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

Baye Dagnew; Achamyeleh Birhanu Teshale; Henok Dagne; Mengistie Diress; Getayeneh Antehunegn Tesema; Reta Dewau; Meseret Derbew Molla; Yigizie Yeshaw

doi:10.1371/journal.pone.0254094

. 2021 Sep 2;16(9):e0254094. doi: 10.1371/journal.pone.0254094

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

Baye Dagnew ^1,^*, Achamyeleh Birhanu Teshale ², Henok Dagne ³, Mengistie Diress ¹, Getayeneh Antehunegn Tesema ², Reta Dewau ⁴, Meseret Derbew Molla ⁵, Yigizie Yeshaw ^1,²

Editor: José Antonio Ortega⁶

PMCID: PMC8412270 PMID: 34473727

Abstract

Background

Knowledge of the ovulatory cycle (KOC) aids women to refrain and engage in sexual intercourse to avoid and to get pregnancy, respectively. The effect of community-level factors on KOC was not yet known in Ethiopia. Therefore, we aimed to investigate the community- and individual-level determinants of KOC among women of childbearing age.

Methods

We used the 2016 Ethiopian Demographic and Health Survey, and total weighted samples of 15,683 women were included. Intra-class correlation, median odds ratio, and deviance were executed for model comparison in which a model with the lowest deviance was the best model i.e. model III in this case. A multivariable multilevel logistic regression model was employed to identify community- and individual-level factors of correct KOC. In the ultimate model, an adjusted odds ratio (AOR) with a 95% confidence interval was reported and variables with a p<0.05 were considered as statistically significant.

Results

In this study, 3,698 [23.58% (95% CI; 22.92–24.25)] participants had correct KOC. Women’s age in years, i.e. 20–24 (AOR = 1.46;1.28–1.68) 25–29 (AOR = 1.72; 1.49–1.99), 30–34 (AOR = 2.21; 1.89–2.58), 35–39 (AOR = 1.78; 1.51–2.09), 40–44 (AOR = 1.97; 1.65–2.37), and 45–49 (AOR = 1.78; 1.44–2.19), knowledge of contraceptive methods (AOR = 3.08; 2.07–4.58), increased women’s educational level, i.e. higher (AOR = 4.24; 3.54–5.07), secondary (AOR = 2.89; 2.48–3.36), and primary (AOR = 1.57; 1.39–1.78), higher household’s wealth index, i.e. richest (AOR = 1.71; 1.35–2.16), richer (AOR = 1.42; 1.16–1.72), middle (AOR = 1.29; 1.07–1.56), and poorer (AOR = 1.24; 1.03–1.48), current contraceptive use (AOR = 1.26; 1.13–1.39), menstruating in the last six weeks (AOR = 1.13; 1.03–1.24), women’s media exposure (AOR = 1.20; 1.07–1.35), and being in the community with a high level of media exposure (AOR = 1.53; 1.24–1.88) were statistically significant with KOC.

Conclusions

Knowledge of the ovulatory cycle was low in this study, which demands health education for women of childbearing age. Special attention should be given to teenagers, those with lower educational, and lower economic status. Besides, the strengthening of media campaigns could increase women’s KOC, which is crucial for preventing unintended pregnancy.

Background

Ovulation is a physiological event noted by the break-up and exit of the dominant follicle from the ovary into the fallopian tube for potential fertilization [1]. The time of ovulation is detected by the knowledge of the basal body temperature and cervical mucus [2]. Understanding of ovulation helps women to conceive a child or escape sexual contact in the fertile period for the sake of contraception [3]. Ovulatory cycle is one of the fertility awareness family planning methods where people use measurements or on-time changes of the body to decide for sexual intercourse on a fertile period [4,5]. It is also useful to recognize certain pathologies [5], evaluate woman’s health [6], and monitor fertility [7] whereas inadequate KOC predisposes to unintended pregnancy [8].

Evidences showed the prevalence of KOC was 32.8% in USA [8], 31.2% in Spain [9], and 15% in India [10]. In Africa, the prevalence of KOC ranged from 10.4%-49% such as studies in Togo (42.8%) [11,12]. Besides, 38% of women of childbearing age reported correct KOC in Ghana [13]. Few pieces of the literature disclosed the association of KOC with low socioeconomic status and encountering unwanted pregnancy [14], age (younger women had inadequate KOC) [15,16], education [14,17–19], and unintended pregnancy [11,16].

Even though KOC is a fundamental aspect of the female reproductive cycle, there is limited study in Ethiopia. Only one study reported the prevalence of knowledge of the ovulation period (i.e. 23.6%) which used the Ethiopian Demographic and Health Survey (EDHS) 2016 [20]. The previous study did not consider the community-level factors (only the individual-level factors were analyzed; even they did not use multilevel analysis which is appropriate for hierarchical data), there was no weighting to account for survey design, and essential variables were missed such as media exposure that plays a key role for the provision of information. Besides, the individual-level factors, knowledge of community-level factors aids to institute interventional strategies. Therefore, this study aimed to identify individual and community-level determinants of correct KOC among women of childbearing age in Ethiopia using the EDHS 2016. The results will be helpful to establish reproductive health specific interventions to advocate appropriate KOC. This will improve women’s health to prevent unintended pregnancy especially for those with poor access and awareness of contraceptive methods, particularly in Sub-Saharan Africa [21]. Forthcoming researchers will get baseline information for further studies on the field.

Methods data source, study design, and study period

We used the latest EDHS (2016) data after a reasonable request from the Measure DHS programme [22] available at (https://dhsprogram.com/Data/terms-of-use.cfm). The Central Statistical Agency (CSA) had surveyed the data in collaboration with the Ethiopian Ministry of Health and Ethiopian Public health institute and also the international classification of functioning, disability and health (ICF) involved in technical assistance. Financial support was obtained from the development partners i.e. United States Agency for International Development (USAID), government of the Netherlands, Global Fund, Irish Aid, World Bank, United Nations Population Fund (UNFPA), United Nations Children’s Fund (UNICEF), and UN Women. The dataset comprised all females aged 15–49 years. This is important data to provide the key health indicators at the national and sub-national level [23]. We used the Ethiopia Population and Housing Census (2007) as a sampling frame for the EDHS 2016 which was a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographic area covering an average of 181 households (~15,369,615 households on average). Each large EA was segmented to minimize the task of household listing, from which only one segment was selected for the survey, with probability proportional allocation to segment size. Household listing was conducted only in the selected segment, i.e. a 2016 EDHS cluster is an EA or a segment of an EA. Then, 18,008 households were selected for the sample, of which 17,067 were occupied. Of the occupied households, 16,650 were successfully interviewed, yielding a response rate of ~98%. Regarding the participants, 15,683 (15–49 years of age) were females and 12,688 (15–59 years of age) were males. The EDHS survey 2016 was a community-based cross-sectional study, and the data was collected from January 18, 2016 to June 27, 2016 [24].

Study area

This survey was conducted in Ethiopia, one of the low-income countries in East Africa with 3°-14° N and 33°–48° E. For administrative purpose, Ethiopia is divided into nine regional states (Afar, Amhara, Benishangul-Gumuz, Gambela Peoples region, Oromia, Harari, Southern Nations, Nationalities and People’s (SNNP), Somali, and Tigray), and two city administrations (Addis Ababa and Dire Dawa).

Population and sample size

The source population was all women of childbearing age (15–49 years old) living in Ethiopia and those living in the selected enumeration areas (EAs) served as the study population. In the selected households, all women who spent the night before the survey day were included in the study. We used a women’s dataset (IR file) and 15,683 (weighted) women of childbearing age were used for the final analysis.

Sampling procedure

The EDHS 2016 survey used a two-stage stratified cluster sampling technique to get the study participants. The stratification was performed using separate regions (the nine regions and Dire Dawa) into rural and urban areas; although Addis Ababa is entirely urban. Then each stratum was divided into clusters or EAs, comprised 200–300 households. In the first stage of sampling, 645 EAs were selected (202 from urban and 443 from rural) and in the second stage, a fixed number of 28 households per cluster were selected randomly from the list of households (~15,369,615 households). The numbers of women interviewed and responded for the outcome variable (KOC) were 15,683 and hence there were no missing cases for the outcome variable.

Data collection

After delivering training about the purpose of the survey, nationally recruited data collectors collected the data using a pretested, structured, and interviewer-administered questionnaire [23]. The questionnaire was prepared in English and then translated into the local languages.

Study variables

The outcome variable was women’s correct KOC. The respondents were asked, "when is the ovulation time?" with six responses designated as "during her period", "after period ended", "middle of the cycle", "before the period begins", "at any time", and "don’t know" [25]. We performed recoding of the variables so that the outcome variable becomes binary (dichotomized) which is useful to execute regression. For this, we created a new variable called correct knowledge of the ovulatory cycle whereby the alternative “middle of the cycle was considered as correct KOC and recoded as 1” and others were recoded as 0”. The independent variables include individual-level factors (age, wealth index of the household, women’s media exposure, educational status of the women, region, religion, marital status) [14–19] and community-level factors; non-aggregated (residence) and aggregated (community-level media exposure, community education level, and community poverty level). Community-level factors were created at cluster level using their respective individual-level factors to assess their effect at the community or cluster level.

Community-level media exposure was created from the women’s exposure to radio, newspaper/magazine, and television (after merging these variables and recoding into Yes and No). Then, we took the proportion of women who had exposure to at least one of these media and categorized into low (if <50% of women had exposure to at least one media) and high (if ≥50% of women had exposure to at least one media) community-level media exposure using national median value since the data is not normally distributed. Community poverty level was created from household wealth index by recoding poorest and poor as poor. Then, the proportion of women from households with poor household wealth index was calculated and categorized as low poverty level (those with ≥ 50%) and higher poverty level (those with <50%) using national median value. Community-level education was created by aggregating the individual level woman’s education at cluster level by taking the proportion of women with no education, which was similar as performed for the wealth index. Then we categorized it as a low and higher level of community education using a national median value like media exposure.

Data management and statistical analysis

Stata version 16 was used for extraction, recoding, and analyzing the data. Before any statistical procedure, sampling weight was executed (using women’s sampling weight) to account for the non-proportional allocation of the sample in each region and the potential variations in the response rates, as well as to get an appropriate statistical estimate. As described, stratification was performed to account for survey design. Tabulation, graphical, and textual presentations were used for descriptive results. We estimated the clustering effect since the DHS data has a hierarchical nature (random-effects) using intra-class correlation coefficient (ICC) and median odds ratio (MOR), which were used to check the need for the advanced models such as multilevel logistic regression analysis. We fitted four models; null model/ (containing only the outcome variable), model I (individual-level factors only), model II (community-level factors only), and model III (the individual- and community-level factors simultaneously). Model III was the best-fitted model for this data since it had the lowest deviance value compared to the others. A multilevel binary logistic regression analysis was performed to identify individual and community-level determinant factors of KOC. Variables with a p-value <0.2 in the bi-variable multilevel regression were considered for the multilevel multivariable logistic regression analysis. Independent factors with a p<0.05 were considered as statistically significant factors of correct KOC. The adjusted odds ratio (AOR) along with its 95% confidence interval (CI) was reported to show the strength and direction of the association between correct KOC and independent factors.

Ethical considerations

We got online permission to access the data from the DHS program, and the dataset was allowed for public use. During the survey, all the ethical issues were secured to ascertain confidentiality.

Results

Sociodemographic profiles of the participants

A total weighted samples of 15,683 women were included for the analysis. About one-fifth (21.56%) were aged 15–19 years with a median age of 27 (IQR = 20–35). Most respondents were from Oromia region (36.35%), Orthodox Christian followers (43.27%), rural dwellers (77.84%), and currently married (65.19%). Of the total participants, 47.81% did not attend formal education and 5.59% achieved higher education. Moreover, 26.55% and 56.07% were from the richest households and had no media exposure, respectively. In terms of contraceptives, 98.32% of women had self-reported knowledge of contraceptive methods, whereas 25.34% of women used contraceptives currently (Table 1).

Table 1. Sociodemographic profiles of the participants on knowledge of the ovulatory cycle in Ethiopia (n = 15683).

Variables	Categories	Weighted frequency (%)	Correct KOC frequency (%)
Age	15–19	3,381 (21.56)	685 (20.26)
	20–24	2,762 (17.61)	752 (27.240)
	25–29	2,957 (18.85)	796 (26.91)
	30–34	2,345 (14.95)	593 (25.31)
	35–39	1,932 (2.32)	401 (20.78)
	40–44	1,289 (8.22)	268 (20.75)
	45–49	1,017 (6.48)	685 (20.26)
Region	Tigray	1,129 (7.20)	199 (5.38)
	Afar	128 (0.82)	17 (0.47)
	Amhara	3,714 (23.68)	620 (16.77)
	Oromia	5,701 (36.35)	1783 (48.21)
	Somali	460 (2.93)	59 (1.60)
	Benishangul-Gumuz	161 (1.02)	150.42)
	SNNPR	3,288 (20.97)	532 (14.40)
	Gambela Peoples region	44 (0.28)	10 (0.26)
	Harari	39 (0.25)	16 (0.43)
	Addis Ababa	930 (5.93)	414 (11.22)
	Dire Dawa	90 (0.58)	32 (0.85)
Religion	Orthodox	6,786 (43.27)	1672 (45.21)
	Catholic	120 (0.76)	40 (1.07)
	Protestant	3,674 (23.43)	840 (22.72)
	Muslim	4,893 (31.20)	1097 (29.67)
	Others	210 (1.34)	49 (1.32)
Marital status	Never in Union	4,037 (25.74)	1018 (27.53)
	Currently married	10,223 (65.19)	2348 (63.48)
	Formerly married	1,423 (9.08)	333 (8.99)
Educational status of women	No education	7,498 (47.81)	1,231 (16.41)
	Primary	5,490 (35.01)	1,307 (23.81)
	Secondary	1,818 (11.59)	693 (38.12)
	Higher	877 (5.59)	468 (53.33)
Wealth index	Poorest	2,633 (16.79)	395 (14.99)
	Poorer	2,809 (17.91)	506 (12.0)
	Middle	2,978 (18.99)	557 (18.69)
	Richer	3,100 (19.76)	677 (21.86)
	Richest	4,163 (26.55)	1,564 (37.56)
Women’s media exposure	No	8,793 (56.07)	1451 (39.23)
Women’s media exposure	Yes	6,890 (43.93)	2247 (60.77)
Residence	Urban	3,476 (22.16)	1300 (37.41)
Residence	Rural	12,207 (77.84)	2,398 (19.64)
Community media exposure level	Low	7,152 (45.60)	1424 (38.50)
Community media exposure level	High	8,531 (54.40)	2274 (61.50)
Community education level	Low	6,754 (43.07)	1677 (45.35)
Community education level	High	8,929 (56.93)	2021 (54.65)
Community poverty level	Low	7,714 (49.18)	145 (39.34)
Community poverty level	High	7,969 (50.82)	2243 (60.66)
Knowledge of contraceptive methods	No	264 (1.68)	14 (0.39)
Knowledge of contraceptive methods	Yes	15,419 (98.32)	3684 (99.61)
Current contraceptive status	No	11,709 (74.66)	2,632 (22.48)
Current contraceptive status	Yes	3,974 (25.34)	1,066 (26.84)

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Knowledge of the ovulatory cycle among women of childbearing age

From the total of 15,683 women of childbearing age, 3,698 [23.58% (95% CI: 22.92–24.25)] had correct KOC. Those who replied to the question "what is the ovulation time?" as “after period ended” were 24.94% and 19.74% did not know the ovulation time when to occur. When performed as per the correct knowledge and incorrect knowledge, 11,985 (76.42%) women had incorrect knowledge (Fig 1).

Determinants of KOC among women of childbearing age in Ethiopia

After running the bi-variable multilevel logistic regression analysis, the candidate variables for multivariable multilevel analysis (p<0.2) were the age of women in years, knowledge of contraceptive methods, educational status of the women, household’s wealth index, current use of contraceptives, menstruation in the last six weeks, women’s media exposure status, residence, community-level poverty, community-level media exposure, and community-level of women’s education. In the multivariable multilevel logistic regression, all the aforementioned individual-level factors and one community-level factor (community-level media exposure) were significantly associated with correct KOC at p<0.05.

The odds of KOC among women aged 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 were 1.46 (95% CI = 1.28–1.68), 1.72 (95% CI = 1.49–1.99), 2.21 (95% CI = 1.89–2.58), 1.78 (95% CI = 1.51–2.09), 1.97 (95% CI = 1.65–2.37), and 1.78 (95% CI = 1.44–2.19) times compared to those aged 15–19 years, respectively. The odds of correct KOC was 3 times (95% CI = 2.07–4.58) among those who had self-reported knowledge of contraceptive methods compared to those who did not know about contraceptives. The odds of KOC was 1.26 times (95% CI = 1.13–1.39) among women who currently used contraceptives relative to those who did not use. Women who attended higher education, secondary and primary education had 4.24 (95% CI = 3.54–5.07), 2.89 (95% CI = 2.48–3.36), and 1.57 (95% CI = 1.39–1.78) odds of KOC, respectively as compared to those who did not attend formal education (no education). Category of women from households with the richest, richer, middle, and poor wealth index level reported 1.71 (95% CI = 1.35–2.16), 1.42 (95% CI = 1.16–1.72), 1.29 (95% CI = 1.07–1.56), and 1.24 times (95% CI = 1.03–1.48) odds of KOC, respectively, in contrast to those who were from the poorest households. The likelihood of KOC was 1.13 (95% CI = 1.03–1.24) and 1.2 (95% CI = 1.07–1.35) times in women who menstruated in the last six weeks before the survey and those who had individual-level media exposure, respectively, compared to the references. Finally, from the community-level factors, only community-level media exposure was associated with KOC in that the odds of KOC was 1.53 times (95% CI = 1.24–1.88) among women from the area of high community-level media exposure compared to their counterparts.

We estimated the clustering effect because of the hierarchical nature of the DHS data. In the random-effects analysis, the ICC in the null model was high. This shows that about 25.15% of the variability of KOC was attributed to the difference between communities/clusters with the remaining 74.85% of the total variation was because of the individual variation. The higher MOR value in the null model revealed that there was a significant difference in KOC between communities/clusters. In the null model, the MOR value was 2.66 (95% CI: 2.514–2.951), depicting that if we witnessed two women from two different clusters, a woman in the cluster with high KOC was 2.66 times higher likelihood of having correct KOC as compared to a woman in the cluster with lower KOC. Also, model fitness was checked using deviance where the model with the lowest deviance was considered as the best fit model i.e. Model III in this case with a deviance of 14832.06 (Table 2).

Table 2. Determinant factors of correct KOC among women of childbearing age in Ethiopia using multilevel logistic regression and the random-effects analysis (n = 16583).

Variables		Null model	Model I AOR (95% CI)	Model II AOR (95% CI)	Model III AOR (95% CI)
Age in years	15–19		1	-	1
	20–24		1.46 (1.28–1.68)^***	-	1.46 (1.28–1.68)^***
	25–29		1.73 (1.50–1.99)^***	-	1.72 (1.49–1.99)^***
	30–34		2.22 (1.90–2.59)^***	-	2.21 (1.89–2.58)^***
	35–39		1.79 (1.52–2.11)^***	-	1.78 (1.51–2.09)^***
	40–44		1.98 (1.66–2.38)^***	-	1.97 (1.65–2.37)^***
	45–49		1.79 (1.46–2.21)^***	-	1.78 (1.44–2.19)^***
Knowledge of contraceptives	No		1	-	1
Knowledge of contraceptives	Yes		3.16 (2.12–4.69)^***	-	3.08 (2.07–4.58)^***
Educational status of women	No education		1	-	1
	Primary		1.57 (1.39–1.77)^***	-	1.57 (1.39–1.78) ^***
	Secondary		2.88 (2.48–3.34)^***	-	2.89 (2.48–3.36) ^***
	Higher		4.26 (3.57–5.09)^***	-	4.24 (3.54–5.07) ^***
Wealth index	Poorest		1	-	1
	Poorer		1.27 (1.07–1.52)^**	-	1.24 (1.03–1.48)^*
	Middle		1.36 (1.13–1.63)^***	-	1.29 (1.07–1.56)^**
	Richer		1.53 (1.27–1.84)^***	-	1.42 (1.16–1.72)^***
	Richest		2.17 (1.80–2.61)^***	-	1.71 (1.35–2.16) ^***
Current contraceptive use	No		1	-	1
Current contraceptive use	Yes		1.26 (1.14–1.40)^***	-	1.26 (1.13–1.39)^***
Menstruated in last six weeks	No		1	-	1
Menstruated in last six weeks	Yes		1.14 (1.04–1.24)^**	-	1.13 (1.03–1.24^**
Women’s media exposure	No		1	-	1
Women’s media exposure	Yes		1.27 (1.13–1.42)^***	-	1.20 (1.07–1.35)^**
Residence	Urban		-	1.76 (1.41–2.20)^***	1.17 (0.91–1.51)
Residence	Rural		-	1	1
Community education	Low		-	1	1
Community education	High		-	1.20 (0.99–1.44)	0.83 (0.68–1.01)
Community poverty	Low		-	1	1
Community poverty	High		-	1.40 (1.11–1.71)^***	1.04 (0.83–1.30)
Community media exposure	Low		-	1	1
Community media exposure	High		-	1.87 (1.52–2.30)^***	1.53 (1.24–1.88) ^***
Parameter		Null model (95% CI)	Model I (95% CI)	Model II (95% CI)	Model III (95% CI)
Community-level variance		1.106 (0.942–1.298)	0.549 (0.453–0.666)	0.550 (0.455–0.664)	0.532 (0.438–0.646)
ICC (%)		25.15 (22.26–28.28)	14.31 (12.10–16.84)	14.32 (12.15–16.79)	13.92 (11.75–16.42)
MOR		2.659 (2.514–2.951)	2.022 (1.895–2.172)	2.022 (1.898–2.169)	1.999 (1.875–2.146)
PCV (%)		1	47.9655	50.3074	49.8207
Log-likelihood		-7878.95	-7428.74	-7729.60	-7416.03
LR test		X² = 1376.13, p<0.001	X² = 476.78, p< 0.001	X² = 530.10, p< 0.001	X² = 459.24, p< 0.001
Deviance		15757.90	14857.48	15459.20	14832.06

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*0.01 < p ≤ 0.05,

**0.001 < p ≤ 0.01,

***p ≤ 0.001.

Discussion

Knowledge of the ovulatory cycle enables the women of childbearing age to plan for pregnancy or avoiding it. The current study aimed to identify the individual and community-level determinant factors of correct KOC among women of childbearing age in Ethiopia. In this study, correct KOC was reported by 23.58% of women in Ethiopia. It is similar to studies conducted in Kenya (23.4%), Gambia (23.1%), and Guinea (23.3%) [11]. The result is higher than other studies in India (15%) [10], and African countries (like Sao Tome and Principe (10.4%), Namibia (13.9%), Nigeria (20.3%), Zambia (21.5%), and Rwanda (21%)) [11]. However, the prevalence found in this study is lower than other studies in African countries (Comoros (49%), Togo (42.8%), Ghana (34%), and Sierra Leone (30.3%)) [11], and the USA (32.8%) [12]. These discrepancies might be because of differences in socioeconomic status, and sociocultural variations between women of childbearing age of these countries.

In the multilevel multivariable analysis, after considering the community level variability, age of women, knowledge of contraceptives, educational status of women, wealth index of the household, current use of contraceptives, menstruating in the last six weeks, women’s media exposure, and community level media exposure were determinants of correct KOC.

After adjusting for other factors, women’s age had a statistically significant association with correct KOC in which women in the advanced age category were more likely to have a correct KOC compared to teenagers (15–19 years). This is supported by a study in Ghana [16] and the USA [15]. A study conducted in Ethiopia using EDHS also supported our finding even though that study used simple logistic regression analysis without adjusting for community-level factors [20]. The reason for this association could be as age is increased, exposure to different reproductive related issues is increased that lets women gain more knowledge. Increased educational status of women was associated with KOC, which is supported by other studies [14,17–19,26]. This explicates that education has a positive impact on health knowledge and behaviour [27]. Women from the richest, richer, middle, and poor households had better correct KOC than those who were from the poorest households. This is supported by another study in Africa [14]. This might be because women with a higher wealth index are more likely to be knowledgeable [28] and have the interest to learn [29].

Self-reported knowledge of contraceptive and the current use of contraceptive methods were also determinants for correct KOC, which is in line with other studies [16,30]. This might be because counselling on contraceptive use increases fertility knowledge [31] and knowledge of ovulation is included in family planning counselling guidelines [32]. Women who menstruated in the last six weeks had 1.13 times KOC compared to their counterparts. There is no literature supporting or against this association, but it might be because women who observed menstruation in the recent weeks are more likely to better understand the time of ovulation than those who did not. Women who did not report menstruation, being a sign of pregnancy, may be an unintended pregnancy, partly because of their low KOC.

Individual-level and community-level media exposure were determinants of KOC. Mass media exposure has a positive impact on comprehensive knowledge [33]. Besides, community-level media exposure increases maternal health service utilization, which is the major way of transmitting health information including family planning such as knowledge of reproductive cycle (ovulation) [34]. The findings of this study suggests the Ethiopian government, particularly the Ministry of health, for designing strategies to increase KOC and other reproductive aspects of women’s health to achieve the good health goal which is one of the 2030 agenda of sustainable development goals [35].

Strength and limitations of the study

This study was based on nationally representative data with appropriate statistical analysis (weighting and multilevel analysis). However, the interpretations of this study could be made by considering the following limitations; we used secondary data, which missed a few important variables, such as pre-survey awareness of the ovulatory cycle among women of childbearing age, for the analysis. Because of the cross-sectional nature, it cannot infer the cause-effect relationship between factors and outcome variables. Social desirability and recall biases were also the expected limitations of this study.

Conclusions

Correct KOC was low, which demands the need to design and implement reproductive health services via community media campaign and health promotion. The investigators of this study would like to recommend the Ethiopian Ministry of Health to focus on the reproductive health of women by giving privilege to the teenagers, those with lower educational level, and lower socioeconomic status. Also, the strengthening of media campaigns could increase women’s correct KOC, which is crucial for precluding unintended pregnancy. Besides, we urge the Ministry of Health, Ministry of Science and Higher education, and the Universities to institute reproductive health promotion projects, including the above factors. We also advise the forthcoming researchers to conduct the level of awareness of women of childbearing age about the ovulatory cycle so that awareness creation can be tailored based on the findings.

Acknowledgments

The authors acknowledged the measures DHS program for providing the dataset and for the permission to do this research.

Data Availability

All the relevant data were included in the manuscript. However, it is ethically not acceptable to share the DHS data set with third parties and anyone who wants the data set can access the Measure DHS program at www.dhsprogram.com, through legal requesting. The authors had no special access privileges others would not have. We used the Ethiopian demographic and health survey (https://dhsprogram.com/methodology/survey/survey-display-478.cfm).

Funding Statement

The authors received no specific funding for this work.

References

1.Holesh JEB, A. N. Lord, M. Physiology, Ovulation. StatPearls. Treasure Island (FL): StatPearls Publishing 2020. [PubMed]
2.Owen M. Physiological signs of ovulation and fertility readily observable by women. Linacre Q. 2013;80(1):17–23. doi: 10.1179/0024363912Z.0000000005 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.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;2(3):238–46. doi: 10.1002/btm2.10058 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Simmons RG, Jennings V. Fertility awareness-based methods of family planning. Best Practice & Research Clinical Obstetrics & Gynaecology. 2020;66:68–82. doi: 10.1016/j.bpobgyn.2019.12.003 [DOI] [PubMed] [Google Scholar]
5.Vigil P, Blackwell LF, Cortés ME. The Importance of Fertility Awareness in the Assessment of a Woman’s Health a Review. Linacre Q. 2012;79(4):426–50. doi: 10.1179/002436312804827109 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Murray DR. Why Predictable Ovulation Is Important: Tennessee Reproductive medicine 2018 https://trmbaby.com/predictable-ovulation/.
7.Billings EL, Brown JB, Billings JJ, Burger HG. Symptoms and hormonal changes accompanying ovulation. Lancet (London, England). 1972;1(7745):282–4. doi: 10.1016/s0140-6736(72)90291-7 [DOI] [PubMed] [Google Scholar]
8.Ayoola AB, Zandee GL, Adams YJ. Women’s Knowledge of Ovulation, the Menstrual Cycle, and Its Associated Reproductive Changes. Birth. 2016;43(3):255–62. doi: 10.1111/birt.12237 [DOI] [PubMed] [Google Scholar]
9.García D, Vassena R, Prat A, Vernaeve V. Increasing fertility knowledge and awareness by tailored education: a randomized controlled trial. Reproductive biomedicine online. 2016;32(1):113–20. doi: 10.1016/j.rbmo.2015.10.008 [DOI] [PubMed] [Google Scholar]
10.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 women’s health. 2018;18(1):177. doi: 10.1186/s12905-018-0669-y [DOI] [PMC free article] [PubMed] [Google Scholar]
11.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;65(4):445–55. doi: 10.1007/s00038-020-01356-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Ayoola AB, Zandee GL, Adams YJ. Women’s Knowledge of Ovulation, the Menstrual Cycle, and Its Associated Reproductive Changes. Birth. 2016;43(3):255–62. doi: 10.1111/birt.12237 [DOI] [PubMed] [Google Scholar]
13.Juayire CA. Knowledge of Ovulatory Cycle and Current Fertility Among Women in Ghana: University of Ghana; 2016.
14.Iyanda AE. Cross-National Variation in Knowledge of Ovulation Timing in Sub-Saharan Africa. Women’s Reproductive Health. 2020;7(2):127–43. [Google Scholar]
15.Lundsberg LS, Pal L, Gariepy AM, Xu X, Chu MC, Illuzzi JL. Knowledge, attitudes, and practices regarding conception and fertility: a population-based survey among reproductive-age United States women. Fertility and sterility. 2014;101(3):767–74. doi: 10.1016/j.fertnstert.2013.12.006 [DOI] [PubMed] [Google Scholar]
16.Juayire CA. Knowledge Of Ovulatory Cycle And Current Fertility Among Women In Ghana 2016 http://ugspace.ug.edu.gh/handle/123456789/22706.
17.Samir VSK. Fertility-reducing dynamics of women’s social status and educational attainment Asian Population studies. 2012;8. [Google Scholar]
18.Bunting L, Boivin J. Knowledge about infertility risk factors, fertility myths and illusory benefits of healthy habits in young people. Human reproduction (Oxford, England). 2008;23(8):1858–64. doi: 10.1093/humrep/den168 [DOI] [PubMed] [Google Scholar]
19.Stern J, Larsson M, Kristiansson P, Tydén T. Introducing reproductive life plan-based information in contraceptive counselling: an RCT. Human reproduction (Oxford, England). 2013;28(9):2450–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.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. International journal of women’s health. 2020;12:701–7. doi: 10.2147/IJWH.S267675 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Chandra-Mouli V, McCarraher DR, Phillips SJ, Williamson NE, Hainsworth G. Contraception for adolescents in low and middle income countries: needs, barriers, and access. Reproductive health. 2014;11(1):1–8. doi: 10.1186/1742-4755-11-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Program D. Conditions of Use for The DHS Program datasets https://dhsprogram.com/Data/terms-of-use.cfm.
23.CSA. Demographic and Health Survey 2016 2016 https://dhsprogram.com/pubs/pdf/FR328/FR328.pdf.
24.ICF CSACEa. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF.; 2016.
25.programme. D. Guide to DHS Statistics 2018 https://www.dhsprogram.com/data/Guide-to-DHS-Statistics/index.cfm.
26.Gyekye EK. Correlates of Knowledge of Ovulation Cycle among Women in Urban Poor Communities in Accra 2014 http://ugspace.ug.edu.gh/handle/123456789/22592.
27.Mocan N, Altindag DT. Education, cognition, health knowledge, and health behavior. The European journal of health economics: HEPAC: health economics in prevention and care. 2014;15(3):265–79. doi: 10.1007/s10198-013-0473-4 [DOI] [PubMed] [Google Scholar]
28.Yaya S, Bishwajit G, Shah V. Wealth, education and urban-rural inequality and maternal healthcare service usage in Malawi. BMJ global health. 2016;1(2):e000085. doi: 10.1136/bmjgh-2016-000085 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Tucker-Drob EM, Briley DA. Socioeconomic Status Modifies Interest-Knowledge Associations among Adolescents. Pers Individ Dif. 2012;53(1):9–15. doi: 10.1016/j.paid.2012.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Mandiwa C, Namondwe B, Makwinja A, Zamawe C. Factors associated with contraceptive use among young women in Malawi: analysis of the 2015–16 Malawi demographic and health survey data. Contraception and Reproductive Medicine. 2018;3(1):12. doi: 10.1186/s40834-018-0065-x [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Skogsdal Y, Fadl H, Cao Y, Karlsson J, Tydén T. An intervention in contraceptive counseling increased the knowledge about fertility and awareness of preconception health-a randomized controlled trial. Upsala journal of medical sciences. 2019;124(3):203–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.USAID. Essential components of family planning benefits packages 2017.
33.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;20(1):1420. doi: 10.1186/s12889-020-09536-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Zamawe COF, Banda M, Dube AN. The impact of a community driven mass media campaign on the utilisation of maternal health care services in rural Malawi. BMC Pregnancy Childbirth. 2016;16:21. doi: 10.1186/s12884-016-0816-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.The United, Nations. The 2030 agenda for sustainable development 2015.

PLoS One. doi: 10.1371/journal.pone.0254094.r001

Decision Letter 0

Jamie Males

24 Mar 2021

PONE-D-20-30888

Individual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A Multilevel analysis based on the 2016 Ethiopian Demographic and Health Survey

PLOS ONE

Dear Dr. Dagnew,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Introduction:

*Please elaborate a bit further on the previous studies conducted on KOC in Africa. What type of studies have been conducted and what were their results?

* Please spell out EDHS at first use.

* Please motivate why determining factors associated with KOC would be important, what implications such knowledge might have to improve womens' reproductive health.

Method:

* Please define the range of what was determined to be the "childbearing age", i.e. the study population.

* Please motivate why p < 0.2 was used to select variables for inclusion in one of part the analyses and p < 0.05 was used to select variables for inclusion in another part of the analysis.

* Please motivate why menstruation within the past 6 weeks was included as one of the variables under investigation, i.e. how this variable might have an association with KOC.

Results:

* Please state the number and % of women with missing values on the outcome variable (KOC).

* Please include CI for the ORs mentioned in the text.

* Figure 1: Please consider if a pie chart might be more appropriate in order to present distribution of responses.

Discussion:

* Limitations: Please specify what important variables you consider were missing for the analysis.

Conclusion:

* Please specify how you consider that the health section should promote reproductive health among the subgroups of women identified in the study.

Pleas note that the manuscript needs considerable language revision before publication.

Reviewer #2: This paper addresses a very important reproductive health subject and will potentially contribute to the literature in this area. However, I have some comments that will help to significantly improve the paper.

Title:

Okay

Abstract

This section is quite okay.

Introduction

While this section is quite okay, authors should also provide evidence of KOC in Europe as provided in America and Asia (India).

Authors should also provide a brief explanation of the significance of the study after the objective statement.

Materials and Methods

Under the data source, authors should put the link of the data source in a bracket. Also, provide some more information on the data including the demographic profile (sex and age) of the participants of the survey. Who conducted the survey and who are the collaborators?

Under population, even though this may be known, provide the age range instead of saying childbearing age.

Under the sampling procedure, the authors should provide a few more details. For instance, what is the total number of women that took part in the survey, and what is the total number of listed households from which the fixed 28 were selected.

Under study variables, authors should provide a better description of the outcome variable. For instance, after describing the nature of the original outcome variable, what is the nature of the final outcome variable and its measurement?

Authors should explain whether there are re-codings or use of original variables and how they were measured.

The authors did not state the level of the community factors. Is it EAs or clusters or others?

Were the independent variables selected arbitrarily or informed by existing literature? If former, explain, and if the latter, provide citations to support the selection of variables.

It is not clear how the authors derived the community-level variables from the individual-level factors. More details should be provided.

Under the statistical analysis, the authors stated that they executed women's sampling weight, however, this is not enough as authors should apply survey design due to the hierarchical nature of the data.

Results

It is better for authors to delete “contraceptive profiles” from the title of the descriptive results.

Authors should connect the descriptive title with the Table 1 title.

Under Knowledge of the ovulatory cycle, 24.94% cannot be said as the majority, so the statement should be revised.

It is more understandable and convenient to have both the fixed and random effects in the same table. Therefore, authors should merge Tables 2 and 3 and synchronized them.

The correct KOC results in Table 3 are descriptive and cannot be presented with the model results in the same table. Authors should consider merging them with Table 1 or creating a new table for them.

Authors should revise the “due to attributed to” in the second sentence under random effects.

Authors should avoid the use of for instance, 3 times better than, in reporting the odds ratios. It is appropriate to say the odds of KOC were 3 times compared to the reference group. This cuts across the fixed effects results and should be revised as such.

Discussion

The authors should explain how sample size could determine differences in KOC prevalence among countries or remove it. I do not find it convincing.

Age in “Age of women” should be all small letters.

Women ages 15-19 years are not young adults as stated under the multivariate analysis discussion but teenagers and should be revised. Also, authors should revise the “comfortable wealth index” to a higher wealth index.

The explanation for menstruation looks unconvincing. The variable is about having or not having menstruation in the past 6 weeks but not about remembering their menstruation. What about those not having menstruation being a sign of pregnancy which may be an unintended pregnancy partly due to their low KOC as shown by the odds ratio.

Conclusion

The first sentence does not read well, revise. It is better to say KOC is low rather than very low.

The “health sector” is not a decision and policymaker and therefore, authors should state the agencies directly involved in this.

General comments

It appears the authors did not follow the journal formats by not providing line numbers and making referring difficult. The required square bracket for in-text citation was replaced with a round bracket among several others including font size of titles. All the sections after the conclusion to the author’s contribution do not fit the journal format. The authors should visit the journal format documents and use them to format the manuscript appropriately. Also, the references have different font and font size from the main manuscript. Finally, the manuscript should be proofread and revised as some of the sentences are not clear enough or include typos and are found under the “Knowledge of the ovulatory cycle” section.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Sep 2;16(9):e0254094. doi: 10.1371/journal.pone.0254094.r002

Author response to Decision Letter 0

2 Apr 2021

Point-by-point response to reviewers

We, the authors, would like to acknowledge the editorial team and the reviewers for giving us the opportunity to revise and improve our manuscript titled “Individual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A Multilevel analysis based on the 2016 Ethiopian Demographic and Health Survey” and ID “PONE-D-20-30888”. We addressed all the concerns of the reviewers and the responses are stated by the point-by-point response letter (thee authors’ replies are italic-bold) as well as corrections are incorporated in the revised manuscript.

Authors’ reply to the comments of the Reviewer #1

Reviewer’s comment: Please elaborate a bit further on the previous studies conducted on KOC in Africa. What type of studies has been conducted and what were their results?

Authors’ reply: Thank you. We included few studies conducted in Africa as in Sub-Saharan Africa and also a study in Ghana and found in the revised manuscript. Besides, most studies in Africa are described in the discussion section to compare our findings (Line#68-70).

Reviewer’s comment: Please spell out EDHS at first use.

Authors’ reply: Thank you. We spelled out as suggested (Line#77).

Reviewer’s comment: Please motivate why determining factors associated with KOC would be important, what implications such knowledge might have to improve women’s' reproductive health.

Authors’ reply: Thank you. The results of this study will be helpful to institute reproductive health specific interventional strategies to equip childbearing age women with adequate and appropriate knowledge of the ovulatory cycle. Correct KOC is important to improve women’s health by preventing unintended pregnancy especially for those with poor access and awareness of contraceptive methods particularly in Sub-Saharan Africa. Identifying the determinant factors is used to target the associated factors that lead women for correct KOC so that strategies will be designed to improve positively associated factors and to prevent the negatively influencing factors. All these are included in the revised manuscript(Line#78-85).

Reviewer’s comment: Please define the range of what was determined to be the "childbearing age", i.e. the study population.

Authors’ reply: Thank you very much for the compliment. We added the range to illustrate the childbearing age as “15-49 years old” (Line#115).

Reviewer’s comment: Please motivate why p < 0.2 was used to select variables for inclusion in one of part the analyses and p < 0.05 was used to select variables for inclusion in another part of the analysis.

Authors’ reply: Thank you. As it is known, in the scientific community variables for the selection of eligible variables for the multivariable analysis we used p-value <0.20 and this is the usual procedure to drop variables that are distal or unnecessary for the data at hand. After we have selected variables using p <0.20 we conducted the multivariable analysis. In the multivariable analysis, we used p value<0.05 for declaring variables as predictors of KOC. The reason why we used p<0.05 is just to minimize the association due to chance.

Reviewer’s comment: Please motivate why menstruation within the past 6 weeks was included as one of the variables under investigation, i.e. how this variable might have an association with KOC.

Authors’ reply: Thank you. We included the variable “menstruation within the past two weeks” in a sense that women who recently practiced menstruation (last menstruation prior to the survey) may have a relatively better knowledge of ovulatory cycle; they are more likely to search out why menstruation occurred (as they observed it), when to occur, and the causes of ovulation. As it is known, the female reproductive cycle comprises two cycle; the ovarian cycle (including the ovulation phase) and uterine cycle (including menstrual phase). After ovulation, if there is no fertilization, there will be withdrawal of female gonadal hormones that results in menstruation after successive event. Therefore, females who observed menstruation are more likely to understand the time of ovulation than those who did not (may be forgotten as it becomes longer). That is why we included this variable as potential exposure variable for the outcome variable (Line#288-90).

Reviewer’s comment: Please state the number and % of women with missing values on the outcome variable (KOC).

Authors’ reply: Thank you. All the women who took part in the survey (15,683) responded to the outcome variable (KOC). Therefore, there is no missing case for the KOC (Line#127-8).

Reviewer’s comment: Please include CI for the ORs mentioned in the text.

Authors’ reply: Thank you for the compliment. We included the 95% CI for each OR mentioned in the text (Line#222-38).

Reviewer’s comment: Figure 1: Please consider if a pie chart might be more appropriate in order to present distribution of responses

Authors’ reply: Thank you. We used pie chart instead of bar chart as suggested in the revised manuscript (separate figure uploaded).

Reviewer’s comment: Please specify what important variables you consider were missing for the analysis.

Authors’ reply: Thank you. We think all the important variables are included in the survey. However, if pre-survey “awareness” about ovulatory cycle is included, it will be fine. Awareness about something is the main way of knowledge gain (Line#304).

Reviewer’s comment: Please specify how you consider that the health section should promote reproductive health among the subgroups of women identified in the study.

Authors’ reply: Thank you. As per our understanding, we urge the Ministry of health, Ministry of science higher education, and the Universities to institute reproductive health promotion projects including the above-mentioned factors. We also advice the forthcoming researchers to conduct the level of awareness of childbearing aged women about ovulatory cycle so that awareness creation can be tailored based on the finding (Line#311-8).

Reviewer’s comment: Please note that the manuscript needs considerable language revision before publication.

Authors’ reply: Thank you very much. We revised the whole manuscript for language revision and we believe we improved the language problems.

Reviewer#2

Reviewer’s comment: While this section is quite okay, authors should also provide evidence of KOC in Europe as provided in America and Asia (India).

Author’s reply: Thank you. As far as our searching strategy, there is scarce data in Europe regarding the proportion of childbearing age women on knowledge of ovulation cycle. But, we found one study in Spain and we included it in the revised manuscript.

Reviewer’s comment: Authors should also provide a brief explanation of the significance of the study after the objective statement.

Author’s reply: Thank you. We included further explanation of the significance of the study after the objective as suggested (Line#77-85).

Reviewer’s comment: Under the data source, authors should put the link of the data source in a bracket. Also, provide some more information on the data including the demographic profile (sex and age) of the participants of the survey. Who conducted the survey and who are the collaborators?

Authors’ reply: Thank you. We put the link in bracket as suggested (Line#88). WE used the Ethiopia Population and Housing Census (2007) as a sampling frame for the EDHS 2016. The census frame is a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographic area covering on average 181 households (~15,369,615 household on average). Each large EA was segmented to minimize the task of household listing from which only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2016 EDHS cluster is either an EA or a segment of an EA. Then, a total of 18,008 households were selected for the sample, of which 17,067 were occupied. Of the occupied households, 16,650 were successfully interviewed, yielding a response rate of ~98%. Regarding the participants, 15,683 (15-49 years of age) were females and 12,688 (15-59 years of age) were males. Regarding the organizations involved in the survey, the Central Statistical Agency (CSA) conducted the survey in collaboration with the Ethiopian Ministry of Health and Ethiopian Public health institute and also the international classification of functioning, disability and health (ICF) involved in technical assistance. The financial support was obtained from the development partners i.e. the United States Agency for International Development (USAID), the government of the Netherlands, the Global Fund, the Irish Aid, the World Bank, the United Nations Population Fund (UNFPA), the United Nations Children’s Fund (UNICEF), and UN Women. The dataset, we used for this analysis, comprises all females aged 15-49 years. This is included in the revised manuscript (Line#87-106).

Reviewer’s comment: Under population, even though this may be known, provide the age range instead of saying childbearing age.

Authors’ reply: Thank you. We included the age range (15-49 years old) as suggested (Line#115).

Reviewer’s comment: Under the sampling procedure, the authors should provide a few more details. For instance, what is the total number of women that took part in the survey, and what is the total number of listed households from which the fixed 28 were selected.

Authors’ reply: Thank you. ~15,369,615 household on average. A total of 15,683 women took part in the survey (all of which responded to the outcome variable KOC) (Line#126-8).

Reviewer’s comment: Under study variables, authors should provide a better description of the outcome variable. For instance, after describing the nature of the original outcome variable, what is the nature of the final outcome variable and its measurement? Authors should explain whether there are re-codings or use of original variables and how they were measured.

Authors’ reply: Thank you. We added the suggested comments in the revised manuscript… We performed recoding of the variables so that the outcome variable become binary (dichotomized) which is useful to execute regression. For this, we created a new variable called correct knowledge of the ovulatory cycle. The alternative “middle of the cycle was coded as 1” and others were coded as 0” (Line#137-44).

Reviewer’s comment: The authors did not state the level of the community factors. Is it EAs or clusters or others?

Authors’ reply: Thank you. When we say community level factors, we mean factors created at cluster level using their respective individual level factors to assess the effect of these variables at community or cluster level and these are stated in the revised manuscript (Line#144-6).

Reviewer’s comment: Were the independent variables selected arbitrarily or informed by existing literature? If former, explain, and if the latter, provide citations to support the selection of variables.

Authors’ reply: We used three approaches to select variables. These were based on the clinical plausibility of variables, based on the existing literature (cited in the background section; Line#70-2)), and based on the variables found in the EDHS dataset. We performed univariate analysis one by one with the outcome variable (KOC) based on the fact that each variable has biological or other association with the outcome variable. In the univariate analysis, those variables with a p<0.2, were selected and entered into multivariable multilevel logistic regression. In the final model, we used p<0.05 to declare statistical significance.

Reviewer’s comment: It is not clear how the authors derived the community-level variables from the individual-level factors. More details should be provided.

Authors’ reply: Thank you very much. We described more detail how the community-level factors are create and amended in the revised manuscript (Line#147-59).

Reviewer’s comment: Under the statistical analysis, the authors stated that they executed women's sampling weight, however, this is not enough as authors should apply survey design due to the hierarchical nature of the data.

Authors’ reply: We have weighted the data using women sample weighting, primary sampling unit, and stratification variable (to account for survey design) and weighting to account for representativeness, non-response rate, and to get appropriate statistical estimate (Line#162-6).

Reviewer’s comment: It is better for authors to delete “contraceptive profiles” from the title of the descriptive results.

Authors reply: Thank you. We deleted contraceptive profiles as suggested (Line#)

Reviewer’s comment: Authors should connect the descriptive title with the Table 1 title

Authors’ reply: We made correction as suggested (Line#).

Reviewer’s comment: Under Knowledge of the ovulatory cycle, 24.94% cannot be said as the majority, so the statement should be revised.

Authors’ reply: Thank you for the compliment. We did as suggested (Line#208).

Reviewer’s comment: It is more understandable and convenient to have both the fixed and random effects in the same table. Therefore, authors should merge Tables 2 and 3 and synchronized them.

Authors’ reply: Thank you we merged table 2 and 3 as suggested.

Reviewer’s comment: The correct KOC results in Table 3 are descriptive and cannot be presented with the model results in the same table. Authors should consider merging them with Table 1 or creating a new table for them.

Authors’ reply: Thank you very much. We merged the results of KOC with table 1 as suggested.

Reviewer’s comment: Authors should revise the “due to attributed to” in the second sentence under random effects. Authors should avoid the use of for instance, 3 times better than, in reporting the odds ratios. It is appropriate to say the odds of KOC were 3 times compared to the reference group. This cuts across the fixed effects results and should be revised as such.

Authors’ reply: Thank you. Thank you. We removed “due to” which is grammatical error (Line#242, and Line#222-239)

Reviewer’s comment: The authors should explain how sample size could determine differences in KOC prevalence among countries or remove it. I do not find it convincing.

Authors’ reply: Thank you. We removed sample size as suggested (Line#266).

Reviewer’s comment: Age in “Age of women” should be all small letters.

Authors’ reply: Thank you. We make it small letter as suggested (Line#268).

Reviewer’s comment: Women ages 15-19 years are not young adults as stated under the multivariate analysis discussion but teenagers and should be revised. Also, authors should revise the “comfortable wealth index” to a higher wealth index.

Authors’ reply: Thank you. We revised as suggested (Line#274=teenagers, and Line#281 higher).

Reviewer’s comment: The explanation for menstruation looks unconvincing. The variable is about having or not having menstruation in the past 6 weeks but not about remembering their menstruation. What about those not having menstruation being a sign of pregnancy which may be an unintended pregnancy partly due to their low KOC as shown by the odds ratio.

Authors’ reply: The female reproductive cycle comprises two cycles; the ovarian cycle (the ovulation is one of the three phases) and uterine cycle (menstrual phase is one of the three phases). After ovulation, if there is no fertilization, there will be withdrawal of female gonadal hormones (estrogen and progesterone) that results in menstruation after successive event. Therefore, females who observed menstruation are more likely to understand the time of ovulation than those who did not (may be forgotten as it becomes longer). We also included the reason suggested for unintended pregnancy (if we capture the comment in the right way (Line#287-91).

Reviewer’s comment: The first sentence does not read well, revise. It is better to say KOC is low rather than very low. The “health sector” is not a decision and policymaker and therefore, authors should state the agencies directly involved in this.

Authors’ reply: Thank you. We corrected as suggested in the revised manuscript (Line# 352, we say it low as suggested). Line#311, 314, and 315 we included Ethiopian ministry of health, Ministry of Science and Higher education, and the Universities).

Reviewer’s comment: It appears the authors did not follow the journal formats by not providing line numbers and making referring difficult. The required square bracket for in-text citation was replaced with a round bracket among several others including font size of titles. All the sections after the conclusion to the author’s contribution do not fit the journal format. The authors should visit the journal format documents and use them to format the manuscript appropriately. Also, the references have different font and font size from the main manuscript. Finally, the manuscript should be proofread and revised as some of the sentences are not clear enough or include typos and are found under the “Knowledge of the ovulatory cycle” section.

Authors’ reply: We made square bracket for in-text citation, we included line number, we corrected the font sizes as 18 for title pages (first heading), 16 for second heading, 14 for 3rd heading and 12 for the body of the text. We corrected the format using the journals guideline, corrected the references font size and style as suggested, and finally we made revisions on typos and grammatical errors throughout the manuscript particularly the suggested section.

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PLoS One. doi: 10.1371/journal.pone.0254094.r003

Decision Letter 1

José Antonio Ortega

28 Apr 2021

PONE-D-20-30888R1

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

Dear Dr. Dagnew,

There has been a change of editor since the last revision, and the two previous reviewers where reassigned for review before the new editor was assigned. Except for minor reservations, they feel that suggested changes were incorporated.

However, upon initial assesment by the new editor, I have to note with much regret that this submission carries out almost identical analysis with the same data as another paper that had been published before the initial submission. Note that PLOS ONE does not accept for publication studies that have already been published elsewhere in the peer-reviewed literature and if a submitted study replicates or is very similar to previous work, authors must provide a sound scientific rationale for the submitted work and clearly reference and discuss the existing literature. Submissions that replicate or are derivative of existing work will likely be rejected if authors do not provide adequate justification.

I know this should have been identified in the previous round and I apologize for the late feedback in that respect.

Reference:

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;12:701-707

https://doi.org/10.2147/IJWH.S267675

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José Antonio Ortega, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: No

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: The authors have effectively addressed the majority of the comments provided by reviewers. However, a few comments were only responded to by the authors without effecting the needed revisions in the manuscript.

1. Authors stated that data used for the study were included in the manuscript but were not actually included. Authors should provided the link to the dhsprogram data for EDHS 2016 in the editorial manager system in place of the "All relevant data are included in the manuscript" statement.

2. The existing literature supporting the variables was only explained but not provided as recommended. Some of these citations should be provided in the Study Variables section to support the variable selection.

3. The first paragraph of the "Study variables" has a different spacing from the rest of the manuscript.

4. The "BMC Series" format of manuscript presented after the conclusion section should be removed (List of abbreviations to author contributions, EXCEPT Acknowledgements) as all these are pre-filled in and generated from the editorial manager system onto the final paper.

5. Some clear grammatical problems remain in the manuscript and authors have to submit the manuscript for expert copy-editing before re-submission.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Helena Litorp

Reviewer #2: No

PLoS One. 2021 Sep 2;16(9):e0254094. doi: 10.1371/journal.pone.0254094.r004

Author response to Decision Letter 1

22 May 2021

Point-by-point response to the editor and reviewers

Academic editor’s comment: I have to note with much regret that this submission carries out almost identical analysis with the same data as another paper that had been published before the initial submission. Note that PLOS ONE does not accept for publication studies that have already been published elsewhere in the peer-reviewed literature and if a submitted study replicates or is very similar to previous work, authors must provide a sound scientific rationale for the submitted work and clearly reference and discuss the existing literature. Submissions that replicate or are derivative of existing work will likely be rejected if authors do not provide adequate justification. I know this should have been identified in the previous round and I apologize for the late feedback in that respect. Ref: https://doi.org/10.2147/IJWH.S267675 . You must discuss this article among the literature review and incorporate its findings. If you can make a convincing case of why the article should be considered despite this, the article would be evaluated further.

Authors’ reply: The previous study used the Ethiopian Demographic and Health Survey (EDHS) 2016 [20]. The previous study did not consider the community level factors (they consider factors only at the individual-level; even they did not use multilevel analysis which is appropriate for hierarchical data), there was no weighting to account for appropriate estimation, and there were missing of essential variables such as media exposure which plays a crucial role for the provision of information including reproductive health. It is useful to consider the community-level factors for designing interventional strategies not only based from the individual-level factors. In a summary, our study differs from the previous one by considering the following;

1. Our study used sampling weight (using women's sampling weight) to account for the non-proportional allocation of the sample in each region and the potential variations in the response rates, as well as to get an appropriate statistical estimate. Besides, stratification was used to account for the survey design

2. We included community-level factors (community-education, community poverty and community media exposure). As explained above, strategies should based from community level factors besides individual level factors.

3. We used multilevel multivariable analysis; clustering effect was performed due to the hierarchical nature of the DHS data. Intra-class correlation coefficient (ICC) and median odds ratio (MOR) were computed to choose appropriate model. We fitted four models; null model/ (containing only the outcome variable), the model I (with individual-level factors only), model II (with community-level factors only), and model III (with the individual- and community-level factors simultaneously). Finally, we used the third model by looking the lowest deviance.

4. In addition to the individual factors included by the previous study, we included media exposure which is crucial factor for the provision of information including reproductive health knowledge.

For more information, we included it in the revised manuscript Line#70-6 and in the discussion section

Reviewers’ comments to the Authors

Authors’ reply to the reviewer#1: We acknowledge the reviewer for accepting our previous replies to the comments. In this phase, there is no comment from the reviewer#1 to be addressed.

Authors’ reply to the reviewer#2

Reviewer #2 (comments): The authors have effectively addressed the majority of the comments provided by reviewers. However, a few comments were only responded to by the authors without effecting the needed revisions in the manuscript.

1. Authors stated that data used for the study were included in the manuscript but were not actually included. Authors should provide the link to the dhsprogram data for EDHS 2016 in the editorial manager system in place of the "All relevant data are included in the manuscript" statement.

Authors’ reply: We provided the link to DHS program in the editorial system instead of “"All relevant data are included in the manuscript" statement. All the data were included in the manuscript. There is no restriction. The data can be accessed from the DHS website after reasonable request (https://dhsprogram.com/Data/terms-of-use.cfm)

Authors’ reply: Thank you very much. We included few citations to the variables section to indicate where we get the variables to consider them as independent (Line#139).

3. The first paragraph of the "Study variables" has a different spacing from the rest of the manuscript.

Authors’ reply: Thank you. We corrected as suggested (formerly it was 1.5 and now we make it double spacing).

Authors’ reply: Thank you. We removed as advised.

5. Some clear grammatical problems remain in the manuscript and authors have to submit the manuscript for expert copy-editing before re-submission.

Authors’ reply: We invited an expert person from the department of literature at the University of Gondar for the revision of the grammatical and standard use of English language. Then, we incorporated the points from the invited person and all the authors revised the manuscript for the correction of grammatical flaws. We hope, we have made improvements in the revised manuscript.

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PLoS One. doi: 10.1371/journal.pone.0254094.r005

Decision Letter 2

José Antonio Ortega

21 Jun 2021

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

PONE-D-20-30888R2

Dear Dr. Dagnew,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Both referees feel that their comments have been properly addressed, and the main concern of the editor, providing a rationale for a study with the same topic and data than previous studies, has also been addressed.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

José Antonio Ortega, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0254094.r006

Acceptance letter

José Antonio Ortega

24 Aug 2021

PONE-D-20-30888R2

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

Dear Dr. Dagnew:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. José Antonio Ortega

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

[pone.0254094.ref001] 1.Holesh JEB, A. N. Lord, M. Physiology, Ovulation. StatPearls. Treasure Island (FL): StatPearls Publishing 2020. [PubMed]

[pone.0254094.ref002] 2.Owen M. Physiological signs of ovulation and fertility readily observable by women. Linacre Q. 2013;80(1):17–23. doi: 10.1179/0024363912Z.0000000005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref003] 3.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;2(3):238–46. doi: 10.1002/btm2.10058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref004] 4.Simmons RG, Jennings V. Fertility awareness-based methods of family planning. Best Practice & Research Clinical Obstetrics & Gynaecology. 2020;66:68–82. doi: 10.1016/j.bpobgyn.2019.12.003 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref005] 5.Vigil P, Blackwell LF, Cortés ME. The Importance of Fertility Awareness in the Assessment of a Woman’s Health a Review. Linacre Q. 2012;79(4):426–50. doi: 10.1179/002436312804827109 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref006] 6.Murray DR. Why Predictable Ovulation Is Important: Tennessee Reproductive medicine 2018 https://trmbaby.com/predictable-ovulation/.

[pone.0254094.ref007] 7.Billings EL, Brown JB, Billings JJ, Burger HG. Symptoms and hormonal changes accompanying ovulation. Lancet (London, England). 1972;1(7745):282–4. doi: 10.1016/s0140-6736(72)90291-7 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref008] 8.Ayoola AB, Zandee GL, Adams YJ. Women’s Knowledge of Ovulation, the Menstrual Cycle, and Its Associated Reproductive Changes. Birth. 2016;43(3):255–62. doi: 10.1111/birt.12237 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref009] 9.García D, Vassena R, Prat A, Vernaeve V. Increasing fertility knowledge and awareness by tailored education: a randomized controlled trial. Reproductive biomedicine online. 2016;32(1):113–20. doi: 10.1016/j.rbmo.2015.10.008 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref010] 10.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 women’s health. 2018;18(1):177. doi: 10.1186/s12905-018-0669-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref011] 11.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;65(4):445–55. doi: 10.1007/s00038-020-01356-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref012] 12.Ayoola AB, Zandee GL, Adams YJ. Women’s Knowledge of Ovulation, the Menstrual Cycle, and Its Associated Reproductive Changes. Birth. 2016;43(3):255–62. doi: 10.1111/birt.12237 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref013] 13.Juayire CA. Knowledge of Ovulatory Cycle and Current Fertility Among Women in Ghana: University of Ghana; 2016.

[pone.0254094.ref014] 14.Iyanda AE. Cross-National Variation in Knowledge of Ovulation Timing in Sub-Saharan Africa. Women’s Reproductive Health. 2020;7(2):127–43. [Google Scholar]

[pone.0254094.ref015] 15.Lundsberg LS, Pal L, Gariepy AM, Xu X, Chu MC, Illuzzi JL. Knowledge, attitudes, and practices regarding conception and fertility: a population-based survey among reproductive-age United States women. Fertility and sterility. 2014;101(3):767–74. doi: 10.1016/j.fertnstert.2013.12.006 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref016] 16.Juayire CA. Knowledge Of Ovulatory Cycle And Current Fertility Among Women In Ghana 2016 http://ugspace.ug.edu.gh/handle/123456789/22706.

[pone.0254094.ref017] 17.Samir VSK. Fertility-reducing dynamics of women’s social status and educational attainment Asian Population studies. 2012;8. [Google Scholar]

[pone.0254094.ref018] 18.Bunting L, Boivin J. Knowledge about infertility risk factors, fertility myths and illusory benefits of healthy habits in young people. Human reproduction (Oxford, England). 2008;23(8):1858–64. doi: 10.1093/humrep/den168 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref019] 19.Stern J, Larsson M, Kristiansson P, Tydén T. Introducing reproductive life plan-based information in contraceptive counselling: an RCT. Human reproduction (Oxford, England). 2013;28(9):2450–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref020] 20.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. International journal of women’s health. 2020;12:701–7. doi: 10.2147/IJWH.S267675 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref021] 21.Chandra-Mouli V, McCarraher DR, Phillips SJ, Williamson NE, Hainsworth G. Contraception for adolescents in low and middle income countries: needs, barriers, and access. Reproductive health. 2014;11(1):1–8. doi: 10.1186/1742-4755-11-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref022] 22.Program D. Conditions of Use for The DHS Program datasets https://dhsprogram.com/Data/terms-of-use.cfm.

[pone.0254094.ref023] 23.CSA. Demographic and Health Survey 2016 2016 https://dhsprogram.com/pubs/pdf/FR328/FR328.pdf.

[pone.0254094.ref024] 24.ICF CSACEa. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF.; 2016.

[pone.0254094.ref025] 25.programme. D. Guide to DHS Statistics 2018 https://www.dhsprogram.com/data/Guide-to-DHS-Statistics/index.cfm.

[pone.0254094.ref026] 26.Gyekye EK. Correlates of Knowledge of Ovulation Cycle among Women in Urban Poor Communities in Accra 2014 http://ugspace.ug.edu.gh/handle/123456789/22592.

[pone.0254094.ref027] 27.Mocan N, Altindag DT. Education, cognition, health knowledge, and health behavior. The European journal of health economics: HEPAC: health economics in prevention and care. 2014;15(3):265–79. doi: 10.1007/s10198-013-0473-4 [DOI] [PubMed] [Google Scholar]

[pone.0254094.ref028] 28.Yaya S, Bishwajit G, Shah V. Wealth, education and urban-rural inequality and maternal healthcare service usage in Malawi. BMJ global health. 2016;1(2):e000085. doi: 10.1136/bmjgh-2016-000085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref029] 29.Tucker-Drob EM, Briley DA. Socioeconomic Status Modifies Interest-Knowledge Associations among Adolescents. Pers Individ Dif. 2012;53(1):9–15. doi: 10.1016/j.paid.2012.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref030] 30.Mandiwa C, Namondwe B, Makwinja A, Zamawe C. Factors associated with contraceptive use among young women in Malawi: analysis of the 2015–16 Malawi demographic and health survey data. Contraception and Reproductive Medicine. 2018;3(1):12. doi: 10.1186/s40834-018-0065-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref031] 31.Skogsdal Y, Fadl H, Cao Y, Karlsson J, Tydén T. An intervention in contraceptive counseling increased the knowledge about fertility and awareness of preconception health-a randomized controlled trial. Upsala journal of medical sciences. 2019;124(3):203–12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref032] 32.USAID. Essential components of family planning benefits packages 2017.

[pone.0254094.ref033] 33.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;20(1):1420. doi: 10.1186/s12889-020-09536-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref034] 34.Zamawe COF, Banda M, Dube AN. The impact of a community driven mass media campaign on the utilisation of maternal health care services in rural Malawi. BMC Pregnancy Childbirth. 2016;16:21. doi: 10.1186/s12884-016-0816-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254094.ref035] 35.The United, Nations. The 2030 agenda for sustainable development 2015.

PERMALINK

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

Baye Dagnew

Achamyeleh Birhanu Teshale

Henok Dagne

Mengistie Diress

Getayeneh Antehunegn Tesema

Reta Dewau

Meseret Derbew Molla

Yigizie Yeshaw

Roles

Abstract

Background

Methods

Results

Conclusions

Background

Methods data source, study design, and study period

Study area

Population and sample size

Sampling procedure

Data collection

Study variables

Data management and statistical analysis

Ethical considerations

Results

Sociodemographic profiles of the participants

Table 1. Sociodemographic profiles of the participants on knowledge of the ovulatory cycle in Ethiopia (n = 15683).

Knowledge of the ovulatory cycle among women of childbearing age

Fig 1. A pie chart depicting the distribution of women with their knowledge of the ovulatory cycle.

Determinants of KOC among women of childbearing age in Ethiopia

Table 2. Determinant factors of correct KOC among women of childbearing age in Ethiopia using multilevel logistic regression and the random-effects analysis (n = 16583).

Discussion

Strength and limitations of the study

Conclusions

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jamie Males

Roles

Author response to Decision Letter 0

Decision Letter 1

José Antonio Ortega

Roles

Author response to Decision Letter 1

Decision Letter 2

José Antonio Ortega

Roles

Acceptance letter

José Antonio Ortega

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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