Mental health dynamics of adolescents: A one-year longitudinal study in Harari, eastern Ethiopia

Gari Hunduma; Yadeta Dessie; Biftu Geda; Tesfaye Assebe Yadeta; Negussie Deyessa

doi:10.1371/journal.pone.0300752

. 2024 Apr 18;19(4):e0300752. doi: 10.1371/journal.pone.0300752

Mental health dynamics of adolescents: A one-year longitudinal study in Harari, eastern Ethiopia

Gari Hunduma ^1,^*, Yadeta Dessie ^2,^#, Biftu Geda ³, Tesfaye Assebe Yadeta ¹, Negussie Deyessa ⁴

Editor: Anthony A Olashore⁵

PMCID: PMC11025968 PMID: 38635790

Abstract

Aims

This study aims to assess the dynamics of in-school adolescents’ mental health problems in Harari regional state, eastern Ethiopia for a year.

Materials and methods

Using multistage sampling technique, we conducted a year-long longitudinal study at three public high schools between March 2020 and 2021. Three hundred fifty-eight in-school adolescents were chosen by systematic random sampling for the baseline assessment, and 328 completed the follow-up assessment. We used self-administered, adolescent version of SDQ-25 Questionnaire to collect the data. Wilcoxon matched-pairs signed-rank test and McNemara’s Chi-squared tests were used to examine the median difference and distribution of mental health problems between times one and two. Random-effects logistic regressions on panel data was used to identify factors associated with mental health problems. A p-value < 0.05 was considered as statistically significant.

Results

The magnitude of overall mental health problems at baseline assessment was 20.11% (95% CI: 16–25), with internalizing problems accounting for 27.14% (95% CI: 23–32) and externalizing problems accounting for 7.01% (95% CI: 4.6–10.3). At the follow-up assessment, these proportions rose to 22.56% (95% CI, 18–27) for overall problems and 10.3% (95% CI, 7.7–14.45) for externalizing problems. On other hand, internalizing problems decreased unexpectedly to 22.86% (95% CI, 18.6–27.7) at follow-up assessment. Internalizing problem scores at time two were significantly lower than baseline among older adolescents, girls and those with average wealth index in our study cohort.

Conclusions

The prevalence of mental health problems were high among the study cohort. The proportion of overall problems and externalizing problems has increased over time, indicating a deterioration in the mental health of the study cohort. However, the decrease in internalizing problems among older adolescents, girls, and those with an average wealth index is a positive sign. The findings highlight that tailored interventions are required to reduce externalizing problems and maintain the decrease in internalizing problems. These interventions should target middle-aged and male adolescents from low-income families.

Introduction

Adolescence is a vulnerable time for mental illness and its effects due to the rapid social, psychological, cognitive, and emotional changes [1, 2]. Studies carried out in developed countries show that adolescent mental health problems, which can present as internalizing or externalizing mental health problems, have sharply increased over the past few decades [3, 4]. Internalizing (depression and anxiety) and externalizing (conduct disorders and hyperactivity) problems are adolescents’ most commonly reported mental health problems [5]. Although nearly half of mental health disorders begin by age 14 and 75% start by age 24, the majority are underdiagnosed and undertreated [6, 7] or start much later [6]. Thus, failure to detect and treat the problem at an early stage can have detrimental long-term effects, including poor academic performance, unemployment, homelessness, poverty, various health problems, and involvement with the juvenile justice system [8–10].

One in seven (10–19 year old) people worldwide suffer from mental health disorders [11] which account for 16% of the worldwide burden of disease and disability [12]. According to a systematic analysis published in 2019, the global prevalence of common mental disorders among adolescents aged 10 to 19 years was 25% and 31.0%, using the GHQ cut-off point of 4 and 3, respectively [13]. It is estimated that 14.3% of adolescents in Sub-Saharan Africa suffer from mental health problems, with 10% having diagnosable psychiatric disorders [14].

A study of Ghanaian high school students found that the overall rate of mental health problems was 58.5%, with internalizing problems accounting for 36.8% and externalizing problems accounting for 16.3% [15]. According to their parents and adolescents, 31.2% and 40.8% of Tanzanian in-school adolescents had mental health problems, respectively [16]. In Uganda, Kinyanda et al. found an identically high self-reported prevalence of 41.4% [17]. Another study in central Kenya found that internalizing and externalizing problems were present in 17.2% and 5.4% of adolescents, respectively [18]. Few studies in Ethiopia have revealed that more than one-third of college or university students have common mental disorders [19]. Furthermore, the prevalence of childhood mental health problems is 12–25%, making it the country’s health sector’s highest burden of mental illnesses [20]. However, because these studies were all cross-sectional, they could not reveal the dynamics underlying adolescent mental health problems.

Adolescents’ mental health problems persist into adulthood, impairing physical and psychological health and limiting opportunities to lead fulfilling lives as adults if not treated promptly [11, 21]. The problems can have long-term and short-term effects on employment, relationships, violence, substance abuse, reproductive and sexual health, educational attainment, and suicide [22]. Each of these factors can then impact the mental health of the next generation. Furthermore, developing mental health problems during adolescence can contribute to high-risk behaviors such as self-harm, tobacco, alcohol and other substance use, and risky sexual behavior [12, 23, 24]. It also exposes adolescents to social exclusion, domestic violence, school dropout, discrimination, stigma, delinquency, and human rights violations [11].

Existing data show that various life factors, including social, demographic, and socioeconomic factors, influence adolescent mental health [25–28]. Exposure to adversity, peer pressure, identity exploration [11, 29, 30] and quality of home life are also factors that can contribute to mental health problems during adolescence [11, 31, 32]. Furthermore, violence (particularly sexual violence) and bullying at school endanger adolescents’ mental health [11, 33, 34].

Early detection and intervention have improved adolescents’ mental health, lowering delinquency, substance misuse, health-risking sexual behaviors, and school failure [10]. Understanding the dynamics of in-school adolescent mental health problems over time and the factors contributing to them is critical for designing effective interventions. However, there is still a scarcity of research on how adolescent mental health changes over time in developing countries like Ethiopia. Furthermore, no longitudinal studies have been conducted in Ethiopia to determine the stability of these issues over time. As a result, this study looked at the dynamics of in-school adolescents’ mental health problems over a year. Therefore, this research aims to assess in-school adolescents’ mental health dynamics in Harari Regional State, eastern Ethiopia, over a year. We hypothesized that the number of in-school adolescents with mental health problems would remain stable throughout the study, and therefore there would be no significant difference between the SDQ score of T1 and T2.

The finding may help to provide crucial data for policymakers in designing, planning, and implementing interventions of risk reduction and scale-up of services for adolescent mental health conditions.

Methods and procedures

Study setting, designs, and samples

A school-based longitudinal study design was conducted in three randomly selected high schools in Harari Regional State from March 2020 to March 2021. Harari region is located in eastern Ethiopia at 511 km from Addis Ababa. Unlike most other Ethiopian regions, the region’s population is concentrated in urban areas (54.2%) [35]. Psychoactive substances like khat (Catha edulis), tobacco, and coffee play a significant role in the region’s commercial operations. In addition, khat use is very common in the neighborhood, with around a quarter of the young people doing it [35]. In most of Hariri’s rural sub-districts, khat is the main cash crop [36, 37].

The study was conducted over two round data collection periods, from March 1 to 16, 2020 (T1) and March 1 to 20, 2021 (T2). Participants in the study ranged in age from 14 to 19 and were enrolled in public high schools in the Harari regional state of eastern Ethiopia. During the data collection period, seven governmental high schools were in the region. We used a simple random sampling technique to select the schools. We listed all schools and randomly selected three schools using computer-generated sampling. We identified grade levels for each selected school, and finally, sections were selected proportionally using the lottery method from each grade level, considering the number of sections. Finally, participants were selected randomly from each section using systematic random sampling.

The baseline assessment was conducted in March 2020 with 358 randomly selected in-school adolescents from randomly selected schools. We collected data from the same participants for the second time in March 2021. In order to ensure that the data collected from participants at different times could be linked, we used a unique ID code to identify participants for T2 data collection after a year. This allowed us to reconcile the data with T1 data analysis.

Three hundred twenty-eight (91.6%) of the 358 in-school adolescents who participated in baseline assessments completed the second assessment phase. We used the same technique and assessment tools for the follow-up assessment at school. The prevalence of mental health problems during the first phase of data collection was used as a baseline assessment. On the other hand, the prevalence of mental health problems in the second phase was used as a one-year follow-up to see if there was any significant change in problem dynamics. The current analysis included adolescents who completed baseline and follow-up assessments after one year.

Data collection tool

We collected data from adolescent students at their schools using a guided, self-administered data collection technique. We used structured, standardized, self-administered questionnaires to collect the data. All data collection instruments were pre-tested in Dire Dawa administrative counselling among similar in-school adolescents in 5% of the sample size. We also calculated Cronbach’s alpha for the reliability and validity of the tool to assess the scales’ internal consistency and reliability before actual data collection.

Accordingly, the calculated Cronbach’s alphas for the SDQ total, externalizing and internalizing, were 0.72, 0.63, and 0.66, respectively. Eight health professionals collected the data. They provided a suitable classroom for participant students in small groups of no more than 20 per session, as well as instructions on completing the questionnaires. Each session had two data collectors whose main role was to facilitate, read the questions to the participants if necessary and then let them choose their acceptable responses based on the questionnaire frameworks. The trained supervisors and the primary investigator closely monitored the data collection process.

Variables and measurements

Mental health problems were dependent variables measured using the strength and difficulties questionnaire (SDQ-25). Socio-demographic variables (age, sex, residence); socio-economic status (education, occupation, and wealth of the family); psychosocial variables (bullying at school, Fear of COVID-19, risk of COVID-19 exposure); behavioral variables (substance use); biological factors (family history of mental illness, known chronic medical problems) are independent variables and were measured by using a standard questionnaire adopted from previous studies.

Mental health problems were assessed using the self-administered Strength and Difficulties Questionnaire (SDQ) [38–40]. The SDQ consists of 25 items divided into five subscales, with a fifth subscale used to determine strengths. Four subscales contribute to the overall difficulty score. Higher scores indicate a greater risk of developing mental health problems. A 3-point Likert-type scale is used in this survey (0 being not true, 1 being somewhat true, and 2 being true). Using the score banding technique outlined by Goodman, the SDQ total difficulties score is broken down into normal (0–13), borderline (14–16), and abnormal scores [41, 42].

We used the sum of the first four problems domains (excluding the prosocial behavior items), to generate a total difficulties score ranging from 0 to 40. The sum of conduct and hyperactivity scales was used to generate externalizing score which ranging from 0 to 20 and the sum of emotional and peer problem scales to generate internalizing score which ranging 0 to 20. The internalizing problem subscale category is normal (0–7), borderline (8) and abnormal (9–20) while the externalizing subscale for normal, borderline and abnormal are (0–8), (9), (10–20) respectively [38, 43]. A borderline category score was considered a cut-off point for each difficulty sub-scores for indicating mental health problems in this study. The SDQ can be used within community samples to separate internalizing and externalizing problems. We looked at the internalizing and externalizing sub-scores and the discrete sum scores for the overall SDQ score in this study.

Cronbach’s alpha is a measure of the internal consistency or reliability of a set of survey items. It quantifies the level of agreement on a standardized 0 to 1 scale, with higher values indicating higher agreement between items. Cronbach’s alpha is used to determine whether a collection of items consistently measures the same characteristic. In this study, we assessed Cronbach’s alpha to evaluate the internal consistency of our survey items. The Cronbach’s α for SDQ total is 0.764 while it is 0.55 and .65 for internalizing and externalizing in the current sample respectively.

Fear of COVID-19 Scale (FCV-19S) was used to assess the severity of fear related to COVID-19 and has already been validated in many languages. It is a uni-dimensional scale that assesses the fear of COVID-19 and was developed by Ahorsu et al.2020 [44]. The instrument has seven items with a 5-point Likert scale (strongly disagree = 1) to (strongly agree = 5). The minimum possible score for each question is one, and the maximum is seven. The score obtained vary from 7 to 35, with higher scores indicating greater fear of COVID-19.

We assessed the risk of covid-19 by asking, “Is there anyone in your family and/or neighbors who are infected with covid-19?” We provided “yes” or “no” options, and if a participant chose “yes” for the first or second, or both, the participant was considered to have a risk of exposure to COVID-19.

Wealth index is a composite measure of the cumulative living standard of a household. It is calculated using data on a household’s ownership of selected set of assets. In this case, the wealth index was measured using the number and kind of known goods (such as televisions, bicycles, and cars; dwelling characteristics such as flooring material; type of drinking water source; and toilet and sanitation facilities) the family owns as reported by the adolescent and analyzed using the principal component analysis [45].

Substance use was defined as the ever-current use of substances such as alcohol, cigarettes, khat, or other illicit drugs. To assess the use of substances among adolescents, we adapted and used a questionnaire from the Global School-Based Health Survey (GSHS), which is recommended by the World Health Organization (WHO). This questionnaire assesses adolescents’ substances (including alcohol, cigarette and others) and their frequency of use in the previous 12 months [46].

Bullying at school was assessed whether a respondent has been bullied at school, they were asked the question “How often have you been bullied at school in the past couple of months?”. If the answer was more than once a week, then the respondent was categorized as “1” for being bullied at school. Otherwise, the respondent was categorized as “0”.

Self-esteem: defined as the “judgment one makes about their self-concept or “attitude one holds toward themselves as an object that measured via assessing a subject’s attitude about themselves as a “thing.” The 10-item Rosenberg Self-Esteem Scale is used to measure global self-esteem, which consists of statements related to feelings of self-worth and self-acceptance [47]. This 10-items scale ranges from (0 = strongly agree) to 3 (strongly disagree). The sum scores for all 10 items range from to 0–30 with higher scores indicating higher self-esteem. Respondents with a total score >25 were classified as having ‘high self-esteem’, scores between 15 and 25 were within the ‘normal self-esteem’ range, and scores below 15 suggested ‘low self-esteem’.

Data analysis

Data were entered into Epi data and analyzed by STAT 16 with a 95% confidence level for all statistical significance tests. Descriptive statistics, including mean, standard deviations, and percentages, were performed to characterize the sample regarding socio-demographic characteristics and outcome variables. Wilcoxon matched-pairs signed-rank test and McNemar’s test Chi-squared tests were used to compare the median score severity charges of mental health problems among different groups of study participants. To find the potential factors associated with individual mental health disorders, we used random-effects logistic regressions on panel data. The dependent variable in each model was each mental health dimension’s state (binary variable). We performed univariate analysis to check for the homoscedasticity and linearity of the variables before feeding them into the model. We ensured that the assumptions of linearity, independence, homoscedasticity, and normality were met before feeding the variables into the regression model. Statistical criteria of P < 0.2 during univariate analysis were used as a guide to enter independent variables into a multivariate model. Statistical significance was declared at (P < 0.05).

Ethical considerations

Haramaya University’s Institutional Health Research Ethics Review Committee (IHRERC) granted ethical approval with reference number IHRERC/149.2019. Participants were informed that participation in the study was entirely voluntary, that declining to participate would have no negative consequences for them or their families, and that they could stop at any time or skip any questions they did not wish to answer. Respondents got clear and adequate information concerning the research, including purpose, procedures, potential risks and benefits, and their right to participate in the study. Both participants and their parents informed that the information gathered would be disseminated to assist in knowledge generation only. For participants aged 13 to 17, written, informed, and signed voluntary consent was obtained from one of the parents or guardians, as well as written and signed voluntary consent from the adolescents. Participants aged 18 and up provided written, informed, and signed consent. The questionnaires did not include personal identifiers to ensure participant confidentiality. The study adhered to the Declaration of Helsinki’s ethical principles for human subjects’ medical research.

Results

Socio-demographic characteristics of participants

Thirty (8.4%) of the 358 in-school adolescents who took the baseline assessment did not receive follow-up assessments. Attrition was primarily due to changing schools and moving to different regions. As a result, 328 in-school adolescents were included in the final analysis, with a response rate of 94.6%. The respondents’ mean age was 17.5 (SD 1.2), ranging from 14 to 20 years. About half the participants (50.9%) were male, and 62.5% primarily lived in urban areas. The majority (86.9%) of the participants were single, and 13.1% were engaged. About 32.9% of the participating adolescents lived with one parent (father or mother). Regarding the physical health of the adolescents, 18.6% experienced at least one chronic illness. About one-fifth (23.2%) of the respondents reported being bullied in school at least once a week during the last two months.

At the time of the data collection, one-eighth of the participants (12.2%) used cigarettes, one-seventh (14.3%) used alcohol, and one-third (32.3%) used khat. More than half of the participants (55.2%) expressed an intense fear of COVID-19, and 18.0% said they were at risk of contracting the virus. Regarding parental characteristics, 66.5% of fathers and 80.5% of mothers were uneducated, while 33.5% of fathers and 19.5% of mothers were. Looking at the family history of mental problems, approximately (13.1) reported that one of their family members had a mental illness (Table 1).

Table 1. Characteristics of matched respondents in baseline survey (T1) and after one year (T2), (n = 328).

Characteristics	Categories	Baseline survey(T1)	After one year (T2)	p-Value**
Age,	Mean ± SD	16.9 (1.3)	17.5(1.2)	0.001
Gender	Male	167 (50.9%)	167 (50.9%)	-
Gender	Female	161 (49.1%)	161 (49.1%)	-
Residence	Urban	205 (62.5)	205 (62.5)	-
Residence	Rural	123 (37.5)	123 (37.5)	-
Marital status	Single	292 (89.0)	285 (86.9%)	0.001
Marital status	Engaged	36 (11.0%)	43 (13.1%)	0.001
Any chronic disease	No	246 (75.0%)	267 (81.4)	0.001
Any chronic disease	Yes	82 (25.0%)	61 (18.6%)	0.001
Current living status	Live with both parents	220 (67.1%)	220 (67.1)	-
Current living status	Live with one parent	108 (32.9%)	108 (32.9)	-
Mother educational status	Not educated	264 (80.49)	264 (80.49)	-
Mother educational status	Educated	64 (19.5)	64 (19.5)	-
Father educational status	Not education	218 (66.5)	218 (66.5)	-
Father educational status	Educated	110 (33.5)	110 (33.5)	-
History of family mental illness	No	285 (86.9)	285 (86.9)	-
History of family mental illness	Yes	43 (13.1)	43 (13.1)	-
Wealth index	Lowest	134 (40.9%)	131 (39.9)	0.001
	Middle	131(39.9%)	132 (40.2)
	Highest	63 (19.2%)	65 (19.8)
Khat use	No	213 (64.9%)	222 (67.7%)	0.001
Khat use	Yes	115 (35.1%)	106 (32.3%)	0.001
Alcohol use	No	265 (80.8%)	281 (85.7%)	0.001
Alcohol use	Yes	63 (19.2%)	47 (14.3%)	0.001
Cigarette use	No	278 (84.8%)	288 (87.8%)	0.001
Cigarette use	Yes	50 (15.2%)	40 (12.2%)	0.001
Self-esteem	Low	37 (11.3%)	41 (12.5%)	0.001
	Normal	213 (64.9%)	200 (61.0%)
	High	78 (23.9%)	87 (26.5%)
Suicide behaviors	No	288 (87.8%)	289 (88.1%)	0.001
Suicide behaviors	Yes	40 (12.2%)	39 (11.9%)	0.001
Bullying at school	No	239 (72.9)	252 (76.8)	0.001
	Yes	89 (27.1)	76 (23.2)	0.001
FCV-19S	Low	NA	147 (44.8%)	-
FCV-19S	High	NA	181 (55.2%)	-
Risk of exposure to COVID-19	No	NA	269 (82.0%)	-
Risk of exposure to COVID-19	Yes	NA	59 (18.0%)	-

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Dynamics of mental health problems for the year-long follow-up

The magnitude of mental health problems among adolescents was 20.11% (95% CI, 16–25) for overall mental health problems, 27.14% (95% CI, 23–32) for internalizing problems, and 7.01% (95% CI, 4.6–10.3) for externalizing problems at the baseline assessment (T1). At the follow-up assessment (T2), this proportion increased to 22.56% (95% CI, 18–27) in overall problems and 10.3% (95% CI, 7.7–14.5) in externalizing problems but decreased to 22.86% (95% CI, 18.6–27.7) in internalizing problems (Table 2).

Table 2. Pattern and prevalence of the self-reported adolescents’ mental health problems with the corresponding time of the survey (T1 & T2): (n = 328).

Mental health problems domain	SDQ-25 profile with its corresponding time of the survey
	Norma		Borderline		Abnormal
	T1	T2	T1	T2	T1	T2
Internalizing problems N, %	239 (72.9)	253 (77.1)	34 (10.4)	31 (9.5)	55 (16.8)	44 (13.4)
Externalizing problems N, %	305 (93.0)	293 (89.3)	8 (2.4)	11 (3.4)	15 (4.6)	24 (7.3)
SDQ Total difficulties N, %	262 (79.9)	254 (77.4)	29 (8.84)	32 (9.8)	37 (11.28)	42 (12.8)

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The data showed that the difficulty score for overall, internalizing, and externalizing issues decreases as adolescents get older. Table 3 shows the observed change for each item as a function of age.

Table 3. Prevalence distributions of self-reported adolescents’ mental health problem symptoms (items) with ages and the corresponding time of the survey (t1 & t2) (N = 328).

Items	At age 14/15		At age 15/16		At age 16/17		At age 17/18		At age 18/19		At age 19/20
Items	T1 (No, %)	T2 (No, %)	T1 (No, %)	T2 (No, %)	T1 (No, %)	T2 (No, %)	T1 (No, %)	T2 (No, %)	T1 (No, %)	T2 (No, %)	T1 (No, %)	T2 (No, %)
Internalizing problems scores
Somatic symptoms	10(10.2)	5(11.4)	6(10.3)	4(6.4)	29(39.8)	25(45.5)	42(55.2)	37(53.4)	29(36.7)	24(34.9)	34(50.8)	26(48.5)
Worries	11(8.7)	9(7.5)	13(10.3)	13(11.0)	38(29.8)	37(31.4)	68(53.8)	64(53.0)	62(48.9)	57(47.0)	61(48.4)	60(50.1)
Unhappy	7(6.6)	8(7.4)	8(9.8)	7(6.5)	24(29.5)	22(29.4)	42(54.4)	40(53.7)	35(47.8)	33(48.7)	38(51.8)	39(54.3)
Nervous in new situations	8(8.2)	8(9.6)	10(10.7)	10(10.5)	29(31.2)	27(32.3)	51(55.5)	43(53.2)	41(43.0)	38(42.9)	47(51.4)	45(51.6)
Many fears	7(9.3)	10(13.7)	8(7.69)	9(11.1)	27(35.38)	29(39.4)	41(51.41)	42(51.9)	38(50.24)	34(39.3)	38(45.96)	36(44.5)
Solitary	19(9.9)	12(11.1)	12(10.7)	13(12.3)	39(34.5)	38(38.3)	54(48.6)	46(42.5)	48(42.7)	42(38.5)	60(53.6)	59(57.3)
Has good friend+	13 (8.9)	14(11.0)	17 (11.7)	16(13.11)	47(35.75)	47(26.6)	76(44.4)	71(51.8)	71 (45.8)	68(45.6)	78 (54.5)	74(51.9)
Generally liked+	13(9.2)	12(8.3)	14(9.7)	16(11.7)	44(30.7)	45(31.5)	78(48.9)	73(51.2)	69(46.0)	67(47.4)	77(55.4)	69(50.0)
Picked on or bullied	9(9.9)	6(7.9)	7(10.7)	6(8.2)	22(31.2)	24(43.6)	33(48.4)	25(46.0)	32(50.4)	19(32.4)	36(49.5)	35(61.9)
Better with adults	13(9.42)	12(8.9)	15(11.00)	16(12.0)	48(33.36)	47(32.9)	75(52.13)	76(53.8)	67(46.96)	61(42.8)	73(47.13)	74(49.7)
Externalizing problem scores
Tempers	6(7.1)	5(9.3)	4(4.7)	5(8.1)	20(29.9)	23(47.9)	31(58.7)	25(50.3)	23(33.5)	16(33.3)	31(65.4)	24(51.2)
Obedient+	13(9.2)	12(7.9)	17(11.8)	15(10.7)	48(35.9)	42(29.9)	79(46.9)	80(51.7)	72(45.6)	70(50.4)	75(50.4)	74(49.5)
Fights or bullies	6(11.5)	6(10.7)	5(9.9)	6(10.0)	18(35.7)	23(49.5)	20(37.4)	17(31.0)	25(48.6)	20(37.3)	29(57.0)	31(61.6)
Lies or cheats	16(11.3)	6(10.7)	4(7.4)	4(7.7)	17(32.3)	19(48.4)	22(42.4)	23(39.4)	23(44.6)	20(20.4)	31(61.9)	26(58.7)
Steals	4(12.12)	3(9.0)	1(3.23)	3(9.0)	12(37.53)	14(44.6)	11(34.11)	9(20.0)	17(53.86)	12(37.3)	19(59.14)	33(71.8)
Restless	9 (8.9)	7(7.9)	8(7.2)	6(7.9)	28(30.1)	28(32.2)	44 (45.0)	44(51.0)	44 (47.2)	33(41.2)	54 (61.4)	51(61.4)
Fidgety	5(5.2)	8(10.6)	7(7.3)	8(9.0)	18(24.7)	22(40.5)	33(54.3)	28(41.0)	30(49.1)	27(43.0)	36(59.4)	33(56.0)
Easily distracted	9(8.7)	7(7.1)	9(9.4)	13(11.5)	28(28.4)	37(39.0)	46(50.6)	47(47.4)	37(38.3)	37(39.0)	61(64.8)	53(55.8)
Thinks before acting+	12(8.72)	12(8.7)	16(9.68)	17(11.1)	49(34.48)	44(32.3)	80(52.55)	75(53.3)	67(42.55)	67(48.4)	77(52.03)	68(46.0)
Good attention+	13(9)	12(10.3)	15(10.69)	16(11.4)	47(31.36)	47(27.4)	82(49)	76(48.7)	71(52.5)	67(48.4)	77(47.11)	77(53.9)

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T1: baseline or time one assessment

T2: follow-up or time two assessment

No (%): number and percent

^{+ positive} statements which need a reverse score

There was no statistically significant difference in the distribution of the severity of mental health problems from year to year. At T1, one-fifth (20.1%) of adolescents tested positive for overall mental health problems, and 22.6% tested positive at T2 (p = 0.3458). The externalizing dimension’s difficulty score also increased, but the difference was insignificant: 7.01% at baseline vs 10.67% at follow-up (p = 0.0441). Internalizing problem scores decreased from 27.13% to 22.87%, but the difference was not statistically significant (p = 0.1444) (Table 4).

Table 4. Longitudinal changes of overall and subscales by severity in March 2020 (T1) and March 2021(T2).

Overall mental health problems at T1	Overall metal health problems at T2 (n, %)
Overall mental health problems at T1	Normal	Borderline	Abnormal	Total at T1	p-value^*
Normal	222 (84.7)	15 (5.7)	25 (9.5)	262	0.3458
Positive for SDQ score
Borderline	18 (62.1)	9 (31.0)	2 (6.9)	29
Abnormal	14 (37.84)	8 (21.62)	15 (40.54)	37
Total at T2	254 (77.4)	32 (9.76)	42 (12.8)	328
Internalizing problems in 2020	Internalizing problems in 2021(n, %)
Internalizing problems in 2020	Normal	Borderline	Abnormal	Total at T1	0.1444
Normal	200 (83.7)	13 (5.4)	26 (10.9)	239
Positive for internalizing problems
Borderline	22 (64.7)	10 (29.4)	2 (5.9)	34
Abnormal	31 (59.55)	8 (20.2)	16 (20.2)	55
Total at T2	253 (77.1)	31 (9.45)	44 (13.41)	328
Externalizing problems in 2020 (n, %)	Externalizing problems in 2021(n, %)
Externalizing problems in 2020 (n, %)	Normal	Borderline	Abnormal	Total at T1	0.0641
Normal	278 (91.2)	10 (3.28)	17 (5.57)	305
Positive for externalizing problems
Borderline	6 (75.0)	1 (12.5)	1 (12.5)	8
Abnormal	9 (60.0)	0	6 (40.0)	15
Total at T2	293 (89.33)	11 (3.35)	24 (7.32)	328

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* Wilcoxon matched-pairs signed-rank test.

However, there was a significant improvement in internalizing SDQ scores in our study cohort at T2 (median (IQR): T1: 7 (6–10) vs T2: 7 (5–9), among females at T2 (median (IQR): T1: 7 (6–10) vs T2: 7 (5–9), p = 0.0300), and among older adolescents at T2 (median (IQR): T1: 7 (5–9) vs T2: 4 (2–8), p = 0.048)) (Table 5).

Table 5. Internalizing and externalizing scores amongst matched participants (n = 328) and its subgroups of males (n = 167) and females (n = 161).

Mental health outcomes	At baseline (T1)	At follow-up (T2)	At baseline (T1)	At follow-up(T2)	p-Value ^*
Mental health outcomes	Mean (± SD)	Mean (± SD)	Median (IQR)	Median (IQR)	p-Value ^*
All participants
SDQ Internalizing	7.71 ± 3.04	7.28 ± 3.04	7 (6–10)	7 (5–9)	0.0079
SDQ Externalizing	4.8 ± 3.32	5.01 ± 3.44	4 (2–7)	4 (2–7)	0.3825
Males
SDQ Internalizing	7.78 ± 2.93	7.52 ± 3.19	7 (6–10)	7 (5–10)	0.1326
SDQ Externalizing	4.95 ± 3.40	5.29 ± 3.44	7 (6–10)	4 (2–8)	0.1683
Females
SDQ Internalizing	7.63 ± 3.16	7.03 ± 2.86	7 (6–10)	7 (5–9)	0.0300
SDQ Externalizing	4.63 ± 3.24	4.71 ± 3.43	7 (6–10)	4 (2–6)	0.9279
Young adolescent
SDQ Internalizing	7.94 (2.9)	7.46 (3.1)	7 (5–9)	7 (5–10)	0.0900
SDQ Externalizing	4.80 (3.15)	5.01 (3.6)	5 (2–7)	5 (2–7)	1.00
Late adolescent
SDQ Internalizing	7.53 (3.14)	7.11 (2.99)	7 (5–9)	4 (2–8)	0.0498
SDQ Externalizing	4.79 (3.45)	5 (3.28)	4 (2–8)	7 (5–9)	0.2752

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* McNemara’s chi² test.

Females (OR: 0.43, 95% CI: 0.26–0.69), late adolescents (aged 17–20 years) (OR: 0.55, 95% CI: 0.33–0.90), and those from middle-class families (OR: 0.42, 95% CI: 0.23–0.77) had significantly lower odds of developing internalizing mental health problems. Similarly, late adolescents (aged 17–20 years) (OR: 0.17, 95% CI: 0.08–0.32), those with average family wealth (OR: 0.30, 95% CI: 0.13–0.68), and those in higher categories (OR: 0.32, 95% CI: 0.14–0.72) had significantly lower odds of developing externalizing mental health problems. Furthermore, having a history of bullying at school (OR: 2.40, 95% CI: 1.02–5.70) significantly increased the odds of developing externalizing mental health problems (Table 6).

Table 6. The association between individual factors and internalizing and externalizing mental health outcomes (random-effects logistic regressions).

Characteristics	Categories	Internalizing problems	P—values	Externalizing problems	P—values
Age	14–16 years	1	1	1	1
Age	17–20 years	0.55 (0.33–0.90)	0.020	0.17 (0.08–0.32)	0.000
Gender	Male	1	1	1	1
Gender	Female	0.43 (0.26–0.69)	0.001	0.51 (0.25–1.02)	0.058
Any chronic disease	No	1	1	1	1
Any chronic disease	Yes	1.67 (0.83–3.20)	0.151	2.23 (0.95–5.20)	0.063
Wealth index	Lowest	1	1	1	1
	Middle	0.42 (0.23–0.77)	0.005	0.30 (0.13-.68)	0.004
	Highest	0.65 (0.36–1.20)	0.165	0.32(0.14–0.72)	0.006
Khat use	No	1	1	1	1
Khat use	Yes	1.02 (0.507–1.59)	0.930	1.21 (0.54–2.70)	0.627
Alcohol drinking	No	1	1	1	1
Alcohol drinking	Yes	0.58 (0.24–1.34)	0.24	1.81 (0.67–4.90)	0.239
Cigarette smoking	No
Cigarette smoking	Yes	1.9 (0.72–5.01)	0.190	2.0 (0.65–6.1)	0.223
Bullying at school	No
Bullying at school	Yes	1.32 (0.66–2.64)	0.428	2.4 (1.02–5.70)	0.043
FCV-19S	Low	1	1	1	1
FCV-19S	High	1.34 (0.81–2.25)	0.256	0.63 (0.31–1.27)	0.201
Risk of exposure to COVID-19	No	1	1	1	1
Risk of exposure to COVID-19	Yes	0.97 (0.47–1.99)	0.934	0.89(0.34–2.35)	0.822

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Discussion

This research aimed to determine the dynamics of in-school adolescents’ mental health problems over a year in eastern Ethiopia. Our findings revealed that an increase in the externalizing sub-scale from the baseline to the follow-up assessment increased the SDQ scale’s median scores. However, there were no statistical differences in the externalizing issues. Over one year, however, there was a significant improvement in the cohort’s internalizing problem scores, particularly among females and older adolescents. During the follow-up study, older age, female gender, and family wealth index were associated with a lower likelihood of mental health issues.

In contrast to prior studies [18, 48], we found that boys had more difficulty scoring in internalizing than girls. In other ways, a study conducted in China in 2021 reported similar results to our findings [49]. Boys’ poor social interaction could explain this conclusion and more exposure to risky behaviors, such as khat use, tobacco use, alcohol use, excessive screen time, and other forms of social media use commonly observed in the region [50–52].

Our findings indicated that the internalizing problem score unexpectedly decreased during the follow-up assessment. This finding is similar to that in the USA [53], China [54], and Kenya [55] which showed a declining rate of youth mental health problems in a follow-up study. According to our findings, a lower incidence of mental health disorders was associated with the female sex among adolescents during this one year. The reason may be due to protective measures such as staying home during the COVID-19 pandemic, which may increase family time and adolescent relationships. It could be because girls have better relationships with their families, peers, and friends than boys. Their gender-based reactions to problems could explain this disparity [56–58].

Our finding suggested that adolescent mental health problems were associated with older age. Compared to earlier adolescents, teenagers aged ≥17 experienced fewer odds of mental health issues. Previous investigations of individual factors associated with teenage mental health problems [54, 59] found similar results. The correlation may be due to cognitive maturity and coping mechanisms of traumatic life events than young teenagers, such as COVID-19 for instance, or better suited to dealing with difficult full-life situations. People with a higher level of education reported less stress and greater control in their daily lives [60–62].

Individuals in life-threatening situations are more likely to develop mental health issues [63, 64]). Because this study was conducted during the COVID-19 pandemic, we anticipated an increase in mental health problems among these susceptible and more vulnerable groups [65]. We assess the fear of COVID-19 and the risk of infection exposure during the pandemic period. In our study sample, there was no significant relationship between adolescent mental health problems and the consequences of the COVID-19 pandemic. Other studies have found that adolescents who fear their COVID-19 score have more mental health problems. COVID-19 has an association with adolescents’ mental health difficulties in research from Turkey [66], North California [67], China [68, 69], and Australia [70, 71]. Fear of the current epidemic has been shown to have the likelihood of developing mental health difficulties like psychological distress, panic disorder, post-traumatic stress symptoms, and significantly moderate to severe depressive symptoms [66, 72, 73].

Fear of developing COVID-19 and contracting the virus may be one of the reasons In contrast, house confinement and accompanying social and physical isolation are all critical risk factors for developing mental health problems [74]. In our study environment, illness perception, community awareness, cultural concerns, health beliefs, contradictory information from all sources, perplexing messages from health professionals, and perspectives of friends and social networks may all play a role in the inverse association [75]. More research with a larger sample size is needed to rule out these surprising findings in our study settings with similar study subjects and conditions to corroborate the findings.

Strengths and limitations of the study

This sample is valuable because it represents a significant population in the region for which mental health status should understand. However, the generalizability of our findings may be limited since the included data from only three schools. Second, even though a multi-method assessment would be ideal for understanding teenagers’ mental health concerns, we collect data using self-report measurements, which may disguise some internalizing and externalizing behaviors. Third, school settings may exacerbate mental health issues, increasing their incidence. The results have also hampered the fact that the data was self-reported, which could have skewed the results due to numerous biases (e.g., method bias, social desirability bias, memory recall bias). Repeating the current study with more prominent and representative samples from inside and beyond the region is recommended to minimize the mentioned limitation.

Conclusion

The overall mental health problems among in-school adolescents were high during the study period. The Follow-up evaluations revealed that internalizing problems had improved significantly. The female sex, older age, and the average income index were associated with lower odds of mental health problems. School-based programs promoting prevention, early identification, and rapid intervention are essential. Tailored interventions for reducing adolescent mental health problems should focus on middle-aged and male adolescents from low-income families.

Supporting information

S1 Data

(DTA)

pone.0300752.s001.dta^{(806.1KB, dta)}

Acknowledgments

We want to thank Haramaya University for giving us the approval to conduct this research. Additionally, we would like to thank the Harari Region Education Office for organizing the participants, instructors, and school administrators. We sincerely thank the data collectors for planning and completing the task with care.

Data Availability

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

Funding Statement

Haramaya University provided financial support for our study under the Scientific Research Grant number “HURG-2020-02-01-92”. The financial support was fifty thousand Ethiopian birr. 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.0300752.r001

Decision Letter 0

Anthony A Olashore

6 Jan 2023

PONE-D-22-31044Mental health dynamics of adolescents: A one-year longitudinal study in Harar, eastern Ethiopia.PLOS ONE

Dear Dr. Hunduma,

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.

Please submit your revised manuscript by Feb 20 2023 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'.
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We look forward to receiving your revised manuscript.

Kind regards,

Anthony A. Olashore, MBCHB, FWACP

Academic Editor

PLOS ONE

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"6724"

At this time, please address the following queries:

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We will update your Data Availability statement to reflect the information you provide in your cover letter.

Additional Editor Comments:

I Agree with the first reviewer; the manuscript is difficult to follow due to extensive grammatical errors. Therefore, I suggest you rewrite the manuscript with the help of a native English speaker.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

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

Reviewer #1: The paper is a good start and a good area of interest. But a coherent flow in some areas is lacking, especially the introduction and discussion. I suggest that the authors re-write the manuscript with the help of a native English speaker.

Reviewer #2: The research is important work which is dire of literature in Africa. The work, though the findings are not statistical significant will serve as a pilot for future larger studies. The authors need to make a few corrections i.e. line 34: 'McNemar's test' instead of 'McNamara's test'. line 69: The article you reference states ''Global prevalence of CMD in adolescents was 25.0% and 31.0%, using the GHQ cut-off point of 4 and 3, respectively. '' The female and male figures are not in the paper. both figures are less then 25 so the sentence does not make sense. line 112: Attrition 358 to 328 - Please provide possible reason and its effects on your result.

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

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

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

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PLOS review 1.docx

pone.0300752.s002.docx^{(14.5KB, docx)}

PLoS One. 2024 Apr 18;19(4):e0300752. doi: 10.1371/journal.pone.0300752.r002

Author response to Decision Letter 0

17 Feb 2023

We wrote a rebuttal letter addressing each point raised by the academic editor and reviewer(s). we uploaded this letter as a separate file labelled 'Response to Reviewers'. 'Manuscriptr' and 'Revised Manuscript with Track Changes'.

Attachment

Submitted filename: Response to Reviewrs.docx

pone.0300752.s003.docx^{(29.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0300752.r003

Decision Letter 1

Anthony A Olashore

11 Aug 2023

PONE-D-22-31044R1Mental health dynamics of adolescents: A one-year longitudinal study in Harar, eastern Ethiopia.

PLOS ONE

Dear Dr. Hunduma

Thank you for submitting your manuscript to PLOS ONE. The reviewers have accepted your manuscript, but I would recommend making some minor edits to your manuscript for it to be accepted for publication.

Editor's comments

1. Your abstract is too lengthy; try summarising it into 250 words or less.

2. Remove the statement ‘We assumed that the number of in-school adolescents with mental

health problems would remain stable throughout the study’ from the abstract.

3. Can you please clarify what you meant by the statement below?

‘To lessen adolescent mental health issues, quick interventions that emphasize middle age, the male gender, and wealth disparity are required.’

Kindly reword this statement …may be something like ‘interventions should focus on middle-aged and male adolescents from low-income families’ it sounds more reasonable.

4. Some parts of the manuscript contain punctuations before and after in-text citations, e.g., lines 82, 88, and 98; kindly edit these typos. Also, check the spacing between the in-text citations and the last words. In some, there is space between them, while in others none, e.g., lines 69, 81, etc. These are all over the manuscript.

5. Line 296 and in other parts of the manuscripts consider replacing ‘khat chewing, tobacco smoking, alcohol drinking’ with ‘the use of khat, tobacco, and alcohol.’ You can even say Khat use, tobacco use, and alcohol use.

6. End your introduction by explaining how your findings could benefit society, such as through policy changes or improving respondents' health.

7. Last statement in your conclusion on page 22, line 348, remove tolerated.

Please address these concerns before I reach a decision about your manuscript.

Please submit your revised manuscript by Sep 25 2023 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,

Anthony A. Olashore, MBCHB, PhD, FWACP

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments: Kindly address the comments listed above and resubmit.

Comments to the Author

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

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Reviewer #2: Line 123 - 'Harare’s' SHOULD READ 'Harari's'

The document reads well. I am happy for it to be accepted for publication.

Reviewer #3: The research is scientifically and socially justified and the authors clearly highlighted the gap in literature that merited the research. The study methodology was appropriate and data appropriately collected and analyzed. The results were well described and the discussion showed the contribution of the study to existing literature. The limitations of the study were reported and they were acceptable. The references cited were appropriate.

**********

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

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

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

Reviewer #2: No

Reviewer #3: Yes: Dr. Radiance Ogundipe

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PLoS One. 2024 Apr 18;19(4):e0300752. doi: 10.1371/journal.pone.0300752.r004

Author response to Decision Letter 1

18 Aug 2023

We have seen that the comments and suggestions offered by the editor have immensely helped us to improve our manuscript. We have considered every comment raised and have responded point by point, indicating how we addressed them and tracking the changes we have made. The changes are presented in the revised manuscript and also our responses to the comments are provided in the table attached with suggested modification of the authors.

Attachment

Submitted filename: Response to Reviewrs.docx

pone.0300752.s004.docx^{(24.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0300752.r005

Decision Letter 2

Anthony A Olashore

9 Jan 2024

PONE-D-22-31044R2Mental health dynamics of adolescents: A one-year longitudinal study in Harar, eastern Ethiopia.PLOS ONE

Dear Dr. Hunduma,

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

ACADEMIC EDITOR: None

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

Please submit your revised manuscript by Feb 23 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,

Anthony A. Olashore, MBCHB, PhD, FWACP

Academic Editor

PLOS ONE

Journal Requirements:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

**********

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

Reviewer #4: Yes

Reviewer #5: Partly

**********

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

Reviewer #4: I Don't Know

Reviewer #5: No

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Reviewer #4: The manuscript has been revised accordingly, and now from my side it is acceptable. The statistical part should be revised by an expert

Reviewer #5: 1. This is a good paper. However, it needs some improvements.

2. The abstract should be re-write after attending to recommendations.

2. Authors wrote a very good introduction. However, there is a need to write done hypothesis as authors wanted to test whether there were differences in SDQ score of T1 and T2.

3. Methods:

-Study setting, designs, and samples:

Line 107: how many schools exist in the region and how authors conducted the random selection? Were they choosing one per cluster, or they listed all schools and use MS Excel or any software for random selection...

Same for selection of participants (line 117).

-Data collection tool:

How did authors do to identify participants after one year for T2 data collection and reconcile with T1 data for the sample selection was dependent T1 and T2: did they use identifiable such names, or ID...

Line 130 and 131: authors state that "...we conducted pretests and confirmed Cronbach's alpha for reliability and validity..." Was this conducted as a pilot study? They included how many participants in the pilot study?

-Variables and Measurements: some variables are in the results section in table 1 but authors did not define them in the methods section e.i. Wealth index, Alcohol use, FCV-19S...

-Regression models are parametric statistic tests. This means that to feed variables in the model, one needs to find out, after univariate analysis, about the homoscedasticity and lineality of these variables, not only about a p value of <0.1 or 0.2.

-Authors repeatedly assess Cronbach alpha while this was not part of the study objectives. Yet they did not even define how they interpreted it in the methods section.

4. Results

-Table 3: It could be better if authors calculated the weighted rate.

**********

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

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

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

Reviewer #4: Yes: PANCHANAN ACHARJEE

Reviewer #5: Yes: Tshitenge, Stephane

**********

PLoS One. 2024 Apr 18;19(4):e0300752. doi: 10.1371/journal.pone.0300752.r006

Author response to Decision Letter 2

19 Jan 2024

Author's Responses to Questions or Responses to reviewers

Review Comments to the Author

Reviewer # 4:

All comments have been addressed. The manuscript has been revised accordingly, and now from my side it is acceptable. The statistical part should be revised by an expert

Response

Thank you for your positive feedback and accepted as presented.

To provide a detailed explanation of the statistical methods used in our study, we start by describing the purpose of each test and how it was applied to our data. Wilcoxon matched-pairs signed-rank test: This test is used to compare two related samples, such as before-and-after measurements or matched pairs of subjects. It tests whether the median difference between the two samples is significantly different from zero. In our study, this test was used to compare the median score severity charges of mental health problems among different groups of study participants as depicted in table 4.

• We used McNemar’s Chi-squared test to compare two paired proportions, such as the proportion of subjects who respond positively during the base line assessment and during the follow up assessment. It tests whether the proportion of subjects who change from one category to another is significantly different from what would be expected by chance. We used this test to compare the severity charges of mental health problems among different groups of study participants (table 5).

• Random-effects logistic regressions on panel data: This method is used to analyze longitudinal data, where the same subjects are measured repeatedly over time. It models the relationship between a binary outcome variable (such as the presence or absence of a mental health disorder) and one or more predictor variables (such as age, gender, or treatment group). In our study, this method was used to find the potential factors associated with individual mental health disorders (table 6).

Review Comments to the Author

Reviewer #5:

1. This is a good paper. However, it needs some improvements.

Response: Thank you for your positive feedback.we try to address the raised issues points by point and we hope that we improved our revised manuscript.

2. The abstract should be re-write after attending to recommendations.

Response: We appreciate your point. The abstract makes this clear under the conclusion part. We re-write the paragraph in this way. “The prevalence of mental health problems were high among the study cohort. The proportion of overall problems and externalizing problems has increased over time, indicating a deterioration in the mental health of the study cohort. However, the decrease in internalizing problems among older adolescents, girls, and those with an average wealth index is a positive sign. The findings highlight that tailored interventions are required to reduce externalizing problems and maintain the decrease in internalizing problems. These interventions should target middle-aged and male adolescents from low-income families”.

3. Authors wrote a very good introduction. However, there is a need to write done hypothesis as authors wanted to test whether there were differences in SDQ score of T1 and T2.

Response: We are grateful and the comment is acceptable. We hypothesized that the number of in-school adolescents with mental health problems would remain stable throughout the study, and therefore there would be no significant difference between the SDQ score of T1 and T2.this is included in the revised manuscript. Please kindly refer lines 104-106.

4. Methods: -Study setting, designs, and samples: Line 107: how many schools exist in the region and how authors conducted the random selection? Were they choosing one per cluster, or they listed all schools and use MS Excel or any software for random selection... Same for selection of participants (line 117).

Response: Thank you for raising this point. During the data collection period, seven governmental high schools were in the region. We used a simple random sampling technique to select the schools. We listed all schools and randomly selected three schools using computer-generated sampling. We identified grade levels for each selected school, and finally, sections were selected proportionally using the lottery method from each grade level, considering the number of sections. Finally, participants were selected randomly from each section using systematic random sampling. All are incorporated in the revised manuscript.

5. Data collection tool: How did authors do to identify participants after one year for T2 data collection and reconcile with T1 data for the sample selection was dependent T1 and T2: did they use identifiable such names, or ID...

Response: Thank you for bringing up such crucial points. In order to ensure that the data collected from participants at different times could be linked, we used a unique ID code to identify participants for T2 data collection after a year. This allowed us to reconcile the data with T1 data analysis. It is included in the revised manuscript.

6. Line 130 and 131: authors state that "...we conducted pretests and confirmed Cronbach's alpha for reliability and validity..." Was this conducted as a pilot study? They included how many participants in the pilot study?

Response: Thank you for your feedback. We conducted pretests on 5% of the sample size to confirm the reliability and validity of the items. All data collection instruments were pre-tested in Dire Dawa administrative counselling among similar in-school adolescents in 5% of the sample size. However, we did not conduct this as a pilot study. We calculated Cronbach’s alpha for the reliability and validity of the tool to assess the scales’ internal consistency and reliability before actual data collection. We apologize for any confusion this may have caused. And it is incorporated the revised manuscript as shown in track change.

7. Variables and Measurements: some variables are in the results section in table 1 but authors did not define them in the methods section e.i. Wealth index, Alcohol use, FCV-19S...

Response: We appreciated your comments. All important variables in the results section are defined in the methods section including Wealth index, substance use, such as alcohol, cigarettes, khat, or other illicit drugs, Bullying at school and Self-esteem:as per the suggestions.

8. Regression models are parametric statistic tests. This means that to feed variables in the model, one needs to find out, after univariate analysis, about the homoscedasticity and lineality of these variables, not only about a p value of <0.1 or 0.2.

Response: Thank you for your feedback. We have checked the assumptions of linearity, independence, homoscedasticity, and normality of the model during our data analysis. We performed univariate analysis to check for the homoscedasticity and linearity of the variables before feeding them into the model. We also checked for normality by examining the distribution of the residuals. We ensured that these assumptions were met before feeding the variables into the regression model and it is incorporated in the revised manuscript. Thank you for bringing this to my attention.

9. Authors repeatedly assess Cronbach alpha while this was not part of the study objectives. Yet they did not even define how they interpreted it in the methods section.

Response: Thank you for your feedback. We acknowledge that Cronbach’s alpha was assessed repeatedly in our study. We will ensure that we only assess Cronbach’s alpha if it is relevant to the study objectives in future studies. Additionally, we defined how Cronbach’s alpha was interpreted in the methods section of the study to help readers understand the rationale behind the assessment of Cronbach’s alpha. It is described as “Cronbach’s alpha is a measure of the internal consistency or reliability of a set of survey items. It quantifies the level of agreement on a standardized 0 to 1 scale, with higher values indicating higher agreement between items. Cronbach’s alpha is used to determine whether a collection of items consistently measures the same characteristic. In this study, we assessed Cronbach’s alpha to evaluate the internal consistency of our survey items” in the revised manuscript.

10. Results -Table 3: It could be better if authors calculated the weighted rate.

Response: Thank you for your feedback. Our intention was to show the prevalence distributions of self-reported adolescents’ mental health problem symptoms with ages and the corresponding time of the survey (T1 & T2). We agree that calculating the weighted rate could be useful in adjusting for differences in the distribution of confounding variables between the study groups. However, we believe that our study groups were similar in terms of these variables, and therefore, calculating the weighted rate may not be necessary. We hope this explanation helps clarify our reasoning for not calculating the weighted rate.”

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PLoS One. doi: 10.1371/journal.pone.0300752.r007

Decision Letter 3

Anthony A Olashore

5 Mar 2024

Mental health dynamics of adolescents: A one-year longitudinal study in Harar, eastern Ethiopia.

PONE-D-22-31044R3

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PLoS One. doi: 10.1371/journal.pone.0300752.r008

Acceptance letter

Anthony A Olashore

21 Mar 2024

PONE-D-22-31044R3

PLOS ONE

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Data Availability Statement

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

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PERMALINK

Mental health dynamics of adolescents: A one-year longitudinal study in Harari, eastern Ethiopia

Gari Hunduma

Yadeta Dessie

Biftu Geda

Tesfaye Assebe Yadeta

Negussie Deyessa

Roles

Abstract

Aims

Materials and methods

Results

Conclusions

Introduction

Methods and procedures

Study setting, designs, and samples

Data collection tool

Variables and measurements

Data analysis

Ethical considerations

Results

Socio-demographic characteristics of participants

Table 1. Characteristics of matched respondents in baseline survey (T1) and after one year (T2), (n = 328).

Dynamics of mental health problems for the year-long follow-up

Table 2. Pattern and prevalence of the self-reported adolescents’ mental health problems with the corresponding time of the survey (T1 & T2): (n = 328).

Table 3. Prevalence distributions of self-reported adolescents’ mental health problem symptoms (items) with ages and the corresponding time of the survey (t1 & t2) (N = 328).

Table 4. Longitudinal changes of overall and subscales by severity in March 2020 (T1) and March 2021(T2).

Table 5. Internalizing and externalizing scores amongst matched participants (n = 328) and its subgroups of males (n = 167) and females (n = 161).

Table 6. The association between individual factors and internalizing and externalizing mental health outcomes (random-effects logistic regressions).

Discussion

Strengths and limitations of the study

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Anthony A Olashore

Roles

Author response to Decision Letter 0

Decision Letter 1

Anthony A Olashore

Roles

Author response to Decision Letter 1

Decision Letter 2

Anthony A Olashore

Roles

Author response to Decision Letter 2

Decision Letter 3

Anthony A Olashore

Roles

Acceptance letter

Anthony A Olashore

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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