NNJ Maternal Knowledge

library(tidyverse)
nnj_mat <- dget("nnj") %>% 
    select(
        sid, mage, meduc, maristat, occup, mincome, nhis, gravid, 
        parity, delplace, matbldgrp, mrhesus, prev_nnj, heardnnj, anc,
        hprof, friend, radio, social, prev_chn, othersnnj, nnj_oth_2, 
        what_is_nnj, danger1, danger2, danger_signs, nnjharm, interval,
        interval2, interval3, interval4, nnj_treat_home, yesjaund, 
        jbabies, ifyes3, resistance2, mat_anemia:mat_syphilis, primip,
        mincome_cat, prev_nnj2, adm_age_hours, comp_death:comp_eye, 
        nnj_noidea:nnj_oth_cause, sunlight:herbs, nnjharm2, moccup_cat) %>% 
    mutate(
        single = case_when(
            maristat == "Single" ~ "Yes",
            maristat == "Married" ~ "No",
            maristat == "Co-habiting" ~ "No"),
        nhis2 = case_when(
            nhis == "None" ~ "No",
            nhis == "National" ~ "Yes",
            nhis == "Private" ~ "Yes"),
        rhesus_neg = case_when(
            mrhesus == "Negative" ~ "Yes",
            mrhesus == "Positive" ~ "No"),
        income_cat = case_when(
            mincome_cat == "Less than GHC320" ~ "<=650GHC",
            mincome_cat == "320 to 650GHC" ~ "<=650GHC",
            mincome_cat == ">650 to 1500GHC" ~ ">650GHC",
            mincome_cat == "Greater than 1500" ~ ">650GHC"),
        what_is_nnj = case_when(
            what_is_nnj == "Right" ~ "Yes",
            what_is_nnj == "Wrong"~ "No") %>% factor())
nnj_mat <-
    nnj_mat %>% 
    labelled::set_variable_labels(
        meduc = "Educational level",
        mage = "Age (years)",
        gravid = "Gravidity",
        single = "Single Mother",
        nhis2 = "Insurance coverage",
        rhesus_neg = "Rhesus negative",
        mincome_cat = "Income level",
        prev_nnj2 = "Previous NNJ",
        income_cat = "Income (GHC)",
        what_is_nnj = "Know what NNJ is"
        )

Table 1A:

gtsummary::theme_gtsummary_compact()

Setting theme "Compact"

nnj_mat %>% 
    select(
        what_is_nnj,  mage, meduc, single, gravid, primip, nhis2, 
        delplace, matbldgrp, rhesus_neg, income_cat, prev_nnj2, 
        moccup_cat) %>% 
    gtsummary::tbl_summary(
        by = what_is_nnj,
        type = list(
            parity = "continuous",
            gravid = "continuous"
            )
    ) %>% 
    gtsummary::add_overall(last=T) %>%
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p()

Table 1: Association between mothers knowing about NNJ and maternal characteristics

Characteristic	No N = 78 1	Yes N = 145 1	Overall N = 223 1	p-value 2
Age (years)	26 (22, 30)	31 (26, 35)	29 (25, 34)	<0.001
Educational level				<0.001
None	9 (12%)	4 (2.8%)	13 (5.8%)
Prim/JHS	44 (56%)	49 (34%)	93 (42%)
Senior Hiigh	21 (27%)	52 (36%)	73 (33%)
Tertiary	4 (5.1%)	40 (28%)	44 (20%)
Single Mother	33 (42%)	17 (12%)	50 (22%)	<0.001
Gravidity	2 (1, 2)	3 (2, 4)	2 (1, 4)	<0.001
Primiparous mother	48 (62%)	53 (37%)	101 (45%)	<0.001
Insurance coverage	67 (86%)	133 (92%)	200 (90%)	0.172
Place of delivery				0.108
KATH	35 (45%)	71 (49%)	106 (48%)
District Hospital	29 (37%)	37 (26%)	66 (30%)
Private Hospital	10 (13%)	33 (23%)	43 (19%)
Others	4 (5.1%)	4 (2.8%)	8 (3.6%)
Mother's blood group				0.814
A	12 (16%)	19 (13%)	31 (14%)
B	13 (18%)	27 (19%)	40 (19%)
AB	2 (2.7%)	2 (1.4%)	4 (1.9%)
O	47 (64%)	93 (66%)	140 (65%)
Unknown	4	4	8
Rhesus negative	2 (2.7%)	20 (14%)	22 (10%)	0.008
Unknown	4	4	8
Income (GHC)				0.012
<=650GHC	33 (79%)	64 (57%)	97 (63%)
>650GHC	9 (21%)	49 (43%)	58 (37%)
Unknown	36	32	68
Previous NNJ	4 (5.1%)	34 (23%)	38 (17%)	<0.001
Mother's Occupation				<0.001
Civil / Public servant	4 (5.1%)	41 (28%)	45 (20%)
Self/Private	47 (60%)	83 (57%)	130 (58%)
Student	6 (7.7%)	7 (4.8%)	13 (5.8%)
Unemployed	21 (27%)	14 (9.7%)	35 (16%)
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Fisher’s exact test; Pearson’s Chi-squared test

Table 1B

nnj_mat %>% 
    glm(what_is_nnj ~ mage + meduc + single + parity + 
            rhesus_neg + prev_nnj2 + moccup_cat,
        data = ., 
        family = binomial(link = "logit")) %>% 
    gtsummary::tbl_regression(
        exponentiate = TRUE,
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p()

Table 2: Adjusted association between mothers knowing about NNJ and maternal characteristics

Characteristic	OR 1	95% CI 1	p-value
Age (years)	1.06	0.97, 1.15	0.184
Educational level
None	—	—
Prim/JHS	2.78	0.69, 12.8	0.161
Senior Hiigh	6.45	1.52, 31.6	0.014
Tertiary	19.1	2.79, 152	0.003
Single Mother
No	—	—
Yes	0.33	0.13, 0.82	0.018
parity	1.08	0.78, 1.49	0.645
Rhesus negative
No	—	—
Yes	3.36	0.79, 23.8	0.145
Previous NNJ
No	—	—
Yes	3.87	1.20, 15.6	0.035
Mother's Occupation
Civil / Public servant	—	—
Self/Private	0.74	0.16, 2.96	0.684
Student	0.80	0.12, 5.29	0.812
Unemployed	0.38	0.07, 1.71	0.216
1 OR = Odds Ratio, CI = Confidence Interval

Table 2

nnj_mat %>% 
    filter(heardnnj == "Yes") %>% 
    mutate(
        sources = (friend == "Yes") + (anc == "Yes")  + 
            (hprof == "Yes")  + (prev_chn == "Yes")  + 
            (radio == "Yes")  + (social == "Yes")  +  
            (othersnnj == "Yes"),
        sources = factor(
            sources, levels = c(1,2,3), labels = c("One","Two", "Three")),
        source_health_prof = case_when(
            (hprof == "Yes") | (anc == "Yes") ~ "Yes", TRUE ~ "No"),
        complication = (comp_death == "Yes") + (comp_retard == "Yes") +                  (comp_damage == "Yes") + (comp_devt == "Yes") + 
            (comp_eye == "Yes"),
        causes_known = (
            nnj_bld_incomp == "Yes") + (nnj_breastfeed == "Yes") + 
            (nnj_infection == "Yes") + (nnj_fever == "Yes") + 
            (nnj_liver_dxs == "Yes")) %>%
    select(
        sources, source_health_prof, nnjharm2, danger_signs, 
        nnj_treat_home, complication, causes_known) %>% 
    gtsummary::tbl_summary(
        digits = list(
            gtsummary::all_categorical() ~ c(0, 1)),
        label = list(
            sources ~ "Source of knowledge on NNJ",
            source_health_prof ~ "Source of knowledge from health personnel",
            nnjharm2 ~ "Knowledge NNJ is harmful",
            causes_known ~ "Number of causes of NNJ mentioned",
            complication ~ "Number of complications of NNJ mentioned")
            ) %>%
    gtsummary::bold_labels()

Table 3: Mothers knowledge about NNJ

Characteristic	N = 151 1
Source of knowledge on NNJ
One	118 (78.1%)
Two	29 (19.2%)
Three	4 (2.6%)
Source of knowledge from health personnel	79 (52.3%)
Knowledge NNJ is harmful	81 (53.6%)
Number of NNJ danger signs mentioned
None	106 (70.2%)
One	30 (19.9%)
Two	15 (9.9%)
NNJ can be treated at home	99 (65.6%)
Number of complications of NNJ mentioned
0	70 (46.4%)
1	67 (44.4%)
2	14 (9.3%)
Number of causes of NNJ mentioned
0	99 (65.6%)
1	49 (32.5%)
2	3 (2.0%)
1 n (%)

Figure

#|label: fig-one
#|fig.width: 9
#|fig.height: 4
#|fig.cap: "Distribution of mothers' knkowledge of causes, complications, "

plot_one <- 
    nnj_mat %>% 
    select(
        friend, anc, hprof, prev_chn, radio, social,  othersnnj,
        nnj_malaria:nnj_oth_cause, sunlight:herbs, comp_death:comp_eye) %>% 
    rename(
        source_friend = friend,
        source_anc = anc,
        source_hprof = hprof, 
        source_prev_chn = prev_chn,
        source_radio = radio,
        source_social = social,
        source_othersnnj = othersnnj,
        tmt_breastfeed = breastfeed,
        tmt_herbs = herbs,
        tmt_glucose = glucose,
        tmt_sunlight = sunlight
        ) %>% 
    pivot_longer(cols = source_friend:comp_eye) %>% 
    filter(value == "Yes") %>% 
    group_by(name) %>% 
    count() %>% 
    ungroup() %>% 
    mutate(
        grp = str_extract(name, "^[a-z]+"),
        perc = n/151,
        grp = case_when(
            grp == "comp" ~ "Complications",
            grp == "nnj" ~ "Cause",
            grp == "source" ~ "Source of Knowledge",
            grp == "tmt" ~ "Treatment"),
        name = case_when(
            name == "comp_damage" ~ "Brain Damage",
            name == "comp_death" ~ "Death",
            name == "comp_devt" ~ "Developmental Delay",
            name == "comp_eye" ~ "Visual Impairment",
            name == "comp_retard" ~ "Mental Retardation",
            name == "nnj_bld_incomp" ~ "Blood incompatibility",
            name == "nnj_breastfeed" ~ "Breastfeeding",
            name == "nnj_candidiasis" ~ "Candidiasis",
            name == "nnj_eye_dxs" ~ "Eye Disease",
            name == "nnj_fever" ~ "Fever",
            name == "nnj_infection" ~ "Infections",
            name == "nnj_liver_dxs" ~ "Liver Disease",
            name == "nnj_malaria" ~ "Malaria",
            name == "nnj_oilyfood" ~ "Oily Foods",
            name == "nnj_oth_cause" ~ "Others",
            name == "source_anc" ~ "Antenatal Clinic",
            name == "source_friend" ~ "Friends/Relatives",
            name == "source_hprof" ~ "Health Professional",
            name == "source_othersnnj" ~ "Others",
            name == "source_prev_chn" ~ "Previous child Birth",
            name == "source_radio" ~ "Radio/Television",
            name == "source_social" ~ "Social Media",
            name == "tmt_breastfeed" ~ "Breastfeeding",
            name == "tmt_glucose" ~ "Oral Glucose",
            name == "tmt_herbs" ~ "Herbs",
            name == "tmt_sunlight" ~ "Sunlight")
        ) %>%
    ggplot(
        aes(
            x = fct_reorder(name, n), 
            y = n, 
            label = paste0(
                n, 
                "(", 
                scales::percent(perc, accuracy = 0.1), ")"))) + 
    geom_bar(stat = 'identity', fill = "gray45") +
    coord_flip()+
    labs(x = NULL, y = NULL)+
    ylim(c(0,100))+
    geom_text(
        vjust = 0.25, 
        hjust = -0.05,
        color= "black", 
        size = 3, 
        fontface="italic")+
    facet_wrap("grp", nrow = 2, scales = "free")+
    theme_light()+
    theme(
        text = element_text(family = "serif", size = 12),
        strip.background = element_rect(fil = "white"),
        strip.text = element_text(colour = "black", size = 10, face = "bold"))

plot_one

ggsave("plot-one.pdf", width = 7, height = 4.5)