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Abstract

Ghana’s under-five mortality rate far exceeds the Sustainable Development Goal (SDG) 3.2 Target of 25 deaths per thousand live births by 2030. Therefore to improve upon the situation, it is imperative that the factors which determine the health status of children are investigated. This study therefore used data from the 2014 Ghana Demographic and Health Survey to investigate the socio-economic determinants of child health status in Ghana by employing the binary probit model. The study revealed that, Ewe, Grusi, Muslim and Christian children, children from urban areas, Greater Accra, Northern, Ashanti, Upper east, Eastern and Central regions, were more probable to contract cough. Also children with uneducated mothers, those whose mothers had uneducated partners as well as those whose mothers had no health insurance were revealed to be more likely to be anaemic. Further, male children and children from non-wealthy households were revealed to be more likely to have suffered diarrhoea, fever and anaemia. Also children with employed mothers and those with mothers with big distance challenges to seek care were found to be more likely to have fever. These findings, point out the essence of socio-economic factors to child health outcomes and hence the need to be given attention in child health survival interventions in Ghana.
Copyright © 2017 Mustapha Immurana, Arabi. U r. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
International Journal of Health, 5 (2) (2017) 100-106
International Journal of Health
Website: www.sciencepubco.com/index.php/IJH
doi: 10.14419/ijh.v5i2.7806
Research paper
Socio-economic factors and child health status in Ghana
Mustapha Immurana 1*, Arabi, U.2
1 Research Scholar, Department of Economics, Mangalore University, Mangalagangotri 574119, Karnataka State, India
2 Professor and Research Guide, Department of Economics, Mangalore University, Mangalagangotri 574119, Karnataka State, India
*Corresponding author E-mail: mustaphaimmurana@gmail.com
Abstract
Ghana’s under-five mortality rate far exceeds the Sustainable Development Goal (SDG) 3.2 Target of 25 deaths per thousand live births
by 2030. Therefore to improve upon the situation, it is imperative that the factors which determine the health status of children are inves-
tigated. This study therefore used data from the 2014 Ghana Demographic and Health Survey to investigate the socio-economic determi-
nants of child health status in Ghana by employing the binary probit model. The study revealed that, Ewe, Grusi, Muslim and Christian
children, children from urban areas, Greater Accra, Northern, Ashanti, Upper east, Eastern and Central regions, were more probable to
contract cough. Also children with uneducated mothers, those whose mothers had uneducated partners as well as those whose mothers
had no health insurance were revealed to be more likely to be anaemic. Further, male children and children from non-wealthy households
were revealed to be more likely to have suffered diarrhoea, fever and anaemia. Also children with employed mothers and those with
mothers with big distance challenges to seek care were found to be more likely to have fever. These findings, point out the essence of
socio-economic factors to child health outcomes and hence the need to be given attention in child health survival interventions in Ghana.
Keywords: Child Health Status; Socio-Economic Factors; Diarrhoea; Anaemia; Cough; Fever; Ghana
1. Introduction
The Health of Children sends signals about the strength of the
future human capital base of any nation and hence economic
growth and development. Therefore it is not surprising that tack-
ling the issue of improving the health of children is a public health
concern globally and hence the sustainable development goal
(SDG) 3.2 which aims at reducing the mortality of children less
than five years to 25 deaths per thousand live births by 2030.
Thus the SDG 3.2 became necessary given that, according to the
World Health Organization [WHO] (2016), 5.9 million children
less than five years of age died in 2015 mostly due to pneumonia,
diarrhoea, birth asphyxia and malaria with about 45 Percent of
these deaths being associated with Malnutrition.
Further, Sub-Saharan African children are more than fourteen
times more likely to die before their fifth birth day relative to
those in the developed region (WHO, 2016) and hence it was not
surprising that according to the United Nations [UN] (2015), al-
most half of the deaths in children less than five years of age in
2015 occurred in Sub-Saharan Africa.
Ghana as a Sub-Saharan African country has an under-five mortal-
ity rate of 60 deaths per thousand live births (Ghana Statistical
Service [GSS], Ghana Health Service [GHS] & ICF International,
2015). In addition, Malaria which has fever as one of its major
signs, is a public health problem in Ghana with children being one
of the most vulnerable groups. Also Pneumonia which has cough
as some of its symptoms combined with some respiratory tract
infections are major killers of younger children in Ghana (GSS,
GHS, & ICF International, 2015). Moreover, it has been reported
that the death of children in Ghana that can be attributed to diar-
rhoea and pneumonia significantly increased in the 2012-2013
period (GHS, n.d). Also, the presence of the anaemia control pro-
gramme in Ghana, tells us about the significance of tackling
anaemia to the health of the entire population in Ghana which
includes child health. Therefore it was not surprising that the GSS,
GHS, & ICF International (2015) contend that 66% of children in
Ghana are anaemic.
Based on the above, this study therefore investigated how socio-
economic factors determine the health status (Fever, Cough, Diar-
rhoea and Anaemia) of children in Ghana. This would help bring
out the factors that are paramount to child health and hence inform
policy makers on how to improve upon the current child mortality
rate in Ghana in our drive towards achieving the SDG 3.2.
2. Literature review
The main theoretical basis of the work was based on the work of
Grossman (1999) which basically tells us that individuals are the
producers of health and hence certain factors enter the health pro-
duction function as inputs. The model points out how factors such
as age, education and income affect the health production func-
tion. However, to identify most of the socio-economic factors with
regard to child health, the model of Mosley and Chen (1984) is of
much relevance. The framework of Mosley and Chen (1984)
points out how socio-economic factors broadly grouped as Indi-
vidual-level indicators or variables: Parental productivity (skill,
education), attitudes, traditions or cultures or norms; Household-
level Indicators or variables: income/wealth e.t.c and Community-
level indicators or variables: health system, ecological setting and
political economy work through some proximate determinants
such as birth interval, age e.t.c to affect child health or survival.
On the related works, Escobar et al. (2015) in Brazil revealed in
addition to other factors that children who were younger, suffering
under nutrition, with low maternal education, with lower house-
hold socioeconomic status and with another child suffering diar-
rhoea in the household had higher risk of Diarrhoea. Fakir and
International Journal of Health
Khan (2015) in Bangladesh found per capita income to improve
the health status of children but not household assets. Further,
better maternal literacy was revealed to tweak the health of male
children at the cost of female children and in addition children
with elderly (older) siblings were revealed to have better nutri-
tional status. Urke and Mittelmark (2015) found intimate partner
violence to have a weak but unwavering influence on signs of
illness in Peru and Bolivia but not in Colombia after restraining
(controlling) childcare. Kadam, Gowri, and Thirumugam (2013) in
India found infant health, mortality and morbidity to be deter-
mined by household income, knowledge and education of parents,
mother’s health pre and during pregnancy, family structure, envi-
ronmental factors such as hygiene and existence and quality of
health services. Joshi, Gupta, Joshi and Vipul (2011) found a high
significant association between maternal factors such as dietary
knowledge, monthly per-capita income, literacy, occupation and
child nutrition in Nepal.
With regard to Ghana, Quansah, Ohene, Norman, Mireku and
Karikari (2016) revealed the major determinants of child health to
be family income, place of residence, maternal education and high
dependency (multiparousity) using a qualitative review of pub-
lished articles. Fosu-Brefo and Arthur (2015) revealed that early
breast feeding immensely impact child health. In addition they
found that ages of the child and mother, mother’s education,
wealth of the household and child’s size at birth had significant
influences on child health in Ghana. Aheto, Keegan, Taylor and
Diggle (2015) found increased risk of malnutrition among children
to be linked with multiple births, unavailability of toilet facilities,
lengthy breast feeding period, small birth size, poverty, mothers
without national health insurance and history of diarrhoea epi-
sodes. Conversely, a rise in maternal body mass index and years
of education were found to be linked with decreased malnutrition.
Abubakari, Kynast-Wolf, and Jahn (2015) among other findings
revealed that female infants, rural mother’s and mothers with diar-
rhoea episodes during pregnancy were associated with low birth
weight in the Northern region of Ghana. Amugsi et al. (2015)
among other findings revealed older children to have a higher risk
of getting cough and diarrhoea relative to younger children in
Ghana. Amugsi, Mittelmark, Lartey, Matanda, and Urke (2014)
revealed that maternal weight, the number of children less than
five years, place of residence and wealth index, child’s age and
mother’s age to be immensely linked with height-for-age Z-scores
(Children’s Growth) in Ghana. Borbor, Kumi-Kyereme, Yendaw
and Adu-Opong (2014) revealed that, younger children, children
from poor households, with higher household size, with lesser
paternal and maternal education, with younger mothers and from a
rural area were more likely to have anaemia. Egbi et al. (2014)
revealed among other findings that, more females had anaemia as
compared to males in the Volta region of Ghana. Dwumoh, Es-
suman and Afagbedzi (2014), Zere, Kirigia, Duale and Akazili
(2012) and Hong (2007) have also conducted similar studies on
Ghana.
However, among all the studies above that had nationwide repre-
sentation, none of them used data that is more current as the one
used by this study (the 2014 Ghana Demographic and Health Sur-
vey). Thus this study would help bring to light the most current
factors that determine child health status in Ghana and hence aid-
ing in our drive towards ameliorating the child mortality rate in
Ghana which is far above the SDG 3.2.
3. Methods
3.1. Data
The study utilised data from the 2014 Ghana Demographic and
Health Survey ([GDHS], the most recent nationally representative
demographic and health survey) which was a cross-sectional sur-
vey carried out by the Ghana Statistical Service, the Ghana Health
Service and host of other partners from early September 2014 to
mid-December, 2014. During the survey, the mothers or caregiv-
ers of children less than five years of age were asked as to whether
the child suffered from cough, diarrhoea and fever in the past 14
days or 24 hours before the survey. However, there was no child
with any of the ailments in the previous 24 hours before the sur-
vey. Therefore this study recoded these health status proxies as 1,
if a child had fever or cough or diarrhoea in the past 14 days be-
fore the survey and 0 if not.
With regards to anaemia testing during the 2014 GDHS, it was
done by using the haemoglobin level in the blood of children.
Thus using sterile and disposable instruments, a drip of capillary
blood was taken with a finger prink and the HemoCue photometer
system was used to measure the Haemoglobin concentration and
hence children with haemoglobin level lower than 11.0 g/dl were
considered anaemic. In the case of anaemia testing, the data cov-
ered children who were present, whose parents consented and had
haemoglobin results that constituted plausible data (GSS, GHS, &
ICF International, 2015).
In the original data, anaemia level in children was coded as 1 (se-
vere), 2 (moderate), 3 (mild) and 4 (not anaemic). This study how-
ever, recoded it by converting 1, 2, and 3 to 1 (anaemic) and 4 was
recoded to 0 (not anaemic).
3.2. Model
Since the health status proxies (fever, cough, diarrhoea and anae-
mia) were coded as 1/0, it means they were all dichotomous. Thus
each of these proxies was a dependent variable. Given the dichot-
omous or binary nature of the dependent variables, the study
adopted the binary probit model as the empirical model of estima-
tion. Therefore we specify a simplified model as:
H= ϢM +ϦC +ϗS + μ (1)
Where H (Health Status), represents the likelihood that a child
suffered a particular ailment. M is mother’s and partner’s charac-
teristics such as age, education, employment e.t.c, C shows child
characteristics such as sex, age and birth order and S shows other
socio-economic variables such as region, ethnicity, household
wealth status, residence type, e.t.c., Ϣ, Ϧ, and ϗ are vector of pa-
rameters of the explanatory variables and μ is the disturbance
term.
In this study all don’t know responses in the data were treated as
missing values and all categorical explanatory variables were
treated as dummy variables. It must be stated that, marital status,
religion and wealth status were recoded as applied in this study.
The Average marginal effects (AMEs) which is seen by many
researchers to be superior to the Marginal effects at the means
(MEM) were reported since marginal effects give more intuitive
meanings to explanatory variables as Williams (2012) contends.
4. Results and discussion
Bivariate Analyses (Descriptive Statistics with Pearson Chi
Square).
In Table 1 can be seen the distribution of the various categorical
explanatory variables with regards to the health status (Diarrhoea,
Fever, Cough and Anaemia) of children. Also the Pearson chi
square showed whether there were significant relationships be-
tween the various explanatory variables and child health status.
The result showed that there were significant association between
child health status (Diarrhoea, Fever, Cough and Anaemia) and
region, religion, ethnicity, mother’s education, partner’s education
and household wealth status. Further, there were significant asso-
ciation between residence and all the child health proxies apart
from Diarrhoea. The results further showed significant association
between Diarrhoea, Fever and Anaemia status of children and
102
International Journal of Health
money to seek medical care by mother, distance to seek medical
care by mother and sex of child. Mother’s health insurance also
was revealed to have significant relationships with fever and
anaemia status of children. Also as can be seen, 11.84% and
12.30% of urban and rural children were revealed to have suffered
from diarrhoea respectively. Also 71.11% and 67.95% of male and
female children were found to have suffered from anaemia respec-
tively. With regards to religious background, it can be seen that,
14.09%, 18.17% and 13.21% of Christian, Muslim (Islam) and
traditional/ spiritualist/no religion faiths children suffered from
fever respectively. Moreover, 74.80% and 55.69% of non-rich and
rich (wealthy) household children were found to have suffered
from anaemia respectively.
Table 1: Bivariate Analyses of Fever, Cough and Diarrhoea in Children and Categorical Explanatory Variables
Variable
Diarrhoea (%)
Chi-square
Fever (%)
Chi-square
Cough (%)
Chi-square
No
Yes
No
Yes
No
Yes
Region
68.2084***
60.6666***
48.4333***
Western
92.67
7.33
89.61
10.39
89.98
10.02
Central
90.65
9.35
87.52
12.48
86.29
13.71
Greater Accra
92.52
7.48
89.16
10.84
84.84
15.16
Volta
92.78
7.22
85.34
14.66
85.53
14.47
Eastern
84.58
15.42
81.89
18.11
78.15
21.85
Ashanti
86.16
13.84
84.03
15.97
85.06
14.94
Brong Ahafo
82.29
17.71
85.76
14.24
88.18
11.82
Northern
85.26
14.74
84.19
15.81
87.63
12.37
Upper East
89.20
10.80
88.11
11.89
87.55
12.45
Upper West
85.96
14.04
75.53
24.47
90.64
9.36
Residence
0.2647
4.2028**
7.7797***
Urban
88.16
11.84
86.36
13.64
84.95
15.05
Rural
87.70
12.30
84.36
15.64
87.56
12.44
Religion
47.6484***
12.5723***
16.9691***
Christian
89.40
10.60
85.91
14.09
85.59
14.41
Islam
81.88
18.13
81.83
18.17
87.36
12.64
Traditional
89.52
10.48
86.79
13.21
92.24
7.76
Ethnicity
19.1660**
44.8535***
29.0961***
Akan
88.39
11.61
87.48
12.52
86.06
13.94
Ga/Dangme
86.99
13.01
84.21
15.79
81.38
18.62
Ewe
91.42
8.58
83.66
16.34
82.98
17.02
Guan
92.19
7.81
80.47
19.53
86.72
13.28
Mole-Dagbani
85.99
14.01
81.70
18.30
88.21
11.79
Grusi
84.68
15.32
80.85
19.15
82.55
17.45
Gurma
88.38
11.62
90.20
9.80
90.38
9.62
Mande
83.53
16.47
80.00
20.00
91.76
8.24
Other
89.11
10.89
90.29
9.71
89.32
10.68
Mother’s Education
13.8479***
10.3394**
9.2275**
Uneducated
86.32
13.68
83.25
16.75
88.00
12.00
Primary
87.72
12.28
85.44
14.56
87.00
13.00
Secondary
88.70
11.30
86.27
13.73
85.43
14.57
Higher
94.26
5.74
89.05
10.95
82.38
17.62
Mother’s Insurance
0.1586
4.7028**
1.3385
Uninsured
87.62
12.38
86.72
13.28
87.32
12.68
Insured
88.00
12.00
84.47
15.53
86.17
13.83
Marital Status
0.4149
1.2585
2.4228
Single
87.49
12.51
85.91
14.09
85.51
14.49
Married
88.09
11.91
84.78
15.22
87.03
12.97
Mother’s Employment
0.2353
10.5486***
0.2513
Unemployed
87.48
12.52
88.22
11.78
86.06
13.94
Employed
88.00
12.00
84.39
15.61
86.63
13.37
Permission to seek medical care/help by mother
0.0233
5.5554**
0.2651
Big Problem
87.63
12.37
80.97
19.03
87.40
12.60
Not a Big Problem
87.90
12.10
85.46
14.54
86.46
13.54
Money to seek medical care/help by mother
6.9179***
6.5344**
0.0003
Big Problem
86.69
13.31
83.90
16.10
86.53
13.47
Not a Big Problem
89.00
11.00
86.34
13.66
86.51
13.49
Distance to seek medical care/help by mother
8.3265***
14.2845***
2.2488
Big Problem
86.03
13.97
82.51
17.49
85.51
14.49
Not a Big Problem
88.75
11.25
86.39
13.61
86.99
13.01
Partner’s Education
17.1184***
20.0196***
6.9674*
Uneducated
86.32
13.68
83.19
16.81
88.69
11.31
Primary
86.50
13.50
82.21
17.79
85.60
14.40
Secondary
88.97
11.03
86.51
13.49
86.25
13.75
Higher
92.62
7.38
89.63
10.37
85.53
14.47
Sex of Household Head
0.2962
0.0107
4.4092**
Male
88.02
11.98
85.13
14.87
87.05
12.95
Female
87.45
12.55
85.25
14.75
84.77
15.23
Sex of Child
15.2349***
4.5617**
1.8207
Male
86.24
13.76
84.18
15.82
87.12
12.88
Female
89.67
10.33
86.22
13.78
85.88
14.12
Wealth Status
20.5603***
17.5064***
3.2486*
Non-Rich
86.66
13.34
83.93
16.07
87.03
12.97
Rich
91.12
8.88
88.40
11.60
85.18
14.82
Source: Authors computation from the 2014 GDHS. Notes: 1. *, **, and *** showing significant difference at 10%, 5% and 1% respectively within the
predictor and fever, cough and diarrhoea among children in Ghana. 2. Traditional in this study means traditional/ spiritualist and no religion.
International Journal of Health
Table 2: Bivariate Analyses of Anaemia in Children and Categorical Explanatory Variables
Variable
Anaemia (%)
Chi-square
Variable
Anaemia (%)
Chi-square
No
Yes
No
Yes
Region
89.0549***
Mother’s Education
90.0295***
Western
31.98
68.02
Uneducated
20.23
79.77
Central
27.27
72.73
Primary
29.59
70.41
Greater Accra
40.00
60.00
Secondary
38.13
61.88
Volta
29.95
70.05
Higher
54.79
45.21
Eastern
32.04
67.96
Mother’s Insurance
11.1827***
Ashanti
47.44
52.56
Uninsured
25.76
74.24
Brong Ahafo
35.56
64.44
Insured
32.54
67.46
Northern
15.12
84.88
Marital Status
0.0561
Upper East
27.06
72.94
Single
30.08
69.92
Upper West
27.41
72.59
Married
30.56
69.44
Residence
35.6130***
Mother’s Employment
0.5367
Urban
37.27
62.73
Unemployed
31.90
68.10
Rural
25.80
74.20
Employed
30.12
69.88
Religion
46.0045***
Permission to seek medical care/help by mother
1.2235
Christian
34.43
65.57
Big Problem
26.35
73.65
Islam
22.74
77.26
Not a Big Problem
30.67
69.33
Traditional
16.06
83.94
Money to seek medical care/help by mother
29.5152***
Ethnicity
67.6267***
Big Problem
25.09
74.91
Akan
37.89
62.11
Not a Big Problem
35.32
64.68
Ga/Dangme
33.68
66.32
Distance to seek medical care/help by mother
17.5654***
Ewe
31.75
68.25
Big Problem
24.57
75.43
Guan
29.51
70.49
Not a Big Problem
33.07
66.93
Mole-Dagbani
25.40
74.60
Partner’s Education
86.3314***
Grusi
32.46
67.54
Uneducated
18.77
81.23
Gurma
13.98
86.02
Primary
25.09
74.91
Mande
23.33
76.67
Secondary
36.28
63.72
Other
15.38
84.62
Higher
45.36
54.64
Sex of Household Head
0.3450
Sex of Child
2.8060*
Male
30.10
69.90
Male
28.89
71.11
Female
31.41
68.59
Female
32.05
67.95
Wealth Status
81.6175***
Non-Rich
25.20
74.80
Rich
44.31
55.69
Source: Authors computation from the 2014 GDHS. Notes: 1. *,**, and *** showing significant difference at 10%, 5% and 1% respectively within the
predictor and anaemia among children in Ghana. 2. Traditional in this study means traditional/ spiritualist/ no religion.
4.1. Regression results
This sub-section discussed the socio-economic factors which influence the health status of children in Ghana using the binary probit re-
gression model.
Table 3: Probit Regressions on Socio-Economic Factors and Child Health Status in Ghana
Dependent Variable
Diarrhoea
Fever
Cough
Anaemia
Independent Variable
AME
AME
AME
AME
Region(Ref: Upper
West)
Western
-.0532818**
-.0920421***
.0118388
.0405946
Central
-.0407197
-.0553546*
.051492**
.0631523
Greater Accra
-.0450493*
-.0806176**
.0433028*
-.018447
Volta
-.0451094*
-.0546221
.0413917
-.0316564
Eastern
.0164755
-.0136191
.1179508***
-.0136766
Ashanti
.0185166
-.0152488
.0649906***
-.1122619**
Brong Ahafo
.0339945
-.0640671**
.0330037
-.0414921
Northern
.0151946
-.0218242
.0625446***
.0561503
Upper East
-.0202869
-.0962828***
.0354082*
.0011709
Residence
(Ref:Rural)
Urban
.0183442
.0096067
.0257733**
.0117291
Religion (Ref: Tradi-
tional)
Christian
.0016575
.0223184
.0539449***
-.0885593**
Islam
.066625***
.0180751
.0451376**
-.0124038
Ethnicity(Ref: other)
Akan
.0359486
.0095064
.0438425
-.1242696*
Ga/Dangme
.0448398
.0462611
.0452426
-.0524946
Ewe
.014563
.0455015
.0680842*
-.0820207
Guan
-.0392575
.0771444
.0458927
-.1424717
Mole-Dagbani
.0049265
.0438047
.0331346
-.1407937**
Grusi
.0347619
.0585399
.106202**
-.1629429**
Gurma
-.0051489
-.0489709
-.0023502
-.0484388
Mande
-.001192
.0735804
-.0011499
-.1423818
Mother’s Education
(Ref: Uneducated)
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International Journal of Health
Primary
-.0023311
-.0076655
-.0189157
-.0473756
Secondary
-.0090166
-.002212
-.0110587
-.0696152**
Higher
-.0307876
-.0237178
.0146928
-.1435621**
Mother’s Insurance(Ref:
Uninsured)
Insured
-.006336
.0162409
.0131865
-.0371926*
Marital Status (Ref:
Single Mothers)
Married
-.0044069
.0002726
-.0005962
-.0004939
Mother’s Employment
(Ref: Unemployed)
Employed
.0089974
.0471955***
.0150347
.0290478
Permission to Seek
Medical Care by Moth-
er( Ref: Not a big prob-
lem)
Big Problem
-.0122711
.0280066
.0007322
-.023274
Money to Seek Medical
Care by Mother( Ref:
Not a big problem)
Big Problem
.0076797
-.0065604
.0018146
.0359314
Distance to Seek Medi-
cal Care by Mother( Ref:
Not a big problem)
Big Problem
.0084423
.0229874*
.0200159
-.0082151
Partner’s Education
(Ref: Uneducated)
Primary
.0088253
.0207113
.0114248
-.0031998
Secondary
-.0021563
-.0114361
-.0089943
-.0614205**
Higher
-.0267544
-.0374555*
-.0176655
-.0764585
Sex of Household
head(Ref: Female Head)
Male head
.0019632
-.0026913
-.0059958
-.0379127
Sex of Child (Ref: Fe-
male)
Male Child
.0382883***
.0197648**
-.0136338
.0513151***
Wealth(Ref: Non-Rich)
Rich
-.0353769**
-.0353859**
-.0088499
-.0605995*
Mother’s Age
.0000505
.0022997*
.0002103
-.0045687**
Age of Household Head
-.0007123*
-.0004081
-.0008065*
-.0010875
Birth Order of Child
.0020555
-.0020639
-.0041992
.0176767**
Childs Age
-.0199801***
.0041434
-.0084934**
-.0569939***
N=5100
Prob>chi2=.0000
N=5109
Prob>chi2=.0000
N=5108
Prob>chi2=.0000
N=2220
Prob>chi2=.0000
Source: Authors computation from the 2014 GDHS. Notes: 1. ***P-value<.01, **P-value<.05, *P-value<.1. 2. Traditional in this study means traditional/
spiritualist/ no religion.
From the results on diarrhoea, on average children from the West-
ern, Volta and Greater regions were all 5% less probable to get
diarrhoea relative to those in the Upper West region. These send
signals on regional differentials in the health status (diarrhoea
status) of children in Ghana. Also on average, Muslim children
and male children were 7% and 4% more probable to have diar-
rhoea relative to those from Spiritualist/Traditional/No religion
faiths and female children respectively. The findings of male chil-
dren is in line with that of Darteh et al. (2014) who revealed stunt-
ing to be higher among males than females in Ghana. This can be
due to the perception of people that male children are naturally
stronger relative to female children and hence may not be given
much care which might make them more susceptible to diseases.
In addition, wealthy households’ children were 4% less probable
to suffer diarrhoea relative to those from non-wealthy homes. This
concurs with the findings of Escobar et al. (2015) in Brazil. The
reason is that wealthy households are more capable to afford in-
puts of child health and cleaner water, environment, etc as com-
pared to non-wealthy households and hence translating in to a
good health for wealthy households’ children.
In addition, on average, a yearly increase in the ages of the house-
hold head and that of the child were revealed to decrease by.07%
and 2% the probability that a child suffered from diarrhoea respec-
tively. This is because older people might have acquired so much
experience concerning disease avoidance and healthy living and
hence would be efficient producers of child health as compared to
younger individuals. The finding on child’s age flouts that of
Fosu-Brefo and Arthur (2015) who revealed rising child’s age to
be linked with lesser health (height-for-age Z score) and Amugsi
et al. (2015) in the case of diarrhoea among children in Ghana.
The reason could be that older children might have developed
stronger immune systems as they grow and hence making them
less prone to diseases.
With regards to Fever, on the average, children from the Western,
Greater Accra, Central, Upper East and Brong Ahafo regions were
less likely to suffer from fever two weeks prior the survey by 9%,
8%, 6%, 10% and 6% respectively relative to those in the Upper
West region. The result on the Western region is similar to that of
Nyarko and Cobblah (2014).
Surprisingly, Children with employed mothers were 5% more
likely to have suffered from fever relative to those with unem-
ployed mothers. This might be that; employed mothers do not
have enough time and care for their children as they give so much
attention to their jobs.
Also children of mothers with big problems in terms of distance to
seek self-medical help were 2% more likely to suffer from fever
relative to children whose mothers had no big problems with dis-
tance to seek self-medical care. This implies big challenges with
distance to seek care for mothers could prevent them from sending
children to health facilities or centres where usually child health
education and child health inputs such as immunisation are ren-
dered and hence may affect the health of their children negatively.
Furthermore, Children with mothers whose partners had higher
level of education were 4% less likely to have suffered from fever
relative to children with mothers whose partners had no education.
This is because; the educated are better efficient producers of child
health relative to the uneducated. This concurs with the result of
Borbor et al. (2014) who found lesser paternal education to be
International Journal of Health
linked with higher probability of anaemia among children in Gha-
na. Also male children were revealed to be 2% more probable to
suffer from fever relative to female children. This is similar to the
results of Darteh et al. (2014) who found stunting to be more
among males than females. The explanation under diarrhoea still
applies here.
Also children form wealthy homes were found to be 4% less prob-
able to suffer from fever relative to those from non-wealthy
homes. Last but not the least, a yearly rise in the age of the mother
was found to increase the likelihood of children suffering fever by
.2%. This might be that, older mothers have many children under
their care and hence would not have much attention for younger
children resulting in poor health status. It might also be that older
mothers (who are more probable to have older children) may hand
over the care of younger children to older children and since they
(older children) lack enough experience in taking good care of
these younger children, may result in bad health. This conflicts the
findings of Fosu-Brefo and Arthur (2015) who revealed increasing
mother’s age to be linked with higher child health (height-for-age
Z score) in Ghana.
Concerning Cough, on the average, Central, Eastern, Greater Ac-
cra, Ashanti, Upper East and Northern region children, were found
to be 5%, 12%, 4%, 6%, 4% and 6% respectively more probable
to have suffered cough relative to those in the Upper West region
(reference group). Further, urban children were revealed to be 3%
more probable to suffer from cough relative to rural children. This
possibly could be the higher pollution levels in urban areas be-
cause of heavy industrial and vehicular emissions and hence lead-
ing to more vulnerability to cough among urban children. This is
contrary to the findings of Borbor et al. (2014) who revealed chil-
dren from rural areas to be more likely to get anaemia in Ghana.
Also Christian and Islamic faiths children were 5% more probable
to have cough relative to those from the Traditional/Spiritualist/No
religion faith. Further on ethnicity, Ewe and Grusi children were
7% and 11% more probable to have cough relative to children in
the other ethnic groups. This reinforces the essence of ethnicity as
a proxy for socio-cultural factors and how they influence child
health. Last but not the least, a yearly increase in the ages of the
child and that of the household head decreased the likelihood of
children contracting cough by .8% and .08% respectively. The
result on child’s age is not in line with that of Amugsi et al. (2015)
who found older children to have a higher risk of contracting
cough relative to younger children in Ghana.
Concerning Anaemia, Ashanti region children were found to be
11% less probable to suffer from anaemia relative to those in the
Upper West region. Also on average, Christian children were 9%
less probable to suffer from anaemia relative to children from
Traditional/Spiritualist/No religion background.
On ethnicity, Akan, Grusi and Mole-Dagbani children were 12%,
16% and 14% respectively less likely to suffer from anaemia rela-
tive to those in the other ethnic groups.
Also on average, children whose mothers had higher and second-
ary education were 14% and 7% respectively less likely to suffer
from anaemia as compared to children with uneducated mothers.
This is in line with the result of Aheto et al. (2015) who revealed
increasing maternal years of education to be linked with decreased
risk of malnutrition, Fosu-Brefo and Arthur (2015) who revealed
that mother’s education (tertiary level) had positive influence on
child health (height-for-age Z-score), Joshi et al. (2011) who
found a high significant linkage between child nutrition and ma-
ternal literacy in Nepal and Borbor et al. (2014) who revealed
children with lower maternal education (uneducated and primary
education), to be more probable to get anaemia.
Furthermore, children whose mothers had health insurance were
revealed to be 4% less likely to suffer from anaemia relative to
those with uninsured mothers. This is in line with the result of
Aheto et al. (2015) who revealed the non-existence of maternal
health insurance to be linked with a higher risk of malnutrition.
The reason is that, health insurance makes medical care relatively
cheaper and hence mothers would utilise for themselves and their
children especially in the case of the national health insurance
scheme in Ghana where there is free health insurance registration
for children and pregnant women. Also since mothers with health
insurance are more likely comparatively to visit health facilities,
they may get health education on good child health practices as
compared to their uninsured counterparts.
In addition, on average, children with mothers whose partners had
secondary education were 6% less probable to suffer from anae-
mia relative to children with mothers whose partners had no edu-
cation.
Also male children were revealed to be 5% more probable to suf-
fer from anaemia relative to female children. This contradicts the
results of Egbi et al. (2014) who revealed females to be more
linked with anaemia rather. This finding is very disturbing given
that it was observed under both diarrhoea and fever.
Also wealthy households’ children were 6% less probable to suf-
fer anaemia relative to those from non-wealthy households. This
concurs with the results of Aheto et al. (2015) who revealed pov-
erty to be linked with increased risk of malnutrition in children,
Fosu-Brefo and Arthur (2015) in the case age-for-height Z-score
and Borbor et al. (2014) who revealed children from poor house-
holds to be more likely to get anaemia. Again the result is similar
to those observed under both diarrhoea and fever above.
Also on average, a yearly increase in the ages of the mother and
that of the child decreased the probability that a child contracted
anaemia by .5% and 6% respectively. These findings are similar to
those of Borbor et al. (2014) in Ghana. This might be that older
mothers have given several births and therefore have gained
enough experience in child care practices. The finding on child’s
age is in line with the results on diarrhoea and cough above.
Last but not the least, a unit rise in child’s birth order was revealed
to increase the probability of suffering from anaemia by 2%. This
might be that, less experienced older children are left with the
responsibility of catering for younger children. Also it could be
that caregivers or mothers have many children and hence may not
have enough attention for their younger (those with higher birth
order) children who need lots of care and attention.
5. Conclusion
Based on the findings, the paper can conclude that residence, re-
gion, ethnicity, wealth status, mother’s employment status, reli-
gion, mother’s health insurance, mother’s education, partner’s
education, distance to seek medical care by mother, sex of the
child, age of the household head, maternal age, child’s age and
birth order of the child are the main determinants of the health
status of children in Ghana.
Therefore intensifying child health interventions in regions with-
out neglecting urban areas could be an effective tool towards im-
proving child health status in Ghana. This is because children
from urban areas, Greater Accra, Northern, Ashanti, Upper east,
Eastern and Central regions were more probable to contract cough.
Targeting religious faiths and ethnic groups with regards to educa-
tion on better child health practices should be embarked upon
since Muslim children were more likely to have suffered from
cough and diarrhoea whiles children from the Traditional/no reli-
gion or spiritualist faiths on one hand and Ewe, Grusi, Christian
children on another hand were more probable to suffer from
anaemia and cough respectively. Such education drives should
point out the need for all children irrespective of their sex to be
given better health attention since male children were more likely
to contract fever, diarrhoea and anaemia relative to their female
counterparts.
Targeting the uneducated mothers and partners with regards to
better child health practices can also be instituted. As a long term
goal, opening education opportunities for all, especially up to at
106
International Journal of Health
least the secondary level could help in bettering child health out-
comes since maternal education up to the secondary and higher
levels and paternal education up to the secondary level were found
to reduce the likelihood of anaemia in Children. Also higher pa-
ternal level of education was revealed to reduce the likelihood of
fever in children.
Strengthening the free maternal health insurance scheme and en-
couraging women without health insurance to register should be
instituted since maternal health insurance was revealed to decrease
the probability of anaemia in children.
Policies that are geared towards given enough maternal leave for
employed nursing mothers can be embarked upon given that chil-
dren with employed mothers were more likely to suffer from fever
relative to those with unemployed mothers. Alternatively, better
regulated child care homes could be established for employed
mothers to send their children for proper care if their jobs would
not give them enough time.
Finally, Poverty reduction strategies as a long term goal as well as
aiding non-wealthy households with regards to better child health
practices should be encouraged since children from wealthy
households were revealed to be less likely to contract diarrhoea,
fever and anaemia relative to those from non-wealthy homes.
Acknowledgement
We are being thankful to the DHS Program for the data
Conflicts of interests
None
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Background: Globally, diarrhea is the second leading cause of death among children under five. In Brazil, mortality due to diarrhea underwent a significant reduction in recent decades principally due to expansion of the primary healthcare network, use of oral rehydration therapy, reduced child undernutrition, and improved access to safe drinking water. The First National Survey of Indigenous People's Health and Nutrition in Brazil, conducted in 2008-2009, was the first survey based on a nationwide representative sample to study the prevalence of diarrhea and associated factors among Indigenous children in the country. Methods: The survey assessed the health and nutritional status of Indigenous children < 5 years of age based on a representative sample of major Brazilian geopolitical regions. A stratified probabilistic sampling was carried out for Indigenous villages. Within villages, children < 5 years of age in sampled households were included in the study. Interviews were based on a seven day recall period. Prevalence rates of acute diarrhea were calculated for independent variables and hierarchical multivariable analyses were conducted to assess associations. Results: Information on diarrhea was obtained for 5,828 children (95.1% of the total sample). The overall prevalence of diarrhea was 23.5%. Regional differences were observed, with the highest rate being in the North (38.1%). Higher risk of diarrhea was observed among younger children and those who had less maternal schooling, lower household socioeconomic status, undernutrition (weight-for-age deficit), presence of another child with diarrhea in the household, and occurrence of upper respiratory infection. Conclusions: According to results of the First National Survey of Indigenous People's Health and Nutrition, almost a quarter of Indigenous children throughout the country had diarrhea during the previous week. This prevalence is substantially higher than that documented in 2006 for Brazilian children < 5 years generally (9.4%). Due to its exceedingly multicausal nature, the set of associated variables that remained associated with child diarrhea in the final multivariable model provide an excellent reflection of the diverse social and health inequities faced by Indigenous peoples in contemporary Brazil.
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