ABSTRACT

Background: Hypertensive disorders in pregnancy (HDP) are a major public health problem. The objective of this study was to describe the epidemiology of HDP in Mezam division. Methods: A cross-sectional study and record review (retrospective) was conducted to determine the prevalence and determinants of behavioural risk factors (BRFs), the prevalence and risk factors of HDP and a prediction model for gestational hypertension (GH) (1210 participants) while a randomized control trial (RCT) (342 participants) to determine the effect of mobile health (mHealth) on BRFs of HDP. Consecutive sampling was used to recruit participants. Level of significance was set at p-value < 0.05. Chi-square test, independent sample t-test, Paired t-test and McNemar test were used in the analysis. Results: The prevalence of BRFs were: smoking (0.8%), excess salt (12.4%), excess alcohol (21.6), stress (48.8%), sedentary lifestyle (49.2%) and fruits/vegetables <5 servings/day (80.7%). Socio-demographic variables; age (≥35years), singles, educational status, occupation and religion were determinants of BRFs of HDP. The prevalence of HDP was 14.5%. Of the HDP types; chronic hypertension (CH) (0.5%), preeclampsia ((PE) superimposed on CH (0.8%), severe PE (1.6%), GH (4.6%) and (PE) (7.0%). For risk factors, family history of hypertension (HTN) [adjusted odd ratio (AOR), 95%CI: 3.7(1.7-11.6)] and BMI ≥25 kg/m² [2.6(1.3-6.7)] for CH; Singles [2.7(1.5-4.7)], family history of HTN [4.4(1.5-13.1)], alcohol [2.8(1.1-5.4)], HTN in previous pregnancy [4.9(1.5-10.2)] for GH;, smoking [5.1(1.3-9.3)],  BMI ≥25 kg/m² [2.7(1.4-7.1)], age first pregnancy (≥35 years) [7.4(1.6-28.4)], caesarean section [3.2(1.4-4.6)], HTN in previous pregnancy [3.5(1.3-61.1)], birth spacing ≥108months [4.8(1.3-61.1)] and blood group (AB) [3.0(1.3-5.9)] for PE. In our RCT, both the control and intervention group were similar at baseline. At post-intervention, there was a significant increase in mean knowledge of HIP, PA, fruits/vegetables and a proportionate decrease in alcohol, stress and salt intake. No such increase/decrease were found in the control group. Regarding the prediction model, five factors were significant and were used to generate the model. The sensitivity, specificity and area under the curve (AUC) value for the derivation data set were 85.9%, 88.8% and 0.828 (95% CI 0.772–0.884) respectively. The model was validated in an independent data set. There was a significant difference (χ²_trend < 0.001) in cumulative incidence in derivation and validation data set. Conclusion: The prevalence of BRFs was high. Age, education, marital status and religion were found as determinants of BRFs. The prevalence of HDP was 14.5% and both behavioural and non-BRFs were found as risk factors of HDP. The mHealth intervention showed great impact in increasing/decreasing BRFs of HIP. For the prediction model, the AUC value was high and could better separate women with/without GH. Thus, mHealth should be used in sensitizing women at community level while the prediction model should be used in screening women at risk in health facilities.

CHAPTER ONE

 INTRODUCTION

1.1. Background of the study

Hypertensive disorders in pregnancy (HDP) remain a major public health problem worldwide, and its prevalence varies from region to region as well as from country to country. HDP remain a major cause of maternal and neonatal morbidity and mortality [5, 6]. It is a condition in which the expectant mother present with raised blood pressure (BP) during pregnancy as defined by the American College of Obstetricians and Gynecologists (ACOG) in 1986 and approved by the WHO [7-9]. This condition can be well-thought-out as a rise in diastolic blood pressure (DBP) of at least 90 mmHg and a systolic blood pressure (SBP) of at least 140 mmHg. It is equally considered as an upsurge in DBP of at least 15 mmHg or an upsurge of 30 mmHg in SBP [7-9]. Besides, the WHO considers only an elevated value of DBP as a criterion for defining this disorder [7]. According to Moodley, [10], the disorder remains one of the main causes of maternal death worldwide.

The prevalence of HDP is roughly 8–10% of all pregnancies worldwide [11-13]. It’s known to complicate 5 in 10 gestations in Nigeria [14, 15]. In Cameroon and other African countries, nearly one tenth of all maternal deaths are linked to HDP [16]. In addition, over 80% of maternal deaths worldwide are due to five direct obstetric causes; haemorrhage, hypertension (HTN), sepsis, obstructed labour and complications of abortion, with hypertension in pregnancy (HIP) contributing ~18%, second after hemorrhage [17].

HDP incorporates a spectrum of conditions, including preexisting HTN, gestational hypertension (GH), preeclampsia (PE) and preeclampsia superimposed on chronic hypertension (PSCH) [18]. About 1% of pregnancies are complicated by chronic hypertension (CH), 5% to 6% by GH without proteinuria, and 2% by PE [19]. The condition can affect normotensive as well as hypertensive individuals and usually occur in the second half of pregnancy (gestational age ≥20 weeks).

In 2008, 358,000 mothers died during or following pregnancy and childbirth and almost all of the deaths (99%) occurred in developing countries, which could have been prevented [17]. Apart from causing mortality, HDP are associated with severe maternal and perinatal morbidity like intrauterine growth retardation, premature delivery, and early neonatal deaths [20]. A 2006 systematic review of the causes of maternal mortality by the WHO revealed that HDP are accountable for 2-43% of maternal deaths across countries and 9.1% of pregnancy-related deaths in Africa [16]. The WHO equally estimates that at least one woman dies every seven minutes from complications of HDP. In sub-Saharan Africa, HDP remain a major cause for concern owing to their increasing incidence, gravity and associated complications [21].

The United Nations Sustainable Development Goal (SDG) 3.1 aims at reducing the global maternal mortality ratio to less than 70 per 100,000 live births by 2030 [22]. The SDGs equally aim at maintaining the momentum of the Millennium Development Goals, which catalyzed a global reduction in maternal deaths from approximately 390,000 in 1990 to 275,000 in 2015 [23]. Although efforts have been made by international organizations to curb these deaths, it still constitutes a major public health problem in Cameroon and other sub-Saharan African countries [24]. Substantial progress has been accomplished in almost all countries worldwide as regards maternal morbidity and mortality; but by every indication, Cameroon and many countries in sub-Saharan Africa failed to reach the Millennium Development Goal (MDG) target of decreasing maternal death by 75 percent from 1990 to 2015. Some studies have estimated the prevalence of HDP to be, 8.2% in the Yaounde Gynaeco-Obstetric and Pediatric Hospital, Cameroon [25], 31.0% reported in urban Cameroon [26], 17% in Nigeria [15] and 5.5% in the Far North Region of Cameroon [27]. A study by Egbe et al., [28] to assess the determinants of maternal mortality in Mezam Division, found that of the 89 deaths due to direct causes (Post-partum haemorrhage, unsafe abortion, HDP etc.), 14.5% were due to HDP.

 Antenatal screening for HTN and proteinuria followed by close monitoring and treatment of PE reduced eclampsia related maternal mortality by 48-68% [29, 30]. Therefore, availability of skilled health personnel with knowledge and skills in managing HTN is vital for prevention of hypertensive related complications [31].

Furthermore, HTN can be categorized into primary (essential) or secondary HTN. Over 90% of all HTN are classified into essential HTN (HTN with no obvious cause, although there are recognizable risk factors [32]. The remaining 10% are classified as secondary HTN (HTN that results from other diseases in the body e.g. kidney disease, cardiovascular disease (CVD), coronary heart disease [33]. Although the exact cause of primary HTN is unknown, there are a number of risk factors that have been associated with the condition. These factors can be grouped into behavioural and non-behavioural risk factors (BRFs) [34, 35]. The non-BRFs are traits or characteristics in the individual that cannot be changed or modified, hence they are out of our control and little or nothing can be done to change them. These factors include age, sex, race, gravidity, preexisting hypertension, family history, genetic composition [36-39] etc. On the other hand, the BRFs are attributes, characteristics, exposures or life style patterns that can be adjusted or changed to avoid the development of the disease. The BRFs include high salt intake (women who add salt to food before eating), sedentary lifestyle/physical inactivity, tobacco use, alcohol consumption, stress and fruits/vegetables <5 servings/day [40] etc.

A number of studies have been carried out in Cameroon [25, 26, 36, 38, 41] with regards to prevalence and risk factors of HDP. However, these studies have mainly been focused in Regional or secondary level health facilities (HF), excluding primary HF. Likewise limited literature exist on the effect of mobile health (mHealth) on BRFs of HTN in Cameroon. Hence to our knowledge this is the first study done in Mezam Division encompassing all the HDP, the behavioural and non-BRFs as well as to assess the effect of mHealth on BRFs of HTN among pregnant women in Mezam.

1.2. Statement of the problem

Despite that HDP are the third most common cause of maternal morbidity and mortality, very limited and mostly descriptive studies have been published on the subject in Cameroon. The condition is apparently increasing over the years in Mezam as obtained from the hospital statistics of Obstetrics and Gynecology units of hospitals in this area according to unpublished data (Annual Report of Causes of Maternal deaths in the North West Region). Follow-up and treatment of pregnant hypertensive women is an important step in the prevention of HDP and serious end-organ damages. The mortality and morbidity for women and children associated with HDP and its complications are a major burden, particularly in less developed countries. Studies shows that the incidence of HDP in developed and developing countries is similar. However, deaths due to eclampsia are few in developed compared to developing countries, indicating there is a missed opportunity to prevent hypertensive related maternal deaths in these countries. Delay in diagnosis and prompt initiation of treatment could result in disastrous consequences for both the mother and the baby. So, firstly, it is quite important to determine how vast the problem is in our hospitals, identify risk factors so as to prevent the preventable causes.

On the one hand, predicting pregnant women at increased risk of HDP is at times challenging and time consuming. With the use of prediction models, the challenge and delay can be overcome. However, prediction models that could assist in this screening process are mostly developed in high income countries, which may not be suitable for less developed countries like Cameroon because of differences in the settings, availability and the cost of investigations used in the model. Thus, developing and validating a prediction model for HDP for our setting would be helpful in screening women at the onset of the disease.

Similarly, mobile health (mHealth) through text messages is viewed as a promising communication channel that offers the potential to improve healthcare delivery and promote behaviour change among vulnerable populations. A systematic review conducted in other countries to investigate the effectiveness of health education to promote physical activity and healthy diets, concluded that mHealth intervention is effective in promoting BRFs of HDP. However, there is scarcity of studies demonstrating the effect of mHealth on modifying BRFs of HDP, especially in developing countries including Cameroon. Likewise, most research regarding HDP has been carried out in secondary and tertiary health facilities, living out primary health facilities. Thus, the current study is conducted to determine the prevalence and determinants of BRFs of HDP, prevalence and risk factors of HDP, the effect of mHealth on the modifiable BRFs of HDP and to develop a model for prediction of GH. By understanding the magnitude of this risk factors on hypertension, future interventions can be undertaken before potential diseases occur.

1.3. Research Questions

1. What is the prevalence and socio-demographic determinants of BRFs of HDP?
2. What is the prevalence and risk factors of HDP?
3. What is the effect of mHealth on modifying BRFs of HDP?
4. Can a prediction model be developed and validated for predicting women with GH?

1.4. Hypothesis

1. The prevalence of BRFs of HDP is low in Mezam.
2. Socio-demographic factors influence of BRFs of HDP.
3. The prevalence of HDP is low in Mezam division
4. Behavioural and non-behavioural factors influence HDP
5. Mobile Health has an effect on BRFs of HDP
6. No predictive model can be developed and validated to screen for GH.

1.5. Objectives

1.5.1. General Objective: To describe the epidemiology of HDP in Mezam division

1.5.2. Specific Objectives:

1. To determine the prevalence of BRFs of HDP in Mezam division
2. To assess the determinants of BRFs of HDP in Mezam division.
3. To determine the prevalence of HDP in Mezam division.
4. To assess the risk factors of HDP in Mezam division.
5. To assess the effect of mHealth on BRFs of HDP.

6. To develop and validate a predictive risk score model for GH in Mezam division