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Prevalence of mental disorders in Uganda: a systematic review protocol

Opio, John Nelson; Tufanaru, Catalin; Aromataris, Edoardo

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JBI Database of Systematic Reviews and Implementation Reports: August 2018 - Volume 16 - Issue 8 - p 1613-1620
doi: 10.11124/JBISRIR-2017-003626
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Mental disorders account for a substantial proportion of health burden. They contributed to approximately 10% of global burden of disease as measured by disability-adjusted life years (DALYs) in 2010.1 The DALY is a quantitative health measure that combined the non-fatal component of the disease or injury as years live with disability (YLDs) and the fatal component as years of life lost (YLLs) or premature death.2 The Global Burden of Disease (GBD) Study 2016 reported mental disorders as the leading cause of YLDs in 2016 affecting an estimated 1.1 billion people globally.3 Despite the existence of a wide range of mental, neurological and substance use disorders,4,5 depression and anxiety disorders are among the top ten causes of ill health in nearly all countries globally.1,3 Importantly, the World Health Organization (WHO) 2017 report on the burden of mental disorders indicates that globally, 80% of people with mental disorders are living in countries classified by the World Bank6 as low- and middle-income countries (LMIC) like Uganda.7

Diagnosis of mental disorders is consistently increasing. Previous studies have shown that the number of cases of mental disorders increased by 41% globally between 1990 and 2010.1 Conversely, other authors have forecast that the prevalence of mental disorders in Sub-Saharan Africa (SSA) is likely to increase by 130% by 2050 due to epidemiological and demographic transitions.8 Uganda is part of SSA. Demographic factors, particularly age, appear to have a strong association with the burden attributable to mental disorders in a population. Empirical evidence suggests that a substantial proportion of all lifetime cases of mental disorders begins to occur early in life (nearly half by the age of 14 and three-quarters by the age of 24).9 These age groups constitute an estimated 80% (50.3% are under 15 years and 30.6% are 15 to 24 years) of the Ugandan population.10 The age distribution and the age of onset of mental disorders can explain the empirical evidence that indicates the higher burden of these conditions among people aged 15 to 49 years.1,11,12 The demographic attribute of mental disorders suggests that Uganda, with about 95% of the population below 60 years, is likely to be disproportionately affected,13 highlighting the need to evaluate the prevalence of mental disorders across all age groups.

The cause of mental disorders was once believed to be mystical.14,15 Nowadays, research indicates that there is a relationship between biological (infection, brain damage and genetics), psychological (fear, failure and anger) and social (education, social class, self-esteem, loss of a relationship and societal expectation) factors that triggers mental disorders.4,7,16,17 In addition, mental disorders are associated with chronic physical illnesses such as human immunodeficiency virus (HIV), coronary heart disease, cancer and diabetes.18 In developed countries, research shows that people with mental disorders die 15 to 20 years earlier due to chronic physical illness.19 These factors present challenges and opportunities for policy-makers and health service planners to improve the mental health of about 40 million people in Uganda. Despite the existence of factors that trigger mental disorders, the prevalence of mental disorders in Uganda remains poorly understood.1,20

The successful planning and implementation of interventions to improve health services for people with mental disorders in any country like Uganda require reliable prevalence data on the mental disorders that are affecting its populations.21,22 Accordingly, in response to the global burden of mental disorders,23 the WHO developed the Mental Health Gap Action Program (mhGAP) in 200824 and the Mental Health Action Plan 2013–202025 in 2013 to guide efforts to improve access to health services for people with mental disorders. Improving access to health services is dependent on interactions between multiple factors within health service delivery systems.22,26,27 These factors include demographic profiles (age, sex and others), social structures and health beliefs of individuals as well as resources available to the individual or family, health information and services. Lastly, the individual or population health needs (perceived or evaluated health status) will also influence access to health services.22,27 For example, studies of behavior of people with mental disorders or their family suggest that their willingness to seek help or utilize health services is driven by their self-perceived health status.28-30 This highlights the importance of prevalence data on mental disorders for each country as one of the stimuli to increase awareness and redirect efforts to improve access to health services by people with mental disorders.

A preliminary search for previous systematic reviews on the prevalence of mental disorders in Uganda was conducted in PROSPERO and PubMed. The search found no specific previous systematic reviews on the prevalence of mental disorders in Uganda.

Inclusion criteria


This review will consider studies that include children below 18 years and/or adults 18 years and over with or without co-existing physical health problems and without restriction on gender. The participants will be representative of the general population (representativeness will be determined by participant recruitment or sampling methods).


This review will include studies that investigate the prevalence of mental disorders defined according to diagnostic criteria in the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) or the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV or DSM-5.4,5 Included studies must use validated diagnostic instruments. Eligible studies must identify/report on one or more specific mental disorders such as depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, disruptive behavioral disorders (such as attention-deficit hyperactivity disorder [ADHD]) and conduct disorders. Eligible studies will report current (one week or one month), period (six months or 12 months) or lifetime estimates of the prevalence of mental disorders. The review will exclude studies that focus on the prevalence of substance use and neurological disorders such as alcohol use disorders, drugs use disorders, dementia, migraine, epilepsy and others. Furthermore, studies will be excluded if diagnosis of mental disorders reported is not based on validated instruments.


This review will include studies that report on prevalence of mental disorders conducted in the community, schools, general hospitals/health facilities/primary healthcare located in Uganda. It will exclude studies in which participants are drawn from prisons or mental health clinics whereby the prevalence of mental disorders is not representative of the general population.

Types of studies

This review will consider observational study designs: population based cross sectional studies and cohort studies. Other study designs that provide indications of prevalence of mental disorders including randomized control trails will be considered for inclusion.

Studies published after the development of the mhGAP-Intervention Guide 2010 will be included to capture recent trends in the prevalence of mental disorders in Uganda. No restriction to language of publication or sample size will be set.


This review will follow the Joanna Briggs Institute (JBI) guidelines for conducting and reporting reviews of prevalence and incidence, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).21,31,32

Search strategy

A three-step search strategy will be used to locate both published and unpublished studies of the prevalence of mental disorders in Uganda.33 An initial limited search of PubMed and PsycINFO has been undertaken followed by analysis of the text words contained in the title and abstract, and of the index terms used to describe the article.34 This informed the development of a search strategy tailored for each information source. Search strings were generated in consultation with a research librarian. A full search strategy for the PubMed electronic database is detailed in Appendix I. Searching for additional studies from gray literature (not commercially owned or published) from government departments, international agencies and academics institution repositories or websites will be conducted using similar keywords from the search strings. Hand searching in gray literature including technical evaluation reports and websites (from Uganda government departments/Ministry of Health and WHO-iris and AfroLib).35 The reference list of all eligible studies will be screened for additional studies.

Information sources

The databases to be searched include: PubMed, Scopus, Google Scholar, PsycInfo, Embase, African Index Medicus (African Journal of Psychiatry, African Health Observatory), Web of Science, program survey reports, conference papers, African local journals, dissertations, bulletins and factsheets.

The trial registers to be searched include: Cochrane Central Register of Controlled Trials The search for unpublished studies will include: survey reports and program evaluation reports.

The following are websites that will be searched in addition to the Uganda Ministry of Health/government website:, WHO Institutional Repository for Information Sharing (WHO-IRIS):, AfroLib:, World Bank group:

Study selection

Following the search, all identified citations will be uploaded into Endnote V8 (Clarivate Analytics, PA, USA) and duplicate citations removed. Two independent reviewers will initially pilot and then screen titles and abstracts against the inclusion criteria. Studies that meet or may potentially meet the inclusion criteria will be retrieved in full, and their details imported into JBI System for Unified Management, Assessment and Review Information (JBI SUMARI) software. The full text of selected studies or reports will be retrieved and assessed in detail against the inclusion criteria using JBI SUMARI. Full text studies that do not meet the inclusion criteria will be excluded and reasons for exclusion will be provided in an appendix in the final systematic review report. Included studies will undergo critical appraisal. The result of the search will be reported in full in the final report and presented in a PRISMA flow diagram. Any disagreements that arise between the reviewers will be resolved through discussion, or with a third reviewer (EA) to determine the final inclusion.

Assessment of methodological quality

Eligible studies will be critically appraised for methodological quality by two independent reviewers using the standardized critical appraisal instrument for prevalence studies in JBI SUMARI.36 Prior to embarking on a full review of all selected papers, an initial piloting of the critical appraisal tool in a subset of the selected papers will be performed by two independent reviewers. Any disagreements or uncertainties that arise during methodological quality assessment will be resolved through discussion or with a third reviewer (EA). The results of critical appraisal will be reported in narrative and tabular formats. All studies that meet the inclusion criteria, regardless of the results of their methodological quality, will undergo data extraction and synthesis (where possible).

Data extraction

Data will be extracted using the standardized data extraction tool for prevalence and incidence.37 Appendix II presents a modified version of the data extraction tool for this review. Prior to extraction, two reviewers will independently pilot the data extraction tool customized in JBI SUMARI with a subset of the selected studies and meet to review the consistency. After piloting, JO will extract the data. The data extracted will include specific details about the participants, condition and other characteristics including:

  • i) General information: author(s) name, study title and aim, year of study and country of study, publication type or source of data (journal or report), socioeconomic data, especially income level, where available;
  • ii) Study characteristics: study design, characteristic of the study population (age and gender), sample size, diagnostic instrument used and sampling methods (in studies investigating mental disorders of children, this will include characteristics of the person providing the information. e.g. parent or caregiver or teacher or child);
  • iii) Outcome information: proportion of people reported with either current or period or life time prevalence of mental disorders, or outcome data to allow an estimation of the effect size such as statistical test and author's conclusion (in cohort study designs that measure prevalence of mental disorders and where data is taken multiple times, only data from the last point will be extracted).

Any disagreements that arise between the reviewers at piloting or where the reviewer is uncertain on particular study details to extract, will be resolved through discussion or with the other reviewers (EA). If required, the author(s) of the paper (s) will be contacted to request for missing or additional data.

Data synthesis

Prevalence data extracted from the included studies will, where possible (e.g. studies using uniform case definitions, the same measures of outcome, context and approaches), be pooled in a statistical meta-analysis using Metafor (Free Software Foundation, Inc., Boston, USA)38 or any other relevant software. Prevalence data from the included studies will be transformed using a Logit transformation to calculate the weighted summary of proportion under a random effect model. The effect size will be expressed as a proportion with 95% confidence intervals around the summary estimate.

Heterogeneity will be assessed using the Chi-squared, Tau-squared and I-squared tests.39,40 To explore potential sources of heterogeneity from the included studies, characteristics likely to modify prevalence estimates will be considered for subgroup analysis. The following subgroups will be analyzed, where possible: age (under 18 years and 18 years and over), diagnostic instruments used, specific diagnosis (e.g. depression, anxiety, etc.), socio-economic status and study design. Sensitivity analyses will be performed to explore the impact of individual studies on the overall calculated prevalence estimates. This will be performed by investigating whether dropping or adding primary studies with (say) slightly non-standard disease definitions will make a difference.

Where statistical pooling in a meta-analysis is not possible due to heterogeneity, the findings will be presented in narrative form including tables and figures to aid in data presentation. Sources of heterogeneity and reason for which it is determined to be inappropriate to pool data will be specified in the systematic review report.


This review will be conducted towards the award of PhD degree for JO. We acknowledge assistance from University of Adelaide Research Librarian Ms. Maureen Bell in the development of the search strategy.


JO is supported by an Australian Government Research Training Program Scholarship. The sponsor had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Appendix I: Search terms and strategy

Search terms for PubMed

  • 1. “Mental disorders”[MeSH Terms]
  • 2. Mental disorders[textword]
  • 3. “Mentally ill persons”[MeSH Terms]
  • 4. Mentally ill person[textword]
  • 5. “Psychopathology”[MeSH Terms]
  • 6. Psychopathology[textword]
  • 7. “Psychiatric”[MeSH Terms]
  • 8. Psychiatric[textword]
  • 9. “Child psychiatry”[MeSH terms]
  • 10. “child psychology” [MeSH terms]
  • 11. “diagnostic and statistical manual of mental disorders”[MeSH Terms]
  • 12. “Prevalence”[MeSH Terms]
  • 13. Prevalence[textword]
  • 14. Epidemiology[textword]
  • 15. “Uganda”[MeSH Terms]
  • 16. Uganda[textword]

Search strategy for PubMed

  • 17. Search 1: #1 OR #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8 OR #9 OR #10 OR #11
  • 18. Search 2: #12 OR #13 OR #14
  • 19. Search 3: #15 OR #16
  • 20. Search 4: #17 AND #18 AND #19
  • 21. Search limited to: #20 AND Limit 2010 to 2018

Appendix II: Data extraction (modified from Munn et al.37)


1. Whiteford HA, Ferrari AJ, Degenhardt L, Feigin V, Vos T. The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS One 2015; 10 2:e0116820.
2. Murray CJ. Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bull World Health Organ 1994; 72 3:429–445.
3. Vos T, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390 10100:1211–1259.
4. American Psychiatric Association, American Psychiatric Association. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders. Fifth Edition.2013; p. 991.
5. World Health Organization, WHO. The ICD-10 Classification of Mental and Behavioural Disorders: Clinical descriptions and diagnostic guidelines. 1993; p. 267.
6. Fantom NS, Umar. The World Bank's Classification of Countries by Income. Policy Research Working Paper; No. 7528. Policy Research Working Paper. Washington, DC: World Bank; 2016. pp. 1–52.
7. WHO. Depression and other common mental disorders Global: health estimates. 2017; Geneva: World Health Organization, pp. 1–24.
8. Charlson FJ, Diminic S, Lund C, Degenhardt L, Whiteford HA. Mental and substance use disorders in Sub-Saharan Africa: predictions of epidemiological changes and mental health workforce requirements for the next 40 years. PLoS One 2014; 9 10:e110208.
9. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of dsm-iv disorders in the national comorbidity survey replication. Arch Gen Psychiatry 2005; 62 6:593–602.
10. Uganda Bureau of Statistics. National Population and Housing Census 2014, Main Report. Uganda Bureau of Statistics; 2016.
11. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013; 382 9904:1575–1586.
12. Vos T, Allen C, Arora M, Barber RMRM, Bhutta ZA, Brown A, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 2016; 388 10053:1545–1602.
13. United Nations Department of Economic and Social Affairs Population Division. World Population Prospects the 2017 revision: key findings and advance tables. Working Paper No. ESA/P/WP/248. NewYork: United Nations Department of Economics and Social Affairs; 2017. p. 53.
14. Porter R. Madness: a brief history. 2002; New York: Oxford University Press, p. 250.
15. Foerschner AM. The History of Mental Illness: From Skull Drills to Happy Pills. Inquiries Journal 2010; 2 09:1–4.
16. Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. Lancet 2007; 370:859–877.
17. Lund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, et al. Poverty and common mental disorders in low and middle income countries: A systematic review. Soc Sci Med 2010; 71 3:517–528.
18. Gelband H, Sankaranarayanan R, Gauvreau CL, Horton S, Anderson BO, Bray F, et al. Costs, affordability, and feasibility of an essential package of cancer control interventions in low-income and middle-income countries: key messages from Disease Control Priorities, 3rd edition. Lancet (London, England) 2015; 387 10033:2133–2144.
19. Thornicroft G, Newman SC, Bland RC, Wahlbeck K, Westman J, Nordentoft M, et al. Physical health disparities and mental illness: the scandal of premature mortality. Br J Psychiatry 2011; 199 6:441–442.
20. Baxter AJ, Patton G, Scott KM, Degenhardt L, Whiteford HA. Global Epidemiology of Mental Disorders: What Are We Missing? PLoS One 2013; 8 6:e65514.
21. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Chapter 5: Systematic reviews of prevalence and incidence. In: Aromataris E, Munn Z (Editors). Joanna Briggs Institute Reviewer's Manual. The Joanna Briggs Institute, 2017. Available from
22. Andersen RM, Aday LA. Access to Medical Care in the U. S.: Realized and Potential. Med Care 2008; 16 7:533–546.
23. WHO. The World Health Report 2001: mental health: new understanding, new hope. Geneva: World Health Organization; 2001.
24. WHO. Mental Health Gap Action Programme - Scaling up care for mental, neurological, and substance use disorders. 2008. p. 44.
25. WHO. Mental Health Action Plan (2013–2020). 2013; Geneva: World Health Organization, p. 50.
26. Aday LA, Andersen R. A Framework for the Study of Access to Medical Care. Health Serv Res 1974; 9 3:208–220.
27. Andersen RM. Revisiting the Behavioral Model and Access to Medical Care: Does it Matter? J Health Soc Behav 1995; 36 1:1–10.
28. Byers AL, Lai AX, Nelson C, Yaffe K. Predictors of Mental Health Services Use Across the Life Course among Racially–Ethnically Diverse Adults. Am J Geriatr Psychiatry 2017; 25 11:1213–1222.
29. Li W, Denson LA, Dorstyn DS. Help-Seeking Intentions and Behaviors among Mainland Chinese College Students: Integrating the Theory of Planned Behavior and Behavioral Model of Health Services Use. Int J Adv Couns 2017; 39 2:125–148.
30. Li W, Dorstyn DS, Denson LA. Predictors of Mental Health Service Use by Young Adults: A Systematic Review. Psychiatr Serv 2016; 67 9:946–956.
31. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: A proposal for reporting. JAMA 2000; 283 15:2008–2012.
32. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc 2015; 13 3:147–153.
33. The Joanna Briggs Institute. The Joanna Briggs Institute Reviewers’ Manual: 2017 edition. Adelaide: The Joanna Briggs Institute; 2017.
34. Wilczynski NL, Haynes RB, Team H. Optimal search strategies for identifying mental health content in MEDLINE: an analytic survey. Ann Gen Psychiatry 2006; 5:4–14.
35. Olsan TH, Bianchi C, White P, Glessner T, Mapstone PL. Finding electronic information for health policy advocacy: a guide to improving search results. J Am Acad Nurse Pract 2011; 23 12:648–658.
36. Munn Z, Moola S, Riitano D, Lisy K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag 2014; 3 33:123–128.
37. Munn Z, Tufanaru C, Aromataris E. Data extraction and synthesis. Am J Nurs 2014; 114 7:49–54.
38. Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw 2010; 36 3:1–48.
39. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J 2003; 327 7414:557–560.
40. Higgins J, Thompson S, Deeks J, Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. J Health Serv Res Policy 2002; 7 1:51–61.

Epidemiology; mental disorders; population; prevalence; Uganda