Determinants of Hypertensive treatment adherence among recent stroke survivors

Author

Dr Priscilla Abrafi Opare-Addo

Published

September 1, 2025

Please Note
  • Table 1 was not drawn because it is common for all papers
  • Hillbone was divided as “Low” & “High” when the score was less than or greater than the median
  • Only one person had Afib so it was dropped from the analysis
  • Everyone had hypertension so it was dropped as well
  • Average monthly income cannot be determined so categorized
  • Not sure where to get stroke severity and duration of stroke
  • Morisky was analyzed initially as dichotomized (Table 2) and then as raw scores (Table 4)

General Summary

Show the code
table_0 <- 
    df_paper_05 %>% 
    select(hillbone, l_forget, l_decide, l_salty, l_shake, l_fasfood,
           l_appoint, l_missched, l_prescrip, l_runout, l_skipmed, 
           l_feelbet, l_feelsick, l_someone, l_careless, a_agebase, 
           a_gender, a_domicile, income, maristat,a_livingsit, educ,
           a_religion, side_effects, pill_burden) %>% 
    gtsummary::tbl_summary(
        statistic = list(
            gtsummary::all_continuous()~"{mean} ({sd})",
            gtsummary::all_categorical()~"{n} ({p})"),
        missing = "no") %>% 
    gtsummary::bold_labels()
table_0
Characteristic N = 5001
Total Hillbone Score 50.9 (4.1)
How often do you forget to take your HBP medicine ?
    All of the time 8 (1.6)
    Most of the Time 11 (2.2)
    Some of the Time 87 (18)
    None of the Time 391 (79)
How often do you decide NOT to take your HBP medicine ?
    All of the time 1 (0.2)
    Most of the Time 12 (2.4)
    Some of the Time 78 (16)
    None of the Time 407 (82)
How often do you eat salty food ?
    All of the time 22 (4.4)
    Most of the Time 41 (8.2)
    Some of the Time 172 (35)
    None of the Time 263 (53)
How often do you shake salt on your food before you eat it ?
    All of the time 8 (1.6)
    Most of the Time 20 (4.0)
    Some of the Time 86 (17)
    None of the Time 384 (77)
How often do you eat fast food ?
    All of the time 7 (1.4)
    Most of the Time 24 (4.8)
    Some of the Time 147 (30)
    None of the Time 320 (64)
How often do you make the next appointment before you leave the doctors office ?
    All of the time 277 (56)
    Most of the Time 50 (10)
    Some of the Time 57 (11)
    None of the Time 114 (23)
How often do you miss scheduled appointments ?
    All of the time 8 (1.6)
    Most of the Time 13 (2.6)
    Some of the Time 73 (15)
    None of the Time 404 (81)
How often do you forget to get prescriptions filled ?
    All of the time 9 (1.8)
    Most of the Time 9 (1.8)
    Some of the Time 51 (10)
    None of the Time 428 (86)
How often do you run out of HBP pills ?
    All of the time 1 (0.2)
    Most of the Time 13 (2.6)
    Some of the Time 84 (17)
    None of the Time 400 (80)
How often do you skip your HBP medicine before you go to the doctor ?
    All of the time 1 (0.2)
    Most of the Time 6 (1.2)
    Some of the Time 60 (12)
    None of the Time 431 (87)
How often do you miss taking your HBP pills when you feel better ?
    All of the time 5 (1.0)
    Most of the Time 18 (3.6)
    Some of the Time 52 (10)
    None of the Time 423 (85)
How often do you miss taking your HBP pills when you feel sick ?
    All of the time 6 (1.2)
    Most of the Time 7 (1.4)
    Some of the Time 35 (7.0)
    None of the Time 450 (90)
How often do you take someone elses HBP pills?
    All of the time 1 (0.2)
    Most of the Time 3 (0.6)
    Some of the Time 9 (1.8)
    None of the Time 485 (97)
How often do you miss taking your HBP pills when you care less ?
    All of the time 2 (0.4)
    Most of the Time 1 (0.2)
    Some of the Time 42 (8.5)
    None of the Time 448 (91)
Age in years 58 (11)
Gender
    Male 281 (56)
    Female 219 (44)
Domicile
    Rural 33 (6.6)
    Semi-Urban 166 (33)
    Urban 301 (60)
Income in GHC
    0-100 174 (35)
    101-250 150 (30)
    251-500 109 (22)
    >500 64 (13)
Marital Status
    Currently Married 333 (67)
    Previously Married 144 (29)
    Never Married 23 (4.6)
Living Status
    Lives Alone 29 (5.8)
    Lives With Spouse and Children 273 (55)
    Lives in a Nursing Home 0 (0)
    Lives With Spouse 30 (6.0)
    Lives With Extended Family 72 (14)
    Lives With Children 96 (19)
Educational Status
    None 49 (9.8)
    Primary 203 (41)
    Secondary 165 (33)
    Tertiary 83 (17)
Religion
    Christianity 448 (90)
    Islam 49 (9.8)
    Other 3 (0.6)
No. of Side Effects 2 (2)
pill_burden 4.73 (1.90)
1 Mean (SD); n (%)

Table 1

Show the code
table_1 <- 
    df_paper_05 %>% 
    filter(!is.na(hillbone_cat)) %>% 
    select(
        hillbone_cat, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, , side_effects,
        tobacco_use, g_alcohol, dm, hyperlipidemia, hosp_cat, pill_burden) %>% 
    gtsummary::tbl_summary(
        by = hillbone_cat,
        statistic = list(
            gtsummary::all_continuous()~"{mean} ({sd})",
            gtsummary::all_categorical()~"{n} ({p})"),
        missing = "no",
        digits = list(gtsummary::all_categorical() ~ c(0,1))
        ) %>% 
    gtsummary::add_overall(last=TRUE) %>% 
    gtsummary::add_p(
        pvalue_fun = ~gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::bold_labels()%>% 
    gtsummary::bold_p() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Sociodemographic and clinical characteristics 
        by treatment adherence status (Hillbone)")

table_1
Table 1: Sociodemographic and clinical characteristics by treatment adherence status (Hillbone)
Characteristic High
N = 2951
Low
N = 1961
Overall
N = 4911
p-value2
Age in years 59 (12) 57 (10) 58 (11) 0.006
Gender


0.391
    Male 160 (54.2) 114 (58.2) 274 (55.8)
    Female 135 (45.8) 82 (41.8) 217 (44.2)
Domicile


0.115
    Rural 14 (4.7) 18 (9.2) 32 (6.5)
    Semi-Urban 104 (35.3) 60 (30.6) 164 (33.4)
    Urban 177 (60.0) 118 (60.2) 295 (60.1)
Educational Status


0.003
    None 35 (11.9) 13 (6.6) 48 (9.8)
    Primary 128 (43.4) 74 (37.8) 202 (41.1)
    Secondary 80 (27.1) 83 (42.3) 163 (33.2)
    Tertiary 52 (17.6) 26 (13.3) 78 (15.9)
Primary Occupation


<0.001
    Skilled 143 (48.5) 88 (44.9) 231 (47.0)
    Manual 43 (14.6) 65 (33.2) 108 (22.0)
    Others 11 (3.7) 9 (4.6) 20 (4.1)
    Retired 42 (14.2) 15 (7.7) 57 (11.6)
    Unemployed 56 (19.0) 19 (9.7) 75 (15.3)
Marital Status


0.412
    Currently Married 192 (65.1) 136 (69.4) 328 (66.8)
    Previously Married 91 (30.8) 50 (25.5) 141 (28.7)
    Never Married 12 (4.1) 10 (5.1) 22 (4.5)
Income in GHC


0.003
    0-100 92 (31.4) 81 (41.5) 173 (35.5)
    101-250 97 (33.1) 49 (25.1) 146 (29.9)
    251-500 74 (25.3) 32 (16.4) 106 (21.7)
    >500 30 (10.2) 33 (16.9) 63 (12.9)
Stroke Type (Choose One)


0.255
    Ischemic Stroke 200 (75.8) 126 (71.2) 326 (73.9)
    Intracerebral Hemorrhagic Stroke 58 (22.0) 42 (23.7) 100 (22.7)
    Ischemic With Hemorrhagic Transformation 5 (1.9) 5 (2.8) 10 (2.3)
    Untyped Stroke (no CT scan available) 1 (0.4) 4 (2.3) 5 (1.1)
NIH Stroke Scale 4.6 (5.3) 4.8 (5.3) 4.7 (5.3) 0.334
Body Mass Index 26.4 (5.8) 27.0 (5.1) 26.6 (5.6) 0.156
No. of Side Effects 1 (2) 2 (2) 2 (2) <0.001
History of tobacco use 23 (7.9) 22 (11.8) 45 (9.4) 0.155
Alcohol use


0.375
    Never used alcohol 183 (62.0) 103 (52.8) 286 (58.4)
    Currently uses alcohol 30 (10.2) 22 (11.3) 52 (10.6)
    Past 12 months 5 (1.7) 6 (3.1) 11 (2.2)
    Past 30 days 3 (1.0) 4 (2.1) 7 (1.4)
    Formerly used alcohol 60 (20.3) 48 (24.6) 108 (22.0)
    Stopped after the stroke occured 14 (4.7) 12 (6.2) 26 (5.3)
Diabetes Mellitus 103 (34.9) 64 (32.7) 167 (34.0) 0.604
Hyperlipidemia 93 (31.5) 65 (33.2) 158 (32.2) 0.704
Health institution category


<0.001
    Primary 87 (29.5) 61 (31.1) 148 (30.1)
    Secondary 41 (13.9) 75 (38.3) 116 (23.6)
    Tertiary 167 (56.6) 60 (30.6) 227 (46.2)
pill_burden 4.91 (1.68) 4.53 (2.16) 4.75 (1.90) 0.064
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
Show the code
file.remove("paper_5_table_1.docx")
[1] TRUE
Show the code
table_1 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_1.docx") 

Table 2

Show the code
table_2 <- 
    df_paper_05 %>% 
    filter(!is.na(morisky_4_cat)) %>% 
    select(
        morisky_4_cat, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, hosp_cat, 
        g_alcohol, dm, hyperlipidemia,side_effects,pill_burden) %>% 
    gtsummary::tbl_summary(
        by = morisky_4_cat,
        statistic = list(
            gtsummary::all_continuous()~"{mean} ({sd})",
            gtsummary::all_categorical()~"{n} ({p})"),
        missing = "no",
        digits = list(gtsummary::all_categorical() ~ c(0,1))
        ) %>% 
    gtsummary::add_overall(last=TRUE) %>% 
    gtsummary::add_p(
        pvalue_fun = ~gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p() %>% 
    gtsummary::modify_caption(
        "**Table 2**: Factors associated with medication adherence: 
        Morisky medication adherence scale dichotomized as  1-7 
        (Low) or 8 (High))")

table_2
Table 2: Factors associated with medication adherence: Morisky medication adherence scale dichotomized as 1-7 (Low) or 8 (High))
Characteristic High
N = 3281
Low
N = 1711
Overall
N = 4991
p-value2
Age in years 58 (12) 58 (11) 58 (11) 0.594
Gender


0.278
    Male 179 (54.6) 102 (59.6) 281 (56.3)
    Female 149 (45.4) 69 (40.4) 218 (43.7)
Domicile


0.456
    Rural 23 (7.0) 10 (5.8) 33 (6.6)
    Semi-Urban 103 (31.4) 63 (36.8) 166 (33.3)
    Urban 202 (61.6) 98 (57.3) 300 (60.1)
Educational Status


0.100
    None 39 (11.9) 10 (5.8) 49 (9.8)
    Primary 129 (39.3) 73 (42.7) 202 (40.5)
    Secondary 102 (31.1) 63 (36.8) 165 (33.1)
    Tertiary 58 (17.7) 25 (14.6) 83 (16.6)
Primary Occupation


<0.001
    Skilled 182 (55.5) 55 (32.2) 237 (47.5)
    Manual 55 (16.8) 53 (31.0) 108 (21.6)
    Others 14 (4.3) 6 (3.5) 20 (4.0)
    Retired 40 (12.2) 18 (10.5) 58 (11.6)
    Unemployed 37 (11.3) 39 (22.8) 76 (15.2)
Marital Status


0.494
    Currently Married 217 (66.2) 116 (67.8) 333 (66.7)
    Previously Married 98 (29.9) 45 (26.3) 143 (28.7)
    Never Married 13 (4.0) 10 (5.8) 23 (4.6)
Income in GHC


<0.001
    0-100 86 (26.5) 88 (51.5) 174 (35.1)
    101-250 111 (34.2) 39 (22.8) 150 (30.2)
    251-500 88 (27.1) 21 (12.3) 109 (22.0)
    >500 40 (12.3) 23 (13.5) 63 (12.7)
Stroke Type (Choose One)


0.203
    Ischemic Stroke 205 (70.9) 126 (78.8) 331 (73.7)
    Intracerebral Hemorrhagic Stroke 75 (26.0) 28 (17.5) 103 (22.9)
    Ischemic With Hemorrhagic Transformation 6 (2.1) 4 (2.5) 10 (2.2)
    Untyped Stroke (no CT scan available) 3 (1.0) 2 (1.3) 5 (1.1)
NIH Stroke Scale 3.7 (4.8) 6.4 (5.8) 4.6 (5.3) <0.001
Body Mass Index 26.5 (5.7) 26.7 (5.1) 26.6 (5.5) 0.668
History of tobacco use 28 (8.8) 17 (10.1) 45 (9.3) 0.657
Health institution category


<0.001
    Primary 83 (25.3) 65 (38.0) 148 (29.7)
    Secondary 53 (16.2) 65 (38.0) 118 (23.6)
    Tertiary 192 (58.5) 41 (24.0) 233 (46.7)
Alcohol use


0.566
    Never used alcohol 201 (61.5) 92 (53.8) 293 (58.8)
    Currently uses alcohol 34 (10.4) 18 (10.5) 52 (10.4)
    Past 12 months 7 (2.1) 4 (2.3) 11 (2.2)
    Past 30 days 5 (1.5) 2 (1.2) 7 (1.4)
    Formerly used alcohol 64 (19.6) 45 (26.3) 109 (21.9)
    Stopped after the stroke occured 16 (4.9) 10 (5.8) 26 (5.2)
Diabetes Mellitus 110 (33.5) 58 (33.9) 168 (33.7) 0.932
Hyperlipidemia 98 (29.9) 62 (36.3) 160 (32.1) 0.147
No. of Side Effects 1 (2) 2 (2) 2 (2) 0.042
pill_burden 4.83 (1.80) 4.58 (2.04) 4.74 (1.89) 0.206
1 Mean (SD); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
Show the code
file.remove("paper_5_table_2.docx")
[1] TRUE
Show the code
table_1 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_2.docx") 

Table 3

Show the code
table_hillbone_crude <- 
    df_paper_05 %>% 
    select(
        hillbone, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, 
        g_alcohol, dm, hyperlipidemia, hosp_cat, side_effects, 
        pill_burden) %>% 
    tbl_uvregression(
        y = hillbone, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    add_global_p() %>% 
    bold_p(t = 0.1)

table_hillbone_adj <- 
    df_paper_05 %>% 
    select(hillbone, a_agebase, educ, a_poccup3, income, g_alcohol, 
           hosp_cat, side_effects, a_gender, pill_burden) %>% 
    glm(hillbone ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p()

table_3 <- 
    tbl_merge(
    list(table_hillbone_crude, table_hillbone_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3**:Univartiate and multivariate linear 
        regression for Hillbone") 

table_3
Table 3:Univartiate and multivariate linear regression for Hillbone
Characteristic
Univariate
Multivariate
N Beta 95% CI p-value Beta 95% CI p-value
Age in years 491 0.04 0.01, 0.07 0.012 0.03 -0.01, 0.06 0.125
Gender 491

0.094


    Male


    Female
0.62 -0.11, 1.3
0.14 -0.62, 0.90 0.719
Domicile 491

0.179


    Rural




    Semi-Urban
0.90 -0.65, 2.4



    Urban
1.3 -0.19, 2.8



Educational Status 491

0.005


    None


    Primary
-0.95 -2.2, 0.32
-0.68 -1.9, 0.52 0.266
    Secondary
-2.0 -3.3, -0.71
-1.6 -2.9, -0.31 0.015
    Tertiary
-0.64 -2.1, 0.82
-0.67 -2.2, 0.83 0.383
Primary Occupation 491

<0.001


    Skilled


    Manual
-1.9 -2.8, -0.95
-1.5 -2.5, -0.61 0.001
    Others
-0.60 -2.4, 1.2
-0.37 -2.2, 1.5 0.694
    Retired
0.81 -0.34, 2.0
0.43 -0.75, 1.6 0.478
    Unemployed
1.0 -0.03, 2.0
0.58 -0.55, 1.7 0.314
Marital Status 491

0.394


    Currently Married




    Previously Married
0.53 -0.28, 1.3



    Never Married
-0.27 -2.0, 1.5



Income in GHC 488

0.009


    0-100


    101-250
0.84 -0.05, 1.7
0.08 -0.84, 0.99 0.867
    251-500
1.4 0.41, 2.4
0.40 -0.65, 1.5 0.454
    >500
-0.39 -1.6, 0.78
-0.13 -1.3, 1.0 0.826
Stroke Type (Choose One) 441

0.201


    Ischemic Stroke




    Intracerebral Hemorrhagic Stroke
-0.35 -1.3, 0.54



    Ischemic With Hemorrhagic Transformation
-1.6 -4.2, 0.90



    Untyped Stroke (no CT scan available)
-3.0 -6.6, 0.52



NIH Stroke Scale 479 -0.04 -0.10, 0.03 0.315


Body Mass Index 471 -0.03 -0.09, 0.04 0.445


History of tobacco use 479

0.216


    No




    Yes
-0.79 -2.0, 0.46



Alcohol use 490

0.026


    Never used alcohol


    Currently uses alcohol
-0.19 -1.4, 1.0
0.20 -0.96, 1.4 0.740
    Past 12 months
-2.0 -4.5, 0.43
-1.7 -4.1, 0.67 0.159
    Past 30 days
-3.3 -6.3, -0.25
-2.4 -5.3, 0.42 0.095
    Formerly used alcohol
-1.2 -2.1, -0.32
-0.68 -1.5, 0.17 0.117
    Stopped after the stroke occured
-0.75 -2.4, 0.88
0.44 -1.1, 2.0 0.576
Diabetes Mellitus 491

0.643


    No




    Yes
-0.18 -0.94, 0.58



Hyperlipidemia 491

0.889


    No




    Yes
0.05 -0.72, 0.83



Health institution category 491

<0.001


    Primary


    Secondary
-2.2 -3.2, -1.3
-1.8 -2.8, -0.84 <0.001
    Tertiary
0.86 0.05, 1.7
0.83 -0.10, 1.8 0.082
No. of Side Effects 491 -0.33 -0.49, -0.17 <0.001 -0.18 -0.34, -0.01 0.035
pill_burden 491 0.31 0.12, 0.50 0.002 0.36 0.17, 0.54 <0.001
Abbreviation: CI = Confidence Interval
Show the code
file.remove("paper_5_table_3.docx")
[1] TRUE
Show the code
table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_3.docx") 

Table 4

Show the code
table_morisky_crude <- 
    gtsummary::tbl_uvregression(
    df_paper_05,
    include = c(
        morisky_4_raw, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, hosp_cat,
        g_alcohol, dm, hyperlipidemia, side_effects, pill_burden),
    method = ordinal::clm,
    y = morisky_4_raw,
    exponentiate = TRUE,
    pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3),
    add_estimate_to_reference_rows = TRUE,
    hide_n = TRUE,
    tidy_fun = function(x, ...) broom::tidy(x, ..., p.values = TRUE),
    ) %>%
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p() 

table_morisky_adj <- 
    gtsummary::tbl_regression(
    ordinal::clm(
        morisky_4_raw ~ g_alcohol + educ + income + nihss_scale + 
        a_poccup3 + hosp_cat + side_effects, 
        data = df_paper_05), 
        exponentiate=TRUE,
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)
        ) %>% 
    gtsummary::bold_p() %>% 
    gtsummary::bold_labels()

table_4 <- 
    tbl_merge(
    list(table_morisky_crude, table_morisky_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 4**:Univartiate and multivariate Ordinal logistic 
        regression for Morisky") 

table_4
Table 4:Univartiate and multivariate Ordinal logistic regression for Morisky
Characteristic
Univariate
Multivariate
OR 95% CI p-value OR 95% CI p-value
Age in years 1.00 0.99, 1.02 0.591


Gender





    Male 1.00



    Female 1.23 0.85, 1.78 0.269


Domicile





    Rural 1.00



    Semi-Urban 0.78 0.34, 1.69 0.545


    Urban 1.00 0.44, 2.11 0.992


Educational Status





    None 1.00

    Primary 0.49 0.22, 0.99 0.058 0.37 0.15, 0.83 0.021
    Secondary 0.41 0.18, 0.84 0.021 0.20 0.08, 0.46 <0.001
    Tertiary 0.61 0.25, 1.36 0.237 0.19 0.07, 0.50 0.001
Primary Occupation





    Skilled 1.00

    Manual 0.32 0.20, 0.52 <0.001 0.28 0.16, 0.50 <0.001
    Others 0.69 0.27, 2.01 0.460 1.05 0.37, 3.36 0.924
    Retired 0.76 0.41, 1.44 0.385 0.55 0.28, 1.12 0.093
    Unemployed 0.39 0.24, 0.65 <0.001 0.42 0.22, 0.79 0.007
Marital Status





    Currently Married 1.00



    Previously Married 1.13 0.75, 1.71 0.566


    Never Married 0.60 0.26, 1.42 0.228


Income in GHC





    0-100 1.00

    101-250 2.54 1.62, 4.03 <0.001 1.89 1.11, 3.24 0.020
    251-500 3.79 2.22, 6.70 <0.001 3.51 1.78, 7.17 <0.001
    >500 1.50 0.86, 2.70 0.163 2.08 1.04, 4.25 0.040
Stroke Type (Choose One)





    Ischemic Stroke 1.00



    Intracerebral Hemorrhagic Stroke 1.46 0.91, 2.40 0.123


    Ischemic With Hemorrhagic Transformation 1.04 0.33, 3.97 0.944


    Untyped Stroke (no CT scan available) 0.94 0.19, 6.90 0.944


NIH Stroke Scale 0.92 0.89, 0.95 <0.001 0.97 0.93, 1.01 0.087
Body Mass Index 0.99 0.96, 1.03 0.738


History of tobacco use





    No 1.00



    Yes 0.90 0.50, 1.70 0.743


Health institution category





    Primary 1.00

    Secondary 0.53 0.34, 0.84 0.007 0.52 0.31, 0.88 0.014
    Tertiary 3.20 2.04, 5.08 <0.001 1.84 1.03, 3.29 0.040
Alcohol use





    Never used alcohol 1.00

    Currently uses alcohol 0.82 0.45, 1.55 0.534 0.51 0.26, 1.04 0.060
    Past 12 months 0.64 0.19, 2.51 0.486 0.44 0.12, 1.90 0.244
    Past 30 days 1.31 0.30, 9.00 0.742 1.26 0.27, 9.13 0.787
    Formerly used alcohol 0.61 0.39, 0.95 0.027 0.69 0.42, 1.15 0.154
    Stopped after the stroke occured 0.78 0.36, 1.79 0.538 1.50 0.63, 3.84 0.380
Diabetes Mellitus





    No 1.00



    Yes 0.98 0.67, 1.43 0.899


Hyperlipidemia





    No 1.00



    Yes 0.81 0.55, 1.18 0.263


No. of Side Effects 0.89 0.83, 0.97 0.005 0.96 0.88, 1.05 0.357
pill_burden 1.07 0.97, 1.18 0.163


Abbreviations: CI = Confidence Interval, OR = Odds Ratio
Show the code
file.remove("paper_5_table_4.docx")
[1] TRUE
Show the code
table_4 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_4.docx") 

Additional tables: Stratified by sex = Male

Show the code
table_hillbone_crude <- 
    df_paper_05 %>% 
    filter(a_gender == "Male") %>% 
    select(
        hillbone, a_agebase, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, 
        g_alcohol, dm, hyperlipidemia, hosp_cat, side_effects, 
        pill_burden) %>% 
    tbl_uvregression(
        y = hillbone, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    add_global_p() %>% 
    bold_p(t = 0.1)

table_hillbone_adj <- 
    df_paper_05 %>% 
    select(hillbone, a_agebase, educ, a_poccup3, income, dm, 
           hosp_cat, side_effects, pill_burden) %>% 
    glm(hillbone ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p()

table_3 <- 
    tbl_merge(
    list(table_hillbone_crude, table_hillbone_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3**:Univartiate and multivariate linear 
        regression for Hillbone") 

table_3
Table 3:Univartiate and multivariate linear regression for Hillbone
Characteristic
Univariate
Multivariate
N Beta 95% CI p-value Beta 95% CI p-value
Age in years 274 0.05 0.00, 0.09 0.045 0.03 0.00, 0.06 0.096
Domicile 274

0.551


    Rural




    Semi-Urban
-1.3 -3.6, 1.0



    Urban
-1.1 -3.3, 1.2



Educational Status 274

0.087


    None


    Primary
-1.1 -3.9, 1.7
-0.75 -1.9, 0.45 0.222
    Secondary
-1.9 -4.6, 0.85
-1.8 -3.1, -0.59 0.004
    Tertiary
-0.32 -3.2, 2.5
-0.94 -2.4, 0.50 0.203
Primary Occupation 274

<0.001


    Skilled


    Manual
-2.8 -4.1, -1.5
-1.5 -2.5, -0.63 <0.001
    Others
0.96 -3.8, 5.7
-0.35 -2.2, 1.5 0.705
    Retired
0.68 -0.81, 2.2
0.30 -0.86, 1.5 0.613
    Unemployed
0.25 -1.5, 2.0
0.66 -0.45, 1.8 0.243
Marital Status 274

0.657


    Currently Married




    Previously Married
-0.10 -1.7, 1.5



    Never Married
-1.1 -3.5, 1.3



Income in GHC 274

0.007


    0-100


    101-250
1.6 0.31, 2.9
0.17 -0.73, 1.1 0.706
    251-500
2.2 0.87, 3.6
0.54 -0.51, 1.6 0.315
    >500
0.51 -1.1, 2.1
-0.07 -1.2, 1.1 0.906
Stroke Type (Choose One) 245

0.541


    Ischemic Stroke




    Intracerebral Hemorrhagic Stroke
-0.05 -1.2, 1.1



    Ischemic With Hemorrhagic Transformation
-1.9 -4.9, 1.0



    Untyped Stroke (no CT scan available)
-1.8 -6.5, 2.9



NIH Stroke Scale 268 -0.04 -0.14, 0.06 0.471


Body Mass Index 267 -0.06 -0.16, 0.05 0.269


History of tobacco use 267

0.571


    No




    Yes
-0.40 -1.8, 1.0



Alcohol use 273

0.508


    Never used alcohol




    Currently uses alcohol
0.07 -1.4, 1.6



    Past 12 months
-1.9 -5.0, 1.1



    Past 30 days
-3.6 -7.8, 0.71



    Formerly used alcohol
-0.29 -1.6, 0.97



    Stopped after the stroke occured
-0.37 -2.3, 1.5



Diabetes Mellitus 274

0.034


    No


    Yes
-1.2 -2.3, -0.09
-0.47 -1.2, 0.27 0.215
Hyperlipidemia 274

0.723


    No




    Yes
0.20 -0.91, 1.3



Health institution category 274

<0.001


    Primary


    Secondary
-2.6 -3.9, -1.3
-1.8 -2.8, -0.84 <0.001
    Tertiary
0.98 -0.14, 2.1
0.79 -0.13, 1.7 0.092
No. of Side Effects 274 -0.36 -0.59, -0.13 0.002 -0.17 -0.33, -0.01 0.040
pill_burden 274 0.27 0.00, 0.54 0.047 0.41 0.23, 0.60 <0.001
Abbreviation: CI = Confidence Interval
Show the code
file.remove("paper_5_table_3_male.docx")
[1] TRUE
Show the code
table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_3_male.docx") 

Additional tables: Stratified by sex = Female

Show the code
table_hillbone_crude <- 
    df_paper_05 %>% 
    filter(a_gender == "Female") %>% 
    select(
        hillbone, a_agebase, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, 
        g_alcohol, dm, hyperlipidemia, hosp_cat, side_effects, 
        pill_burden) %>% 
    tbl_uvregression(
        y = hillbone, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    add_global_p() %>% 
    bold_p(t = 0.1)

table_hillbone_adj <- 
    df_paper_05 %>% 
    select(hillbone, a_agebase, educ, a_poccup3, a_domicile,g_alcohol,
           hosp_cat, side_effects, pill_burden) %>% 
    glm(hillbone ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p()

table_3 <- 
    tbl_merge(
    list(table_hillbone_crude, table_hillbone_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3**:Univartiate and multivariate linear 
        regression for Hillbone") 

table_3
Table 3:Univartiate and multivariate linear regression for Hillbone
Characteristic
Univariate
Multivariate
N Beta 95% CI p-value Beta 95% CI p-value
Age in years 217 0.03 -0.01, 0.08 0.180 0.02 -0.01, 0.06 0.156
Domicile 217

0.001


    Rural


    Semi-Urban
3.0 0.99, 5.0
0.16 -1.3, 1.6 0.831
    Urban
3.5 1.6, 5.4
0.52 -0.88, 1.9 0.471
Educational Status 217

0.152


    None


    Primary
-0.72 -2.1, 0.69
-0.68 -1.9, 0.52 0.267
    Secondary
-1.8 -3.4, -0.21
-1.7 -2.9, -0.42 0.009
    Tertiary
-1.1 -3.3, 1.2
-0.66 -2.1, 0.75 0.359
Primary Occupation 217

0.024


    Skilled


    Manual
-0.85 -2.2, 0.48
-1.5 -2.4, -0.54 0.002
    Others
-0.89 -2.9, 1.1
-0.31 -2.1, 1.5 0.733
    Retired
1.0 -0.80, 2.9
0.55 -0.62, 1.7 0.359
    Unemployed
1.3 0.01, 2.7
0.61 -0.47, 1.7 0.267
Marital Status 217

0.521


    Currently Married




    Previously Married
0.56 -0.49, 1.6



    Never Married
0.89 -1.7, 3.5



Income in GHC 214

0.330


    0-100




    101-250
0.24 -1.0, 1.5



    251-500
0.62 -0.79, 2.0



    >500
-1.2 -3.1, 0.59



Stroke Type (Choose One) 196

0.281


    Ischemic Stroke




    Intracerebral Hemorrhagic Stroke
-0.74 -2.2, 0.75



    Ischemic With Hemorrhagic Transformation
0.24 -5.2, 5.7



    Untyped Stroke (no CT scan available)
-4.8 -10, 0.65



NIH Stroke Scale 211 -0.04 -0.13, 0.05 0.426


Body Mass Index 204 -0.03 -0.11, 0.06 0.546


History of tobacco use 212

0.227


    No




    Yes
-3.3 -8.6, 2.1



Alcohol use 217

0.017


    Never used alcohol


    Currently uses alcohol
0.13 -2.1, 2.3
0.02 -1.1, 1.2 0.974
    Past 12 months
-1.4 -5.7, 2.9
-1.4 -3.7, 0.84 0.216
    Past 30 days
-2.7 -7.0, 1.6
-2.7 -5.5, 0.14 0.063
    Formerly used alcohol
-2.3 -3.6, -1.0
-0.73 -1.6, 0.11 0.090
    Stopped after the stroke occured
-0.37 -4.7, 3.9
0.33 -1.2, 1.9 0.667
Diabetes Mellitus 217

0.148


    No




    Yes
0.77 -0.27, 1.8



Hyperlipidemia 217

0.712


    No




    Yes
-0.20 -1.3, 0.87



Health institution category 217

<0.001


    Primary


    Secondary
-1.8 -3.1, -0.40
-1.7 -2.7, -0.74 <0.001
    Tertiary
0.73 -0.42, 1.9
0.98 0.08, 1.9 0.032
No. of Side Effects 217 -0.28 -0.50, -0.05 0.016 -0.18 -0.34, -0.02 0.025
pill_burden 217 0.32 0.06, 0.59 0.017 0.37 0.19, 0.54 <0.001
Abbreviation: CI = Confidence Interval
Show the code
file.remove("paper_5_table_3_female.docx")
[1] TRUE
Show the code
table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_3_female.docx") 

Additional Tables: Stratified by Age < 50

Show the code
table_hillbone_crude <- 
    df_paper_05 %>% 
    filter(a_agebase < 50) %>% 
    select(
        hillbone, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, 
        g_alcohol, dm, hyperlipidemia, hosp_cat, side_effects, 
        pill_burden) %>% 
    tbl_uvregression(
        y = hillbone, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    add_global_p() %>% 
    bold_p(t = 0.1)

table_hillbone_adj <- 
    df_paper_05 %>% 
    select(hillbone, a_agebase, educ, a_poccup3, income, 
           hosp_cat, side_effects, a_gender, dm, d_st_type, 
           nihss_scale) %>% 
    glm(hillbone ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p()

table_3 <- 
    tbl_merge(
    list(table_hillbone_crude, table_hillbone_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3**:Univartiate and multivariate linear 
        regression for Hillbone") 

table_3
Table 3:Univartiate and multivariate linear regression for Hillbone
Characteristic
Univariate
Multivariate
N Beta 95% CI p-value Beta 95% CI p-value
Age in years 102 -0.07 -0.22, 0.09 0.392 0.03 -0.01, 0.06 0.145
Gender 102

0.274


    Male


    Female
0.97 -0.78, 2.7
0.25 -0.54, 1.0 0.537
Domicile 102

0.939


    Rural




    Semi-Urban
-0.38 -4.5, 3.7



    Urban
-0.60 -4.6, 3.4



Educational Status 102

0.085


    None


    Primary
-2.7 -6.4, 0.94
-0.50 -1.8, 0.84 0.469
    Secondary
-3.6 -7.3, 0.03
-1.5 -2.9, -0.07 0.040
    Tertiary
-1.2 -5.1, 2.7
-1.1 -2.8, 0.49 0.172
Primary Occupation 102

0.001


    Skilled


    Manual
-3.8 -5.6, -1.9
-1.5 -2.5, -0.49 0.004
    Others
-1.2 -5.9, 3.6
-0.38 -2.4, 1.6 0.715
    Unemployed
-0.60 -3.4, 2.2
0.04 -1.2, 1.2 0.953
Marital Status 102

0.706


    Currently Married




    Previously Married
-0.83 -3.3, 1.7



    Never Married
-0.90 -3.9, 2.1



Income in GHC 101

0.015


    0-100


    101-250
1.8 -0.27, 3.9
0.34 -0.63, 1.3 0.489
    251-500
3.5 1.3, 5.6
1.1 0.00, 2.3 0.052
    >500
1.1 -1.9, 4.1
0.80 -0.51, 2.1 0.235
Stroke Type (Choose One) 95

0.041


    Ischemic Stroke


    Intracerebral Hemorrhagic Stroke
0.10 -1.7, 1.9
-0.48 -1.4, 0.46 0.318
    Ischemic With Hemorrhagic Transformation
-5.5 -10, -0.60
-0.10 -2.5, 2.3 0.937
    Untyped Stroke (no CT scan available)
-7.8 -16, 0.48
-0.35 -4.1, 3.4 0.855
NIH Stroke Scale 101 -0.18 -0.31, -0.04 0.011 0.00 -0.08, 0.08 0.958
Body Mass Index 100 -0.04 -0.22, 0.13 0.617


History of tobacco use 101

0.100


    No




    Yes
-4.2 -9.1, 0.81



Alcohol use 102

0.764


    Never used alcohol




    Currently uses alcohol
-0.18 -2.6, 2.3



    Past 12 months
0.45 -5.8, 6.7



    Past 30 days
1.4 -4.8, 7.7



    Formerly used alcohol
-1.6 -3.8, 0.63



    Stopped after the stroke occured
-1.1 -5.5, 3.4



Diabetes Mellitus 102

0.092


    No


    Yes
-1.8 -4.0, 0.31
-0.33 -1.1, 0.45 0.409
Hyperlipidemia 102

0.322


    No




    Yes
-0.94 -2.8, 0.93



Health institution category 102

<0.001


    Primary


    Secondary
-4.3 -6.4, -2.3
-2.6 -3.6, -1.5 <0.001
    Tertiary
1.5 -0.21, 3.2
-0.20 -1.2, 0.83 0.701
No. of Side Effects 102 -0.55 -0.86, -0.25 <0.001 -0.17 -0.34, -0.01 0.043
pill_burden 102 0.00 -0.47, 0.47 0.990


    Retired



-0.03 -1.3, 1.2 0.966
Abbreviation: CI = Confidence Interval
Show the code
file.remove("paper_5_table_3_less_50.docx")
[1] TRUE
Show the code
table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_3_less_50.docx") 

Additional Tables: Stratified by Age >= 50

Show the code
table_hillbone_crude <- 
    df_paper_05 %>% 
    filter(a_agebase >= 50) %>% 
    select(
        hillbone, a_agebase, a_gender, a_domicile, educ, a_poccup3, 
        maristat, income, d_st_type, nihss_scale, bmi, tobacco_use, 
        g_alcohol, dm, hyperlipidemia, hosp_cat, side_effects, 
        pill_burden) %>% 
    tbl_uvregression(
        y = hillbone, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    add_global_p() %>% 
    bold_p(t = 0.1)

table_hillbone_adj <- 
    df_paper_05 %>% 
    select(hillbone, a_agebase, educ, a_poccup3, g_alcohol, 
           hosp_cat, side_effects, a_gender, pill_burden, a_domicile) %>% 
    glm(hillbone ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p()

table_3 <- 
    tbl_merge(
    list(table_hillbone_crude, table_hillbone_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3**:Univartiate and multivariate linear 
        regression for Hillbone") 

table_3
Table 3:Univartiate and multivariate linear regression for Hillbone
Characteristic
Univariate
Multivariate
N Beta 95% CI p-value Beta 95% CI p-value
Age in years 389 0.09 0.05, 0.14 <0.001 0.02 -0.01, 0.06 0.154
Gender 389

0.202


    Male


    Female
0.52 -0.28, 1.3
0.13 -0.63, 0.88 0.745
Domicile 389

0.077


    Rural


    Semi-Urban
1.1 -0.55, 2.8
0.18 -1.3, 1.6 0.811
    Urban
1.7 0.09, 3.3
0.52 -0.88, 1.9 0.465
Educational Status 389

0.046


    None


    Primary
-0.66 -2.0, 0.70
-0.66 -1.9, 0.55 0.285
    Secondary
-1.7 -3.1, -0.32
-1.6 -2.9, -0.34 0.013
    Tertiary
-0.70 -2.3, 0.90
-0.60 -2.1, 0.85 0.417
Primary Occupation 389

<0.001


    Skilled


    Manual
-1.2 -2.2, -0.14
-1.5 -2.4, -0.54 0.002
    Others
-0.35 -2.3, 1.6
-0.37 -2.2, 1.5 0.694
    Retired
1.1 -0.09, 2.3
0.54 -0.62, 1.7 0.362
    Unemployed
1.4 0.28, 2.5
0.58 -0.52, 1.7 0.303
Marital Status 389

0.268


    Currently Married




    Previously Married
0.71 -0.15, 1.6



    Never Married
0.10 -2.1, 2.4



Income in GHC 387

0.114


    0-100




    101-250
0.51 -0.49, 1.5



    251-500
0.75 -0.36, 1.9



    >500
-0.81 -2.1, 0.47



Stroke Type (Choose One) 346

0.632


    Ischemic Stroke




    Intracerebral Hemorrhagic Stroke
-0.54 -1.6, 0.54



    Ischemic With Hemorrhagic Transformation
0.08 -2.9, 3.1



    Untyped Stroke (no CT scan available)
-1.8 -5.7, 2.1



NIH Stroke Scale 378 0.02 -0.06, 0.09 0.702


Body Mass Index 371 -0.02 -0.09, 0.05 0.544


History of tobacco use 378

0.376


    No




    Yes
-0.58 -1.9, 0.71



Alcohol use 388

0.010


    Never used alcohol


    Currently uses alcohol
-0.16 -1.6, 1.2
0.05 -1.1, 1.2 0.934
    Past 12 months
-2.6 -5.2, 0.08
-1.4 -3.7, 0.85 0.219
    Past 30 days
-5.2 -8.7, -1.6
-2.7 -5.5, 0.15 0.064
    Formerly used alcohol
-1.1 -2.1, -0.15
-0.72 -1.6, 0.13 0.098
    Stopped after the stroke occured
-0.71 -2.5, 1.0
0.38 -1.2, 1.9 0.634
Diabetes Mellitus 389

0.888


    No




    Yes
0.06 -0.77, 0.89



Hyperlipidemia 389

0.511


    No




    Yes
0.29 -0.57, 1.1



Health institution category 389

<0.001


    Primary


    Secondary
-1.8 -2.8, -0.69
-1.7 -2.7, -0.75 <0.001
    Tertiary
0.69 -0.22, 1.6
0.97 0.06, 1.9 0.036
No. of Side Effects 389 -0.23 -0.42, -0.04 0.016 -0.18 -0.34, -0.02 0.026
pill_burden 389 0.38 0.17, 0.58 <0.001 0.36 0.18, 0.54 <0.001
Abbreviation: CI = Confidence Interval
Show the code
file.remove("paper_5_table_3_greater_50.docx")
[1] TRUE
Show the code
table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_5_table_3_greater_50.docx")