Maternal Risk Factors and Adverse Birth Outcomes Associated with Preeclampsia: A Prospective Cohort Study in Zanzibar ()
1. Introduction
Hypertensive disorders of pregnancy (HDPs) account for approximately 16% of maternal mortality and are responsible for 70,000 maternal deaths globally [1] [2]. The global incidence of hypertensive disorders during pregnancy rose from 31.33 million to 36.10 million, representing a total increase rate of 15.24% between 1990 and 2021 [3]. Preeclampsia and eclampsia (PE/E) are among the most common (HDPs), and are the second most common cause of maternal and perinatal morbidity and mortality worldwide [4]. In sub-Saharan Africa, the overall incidence of preeclampsia was 13%, and the trend analysis found an increase from 2.22% in 2010 to 2.67% in 2018 [5].
Preeclampsia (PE) is a pregnancy-related complication characterized by a syndrome with multisystem involvement and damage based on hypertension (systolic blood pressure of 140 mmHg or diastolic blood pressure of 90 mmHg) that first shows up after 20 weeks of gestation [6]-[8]. PE is represented as a gestational condition with a hypertensive disorder diagnosed after 20 weeks of gestation and coexisting proteinuria or generalized oedema, and certain forms of hematologic disorders such as thrombocytopenia or signs of end-organ damage, including renal impairment, abnormal liver function, pulmonary oedema, and cerebral and visual disturbance [9].
Pregnant women with high blood pressure equal to 140/90 mmHg systolic and diastolic, as well as albumin protein in the urine more than 30 mg/L of albumin, indicate kidney damage, and the presence of these two crucial complications at the same time in pregnancy may point to preeclampsia [10]. The primary mechanism of preeclampsia begins with the inadequate remodeling of the spiral arteries that supply maternal blood to the intervillous space of the placenta, resulting in suboptimal placentation. This causes hypoxia in the placental tissue, triggering oxidative stress and a reduction in nitric oxide (NO) production, which in turn leads to vasoconstriction, placental ischemia, and endothelial dysfunction [11].
According to the World Health Organization (WHO), preeclampsia is seven times more prevalent in low and middle-income countries (LIMCs) [12]. Maternal health remains one of the major public health problems in low- and middle-income countries (LMIC) [13]. In SSA, HDPs are the second leading cause of maternal mortality, 22.1%, in 2020 [14].
Tanzania’s maternal mortality ratio (MMR) significantly dropped to 104 per 100,000 live births in 2022 [15]. Despite some progress in reducing maternal deaths in Tanzania, the maternal mortality rate (MMR) in Zanzibar remains high at 287 per 100,000 live births. The prevalence of pre-eclampsia is 4.2% in mainland Tanzania and 14.3% in Zanzibar [16] [17]. The global maternal mortality ratio is unlikely to meet the Sustainable Development Goal (SDG) target 3.1, which aims for a rate lower than 70 per 100,000 live births by the year 2030 [2].In Tanzania, several factors contribute to the reduction in maternal mortality, including increased ANC attendance, a decrease in teenage pregnancies, a rise in deliveries at health facilities, decentralizing high-quality comprehensive emergency obstetric and new-born care (EmONC) to lower-level facilities, and more deliveries assisted by skilled healthcare providers [18].
Numerous factors, including chronic hypertension, inappropriate weight gain, unfavorable socioeconomic conditions, presence of chronic diseases, personal and/or family history of PE, nulliparity, multifetal gestation, maternal age, pre-existing diabetes mellitus, having more than one partner and alcohol intake, body mass index > 30 kg/m and Previous history of caesarean section have been associated with preeclampsia [12] [17] [19]-[26].
Severe preeclampsia is linked to severe maternal and perinatal effects, including eclampsia, HELLP syndrome, intracerebral bleeding, cardiovascular diseases, type 2 diabetes, acute renal failure, miscarriage, respiratory distress syndrome, premature delivery, placental abruption, intrauterine growth restriction, obstructed labour and an increased risk of caesarean section, with adverse perinatal outcomes including low birth weight, birth asphyxia, cognitive and psychiatric impairment in offspring, and neonatal death [27]-[31].
Despite its high burden, there remains limited data on maternal risk factors and adverse birth outcomes related to preeclampsia in Tanzania, particularly in Zanzibar. However, in Zanzibar, a facility-based study shows the higher prevalence of severe pre-eclampsia among postpartum women of 26.3%, which is reflected in the number of deaths and complications associated with PE [19]. Given the need to reduce maternal and neonatal mortality and morbidity, early detection and management of preeclampsia are crucial. This study, therefore, aimed to assess maternal risk factors and adverse birth outcomes associated with preeclampsia among pregnant women of Unguja Zanzibar.
2. Methods
2.1. Study Design, Study Area, and Study Period
This prospective cohort study was carried out from April to October 2021 at three public health facilities in three regions of Unguja, Zanzibar. One of these facilities is the Mnazi Mmoja hospital in the Urban region, which has an annual live birth delivery count of 13,291. Kivunge hospital is located in the North “A” region, serving 4000 live births annually, and Makunduchi hospital in the South region, with 1000 live births per year [32]. During the study period, these were the only public health facilities that offered Comprehensive Emergency Obstetric and New-born Care (CEmONC) in Unguja. Moreover, the Urban region recorded the highest proportion of women of childbearing age at 60.9%, with Unguja’s south region coming next at 54.0% [23]. The participants were enrolled at antenatal clinics (ANC) following their antenatal care visits in the selected health facilities in Unguja, Zanzibar.
2.2. Study Population, Sampling Technique, and Sample Size Estimation
Eligible participants were pregnant women at ≥20 weeks of gestation attending ANC visits at selected facilities. Inclusion criteria were willingness or intention to deliver within Unguja and their availability for follow-up up to 28 days postpartum/ during the study period. Systematic random sampling was used to select participants from the selected health facilities. Enrolment was undertaken after the women had given consent to participate in the study.
A sample size of 728 pregnant women was estimated to be required, using Kish’s formula (Kish, 1965), based on a 19.5% estimated prevalence of PE/E [33], 95% confidence level, 4% margin of error, design effect of 1.5, 20% anticipated loss to follow-up and a 5% non-response rate. Allocation across sites was based on the average number of women booking at ANC per month. 488 from Mnazi Mmoja, 124 from Kivunge, and 116 from Makunduchi. Pregnant women were followed across three distinct phases. Phase one involved three antenatal follow-up visits at 20, 28, and 36 weeks of gestation. Phase two occurred within 24 hours after childbirth. Phase three included postnatal follow-up conducted at 72 hours, 14 days, and 28 days after childbirth.
3. Data Collection Method and Procedure
Participants were recruited from April and May 2021, then followed up to October 2021, as they registered for booking at the registration desk of the antenatal clinic until the sample size estimated was reached. On every recruitment day, two research assistants sat with the registration nurses to screen women for the gestational age required by the study. Then, women were informed about the study and referred for an ultrasound scan to confirm the gestational age in each health facility. After the ultrasound results, all other inclusion criteria were evaluated. Clients who met all criteria were directed to another room for informed consent and were assigned an identification number for data collection procedures.
Data were collected using structured and semi-structured questionnaires adapted from validated tools [34]-[36] and then modified to gather the desired information from the sample population. The tool was translated into Kiswahili and back-translated into English to ensure consistency. The questionnaire consisted of a total of 244 questions. In phase 1 (ANC visits), a total of 142 questions were asked, assessing socio-demographic, reproductive and obstetric factors, prior and family medical history, and history of maternal complications, current behavioral and obstetric conditions. In Phase 2 (Delivery), another 45 questions were asked, assessing the current conditions of the mother and the new-born baby. Phase 3 (Postnatal) consisted of 57 questions, assessing the condition of the mother and the new-born baby. The PI and RAs ensured that every woman enrolled in this study had her blood pressure monitored and urine tested for protein in all seven visits of the data collection period. The Clinical reports, documented from the client’s ANC card, including the routine laboratory results, ultrasound scan, and partograph, were gathered into the data collection forms by the PI and research assistants. Additionally, the participants’ gestational ages were confirmed through ultrasound scans, Blood pressure measurements were taken, and cases of hypertension (≥140/90 mmHg) accompanied by protein in urine (proteinuria) were recorded. Maternal and neonatal outcomes were documented at each follow-up visit during the data collection period across all three phases.
3.1. Follow-Up of the Study Participants
In this study, a total of seven follow-up visits were carried out: three ANC visits at 20, 28, and 36 weeks of gestation; one visit within 24 hours of delivery; and three visits at 72 hours, 14 days, and 28 days postpartum. All data collected were entered into a prepared database.
To enhance follow-up feasibility, participants were given a follow-up card containing contact information, visit schedules, and the research team’s telephone numbers. Participants were reminded of the next ANC visits and were advised to report to the hospital in case of any complications during pregnancy. A reminder call was made to participants one week before and after their expected delivery date (EDD) based on the ultrasound scan, which worked well for most women. The research team, along with obstetricians, increased the timing of ANC and follow-up visits for women suspected of having preeclampsia based on the severity of their condition. The research team made sure that the women were contacted and followed up weekly, and they were advised to go to the hospital if they noticed any warning signs.
3.2. Advanced Birth Measures
Maternal outcome measures were defined as adverse health effects experienced by pregnant women diagnosed with or without preeclampsia in Unguja. These outcomes included chronic hypertension, eclampsia, HELLP syndrome, renal failure, and maternal death. Data were collected from the ANC (during the recruitment) phase through a 28-day postpartum follow-up. Neonatal outcomes were assessed for new-borns and fetuses born to the same cohort of women (mothers with or without preeclampsia). These included preterm birth, low birth weight, neonatal intensive care unit (NICU) admissions, stillbirth, and neonatal death. These outcomes were monitored from the time of birth up to 28 days postnatal. All outcomes were prospectively recorded as part of the present study data collection process.
3.3. Quality Control
Six bilingual (Swahili and English) female and male nurse officers were trained as research assistants (RAs). The training lasted for 3 days, and they covered the procedure for data collection, ethical conduct, and research tools. The training was facilitated by the principal investigator (PI). A pilot study was conducted at Alrahma Private Hospital before the commencement of data collection. This health facility (Alrahma Private Hospital), was not selected for the study data collection. After making the needed changes and corrections to the questionnaire, Cronbach’s alpha was used to check how reliable the instrument was, and it scored 0.70, which is generally seen as acceptable. PI reviewed the data daily to ensure completeness, consistency, and accuracy. Regular team meetings addressed emerging issues, including blood pressure (B/P) readings, urine protein tests, and laboratory examinations such as creatinine and liver function tests. Standardized protocols were followed for clinical measurements. This included using the correct cuff size for B/P readings and ensuring proper patient positioning. Additionally, the research team discussed any concerns related to the mother and baby.
Later on, the group of pregnant women with preeclampsia was obtained due to the expert categorization where by preeclampsia was defined as a combination of having a high blood pressure (systolic reading of 140 mmHg or higher) and diastolic reading of 90 mmHg or higher, along with albumin levels exceeding 30 mg/L in the urine.
3.4. Statistical Analysis
Data analysis was performed using SPSS software (version 26.0). Descriptive statistics (means, standard deviations, frequencies) were used to summarize the baseline characteristics. Chi-square or Fisher’s exact tests were used to determine the statistical association between a set of independent variables and PE/E in a bivariate analysis. Incidence and risk ratios were calculated to compare outcomes between the exposed (PE/E) and non-exposed groups, using a multivariable binomial regression model. A p-value of <0.05 was considered statistically significant, and 95% confidence intervals were reported where applicable.
4. Results
4.1. Socio-Demographic Characteristics
Most women were aged 20 - 34 (77.6%), married (97.4%), and had secondary education (76.2%). More than half (59.3%) were housewives with a median monthly income of 16,000 Tsh (approximately $6), (IQR 7,000 - 56,500). Two-thirds (66.9%) resided in urban areas (Table 1).
Table 1. Socio-demographic characteristics of study participants (N = 728).
Variable |
n (%) |
Age (Years) |
<20 |
21 (2.9) |
20 - 34 |
565 (77.6) |
≥35 |
142 (19.5) |
Median age |
28 (24,33) |
Marital Status |
Never married/widowed/divorced |
19 (2.6) |
Married |
709 (97.4) |
Education Level |
No formal education |
15 (2.1) |
Primary education |
107 (14.7) |
Secondary education |
555 (76.2) |
College/ University |
51 (7.0) |
Current Occupation |
Government employee |
56 (7.7) |
Self-employed |
217 (29.8) |
Housewife |
432 (59.3) |
Private employee |
23 (3.2) |
Monthly Income (Tsh) |
<150,000 |
616 (84.6) |
150,000 - 300,000 |
84 (11.5) |
>300,000 |
28 (3.9) |
Median (IQR) |
16,000 (7000 - 56,500) |
Area of residence |
Urban |
487 (66.9) |
Rural |
241 (33.1) |
Type of family |
Nuclear family |
649 (89.2) |
Joint/Extended family |
79 (10.8) |
4.2. New-Born Characteristics
Of the new-borns, 51.5% were male. The mean birth weight was 3.1 kg (SD ±0.5), and 82.8% weighed between 2.5 - 3.9 kg. Their mean gestational age was 36.9 weeks (SD ±2.4), with a mean Apgar score at 1 minute of 8.3 (SD ±2.2) (Table 2).
Table 2. New-born characteristics of the study population (N = 714*).
New-born characteristics |
n (%) |
Sex |
Male |
371 (51.5) |
Female |
350 (48.5) |
Birthweight (Kg) |
<2.5 |
91 (13.2) |
2.5 - 3.9 |
573 (82.8) |
≥4 |
28 (4.0) |
Apgar score at 1 minute |
<3 |
37 (5.4) |
4 - 6 |
16 (2.3) |
≥7 |
632 (92.3) |
Mean(±SD) |
8.3 (±2.2) |
Gestation age (weeks) |
<28 |
9 (1.2) |
28 - 34 |
45 (6.2) |
35 - 36 |
302 (41.8) |
≥37 |
367 (50.8) |
Mean (±SD) |
36.9 (±2.4) |
* = Missing due to 9 Abortion, 5 Loss of follow up.
4.3. Factors Associated with Preeclampsia
4.3.1. Reproductive Factors
Of the enrolled women, 728 were followed up through 28 days post-delivery (Figure 1). Among these, 5 pregnant mothers were lost during the first phase. About 723 (99%) of pregnant women were followed until delivery, of whom 55 developed preeclampsia, and nine women had an abortion and could not be followed up, resulting in a cohort of 714 women followed up until 28 days post-delivery. None of the studied reproductive factors showed a statistically significant association with preeclampsia in either crude or adjusted models (p > 0.05 for all comparisons) (Table 3).
4.3.2. Medical and Family History Factors
Women with a history of chronic hypertension had 3.6 times higher odds of developing preeclampsia (aOR= 3.69, 95%CI: 1.01-13.54, p =0.049). Similarly, a previous history of preeclampsia (aOR 3.19, 95% CI: 1.04 - 9.72, p = 0.041) and a family history of hypertension (aOR = 1.92, 95% CI: 1.05 - 3.51, p = 0.033) were independently associated with a higher risk of preeclampsia (Table 4).
4.3.3. Previous Maternal/Obstetric Complications
Women with a previous history of preeclampsia or eclampsia were significantly more likely to develop preeclampsia in their subsequent pregnancy than their counterparts (aOR = 3.11, 95% CI: 1.11 - 8.71, p = 0.031) (Table 5).
Figure 1. Flow chart of the participants recruited for the study.
Table 3. Reproductive Factors associated with Preeclampsia (N = 728).
Variables |
Total |
PREECLAMPSIA |
COR |
p-Value |
AOR |
p-Value |
Yes (%) |
No (%) |
Reproductive History |
Age at menarche |
<12 |
205 |
20 (9.8) |
185 (90.2) |
1 |
|
|
|
13 - 15 |
432 |
32 (7.4) |
400 (92.6) |
0.74 (0.41 - 1.33) |
0.313 |
1.47 (0.68 - 3.17) |
0.323 |
≥16 |
91 |
4 (4.4) |
87 (95.6) |
0.43 (0.14 - 1.28) |
0.129 |
0.47 (0.09 - 2.25) |
0.343 |
Ever been pregnant before |
No |
136 |
10 (7.4) |
126 (92.7) |
1 |
|
|
|
Yes |
592 |
46 (7.8) |
546 (92.2) |
1.06 (0.52 - 2.16) |
0.869 |
1.68 (0.47 - 5.93) |
0.423 |
Age at first pregnancy in (years) |
≤18 years |
265 |
23 (8.7) |
242 (91.3) |
1 |
|
|
|
19 - 25 years |
375 |
24 (6.4) |
351 (93.6) |
0.72 (0.39 - 1.30) |
0.278 |
0.61 (0.26 - 1.44) |
0.257 |
26 - 35 years |
88 |
9 (10.2) |
79 (89.8) |
1.19 (0.53 - 2.69) |
0.662 |
0.84 (0.28 - 2.45) |
0.743 |
Parity |
0 |
182 |
15 (8.2) |
167 (91.8) |
1 |
|
|
|
1 - 4 |
487 |
31 (6.4) |
456 (93.6) |
0.76 (0.39 - 1.44) |
0.395 |
0.62 (0.17 - 2.26) |
0.469 |
≥5 |
59 |
10 (16.9) |
49 (83.1) |
2.27 (0.96 - 5.38) |
0.062 |
1.72 (0.39 - 7.52) |
0.474 |
Age of the last child |
≤2 years |
171 |
22 (12.9) |
149 (87.1) |
1 |
|
|
|
3 - 4 years |
110 |
15 (13.6) |
95 (86.4) |
1.07 (0.53 - 2.16) |
0.852 |
1.31 (0.53 - 3.21) |
0.560 |
>5 years |
82 |
10 (12.2) |
72 (87.8) |
0.94 (0.42 - 2.09) |
0.881 |
1.25 (0.47 - 3.32) |
0.661 |
Paternal change |
No |
114 |
8 (7.0) |
106 (93.0) |
1 |
|
|
|
Yes |
514 |
38 (7.4) |
476 (92.6) |
1.06 (0.48 - 2.33) |
0.889 |
1.18 (0.49 - 2.83) |
0.706 |
Table 4. Previous Medical and Family History Factors associated with Preeclampsia (N = 728).
|
|
PREECLAMPSIA |
|
|
|
|
Variable |
Total |
Yes |
No |
cOR |
p-Value |
aOR |
p-Value |
History of Chronic Hypertension |
No |
712 |
52 (7.3) |
660 (92.7) |
1 |
|
|
|
Yes |
15 |
4 (26.7) |
11 (73.3) |
4.62 (1.42 - 15.00) |
0.011 |
3.69 (1.01 - 13.54) |
0.049 |
History of preeclampsia |
No |
634 |
39 (6.2) |
595 (93.9) |
1 |
|
|
|
Yes |
37 |
9 (24.3) |
28 (75.7) |
4.90 (2.161 - 11.11) |
<0.001 |
3.19 (1.04 - 9.72) |
0.041 |
Previous history of Eclampsia |
No |
647 |
56 (8.0) |
645 (92.0) |
1 |
|
|
|
Yes |
21 |
0 (0.0) |
26 (100.0) |
4.39 (1.53 - 12.55) |
0.006 |
1.75 (0.43 - 7.23) |
0.434 |
Family history of hypertension |
No |
468 |
28 (6.0) |
440 (94.0) |
1 |
|
|
|
Yes |
260 |
28 (10.8) |
232 (89.2) |
1.89 (1.09 - 3.28) |
0.022 |
1.92 (1.05 - 3.51) |
0.033 |
Family history of Pre/Eclampsia |
No |
705 |
653 (92.6) |
52 (7.4) |
1 |
|
|
|
Yes |
23 |
19 (82.6) |
4 (17.4) |
2.64 (0.87 - 8.06) |
0.0870 |
0.76 (0.34 - 1.69) |
0.495 |
Family history of Diabetes Mellitus |
No |
587 |
46 (7.8) |
514 (92.2) |
1 |
|
|
|
Yes |
141 |
10 (7.1) |
131 (92.9) |
0.89 (0.44 - 1.83) |
0.766 |
|
|
Chronic disease in a previous pregnancy |
No |
658 |
47 (7.1) |
611 (92.9) |
1 |
|
|
|
Yes |
23 |
2 (8.7) |
21 (91.3) |
1.24 (0.28 - 5.44) |
0.772 |
|
|
Table 5. Previous maternal/obstetric complications associated with Preeclampsia (N = 728).
Variables |
Total |
PREECLAMPSIA |
cOR |
p-Value |
aOR |
p-Value |
Yes (%) |
No (%) |
Abortion |
No |
659 |
52 (7.9) |
607 (92.1) |
1 |
|
|
|
Yes |
69 |
4 (5.8) |
65 (94.2) |
0.72 (0.25 - 2.05) |
0.536 |
|
|
Preeclampsia/eclampsia |
No |
634 |
39 (6.2) |
595 (93.9) |
1 |
|
|
|
Yes |
37 |
9 (24.3) |
28 (75.7) |
4.90 (2.16 - 11.11) |
<0.001 |
3.11 (1.11 - 8.71) |
0.031 |
Premature labour |
No |
310 |
33 (10.7) |
277 (89.4) |
1 |
|
|
|
Yes |
51 |
13 (25.5) |
38 (74.5) |
2.87 (1.39 - 5.93) |
0.004 |
2.11 (0.91 - 4.92) |
0.082 |
4.3.4. Maternal and Foetal Adverse Birth Outcomes
In adjusted analysis, high blood pressure (SBP_ ≥ 140 mmHg) at first ANC visit was strongly associated with developing PE/E, (aOR = 12.23, 95% CI: 1.02 - 146.77, p = 0.048) compared to low blood pressure of SBP < 140 mmHg. Proteinuria at any level was also a significant predictor: for preeclampsia (aOR = 81.65 (95% CI: 18.89 - 353.02) (1+); 50.62 (95% CI: 3.16 - 809.57) (2+), and 273.79 (95%CI: 20.40 - 3674.21) (3+), all p < 0.001). On the other hand, women who gave birth to an infant weighing 2.5 - 3.9 kg had significantly lower odds of developing preeclampsia (aOR = 0.35, 95% CI: 0.15 - 0.82, p = 0.016) as compared to their counterparts who gave birth to low-birth-weight infants (Table 6).
Table 6. Maternal and fetal adverse birth outcomes associated with Preeclampsia (N = 728).
Variables |
Total |
PREECLAMPSIA |
χ2 p Value |
cOR |
p-Value |
aOR |
P-Value |
Yes (%) |
No (%) |
Number of ANC visit |
1 |
20 |
2 (10.0) |
18 (90.0) |
0.927 |
1 |
|
|
|
2 - 3 |
119 |
10 (8.4) |
109 (91.6) |
|
0.83 (0.17 - 4.08) |
0.814 |
|
|
4+ |
409 |
32 (7.8) |
377 (92.2) |
|
0.76 (0.17 - 3.44) |
0.726 |
|
|
1st_ANCSBP_mmHg |
<100 |
67 |
2 (3.0) |
65 (97.0) |
<0.001 |
1 |
|
|
|
100 - 139 |
556 |
32 (5.8) |
524 (94.2) |
|
1.98 (0.46 - 8.47) |
0.355 |
1.03 (0.11 - 9.32) |
0.981 |
≥140 |
50 |
21 42.0) |
29 (58.0) |
|
23.53 (5.17 - 107.07) |
<0.001 |
12.23 (1.02 - 146.77) |
0.048 |
1st_ANC DPB_mmHg |
<60 |
55 |
2 (3.6) |
53 (96.4) |
<0.001 |
1 |
|
|
|
60 - 89 |
558 |
35 (6.3) |
523 (93.7) |
|
1.77 (0.41-7.58) |
0.440 |
7.61 (0.40-142.84) |
0.175 |
≥90 |
60 |
18(30.0) |
42 (70.0) |
|
11.36 (2.49-1.72) |
0.002 |
11.80 (0.51-270.78) |
0.123 |
Trimester registered ANC |
≤13 weeks |
137 |
7 (5.1) |
130 (94.9) |
0.452 |
1 |
|
|
|
14 - 27 weeks |
566 |
47 (8.3) |
519 (91.7) |
|
1.68 (0.74-3.81) |
0.212 |
|
|
≥28 weeks |
25 |
2 (8.0) |
23 (92.0) |
|
1.61 (0.32-8.27) |
0.565 |
|
|
GA_ at 1st_ANC_clinic |
<16 weeks |
150 |
10 (6.7) |
140 (93.3) |
0.555 |
1 |
|
|
|
17 - 20 weeks |
33 |
2 (6.1) |
31 (93.9) |
|
0.90 (0.19 - 4.33) |
0.899 |
|
|
21 - 28 weeks |
100 |
5 (5.0) |
95 (95.0) |
|
0.74 (0.24 - 2.22) |
0.588 |
|
|
≥29 weeks |
445 |
39 (8.8) |
406 (91.2) |
|
1.34 (0.65 - 2.77) |
0.420 |
|
|
Proteinuria(20_27wks) |
Nil |
330 |
21 (6.4) |
309 (93.6) |
|
1 |
|
|
|
≤+1 |
18 |
14(77.8) |
4 (22.2) |
<0.001 |
51.5 (15.58 - 170.28) |
<0.001 |
81.65 (18.89 - 353.02) |
<0.001 |
+2 |
5 |
4 (80.0) |
1 (20.0) |
|
58.86 (6.29 - 550.35) |
<0.001 |
50.62 (3.16 - 809.57) |
0.006 |
≥±3 |
10 |
8 (80.0) |
2 (20.0) |
|
58.86 (11.75 - 294.83) |
<0.001 |
273.79 (20.40 - 3674.21) |
<0.001 |
Haemoglobin level g/dl |
<8.5 |
56 |
1 (1.8) |
55 (98.2) |
0.040 |
1 |
|
|
|
8.5 - 10.9 |
422 |
21 (5.0) |
401 (95.0) |
|
2.88 (0.38 - 21.84) |
0.306 |
|
|
≥11 |
192 |
18 (9.4) |
174 (90.6) |
|
5.69 (0.74 - 43.59) |
0.094 |
|
|
Maternal adverse outcomes |
The facility in which the women have labour |
Others |
45 |
4 (8.7) |
41 (91.3) |
0.002 |
1 |
|
|
|
Mnazi Mmoja |
461 |
47 (10.2) |
414 (89.8) |
|
1.19 (0.41 - 3.47) |
0.747 |
0.83 (0.16 - 4.17) |
0.817 |
Kivunge |
102 |
3 (2.9) |
99 (97.1) |
|
0.32 (0.07 - 1.48) |
0.145 |
0.33 (0.04 - 2.45) |
0.279 |
Makunduchi |
115 |
1 (0.9) |
114 (99.1) |
|
0.09 (0.01 - 0.85) |
0.035 |
0.23 (0.02 - 3.52) |
0.294 |
The gestational age (weeks) |
<28 |
9 |
2 (22.2) |
7 (77.8) |
<0.001 |
1 |
|
|
|
28 - 34 |
45 |
11 (24.4) |
34 (75.6) |
|
1.13 (0.20 - 6.27) |
0.887 |
0.45 (0.06 - 3.15) |
0.421 |
35 - 36 |
302 |
25 (8.3) |
277 (91.7) |
|
0.32 (0.06 - 1.61) |
0.167 |
0.38 (0.05 - 2.57) |
0.317 |
≥37 |
367 |
17 (4.6) |
350 (95.4) |
|
0.17 (0.03 - 0.88) |
0.035 |
0.26 (0.04 - 1.92) |
0.188 |
Birth complications |
Placenta Previa |
|
|
|
|
|
|
|
|
No |
721 |
55 (7.6) |
666 (92.4) |
0.511 |
1 |
|
|
|
Yes |
7 |
1 (14.3) |
6 (85.7) |
|
2.02 (0.24 - 17.06) |
0.519 |
|
|
Foetal outcomes |
Low birth weight (kg) |
<2.5 |
91 |
21 (23.1) |
70 (76.9) |
<0.001 |
1 |
|
|
|
2.5 - 3.9 |
573 |
29 (5.1) |
544 (94.9) |
|
0.18 (0.09 - 0.33) |
<0.001 |
0.35 (0.15 - 0.82) |
0.016 |
4 - 5 |
28 |
3 (10.7) |
25 (89.3) |
|
0.4 (0.11 - 1.46) |
0.165 |
0.68 (0.15 - 3.17) |
0.628 |
Severe birth asphyxia |
No |
715 |
54 (7.6) |
661 (92.5) |
0.294 |
1 |
|
|
|
Yes |
13 |
2 (15.4 |
11 (84.6) |
|
2.23 (0.48 - 10.29) |
0.306 |
|
|
Low Apgar score |
≤3 |
40 |
6 (15.0) |
34 (85.0) |
0.110 |
1 |
|
|
|
4 - 6 |
15 |
2 (13.3) |
13 (86.7) |
|
0.87 (0.16 - 4.88) |
0.876 |
|
|
≥7 |
630 |
43 (6.8) |
587(93.2) |
|
0.42 (0.17 - 1.04) |
0.061 |
|
|
Premature birth |
No |
367 |
55 (7.6) |
670 (92.4) |
0.095 |
1 |
|
|
|
Yes |
347 |
1 (33.3) |
2 (66.7) |
|
6.09 (0.54 - 68.23) |
0.143 |
|
|
Stillbirth fresh |
No |
350 |
45 (12.9) |
305 (87.1) |
0.876 |
1 |
|
|
|
Yes |
14 |
2 (14.3) |
12 (85.7) |
|
1.13 (0.24 - 5.21) |
0.876 |
|
|
Stillbirth macerated |
|
No |
352 |
44 (12.5) |
308 (87.5) |
0.204 |
1 |
|
|
|
Yes |
12 |
3 (25.0) |
9 (75.0) |
|
2.33 (0.61 - 8.95) |
0.217 |
|
|
Neonatal death |
No |
669 |
50 (7.3) |
619 (92.7) |
0.143 |
1 |
|
|
|
Yes |
45 |
6 (13.3) |
39 (86.7) |
|
1.95 (0.79 - 4.82) |
0.149 |
|
|
Of the enrolled women, 714 were followed up until 28 days post-delivery. Among them, 345 (48.3%) experienced premature labour; of these, 45 (6.3%) had premature labour between 28 and 34 weeks of gestation, while 302 (42.3%) had premature labour during 35 to 36 weeks of pregnancy. Among the 714 babies born, 45 (6.3%) died within 28 days post-delivery. None of these maternal and neonatal adverse birth outcomes studied showed a statistically significant association with preeclampsia in either crude or adjusted models (p > 0.05 for all comparisons) (Table 6).
In summary, individual and family history of hypertensive disorders, high BP at first ANC visit, and proteinuria were the important predictors for preeclampsia. None of the sociodemographic and reproductive variables showed a statistically significant association with preeclampsia.
5. Discussion
In a recent finding, the occurrence of preeclampsia was 7.7%. The median age of women diagnosed with preeclampsia was 28 years. A family history of hypertension, history of chronic hypertension, and history of preeclampsia were predictors of preeclampsia development.
In the present study, the incidence of preeclampsia was found to be 7.7%. This finding is consistence with findings from various regions, including the Eastern, Western, and developing other nations, where the incidence ranges from 2% to 8% of pregnancies [37]. However, our findings differ from those reported in certain sub-Saharan African countries and Canada, where the incidence was reported at 13%, 8.8% [5] [38]. These variations may be attributed to differences in sociodemographic characteristics, methodological approaches, genetic predispositions, and geographic factors, as supported by previous studies.
5.1. Factors Associated with Preeclampsia
5.1.1. Reproductive Factors
Additionally, in the current study, changes in paternity were not statistically associated with the development of preeclampsia. This finding contrasts with a previous study conducted in Ethiopia, which reported a significant association between paternity change and the risk of preeclampsia [39]. The contrasts could have been caused by the inherited family pattern, which may play a role in the development of pre-eclampsia, as the placenta plays a significant role in the disease’s pathogenesis [40]. Moreover, the current findings contrast with those of another study carried out in Zanzibar, which suggested that women who changed partners had a 7.7 times greater risk of developing preeclampsia [19].
This variation could be attributed to the fact that the present study was limited to three public hospitals in Unguja, whereas the other study encompassed both Unguja and Pemba islands. Moreover, the variance may arise from the different study designs and populations: our research involved the enrollment and follow-up of pregnant women from antenatal care (ANC) through to the postnatal period, whereas the other study concentrated on postnatal mothers only. On the other hand, our research indicated that the risk of developing preeclampsia was 1.2 times greater for women with previous pregnancies than for those without any prior pregnancies. This could be attributed to the disease’s aetiology, as multigravida women have an increased risk of developing preeclampsia, considering they may have experienced this condition before.
Similar findings were reported in other sub-Saharan African studies, where the multigravida women had 1.89 times higher risks for preeclampsia compared to the nullipara women [5]. However, this contrasts with a study from Ghana, where primigravida status was not associated with preeclampsia [25]. This variation may be attributed to the sociodemographic characteristics of study participants.
5.1.2. Medical and Family History Factors
The present research demonstrated that the likelihood of developing preeclampsia was 3.7 times higher for women with a history of chronic hypertension and 3.9 times higher for those with a family history of hypertension. Similar to the studies conducted in China, Malaysia, Colombia, Pakistan, Ethiopia, Brazil, Sierra Leone, and Zanzibar, which reported that a history of chronic hypertension and a family history of hypertension were predictors of preeclampsia [6] [10] [19] [26] [41]-[45]. On the other hand, the current findings were inconsistent with the studies done in Bangladesh, Uganda, and Ghana, which revealed that a previous history of hypertension was not associated with preeclampsia [4] [25] [46]. This could be due to the geographical area of the study participants.
5.1.3. Previous Maternal/Obstetric Complications
Furthermore, women who had a prior history of preeclampsia or eclampsia were 3.2 times more likely to develop preeclampsia in their following pregnancy compared to their counterparts. The findings were in line with the findings reported in Colombia and Bangladesh women with a previous history of preeclampsia had a 4 to 5 times higher risk of developing preeclampsia [47] [48]. This could be due to the genetic factors that might contribute to the physiological predisposition of preeclampsia.
5.2. Adverse Birth Outcomes Associated with Preeclampsia
In a recent study, the present findings demonstrate that women with preeclampsia had higher odds of having low-birth-weight infants compared to their counterparts. The finding were in line with other studies done in Malaysia, Cameroon, Nigeria, Uganda, Zimbabwe, Southern and Northeast Ethiopia, China and Thailand, reported that babies born to preeclamptic women were at a higher risk of perinatal death, low birth weight, birth asphyxia, neonatal intensive care unit (NICU) admission and preterm birth than babies born to normotensive women [1] [30] [42] [49]-[53]. On the other hand, premature labour and neonatal deaths were not significantly associated with preeclampsia in the present study, as reported in the previous studies. The divergences may result from methodological factors of the study utilized, such as the study setting, sample size, and study design. The current finding suggests a statistical correlation for women with high blood pressure (SBP_≥ 140 mmHg) during their first ANC visit, and had 12 times higher risk of preeclampsia than those with SBP < 140 mmHg. However, the results contradict those of a prospective study conducted in Uganda, which identified diastolic hypertension ≥ 90 mmHg as significant to preeclampsia [46].
6. Limitations of the Study
This longitudinal population-based cohort study reflects a follow-up of pregnant women who attended antenatal clinics from 20 weeks of gestation until 28 days after delivery. The information was recorded in the medical charts of all women during ANC visits up to 28 days post-delivery. Limitations of this study are the absence of a control group consisting of pregnant women with or without preeclampsia, impeding a direct comparison of disease outcomes, as well as the loss of information regarding blood sample investigations. Furthermore, referring to the study design, study site, and being conducted in the health facilities, the generalizability may be limited to those attending public health facilities in Unguja. This approach, which is based in hospitals, only includes women who go to the three chosen hospitals. This is because many women die without having visited a health facility, and also because perinatal deaths were considered only for those at an institution. We also included family history of hypertension and preeclampsia as variables in our study; these may have been affected by potential recall bias.
Depending on the time elapsed between the event and the interview, participants may struggle to recall their family’s medical history, potentially resulting in misclassification. This could influence the findings related to the family history of hypertension and preeclampsia risk. In our analysis, we conducted a logistic regression; however, we acknowledge that we did not control for all potentially relevant confounders, such as obesity, socioeconomic status, and diabetes. The uncontrolled confounding factor could have affected the observed associations in various ways.
7. Implications of the Study
Preeclampsia is a pregnancy complication characterized by elevated blood pressure and organ damage, which significantly increases the risk of adverse maternal and perinatal outcomes. This study has identified various maternal risk factors that contribute to these outcomes, including a family history of hypertension, chronic hypertension, and previous eclampsia. Therefore, the findings of this study are essential for the early detection and management of preeclampsia. Targeted preventive measures may improve maternal and newborn outcomes for women with preeclampsia, reducing the risk of complications and enhancing the overall quality of maternal and newborn care.
By implementing the findings from this study, healthcare professionals can better identify at-risk women and provide timely interventions, ultimately fostering healthier pregnancies and promoting positive health trajectories for both mothers and their infants.
8. Conclusion
Preeclampsia remains a significant public health issue in Unguja, Zanzibar, affecting maternal and neonatal health outcomes considerably. This research identified a family history of hypertension, chronic hypertension, and previous eclampsia as key risk factors. These results highlight the necessity of early detection, proactive antenatal care, and focused health education. It is also advised to enhance maternal healthcare systems with screening protocols, preventive measures, and management guidelines customized to the recognized risk factors. Furthermore, the study indicates that timely interventions can effectively alleviate the burden of preeclampsia and enhance pregnancy outcomes in comparable low-resource environments.
Acknowledgements
The authors would like to thank the pregnant women who participated in this study for their willingness and the time devoted to this study. We would also like to thank the data collectors for dedicating themselves to participating in this study.
Ethical Considerations
The study has been approved by the Kilimanjaro Christian Medical University College Research and Ethical Review Committee (CRERC) with a certificate number 2495. Permission to conduct the study was obtained from the Zanzibar Medical Research Council (ZAHREC). A written consent form was obtained from participants to confirm their willingness to participate in the study.
Availability of Data and Materials
The data supporting this study’s findings are available from the corresponding author.
Consent for Publication
Not applicable.
Sources of Funding
There were no external sources of funding used for this study.
Authors’ Contributions
NIY originated the study, contributed to the study design, analysis, and drafted the manuscript. JS, RNM, and MJM contributed to the design of the study and critically revised the draft manuscript, providing constructive comments, ideas, and reviews. All authors read and approved the final manuscript.
List of Abbreviations
ANC |
Antenatal Care |
AOR |
Adjusted Odds Ratio |
COR |
Crude Odds Ratio |
CI |
Confidence Interval |
SD |
Standard Deviation |
LMICs |
low and middle-income countries |
PE/E |
Preeclampsia/Eclampsia |
ANCSBP |
Antenatal clinic systolic blood pressure |
ANCDBP |
Antenatal clinic Diastolic blood pressure |
EDDs |
expected date of delivery |
Ras |
research assistants |