Pings-2 BP Data Analysis

Load rerquired packages

Read in the data

Code

df_bp <-dget("df_pings2_bp_data")

Boxplot of BPs for sites

Code

df_bp %>% 
    select(
        Site, sbp_m0,  dbp_m0, sbp_m1, dbp_m1, sbp_m3, dbp_m3, sbp_m6, dbp_m6, 
        sbp_m9, dbp_m9, sbp_m12, dbp_m12) %>% 
    pivot_longer(
        cols = c(starts_with("sbp"), starts_with("dbp")), 
        names_to = "measure", values_to = "values") %>% 
     mutate(
        mth = str_extract(measure, "\\d+$") %>% as.numeric() %>% as.factor(),
        vital = str_extract(measure,  "^[a-z]+")) %>% 
    ggplot(aes(x=mth, y=values, color = vital)) + 
    geom_boxplot() + 
    theme_light() +
    labs(x = "Month", y = "Pressure (mmHg)")+
    facet_wrap(vars(Site), nrow = 4)

Figure 1: Boxplot of Blood Pressure for each Site

Measured blood pressure per site

Code

df_bp %>% 
    select(Site, sbp_m0, sbp_m1, sbp_m3, sbp_m6, sbp_m9, sbp_m12) %>% 
    pivot_longer(
        cols = c(starts_with("sbp")), 
        names_to = "measure", values_to = "values") %>% 
     mutate(
        mth = str_extract(measure, "\\d+$") %>% as.numeric() %>% as.factor(),
        vital = str_extract(measure,  "^[a-z]+"),
        mth = paste("Month", as.character(mth))) %>% 
    drop_na(values) %>% 
    group_by(Site, mth) %>% 
    summarise(count = n()) %>% 
    pivot_wider(id_cols = Site, names_from = mth, values_from = count) %>% 
    relocate(`Month 12`, .after = `Month 9`) %>% 
    ungroup() %>% 
    kableExtra::kable()

Table 1: Distribution of measured systolic blood pressure per visit, per Site

Site	Month 0	Month 1	Month 3	Month 6	Month 9	Month 12
AGOGO SITE	28	28	27	27	27	27
ANKAASE SITE	23	23	21	21	21	20
CCTH SITE	51	51	46	44	42	37
KATH SITE	125	121	114	109	105	109
KNUST SITE	52	24	25	23	19	28
KORLE-BU SITE	57	52	48	45	40	45
KUMASI SOUTH HOSPITAL	38	33	33	33	30	29
KWADASO SITE	36	35	33	33	32	30
MANHYIA GOVT HOSPITAL	42	35	30	29	31	25
TAFO SITE	47	47	47	47	46	45

Code

df_bp_long <- 
    df_bp %>% 
    select(
        Site, sid=`RedCap ID`, sbp_m0, sbp_m1, sbp_m3, sbp_m6, sbp_m9, sbp_m12, 
        dbp_m0, dbp_m1, dbp_m3, dbp_m6, dbp_m9, dbp_m12) %>% 
    pivot_longer(
        cols = c(starts_with("sbp"),starts_with("dbp")), 
        names_to = "measure", 
        values_to = "values") %>% 
    filter(!is.na(values)) %>% 
    mutate(
        mth = str_extract(measure, "\\d+$") %>% as.numeric() %>% as.factor(),
        vital = str_extract(measure,  "^[a-z]+"),
        mth = paste("Month", as.character(mth))) %>% 
    select(sid, values, mth, vital, Site) %>% 
    pivot_wider(id_cols = c(sid, mth), values_from = values, names_from = vital) %>% 
    arrange(sid, mth) %>% 
    mutate(
        hpt_status = case_when(
            sbp >= 140 | dbp >= 90 ~ "Hypertensive",
            sbp >= 120 | dbp >= 80 ~ "Borderline",
            sbp < 120  | dbp <  80 ~ "Normal") %>% 
            factor(levels = c("Normal","Borderline", "Hypertensive")),
        mth = factor(
            mth, 
            levels = c(
                "Month 0", "Month 1", "Month 3", "Month 6", "Month 9", 
                "Month 12")
                )
            )

df_bp_long %>% 
    select(hpt_status, mth) %>% 
    gtsummary::tbl_summary(by=mth) %>% 
    gtsummary::bold_labels()

Table 2: Hypertension status at visits

Characteristic	Month 0, N = 499¹	Month 1, N = 449¹	Month 3, N = 424¹	Month 6, N = 411¹	Month 9, N = 393¹	Month 12, N = 395¹
hpt_status
Normal	3 (0.6%)	37 (8.2%)	24 (5.7%)	28 (6.8%)	30 (7.6%)	41 (10%)
Borderline	26 (5.2%)	122 (27%)	145 (34%)	122 (30%)	127 (32%)	162 (41%)
Hypertensive	470 (94%)	290 (65%)	255 (60%)	261 (64%)	236 (60%)	192 (49%)
¹ n (%)

Code

df_bp_long %>%
    ggplot(aes(x = factor(mth), fill = hpt_status)) +
    geom_bar(position = "fill") +
    stat_count(
        geom = "text",
        aes(
            label = paste(after_stat(count))),
             position=position_fill(vjust=0.5), colour="white"
            ) +
    theme_light()+
    labs(
        fill = "Hypertension Status",
        x = "Months after recruitment",
        y = "Count",
        title = "Blood Pressure status over the study period"
        ) +
    scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
    theme(legend.position="top")

Figure 2: Hypertension status are review

Code

# df_bp_long %>% 
# #    filter(vital == "sbp") %>% 
#     ggplot(aes(x = as.numeric(as.character(mth)), y = values, group = sid)) +
#     geom_line(alpha =.4, col = "pink") +
#     stat_summary(
#         fun.data = mean_sdl, 
#         geom="line", 
#         colour = "black", 
#         linewidth = .8, 
#         group=1, linetype = 2) +
#         theme_light() +
#     labs(x = "Months")+
#     scale_x_continuous(breaks=c(0,1,3, 6, 9, 12))+
#     facet_wrap(vars(vital), nrow = 2, scales = "free",)

Code

# df_bp %>% 
#     arrange(dbp_m0_2) %>%
#     filter(!is.na(dbp_m0_2)) %>% 
#     arrange(sbp_m0_2) %>%
#     mutate(n = row_number()) %>% 
#     ggplot()+
#     geom_point(aes(y = sbp_m0_2, x = n), color = "red") +
#     geom_point(aes(y = sbp_m0_3, x = n), color = "blue")+
#     geom_segment(aes(y=sbp_m0_2 , yend = sbp_m0_3, x=n,  xend = n), show.legend = F)