Validation of the English and Swahili Adaptation of the Patient Health Questionnaire–9 for Use Among Adolescents in Kenya

Tele, Albert; Carvajal, Liliana; Nyongesa, Vincent; Åhs, Jill W.; Mwaniga, Shillah; Kathono, Joseph; Yator, Obadia; Njuguna, Simon; Kanyanya, Ian; Amin, Nabila; Kohrt, Brandon A.; Wambua, Grace Nduku; Kumar, Manasi

doi:10.1016/j.jadohealth.2022.10.003

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…These findings reveal that among youth presenting to medical clinics at an academic medical center serving largely rural and 25.5) *For n = 196 patients (n = 35 are < 12 years old, and n = 161 are 12-17 years old) with at least 1 follow-up visit after initial screening **Remission status is calculated for 98 youth who had at least 4 months of follow-up after initial screening 12 and older (Johnson et al 2002). Although recent work has used and validated the PHQ-A in children as young as 10 years of age (Horowitz et al 2021;Tele et al 2023), the measure has not been validated in children as young as age 8. As such, the results for patients under the age of 12 in this study should be interpreted cautiously.…”

Section: Discussionmentioning

confidence: 99%

Integration of Measurement-Based Care for Youth Depression and Suicidality Using VitalSign6

Mitchell,

Wakefield,

Rezaeizadeh

et al. 2024

Child Psychiatry Hum Dev

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Depression and suicidality are prevalent in youth and are associated with a range of negative outcomes. The current study aimed to evaluate a measurement-based care (MBC) software (VitalSign6) tool to improve the screening and treatment of depression and suicidality in youth aged 8–17 years within a rural, underserved population. To assess for depression and suicidality, the Patient Health Questionnaire-2 was administered as an initial screen, and the Patient Health Questionnaire-9 Modified for Adolescents (PHQ-9-A) was administered if the initial screen was positive. Data were collected at medical clinics over one year, and descriptive statistics and t-tests or Wilcoxon-Mann-Whitney tests were conducted. A total of 1,984 youth were initially screened (mean age of 13 years; 51.6% female); 24.2% screened positive for depression, and 14.9% endorsed suicidality. Of those who screened positive, the mean PHQ-9-A score was 12.8; 66.9% had PHQ-9-A scores in the moderate to severe range, and 44.2% endorsed suicidality. Almost half of the youth who screened positive for depression had at least one follow-up assessment, and about one quarter achieved remission 4 months after initial screening. Adolescents (12–17 years) had higher PHQ-9-A scores, higher suicidality, and more follow-up assessments than younger youth (8–11 years). Younger youth had higher rates of remission. The widespread use of MBC was feasible in this setting. It is important to utilize MBC to identify and treat youth with depression and suicidality and to do so in younger populations to improve their trajectory over time; VitalSign6 is one tool to help achieve these goals.

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Section: Discussionmentioning

confidence: 99%

Integration of Measurement-Based Care for Youth Depression and Suicidality Using VitalSign6

Mitchell,

Wakefield,

Rezaeizadeh

et al. 2024

Child Psychiatry Hum Dev

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“…A score of 5–9, 10–14, 15–19, and 20–27 represents the cut-off points for mild, moderate, moderately severe, and severe depression, respectively. PHQ-9 has been used and validated for use in adolescents from Africa [ 2 , 20 , 43 ]. The tool has been validated in the Swahili language [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Mental health and psychological well-being of Kenyan adolescents from Nairobi and the Coast regions in the context of COVID-19

Mbithi

Mabrouk

Sarki

et al. 2023

Child Adolesc Psychiatry Ment Health

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Background Despite the high burden of mental health problems during adolescence and its associated negative consequences, it has remained neglected especially in sub-Saharan Africa. The 2019 novel Coronavirus disease (COVID-19) pandemic has placed additional stress on adolescent mental health. However, there are few studies documenting the burden of mental health problems and even fewer mental health services in the region. In relation to the limited body of knowledge, the present study aims to determine the psychological well-being of adolescents and to assess the risks and associated factors of mental health problems among adolescents in the context of COVID-19 pandemic in Kenya. Methods We conducted a cross-sectional survey in 2022 among adolescents aged 13–19 years living in Nairobi, and the Coast region of Kenya. We utilized standardized psychological assessment tools including the Patient Health Questionnaire, Generalized Anxiety Scale, Strengths and Difficulties Questionnaire, The World Health Organization- Five Well-Being Index Scale, and the Pandemic Anxiety Scale, to evaluate the psychological wellbeing of the adolescents. A linear regression model was used to evaluate the correlates associated with quality of life, pandemic anxiety, and emotional and behavioural problems among adolescents. Subsequently, a logistic regression model was used to assess factors associated with depression and general anxiety disorders. Variables with a p-value < 0.25 in the univariate model were included in the multivariable regression model. Results The results are based on 797 participants who met the inclusion criteria. We found the prevalence of depression to be relatively higher among out-of-school adolescents at 36.0% compared to school-going adolescents at 20.6%. Furthermore, out-of-school adolescents had significantly higher anxiety scores when compared to their school-going counterparts (27.7% vs 19.1%) respectively. In-school adolescents had a better quality of life scores, lower pandemic anxiety scores, and lower emotional and behavioral problems scores compared to their out-of-school counterparts. Key risk factors associated with depression include; being out-of-school (OR = 1.96 (95% CI 1.33- 2.88) p-value = 0.001), loneliness (OR = 10.68 (95% CI 4.49–22.86) p-value < 0.001), and living in an unsafe neighborhood (OR = 2.24 (95% CI 1.52–3.29) p-value < 0.001). An older age (OR = 1.16 (95% CI 1.03–1.30) p-value = 0.015), being out-of-school (OR = 1.81 (95% CI 1.19–2.77) p-value = 0.006), and living in an unsafe neighborhood (OR = 2.01 (95% CI 1.33–3.04) p-value = 0.001 were key factors associated with anxiety. Furthermore, key factors positively correlated with quality of life include; high socioeconomic status (ß (Std.Err) = 0.58 (0.14) p-value < 0.001, talking to friends often (ß (Std.Err) = 2.32 (0.53) p-value < 0.001, and being close to parents (ß (Std.Err) = 1.37 (0.62) = 0.026. Conclusion Our findings imply that mental health support services targeting adolescents in the country should be prioritized, especially for those who are out-of-school.

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“…As such, our understanding of the constructs measured by these Western-derived instruments rely on assumptions about the generalizability of Western-derived research methods and nosological constructs across diverse populations ( Atilola, 2015 ). And whereas there is a lot of utility in using similar measures across cultures, to develop a robust understanding of what we are measuring, we should aim to complement these tools with culturally adapted or locally co-developed measures ( Nyongesa et al, 2022 ; Tele et al, 2023 ). Thus, we need to measure what matters for all stakeholders and school staff (and through co-creation we can identify what matters to different stakeholders) and we need to measure it meaningful by taking into consideration not only psychometric properties but whether the measure captures all culturally – salient features of the outcome we are assessing.…”

Section: What We Have Learnedmentioning

confidence: 99%

Lessons learned from conducting mental health intervention research in schools in the global south: Our experiences in South Africa and Kenya

Loades

Coetzee

Osborn

et al. 2023

Clin Child Psychol Psychiatry

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Most of the world’s population of young people live in lower-and middle-income countries (LMICs; (Weine, Horvath Marques, Singh, & Pringle, 2020)), and these young people experience heightened rates of known risk factors for developing mental disorders such as poverty and exposure to trauma (Atwoli, Stein, Koenen, & McLaughlin, 2015). Access to professional psychological treatments is limited in LMICs due to structural barriers (e.g., a dearth of trained professionals) and cultural factors like stigma and beliefs about mental health and illness. Therefore, schools, which are widely attended, may be a good location for providing mental health interventions, and it is important that we develop and evaluate feasible, acceptable, effective, and scalable interventions for use in this context. Yet under 10% of clinical trials of psychotherapies (Venturo-Conerly, Eisenman, Wasil, Singla, & Weisz, 2022) have been conducted in LMICs. And there are particular challenges to conducting research in schools, as has been highlighted in the UK context by Moore et al. (2022). Building on that commentary, our aim herein is to share our learnings from conducting psychotherapy research in schools in Kenya and South Africa.

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Validation of the English and Swahili Adaptation of the Patient Health Questionnaire–9 for Use Among Adolescents in Kenya

Cited by 15 publications

References 42 publications

Integration of Measurement-Based Care for Youth Depression and Suicidality Using VitalSign6

Integration of Measurement-Based Care for Youth Depression and Suicidality Using VitalSign6

Mental health and psychological well-being of Kenyan adolescents from Nairobi and the Coast regions in the context of COVID-19

Lessons learned from conducting mental health intervention research in schools in the global south: Our experiences in South Africa and Kenya

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