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Point Prevalence of Common Mental Health Conditions in Nigeria: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesizes data from 103 reports to provide pooled point prevalence estimates for ten common mental health conditions in Nigeria, highlighting substantial heterogeneity and distinguishing between screening and diagnostic rates to guide research planning rather than serving as definitive national population rates.

Original authors: JAMES SUCCESS ODUBIA

Published 2026-08-13

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: JAMES SUCCESS ODUBIA

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the human mind as a vast, bustling city. In this city, sometimes the traffic gets jammed, the lights flicker, or the power grid sags. These aren't just "bad days"; they are specific breakdowns in how the city runs, known as mental health conditions. Scientists who study these cities are called epidemiologists. Their job isn't to fix the traffic lights one by one, but to count how many intersections are currently jammed. This count is called "prevalence." It's a snapshot of how many people are struggling right now, not how many might struggle later in life. Knowing these numbers is like having a map of the city's potholes; without it, the city planners (doctors, policymakers, and researchers) can't build enough roads or hire enough mechanics to handle the traffic. But here's the tricky part: if you count potholes in a race track, you get a different number than if you count them in a quiet neighborhood. The same goes for mental health. A "screen" is like a quick glance to see if a car might have a flat tire, while a "diagnosis" is a mechanic actually lifting the car to confirm it. Mixing these up is like trying to use a race track map to navigate a quiet suburb—it leads to confusion and wasted resources.

Enter a new mapmaker, James Success Odubia, who decided to tackle the messy, scattered puzzle of mental health in Nigeria. Nigeria is a massive, vibrant country with a huge, young population, but the data about its mental health "traffic jams" was scattered across thousands of different studies, each looking at different groups of people using different tools. Some studies looked at students, others at prisoners, some at people in hospitals, and others at those who had survived wars or disasters. It was like having a million different photos of the city, but none of them were taken from the same angle or at the same time. Odubia's mission was to gather all these photos, sort them out, and create a single, clearer picture of what's happening right now.

This paper acts as a giant digital magnifying glass, combining 103 different reports into one massive "meta-analysis." Think of it as taking 133 separate snapshots of mental health in Nigeria and blending them together to see the big picture. The researchers were very careful to keep their categories straight. They didn't mix up the "quick glances" (screens) with the "mechanic confirmations" (diagnoses), and they didn't mix up the "race tracks" (high-risk groups like prisoners or trauma survivors) with the "quiet neighborhoods" (the general public).

Here is what their blended map revealed:

The Heavy Hitters: The most common "traffic jams" were found in the areas of depression and anxiety. When researchers used the "quick glance" method (screening), they found that about 30.2% of people showed signs of depression, and 34.8% showed signs of anxiety. That's roughly one in three people. However, when they looked for the "mechanic confirmations" (diagnoses), the number for depression dropped to 20.2%. This gap makes sense: a quick glance catches more potential problems, while a strict diagnosis is harder to meet.
The Substance Scene: For substance use (like alcohol or drugs), the current prevalence was around 16.4%. Interestingly, when they looked at alcohol specifically, it was about 13.1%.
The Memory Lane: For older adults, diagnosed dementia was found in about 2.2% of the population. However, if you just "screen" for memory issues without a full diagnosis, the number jumps to 24.1%. This suggests many people have memory hiccups that haven't been officially labeled as dementia yet.
The Trauma Zones: This is where the map gets very specific. For Post-Traumatic Stress Disorder (PTSD), the numbers depend entirely on who you ask. In the general population, diagnosed PTSD was about 12.2%. But in groups exposed to conflict, displacement, or violence, the "quick glance" screening showed a staggering 58.8%—more than half of those people showed signs of PTSD. This isn't a number for the whole country; it's a warning sign for specific, high-risk communities.
The Youth and the Mind: For children and teens, ADHD was found in about 8.5% of the population. Suicidal thoughts were reported by 20.4% of people in the studies, while actual suicide attempts were much rarer, at 2.3%.
The Missing Piece: The researchers looked hard for data on Bipolar Disorder but found zero eligible studies that met their strict rules. So, for now, there is no reliable number for this condition in this specific review.

The author is very honest about the "fog" on their map. They found that the data was incredibly mixed up (a statistical term called "heterogeneity" was very high). This means the numbers aren't a single, solid fact for every Nigerian; they are averages of many different, very different groups. For example, the PTSD number is high because many studies focused on people who had already been through terrible things. If you applied that number to a peaceful village, it would be wrong.

The paper explicitly rules out the idea that these numbers are a single, national "scorecard" for the entire country. You can't take the 30.2% depression screen number and say, "Okay, 30% of all Nigerians are depressed." Instead, these numbers are "planning values." They are tools for researchers to say, "If I want to study a group of students in a university, I should expect about 30% to show signs of depression, so I need to plan my study size accordingly."

In short, this paper doesn't solve the traffic jams, but it finally gives the city planners a much better, more detailed map. It shows where the jams are thickest, warns them not to use a race track map for a quiet street, and highlights exactly where they need to build new roads (conduct new research) because some areas, like Bipolar Disorder, are still completely blank on the map. The message is clear: the burden is real, the numbers vary wildly depending on who you ask, and we need to be very careful about which numbers we use for which job.

Technical Summary: Point Prevalence of Common Mental Health Conditions in Nigeria

Problem Statement
Mental health prevalence data in Nigeria are currently fragmented across diverse conditions, measurement instruments, and high-risk settings. This dispersion limits the utility of existing estimates for research planning, specifically for calculating sample sizes and anticipating service demand. A critical methodological challenge is that prevalence is not a fixed property; it varies significantly based on population demographics, case definitions (screening vs. diagnostic), instruments, cutoffs, and recall windows. Combining disparate metrics (e.g., lifetime vs. current, screening vs. diagnosis) obscures genuine differences and yields "anticipated proportions" ( $p$ ) with no clear operational meaning for study design. While the Nigerian Survey of Mental Health and Well-Being provides foundational lifetime and 12-month estimates, there is a lack of synthesized data regarding current or point prevalence for ten specific mental health condition families using validated, contemporary measures.

Methodology
This systematic review and meta-analysis followed the PRISMA 2020 guidelines and a prospectively registered protocol (PROSPERO).

Search Strategy: Searches were conducted on August 6, 2026, across PubMed/MEDLINE, Europe PMC, African Journals Online (AJOL), and Google Scholar (first 20 pages per condition).
Eligibility Criteria: The review included primary human studies from Nigeria reporting current (present-state) or $\le$ $\leq$ 30-day prevalence for ten prespecified condition families: depression, anxiety, substance-use disorders (SUD), dementia/cognitive impairment, psychosis, bipolar disorder, perinatal depression, PTSD, suicidality, and ADHD/child disorders.
- Inclusions: Cross-sectional studies, baseline cohort data, peer-reviewed articles, and accessible theses using validated screening thresholds or diagnostic interviews.
- Exclusions: Lifetime-only estimates, unvalidated checklists, unclear denominators, and studies where the recall window exceeded 30 days (with specific exceptions for AUDIT/ASSIST treated as secondary).
Data Synthesis: A single reviewer performed adjudication with computational cross-checks. Risk of bias was assessed using the nine-item JBI checklist for prevalence studies.
Statistical Analysis: For groups with $k \ge 2$ independent estimates, event counts and denominators were logit-transformed and pooled using inverse-variance random-effects models with restricted maximum likelihood (REML) for between-study variance. Screening and diagnostic estimates were kept separate. Heterogeneity was quantified using $I^2$ , $\tau^2$ , and 95% prediction intervals.

Key Contributions
The review synthesizes 103 reports contributing 133 condition-specific estimates. Its primary contribution is the provision of distinct, methodologically stratified planning values ( $p$ ) for researchers, explicitly separating screening from diagnostic outcomes and current from longer recall periods. It highlights that "planning values" must match the target population and measurement method of the proposed study rather than serving as universal national rates.

Results
The pooled prevalence estimates (95% CI) for the ten condition families are as follows:

Depression: Screening prevalence was 30.2% (24.6%–36.5%; $k=27$ ), while diagnosed prevalence was 20.2% (15.4%–26.0%; $k=6$ ).
Anxiety: Screening prevalence was 34.8% (25.9%–44.9%; $k=19$ ). Only one diagnostic estimate was available (8.1% in HIV-positive adults).
Substance-Use Disorders (SUD): Current any-SUD prevalence was 16.4% (11.9%–22.3%; $k=2$ ); current alcohol disorder was 13.1% (8.7%–19.1%; $k=2$ ).
Dementia/Cognitive Impairment: Diagnosed dementia prevalence was 2.2% (0.7%–6.9%; $k=6$ ), whereas cognitive impairment screening prevalence was 24.1% (21.7%–26.6%; $k=2$ ).
Psychosis: Diagnosed psychosis prevalence was 4.2% (1.3%–12.7%; $k=2$ ), derived exclusively from correctional/justice-involved populations.
Bipolar Disorder: No eligible current point-prevalence estimate was found; this remains an evidence gap.
Perinatal Depression: Antenatal screening prevalence was 16.7% (8.4%–30.4%; $k=4$ ); postpartum screening prevalence was 18.5% (9.0%–34.4%; $k=8$ ).
PTSD: Diagnosed PTSD prevalence was 12.2% (4.5%–29.3%; $k=6$ ). However, screening prevalence in trauma-exposed samples was significantly higher at 58.8% (37.1%–77.5%; $k=6$ ).
Suicidality: Current suicidal ideation prevalence was 20.4% (11.6%–33.4%; $k=11$ ); current suicide attempts were 2.3% (0.7%–6.9%; $k=3$ ).
ADHD/Child Disorders: ADHD prevalence was 8.5% (5.3%–13.4%; $k=9$ ). Broad "any child disorder" estimates were 46.7% (12.3%–84.6%; $k=4$ ), reflecting high heterogeneity between routine and high-risk samples.

Significance and Limitations
The author asserts that these estimates serve as "planning values" for populations resembling the contributing studies, not as national population rates. The review underscores that heterogeneity is usually substantial ( $I^2$ ranging from 58.0% to 99.3%), driven by differences in setting (e.g., clinical vs. community, displaced vs. general), instruments, and risk enrichment.

The paper explicitly notes limitations:

Search Constraints: Lack of access to Embase, PsycINFO, and Scopus; reliance on Google Scholar with rank instability.
Adjudication: Final screening and risk-of-bias scoring were performed by a single reviewer rather than dual independent reviewers.
Data Reconstruction: 65 estimates required reconstructing event counts from rounded percentages.
Sparse Data: Several conditions (e.g., bipolar, psychosis, specific SUD strata) had very few studies ( $k=2$ or 0), resulting in wide confidence intervals and unstable prediction intervals.

The author concludes that researchers must select estimates that strictly match their study's population, instrument, and recall window. For sparse outcomes or extreme uncertainty, they recommend conservative sensitivity calculations (e.g., using the upper confidence limit or $p=0.50$ ) rather than relying on the pooled mean. The findings highlight a substantial burden of mental health conditions in high-risk systems (perinatal care, HIV services, correctional facilities, displacement settings) but caution against generalizing these to the entire Nigerian population without further nationally representative diagnostic surveys.

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