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Many research on the factors that influence whether or not women utilize SBAs have concentrated on socio-demographic and maternal variables despite the fact that males have an important role in the family as the primary decision-makers. Furthermore, most initiatives aimed at addressing these variables and, by extension, increasing male spouse engagement in the use of prenatal care and utilization of maternal health services, have mostly targeted the mothers. Very little has been done to engage fathers in the issue of maternal health. Family planning research focusing on males has found that they are crucial to the process, whether by active engagement or by making it possible for their spouses to take contraception (Mulick, et al., 2008). Male participation has been sluggish, and this is likely a contributing factor to the sub-optimal progress towards the attainment of United Nations SDGs 3 and 5, which aim to ensure health and promote well-being for everyone at all ages and to achieve gender equality and empower all women and girls (UN, 2015). For fathers to reap the advantages of their partners’ prenatal care utilization, their involvement must extend all the way through the labor and delivery process, not just to the hospital lobby (Cohen and Burger, 2000).

Most people feel that men who are aware of the potential risks to their women during pregnancy are more inclined to take immediate action (Cohen and Burger, 2000). Most of this training is provided to mothers in Kenya as part of their prenatal care. In order to improve the utilization of prenatal care, it will be important to identify the factors linked with male husband engagement on the utilization of antenatal care by mothers. Few studies have directly addressed the question of whether male partners who actively engage in prenatal care encourage other men to encourage their women to utilize antenatal care and to seek professional attention during the antenatal care sessions.

According to the Health Care Policy (2012- 2030), all pregnant women should be able to afford prenatal care and give birth in a hospital or other facility with trained medical staff present at no cost to them. When comparing counties in Kenya, such as Kirinyaga, Nyeri, Nairobi, Meru, and Mombasa, there are significant differences in the rates at which women use prenatal care, delivery, and subsequent postnatal services (UNFPA, 2013). counties like West Pokot, Kilifi, Mandera, Turkana, and Wajir report utilization rates between 5% and 17%, while other counties have utilization rates above 70%. (UNFPA, 2014). Therefore, the goal of this study was to fill in the blanks that remain despite attempts to improve the number of prenatal and postnatal care visits and thereby decrease the maternal infant mortality rate in the various counties and nationwide.

It is of major policy significance to the government and other stakeholders, notably funders, to understand the variables impacting the utilization of adequate prenatal care content. Until far, there has been no research conducted in Kenya to identify the factors that affect the uptake of prenatal care information. Several international research have demonstrated the elements that influence the male partner’s engagement in the utilization of prenatal care and the substance of such services (Osungbade, et al., 2008). Research in Uganda was conducted without focusing on the specifics of prenatal care in order to better understand the overall context in which it is used (Kyomuhendo, 2007).

Similar research has been done on the variables that affect prenatal care utilization in the big picture. Factors that affect how often pregnant women seek out prenatal care include the education and work status of their mothers, their age, their socioeconomic status, and their exposure to the media (Saxena, et al., 2006). Disparities in prenatal care access and utilization may be traced back to prospective mothers’ varying levels of socioeconomic class. There are several factors that determine why women consistently use prenatal care services, but one of the most crucial is proximity to the health institution. Polygamy, a well-educated spouse, equality, and women’s agency are also major contributors. Surprisingly, factors such as a history of stillbirth or a C-section delivery did not have a role in the decision to schedule an early prenatal visit (Adenkale and Isawumi, 2008). Awareness of care throughout pregnancy and understanding of pregnancy-related problems have been shown to have a significant impact on the use of prenatal care services in India (Saxena et al., 2006).

Gender mainstreaming in reproductive health should place a greater emphasis on how to involve men, who play a vital role in women’s reproductive health, in a variety of treatments. Men are equally responsible for their own sexual health and the well-being of their spouses and children (Kululanga, 2011). The promotion of certain health seeking behaviors, such as the use of prenatal care services, has been demonstrated to rise after males are educated about the value of health care for the family (Britta, 2004). Traditional approaches to maternal health education have failed to take into account the multiple spheres of influence that shape a pregnant woman’s choices about her health. Despite the importance of men’s involvement in maternal and child health care, there is less data on the role that fathers have in their partners’ prenatal care decisions or the impact they have on their children’s diets in the first years of life. Male partners may be dissuaded from accompanying their pregnant wives to doctor’s appointments because of the stigma associated with males using MCH services and the widespread but false belief that pregnancy and childbirth are inherently feminine experiences (Ilyazu et al., 2010). The Samburu believe women’s reproductive health to be a “woman’s affair,” therefore it is considered dishonorable for men to be involved in this area.


Studies in epidemiology often employ randomized control trials because they allow for controlled tests to be conducted on human patients and other subjects. Randomized control trials, as opposed to case control and cross-sectional studies, evaluate interventions with the goal of preventing disease development and determining which ones work best in treating existing cases. Randomized controlled trials (RCTs) are based on the experimental concept of randomization, which has been used in a variety of fields, including biology and medicine. This reduces the potential for selection bias and makes it simpler to recover from the occasional incidence of prejudice.

Chirakalwasan et al. (2018) conduct a randomized controlled experiment to investigate the association between obstructive sleep apnea and excess body fat. Patients with OSA were randomly allocated to either a CPAP treatment group or a waiting group, with the former receiving CPAP treatment every night for two weeks. Pregnancy and glucose metabolism outcomes were the primary focus of the research. A total of 18 patients were randomly assigned to each of the 18 control groups after being exposed to CPAP.

Because the randomization was open, the participants may choose between joining the waiting list and taking part in the experiment, making a randomized control trial a suitable method for this study. This is helpful since it increases the likelihood that participants will follow the study’s protocol. However, the researchers note that CPAP adherence is poor compared to a prior study on the use of CPAP in people with obstructive sleep apnea and diabetes (Bourjeily et al., 2015) and. The results revealed that the glucose levels of obstructive sleep apnea patients randomly assigned to use CPAP did not improve. Because of the low rate of CPAP use, this study design may not have been ideal for examining the epidemiological connection between obstructive sleep apnea and obesity, both of which have indirect effects on diabetes. Nonetheless, the study successfully argued that questionnaire data cannot be used to establish obstructive sleep Apnea in pregnant women.

Therefore, this study concludes that a randomized controlled trial (RCT) was not sufficiently successful in proving the effect of obstructive sleep apnea on diabetes due of the aforementioned limitations.

Cross-sectional studies, in which the researcher takes contemporaneous measurements of both study participants’ outcomes and their exposures, are possible within the framework of observational research designs. Unlike case-control studies and cohort studies, in which participants are chosen based on whether or not they were exposed to the experimental condition, these investigations randomly assign people to either the treatment or control group. Because of this, inclusion and exclusion criteria form the backbone of cross-sectional research (Setia, 2016).

In this article, we look at the results of a cross-sectional research (Fredheim et al., 2011) that analyzed the correlation between severe obesity, diabetes, and OSA. Extreme obesity was defined in this study as a body mass index (BMI) of 40 or above. ” Clinical trials, bariatric surgery, intensive lifestyle intervention research, and therapy data for morbid obesity” were all incorporated into the study’s cross-sectional design. A total of 137 people from a single obesity clinic participated in the study. Patients with a body mass index (BMI) lower than 40kg/m2 and 3 patients owing to missing oral glucose tolerance test were screened out of the research, thus only those with extreme obesity (obesity grade III) were included. Nine more patients did not match sleep registration criteria, reducing the research population to 137.

Obstructive sleep apnea and glycemic tolerance were two of the primary factors examined. Due to the cross-sectional study design, the main exposures used to determine the effect of obstructive sleep apnea on obesity and, by extension, type 2 diabetes and prediabetes, were “age, gender, anthropometric measures, hypertension, smoking, alcohol consumption, insulin resistance, and relevant medication.”  It is clear from this paper’s analysis that there are both benefits and drawbacks to using this research approach for this particular case study. The use of this methodology is beneficial to the current study since there is a large pool of people who meet the inclusion criteria of being morbidly obese, having impaired glucose tolerance, and suffering from obstructive sleep apnea.

It was appropriate to utilize a cross-sectional research design for this investigation because of the information it may provide on the frequency with which a certain health problem is experienced by a given demographic. The findings of the study by (Fredheim et al., 2011) on the consequences of OSA in extremely obese individuals cannot be extrapolated to other centers because the sample population was recruited from a single health center. Additionally, clinically based samples are incorporated into the population-based design. Additionally, this type of research is less time-consuming and more cost-effective. This means they may be done at any point in the process, even before the design stages of a cohort study or as part of a cohort study’s baseline (Wang & Cheng, 2020). Studying what caused what would have been too time-consuming given the study’s limited time range.

The results of this study make it impossible to draw any conclusions about the relative contributions of OSA and poor glucose tolerance to rising BMIs, prediabetes, and type 2 diabetes. The importance of sleep apnea (OSA) in the development and prevalence of diabetes would have been greatly enhanced if the researcher had supplied the impacts of the factors on the individuals over time. Since all of the participants came from the same obesity clinic, the research suffers from a lack of ethnic variety. The inclusion criteria also exclude those who are only slightly overweight. Two major types of information bias—recall and detection—are at play in this investigation. The primary reason for this is because the risk of developing obstructive sleep apnea, pre-diabetes, or type 2 diabetes is evaluated at the same time as the exposure to excessive obesity.

After considering the aforementioned, it can be concluded that the study’s sampling approach retains validity and relevance.


Medical data from all across the world have confirmed the existence of a global epidemic: obstructive sleep apnea. Therefore, in nations where it has occurred, which is nearly everywhere, it is already recognized as a health danger. As a result, a case control study is an efficient method since it requires less time and effort from the researcher in demonstrating the uniqueness of the condition (Belbasis & Bellou, 2018). Case control studies are research designs in which individuals with and without an illness or result of interest (the cases and controls, respectively) are compared.


After comparing the two groups, a retrospective analysis is performed to determine whether or not there is a relationship between the disease and the risk factor by comparing the rates at which each group was exposed to the risk factor.

Due to the foregoing, case control studies are often observational, which means no active measures are used to affect the illness or its progression. Since this is an area where progress can only be made in retrospect, they will often resort to probabilistic guesswork to achieve their final aim.

Case-control methodology was employed by (Reutrakul & Mokhlesi, 2017) to examine the link between OSA and diabetes. Type 2 diabetes, type 1 diabetes, treatment outcomes, hypoxemia, sleep disruption, and glucose metabolism were the primary foci of their investigation. The authors used the classic symptoms of OSA in their research design and compared them to those of individuals with impaired glucose metabolism. The purpose of the review research was to investigate the role of OSA in the pathophysiology of T2D. The study also analyzed the correlation between glucose metabolism dysregulation and OSA by contrasting case and control groups. We also looked at how treating OSA affects glucose metabolism. Finally, we looked at the research linking OSA to diabetes and gestational diabetes complications.


There are a number of reasons why the adoption of a case control strategy in this study is consistent with epidemiological investigations. The use of a case-control research, as outlined by, allows for the examination of several factors that differentiate cases from controls (Vandenbroucke & Pearce, 2012). The purpose of the study was not to affect the ongoing course of obstructive sleep apnea in the case populations. Instead, the study aimed to identify patterns in order to better direct future studies. Because of the impacts of respiratory regulation and the development of breathing problems during sleep, the study indicated that obstructive sleep apnea and type-2 diabetes are related in both directions. Obstructive sleep apnea was found to have an effect on insulin resistance and increase the probability of acquiring type-2 diabetes when comparing case individuals to controls. In this respect, the study just highlights the problem of pathogenesis, as is the case with case control studies (Pearce, 2016), without providing much information about how the authors’ findings could affect the treatment of the condition or any other related intervention.


Case control studies are useful in epidemiological research for revealing patterns, but not for making treatment recommendations or altering illness progression, as shown in a study by Borgan et al (2020). As this study shows, case control studies are crucial in epidemiology for both determining outcomes and collecting exposures of interest in retrospect.

All of the statistical tests used in the study are represented in the findings section, indicating that they were crucial in establishing a clear conclusion. The mean and standard deviation, which can be seen in figure 2, have been represented in the aforementioned descriptive statistics (Benfer et al., 2019). There were 8514 participants in the study, and the average ratio of listeners per hour was 1.24. The likelihood of women in South Africa having access to the media has been shown below. A population mean will be distinct from a sample mean, as indicated by the standard of error being >0.5 in this case. It is clear from the table that all of the columns are more than 0.5, hence the descriptive statistics indicate that women in South Africa receive less than males in terms of media exposure (Owusu-Addo et al. 2018).


Figure 3 in the results section depicts the ANOVA table, where several observations were compiled for analysis. Standard error of the distribution is estimated to be 0.003 for the “ever had a terminated pregnancy” variable, with a mean of 0.09 and a standard deviation of around 0.288. (Weber et al. 2019). This data shows that more women in the county have chosen to end their pregnancies. The table also reflects the previously mentioned finding that women are less likely to be exposed to mass media. The average “education level of partner or husband” was 1.88, with a standard deviation of 0.809 and a standard error of the mean of 0.809.

In addition, the previously mentioned chi-square test was used to a research in which the two independent variables were the “number of antenatal visits during pregnancy” and the “age of the husband” (Schmalor and Heine, 2022). Other than that, the chi-square results have mirrored different parts of the examination. Figure 4 shows that the total number of samples collected thus far amounts to 1218, or 8.6 percent of the entire sample size for the project. Not included in the sample was a population of 12926, which had a 91.4 percent confidence interval. In light of this, the test shows that the legal marriage age in South Africa is lower than what is required by law (Amrhein et al. 2019). As a result, it has led to problems for pregnant women.

The preceding section’s regression analysis demonstrated the interconnectedness of the various factors. All of them have been shown to have beta values lower than 1. The number of children aged 5 and under has a significance value of 0.031, and its beta value is 0.19. In addition, the study’s figure depicts the regression of the married status of working professionals in South Africa, where the significant value was estimated to be 0.595. For this reason, the majority of employed professionals have reported their marital status as “married” (Ariyani and Hidayati, 2018). The t-value for the constant standard of error was 2.162, while the standard error was 3.839. The respondents’ current ages were used to determine a standard error of 0.064, beta coefficient of -0.023, and t-value of -0.818.


The identification of health-related outcomes and the suggestion of remedies are two areas where epidemiological research are useful. Due to its bias in adherence follow-up, the randomized controlled trial employed in the aforementioned study cannot be relied upon for the current analysis. The case-control and cross-sectional studies, on the other hand, were able to effectively eliminate inadvertent bias, and therefore, reveal the results.


Belbasis, L., & Bellou, V. (2018). Introduction to Epidemiological Studies. In E. Evangelou (Ed.), Genetic Epidemiology: Methods and Protocols (pp. 1–6). Springer.

Borgan, Ø. (2020). Handbook of Statistical Methods for Case-Control Studies. Taylor & Francis Group.

Bourjeily, G., Sharkey, K. M., Mazer, J., Moore, R., Martin, S., & Millman, R. (2015). Central sleep apnea in pregnant women with sleep disordered breathing. Sleep & Breathing = Schlaf & Atmung, 19(3), 835–840.

Centers for Disease Control and Prevention. (2012). Principles of Epidemiology in Public Health Practice, Third Edition: An Introduction. 511.

Chirakalwasan, N., Amnakkittikul, S., Wanitcharoenkul, E., Charoensri, S., Saetung, S., Chanprasertyothin, S., Chailurkit, L., Panburana, P., Bumrungphuet, S., Thakkinstian, A., & Reutrakul, S. (2018). Continuous Positive Airway Pressure Therapy in Gestational Diabetes With Obstructive Sleep Apnea: A Randomized Controlled Trial. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 14(3), 327–336.

Fredheim, J. M., Rollheim, J., Omland, T., Hofsø, D., Røislien, J., Vegsgaard, K., & Hjelmesæth,

Gordis, L. (2013). Epidemiology E-Book. Elsevier Health Sciences.

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Pearce, N. (2016). Analysis of matched case-control studies. BMJ, 352, i969.

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Reutrakul, S., & Mokhlesi, B. (2017). Obstructive Sleep Apnea and Diabetes: A State of the Art Review. Chest, 152(5), 1070–1086.

Schriger, D. L. (2008). Annals of Emergency Medicine. Annals of Emergency Medicine, 52(4), 480.

Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian Journal of Dermatology, 61(3), 261–264.

Szklo, M., & Nieto, F. J. (2014). Epidemiology: Beyond the Basics. Jones & Bartlett Publishers. Vandenbroucke, J. P., & Pearce, N. (2012). Case–control studies: Basic concepts. International

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