 0.662


    Lives With Extended Family
-7.7 -21, 6.0 0.268


    Lives With Children
-10 -23, 2.8 0.125


Religion 217





    Christianity
— —



    Islam
-6.5 -17, 3.7 0.210


    Other
-18 -62, 25 0.409


Domicile 217





    Rural
— —
— —
    Semi-Urban
1.1 -11, 13 0.859 -6.3 -20, 7.2 0.359
    Urban
8.8 -2.3, 20 0.119 1.2 -11, 14 0.850
Income in GHC 214





    0-100
— —
— —
    101-250
11 4.0, 18 0.002 7.5 -0.74, 16 0.074
    251-500
8.0 0.16, 16 0.046 10 0.76, 20 0.035
    >500
-6.3 -16, 3.8 0.222 -2.3 -14, 9.7 0.704
Stroke Type (Choose One) 196





    Ischemic Stroke
— —
— —
    Intracerebral Hemorrhagic Stroke
4.2 -4.1, 12 0.321 -16 -45, 14 0.295
    Ischemic With Hemorrhagic Transformation
-29 -60, 0.84 0.057 26 -30, 83 0.359
    Untyped Stroke (no CT scan available)
-32 -62, -1.7 0.039


Stroke Subtype ( with results of Brain CT scan) 184





    Ischaemic
— —
— —
    Haemorrhage infarct
-4.0 -28, 20 0.743 3.6 -37, 44 0.861
    Haemorrhagic
8.8 0.40, 17 0.040 17 -13, 46 0.265
    Ischaemic and Haemorrhagic
-39 -69, -9.8 0.009 -59 -128, 9.3 0.090
Stroke Location 162





    Anterior Circulation
— —
— —
    Posterior Circulation
2.6 -5.9, 11 0.549 6.9 -1.3, 15 0.097
Body Mass Index 205 0.65 0.16, 1.1 0.010


Barthels Index 214 0.23 0.14, 0.33 <0.001 0.17 0.05, 0.30 0.006
a_livingsit






    Lives Alone



— —
    Lives With Spouse and Children



0.67 -19, 21 0.947
    Lives With Spouse



-4.9 -28, 18 0.675
    Lives With Extended Family



-13 -30, 3.7 0.126
    Lives With Children



-8.5 -25, 7.6 0.299
Abbreviation: CI = Confidence Interval