Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Assessing geographical variation in ovulatory cycle knowledge among women of reproductive age in Sierra Leone: Analysis of the 2019 Demographic and Health Survey

  • Edward Kwabena Ameyaw ,

    Contributed equally to this work with: Edward Kwabena Ameyaw, Daniel Woytowich, Padmore Adusei Amoah

    Roles Data curation, Formal analysis

    Affiliation Institute of Policy Studies and School of Graduate Studies, Lingnan University, Tuen Mun, Hong Kong

  • Daniel Woytowich ,

    Contributed equally to this work with: Edward Kwabena Ameyaw, Daniel Woytowich, Padmore Adusei Amoah

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation California State University Los Angeles, Los Angeles, California, United States of America

  • Fred Yao Gbagbo ,

    Roles Conceptualization, Methodology, Writing – original draft

    gbagbofredyao2002@yahoo.co.uk, fygbagbo@uew.edu.gh

    ‡ FYG and PAA also contributed equally to this work.

    Affiliation University of Education, Winneba, Ghana

  • Padmore Adusei Amoah

    Contributed equally to this work with: Edward Kwabena Ameyaw, Daniel Woytowich, Padmore Adusei Amoah

    Roles Data curation, Formal analysis, Supervision, Writing – review & editing

    ‡ FYG and PAA also contributed equally to this work.

    Affiliations Institute of Policy Studies and School of Graduate Studies, Lingnan University, Tuen Mun, Hong Kong, Department of Psychology, School of Graduate Studies, Institute of Policy Studies, Lingnan University, Tuen Mun, Hong Kong SAR

Abstract

Background

Sierra Leone has poor indicators of reproductive health and a high prevalence of unintended pregnancies. To date, no study has explored determinants of ovulatory cycle knowledge in Sierra Leone. We investigated geographic region to determine where the needs for improved ovulatory cycle knowledge are greatest in Sierra Leone.

Methods

This is a cross-sectional study of women of reproductive age (n = 15,574) based on the 2019 Sierra Leone Demographic and Health Survey. Geographic region and sociodemographic covariates were included in a multivariate logistic regression model predicting the odds that participants possessed accurate knowledge of when in the ovulatory cycle pregnancy initiation is most likely.

Results

In Sierra Leone, 39.8% (CI = 37.4–40.9) of 15-49-year-old women had accurate knowledge of the ovulatory cycle. Women in the Northern and Southern regions possessed the highest prevalence of correct knowledge (46.7%, CI = 43.1–50.3 and 45.1%, CI = 41.9–48.2, respectively). Women from the Northwestern (AOR = 0.29, CI = 0.22–0.38), Eastern (AOR = 0.55, CI = 0.41–0.72), and Western regions (AOR = 0.63, CI = 0.50–0.80) had significantly lower odds of accurate ovulatory cycle knowledge compared to others. Women aged 15–19, those with a primary school education, and participants with a parity of none all had the lowest odds of correct ovulatory cycle knowledge as well.

Conclusion

Less than four in ten women in Sierra Leone had accurate knowledge of when in the ovulatory cycle pregnancy is most likely to occur. This suggests that family planning outreach programs should include education on the ovulatory cycle and the importance of understanding the implications of its timing. This can reduce the risk of unintended pregnancies throughout Sierra Leone, and can have an especially positive impact in the Northwestern, Eastern, and Western regions, where ovulatory cycle knowledge was significantly lower.

Background

During ovulation, the major follicle ruptures and releases an egg into the fallopian tube where fertilization can occur if sperm is present [1]. An understanding of this relationship between ovulation and pregnancy initiation, and accurate knowledge of when in the menstrual cycle ovulation occurs, are both invaluable pieces of family planning information [2, 3]. Ovulation generally occurs at about the midpoint of the menstrual cycle. Therefore, for the average menstrual cycle of 28 days, ovulation will occur about 14 days after the onset of menstruation (i.e., when the period begins) [4]. It is important to note however that there can be variation in menstrual cycle length, which means that the occurrence of ovulation can vary from the typical 14-day mark [5]. In any case, menstruation can be used to estimate when one will ovulate [3]. Women who have a basic knowledge of the aforementioned physiological phenomena are better able to time their pregnancies, or avoid pregnancy if that is their goal [2, 3].

Indicators of ovulation can include mood changes, increases in basal body temperature, the increased production of clear and non-viscous cervical mucous, among others [3, 6, 7]. Women wishing to become pregnant can increase their chances of doing so by having unprotected intercourse in the days leading up to ovulation, while women not wishing to become pregnant should at a minimum have protected sex, ensure the proper use of another birth control method, or abstain from sex altogether during this timeframe [810]. When ovulation occurs in relation to when unprotected sex was had will also dictate how effective levonorgestrel-based emergency contraception will be [11]. Inadequate knowledge of the ovulatory cycle is therefore a prime determinant of unintended and mistimed pregnancies [1215], which carry with them heightened risks of miscarriages, abortions, stillbirths, and childhood morbidity [1620].

Despite the benefits of being familiar with one’s ovulatory cycle, empirical evidence shows that accurate knowledge of the ovulatory cycle is generally low worldwide [12, 2124]. Part of the reason for this may lie in the fact that discussions regarding menstrual cycle issues are still taboo for many individuals, families, and societies [21, 25]. In Sub-Saharan Africa, investigations into the proportion of women with correct ovulatory cycle knowledge have produced varied results, but again show relatively low prevalence of accurate knowledge [15, 26]. The prevalence of correct knowledge was as low as 10.4% in São Tomé and Príncipe and as high as 49.0% in Comoros in one multi-country assessment of 15–24-year-olds [15]. The same study revealed that in 2013, 15–24-year-old women in Sierra Leone had a 30.3% prevalence of proper ovulatory cycle knowledge [15].

In addition to low ovulatory cycle knowledge, Sierra Leone has some of the worst maternal and pregnancy-related health indicators in the world. The maternal mortality rate in Sierra Leone (1,360 per 100,000 live births) is the highest in the world [27]. Their neonatal mortality rate (34 per 1,000 live births) is among the highest in the world as well [27]. Also notable is the fact that abortion is illegal in Sierra Leone unless it is needed to save the mother’s life [28]. All of these issues make avoiding unintended pregnancies in Sierra Leone especially important, of which having correct ovulatory cycle knowledge is an integral part. Unfortunately, to our knowledge, there are no studies about the association between geographic and other sociodemographic factors with ovulatory cycle knowledge in Sierra Leone. Research on this subject is a necessity if a complement of factors that may predispose women to a lower prevalence of ovulatory cycle knowledge, which places them and their offspring at risk, are to be elucidated [2931].

To begin to fill this knowledge gap, this study will assess the association between Sierra Leone’s five regions (Eastern province, Northern province, Northwestern province, Southern province, and Western area (Fig 1) and other covariates with correct ovulatory cycle knowledge. Understanding which regions may have a higher or lower prevalence of accurate knowledge, and also understanding if region alone is maintained as a significant predictor in a multivariate model can help in more efficiently targeting family planning educational resources in Sierra Leone on a geographical basis. It can also provide insight into the regions of Sierra Leone that may be at higher risk of unintended pregnancies, as well as catalyze subsequent research into sociocultural covariates that may lead to differing levels of ovulatory cycle knowledge. All these have policy and programme implications to chart a path forward in the area of enhanced sexual and reproductive health of women in reproductive ages in Sierra Leone.

Methods

Study design

This is a quantitative cross-sectional study that uses secondary data from the 2019 Demographic and Health Survey (DHS) of Sierra Leone [32].

Data source

Permission to use the dataset was obtained from the Measure DHS Program [33]. The 2019 Sierra Leone survey was part of DHS Version VII (i.e., DHS surveys are conducted roughly every 5 years, so version VII is the seventh iteration). The final report of the 2019 Sierra Leone DHS can be found here: https://dhsprogram.com/publications/publication-FR365-DHS-Final-Reports.cfm; while the full dataset can be downloaded here: https://dhsprogram.com/data/available-datasets.cfm. DHS are nationally representative household surveys that utilize a two-stage stratified cluster sampling design. DHS surveys employ standardized data collection procedures which allow for consistency and comparability across regions and populations, generally have response rates of over 90%, accurately represent marginalized groups with complex sampling techniques, and provide up-to-date and comprehensive training for interviewers [34]. Survey techniques and protocols were approved by both ICF International, who provided technical assistance through The DHS Program, and Statistics Sierra Leone, who implemented the survey in-country on behalf of the Sierra Leone Ministry of Health and Sanitation. Funding for the 2019 Sierra Leone DHS was provided by the United States Agency for International Development, the Global Fund, Department for International Development, the United Nations Population Fund, World Health Organization, and World Bank.

Sampling

In the first sampling stage, enumeration areas were drawn from the national census according to population and stratification characteristics. In the second stage, systematic sampling was used to select households from enumeration areas where the interviews would take place [35]. In all subsequent calculations, sampling weights were applied to adjust for DHS’s complex survey design. Further information about sampling, weighting, and other DHS methodologies can be found in the Guide to DHS Statistics, DHS-7, Version 2 [35]. Out of the 13,399 households enlisted for the survey, data was collected from 15,574 respondents [32]. The survey interviews were conducted from May to August 2019 [32].

Inclusion criteria

The respondents were women of reproductive age (15–49 years) at the time of the survey who had provided information on all variables required for the analysis.

Outcome variable

The outcome of interest was captured by the DHS variable ‘V217’, which asked respondents which point in the ovulatory cycle presented the highest risk of pregnancy. Possible answers to the originally worded question were: ‘during her period’, ‘after period ended’, ‘middle of the cycle’, ‘before the period begins’, ‘at any time’, ‘other’, and ‘don’t know’. We recoded this into a binary variable reflective of correct knowledge of the ovulatory cycle by recoding responses of ‘middle of the cycle’ into ‘1’, meaning those respondents had correct knowledge of the ovulatory cycle; and all other responses into ‘0’, meaning those respondents did not have accurate knowledge of the ovulatory cycle. This method of recoding the ‘V217’ DHS variable has been used in other notable studies assessing the prevalence of accurate ovulatory cycle knowledge [2931].

Independent variables

The main predicting variable was regional category, consisting of ‘Eastern’, ‘Northern’, ‘Northwestern’, ‘Southern’, and ‘Western’ regions of Sierra Leone. These are illustrated in Fig 1, which depicts the map showing the Regions of Sierra Leone.

In addition to the Sierra Leonean region, twelve other sociodemographic covariates were selected according to previous literature and epidemiological plausibility [2931]. These included age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years), type of residence (urban, rural), religion (Christian, Islam), wealth index (poorest, poorer, middle, richer, richest), highest education level achieved (no education, primary, secondary, higher), marital status (never married or in the union, married or cohabitating, widowed/divorced/separated), parity (none, 1–2, 3–4, >5 or more), contraception knowledge (knows no method, knows folkloric method, knows traditional method, knows modern method), contraceptive use and intention (currently using modern contraception, currently using a traditional method, not using contraception but intends to use at a later date, never intends to use contraception), and the frequency of reading newspapers/magazines, listening to the radio, and watching television (not at all, less than once a week, at least once a week).

Data analysis

The percentage of respondents with accurate knowledge of the ovulatory cycle was estimated across the regions of Sierra Leone and all aforementioned covariates in a descriptive analysis. The statistical significance of the bivariate association between the independent variables and the outcome variable was initially assessed using chi-square (χ2) tests of independence. Next, a bivariate logistic regression model (Model 1) was used to produce an unadjusted odd ratio (OR) of the association between living in one of the five Sierra Leone regions and correct ovulatory cycle knowledge. Model 2 was a multivariate logistic regression model in which all sociodemographic variables that had statistically significant (p<0.05) bivariate associations in initial tests of independence were included and controlled for. Model 2 produced adjusted odds ratios (aORs) and corresponding 95% confidence intervals (CI) that reflected their precision and significance. Incorrect ovulatory cycle knowledge was the reference group meaning that an OR greater than one (1) indicated that respondents in that corresponding category had a higher likelihood of possessing correct ovulatory cycle knowledge. We performed sub-group analyses by conducting a multivariable logistic regression on samples from each of the administrative regions as well. Missing data were dropped because it was minimal (less than 2%). All statistical analyses were carried out using Stata Version 17 and sample weights were applied using procedures outlined in the Guide to DHS Statistics, DHS-7, Version 2 [35].

Ethics approval

Our study reports on research involving human participants, however, ethical approval was not applicable for our secondary analysis since The DHS Program already had ethical clearance for conducting the primary survey and made the data available for use by the public. Because we strictly followed the conditions of the institutional review board that approved the DHS in Sierra Leone, further ethical approval was not required. Additionally, our methodology did not require informed consent from participants because informed consent was already provided to the original implementers of the survey. We did request permission from The DHS Program to use the 2019 Sierra Leone DHS dataset, which was approved before we began our analysis.

Results

Descriptive results

Table 1 shows the raw counts and weighted percentages of respondents across all the sociodemographic variables of interest. Out of the total sample (n = 15,574), 39.8% (CI = 37.4–40.9) had correct knowledge of the ovulatory cycle. Regarding region, women from the Northern region of Sierra Leone had the highest prevalence of correct ovulatory cycle knowledge (46.7%, CI = 43.1–50.3), whereas women from the Northwestern region had the lowest (20.1%, CI = 16.7–24.0), followed by women from the Eastern (33.3%, CI = 28.5–38.5), Western (39.2%, CI = 35.4–43.2), and Southern regions (45.1%, CI = 41.9–48.2). Women aged 15–19 had the lowest percentage (27.8%, CI = 25.5–30.2) of ovulatory cycle knowledge whereas 35–39-year-olds had the highest (42.1%, CI = 39.3–44.9). Urban women had slightly higher (39.1%, CI = 36.2–42.0) ovulatory cycle knowledge than rural women (36.5%, CI = 34.3–38.6); however, there was no statistically significant difference between the two (p = 0.153). Religion (p = 0.102) and wealth index (p = 0.530) also had no significant bivariate associations with ovulatory cycle knowledge across categories.

thumbnail
Table 1. Accurate ovulatory cycle knowledge stratified by sociodemographic characteristics.

https://doi.org/10.1371/journal.pone.0300239.t001

Women with no education (36.3%, CI = 34.3–38.3) and primary educations (31.2%, CI = 28.6–33.9) had the lowest prevalence of ovulatory cycle knowledge whereas those with ‘higher’ educations had the highest (54.1%, CI = 48.0–60.0). Respondents who were never married/in a union had the lowest percentage of correct ovulatory cycle knowledge (34.3%, CI = 32.0–36.7) while those married/cohabitating had the highest (39.4%, CI = 37.5–41.3). Respondents with a parity of none had the lowest percentage of correct ovulatory cycle knowledge (30.9%, CI = 28.6–33.3) whereas respondents with 1–2 prior births (41.0%, CI = 38.7–43.4) and 3–4 prior births (41.0%, CI = 38.6–43.4) had the highest.

Contraception knowledge had no association with ovulatory cycle knowledge (p = 0.396). As for current contraceptive use and intention, respondents who reported never intending to use contraception had the lowest prevalence of correct ovulatory cycle knowledge (33.1%, CI = 30.8–35.4), whereas women currently using a traditional method of contraception had the highest (49.6%, CI = 31.7–67.6). Lastly, women who read the newspaper/magazine at least once a week (51.5%, CI = 44.5–58.4) and/or listened to the radio at least once a week (43.2%, CI = 39.9–46.5) had a higher prevalence of ovulatory cycle knowledge than women who engaged in those respective activities less often. Frequency of watching television did not have an association with ovulatory cycle knowledge (p = 0.457).

Logistic regression results

Table 2 shows the logistic regression results for the bivariate unadjusted model (Model 1) and the multivariate adjusted model (Model 2). In Model 1, only the main predictor of interest (region) was assessed for its association with ovulatory cycle knowledge. Women of the Northwestern region, compared to the reference group of Northern region women, had the lowest odds of correct ovulatory cycle knowledge (OR = 0.29, CI = 0.22–0.38), followed by women of the Eastern region (OR = 0.57, CI = 0.43–0.74) and Western region (OR = 0.74, CI = 0.59–0.92), while women of the Southern region did not have significantly different odds of ovulatory cycle knowledge (OR = 0.94, CI = 0.77–1.13) compared to the reference group. We also performed subgroup analyses for each of the five regions with multivariable regressions. However, no significant variation was observed relative to our model results, which is being interpreted in this section.

thumbnail
Table 2. Bivariate and multivariate logistic regression results for determinants of ovulatory cycle knowledge.

https://doi.org/10.1371/journal.pone.0300239.t002

In bivariate tests of independence (Table 1), age, education level, marital status, parity, contraceptive use and intention, frequency of reading the newspaper/magazine, and frequency of listening to the radio, in addition to region, all had statistically significant associations (p<0.001) with ovulatory cycle knowledge. Therefore, these variables were included in the Model 2 regression model. In Model 2, women living in the Northwestern region had the lowest prevalence of ovulatory cycle knowledge (aOR = 0.29, CI = 0.22–0.38), followed by those in the Eastern (aOR = 0.55, CI = 0.41–0.72) and Western regions (aOR = 0.63, CI = 0.50–0.80) as compared to women from the Northern region. Regarding age, women from older age groups (40–44 and 45–49 years) did not have statistically different aORs compared to the reference group of 35–39-year-olds. Women from younger age groups had significantly lower odds of correct ovulatory cycle knowledge, with those from the 15–19-year age category demonstrating the lowest (aOR = 0.51, CI = 0.41–0.63).

Women with no education (aOR = 0.48, CI = 0.38–0.62), primary education (aOR = 0.44, CI = 0.33–0.57), and secondary education (aOR = 0.72, CI = 0.57–0.92) had significantly lower odds of ovulatory cycle knowledge than those with ‘higher’ educations (reference group). In Model 2, no marital status category had significantly different odds of having correct ovulatory cycle knowledge. Regarding parity, the only category with significantly lower odds of possessing correct ovulatory cycle knowledge were women with a parity of none (aOR = 0.75, CI = 0.65–0.87), and there was no significant difference in odds between those with 1–2 previous births (reference group), 3–4 previous births (aOR = 1.01, CI = 0.89–1.16), and 5 or more previous births (aOR = 0.97, CI = 0.83–1.13). Categories of contraceptive use and intention and frequency of reading the newspaper/magazine became statistically insignificant in Model 2. Regarding the frequency of listening to the radio, women who listened to the radio less than once a week had very slightly lower odds of possessing correct ovulatory cycle knowledge (aOR = 0.80, CI = 0.67–0.96) than those who listened at least once a week (reference group).

Discussion

This study estimated the prevalence of correct ovulatory cycle knowledge across Sierra Leone and sought to determine if region, along with other sociodemographic factors, was associated with significantly higher or lower levels of that knowledge. In all, 39.8% of 15–49-year-old women in Sierra Leone demonstrated correct knowledge of when in the ovulatory cycle fertilization/pregnancy is most likely. Iyanda and colleagues’ [15] findings from nearby countries showed that 23.3%, 11.5%, 23.1%, and 36.6% of 15–24-year-old women from Guinea (2012), Liberia (2013), Gambia (2013), and Burkina Faso (2010), respectively, had accurate ovulatory cycle knowledge. The same study showed that 30.3% of 15–24-year-olds in Sierra Leone in 2013 had correct ovulatory cycle knowledge, compared to our finding of 39.8% for women of all reproductive ages in 2019 [15]. It is possible that more recent percentages from Guinea, Liberia, Gambia, and Burkina Faso from women of all reproductive ages would be higher than Iyanda and colleagues’ findings from 15-24-year-olds as well [15]. Getahun and Nigatu [29] found that 23.6% of 15–49-year-old women in Ethiopia in 2016 had accurate ovulatory cycle knowledge.

While intercountry differences are apparent in the aforementioned results, previous studies and ours alike demonstrate a low level of accurate knowledge of the ovulatory cycle among women in Sub-Saharan Africa. This low level of knowledge is a major public health concern since women who lack awareness about when they ovulate, and of the significance of ovulation and its role in fertilization, are more likely to experience unintended pregnancies and their repercussions [1215]. This is especially important to consider for countries in which abortion is illegal, such as Sierra Leone [28].

Our multivariate analysis showed that the main determinant of interest, region of Sierra Leone, was statistically significant in its association with accurate ovulatory cycle knowledge. Age, educational level, and parity were also statistically significant determinants. Women residing in Sierra Leone’s Northwestern, Western, and Eastern regions had significantly lower odds of accurate ovulatory cycle knowledge compared to those in the Northern region after the other sociodemographic variables were factored into the multiple regression. In our initial bivariate test of independence, urban/rural status surprisingly was not associated with ovulatory cycle knowledge and so was not included in the regression model. Previous research has generally shown disparities in health knowledge along the urban/rural divide in Africa [29, 3639]. Since we found no association between urban/rural status and ovulatory cycle knowledge but did find a significant association between region at the province level and ovulatory cycle knowledge, our results indicate that in Sierra Leone there are other geographically linked influences at play other than the urban/rural divide when accounting for ovulatory cycle awareness. Sociocultural differences and norms that may be associated with regions, such as the influence of religion, local government, gender power disparities, family dynamics, and the resulting ability for certain peoples to have open discussions about issues like family planning may have contributed to the regional differences we observed. Future researchers are encouraged to elucidate other determinants that may shed light on why we observed such large differences in the odds of having correct ovulatory cycle knowledge between Sierra Leone’s five geographic regions.

This study also showed that women with no previous births had significantly lower levels of ovulatory cycle knowledge compared to those who had previously given birth (irrespective of the number of births). This result is not surprising and was similar to the aforementioned Ethiopian study where currently pregnant women were found to be more knowledgeable about the ovulatory cycle compared to women who have never conceived [29]. The likely explanation for the association between parity and knowledge of the ovulatory cycle is that women who have already had experience with conception or giving birth are more likely to have interacted with skilled health personnel and/or counselors that would have provided reproductive health information. This finding is another possible reminder of the benefits of health system contact regarding family planning and reproductive health.

Higher education was associated with increased odds of correct ovulatory cycle knowledge among respondents, which is consistent with findings from previous studies [30, 40]. This association can likely be attributed to the fact that education generally has a positive impact on knowledge of health and health-related behavior [4143]. Education is also a strong social determinant of health since it provides avenues for employment, independence, empowerment, and subsequent increased ability for women to discover and seek out knowledge for themselves [44, 45]. Women of the youngest age group, 15–19-year-olds, had the lowest odds of ovulatory cycle knowledge compared to those of older ages. This pattern of younger age being correlated with lower ovulatory cycle knowledge was generally consistent with findings from similar studies [29, 30].

Finally, the global association between the frequency of radio listening and ovulatory cycle knowledge was weak since women who did not listen to the radio at all did not have significantly lower odds of accurate knowledge. However, women who listened to the radio less than once a week had slightly lower odds of having correct ovulatory cycle knowledge as opposed to those who listened more than once per week. This very weak finding concerning radio listening and the fact that there was no association between ovulatory cycle knowledge and newspaper/magazine reading contradicts the generally accepted paradigm that access to mass media usually increases one’s health knowledge [37, 46]. However, this may also simply be an indicator that no information on the ovulatory cycle is currently being provided through these sources in Sierra Leone. Our findings regarding media exposure in Sierra Leone should be explored in more detail.

In summary, less than 4 in 10 women of reproductive age in Sierra Leone possess correct knowledge about their ovulatory cycle and its implications on the chances of becoming pregnant. Region of residence, age, education level, and parity were significantly associated with ovulatory cycle knowledge. Women living in the Northwestern, Eastern, and Western regions, as opposed to Sierra Leone’s Northern and Southern provinces, had significantly lower odds of having correct ovulatory cycle knowledge even though Sierra Leone’s capital is in the Western region. Women living in Sierra Leone’s Northwestern region, as compared to those in the Northern region, had the lowest odds of ovulatory cycle knowledge, followed by women in the Eastern, Western, and Southern regions. The differences in ovulatory cycle knowledge across regions of Sierra Leone suggest that the government and other relevant stakeholders should consider contextually appropriate strategies to enhance the public’s knowledge on the ovulatory cycle and its consequences for fertility. Policymakers and community health educators can invest in the development of educational strategies and resources like teach-the-teacher campaigns; easy to understand pamphlets that can be disseminated in communities, schools, and workplaces of healthcare providers; mobile applications that can help women keep track of their cycle; and illustrative videos that outreach workers can use when working with women with low literacy. These resources can be especially targeted to Sierra Leone’s Northwestern, Eastern, and Western areas to minimize the regional disparities in ovulatory cycle knowledge observed in this study.

Strengths and limitations

A strength of this study is that the DHS dataset used was produced from a complex sampling procedure that ensures appropriate subpopulation representativeness, thereby maximizing the validity of results for our population of interest. The multivariate logistic regression method employed is a reliable way of testing a survey question on knowledge that was easily and accurately dichotomized into ‘correct’ and ‘incorrect’ answers. This dataset is also publicly available, which makes our study easily reproducible and/or built upon by other researchers. The same variable on ovulatory cycle knowledge is also available in most other country’s DHS surveys, which makes comparable intercountry analyses on this topic possible as well. However, researchers interested in multi-country analyses on the DHS ovulatory cycle knowledge variable will need to consider issues like the year in which the surveys were done, differing reproductive health practices and policies between the countries included, and a myriad of other contextual differences. Also, DHS surveys are replicated roughly every 5 years, which makes a reliable follow-up study of ovulatory cycle knowledge in Sierra Leone possible, which can help researchers and implementers understand how this variable is trending over time. Lastly, this project is the first of its kind, to the best of our knowledge, that has elucidated region of residence and examined other sociodemographic characteristics as predictors of ovulatory cycle knowledge in Sierra Leone. There are several weaknesses to this study as well. One is that it was based on data obtained from a cross-sectional survey that used self-reported measures, which means recall bias must be taken into account. Also, temporal inferences between the sociodemographic covariates and knowledge about the ovulatory cycle could not be ascertained. Social desirability bias often must be considered with sensitive topics like family planning knowledge and practices. The risk of social desirability bias is probably minimal in this study though since there likely would have been no preconceived notion of what the expected answer to the ovulatory cycle question would have been. However, it is possible that respondents that did not know the answer could have guessed in an effort to display that they had some ovulatory cycle knowledge which could have skewed results slightly. Also, while we were able to assess the relationship between residence in Sierra Leone’s five broad regions and ovulatory cycle knowledge, which is a necessary first step in looking at geographic influences on ovulatory cycle knowledge, it would have been ideal to also be able to study this topic at an even more granular town or district level. However, that was not possible with this dataset.

Conclusion

Lack of awareness and understanding about the ovulatory cycle are key contributors to unwanted and mistimed pregnancies. This study found that prevalence of accurate ovulatory cycle knowledge across Sierra Leone is low among women of reproductive age. This is especially concerning since reproductive health indicators are so poor and abortion is illegal in Sierra Leone. In particular, accurate ovulatory cycle knowledge was lowest among 15–19-year-old women, those with primary school educations, those with a parity of none, and in women living in Sierra Leone’s Northwestern, Eastern, and Western regions, as opposed to the those living in the Northern and Southern regions. Findings therefore suggest that increased awareness and appreciation of the importance of the ovulatory cycle’s role in reproductive health is necessary across all of Sierra Leone, but that resources should especially be targeted towards women belonging to the aforementioned geographic and sociodemographic subgroups.

This epidemiological study augments the current corpus of knowledge and discourse on SSA women’s comprehension of the ovulatory cycle and its importance in reproductive health and family planning. Specifically, it contributes to the scientific literature by clearly elucidating a link between geography and ovulatory cycle knowledge among women of reproductive age in Sierra Leone, a phenomenon which has not been documented in the existing literature. This finding provides actionable direction to policymakers and implementers looking to efficiently address geographical imbalances in ovulatory cycle knowledge in Sierra Leone with targeted outreach. Future research can explore the sociocultural mechanisms behind the stark disparities in ovulatory cycle knowledge we observed between Sierra Leone’s five regions. Our findings also suggest that studying geographic and sociodemographic determinants of ovulatory cycle knowledge in other countries may provide useful programmatic insights. A more complete understanding of how ovulatory cycle knowledge can be increased in high-risk regions can play an important role in improving reproductive health and decreasing unintended pregnancies throughout the world.

Supporting information

S1 Checklist. PLOS ONE clinical studies checklist.

https://doi.org/10.1371/journal.pone.0300239.s001

(DOCX)

Acknowledgments

We are grateful to all those who provided information and direction for this paper and to the Measure DHS program.

References

  1. 1. Holesh JE, Bass AN, Lord M. Physiology, ovulation. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022.
  2. 2. Simmons RG, Jennings V. Fertility awareness-based methods of family planning. Best Pract Res Clin Obstet Gynaecol. 2020 Jul; 66:68–82. pmid:32169418
  3. 3. Vigil P, Blackwell LF, Cortés ME. The importance of fertility awareness in the assessment of a woman’s health a review. Linacre Q. 2012 Nov; 79(4):426–450. pmid:30082987
  4. 4. Marnach, M. What ovulation signs can I look out for if I’m trying to conceive? [Internet]. Mayo Foundation for Medical Education and Research (MFMER); 2022 Dec 7 [cited 2022 Dec 20]. https://www.mayoclinic.org/healthy-lifestyle/getting-pregnant/expert-answers/ovulation-signs/faq-20058000#:~:text=In%20an%20average%2028%2Dday,next%20menstrual%20period%20may%20vary.
  5. 5. Mayo Clinic Staff. Menstrual cycle: What’s normal, what’s not [Internet]. Mayo Foundation for Medical Education and Research (MFMER); 2022 Dec 6 [cited 2022 Dec 20]. https://www.mayoclinic.org/healthy-lifestyle/womens-health/in-depth/menstrual-cycle/art-20047186#:~:text=Menstrual%20flow%20might%20occur%20every,more%20regular%20as%20you%20age.
  6. 6. Owen M. Physiological signs of ovulation and fertility readily observable by women. Linacre Q. 2013 Feb; 80(1):17–23. pmid:24845657
  7. 7. Billings EL, Brown JB, Billings JJ, Burger HG. Symptoms and hormonal changes accompanying ovulation. Lancet. 1972 Feb 5; 1(7745):282–4. pmid:4109930
  8. 8. Ecochard R, Marret H, Rabilloud M, Bradaï R, Boehringer H, Girotto S, et al. Sensitivity and specificity of ultrasound indices of ovulation in spontaneous cycles. Eur J Obstet Gynecol Reprod Biol. 2000 Jul; 91(1):59–64. pmid:10817880
  9. 9. Pallone SR, Bergus GR. Fertility awareness-based methods: another option for family planning. J Am Board Fam Med. 2009 Mar-Apr;22(2):147–57. pmid:19264938
  10. 10. Su H-W, Yi Y-C, Wei T-Y, Chang T-C, Cheng C-M. Detection of ovulation, a review of currently available methods. Bioeng Transl Med. 2017 May 16; 2(3):238–246. pmid:29313033
  11. 11. Novikova N, Weisberg E, Stanczyk FZ, Croxatto HB, Fraser IS. Effectiveness of levonorgestrel emergency contraception given before or after ovulation—a pilot study. Contraception. 2007 Feb; 75(2):112–8. pmid:17241840
  12. 12. Ayoola AB, Zandee GL, Adams YJ. Women’s knowledge of ovulation, the menstrual cycle, and its associated reproductive changes. Birth. 2016 Sep; 43(3):255–62. pmid:27157718
  13. 13. Shaheen AA, Diaaeldin M, Chaaya M, Roueiheb ZE. Unintended pregnancy in Egypt: evidence from the national study on women giving birth in 1999. East Mediterr Health J. 2007 Nov-Dec;13(6):1392–404. pmid:18341189
  14. 14. Geda YF. Determinants of teenage pregnancy in Ethiopia: A Case–control study, 2019. Current Medical Issues. 2019; 17(4):112–117.
  15. 15. Iyanda AE, Dinkins BJ, Osayomi T, Adeusi TJ, Lu Y, Oppong JR. Fertility knowledge, contraceptive use and unintentional pregnancy in 29 African countries: a cross-sectional study. Int J Public Health. 2020 May;65(4):445–455. pmid:32270234
  16. 16. Assefa N, Berhane Y, Worku A, Tsui A. The hazard of pregnancy loss and stillbirth among women in Kersa, East Ethiopia: a follow up study. Sex Reprod Healthc. 2012 Oct;3(3):107–12. pmid:22980735
  17. 17. Hall JA, Barrett G, Copas A, Phiri T, Malata A, Stephenson J. Reassessing pregnancy intention and its relation to maternal, perinatal and neonatal outcomes in a low-income setting: A cohort study. PLoS One. 2018 Oct 18;13(10):e0205487. pmid:30335769
  18. 18. Hall JA, Benton L, Copas A, Stephenson J. Pregnancy intention and pregnancy outcome: Systematic review and meta-analysis. Matern Child Health J. 2017; 21(3): 670–704. pmid:28093686
  19. 19. Bearak J, Popinchalk A, Ganatra B, Moller A-B, Tunçalp Ö, Beavin C, et al. Unintended pregnancy and abortion by income, region, and the legal status of abortion: estimates from a comprehensive model for 1990–2019. Lancet Glob Health. 2020 Sep;8(9):e1152–e1161. pmid:32710833
  20. 20. Sedgh G, Singh S, Hussain R. Intended and unintended pregnancies worldwide in 2012 and recent trends. Stud Fam Plann. 2014 Sep;45(3):301–14. pmid:25207494
  21. 21. Critchley HOD, Babayev E, Bulun SE, Clark S, Garcia-Grau I, Gregersen PK, et al. Menstruation: science and society. Am J Obstet Gynecol. 2020 Nov;223(5):624–664. pmid:32707266
  22. 22. Iyanda AE. Cross-national variation in knowledge of ovulation timing in Sub-Saharan Africa. Women’s Reproductive Health. 2020 May 25; 7(2):127–143.
  23. 23. García D, Vassena R, Prat A, Vernaeve V. Increasing fertility knowledge and awareness by tailored education: a randomized controlled trial. Reprod Biomed Online. 2016 Jan;32(1):113–20. pmid:26611499
  24. 24. Mahey R, Gupta M, Kandpal S, Malhotra N, Vanamail P, Singh N, et al. Fertility awareness and knowledge among Indian women attending an infertility clinic: a cross-sectional study. BMC Womens Health. 2018 Oct 29;18(1):177. pmid:30373587
  25. 25. Mason L, Nyothach E, Alexander K, Odhiambo FO, Eleveld A, Vulule J, et al. ’We keep it secret so no one should know’—a qualitative study to explore young schoolgirls attitudes and experiences with menstruation in rural western Kenya. PLoS One. 2013 Nov 14;8(11):e79132. pmid:24244435
  26. 26. Juayire CA. Knowledge of ovulatory cycle and current fertility among women in Ghana [dissertation]. Legon, Ghana: Regional Institute for Population Studies, University of Ghana; 2016 Jul. https://ugspace.ug.edu.gh/bitstream/handle/123456789/22706/Knowledge%20of%20Ovulatory%20Cycle%20and%20Current%20Fertility%20among%20Women%20in%20Ghana%20-%202016.pdf?sequence=1&isAllowed=y.
  27. 27. Unicef. Sierra Leone: Maternal, neonatal, and child health [Internet]. Unicef; 2022 [cited 2022 Dec 18]. https://www.unicef.org/sierraleone/maternal-neonatal-and-child-health#:~:text=The%20maternal%20mortality%20rate%20in,in%20every%20100%2C000%20live%20births.
  28. 28. HowToUseAbortionPill.org. Abortion laws in Sierra Leone [Internet]. HowToUseAbortionPill.org; 2022 [cited 2022 Dec 3]. https://www.howtouseabortionpill.org/abortion-laws-by-country/sierra-leone/.
  29. 29. Getahun MB, Nigatu AG. Knowledge of the ovulatory period and associated factors among reproductive women in Ethiopia: A population-based study using the 2016 Ethiopian Demographic Health Survey. Int J Womens Health. 2020 Sep 8;12:701–707. pmid:32982474
  30. 30. Dagnew B, Teshale AB, Dagne H, Diress M, Tesema GA, Dewau R, et al. Individual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A multilevel analysis based on 2016 Ethiopian Demographic and Health Survey. PLoS One. 2021 Sep 2;16(9):e0254094. pmid:34473727
  31. 31. Zegeye B, Adjei NK, Idriss-Wheeler D, Yaya S. Individual and community-level determinants of knowledge of ovulatory cycle among women of reproductive age in 29 African countries: a multilevel analysis. BMC Womens Health. 2022 Sep 29;22(1):394. pmid:36175854
  32. 32. The Demographic and Health Survey Program. Sierra Leone DHS, 2019. Rockville, MD: ICF; 2019. https://dhsprogram.com/publications/publication-FR365-DHS-Final-Reports.cfm.
  33. 33. The Demographic and Health Survey Program. The DHS Program: Demographic and Health Surveys. Rockville, MD: ICF; 2022. https://dhsprogram.com/.
  34. 34. Corsi DJ, Neuman M, Finlay JE, Subramanian SV. Demographic and health surveys: a profile. Int J Epidemiol. 2012 Dec; 41(6):1602–13. pmid:23148108
  35. 35. Croft TN, Marshall AMJ, Allen CK, et al. Guide to DHS statistics: DHS-7 (version 2). [Internet] Rockville, MD: ICF; 2020 May [cited 2022 Nov 27]. https://www.dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_DHS-7_v2.pdf.
  36. 36. Fenny A, Crentsil A, Asuman D. Determinants and distribution of comprehensive HIV/AIDS knowledge in Ghana. Global Journal of Health Science. 2017; 9(12).
  37. 37. Agegnehu CD, Tesema GA. Effect of mass media on comprehensive knowledge of HIV/AIDS and its spatial distribution among reproductive-age women in Ethiopia: a spatial and multilevel analysis. BMC Public Health. 2020 Sep 17; 20(1):1420. pmid:32943042
  38. 38. Rhine L. The impact of information technology on health information access in Sub-Saharan Africa: The divide within the divide. Information Development. 2016 Jun 30; 22(4).
  39. 39. White JS, Speizer IS. Can family planning outreach bridge the urban-rural divide in Zambia? BMC Health Serv Res. 2007 Sep 5;7:143. pmid:17803805
  40. 40. Gyekye EK. Correlates of knowledge of ovulation cycle among women in urban poor communities in Accra [dissertation]. Legon, Ghana: Regional Institute for Population Studies, University of Ghana; 2014 Jun. https://ugspace.ug.edu.gh/bitstream/handle/123456789/22592/Correlates%20of%20Knowledge%20of%20Ovulation%20Cycle%20among%20Women%20in%20Urban%20Poor%20Communities%20in%20Accra%20Eugene%20Kwasi%20Gyekye.pdf?sequence=1&isAllowed=y.
  41. 41. Mocan N, Altindag DT. Education, cognition, health knowledge, and health behavior. Eur J Health Econ. 2014 Apr;15(3):265–79. pmid:23546739
  42. 42. Schuller T, Preston J, Hammond C, Brassett-Grundy A, Bynner J. The benefits of learning: The impact of education on health, family life and social capital. 1st ed. Routledge; 2004.
  43. 43. Higgins C, Lavin T, Metcalfe O. Health impacts of education: A review. Institute of Public Health in Ireland; 2008 Nov 4. https://publichealth.ie/files/file/Health%20Impacts%20of%20Education.pdf.
  44. 44. The Lancet Public Health. Education: A neglected social determinant of health. Lancet Public Health. 2020 Jul; 5(7):e361. pmid:32619534
  45. 45. Shankar J, Ip E, Khalema E, Couture J, Tan S, Zulla RT, et al. Education as a social determinant of health: Issues facing indigenous and visible minority students in postsecondary education in western Canada. Int J Environ Res Public Health. 2013 Sep; 10(9): 3908–3929. pmid:23989527
  46. 46. T Dimbuene Z, K Defo B. Fostering accurate HIV/AIDS knowledge among unmarried youths in Cameroon: do family environment and peers matter? BMC Public Health. 2011 May 19; 11:348. pmid:21595931