Table 1:1 Represents perinatal mortality among pregnancy women by Social demographic characteristics in Geita region.
Variables
|
Macerated Stillbirth (MSB)
|
Fresh Stillbirth (FSB)
|
Early perinatal deaths (EPD)
|
Age
< 20
21-30
Above 30
|
|
|
|
73 (31.88%)
|
18 (19.56%)
|
12 (35.53%)
|
98 (42.79%)
|
53 (57.61%)
|
13 (38.24%)
|
58 (25.33%)
|
21 (22.83%)
|
9 (26.23%)
|
Education level
No education
primary
Secondary
college
University
|
|
|
|
29 (69.05%)
|
6 (14.28%)
|
7 (16.67%)
|
9 (69.23%)
|
3 (23.08%)
|
1 (7.69%)
|
3 (33.33%)
|
2 (22.22%)
|
4 (44.45%)
|
1 (100.0%)
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
Unknown causes
|
169 (73.79%)
|
1 (1.09%)
|
0 (0.00%)
|
Peripartum birth asphyxia
|
12 (5.24%)
|
0
|
0 (0.00%)
|
Intrapartum birth asphyxia
|
20 (8.73%)
|
71 (77.17%)
|
0 (0.00%)
|
Severe birth asphyxia
|
0 (0.00%)
|
5 (5.43%)
|
20 (58.82%)
|
antepartum Haemorrhage (APH)
|
0 (0.00%)
|
2 (2.17%)
|
0 (0.00%)
|
Malaria in pregnancyy
|
2 (0.87%)
|
0 (0.00%)
|
1 (2.94%)
|
Eclampsia
|
10 (4.37%)
|
1 (1.09%)
|
0 (0.00%)
|
Premature complication
|
0 (0.00%)
|
0 (0.00%)
|
8 (23.53%)
|
Pre-Eclampsia
|
3 (1.31%)
|
5 (5.43%)
|
0 (0.00%)
|
Hypertension
|
7 (3.05%)
|
2 (2.17%)
|
0 (0.00%)
|
Congenital malformation
|
0 (0.00%)
|
5 (5.43%)
|
0 (0.00%)
|
Hepatitis B
|
1 (0.44%)
|
0 (0.00%)
|
0 (0.00%)
|
syphilis
|
0 (0.00%)
|
0 (0.00%)
|
1 (2.94%)
|
Anemia in pregnancy
|
3 (1.31%)
|
0 (0.00%)
|
1 (2.94%)
|
Sepsis
|
2 (0.87%)
|
0 (0.00%)
|
2 (5.88%)
|
Aspiration pneumonia
|
0 (0.00%)
|
0 (0.00%)
|
1 (2.94%)
|
Source; Geita Regional Referral Hospital (GRRH) discussed perinatal deaths
The table 1.1 above represents perinatal mortality among pregnant women in the Geita region, based on their social demographic characteristics. The data is divided into three categories: Macerated Stillbirth (MSB), Fresh Stillbirth (FSB), and Early Perinatal Deaths (EPD).
The table shows that the highest percentage of MSB occurred among pregnant women above the age of 30 (35.53%), while the highest percentage of FSB occurred among pregnant women between the ages of 21-30 (57.61%). The highest percentage of EPD occurred among pregnant women between the ages of 21-30 (22.83%).
In terms of education level, the highest percentage of MSB occurred among pregnant women with no education (69.05%), while the highest percentage of FSB occurred among pregnant women with secondary education (44.45%). The only case of EPD in the education category occurred among pregnant women with college education.
The data also shows that unknown causes were responsible for the highest percentage of MSB (73.79%) while intrapartum birth asphyxia was responsible for the highest percentage of FSB (77.17%). Severe birth asphyxia was responsible for the highest percentage of EPD (58.82%).
Table 1: 2 Multiple linear regressions to show Perinatal mortality associated factors.
Model
|
B
|
Std. Error
|
t
|
Sig.
|
Age categories
|
|
|
|
|
21-30
|
9.23
|
2.462
|
3.386
|
0.0241
|
31 and above
|
8.743
|
1.956
|
2.847
|
0.0458
|
Education level
|
|
|
|
|
Primary
|
5.39
|
2.65
|
2.401
|
0.148
|
Secondary
|
2.026
|
4.641
|
0.744
|
0.461
|
Collage
|
1.478
|
5.923
|
0.593
|
0.037
|
Unknown cause
|
4.583
|
1.582
|
2.896
|
0.006
|
Premature complication
|
5.65
|
2.063
|
-2.738
|
0.009
|
Severe birth asphyxia
|
5.25
|
1.769
|
-2.967
|
0.005
|
Neonatal sepsis
|
6.158
|
2.961
|
-2.111
|
0.04
|
Anaemia
|
7.27
|
4.035
|
-1.797
|
0.079
|
Aspiration pneumonia
|
7.279
|
2.961
|
-2.449
|
0.018
|
Anaemia in pregnancy
|
6.211
|
1.831
|
-2.111
|
0.04
|
Hypertension
|
6.456
|
4.035
|
-1.549
|
0.129
|
Eclampsia
|
7.25
|
2.791
|
-2.449
|
0.018
|
Pre-eclampsia
|
5.523
|
2.063
|
-2.544
|
0.015
|
Malaria in pregnancy
|
8.23
|
2.238
|
-3.24
|
0.002
|
Syphilis
|
7.15
|
4.265
|
1.797
|
0.079
|
Hepatitis B
|
9.214
|
4.035
|
1.797
|
0.079
|
Congenital malformation
|
3.251
|
2.041
|
-0.806
|
0.425
|
Peripartum asphyxia
|
2.572
|
4.015
|
0.682
|
0.499
|
(Constant)
|
8.25
|
1.119
|
7.373
|
0.000
|
Source; (GRRH) discussed perinatal deaths
Table 1.2 shows the results of a multiple linear regression analysis that was conducted to identify the factors associated with perinatal mortality. The table has different columns that include Model, B, Std. Error, t, and Sig.
The Model column shows the independent variables that were included in the analysis, while the B column shows the beta coefficients, which represent the strength and direction of the relationship between each independent variable and the dependent variable (perinatal mortality). The Std. Error column shows the standard error of the estimate, which indicates how far the observed values are likely to deviate from the true regression line. The t column shows the t-value, which measures the size of the difference relative to the variation in the sample data. Finally, the Sig. column shows the significance level, which indicates the probability of obtaining the observed results by chance.
From the table, we can see that several factors are associated with perinatal mortality, including age categories, education level, and various health conditions such as premature complication, severe birth asphyxia, neonatal sepsis, aspiration pneumonia, among others.
The results suggest that older mothers (31 and above) have a higher risk of perinatal mortality than younger mothers (21-30). Furthermore, it appears that having a college education is associated with a lower risk of perinatal mortality compared to having only a primary or secondary education.
Additionally, several health conditions are associated with an increased risk of perinatal mortality, including Unknown cause, premature complication, severe birth asphyxia, and neonatal sepsis, and aspiration pneumonia, malaria in pregnancy, eclampsia, pre-eclampsia, syphilis, and hepatitis B.
However, some factors such as congenital malformation and peripartum asphyxia do not appear to have a significant association with perinatal mortality.
The constant term has a coefficient estimate of 8.25, which represents the expected level of perinatal mortality when all the other variables are equal to zero. The constant term is significant (p < 0.001), which indicates that there are other factors not included in the model that may also be associated with perinatal mortality.
Table 2: Association between Sex of Children and perinatal mortality
Variable
|
Categories
|
Macerated Stillbirth (MSB)
|
Fresh Stillbirth (FSB)
|
Early perinatal Mortality (EPM)
|
Sex
|
Male
|
98 (42.79%)
|
36 (39.13%)
|
13 (38.24%)
|
Female
|
131 (57.21%)
|
56 (60.87%)
|
21 (61.76%)
|
Total
|
229 (100%)
|
92 (100%)
|
34 (100%)
|
Source; (GRRH) discussed perinatal deaths
Table 2 shows the association between the sex of children and perinatal mortality. The variables are Macerated Stillbirth (MSB), Fresh Stillbirth (FSB), and Early Perinatal Mortality (EPM). The categories are Male and Female. The table shows that there were a total of 229 cases of perinatal mortality, out of which 42.79% were male and 57.21% were female. In terms of MSB, 98 cases were male and 131 were female. For FSB, 36 cases were male and 56 were female. Lastly, for EPM, 13 cases were male and 21 were female.
The findings from the qualitative interviews reveal important insights into the challenges faced by mothers during pregnancy and childbirth. One of the key issues highlighted is the low antenatal attendance by mothers, with some attending as few as one to five visits instead of the recommended eight visits. One participant commented;
"A lot of mothers are not aware of the importance of antenatal care and the risks associated with not attending early enough. Some mothers only come to the clinic to ask RCH card when they are already experiencing complications, which make it difficult to manage their health effectively."(Midwife RCH-Geita)
The lack of early visits can lead to serious health complications for both the mother and the baby, such as congenital malformations and malaria. A grandmultparous interviewed during the study commented;
"I didn't see the need to attend antenatal clinics regularly since I was feeling okay, it was a fifth pregnancy and my four children were well. But when I developed complications later on, I regretted not attending all the visits." (Postnatal mother –GRRH)
Furthermore, economic barriers and geographical location are also significant challenges, especially for mothers who live far away from the healthcare facility. In some cases, mothers may opt to seek the services of traditional birth attendants due to a lack of transport or financial resources. However, this can lead to delays in referral and treatment, which can have fatal consequences for both the mother and the baby. One participant commented;
"I only attended two antenatal visits because the healthcare facility is far from my house and I don't have enough money to pay for transport. I know it's not enough, but what can I do? I have to look for money to feed my family." (FGD Pregnant RCH-Geita)
In addition to these challenges, there are also issues related to screening for pregnancy at ANC clinics, as well as the availability of skilled human resources for reproductive health. For instance, some mothers are not tested for certain conditions, while others face delays in getting referrals due to a lack of equipped ambulances or trained staff.one participant commented
"I had to wait for hours before being referred to a hospital for further tests. The facility had no ambulance and the midwife had to call around to find a private vehicle to take me. It was a very stressful experience"(postnatal mother –GRRH)
Overall, these findings underscore the need for better education and awareness among mothers, as well as improved access to healthcare services and skilled human resources. It is imperative that these issues are addressed to ensure better health outcomes for mothers and their babies.