ABSTRACT
Background:
Pregnancy complications are one of the major public health concerns. One of the main causes of preventable complications is the absence of or inadequate provision of prenatal care. The present study was conducted to investigate whether Extended Parallel Process Model’s constructs can predict the utilization of prenatal care services.
Methods:
The present longitudinal prospective study was conducted on 192 pregnant women selected through the multi-stage sampling of health facilities in Qeshm, Hormozgan province, from April to June 2015. Participants were followed up from the first half of pregnancy until their childbirth to assess adequate or inadequate/non-utilization of prenatal care services. Data were collected using the structured Risk Behavior Diagnosis Scale. The analysis of the data was carried out in SPSS-22 using one-way ANOVA, linear regression and logistic regression analysis. The level of significance was set at 0.05.
Results:
Totally, 178 pregnant women with a mean age of 25.31±5.42 completed the study. Perceived self-efficacy (OR=25.23; P<0.001) and perceived susceptibility (OR=0.048; P<0.001) were two predictors of the intention to utilize prenatal care. Husband’s occupation in the labor market (OR=0.43; P=0.02), unwanted pregnancy (OR=0.352; P<0.001), and the need to care for the minors or elderly at home (OR=0.35; P=0.045) were associated with lower odds of receiving prenatal care.
Conclusion:
The model showed that when perceived efficacy of the prenatal care services overcame the perceived threat, the likelihood of prenatal care usage will increase. This study identified some modifiable factors associated with prenatal care usage by women, providing key targets for appropriate clinical interventions.
KEYWORDS: Prenatal care, Forecasting, Health promotion
INTRODUCTION
Regular prenatal care reduces the pregnancy and childbirth complications by decreasing their risk factors through training, social support, diagnosis, early referral and appropriate treatment.1-3 Despite their positive outcomes, some mothers do not take advantage of the prenatal care services during pregnancy offered for a variety of reasons so that pregnancy outcomes may lead to unexpected complications including maternal death.4-6
There is an inverse correlation between the risk of maternal morbidity and death and the access to trained healthcare personnel during pregnancy and childbirth.7 The majority of these problems occur during or after a seemingly normal process of childbirth, as it is possible to prevent these events by utilizing preventive pregnancy care.8 The lack of adequate care sensitivity on the part of the pregnant woman and her family, insufficient knowledge about the high-risk complications of pregnancy, satisfaction with previous childbirth experiences, unwanted pregnancy, trust on self-care, and distrust in care providers are some of the reasons for inadequate care utilization during this period.5,9
In Iran, like many other countries, the most primary healthcare services for urban and rural populations are health centers and health facilities, respectively. They have been providing free primary health care services for many years including preconception, prenatal and postpartum care, and active follow-up has been conducted by health staffs through home visits whenever necessary.
Nevertheless, the non-utilization or inadequate utilization of pregnancy care was responsible for 17% of the cases of cause-specific maternal mortality in 2010 in Iran, especially in underprivileged and less-developed regions of the country. From 2005 to 2011, Hormozgan province in the south of Iran was reported to be one of the highest maternal mortality rates of 39.7 per 100,000 live births;10 and Qeshm island in this province had the highest rates of maternal mortality as well (62.22 per 100,000 live births in 2013). One of the main attributable causes of maternal mortality in this town is the irregular or inadequate utilization of prenatal care services by pregnant women and the delayed decision by their families to refer the mothers to obstetrics services even in presence of an underlying disease during pregnancy.11
Given the aforementioned health problem in this region of the country, identifying the obstacles and facilitators of prenatal care utilization by pregnant women based on a scientific approach appear to be essential. Furthermore, although every day health messages are being rendered by the health staff to the target group, data show their inadequate effectiveness or failure to change health related behaviors in this domain. The literature suggests that one of the most appropriate models for the assessment of health related behaviors is the Extended Parallel Process Model (EPPM). This is one of the models of cognitive and affective responses to fear. Fear appeals have been shown to be effective in attitude change and self-care behaviors through health messages.12-16 According to the fear appeal models including EPPM, people tend to evaluate the benefits and harms of the health behavior-related messages first and then react accordingly.17 They may evaluate the message as either a threat or efficacy, both of which lead to three possible reactions: indifference, rejection of the message, and acceptance of the message18 (figure 1).
Figure1.
Simple Description of Witte’s Extended Parallel Process Model for fear appeals (according to Witte, et al., 2001)18
There are two key elements within the EPPM: threat and efficacy. This approach assumes that the higher the individual’s perceived efficacy (a combination of perceived self-efficacy and perceived response-efficacy), the more likely the behavior change and the higher the intention to practice health behaviors; conversely, the higher the perceived threat (a combination of perceived susceptibility and perceived severity), compared to perceived efficacy, the lower the likelihood of behavior change. This model implies that those individuals who perceive more severe threat are more likely to move into self-protective behaviors and believe that their actions are effective to avert the threat; this can increase the feasibility and ease of the recommended messages. The difference between the scores of perceived threat and perceived efficacy in the model is referred to as the Critical Point, indicating the likelihood of the individual’s performing the intended behavior.18 Behavior involves the individual’s attitude, beliefs and subjective norms, which affect his/her behavioral control; a greater certainty about a task, thus, leads to a greater intention to perform it.19 As EPPM suggests, behaviors are determined by the weighting of perceived efficacy against threat;18 the question remains as to whether or not behavioral intention can always predict a certain health related behavior.
The present study aimed to predict prenatal care utilization as a healthy behavior in the rural areas of Qeshm using the EPPM. The research questions were whether the constructs of the EPPM are able to predict pregnant women’s intention to utilize prenatal care services, and if so, whether the intention to utilize prenatal care can predict the adequate utilization of these services by pregnant women.
MATERIALS AND METHODS
In this longitudinal prospective study, 192 pregnant women living in rural areas of Qeshm, were enrolled in the study. The participants were a group of pregnant women covered by the health facilities of Qeshm in 2015 selected according to the study’s inclusion criteria, namely having an Iranian nationality, permanent residence in rural areas of Qeshm and being in the first trimester of pregnancy based on the last menstrual period or ultrasound. The exclusion criteria in this study consisted of unwillingness to continue participation in the study and abortion due to any reason. However, in cases of preterm birth due to medical or midwifery reasons, the adequacy and inadequacy of the participant’s prenatal care utilization was assessed according to the mother’s first pregnancy visit and the number of prenatal care visits completed on the basis of her gestational age.
The participants were selected through multi-stage sampling. First, a list was prepared of all the rural health facilities covered by the urban and rural health centers in the area. The total number of women with health records in each health facility was then obtained. At least one health facility covering the largest population of women was then selected non-randomly and based on geographical distribution and the distance from each health facility to the satellite villages covered by that facility. Then, a quota was accordingly assigned to each health facility and a convenience sampling was performed in each quota. In the health facilities that covered only a small number of pregnant women, however, all the women were included in the study through the census method.
A total of 1100 pregnant women had health records in the surveyed health facilities. The initial study sample size was determined as 160, using correlation sample size formula20 (with an effect size of 0.22, first-type error of 0.05, and a study power of 0.8). However, the total sample size was increased to 192 to take account of a potential sample loss of 20%.
Sampling was carried out from 19 health facilities for two months, except during the weekends and holidays, from early April to late June 2015.
The study setting comprised all the 45 villages of Qeshm and the study population consisted of the pregnant women covered by the health facilities of the Qeshm health network affiliated to Hormozagan University of Medical Sciences.
According to the National Guideline for Midwifery and Childbirth Services in Iran, receiving at least five out of eight visits of prenatal care is regarded as the adequate utilization of care. For low-risk pregnancies, the criterion is to get a minimum of two visits in the first half of pregnancy and six visits in the second half.21 In the present study, a minimum of six visits of prenatal care (one in the first half of pregnancy and five in the second half) was considered to be adequate because, first, few to no women presented for preconception counseling, and the diagnosis and treatment of their underlying disorders was, therefore, delayed, and second, due to the likelihood of the women’s failure to pay regular and timely visits to the health facilities given the pregnancy records in this region and the citizens’ tendency not to seek care in the private sector unless in cases of emergency, which is unlike in urban areas; therefore, women who had received less than six visits of prenatal care or had not received any visit at all were considered as the cases of inadequate utilization and non-utilization of prenatal care. The adequacy of care was determined at the end of the follow-up period, i.e. at 38-40 weeks of gestation, based on the gestational age and the ideal number of visits for the given age.
The researchers followed-up the participants until the end of pregnancy and delivery and the number of prenatal visits were recorded for each person.
Data were collected using the structured Risk Behavior Diagnosis Scale (RBDS) based on the EPPM.17 The original scale is a 12- item survey theoretically grounded in the EPPM. RBDS has two main constructs, namely perceived threat (consisting of the perceived severity and perceived susceptibility) and perceived efficacy (consisting of the perceived self-efficacy and perceived response-efficacy). The researcher then developed his/her targeted health message on the basis of theses sub-scales.18 The study’s questionnaire included 24 items on the basis of the research objectives and health messages were developed for each construct in the model accordingly to predict the outcome measures. All the questionnaire’s statements were scored based on a 5-point Likert scale from 1 (totally disagree) to 5 (totally agree).
Perceived severity and perceived susceptibility evaluated the perceptions of the risk and effects of not visiting on time for receiving prenatal care and women’s exposure to the undiagnosed complications of pregnancy and childbirth in the case of failure to visit for prenatal care or receiving irregular care, respectively. Perceived response-efficacy and perceived self-efficacy evaluated the women’s subjective perceptions of the efficacy/benefits of prenatal care, and their perceptions of their own ability to get regular and timely visits in health facilities for receiving prenatal care, respectively. Finally, behavioral intention assessed the women’s intention to pay regular and timely visits to health facilities for receiving prenatal care in their current pregnancy.
Since the subjective barriers to performing a behavior are often affected by several factors, the non-personal barriers that were likely to prevent the desirable utilization of prenatal care such as the great distance to the health center, lack of access to transportation, need to care for a minor or an elderly, etc. were also asked with one semi-structured question.
The adequacy or inadequacy of prenatal care utilization was determined by calculating the number of visits in accordance with the woman’s gestational age in each trimester, and standard statistical tests were then used to examine the relationship between each of the EPPM constructs and the variable of behavioral intention and the adequacy of prenatal care.
The validity of the study tool was confirmed by assessing its face and content validity.22 At first, to assess the face validity, the questionnaire was distributed among 10 experts in reproductive health and health education and health promotion. Second, to determine the Content Validity Index (CVI) of the tool using Walts and Bussel’s criteria and the Content Validity Ratio (CVR) based on the minimum acceptable value according to Lawshe’s chart,23 the questionnaire was distributed among 12 experts familiar with the study’s subject and methods. The CVI and CVR of the entire scale were 0.94 and 0.87, respectively. The internal consistency of the tool was confirmed using Cronbach’s alpha coefficient of 0.93, as calculated by the responses given by 20 eligible women, and with an acceptable level of 0.7.24 The stability of the instrument over time was assessed using the test-retest method with a minimum of two-week interval and the correlation between the test and retest scores was calculated as 0.87, indicating the good stability of the instrument.24
After obtaining written consents, we collected the data through self-completion questionnaires that were completed by the researcher after a full explanation of the statements, followed by an interview, in case the respondent was illiterate. All the participants received prenatal care by the health facility staff and were followed up by the researcher until weeks 38-40 of gestation. The number of prenatal care visits completed was then calculated. As attrition is an inevitable problem in almost every longitudinal epidemiological study, to prevent the loss of study’s power and potential bias, the researchers obtained at least two contact phone numbers and postal address from the eligible women presenting to the health facilities. In addition, if a pregnant woman who delayed her visits and avoided visiting to receive care even after a reminder phone call made by the facility staff, she was visited at home by both health staff and researcher, and in the case of refusal to get care, this event was reported to the health network by a phone call, and written official reports were sent for further interventions by family health experts; the results of these services were then recorded on special care forms.
The raw data obtained were analyzed in SPSS, version 22. The normal distribution of the data was confirmed through the Kolmogorov-Smirnov test. Descriptive statistics were used to determine the central and distribution indices and inferential statistics to find the Pearson correlation coefficient between the model’s constructs and behavioral intention. The difference between the mean scores of the model’s constructs and the demographic variables was calculated using the one-way ANOVA, the independent samples t-test, and the Chi-squared test (X2). The linear regression analysis was used to determine the association between the model’s constructs and behavioral intention, and the logistic regression analysis determined the association with the adequacy of prenatal care. The level of significance was set at 0.05. The control over the effect of the confounding variables on behavioral intention and the adequacy of prenatal care was also examined. The critical point was calculated by subtracting the total score of the perceived threat from the total score of the perceived efficacy and their association with the outcome variables was also determined.
To comply with ethical considerations, the research project was approved by the deputy of research under the Ethical Code sbmu2.rec.2015.22. Written informed consent to enter the study was obtained from the participants and they were ensured about the confidentiality of their data and their right to withdraw from the study at any time; they were also ensured that their withdrawal would not affect their right to receive the routine care.
RESULTS
Initially, all the 192 questionnaires were self-completed by the study participants or by the researcher through interviews. Except for 14 women who had to terminate their pregnancy before the 37th weeks of pregnancy for medical or obstetric reasons, the remaining participants (178) were followed-up until weeks 39-40 of gestation. Seven out of all the remained participants refused to attend the health facilities, so with obtaining their agreements, they were visited regularly by the researcher at their own homes until the childbirth.
The mean age of the participants was 25.31±5.42, the mean gestational age at the time of entering the study was 10 weeks, and one third of them were first time mothers. Table 1 presents some of participants’ characteristics.
Table 1.
The participants’ demographic and reproductive information
Variable | N (%) |
---|---|
Education | |
Illiterate | 13 (6.78) |
Lower secondary school | 59 (30.72) |
Upper secondary school | 109 (78) |
Post-secondary education | 11 (5.72) |
Employment status | |
Housewife | 176 (91.65) |
Corporate employee | 3 (1.55) |
Self-employed | 10 (5.20) |
Semi-manual skilled (laborer) | 3 (1.60) |
Spouse’s education | |
Illiterate | 13 (6.78) |
Lower secondary school | 59 (30.72) |
Upper secondary school | 109 (56.78) |
Post-secondary education | 11 (5.72) |
Spouse’s employment status | |
Corporate employee | 15 (7.81) |
Self-employed | 113 (58.85) |
Unemployed | 13 (6.77) |
Semi-manual skilled (laborer) | 51 (26.57) |
Household size | |
2-3 persons | 63 (32.81) |
4-6 persons | 88 (45.84) |
7 and above | 41 (21.35) |
Self-assessed economic status | |
Low | 134 (69.79) |
Intermediate | 58 (30.21) |
High | 0 (0) |
Pregnancy intention | |
Intended pregnancy | 170 (88.55) |
Unintended pregnancy | 22 (11.45) |
Past diagnoses or medical history * | |
Yes | 33 (17.18) |
No | 159 (82.82) |
Including gestational diabetes, pregnancy-induced hypertension, heart disease and asthma
The most common medical disorders in the women’s current pregnancy included iron deficiency anemia and digestive disorders. Four of the women had to have C-sections before their 37th week of gestation due to pregnancy-induced hypertension, gestational diabetes mellitus, and asthma. All the other infants were born at full term and none had to be admitted to neonatal intensive care unit (NICU).
Overall, 26.6% of the participants had received inadequate prenatal care, 8.9% only 1-2 visits, 17.7% a maximum of 3-4 visits during their entire pregnancy and 6.8% had received no care at all. The most common reasons mentioned by the participants who did not receive prenatal care in health facilities included the great distance from their home to the health facility (20.3%), lack of access to transportation (9.3%) and the necessity to care for a minor or an elderly at home (8.9%); however, nearly half of them (44.3%) gave no reason for their inadequate utilization of prenatal care.
The comparison of the scores obtained for the RBDS items assessing the EPPM constructs and the critical point revealed significant differences between the utilizer and non-utilizer participants (Table 2). This finding suggests that all (100%) of the women who had negative critical point at the beginning of the study, based on the questionnaires results, did not receive adequate or any prenatal care during pregnancy. In contrast, most (88.8%) of the women who received positive critical points ended up receiving adequate prenatal care (P<0.001).
Table 2.
Comparison of the scores of the Risk Behavior Diagnosis Scale items in the pregnant women receiving adequate and inadequate/no prenatal care
Subscale/items | Adequate utilization utilization/Inadequate | Non-utilization | P value* (95% CI) |
---|---|---|---|
Mean±SD | Mean±SD | ||
Perceived severity (6) | 25.26±3.89 | 22.46±4.37 | <0.001 (1.46-4.14) |
Perceived susceptibility (6) | 23.24±5.06 | 19.41±5.48 | <0.001 (2.10-5.54) |
Perceived Response-efficacy (6) | 27.29±3.73 | 22.26±4.21 | <0.001 (3.74-6.31) |
Perceived self-efficacy (6) | 25.28±4.60 | 17.59±4.02 | <0.001 (6.20-9.18) |
Behavioral intention (4) | 18.61±1.66 | 12.59±1.00 | <0.001 (5.51-6.53) |
Critical point | 4.10±3.81 | -2.02±2.54 | <0.001 (4.93-7.30) |
Two independent-samples t-tests
As to the first research question, a significant and positive correlation was obtained between all the model constructs and the women’s behavioral intention for receiving prenatal care. The highest correlation was observed between perceived self-efficacy and the intention to receive prenatal care (r=0.75, P<0.001). Similarly, significant and positive correlations were observed among intentional behavior and perceived response-efficacy (r=0.59, P<0.001), perceived susceptibility (r=0.54, P<0.001), and perceived severity (r=0.48, P<0.001).
The result of the stepwise linear regression analysis confirmed this finding. The perceived self-efficacy was associated with an increase, and the perceived susceptibility was associated with a decrease on the intention to receive care. With each unit of increase in the score of perceived self-efficacy, receiving care increased by 0.96 (95% CI=0.76 -1.13, P<0.001).
Conversely, with each unit of increase in the score of perceived susceptibility, receiving care decreased by -0.28 (95% CI=-0. 27 to -0.13, P<0.001). The demographic variables that were associated with a reduction in the likelihood of behavioral intention included:
Being a housewife (adjusted Beta=-0.17; P=0.012; CI=-0.48 to -0.11), the husband’s occupation in the labor market (adjusted Beta=-0.20; P=0.005; CI=-0.46-0.03), an increased household size (adjusted Beta=-0.24; P<0.001; CI=-0. 36 to -0.10), an increased number of daughters (adjusted Beta=-0.29; P<0.001; CI=-0.37 to -0.11), unwanted pregnancy (adjusted Beta=-0.38; P<0.001; CI=-0.47 to -0.20), and the need to care for minors and elderly at home (adjusted Beta=-0.15; P=0.035; CI=0.40 to -0.13). However, we did not observe a significant association for other demographic variables. Overall, according to the stepwise linear regression, the model components explained 56% of the distribution of the factors pertaining to behavioral intention (adjusted R2=0.55).
To answer the second research question, the effect of the EPPM constructs and behavioral intention on the adequacy of prenatal care was assessed using the conditional backward technique in Logistic Regression (alpha-to-remove=0.1). The results showed that with unit of increase in the scores both of perceived susceptibility and perceived severity, the odds of receiving care reduced to 0.04(Odds ratio=0.04; P<0.001). In contrast, with each unit of increase in the scores of perceived self-efficacy and perceived response-efficacy, the odds of receiving care increased by more than 25 and 16, respectively. Moreover, with each unit of increase in the scores of behavioral intention and critical point, the odds of receiving care increased by 1.88 and 9.34, respectively (Table 3).
Table 3.
The results of the logistic regression analysis for assessing the predictive power of the model variables for determining the adequacy of prenatal care utilization
Variable | Coefficient | Odds Ratio | P value (95% CI) |
---|---|---|---|
Perceived Self-Efficacy | 3.22 | 25.23 | 0.001(5.02-124.86) |
Perceived Response-efficacy | 2.81 | 16.75 | 0.001 (3.82-72.24) |
Perceived Susceptibility | -3.03 | 0.04 | 0.001 (0.01-0.21) |
Perceived Severity | -3.09 | 0.04 | 0.001(0.0005-4.12) |
Behavioral Intention | 0.63 | 1.88 | 0.004 (1.06-3.31) |
Critical Point | 2.23 | 9.34 | 0.028(1.25-68.66) |
Husband’s occupation in the labor market | -0.84 | 0.43 | 0.02( 0.11-0.83) |
Need to care for minors /elderly | -1.03 | 0.35 | 0.04 ( 0.05-0.70) |
Unwanted pregnancy | -1.04 | 0.35 | 0.001( 0.06-0.98) |
Including gestational diabetes, pregnancy-induced hypertension, heart disease and asthma
DISCUSSION
The present study applied, for the first time, the EPPM for prediction of intention to receive prenatal care in pregnant women in rural areas. In line with the classic theory of the model, the present study examined the participants’ perceptions of the threat of not receiving prenatal care and predicted their likelihood of developing maternal and neonatal complications. It was found that a large portion of the women accepted the message and, therefore, they were placed in the perceived threat stage. In addition, the predictive value of the EPPM constructs was confirmed by comparing the critical points between the groups of women receiving adequate and inadequate/no care. Therefore, the significant difference between the two groups in terms of the mean critical point (positive, zero and negative) demonstrates the power of this variable in predicting the adequacy of prenatal care utilization.
A number of similar studies have also confirmed the predictive value of this variable in determining the people’s behavioral intention and desired behavior.15,25,26 Nevertheless, not all the women within a positive critical point necessarily received adequate care, as 11.2% of them did not utilize adequate prenatal care services. This finding demonstrates the importance of more careful follow-up with participants who have had a critical point of zero.
According to the EPPM theory, having similar scores for perceived efficacy and perceived threat suggests equal perceptions of threat and fear; however, at this stage, people are likely to more intensely perceive the fear and regard the available recommended strategies as ineffective rather than adopting strategies to control the threat and pursue self-care measures; therefore, they may shift from the stage of threat control to fear control. This finding is often attributed to the participants’ lack of access to effective risk control strategies or their perception of the strategies as ineffective.17,18 In the present study, this finding may be attributed to how nearly two-thirds of the participants reported their monthly income as insufficient, especially after the 6.1 Richter earthquake occurring in the island of Qeshm in 2008, upon which people had to spend a large portion of their income on house repairs and reconstruction; also, the residents’ meager income, which is often earned through manual labor and fishery, suffices only for the bare necessities such as drinking water and food and cannot be spent on healthcare.
The other factors involved in the participants’ poor perceptions about the efficacy of these services included the costs and difficulty of access to diagnostic and medical tools and strategies; false assumptions about the ineffectiveness of the preventive, diagnostic or medical measures and strategies offered as part of prenatal care; and mostly the incorrect traditional beliefs favoring self-treatment instead of utilizing midwifery services, especially among illiterate and low-literate women from highly populated and extended families in whose health behaviors the role of important others is especially highlighted. Previous studies have shown that women’s own knowledge and literacy is the main predictor of their own and their family’s utilization of healthcare services,27 while inadequate literacy is associated with poorer health behaviors, poorer self-care and less concern about health behaviors.28
It has been suggested that one of the reasons for the delayed utilization of prenatal care services was the great distance from home to the health center, which was proposed as an important barrier to receiving care.29 Similarly, findings of a systematic review suggest that difficult access to a health center or clinic, long waiting period to receive prenatal care and poor family’s economic situation were proposed as a fundamental barrier to receiving care services in the view of women.30
Answering the first question of the study showed that all the EPPM constructs were independently associated with the intention to receive prenatal care, but the strongest predictor of behavioral intention remained perceived self-efficacy and perceived susceptibility. The results of a meta-analysis of 22 experimental studies on the role of risk assessment theories in people’s change of practices revealed the highest mean score obtained in the construct of perceived threat pertains to perceived severity rather than perceived susceptibility and to perceived response-efficacy rather than perceived self-efficacy in the construct of perceived efficacy.31 This finding shows when perceived efficacy outweighs perceived threat, people are motivated to deal with the threat which also increased their behavioral intention and performance of healthy behaviors. Nevertheless, the likelihood of rational decision-making, behavioral intention and proper health practices increase when participants assess the risks and perceive their severity only if they simultaneously perceive response-efficacy of the health messages, demonstrate appropriate self-efficacy, reduce perceived barriers such as costs, and know how to replace more appropriate and acceptable options for behavior change.32
It is assumed that behaviors change according to a set of assumptions and beliefs that require the passage of time and are often due to the merging of cognitive, psychological and social functions, and the individual’s intention to change or modify the intended action is formed to answer whether the proposed methods can satisfy his/her needs and how much the recommended health strategies are able to meet his/her demands. In other words, they may believe that they are able to do a specific health behavior, but they may not do it, because they would like to be assured about consequences of that certain behavior.19 Our research hypothesis was whether the behavioral intention always leads to the intended action change. In response to this question and as a confirmation of the second question of the study, findings indicated the predictive role of this variable for the intended action, as it had double predictive power in women with adequate prenatal care compared with inadequate care or non-utilizers. The predictive role of behavioral intention for the intended action has been shown in several studies.33,34
Each of the model’s constructs not only was independently associated with the increased likelihood of the intention to receive prenatal care, but also demonstrated itself as a strong predictor of the adequacy of care utilization. An important finding of this study was the independent role of the individuals’ perception of self-efficacy, which showed a strong association with the intention to utilize prenatal care and its adequacy. Self-efficacy or the perceived ability to successfully carry out a task is a major principle that links knowledge to action. As self-efficacy is a vital part of medical plans and aims to obviate negative emotions and strengthen coping behaviors, he believes that the most important prerequisite for behavior change is to have an adequate perception of self-efficacy.35 This finding acknowledges the importance of people’s perceived self-efficacy in the realization of the expected behaviors and improving self-efficacy through effective interventions which can increase the likelihood of performing the intended healthy behavior.27,37 Nevertheless, the results of a meta-analysis of 16 clinical trials showed that the behavioral intention increases when perceived self-efficacy overcomes perceived threat; however, this change does not always lead to the adoption of desirable healthy behaviors.38
Assessing the differences between the role of demographic and underlying variables in the participants’ intention shows that although some barriers such as the woman being a housewife, household size and the increased number of daughters were associated with a reduced intention to utilize prenatal care services, they were unable to maintain their role in determining the adequacy of care. These findings suggest that these factors were unable to affect the mothers’ visits for receiving prenatal care and their adequate utilization of the services, and more important factors were, therefore, found to be at play in the utilization of prenatal care services. However, a few barriers were identified in some studies to reduce the likelihood of women’s utilization of midwifery care services, including medical conditions during pregnancy,39 increased number of children,40 mother’s young or old age,30 the unavailability of a trustworthy person for babysitting, and needing the husband’s permission for receiving prenatal care.41 In contrast, having the encouragement of a family member for receiving care9 and having an independent source of income and being employed41 were among the factors that facilitated the early initiation and adequate utilization of prenatal care for pregnant women.
The findings of this study using this model suggest important insights into the process through which high perceived efficacy increases the prenatal care usage and how high perceived threat might compromise its effectiveness. However, our study’s samples only consisted of the women with family health records in rural health facilities and the researchers had no access to all the women in this region, such as those who had never visited these facilities; this limitation makes the interpretation of the results difficult and challenges their generalizability to the entire population of pregnant women in the region, even though attempts were made to include a large part of the target group in the sampling.
CONCLUSION
This study showed that a variety of psychological, attitudinal and structural factors could contribute to limited prenatal care utilization in the rural regions of Iran. Thus, health care providers like midwives and other staff who are responsible for maternal health care system should be warned about the impeding factors that affect utilizing prenatal care services. Because of the limited number of studies in Iran in this domain, especially in deprived regions, future research like a qualitative study is recommended on the women who are hard to reach and have never visited the health staff and explore the predisposing causes to further increase our current knowledge in this domain.
ACKNOWLEDGEMENT
The authors would like to thank the Research Center of Hormozgan University of Medical Sciences, the social workers and the pregnant women who helped us to conduct this research. This project is a part of the master’s degree dissertation of the second author and was approved in the 13th of July, 2015; also, it was funded by the vice chancellor for research of Shahid Beheshti University of Medical Sciences, Tehran, Iran, with grant Number 4503.
Conflict of Interest:None declared.
REFRENCES
- 1.Debiec KE, Paul KJ, Mitchell CM, Hitti JE. Inadequate prenatal care and risk of preterm delivery among adolescents: a retrospective study over 10 years. American Journal of Obstetrics and Gynecology. 2010;203:122.e1–6. doi: 10.1016/j.ajog.2010.03.001. [DOI] [PubMed] [Google Scholar]
- 2.Chiavarini M, Lanari D, Minelli L, Salmasi L. Socio-demographic determinants and access to prenatal care in Italy. BMC Health Services Research. 2014;14:174. doi: 10.1186/1472-6963-14-174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Heberlein EC, Picklesimer AH, Billings DL, et al. The comparative effects of group prenatal care on psychosocial outcomes. Archives of Women’s Mental Health. 2016;19:259–69. doi: 10.1007/s00737-015-0564-6. [DOI] [PubMed] [Google Scholar]
- 4.Martínez-García E, Olvera-Porcel MC, de Dios Luna-del Castillo J, et al. Inadequate prenatal care and maternal country of birth: a retrospective study of southeast Spain. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2012;165:199–204. doi: 10.1016/j.ejogrb.2012.08.004. [DOI] [PubMed] [Google Scholar]
- 5.Heaman MI, Moffatt M, Elliott L, et al. Barriers, motivators and facilitators related to prenatal care utilization among inner-city women in Winnipeg, Canada: a case–control study. BMC Pregnancy and Childbirth. 2014;14:227. doi: 10.1186/1471-2393-14-227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hagey J, Rulisa S, Pérez-Escamilla R. Barriers and solutions for timely initiation of antenatal care in Kigali, Rwanda: Health facility professionals’ perspective. Midwifery. 2014;30:96–102. doi: 10.1016/j.midw.2013.01.016. [DOI] [PubMed] [Google Scholar]
- 7.World Health Organization, UNICEF. Trends in maternal mortality: 1990 to 2013: estimates by WHO, UNICEF, UNFPA, The World Bank and the United Nations Population Division [Internet]. Geneva: WHO; 2014. [Cited 20 November 2016] Available from: [http://apps.who.int/iris/bitstream/10665/112682/ 2/9789241507226_eng.pdf. ]
- 8.Say L, Chou D, Gemmill A, et al. Global causes of maternal death: a WHO systematic analysis. The Lancet Global Health. 2014;302:e323–33. doi: 10.1016/S2214-109X(14)70227-X. [DOI] [PubMed] [Google Scholar]
- 9.Haddrill R, Jones GL, Mitchell CA, Anumba DO. Understanding delayed access to antenatal care: a qualitative interview study. BMC Pregnancy and Childbirth. 2014;14:207. doi: 10.1186/1471-2393-14-207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rajaie M, Zare S, Dadipour S, et al. Determining the frequency and causes of maternal mortality in Hormozgan province (Iran ) during 2005-2011. Iranian Journal of Obstetrics, Gynecology and Infertility. 2014;16:9–14. [In Persian] [Google Scholar]
- 11.Moradi S, Roozbeh N, Mobarak Abadi A, et al. Prevalence and effective factors of maternal mortality in Hormozgan Province from 2007 until 2011. Bimonthly Journal of Hormozgan University of Medical Sciences. 2015;19:59–64. [Google Scholar]
- 12.Shi J, Smith SW. The effects of fear appeal message repetition on perceived threat, perceived efficacy, and behavioral intention in the extended parallel process model. Health Communication. 2016;331:275–86. doi: 10.1080/10410236.2014.948145. [DOI] [PubMed] [Google Scholar]
- 13.So J. A further extension of the extended parallel process model (E-EPPM): Implications of cognitive appraisal theory of emotion and dispositional coping style. Health Communication. 2013;28:72–83. doi: 10.1080/10410236.2012.708633. [DOI] [PubMed] [Google Scholar]
- 14.Goodall CE, Reed P. Threat and efficacy uncertainty in news coverage about bed bugs as unique predictors of information seeking and avoidance: An extension of the EPPM. Health Communication. 2013;28:63–71. doi: 10.1080/10410236.2012.689096. [DOI] [PubMed] [Google Scholar]
- 15.Hajian S, Shariati M, Mirzaii Najmabadi KM, et al. Psychological predictors of intention to deliver vaginally through the extended parallel process model: a mixed-method approach in pregnant Iranian women. Oman Med J. 2013;28:395–403. doi: 10.5001/omj.2013.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Leung DY, Wong EM, Chan CW. Determinants of participation in colorectal cancer screening among community-dwelling Chinese older people: Testing a comprehensive model using a descriptive correlational study. European Journal of Oncology Nursing. 2016;3021:17–23. doi: 10.1016/j.ejon.2015.11.004. [DOI] [PubMed] [Google Scholar]
- 17.Dillard JP, Li R, Meczkowski E, et al. Fear Responses to threat appeals functional form, methodological considerations, and correspondence between static and dynamic data. Communication Research. 2016;24:1–22. [Google Scholar]
- 18.Witte K, Meyer G, Martell D. Effective health risk messages: A step-by-step guide. Los Angeles, CA: Sage Publications; 2001. pp. 42–6. [Google Scholar]
- 19.Ajzen I. The theory of planned behavior: reactions and reflections. Psychology & Health. 2011;26:1113–27. doi: 10.1080/08870446.2011.613995. [DOI] [PubMed] [Google Scholar]
- 20.Hulley SB, Cummings SR, Browner WS, et al. Designing clinical research: an epidemiologic approach. 4th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2013. p. 79. [Google Scholar]
- 21.Iranian Ministry of Health and Medical Education. Iranian national program on safe motherhood, integrated care on mother’s health, outside hospital services Guide. 3th ed. Tehran: Iranian Ministry of Health and Medical Education; 2010. [cited 20 November 2016] Available from: [http://phc.mui.ac.ir/sites/phc.mui.ac.ir/files/docs/booklet-mama-chap%208-92.pdf. ]
- 22.Soundy A, Rosenbaum S, Elder T, et al. The Hope and Adaptation Scale (HAS): Establishing Face and Content Validity. Open Journal of Therapy and Rehabilitation. 2016;4:76–86. [Google Scholar]
- 23.Gilbert GE, Prion S. Making Sense of Methods and Measurement: Lawshe’s Content Validity Index. Clinical Simulation in Nursing. 2016;12:530–1. [Google Scholar]
- 24.Diedenhofen B, Musch J. Cocron: A Web Interface and R Package for the Statistical Comparison of Cronbach’s Alpha Coefficients. International Journal of Internet Science. 2016;11:51–60. [Google Scholar]
- 25.Basil M, Basil D, Deshpande S, Lavack AM. Applying the Extended Parallel Process Model to workplace safety messages. Health Communication. 2013;28:29–39. doi: 10.1080/10410236.2012.708632. [DOI] [PubMed] [Google Scholar]
- 26.Xue W, Hine DW, Marks ADG, et al. Combining threat and efficacy messaging to increase public engagement with climate change in Beijing, China. Climatic Change. 2016;137:43–55. [Google Scholar]
- 27.Lam Y, Broaddus ET, Surkan PJ. Literacy and healthcare-seeking among women with low educational attainment: analysis of cross-sectional data from the 2011 Nepal demographic and health survey. International Journal for Equity in Health. 2013;12:95. doi: 10.1186/1475-9276-12-95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Levin-Zamir D, Baron-Epel OB, Cohen V, Elhayany A. The association of health literacy with health behavior, socioeconomic indicators, and self-assessed health from a national adult survey in Israel. Journal of Health Communication. 2016;21:61–8. doi: 10.1080/10810730.2016.1207115. [DOI] [PubMed] [Google Scholar]
- 29.Manzi A, Munyaneza F, Mujawase F, et al. Assessing predictors of delayed antenatal care visits in Rwanda: a secondary analysis of Rwanda demographic and health survey 2010. BMC Pregnancy and Childbirth. 2014;14:290. doi: 10.1186/1471-2393-14-290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Roozbeh N, Nahidi F, Hajian S. Barriers related to prenatal care utilization among women. Saudi Medical Journal. 2016;37:1319–27. [Google Scholar]
- 31.Sheeran P, Harris PR, Epton T. Does heightening risk appraisals change people’s intentions and behavior? A meta-analysis of experimental studies. Psychological Bulletin. 2014;140:511–43. doi: 10.1037/a0033065. [DOI] [PubMed] [Google Scholar]
- 32.Thrasher JF, Swayampakala K, Borland R, et al. Influences of Self-Efficacy, Response Efficacy, and Reactance on Responses to Cigarette Health Warnings: A Longitudinal Study of Adult Smokers in Australia and Canada. Health Communication. 2016;31:1517–26. doi: 10.1080/10410236.2015.1089456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ajzen I, Joyce N, Sheikh S, Cote NG. Knowledge and the prediction of behavior: The role of information accuracy in the theory of planned behavior. Basic and Applied Social Psychology. 2011;33:101–17. [Google Scholar]
- 34.Fisher WA, Kohut T, Salisbury C, Salvadori MI. Understanding human papillomavirus vaccination intentions: Comparative utility of the theory of reasoned action and the theory of planned behavior in vaccine target age women and men. The Journal of Sexual Medicine. 2013;10:2455–64. doi: 10.1111/jsm.12211. [DOI] [PubMed] [Google Scholar]
- 35.Beauchamp MR. Disentangling motivation from self-efficacy: implications for measurement, theory-development, and intervention. Health Psychology Review. 2016;10:129–32. doi: 10.1080/17437199.2016.1162666. [DOI] [PubMed] [Google Scholar]
- 36.Kanani S, Allahverdipour H, Asghari Jafarabadi M. Modeling the Intention to Choose Natural Vaginal Delivery: Using Reasoned Action and Social Cognitive Theories. Health Promotion Perspectives. 2015;5:24–33. doi: 10.15171/hpp.2015.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Luszczynska A, Hagger MS, Banik A, et al. Self-efficacy, planning, or a combination of both? A longitudinal experimental study comparing effects of three interventions on adolescents’ body fat. Plos One. 2016;11:e0159125. doi: 10.1371/journal.pone.0159125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Sweeney AM, Moyer A. Self-affirmation and responses to health messages: A meta-analysis on intentions and behavior. Health Psychology. 2015;34:149–59. doi: 10.1037/hea0000110. [DOI] [PubMed] [Google Scholar]
- 39.Bernardes ACF, da Silva RA, Coimbra LC, et al. Inadequate prenatal care utilization and associated factors in São Luís, Brazil. BMC Pregnancy and Childbirth. 2014;14:266. doi: 10.1186/1471-2393-14-266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Hawley NL, Brown C, Nu’usolia O, et al. Barriers to adequate prenatal care utilization in American Samoa. Maternal and Child Health Journal. 2014;18:2284–92. doi: 10.1007/s10995-013-1368-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Khanal V, da Cruz JLNB, Mishra SR, et al. Under-utilization of antenatal care services in Timor-Leste: results from Demographic and Health Survey 2009–2010. BMC Pregnancy and Childbirth. 2015;15:211. doi: 10.1186/s12884-015-0646-5. [DOI] [PMC free article] [PubMed] [Google Scholar]