Effect of community health education on mothers’ knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trial

Amanuel Yoseph; Wondwosen Teklesilasie; Francisco Guillen-Grima; Ayalew Astatkie

doi:10.1371/journal.pone.0312267

. 2024 Nov 27;19(11):e0312267. doi: 10.1371/journal.pone.0312267

Effect of community health education on mothers’ knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trial

Amanuel Yoseph ^1,^*, Wondwosen Teklesilasie ¹, Francisco Guillen-Grima ^2,^3,^4,⁵, Ayalew Astatkie ¹

Editor: Doris Verónica Ortega-Altamirano⁶

PMCID: PMC11602057 PMID: 39602439

Abstract

Introduction

Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers’ knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women’s groups on these outcomes has yet to be comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women’s groups on mothers’ knowledge regarding ODS and BPCR practices in southern Ethiopia.

Methods

An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multilevel mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects.

Results

One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14–2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02–2.39).

Conclusions

A community-based HEI led by women’s groups improved mothers’ knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women’s knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations.

Trial registration

NCT05865873.

Introduction

Maternity is a normal process that causes several anatomical and physiological changes during the prenatal, intrapartum, and postpartum periods [1, 2]. Every period would be a positive experience in terms of assumptions, confirming that mothers and newborns attain their maximum potential for health and welfare [3, 4]. However, it may contain unexpected complications that expose the women and their fetuses to dangers and related morbidities and mortalities [5]. These complications are manifest as obstetric danger signs (ODS) [6–8].

According to the World Health Organization (WHO) 2022 report, direct obstetric complications are the leading cause of maternal death, with bleeding being the first cause globally and accounting for nearly 28% of all deaths [1, 2]. Studies also reported that hemorrhaging and eclampsia are the leading causes of maternal death in Africa [9, 10]. Nevertheless, many of these complications are preventable if the women are aware of them and are identified early, adequately treated, and managed [2]. However, most women have poor knowledge of ODS, particularly in developing countries [1].

The fundamental cause of maternal mortality in many low-income countries during prenatal, intrapartum, and postpartum periods is described in terms of the three critical delays model. These include delays in identifying life-threatening ODS and deciding to seek health care, delaying arrival at the health facility, and delaying receiving timely, sufficient, and effective care at the health facility [11]. Due to poor knowledge of ODS, women postpone seeking obstetric treatment, contributing to high maternal morbidity and mortality in developing countries [12, 13].

Studies from Ethiopia also reported that women had poor knowledge of ODS, which ranged between 15.5 and 48% [14–16], contributing to high maternal mortality in the country (412 maternal deaths per 100,000 LBs) [17] and very high mortality in rural settings (1142 maternal deaths per 100,000 LBs) [18].

During pregnancy, pregnant women and their families must prepare to welcome a newborn and potentially overcome any unpredicted obstetric complications. This practice is known as the birth preparedness and complication readiness (BPCR) plan [6, 19]. The BPCR plan is a method to encourage the timely utilization of skilled care, particularly during childbirth, according to the notion that preparing for delivery decreases delays in accessing this care [20]. In developing countries, the proportion of women preparing for birth and its obstetric complications is low (35%) [21]. Only 32% of pregnant women in Ethiopia had a birth preparedness and complications readiness plan [22]. The figure is much inferior in southern Ethiopia (18.3%) [20].

The Ethiopian government has designed different strategies and initiatives to increase mothers’ knowledge of ODS and BPCR plans, which is very important to promote women’s health and improve their survival [23, 24]. Regardless of the Ethiopian government’s strategies and initiatives, mothers’ knowledge of ODS and the BPCR practice still needs to improve at the country level and is very low in rural settings [14, 15, 20, 22]. Hence, health education is one of the approaches to increasing a mother’s knowledge of ODS and the practice of BPCR. It is a method focused on education and communication to develop the desired behavior change [25, 26]. However, the effect of a health education intervention on women’s knowledge regarding ODS and the BPCR practice has yet to be comprehensively studied, and the evidence needs to be stronger in designing effective and efficient strategies. Studies conducted in Sokoto State, Nigeria [27], Korogwe district of rural Tanzania [28], Mundri East County, South Sudan [29], and Lagos State, Nigeria [30] showed positive effects of HEI. However, the strength of the evidence these studies delivered is weak due to epidemiological and statistical limitations. For example, one of the studies used a purposive sampling method, lacked randomization, and had inadequate power, as evidenced by a small sample size (only 70 women) [31]. Another study utilized a convenient sampling method with a small sample size (only 120 participants) and subsequent low power [32]. Besides, these studies were quasi-experimental and lacked one or more true experimental study elements, such as random allocation of subjects to study groups, which frequently led to confounding and made it difficult to establish causality. It also has lower internal validity because other variables may account for the results, and it is not easy to know if all confounding variables have been included [33].

Furthermore, more cluster-randomized controlled trial (cRCT) studies are needed to evaluate the effect of community-based HEIs on ODS knowledge and BPCR practice in developing countries, including Ethiopia. Using a cRCT permits the research to provide strong evidence of whether or not an HEI has the intended causal effect on outcomes. Therefore, we aimed to assess the effect of health education on mothers’ knowledge of ODS and BPCR practice in southern Ethiopia. The research question to be answered by the present trial is, in pregnant women in the Sidama region of southern Ethiopia, how does a community-based health education intervention facilitated by women’s groups compare to routine health education in improving knowledge of obstetric danger signs and increasing the practice of birth preparedness and complication readiness?

Methods

Study area

This study was conducted in the Northern Zone of Ethiopia’s Sidama Region. Sidama Region was established on June 18, 2020. It is the country’s second-smallest regional state by geographical area, after Harari, and the fifth-most populous [34]. It is situated in southern Ethiopia and is divided into four zones, namely the Southern, Northern, Central, and Eastern zones, as well as one city administration [35]. The northern zone is 273 kilometers south of Addis Ababa. It is divided into eight districts and two town administrations. The zone contains 162 kebeles (Ethiopia’s bottom administrative units). According to the Sidama Region Health Bureau, the zone has a population of 1.29 million people. Women of reproductive age (WRA) account for 23.3% of the population. The zone has 144 health posts, 36 health centers, one general hospital, and four primary hospitals. The zone’s potential health service coverage by public HFs is 70% [36].

Study design and population

A community-based, parallel-group, two-arm cRCT was implemented from January 10 to August 1, 2023, among pregnant women in the Northern Zone of Sidama Region, Ethiopia. In this study, kebeles, which are subsets of districts, were considered clusters. We included all pregnant women whose gestational age was less than or equal to 12 weeks and who lived in the zone for at least six months. Pregnant mothers who planned to change residence during the implementation of the intervention had not voluntarily provided consent or had severe illnesses were excluded from this study. Severe maternal illness in the context of this study includes severe chronic diseases, mental illness, and severe hyperemesis gravidarum that need strict hospital follow-up. We identified 1,126 pregnancies using the existing women’s development team (WDT) and HEW structures. They surveyed all the eligible households to check for the presence of pregnant women in the household. Pregnant women were identified using a two-stage screening procedure. First, women were asked about the symptoms and signs of pregnancy. If women reported symptoms and signs of pregnancy, they were subjected to the second screening process using a urine human chorionic gonadotropin (HCG) test. A urine test was performed on women who had missed their menstrual cyclic period for six weeks or longer. Women were recruited for the study if the urine HCG test results were positive. This study was designed to detect and enroll pregnant women before 12 weeks of pregnancy via home-to-home visits. WDTs and HEWs visits confirmed the women’s pregnancy, screened for eligibility, and obtained written informed consent before randomization. The recruitment period lasted from November 1st to December 31st, 2022. We reported this study based on the CONSORT 2010 statement: extension to cluster randomized trials guideline, and the filled-in checklist is provided as S1 File.

Sample size calculation

The minimum required sample size was calculated using OpenEpi version 3.01 based on the following considerations. Due to the lack of a previous cRCT on the same topic, assumptions on the proportion of women with knowledge of ODS in the control and intervention arms were taken from a previous quasi-experimental study [31]. The proportion before the intervention was taken as the proportion in the control arm, and the proportion after the intervention was taken as the proportion in the intervention arm. Accordingly, P1 = 45.7% (proportion of women’s knowledge regarding ODS during pregnancy) in the control group, and P2 = 62.9% (proportion of women’s knowledge regarding ODS during pregnancy in the intervention arm) [31]. A confidence level of 95% and a power of 80% were also considered. Accordingly, the estimated effective sample size for individual-based randomization was 286 for both groups.

We used the cluster randomization method to assign study participants to the intervention and control arms due to the nature of the intervention, which is more applicable at the group level. This design decreases the intervention’s spillover effect and provides logistical simplicity. However, clustering in sample size calculation requires considering the effect of clustering and calculating a variance inflation factor (VIF) to increase the study’s statistical power [37, 38]. The minimum number of clusters was computed by multiplying both groups’ effective sample size and interclass correlation coefficient (ICC) factors [39]. We received the typical value of the ICC factor of 0.05 from the range of values (0.01 to 0.05) based on the recommendations [39–41].

Consequently, the minimum needed cluster number was 286*0.05 = 3.29 for both groups. Nevertheless, to maintain the cluster’s sufficiency and adequate power [37, 38], 24 clusters were included in this study. The effective sample size was multiplied by a variance inflation factor of 1.55 to account for the effect of the cluster. The VIF was calculated assuming an equal cluster size of 12 study subjects from 24 clusters. We used an ICC value of 0.05 (VIF = 1+ [(n-1) ICC]), where ’n’ is the average cluster size [39–41]. Thus, the final estimated sample size was 444 (222 in the intervention arm and 222 in the control arm).

We also calculated the minimum sample size needed to evaluate the effect of HEI on BPCR practice. In this case, P1 = 3.0% (proportion of women who practice BPCR in the control group) and P2 = 15.5% (proportion of women who practice BPCR in the intervention group) [42]. The level of confidence was taken to be 95% and power 80%. Based on these considerations, the final estimated sample size was 342 for both groups (171 for the intervention group and 171 for the control group). However, this study was part of a larger project, and the sample size calculated for another study, designed to evaluate the effect of the intervention on maternal health service utilization (MHSU), was 1,126 (i.e., more prominent). Hence, the sample size of 1,126 obtained for the other study was utilized as well, as it would suffice for both studies.

Randomization

Randomization was done after securing consent and enrolling all study participants. The randomization was done by an autonomous, blinded statistician using an SPSS random number generator. Stratified by place of residence, kebeles were allocated by simple random assignment to either the HEI or control group. A cluster in our study was the kebeles (lowest administrative units in Ethiopia) of each district, which offered logistical simplicity and reduced the intervention spillover into the control arm. The study consisted of 24 kebeles from four randomly selected districts. These kebeles were stratified into two strata based on the place of residence: rural and urban kebeles. Stratification based on location reduces stratum variation and helps to balance the baseline covariates between the two arms [37, 38]. Also, it helps to balance the rural/urban disparity of maternal knowledge of ODS and BPCR practice. Similar clusters from each stratum were allocated to both groups to increase the resemblance between the two arms. Hence, three urban kebeles from the four districts were allocated to each group using an SPSS random number generator (in total, six kebeles). Similarly, nine rural kebeles from the four districts were allocated to each arm (in total, 18 kebeles). Finally, we included 47 pregnant women from each cluster.

HEI procedure

WDT leaders who can read and write the Sidaamu Afoo language and are willing to do the intervention were recruited to deliver it. Following the recruitment, intensive training was given for three days on topics such as normal pregnancy and childbirth, ODS during pregnancy, delivery, and the postpartum period, the practice of BPCR, and maternal health service utilization (MHSU). The training also included intervention delivery strategies like properly handling pre-recorded audio material and posters, ethical considerations, and who to contact with specific concerns related to the study intervention. The HEI was facilitated by WDT leaders at a small community meeting place using pre-recorded audio-based messages twice per month (detailed information provided in the S2 File).

Study variables

For this study, the dependent variables were mothers’ knowledge of ODS and BPCR practices. Each outcome variable has a binary outcome and was assessed using self-reported data from women. Maternal knowledge regarding ODS was measured using 30 questions during three phases, namely antepartum (9 questions), intrapartum (12 questions), and postpartum (9 questions). The correct answers were assigned a score of 1, while the incorrect answers were assigned a score of 0. The total knowledge scores range from 0 to 30. The study respondents who spontaneously mentioned at least 3 ODS during each phase were classified as having "good knowledge," and those who spontaneously mentioned two or fewer ODS were classified as having "poor knowledge" [43]. BPCR practice was measured using a questionnaire having five components as to whether or not the woman planned for her index pregnancy, such as identifying a closer proper HF for childbirth; finding and communicating an SBA; saving money; preparing material resources for childbirth and preparing for other associated costs; preparing or arranging transportation to a proper HF in case of childbirth and obstetric emergency; and identifying and fixing compatible blood group givers in case of blood requirements. If a woman prearranged at least two components out of 5, she was considered as having "well prepared" and otherwise considered "poorly prepared" [21, 44]. The intervention or exposure variable was health education. The intervention group received routine plus pre-recorded audio-based HEI augmented by posters in a small community meeting twice a month for six months, while the comparator group received the routine health education package until delivery as per the Ethiopian guideline [45]. Table 1 of S2 File contains information on how the variables were measured for this study.

Blinding

Because of the nature of the intervention, neither the research team members nor the study participants could be blinded (open-label). However, the data collectors (outcome assessors) were masked or unaware of the subject’s group allocation.

Data collection procedures

We used a pre-tested and structured questionnaire to collect data (S3 File). It was adopted from earlier research of a similar nature [21, 43, 44]. The questionnaire was initially developed in English and translated into the Sidaamu Afoo language (primarily spoken in the study area). The questionnaire was translated back to English to ensure its consistency and originality. Two translators, English experts, and fluent Sidaamu Afoo speakers carried out the forward and backward translations. The translated questionnaire was reviewed by the principal investigator (PI) and a third individual who was likewise fluent in both languages. Then, based on the issues identified during the evaluation, any inaccuracy or inconsistency between the two versions was addressed.

The PI trained the data collectors and supervisors for two days before data collection on the significance of the study, data collection processes, aims, methodologies, and ethical considerations. Before data collection, the tool was pre-tested on 5% of the sample in the Dale district of the Sidama region and revised prior to actual data collection. The data were collected after seven weeks of delivery (end of the postnatal period). The health professionals with bachelor’s degrees who were blinded about the intervention status collected the data through a face-to-face interviewer-administered questionnaire at the women’s homes utilizing the Open Data Kit (ODK) smartphone application.

To minimize the risk of bias, we exerted maximum efforts to maximize response and follow-up rates by intensively training data collectors and supervisors, masking outcomes assessors to the intervention assignment, and applying randomization. Daily, data was archived and uploaded to the Kobo Toolbox server.

Data analysis technique

We reported absolute frequencies and percentages for categorical variables as summary measures. The mean with standard deviation (SD) was reported as a descriptive measure for numerical variables after the distribution was checked for normality. The wealth index was derived using principal component analysis (PCA) as a combined indicator of life standards based on 44 questions relating to ownership of carefully chosen household assets and basic amenities [46, 47]. Table 2 of S2 File describes this study’s wealth index calculation process.

We used intention-to-treat analysis (ITA), which means we analyzed women included in the trial at the beginning and had the outcomes measured. We randomly allocated the intervention at the cluster level but evaluated the outcome at the individual level. A chi-square test was used to assess the effect of HEI on mothers’ knowledge regarding ODS and BPCR practice in an unadjusted analysis.

A modified Poisson regression with robust standard error was used to calculate the risk ratios with 95% confidence intervals (CIs) for the effect of intervention on outcomes. We first performed a mixed effect-multilevel logistic regression with a random intercept model to determine whether a multilevel analysis was necessary. This model provides information on ICC, which is used to decide whether or not a multilevel model is necessary [48, 49]. The multilevel analysis model must be considered if the random intercept variance is significant or the ICC value exceeds 5%.

Four models were evaluated: Model 1 was the empty model, Model 2 included the intervention variable and other individual-level covariates, Model 3 contained only community-level covariates, and Model 4 contained the intervention variable and individual and community-level covariates. The random effect model was evaluated using the ICC value [50]. The ICC value was used to determine the proportion of variability in ODS knowledge and BPCR practice due to the clustering variable. The Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood with likelihood ratio test were used to determine which model best suited the data. The best-fitting model can be indicated by the lowest value of these criteria or a significant likelihood ratio test [51].

A multivariable regression model included the intervention variable, covariates with p-values < 0.25 on bi-variable analysis, and other covariates known to have practical significance with relevant support from the medical literature [52]. Effect modification was evaluated by entering interaction terms into the multivariable analysis model one at a time. Multicollinearity among the independent variables was also evaluated using a multiple linear regression model. We declared that the effect of multicollinearity would be less likely when the variance inflation factor was less than 5 for all variables [53].

Statistical significance was set at a p-value of < 0.05 for this analysis. This statistical significance level was adjusted to account for type I error inflation, which can result from the effect of multiple comparisons or testing problems in a single study. We adjusted it using the Bonferroni correction method. The adjusted significance level was computed by dividing the preset significance level by the number of statistical tests conducted (outcome variables). In our case, the adjusted level of significance was 0.05/5 = 0.01. Thus, a statistically significant association was declared when the p-value was less than 0.01 [54, 55]. ARRs with 99% CIs were utilized to assess whether a statistically significant association existed and its strength. A statistically significant association between the exposures and outcome variables was validated when the 99% CIs of the ARRs did not contain 1. The complete Stata set of data on which this manuscript is based was provided in the S4 File.

Ethics statement

This study received ethical approval from the institutional review board (IRB) at the College of Medicine and Health Sciences of Hawassa University, with reference number IRB/076/15. A support letter was obtained from the School of Public Health of Hawassa University, Sidama Region Health Bureau, district health offices, and kebele administrators.

Two levels of consent were obtained before conducting the actual study. First, community leaders approved on behalf of the community before randomization. Second, written informed consent was received from all study participants who met inclusion criteria before enrollment. Before signing informed written consent, study participants were informed about the purpose of the study, data collection techniques, voluntary participation, privacy, potential benefits, and dangers. All data collection methods and intervention techniques were carried out with confidentiality. After obtaining ethical approval, we registered the trial protocol at ClinicalTrials.gov with registration number NCT05865873 and trial protocol is provided in S5 File.

Result

Fig 1 summarizes the details of the trial’s randomization, recruitment, and eligibility procedures. Between November and December 2022, we assessed 1,440 pregnant mothers for eligibility; 1,126 from 24 clusters satisfied the criteria and were recruited for the study. WDT leaders and HEWs successfully communicated and recruited 1,126 eligible pregnant women from the 24 clusters (563 participants in 12 intervention clusters and 563 in 12 control clusters). A total of 1,070 (95.02%) women were available for outcome assessment during the data collection period: 540 in the intervention (95.91%) and 530 in the control (94.13%) groups. The study’s overall response rate was 95.02%. The proportion of women lost to follow-up was comparable among both groups (4.98% in the intervention group vs. 5.87% in the control group). The mean (± SD) gestational age of women at recruitment was 10.72 (± 4.14) weeks.

Table 1 presents a summary of the socio-demographic and economic features of the participants. Most socio-demographic and economic features were comparable or well-balanced, with most respondents being of Sidama ethnicity, Protestant Christian region followers, married, and enrolled in primary school at the time of the interview. The mean (± SD) of the age of women was 29.21 (± 7.06) years in the intervention group and 28.76 (± 6.97) years in the control group. Four hundred ninety-six (91.9%) women in the intervention group and 478 (90.2%) women in the control group had attended formal education. Four hundred nine (77.2%) of the women in control and 386 (71.5%) in intervention groups were homemakers, whereas government employees constituted merely 7.7% in control and 13.1% in intervention groups. More than half of women, 306 (56.7%), in the intervention group and 231 (43.6%) in the control group had access to mass media like radio, television, and newspapers.

Table 1. Socio-demographic characteristics of the trial participants (N = 1,070).

Variables	Intervention group	Control group	Total	P- value
Variables	N (%)	N (%)	N (%)	P- value
Ethnicity				0.431
Sidama	485 (89.8)	470 (88.7)	955 (89.3)
Amhara	23 (4.3)	18 (3.4)	41 (3.8)
Gurage	12 (2.2)	12 (2.3)	24 (2.2)
Wolayita	20 (3.7)	30 (5.7)	50 (4.7)
Religions				0.311
Protestant	422 (78.1)	391 (73.8)	813 (76.0)
Orthodox	45 (8.3)	32 (6.0)	77 (7.2)
Catholic	38 (7.0)	66 (12.5)	104 (9.7)
Muslim	35 (6.5)	41 (7.7)	76 (7.1)
Mothers’ education status				0.461
Cannot read and write	36 (6.7)	39 (7.4)	75 (7.0)
Can read and write only (without formal education)	8 (1.5)	13 (2.5)	21 (2.0)
Have formal education	496 (91.9)	478 (90.2)	974 (91.0)
Women’s occupation status				0.001
Homemaker	386 (71.5)	409 (77.2)	795 (74.3)
Farmer	12 (2.2)	37 (7.0)	49 (4.6)
Government employee	71 (13.1)	41 (7.7)	112 (10.5)
Merchant	71 (13.1)	43 (8.1)	114 (10.7)
Husband occupation status				0.001
Government employee	77 (14.3)	40 (7.5)	117 (10.9)
Merchant	299 (55.4)	247 (46.6)	546 (51.0)
Farmer	164 (30.4)	243 (45.8)	407 (38.0)
Use of mass media				0.001
No	234 (43.3)	299 (56.4)	533 (49.8)
Yes	306 (56.7)	231 (43.6)	537 (50.2)
Wealth quintile				0.001
Lowest	131 (24.3)	82 (15.5)	213 (19.9)
Second	77 (14.3)	138 (26.0)	215 (20.1)
Middle	88 (16.3)	126 (23.8)	214 (20.0)
Fourth	113 (20.9)	101 (19.1)	214 (20.0)
Highest	131 (24.3)	83 (15.7)	214 (20.0)

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Note: The p-value in this table is based on the chi-square test.

Reproductive health characteristics

Most of the reproductive health characteristics were comparable between the intervention and control arms. The mean (± SD) of the age at first marriage of the women was 18.62 (± 0.97) years in the intervention group and 18.52 (± 1.21) years in the control group. Approximately one-tenth (10.9%) of the women in the intervention group and 12.6% in the control group had a previous history of abortion. Nearly half, 259 (48.9%), of the women in the intervention group had given birth to two to four children, compared to 277 (51.3%) women in the control group. Seven percent of study participants had experienced a stillbirth at least once in the intervention group, compared to nine percent in the control group. In more than two-thirds (65.1%) of the women in the control group, the last pregnancy was planned, compared to 81.5% in the interventional groups (Table 2).

Table 2. Reproductive characteristics of the trial participants (N = 1,070).

Variables	Intervention group	Control group	Total	P- value
Variables	N (%)	N (%)	N (%)	P- value
Previous history of abortions
No	481 (89.1)	463 (87.4)	944 (88.2)	0.384
Yes	59 (10.9)	67 (12.6)	126 (11.8)
Total number of deliveries
1	182 (33.7)	182 (34.3)	364 (34.1)	0.942
2–4	228 (42.2)	225 (42.5)	453 (42.3)
≥5	130 (24.1)	123 (23.2)	253 (23.6)
Previous history of neonatal death				0.041
No	527 (97.6)	505 (95.3)	1032 (96.4)
Yes	13 (2.4)	25 (4.7)	38 (3.6)
Last pregnancy planned				0.001
No	100 (18.5)	185 (34.9)	285 (26.6)
Yes	440 (81.5)	345 (65.1)	785 (73.4)
Encountered ODS during last pregnancy				0.012
No	501 (92.8)	468 (88.3)	969 (90.6)
Yes	39 (7.2)	62 (11.7)	101 (9.4)
Faced ODS during last childbirth				0.926
No	495 (91.7)	485 (91.5)	980 (91.6)
Yes	45 (8.3)	45 (8.5)	90 (8.4)
Confronted ODS during last postpartum period				0.444
No	500 (92.6)	497 (93.8)	997 (93.2)
Yes	40 (7.4)	33 (6.2)	73 (6.8)

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Note: The p-value in this table is based on the chi-square test for categorical data and the t-test for numeric data.

Description of the proportion of ODS knowledge and BPCR practice

Severe headaches (87.6% in the intervention group vs. 74.5% in the control group) and excessive vaginal bleeding (58.3% in the intervention group vs. 57.7% in the control group) were the most commonly mentioned ODS during pregnancy. Also, excessive vaginal bleeding (94.3% in the intervention group vs. 88.7% in the control group) was the most common ODS mentioned by women during childbirth, while prolapsed cord (11.5% in the intervention group vs. 4.7% in the control group) was the least mentioned. Likewise, excessive vaginal bleeding (93.7% in the intervention group vs. 86.2% in the control group) was the most common ODS mentioned by women during the postpartum period, while an inverted nipple (21.1% in the intervention group vs. 9.8% in the control group) was the least commonly mentioned ODS (Table 3 of S2 File). Overall, 68.7% of the mothers in the intervention group vs. 36.2% in the control group had good knowledge of ODS (P-value < 0.001) (Fig 2).

Saving money and material resources (97.1% in the intervention group vs. 92.7% in the control group) was the BPCR plan most commonly practiced by the women while identifying and fixing compatible blood group givers (6.9% in the intervention group vs. 5.6% in the control group) was the least commonly mentioned (Table 4 of S2 File). Overall, 64.3% of the mothers in the intervention group vs. 38.9% in the control group had BPCR practice (P-value < 0.001) (Fig 3).

Effect of health education intervention on mothers’ knowledge regarding ODS

In unadjusted analysis, the mothers’ knowledge of ODS was significantly higher in the intervention group (68.7%) than in the control group (36.2%) (P-value < 0.001). After being adjusted for confounders and clustering, women who had received HEI had a 71% higher likelihood of ODS knowledge than women in the control group (ARR = 1.71; 99% CI: 1.14–2.57) (Table 3).

Table 3. Effect of health education intervention on knowledge of ODS among women of reproductive age in the Northern zone of the Sidama region, Ethiopia, 2023 (N = 1,070).

Variables	Knowledge of obstetric danger sign		CRR (99% CI)	ARR (99% CI)
	Good	Poor
	N (%)	N (%)
Study group
Control	192 (36.2)	338 (63.8)	Ref	Ref
Intervention	371 (68.7)	169 (31.3)	1.92 (1.37, 2.69)	1.71 (1.14, 2.57)^*

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Variables adjusted in the final model were women’s occupation, husband’s occupation, use of mass media, wealth quintile, previous history of neonatal death, last pregnancy planned, faced health problems during the pregnancy, road access, received model family training, availability of transport, place of residence, cluster-level mass media use, cluster-level distance, and cluster-level poverty.

*: significant association (p < 0.01); CI: confidence interval; ©: continuous variable; CRR: crude risk ratio; ARR: adjusted risk ratio; Ref: reference group.

Effect of health education intervention on BPCR practice

The BPCR practice was significantly different between the intervention group (64.3%) and the control group (38.9%) in unadjusted analysis (P-value < 0.001). After adjustment for confounders and clustering, women who had received HEI had 1.55 times higher likelihoods of BPCR practice than women in the control group (ARR = 1.55; 99% CI: 1.12–2.16) (Table 4).

Table 4. Effect of health education intervention on BPCR practice among women of reproductive age in the Northern zone of the Sidama region, Ethiopia, 2023 (N = 1,070).

Variables	Birth preparedness and complication readiness		CRR (99% CI)	ARR (99% CI)
	Poor	Well
	N (%)	N (%)
Study group
Control	206 (38.9)	324 (61.1)	Ref	Ref
Intervention	347 (64.3)	193 (35.7)	1.70 (1.18, 2.44)	1.55 (1.01, 2.39)^*

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*: significant association (p < 0.01); CI: confidence interval; ©: continuous variable; CRR: crude risk ratio; ARR: adjusted risk ratio; Ref: reference group.

Random effect model of ODS knowledge and BPCR practice

The multilevel mixed-effects modified Poisson regression with robust variance model fitted better than the ordinary model (p <0.001). The ICC value calculated using the intercept-only multilevel binary logistic model revealed that 27.46% of the variability in ODS knowledge and 38.78% in BPCR practice were related to membership in kebeles (Table 5 of S2 File).

Model selection criteria

The model fitness evaluation test of ODS knowledge showed that the empty model was the least fit (AIC = 1801.97, BIC = 1811.92, and log-likelihood = -898.98). However, there was significant progress in the fitness of the models, particularly in the final model (AIC = 1775.71, BIC = 1790.15, and log-likelihood = -864.85). Thus, the final model is best fitted compared to the other models. Similarly, the model fitness significantly improved from the empty model to the final model in cases of BPCR practice (Table 5 of S2 File).

Discussion

We evaluated the effect of community-based health education intervention facilitated by women’s groups (women’s development team) on mothers’ knowledge regarding ODS and BPCR practice in the Sidama region of southern Ethiopia. Severe vaginal bleeding was the most common ODS mentioned by women during pregnancy, childbirth, and postpartum periods in both study arms. Saving money and materials was the most frequently mentioned BPCR practice in both study groups. The HEI significantly improved women’s knowledge of ODS and BPCR practices.

The finding that severe vaginal bleeding was reported to be the most commonly mentioned ODS in both study arms during pregnancy, childbirth, and the postpartum period is similar to other findings from Ethiopia [56–60] and elsewhere [21, 59]. The similarity in findings might be because facility-based ANC counseling by HCPs often focuses on the commonest ODS like severe bleeding, reduced or absent fetal movement, and mal-presentation, which might increase the mother’s recall of severe vaginal bleeding signs due to frequent information obtained from HCPs. [60]. The other reason might be that the community-based pregnant women forum also mainly focuses on a few common ODS during their meetings. HEWs allow the women to discuss the common ODS in their local setting [45]. Even if our HEI provided balanced information about all danger signs because all are equally important in our study, the most common one was severe vaginal bleeding, which might be an eco-effect from facility-based ANC counseling by HCPs and the community-based pregnant women forum.

Saving money and materials was the most frequently mentioned BPCR practice in both study groups. Similar findings were reported from earlier studies in the North Shewa zone [61], Northwestern Ethiopia [62], and rural Uganda [21]. In rural societies, it is customary to prepare money and materials such as towels, cups for newborns, butter, and porridge flour. Due to its ecological impact, this custom became commonplace in our study despite our equal emphasis on all components during HEI.

Community-based HEI increased the likelihood of women’s knowledge regarding ODS. Consistent results were reported from the studies done in Sokoto State, Nigeria [27], rural Egypt [63], Lagos State, Nigeria [64], Zagazig University Hospitals [65], Nicaragua [66], and Cairo University Hospitals [67]. The reason could be that most earlier studies revealed that advanced levels of education were linked to enhanced cognitive skills, improved information processing abilities, and better values, which help to increase knowledge [68–70].

The current finding has a significant advantage because it considers the effects of community-based HEI as opposed to various intervention packages provided by those previously conducted studies [71–75]. The evidence in this study shows the effectiveness of HEI in increasing mothers’ knowledge of ODS. The women’s ability to identify ODS should minimize the delay in receiving basic emergency obstetric care, leading to higher rates of MHSU. However, there needs to be more evidence to comprehensively assess the effects of knowledge learned from HEI on MHSU. Thus, our studies in another paper evaluated whether this knowledge of ODS obtained from HEI might be changed into an increase in MHSU in a study setting similar to those observed following intervention in Nigeria [27], Egypt [63], and Nicaragua [66]. Thus, HEI could be a beneficial supplement to women’s education in rural areas, increasing the utilization of MHS and preventing delays in emergency obstetric care. Promoting community-based health education in rural areas could be an effective way to increase MHSU in areas with low levels of education.

Community-based HEI increased the likelihood of women’s BPCR practice. This result agreed with previous studies conducted in the Korogwe district of rural Tanzania [28], Zaria Metropolis [76], Mirzapur of Bangladesh [75], and Mundri East County, South Sudan [29]. This result agrees with the theory of reasoned action, which states that a person’s intention determines whether they do a specific behavior. It is a function of one’s attitude and the effect of the social environment, which can be positive or negative for a particular behavior [77, 78]. Thus, the women believe practicing BPCR will lead to a whole-positive outcome (i.e., HFD use). Then, they will hold a positive attitude toward executing that behavior (BPCR practice). Our result inferred that women’s exposure to six months of community-based HEI has predisposed, modified, influenced, and changed their attitude toward BPCR practice. The other reason might be that well-prepared women have better knowledge of ODS and good communication with HCPs.

Consequently, they may have organized all the required prearrangements efficiently and effectively. Other researchers argued that women with good knowledge of ODS are more likely to be birth-prepared and have complications readiness practice [21, 79, 80]. Moreover, the community-based HEI might reach a broader audience of women and their families who may not access regular prenatal care at the HF level. The content of HEIs may also reach male listeners, who have an influential role in healthcare decision-making, and this may thus lead to an increase in BPCR practice.

It is relevant to note that community-based HEI significantly affects women’s knowledge of ODS and BPCR practice among women in our study setting. To ensure the sustainability of knowledge obtained during HEI sessions and maintain the BPCR practice of women accessing MHS, the regional health bureau, and district office must reinforce women’s health-seeking behaviors in the community of the study setting by using an already established structure. This reinforcement will help decrease maternal morbidity and mortality rates among women. The best way to deal with the first delays is to raise knowledge of ODS and BPCR practices with improved transportation networks. Meanwhile, their influence depends on the availability of high-quality health services at health facilities. Thus, focusing on both the demand and supply sides is crucial, even though they are only somewhat related (confidence in the quality of healthcare services increases demand) [73].

cRCT design is appropriate for interventions delivered at the group level and utilized when individual-level randomization is unlikely, or intervention is logically applicable to an entire group or naturally-existing clusters like schools, clinical practices, villages, kebeles, and enumeration areas (EAs) where the study subjects are school children, patients, villages, and inhabitants of kebeles [81, 82]. When allocating identifiable groups, cRCT is regarded as the strongest design in public health research intervention [83]. Based on the CONSORT 2010 statements, extension to cluster randomized trials, cRCTs are preferred when there is a risk of inadvertent spillover of intervention effects from one group to another (contamination) [84].

However, cross-contamination can happen in our study when women from one cluster contact women from others. In rural communities (relatives, friends, and neighbors), there are several chances for social mixing or interaction via migration or travel between control and intervention clusters. Likewise, there may be direct involvement or participation of dwellers from control clusters in intervention undertakings or, more probably, an informal dialogue of thoughts arising from intervention undertakings. Due to this, the control cluster dwellers may gain or obtain some basic information from hearing health messages provided for the intervention clusters. The most common problem with this type of cross-contamination may be the dilution of the effect of intervention differences between two arms [81, 82]. To prevent information cross-contamination between the two arms, we considered several measures. One such measure was the creation of a buffer zone, established by using at least four kebeles between the control and intervention clusters. The establishment was achieved using a map of each district. We assigned a midwife to clarify any issues and concerns from outside the study area regarding the HEI procedure during the intervention period. HEWs from a particular cluster made cRCT feasible and appropriate to prevent contamination. We faced some challenges during the implementation of the intervention components. During the early stages of pregnancy, due to cultural taboos, women were unwilling to notify their pregnancy status and underwent an HCG test. However, this has had no significant influence on our research findings.

The gold standard of randomized trials is determined by two qualities, namely randomization and double-blinding [85]. We could not blind study participants and the research team due to the nature of the intervention, but we masked data collectors (outcome assessors). However, this would not preclude the occurrence of bias, which might lead to an underestimation or overestimation of the intervention effect. Also, information bias could influence our results because the intervention was open-label, and the data were obtained from the women’s self-report. Although difficult to quantify, women’s knowledge of their exposure status (whether they received the intervention) likely influenced their self-reported responses to the knowledge and practice questions, thus leading to information bias. There is a potential for deliberately misreporting personally related variables like saving money and materials, preparing or arranging transportation to a proper HF in childbirth and obstetric emergencies, and identifying and fixing compatible blood group givers in case of blood requirements (social desirability bias). Therefore, the extent of these variables might have been overvalued, and as such, the association of the intervention with the magnitude of BPCR practice might have been overestimated. Regardless of these limitations, the results of this trial are sufficiently valid to develop appropriate intervention strategies and inform policy or program development.

Randomization is frequently assumed to eliminate selection bias and create similar groups regarding measurable and unmeasurable confounders. However, this assumption may not hold in cluster trials, especially when limited clusters are available. When a limited number of clusters are available, the risk of a baseline imbalance between the two arms can be significant [37, 38]. In this case, accounting for the effect of baseline covariates using multivariable analysis and assessing the covariates for intervention effect modification is frequently suggested [53, 85]. So, cognizant of this, we have accounted for the measured sources of confounders and assessed effect modification in our analysis. Most individual and community-level covariates were comparable between the intervention and control arms. However, some of them showed a significant imbalance between two arms like women’s occupation, husband’s occupation, mass media use, wealth index, women’s pregnancy planned status, model family training, cluster-level distance to reach the nearest health facility, cluster-level mass media use, place of residence, and cluster-level mass media, and these variables were adjusted for in the multivariable analysis. We also evaluated whether any of the covariates above modified the effect of the intervention. It was found that there was no significant effect modification because none of the interaction terms were statistically significant. Thus, our findings were not affected by modifying the covariates effect and were solely due to the intervention effect because we ruled out the internal validity threat [53].

The ICC value revealed that membership in kebeles explained 27.46% of the variability in ODS knowledge and 38.78% in BPCR practice. This result indicates that multilevel analysis should be considered because the ICC value is greater than 5%, which we considered [48, 49]. The units of analysis are treated as independent observations in traditional methods of ordinary regression. Regression coefficient standard errors will be underestimated if hierarchical structures are not recognized, which could result in an overestimation of statistical significance. Ignoring the clustering effect will likely affect the coefficients of higher-level determinants or standard errors. The effects of group-level determinants are confounded with the effects of group dummies in a fixed-effects ordinary model, so it is impossible to distinguish between effects due to unobserved and observed group characteristics. The effects of both types of variables can be estimated in a multilevel (random effects) model [49].

Another limitation involves the retention of women’s knowledge of ODS. In this study, women’s knowledge was measured immediately after the intervention. Future studies should measure the long-term retention of knowledge of ODS gained through HEIs at later intervals and with repeat exposure and the impact on the use of obstetric care services and maternal morbidity and mortality. We only had one follow-up period (i.e., six months), and we cannot conclude if knowledge and practice were maintained over more extended periods, especially in the intervention group. Also, the residual effect of the HEI needs to be evaluated via a post-project study after a few years of project completion to ensure persistent effects of the intervention in the study setting.

Moreover, we faced challenges in assessing the outcomes of all enrolled women due to various reasons, such as 24 mothers changing residence, 17 having abortions, 13 experiencing stillbirths, and 2 encountering maternal deaths. Consequently, there were missing outcome data, which is not in line with the principle of randomization. Randomization guarantees the comparability of two arms, meaning that they are balanced for HEI and unknown and known confounders only in the manner in which they were initially randomized. The missing women in the two arms can no longer be considered balanced when some members of either or both groups are eliminated. This bias might lead to an underestimation or overestimation of the intervention effect. Also, this situation decreases the sample size. It compromises the study’s statistical power, making it unable to detect the true effect of the intervention or more susceptible to type II error (a high false negative result) [85]. However, this is minimal in our case because the percentage of women lost to follow-up in both groups was similar (4.98% in the intervention group vs. 5.87% in the control group). Also, we lost only 4.8% of randomized women, which is in line with the culture of less than 5% loss to follow-up, considered a low risk of bias in cRCTs that does not significantly affect ITTA results [85]. Moreover, we conducted a post-hoc analysis of power and obtained a statistical power of 100% for both ODS knowledge and BPCR practice, which is strong enough to detect intervention effects.

This trial has several strengths. From these, we registered the trial protocol at ClinicalTrials.gov with registration number NCT05865873 after obtaining ethical approval to avoid duplication. We used a cRCT study design comprising interventional and comparator groups to ascertain the temporal relationship, an essential epidemiological design to establish causality between intervention and outcome. Our sample size was large, which means it is adequate to identify the effects of HEI on outcomes. Hence, the findings are generalizable to all women of reproductive age in study settings and vital to developing applicable policy strategies for efficient and effective promotion of women’s knowledge of ODS and BPCR practice in the Sidama region and other parts of the country with similar contexts. A study from Sudan [29] and Tanzania [28] also found consistent results, suggesting that this inference may also apply to developing countries at comparable stages of socioeconomic development, culture, and health service access.

In the current study, women had significantly higher knowledge of ODS and BPCR practice after six months of HEI. In another paper, we comprehensively investigated the link between increased knowledge of ODS and BPCR practice and changes in women’s behaviors that promote MHSU.

Conclusions

The increased knowledge of ODS and BPCR practice among women in the study setting, resulting from community-based HEI, is particularly important for scaling the intervention to other regional, country, or similar settings. Therefore, our results support the WHO recommendation to include HEI in community-based health extension programs. Community-based HEI should be considered when planning interventions to increase women’s knowledge of ODS and improve BPCR practice.

Supporting information

S1 File. CONSORT 2010 extension to cluster randomized controlled trial checklist.

(DOC)

pone.0312267.s001.doc^{(90.5KB, doc)}

S2 File. Some important information in the method and result section.

(DOCX)

pone.0312267.s002.docx^{(63.3KB, docx)}

S3 File. English and Sidamu afoo versions questionnaire.

(DOCX)

pone.0312267.s003.docx^{(142.2KB, docx)}

S4 File. Stata data set

(DTA)

pone.0312267.s004.dta^{(3.4MB, dta)}

S5 File. Study protocol.

(DOCX)

pone.0312267.s005.docx^{(95.4KB, docx)}

Acknowledgments

Our profound gratitude goes to former Sidama Media Network manager Mr. Birhanu Hankara and Ms. Selamawit Tibo, a media expert, for their cooperation and facilitation of recording high-quality HEI audio material. We also thank Ms. Mihrete Sunura for her excellent narration of the HEI messages in a language acceptable to the community and cultural context and her immense commitment and response to all phone calls from HEWs during the intervention period. Further, we thank all HEWs for their tremendous assistance during the study. From the bottom of our hearts, we want to thank Mr. Misale Jilo for his assistance in translating the research tools and the health education message, audio material development facilitation, and data collection supervision. We are also grateful to the study participants, supervisors, data collectors, and administrators at various levels in the Sidama Region who contributed directly and indirectly to this study. Finally, our greatest thanks go to Netsanet Kibru for her support in printing posters and funding portable Bluetooth devices (Gepps’s).

Abbreviations

AIC: Akaike’s Information Criterion
ANC: Antenatal Care
ARRs: Adjusted Risk Ratio
BIC: Bayesian Information Criterion
BPCR: Birth Preparedness and Complication Readiness
CI: Confidence Interval
cRCT: Cluster Randomized Controlled Trial
CRR: Crude Risk Ratio
EAs: Enumeration areas
GTP: Growth and Transformation Plan
HCG: Human Chorionic Gonadotropin
HCP: Health Care Provider
HEI: Health Education Intervention
HEW: Health Extension Worker
HF: Health Facility
HFD: Health facility Delivery
ICC: Intra-Cluster Correlation Coefficient
IRB: Institutional Review Board
ITA: Intention to Treat Analysis
MHSU: Maternal Health Service Utilization
ODK: Open Data Kit
ODS: Obstetric Danger Sign
PCA: Principal Component Analysis
PI: Principal Investigator
PNC: Postnatal Care
SBA: Skilled Birth Attendant
SD: Standard Deviation
VIF: Variance Inflation Factor
WDA: Women Development Army
WDT: Women Development Team
WHO: World Health Organization
WRA: Women of Reproductive Age

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The authors received funding from Hawassa University and the Sidama Region President Office. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0312267.r001

Decision Letter 0

Doris Verónica Ortega-Altamirano

31 Jul 2024

PONE-D-23-40070Effect of community-based health education led by women's groups on mothers' knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trial.

PLOS ONE

Dear Dr. Yoseph Samago,

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.

I currently have four reviews for your paper PONE-D-23-40070. The reviewers, who have important expertise in areas your paper covers, including health interventions and methodology, overall found the manuscript of potential interest and useful findings, however they raised important topics that need be addressed by author in the resubmission..

Taking into account the reviewers' comments will improve the manuscript for acceptance. The changes should be observed mainly in terms of synthesizing the introduction, strengthening the methodology and strengthening the discussion and conclusions.

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We look forward to receiving your revised manuscript.

Kind regards,

Doris Verónica Ortega-Altamirano, PhD

Academic Editor

PLOS ONE

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[Hawassa University and Sidama region president office].

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

**********

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: 1. There are a lot Keywords, usually require about 3-8 keywords but 9 are a lot.

2. The title is so long too. I suggest: "community health education on mothers' knowledge

with obstetric risk signs, birth preparedness and complication readiness practices in

southern Ethiopia: A cluster randomized controlled trial"

3. Focus on the variables identified in the title so as not to make the writing so extensive.

**********

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Reviewer #1: Yes: Claudia MACIAS-CARRILLO

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PLoS One. 2024 Nov 27;19(11):e0312267. doi: 10.1371/journal.pone.0312267.r002

Author response to Decision Letter 0

6 Aug 2024

Point-by-point responses to reviewers’ and editor’s comments

Academic editor

Comment 1: I currently have four reviews for your paper PONE-D-23-40070. The reviewers, who have important expertise in areas your paper covers, including health interventions and methodology, overall found the manuscript of potential interest and useful findings; however they raised important topics that need be addressed by author in the resubmission. Taking into account the reviewers' comments will improve the manuscript for acceptance. The changes should be observed mainly in terms of synthesizing the introduction, strengthening the methodology and strengthening the discussion and conclusions.

Authors’ response: Thank you for your kind remark. We duly considered all comments and have revised the manuscript accordingly.

Comment 2: My suggestions: follow the criteria for publications in PLOS ONE, reduce the number of keywords, and check consistency between all sections of the manuscript.

Authors’ response: Thank you a lot for this valuable comment. We have accepted the comment and made the required revision. The keywords have been edited to be consistent with PLOS ONE publications criteria as per your suggestion. Besides, we have checked the consistency of all section of our manuscript.

Comment 3: If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

Authors’ response: Thank you for this important notice. However, we didn’t change our financial disclosure because we have correctly stated it in the online submission system. Besides, we have already followed the figures files preparation style of PLOS ONE journal.

Comment 4: If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future.

Authors’ response: Thank you for this recommendation. This study hasn’t laboratory protocol but has a study protocol. We have already provided the study protocol as a supplementary material.

Reviewer 1

Comment 1: There are a lot Keywords, usually require about 3-8 keywords but 9 are a lot.

Authors’ response: Thank you very much for your vital comments. As per your comment and comment 2 from the editor, we have revised the keywords to align with journal requirement.

Comment 2: The title is so long too. I suggest: "community health education on mothers' knowledge with obstetric risk signs, birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trial"

Authors’ response: Thank you, too, for this comment. We have accepted your comment and made the required revisions as per your comment. The slight deviation in the title from your suggested title is meant only for grammatical correctness and clarity.

Comment 3: Focus on the variables identified in the title so as not to make the writing so extensive.

Authors’ response: Thank you for pointing this out. Yes, what you pointed out is a possibility. However, the clear definition of the exposures and outcomes are very important to provide comprehensive information for readers on how variables are measured. Moreover, the variables measurement is not accurately and consistently provided across the medical literature that precludes the replication of measurement and comparison of findings by scholar in different setting and time. Thus, we provided the clear definition separately to each variable to facilitate replication and comparison by different researchers.

Journal requirements

Requirement 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Authors’ response: We have followed PLOS ONE’s style requirements, including file naming conventions, in preparing the manuscript.

Requirement 2: Note from Emily Chenette, Editor in Chief of PLOS ONE, and Iain Hrynaszkiewicz, Director of Open Research Solutions at PLOS: Did you know that depositing data in a repository is associated with up to a 25% citation advantage (https://doi.org/10.1371/journal.pone.0230416)? If you’ve not already done so, consider depositing your raw data in a repository to ensure your work is read, appreciated and cited by the largest possible audience. You’ll also earn an Accessible Data icon on your published paper if you deposit your data in any participating repository (https://plos.org/open-science/open-data/#accessible-data).

Authors’ response: Thank you very much for this vital information. Now we have provided the full data set on repository in which the manuscript is prepared to take advantage of citation as per your suggestion.

Requirement 3: Thank you for stating the following financial disclosure:

[Hawassa University and Sidama region president office]. Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed.

Please respond by return e-mail so that we can amend your financial disclosure and competing interests on your behalf.

Authors’ response: As we also responded to the above comment (comment 3), the financial disclosure statement has been correctly stated as per your suggestion during first online submission in the system. All the required details are provided.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0312267.s006.docx^{(43.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0312267.r003

Decision Letter 1

Doris Verónica Ortega-Altamirano

15 Aug 2024

PONE-D-23-40070R1Effect of community health education on mothers' knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trialPLOS ONE

Dear Dr. Yoseph ,

==============================

The comments and suggestions of 2-4 reviewers were not answered in their previous submission. I realized that it was a mistake and did not arrive properly. I apologize for the inconvenience. The decision on the Major Revision is still valid and may be answered by the authors in the following submission.

==============================

Please submit your revised manuscript by Sep 29 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Doris Verónica Ortega-Altamirano, PhD

Academic Editor

PLOS ONE

Additional Editor Comments:

The comments and suggestions of 2-4 reviewers were not answered in their previous submission (R1). I realized that it was a mistake and did not arrive properly. I apologize for the inconvenience.

Reviewer 1

1. There are a lot Keywords, usually require about 3-8 keywords but 9 are a lot.

2. The title is so long too. I suggest: "community health education on mothers' knowledge

with obstetric risk signs, birth preparedness and complication readiness practices in

southern Ethiopia: A cluster randomized controlled trial"

3. Focus on the variables identified in the title so as not to make the writing so extensive.

Reviewer 2

Comments to authors:

Thank you for the opportunity to review this manuscript. Their results highlight the effectiveness of community-based interventions to increase women’s knowledge about pregnancy complications and best practices which is necessary to reduce maternal and infant mortality rates worldwide. The manuscript would benefit from some editing and summarizing some sections as it is very long and hard to follow at times. Please see comments below.

Introduction

1. The introduction could be significantly shortened, and some of the information used more in the discussion section. I would suggest the following structure;

a. Paragraph describing ODS including some prevalence data

b. Paragraph addressing the women’s knowledge gap of ODS

c. Paragraph describing interventions and government efforts

d. Paragraph describing the lack of research and objectives of this manuscript

2. It would be good for the authors to add some data on prevalence of ODS and maternal mortality particularly in Ethiopia.

Methods

3. Adding a flow chart or diagram depicting the participant’s selection and randomization process would be helpful to follow the study design, population and randomization paragraphs a little better.

4. Study variables: I suggest the authors only focus on variables that are relevant for this manuscript.

5. I suggest moving the description of the intervention to supplementary material and including only a brief summary of it in the manuscript. I also think it should be placed before talking about the outcomes.

6. Page 14 paragraph 2 talks about data collection and then goes back to randomization, this is a little confusing. I would suggest authors to mention everything that has to do with randomization in the appropriate paragraph and not coming both.

7. While the authors offer very detailed information and justifications for their model selection, I’m a little confused if their multilevel models were linear or Poisson models. It is not the standard to estimate ICC from a Poisson model and usually variance portioning is used. If fitting a logistic model then MOR is what is usually estimated as opposed to ICC If authors estimated ICC from non-linear models I would suggest that they add the formula of how they did it to the manuscript.

8. Why did authors decide the cutoff of p < 0.25 to be included in multivariate models

9. What was the reasoning behind the effect modification tests? Which variables were tested? I would suggest authors to elaborate more on this.

10. In general, authors provide many statistical terms and details to the methods section that makes it hard to follow. I would suggest authors simplify it and add only the important information readers would need in order to replicate this analysis. The section is very hard to follow and all the justification and details can be distracting.

Results

11. On the description of table 1, it would be important to highlight the differences between groups, given that this is an RCT and Table 1 should be used to assess balance between groups. The statistically significant differences for Mass Media access and wealth index are important and could be associated with the outcome as well so should be highlighted.

12. Similar to previous comment, it is important to highlight significant differences between groups, previous history of neonatal death and ODS during last pregnancy could be associated with the outcomes as well and should be highlighted as potential confounders.

13. I find tables 3 and 4 a little confusing. I would separate the N(%) of good and poor knowledge into a separate table and just leave the model results. Also, it is not very clear which models the authors are showing. In the methods they refer to a sequence of four models and they only show results for 2, clarifying that would be very helpful.

Discussion

14. While the information provided is good and authors do a good job of comparing their findings to other literature, the limitations section is very long and disorganized. I would strongly advise to summarize better the limitations with regards to clustered RCTs and then they can talk about limitations of analysis or other types instead of going back an forth which makes it hard to follow.

Reviewer 3

Amanuel Yoseph and co-authors evaluated the effect of community-based health education intervention facilitated by women's groups (women's development team) on mothers' knowledge of SDG and BPCR practice in Sidama region in the southern Ethiopia.

I would like to thank the authors for this excellent manuscript. A problem that affects a high proportion of women in underdeveloped countries is outlined: high maternal morbidity and mortality. But, even more importantly, the authors present evidence on effective strategies to mitigate this important public health problem. Adding to the relevance of the topic is the correct design, the careful handling of ethical aspects, a robust analysis and the writing of the manuscript.

My only suggestion is to review the title, its length seems a bit excessive to me. Additionally, I consider the comment "while another paper is focused on three skilled [...], in the "Study variables" section, unnecessary.

Congratulations, I really enjoyed reading the manuscript

Reviewer 4

The topic is relevant because of the social impact and inequalities that occur in developing countries. It is essential to recognize indicators of potential risks during childbirth in order to make informed decisions and prevent complications that could lead to the death of both mother and baby. However, we have some observations.

Overall, the current version of the article is quite dense and difficult to understand because of long and ambiguous sections. To ensure clarity for the reader, it is essential to reorganize the structure of the paper. In addition, it is substantial in the paper, to balance the section statistical analysis with importance of the impact of the intervention, will allowing the validity of the study to be measured.

The specific comments below recommend reducing the text in certain sections:

Introduction Section: From our perspective, the ideas in this section are long and repetitive. We recommend condensing sentences 2, 3, and 8. Sentence 6 could be rewritten or omitted.

In order to develop an educational intervention that addresses complications and reduces maternal and infant mortality rate, it is crucial to have a solid theoretical and causal framework. The causal framework is a diagram that considers the factors associated with social knowledge and establishes clear theoretical relationships between these factors. By incorporating a causal framework, the intervention can show its function and impact, highlighting the desired variable of change and the desired health outcomes. In addition, the baseline measure allows identifying should identify the variables that should be included in the analysis conceptual and ensure factor similarity between the intervention and non-intervention groups. It also allows for including relevant variables in the statistical analysis. The causal framework will justify the importance and scope of the intervention. For further help, see the following references:

• Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Thousand Oaks, CA: SAGE Publications. This classic program evaluation text underscores the importance of understanding and documenting causal relationships in intervention programs.

Centers for Disease Control and Prevention (CDC). (2011). Developing an Effective Evaluation Plan. Atlanta, GA: CDC, National Center for Chronic Disease Prevention and Health Promotion. This paper details the importance of causal and logic frameworks in health program evaluation.

• Funnell, S. C., & Rogers, P. J. (2011). Purposeful Program Theory: Effective Use of Theories of Change and Logic Models. San Francisco, CA: Jossey-Bass. This book provides comprehensive guidance on how to develop and use theories of change and logic models in program evaluation.

• Weiss, C. H. (1997). Theory-Based Evaluation: Past, Present, and Future. New Directions for Evaluation, 1997(76), 41-55. This article reviews the history and utility of theory-based evaluations, including the importance of causal frameworks.

• Bamberger, M., Rugh, J., & Mabry, L. (2012). RealWorld Evaluation: Working Under Budget, Time, Data, and Political Constraints (2nd ed.). Thousand Oaks, CA: SAGE Publications. This book addresses the challenges and strategies for evaluation in real-world settings, including justifying interventions through causal frameworks.

Method Section:

As a general comment, this section should be revised and summarized.

It is recommended that the participant recruitment process be described in more detail both in the text and in Figure 1.

I am confused by the way participant recruitment is described. If pregnant women were selected by visiting all households, if so, how was the response rate assessed if some households had a pregnant woman who did not want to be interviewed, or was not home because she had been hospitalized for complications of pregnancy? Other questions: How many households were visited in total? What is the rate of refusal or nonparticipation? During what stage of the recruitment process must women provide consent to take part? What was the community consent process? Given that the intervention was part of a major project, at what point in the study process were the groups randomized?

How do you measure the equivalence of the two groups in personal, social, and health service variables that theoretically take part in knowledge?

In the sample size calculation section, if a household census was conducted and pregnant women were identified, the most appropriate thing to do is to identify the statistical power or precision got when identifying knowledge in the total number of women who took part in the study.

On the other hand, what were the criteria for generating the clusters?

It would be appropriate to summarize this section as well.

After randomization: Are the factors that determine knowledge the same in both groups (intervened and non-intervened)? How do you check if randomization worked in creating kebeles?

In the variables section of the study, when it is noted that “The respondents in the study spontaneously mentioned three questions… spontaneous knowledge was defined… they could spontaneously name two or more…” Question: During the validation of the questionnaire and in particular of the knowledge questions, was the potential information bias that could influence the results quantified?, and in this context, did they measure its potential impact on the misclassification of knowledge in the statistical analysis?

Regarding the measurement of conceptual constructs such as “knowledge” during the analysis of the information, does the questions have the same value to measure the dimensions of the Knowledge construct?

In the last sentence of the same section, they show that this study is part of another larger project with another aim. It is not clear if it affected the creation of representativeness of women in the community or the recruitment and randomization process. Can you clarify?

IES process section. How did the topics offered to improve knowledge used in the intervention emerge? What competencies should participants have at the end of the educational training? Were assessments conducted before the intervention? What were the assessment results at the beginning and during the training process? If so, is there a parameter at the beginning of the intervention to measure the difference in knowledge between the intervention and non-intervention groups? If so, were these controlled for in the statistical analysis? Please comment.

Regarding the sections on data collection procedures, data techniques, and ethical statements, please reduce the length of these sections and rewrite them.

Results section. The statistical analysis should begin by evaluating the difference in the knowledge indicators that allow the initial counterfactual to be evaluated and induce the final counterfactual. It is important, measure the change before and after the intervention in both groups. A knowledge index be developed to meet the stated aim.

The tables are extensive. Please include the results of the important variables.

Explain the statistical differences that allow the identification of variables used in the adjusted statistical model.

Tables 3 and 4, please include at the bottom of the table the variables used to adjust the final model, and show the most essential findings that allow the subsequent discussion of the results.

Create a bar graph showing the percentage of increase in knowledge caused by the intervention that is the objective of the study.

In the Discussion section, it is recommended that the authors include the percentage of change in knowledge about the SDG and BPCR practices got at the end of the intervention.

An important aspect of the discussion of the study findings is the statistical analysis, which includes both intention to treat and final analysis.

The authors refer to an increased likelihood of women performing BPCR after the intervention, but do not specify which specific outcomes they are referring to.

The discussion mentions potential cost and material savings, but it is unclear whether this was one of the study’s goals.

In conclusion, the paper cannot be published as it is, observations and comments are necessary.

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PLoS One. 2024 Nov 27;19(11):e0312267. doi: 10.1371/journal.pone.0312267.r004

Author response to Decision Letter 1

28 Sep 2024

Point-by-point responses to reviewers’ and editor’s comments

Reviewer 1

Comment 1: There are a lot Keywords, usually require about 3-8 keywords but 9 are a lot.

Authors’ response: Thank you very much for your vital comments. As per your comment and comment 2 from editor, we have revised the keywords to align with journal requirement.

Authors’ response: Thank you, too, for this comment. We have accepted your comment and made the required revisions as per your comment.

Comment 3: Focus on the variables identified in the title so as not to make the writing so extensive.

Authors’ response: Thank you for pointing this out. This comment is also shared by reviewers 2 and 4; we have revised this section of the manuscript as per the suggestions of the reviewers.

Reviewer 2

Comment 1: The introduction could be significantly shortened, and some of the information used more in the discussion section. I would suggest the following structure; a. Paragraph describing ODS including some prevalence data b. Paragraph addressing the women’s knowledge gap of ODS c. Paragraph describing interventions and government efforts d. Paragraph describing the lack of research and objectives of this manuscript 2. It would be good for the authors to add some data on prevalence of ODS and maternal mortality particularly in Ethiopia.

Authors’ response: Thank you a lot for this valuable comment. We have accepted the comment and made the required revision. The introduction has been edited to be more logical and coherent by including prevalence of ODS and maternal mortality data as per your suggestion.

Comment 2: Adding a flow chart or diagram depicting the participant’s selection and randomization process would be helpful to follow the study design, population and randomization paragraphs a little better.

Authors’ response: Dear reviewer, thank you for your critical comment. What you raised is very important to improve this section of the manuscript. However, the flow chart or diagram is already published in another paper (see figure 2). We haven’t provided the figure because to avoid dual publication of the same figure that can lead to scientific misconduct. See the link to the publication: https://doi.org/10.3390/healthcare12101045.

Comment 3: Study variables: I suggest the authors only focus on variables that are relevant for this manuscript.

Authors’ response: Thank you for pointing this out. As indicated in the comment (reviewers 1 and 3), we have revised this section of the manuscript to make it more concise as per the suggestions of the reviewers.

Comment 4: I suggest moving the description of the intervention to supplementary material and including only a brief summary of it in the manuscript. I also think it should be placed before talking about the outcomes.

Authors’ response: Thank you for this comment, too. We have carefully revised this section to improve the readability of the manuscript as per your comment.

Comment 5: Page 14 paragraph 2 talks about data collection and then goes back to randomization, this is a little confusing. I would suggest authors to mention everything that has to do with randomization in the appropriate paragraph and not coming both.

Authors’ response: Thank you for your vital comment and for fixing our mistake. Now we have revised it based on your suggestion.

Comment 6: While the authors offer very detailed information and justifications for their model selection, I’m a little confused if their multilevel models were linear or Poisson models. It is not the standard to estimate ICC from a Poisson model and usually variance portioning is used. If fitting a logistic model then MOR is what is usually estimated as opposed to ICC If authors estimated ICC from non-linear models I would suggest that they add the formula of how they did it to the manuscript.

Authors’ response: Dear reviewer, thank you for your vital comment. As you correctly pointed out, it is not the standard and possible to estimate ICC from a Poisson model. However, we have provided a clear description of ICC estimate calculation using a logistic regression model in the data analysis and result section of this manuscript. To the best of our knowledge, it is possible to calculate the ICC estimate using a logistic regression model to determine whether or not a multilevel analysis is required. Besides, we have provided a stata do file that shows the formula for ICC estimation using stata software as per your request.

Comment 7: Why did authors decide the cutoff of p < 0.25 to be included in multivariate models.

Authors’ response: Thank you again. You raised a very critical point, but we haven’t only used the p-value cutoff point to screen candidate variables for multivariable analysis. A p-value of less than 0.25 and other variables of known clinical and social significance were included for additional multivariable analysis for this study. Utilizing a cutoff value of 0.25 is supported by most statistics books, statisticians, and literatures. We have decided to use the cutoff of p < 0.25 and other variables of known clinical and social importance to include candidate variables into multivariate models because most statistics books, literatures, and statisticians recommend by rule of thumb. Dear reviewer, kindly see the following references:

1. Bendel RB, Afifi AA. Comparison of stopping rules in forward “stepwise” regression. Journal of the American Statistical association. 1977 Mar 1;72(357):46-53.

2. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. American journal of epidemiology. 1989 Jan 1;129(1):125-37.

3. Model building strategy for logistic regression: purposeful selection available from https://atm.amegroups.org/article/view/9400/pdf

Comment 8: What was the reasoning behind the effect modification tests? Which variables were tested? I would suggest authors to elaborate more on this.

Authors’ response: Thank you too for this comment. The reason we conducted the effect modification tests was to assess variables that modify the effect of our intervention. We have already provided the variables that were tested for effect modification and every detail of effect modifiers in supplementary file 1.

Comment 9: In general, authors provide many statistical terms and details to the methods section that makes it hard to follow. I would suggest authors simplify it and add only the important information readers would need in order to replicate this analysis. The section is very hard to follow and all the justification and details can be distracting.

Authors’ response: Thank you a lot for this appreciated comment. We have accepted the suggestion and done the needed revision. Now this section has been revised as per your suggestion.

Comment 10: On the description of table 1, it would be important to highlight the differences between groups, given that this is an RCT and Table 1 should be used to assess balance between groups. The statistically significant differences for Mass Media access and wealth index are important and could be associated with the outcome as well so should be highlighted

Authors’ response: Thank you for this remark. We have highlighted all variables that significantly differ between two arms during bivariable analysis. These variables were considered as potential confounders and controlled using multivariable analysis.

Comment 11: Similar to previous comment, it is important to highlight significant differences between groups, previous history of neonatal death and ODS during last pregnancy could be associated with the outcomes as well and should be highlighted as potential confounders.

Authors’ response: Thank you for this remark, too. As indicated in above comment 10, these variables were considered potential confounders and controlled using multivariable analysis.

Comment 12: I find tables 3 and 4 a little confusing. I would separate the N(%) of good and poor knowledge into a separate table and just leave the model results. Also, it is not very clear which models the authors are showing. In the methods they refer to a sequence of four models and they only show results for 2, clarifying that would be very helpful.

Authors’ response: Thank you for the likely comment. This comment is also shared by reviewer 4; we have accepted comment and revised tables 3 and 4 as per suggestions of reviewers. We have provided the results of the final model (model 4) in this manuscript, but not for two models. The final model that contains both individual and community-level determinants was used to present the findings of this manuscript because it is the best fitted data as compared to the other three consecutive models (models 0, 1, and 2).

Comment 13: While the information provided is good and authors do a good job of comparing their findings to other literature, the limitations section is very long and disorganized. I would strongly advise to summarize better the limitations with regards to clustered RCTs and then they can talk about limitations of analysis or other types instead of going back and forth which makes it hard to follow.

Authors’ response: Thank you for this comment. We have accepted your comment and did the required revisions as per your comment to make the section more concise.

Reviewer 3

Comment 1: Amanuel Yoseph and co-authors evaluated the effect of community-based health education intervention facilitated by women's groups (women's development team) on mothers' knowledge of SDG and BPCR practice in Sidama region in the southern Ethiopia. I would like to thank the authors for this excellent manuscript. A problem that affects a high proportion of women in underdeveloped countries is outlined: high maternal morbidity and mortality. But, even more importantly, the authors present evidence on effective strategies to mitigate this important public health problem. Adding to the relevance of the topic is the correct design, the careful handling of ethical aspects, a robust analysis and the writing of the manuscript.

Authors’ response: Thank you for your kind remark.

Comment 2: My only suggestion is to review the title, its length seems a bit excessive to me.

Authors’ response: Thank you a lot for this valuable comment. This comment is also shared by reviewer 1; we have accepted the suggestion and made the required revision as per the suggestion of both reviewers.

Comment 3: Additionally, I consider the comment "while another paper is focused on three skilled [...], in the "Study variables" section, unnecessary

Authors’ response: Thank you for the genuine and plausible comment. We have removed this unnecessary description from the manuscript as per your suggestion.

Comment 4: Congratulations, I really enjoyed reading the manuscript.

Authors’ response: Thank you very much again for your kind remark.

Reviewer 4

Comment 1: Introduction Section: From our perspective, the ideas in this section are long and repetitive. We recommend condensing sentences 2, 3, and 8. Sentence 6 could be rewritten or omitted.

Authors’ response: Thank you for this comment. We have accepted your comment and did the required revisions as per your comment to make the introduction section more concise.

Comment 2: In order to develop an educational intervention that addresses complications and reduces maternal and infant mortality rate, it is crucial to have a solid theoretical and causal framework. The causal framework is a diagram that considers the factors associated with social knowledge and establishes clear theoretical relationships between these factors. By incorporating a causal framework, the intervention can show its function and impact, highlighting the desired variable of change and the desired health outcomes. In addition, the baseline measure allows identifying should identify the variables that should be included in the analysis conceptual and ensure factor similarity between the intervention and non-intervention groups. It also allows for including relevant variables in the statistical analysis. The causal framework will justify the importance and scope of the intervention. For further help, see the following references:

Authors’ response: Thank you for the important comment. This study was developed based on the theoretical framework, which was well elaborated in another publication of this project. We have already provided all details of the theoretical and causal framework as per your suggestion in another publication. It was denied from this manuscript to avoid plagiarism of similar theoretical frameworks and figures from that paper. Dear reviewer, kindly see this link to the publication: https://doi.org/10.3390/healthcare12101045.

Comment 3: It is recommended that the participant recruitment process be described in more detail both in the text and in Figure 1.

Authors’ response: Thank you for this comment. Our manuscript participant recruitment process seems to contain detailed information. However, we have revised this section based on your suggestion to make it clearer for the reader.

Comment 4: I am confused by the way participant recruitment is described. If pregnant women were selected by visiting all households, if so, how was the response rate assessed if some households had a pregnant woman who did not want to be interviewed, or was not home because she had been hospitalized for complications of pregnancy?

Authors’ response: Dear reviewer, thank you for your vital comment. As you correctly pointed out, there is a possibility of refusal of study participants due to different reasons. However, we haven’t practical experienced the concern you raised because we provided detail information about the aims and significance of the study, method of selection, benefits, and harms of the study to pregnant women. Due to this, study participants actively participated in this study during data collection. Besides, our data collectors visited a single pregnant home three times to avoid non-response due to absenteeism. If women were absent from home after three consecutive visits, they were considered non-respondent. We had never experienced hospitalized women during data collection because our project data was collected after 45 days of childbirth. We only experienced non-response due to change of place, abortion, stillbirths, and mortality during our data collection, as clearly shown in Figure 1.

Comment 5: Other questions: How many households were visited in total?

Authors’ response: Thanks for this query. However, we have clearly provided the information in the respondent details part of the result. Between November and December 2022, we assessed 1,440 pregnant mothers or households for eligibility; 1,126 from 24 clusters satisfied the criteria and were recruited for the study.

Comment 6: What is the rate of refusal or nonparticipation?

Authors’ response: Thanks for this query, too. A total of 1,070 (95.02%) women were available for outcome assessment during the data collection period: 540 in the intervention (94.91%) and 530 in the control (94.13%). The study's overall response rate was 95.02%. The proportion of women lost to follow-up was comparable among both groups (4.98% in the intervention group vs. 5.87% in the control group).

Comment 7: During what stage of the recruitment process must women provide consent to take part? What was the community consent process? Given that the intervention was part of a major project, at what point in the study process were the groups randomized?

Authors’ response: Thanks for this comment. The written informed consent was received from all pregnant women who met inclusion criteria before randomization and enrollment. The community leaders approved on behalf of the community before recruitment and randomization. Randomization was conducted after successful screening, consent, and recruitment of study participants.

Comment 8: How do you measure the equivalence of the two groups in personal, social, and health service variables that theoretically take part in knowledge?

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PLoS One. doi: 10.1371/journal.pone.0312267.r005

Decision Letter 2

Doris Verónica Ortega-Altamirano

4 Oct 2024

Effect of community health education on mothers' knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster-randomized controlled trial

PONE-D-23-40070R2

Dear Dr. Amanuel,

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.

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The manuscript is ready for publication

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0312267.r006

Acceptance letter

Doris Verónica Ortega-Altamirano

18 Oct 2024

PONE-D-23-40070R2

PLOS ONE

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Associated Data

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

Supplementary Materials

S1 File. CONSORT 2010 extension to cluster randomized controlled trial checklist.

(DOC)

pone.0312267.s001.doc^{(90.5KB, doc)}

S2 File. Some important information in the method and result section.

(DOCX)

pone.0312267.s002.docx^{(63.3KB, docx)}

S3 File. English and Sidamu afoo versions questionnaire.

(DOCX)

pone.0312267.s003.docx^{(142.2KB, docx)}

S4 File. Stata data set

(DTA)

pone.0312267.s004.dta^{(3.4MB, dta)}

S5 File. Study protocol.

(DOCX)

pone.0312267.s005.docx^{(95.4KB, docx)}

Attachment

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pone.0312267.s007.pdf^{(101.1KB, pdf)}

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pone.0312267.s008.docx^{(57.4KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

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PERMALINK

Effect of community health education on mothers’ knowledge of obstetric danger signs and birth preparedness and complication readiness practices in southern Ethiopia: A cluster randomized controlled trial

Amanuel Yoseph

Wondwosen Teklesilasie

Francisco Guillen-Grima

Ayalew Astatkie

Roles

Abstract

Introduction

Methods

Results

Conclusions

Trial registration

Introduction

Methods

Study area

Study design and population

Sample size calculation

Randomization

HEI procedure

Study variables

Blinding

Data collection procedures

Data analysis technique

Ethics statement

Result

Fig 1. Trial profile.

Table 1. Socio-demographic characteristics of the trial participants (N = 1,070).

Reproductive health characteristics

Table 2. Reproductive characteristics of the trial participants (N = 1,070).

Description of the proportion of ODS knowledge and BPCR practice

Fig 2. Level of knowledge of obstetric danger signs among women in control and interventional arms.

Fig 3. Level of birth preparedness and complication readiness practice among women in control and interventional arms.

Effect of health education intervention on mothers’ knowledge regarding ODS

Table 3. Effect of health education intervention on knowledge of ODS among women of reproductive age in the Northern zone of the Sidama region, Ethiopia, 2023 (N = 1,070).

Effect of health education intervention on BPCR practice

Table 4. Effect of health education intervention on BPCR practice among women of reproductive age in the Northern zone of the Sidama region, Ethiopia, 2023 (N = 1,070).

Random effect model of ODS knowledge and BPCR practice

Model selection criteria

Discussion

Conclusions

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Doris Verónica Ortega-Altamirano

Roles

Author response to Decision Letter 0

Decision Letter 1

Doris Verónica Ortega-Altamirano

Roles

Author response to Decision Letter 1

Decision Letter 2

Doris Verónica Ortega-Altamirano

Roles

Acceptance letter

Doris Verónica Ortega-Altamirano

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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