International Journal of Community Medicine and Public Health Mwangi DK et al. Int J Community Med Public Health. 2021 Sep;8(9):4161-4166 http://www.ijcmph.com pISSN 2394-6032 | eISSN 2394-6040
DOI: https://dx.doi.org/10.18203/2394-6040.ijcmph20213513
Original Research Article
Risky behavioral factors associated with preterm deliveries among
women of childbearing age (15-49 years) at Pumwani Maternity
Hospital, Nairobi County, Kenya
Daniel K. Mwangi1*, Stanley K. Waithaka2, Alfred O. Odongo3
1Department of Epidemiology and Biostatistics, 2Medical School, 3College of Health Sciences, School of Public Health, Mount Kenya University, Kenya Received: 29 June 2021 Accepted: 07 August 2021 *Correspondence: Dr. Daniel K. Mwangi, E-mail: kamaujan09@gmail.com Copyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT Background: Worldwide, pre-term delivery (PTD) or pre-term birth (PTB) is on the rise. World Health Organization (WHO) data shows an increase from 2 million in 1990 to 2.2 million in 2012 with about 60% of the preterm deliveries occurring in developing countries and 12.3% in Kenya. The study objective was to determine risky behavioral factors associated with preterm deliveries among women of childbearing age (15- 49 years) at Pumwani Maternity Hospital (PMH). Methods: Maternity delivery register (MDR) was used to identify and recruit eligible study participants. A sampling frame constituting of serial numbers assigned to the mothers was made. Data was collected using pretested and structured questionnaire and a data abstraction tool. The statistical package for social sciences (SPSS) version 23 was used to perform statistical analysis. Descriptive statistics were used to summarize categorical variables. Chi-square was used to examine the differences among the categorical variables. Prevalence adjusted odds ratios (PAOR) with their respective 95% confidence interval (CI) was used to estimate the strengths of associations. Results: The study recorded proportional differences among the study variables in their association with preterm deliveries. Among them, 100%, 40%, 22.2% and 17.1% of the mothers who used firewood, charcoal, kerosene and gas/electricity respectively had preterm deliveries. Conclusions: There was no statistically significant association between the study variables (general maternal smoking, maternal smoking during pregnancy, husband smoking, husband smoking during pregnancy, main household type of fuel) and preterm delivery. Keywords: Risky behavioral factors, Preterm deliveries, Maternal smoking
INTRODUCTION
Preterm delivery (PTD) is a multifactorial syndrome that has been on the increase worldwide. It’s a complex triad of events that lead to preterm birth. Factors that cause preterm births can broadly be classified as maternal medical, obstetric, risky behavioral factors, fetal factors, social- economic and demographic factors. A lot of effort is being put into research on how to prevent preterm births. Prevention in child death is one of seventeen sustainable
development goals (SDG)-3 (formerly MDG-4) which advocated for an increase in child survival.1 Many developing countries are facing socio-economic burden brought about by preterm births.2
Preterm delivery (PTD) is definedas all birthsbefore 37 completed weeks1of gestation or fewer1than 259 dayssince thefirst day of a1woman’s last menstrual1period.3 Of the1estimated 130 million1babies born each1year globally, approximately 15 millionare
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born1preterm which is about 11 % of all deliveries.4 Prematurity1is a major1determinant of1neonatal mortalityand morbidity as1well as a significant1contributor to long1term adverse health1outcomes.5 For instance, of the estimated 3.1 million1neonatal deaths that1occurred globally in 2010, about 1.08 million (35%) were directly1related to preterm1birth.6 Complicationsof preterm1birth are the single1largest direct causeof neonatal deathsand the second most1common causeof under-5 deaths after1pneumonia. Prematurity is a major1hindrance to theattainment of the SDG number 3--formerly MDG-4 target given1its contribution1to neonatal mortality. Toaccelerate achievement1of this SGD, there is a need to determine factorsassociated with1preterm birth in1order to reduce preterm births.7
Worldwide, almost 1 million die due to complicationsassociated with prematurity. More than 60% of the 15 million preterm1deliveries occur in Africa and South Asia. In contrast, the United States is among the top 10 nations which has many preterm births. In Africa, around 12% of births are premature compared with 9% in the developed countries.8 Countries with a high number of preterm deliveries per year are in the Asian continent with India at 3,519,100 and China at 1,172,300 preterm deliveries in 2012. African countries with high rates of preterm deliveries are Nigeria at 773,600, the Democratic Republic of Congo at 341,400 while Kenya is at 193,000. For countries in Africa, the rate is double or triple compared to developed countries and this creates pressure on the parents and the health system. The rate per 100 live births is higher in Sub Saharan Africa (SSA) countries with Malawi leading at 18.1%, Congo at 16.7%, Zimbabwe at 16.6%, and Kenya at 12.3%. Developed countries have a low PTD rate per 100 live births with countries such as Belarus at 4.1, Finland at 5.5, and Japan and Sweden at 5.9 per 100 live births.
A study found that when other variables are controlled, women who cook with biomass fuels are significantly more likely to have a preterm birth than women who cook with cleaner fuels (OR=1.44; 95 percent CI: 1.04, 1.97) Women who cook with biofuels are twice as likely to have had two or more stillbirths as women who cook with
cleaner fuels (RRR=2.01; 95 percent CI: 1.11, 3.62). The adjusted impact of active cigarette smoking was positive (OR=1.23) but not statistically significant.9 In Denmark, poverty, lack of education, and smoking contributed to a high number of PTD. Women who had <10 years of education and smoked had the highest number of PTD compared with those who had >12 years of1education in Denmark.10 A study in Japan investigated the effects of physical activity during pregnancy on preterm delivery and mode of delivery. Compared to the medium group, no significant difference was found in the incidence of preterm delivery in the low and high groups, however, that of the very low group increased significantly (OR=1.16, 95% confidence interval [CI],1.05–1.29, p=0.004).11
Problem statement
Preterm1birth is a global problem1with World Health Organization (WHO) estimating the prevalence to range between 5-18% across 184 countries. Locally, studies conducted in Kenyatta National Hospital found the rate of preterm deliveries to be at 15.7% in 2001 and 18.3% in 2014, while nationally it stands at 12.3%. There are multiple factors associated with PTD but understanding the epidemiology of these factors remains limited in Kenya. The factors can be maternal medical, obstetrics, social, foetal, economic, and demographic related. PTD causes a lot of emotional problems, coupled with financial constraints to the family. Prematurity is also associated with poor neurodevelopment, autism spectrum disorders, and chronic illnesses in adulthood like hypertension and type II diabetes. To contribute to a significant reduction in the number of preterm deliveries, it will be important to determine the associated factors with a view of designing suitable interventional programs. Pumwani maternity hospital was selected since it is an obstetric and referral hospital for delivery of expectant mothers in Nairobi, and adjoining counties. It is the largest maternity hospital in the country. The objective of the study was to determine risky behavioral factors associated with preterm deliveries among women of childbearing age (15-49 years).
Conceptual frame work
The frame work is given in Figure 1.
Figure 1: Conceptual framework.
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METHODS
Study design
This study employed a hospital-based cross-sectional study design. In cross-sectional designs, the outcomes of the participants and the exposures are measured. The study wasconducted at Pumwani Maternity Hospital (PMH). This is the largest obstetric hospital in the republic of Kenya and it was started in 1928 to serve the population of Nairobi.
The hospital is located in the Pumwani ward which is in Kamukunji constituency, Nairobi County. The studypopulation comprisedall mothers aged between 15 to 49 years who delivered at Pumwani maternity hospital during the study period.
Sample size and sampling techniques
The sample1was determined through the use of Fishers’ formula of 1998.12
𝑛= 𝑍2𝑃𝑞/𝑑2
n=the desired sample size, Z2=a standard deviation of 95% and a confidence interval of 1.96, P=proportion preterm deliveries in Kenya 12.3%, q=1-P, d=degree of accuracy desired at 0.05.
(1.96)2×0.123×0.877
(0.05)2= 165.75 = 166 mothers
𝑛=
To account for non-respondents and we take 15% of the sample size.
166 × 15/100 = 24.5 = 25
Total sample size = 166+25 =191 mothers
Hospital delivery records (HDRs) were used to identify and select 191 eligible study participants. Systematic random sampling was adopted. Pumwani maternity hospital has 50 to 70 deliveries per day on average. The desired sample size per day was 20 mothers. The first mother was randomly selected and then every 3rd mother was systematically selected.
Validity and reliability research tools
For validity and reliability, a pilot studywas conducted at Kayole 1 health center to test the validity and reliability of the questionnaire. This involved 24 motherswho delivered at Kayole 1 health center and was between the ages of 15—49 years. The reliability was checked through the test-retest method during piloting. The reliability was tested using Cronbach alpha which was acceptable at 0.7.
The data was collected using a structured questionnaire, information abstracted from1the maternal and1neonatal medical1records. The information captured involved demographics, social, medical, obstetric, and neonatal data from the client. All the required information was collected within 12 hours after delivery.
Data analysis
Statistical package for social sciences (SPSS) version 23 was used to perform statistical analysis. Descriptive statistics, proportions were used to summarize categorical variables. Chi-square of Fishers’ exact tests was used to examine the differences among the categorical variables.
Prevalence odds ratios (PORs) and 95% confidence interval (CI) were used to estimate the strengths of associations between the independent and dependent variables.
Ethical considerations
This study involved human subjects aged between 15—49 years who had delivered hence ethical consideration was imperative. Permission for the research was sought from the Pumwani maternity hospital ethics review board, Mount Kenya university ethical review committee, NACOSTI, participating mothers and from guardians of mothers regardless of their age.
During study period, the PI had access to private and confidential information from the medical records of the mother. To mitigate the potential effects of this, the PI ensured safety of the information by treating such information with confidentiality it deserves. The PI also used serial numbers assigned to the mothers and names or contact details of the respondents were not recorded anywhere on the questionnaires.
RESULTS
Prevalence of preterm deliveries
The prevalence of preterm1delivery among women of child1bearing age (15-49 years) at PMH was assessed.
From the findings, 166 women of1child bearing1age (15- 49 years) at PMH participated in the study out of which 37 had preterm deliveries. This presented a prevalence of 22.3%.
Risky behavioral factors associated with preterm deliveries
The study1sought to reveal the risky behaviors of the mothersassociated with preterm deliveries.
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Table 1: Association between risky behaviors and preterm delivery.
DISCUSSION
The study assessed some risky behaviors associated with preterm delivery. The study recorded proportional differences among the study variables in their association with preterm deliveries. Among them, 20% and 10% of the mothers who took alcohol and took alcohol during pregnancy respectively had preterm deliveries. In addition, 20%, 15.2%, 11.1% of mothers who smoke, whose husbands smoke and whose husbands smoke during pregnancy respectively had preterm deliveries. The association between drinking alcohol and drinking alcohol
during pregnancy and preterm delivery were statistically insignificant. Substance abuse like alcohol intake and smoking contributed to 33% of preterm in Sweden.13 Alcohol consumption during the second and third trimesters, but not during the first trimester, was associated with an increased risk of preterm delivery. Heavy alcohol consumption (≥300 g ethanol/week) during the second and third trimesters was associated with a four‐fold higher risk compared with non‐drinkers. Light alcohol consumption (1–149 g ethanol/week) tended to be associated with a lower risk of preterm delivery.14
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Further, the study shows that 100%, 40%, 22.2% and 17.1% of the mothers who used firewood, charcoal, kerosene and gas/electricity respectively had preterm deliveries.
The association between the main type of fuel and preterm delivery was statistically insignificant. However, most of the mothers1who had preterm1deliveries used biomass fuels. This implies that biomass fuels increase the risk of preterm1delivery. Women who cook with biomass fuels are substantially more likely to have had a stillbirth than women who cook with cleaner fuels. Women who cook with biofuels are twice as likely as those who cook with cleaner fuels to have had two or more preterm deliveries.15
CONCLUSION
The association between drinking alcohol, drinking alcohol during pregnancy, smoking, andsmoking during1pregnancy for the mother or the husband and preterm delivery were statistically insignificant. The association between the main type of fuel and preterm delivery was statistically insignificant. Most of the mothers who had1preterm deliveries used biomass which increase the1risk of preterm1delivery.
Recommendations
The study1recommends that there is need for pre- conception care to be included in maternal child health. There is need for awareness creation on the risky behaviors especially among the pregnant mothers so as to reduce the likelihood of preterm delivery among the mothers. More research should be conducted on the association of emotional factors with PTD.
ACKNOWLEDGEMENTS
Authors would like to appreciate my two supervisors Dr. Stanley Waithaka and Dr. Alfred Owino who guided during this research. Also to all mothers who took part in this study. They would also like to thank the staff and administration of Pumwani Maternity Hospital for the opportunity to conduct this study at the hospital.
Funding: No funding sources Conflict of interest: None declared Ethical approval: The study was approved by the Institutional Ethics Committee
REFERENCES
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Cite this article as: Mwangi DK, Waithaka SK, Odongo AO. Risky behavioral factors associated with preterm deliveries among women of childbearing age (15-49 years) at Pumwani Maternity Hospital, Nairobi County, Kenya. Int J Community Med Public Health 2021;8:4161-6.
International Journal of Community Medicine and Public Health | September 2021 | Vol 8 | Issue 9 Page 4166