I am doing research on pathway toncare and barrier to treatment in substance use disorder. I have use WHO Encounter forn and BACE v3 . I n3ed prevalence studies to calculate sample size in our context like nepal and india
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"substance use disorder" AND "prevalence" AND India
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"pathway to care" AND "substance use" AND ("Nepal" OR "India" OR "South Asia")
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substance use disorder prevalence Nepal India sample size calculation epidemiology 2020-2025
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"alcohol use disorder" AND prevalence AND Nepal
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substance use prevalence India community survey epidemiology
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barriers treatment seeking substance use South Asia
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BACE v3 "Barriers to Access and Care Engagement" substance use disorder validation
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PMID: 37830122
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PMID: 31455924
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India national mental health survey 2015-16 substance use disorder prevalence alcohol opioid cannabis
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Nepal substance use survey prevalence alcohol opioid drug 2020 2021 2022 2023
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https://pmc.ncbi.nlm.nih.gov/articles/PMC5419008
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Nepal mental health survey substance use opioid alcohol prevalence community
I now have comprehensive data. Let me compile the full evidence-based response.
Prevalence Data for Sample Size Calculation: Substance Use Disorder Research in Nepal & India
Your Research Framework
You are using two excellent instruments:
- WHO Encounter Form (Pathways to Care) - maps the route patients take before reaching psychiatric/addiction services
- BACE v3 (Barriers to Access and Care Engagement, v3) - a validated tool assessing perceived barriers to treatment engagement in SUD
Both require an adequate sample size, which depends on reliable local prevalence estimates. Here is the best available evidence by substance and country.
INDIA - Key Prevalence Sources
1. National Mental Health Survey (NMHS) 2015-16 - NIMHANS
The most cited and methodologically robust source for India (n=34,802 across 12 states)
| Disorder | Current Prevalence | Lifetime Prevalence |
|---|---|---|
| Alcohol Use Disorder | 4.6-4.7% | ~8-9% |
| - Males specifically | 9.1% | higher |
| - Females | 0.5% | lower |
| Tobacco Use Disorder | 20.9% | - |
| Other Drug Use Disorders (non-tobacco) | 0.6% | - |
| Any Substance Use Disorder | ~25-27% (incl. tobacco) | - |
| Any mental disorder (all types) | 10.6% | - |
- Treatment gap: 86.3% for AUD, 91.8% for tobacco, ~73% for other drugs
- Source: NMHS India 2015-16 (PMC5419008)
2. National Survey on Magnitude of Substance Use in India (2019) - Ministry of Social Justice & Empowerment
The most comprehensive substance-specific national survey (all age groups 10-75 years)
| Substance | Current Use (Past 12 months) | Harmful Use/Dependence |
|---|---|---|
| Alcohol | ~14.6% overall; 27.3% males, 1.6% females | ~5.2% AUD nationally |
| Cannabis | 2.8% (3.1 crore individuals) | ~25% of users = ~0.7% population |
| Opioids | ~2.1% (2.26 crore) | Substantial - India's opioid rate is 3x global average |
| - Heroin | Now surpasses opium as primary opioid | - |
| Sedative-hypnotics | 1.18% | - |
| Inhalants | 0.58% adults; 1.17% children/adolescents | - |
| Cocaine | 0.10% | - |
| Treatment gap for illicit drugs: only 1 in 4 with dependence receive treatment | ||
| Alcohol dependence treatment gap: only 1 in 38 ever receives treatment |
NEPAL - Key Prevalence Sources
1. Nepal NMHS Pilot Study (Jha AK et al., 2019 - PMID: 31455924)
Pilot study, n=1,647 from three districts (Dhanusha, Bhaktapur, Dolakha), using MINI for DSM-5
| Finding | Rate |
|---|---|
| Any mental disorder, adults (current) | 13.2% |
| Substance Use Disorder (leading disorder, adults) | Among top 3 most prevalent |
| Alcohol Use Disorder (current lifetime) | 4.2% (pilot data) |
| Alcohol Use Disorder range | 3.6-4.8% (95% CI) |
| Current suicidality | 10.9% adults |
- Note: Substance Use Disorder and Alcohol Use Disorder were the most common mental disorders in adults alongside dissociative conversion disorder and MDD.
2. Contextual Data for Nepal
- Nepal has among the highest alcohol burden in South Asia, especially in hill communities and certain ethnic/caste groups where home-brewed alcohol is traditional.
- A 2024 study from Bhaktapur (PMID: 39574058) found problematic alcohol consumption among management undergraduates, indicating significant younger adult burden.
- Opioid use: Nepal is situated on major heroin transit routes; heroin and pharmaceutical opioid misuse are rising.
- A 2024 conflict-exposure study (PMID: 37755324) specifically documented AUD onset in Nepali communities exposed to armed conflict.
Sample Size Calculation Guidance
For your Pathways to Care / BACE v3 study, use these prevalence figures depending on your population:
Recommended Prevalence Estimates to Use
| Setting | Substance | Prevalence (p) | Source |
|---|---|---|---|
| India - general population | AUD | 4.6-5.2% | NMHS 2016 / MoSJE 2019 |
| India - males only | AUD | 9.1% | NMHS 2016 |
| India - general | Any SUD (incl. tobacco) | 25-27% | NMHS 2016 |
| India - general | Opioid use disorder | ~0.7-1% | MoSJE 2019 |
| Nepal - general population | AUD | 4.2% | Nepal NMHS Pilot 2019 |
| Nepal - general | Any SUD | ~13-15% | Pilot NMHS; use 13.2% |
| Hospital/treatment-seeking | Any SUD | 50-80% (varies by facility) | use local facility data |
Standard Formula (Cochran's Formula for cross-sectional)
n = Z² × p × (1-p) / d²
Where:
- Z = 1.96 (95% CI)
- p = prevalence estimate from above
- d = acceptable margin of error (typically 0.05 for 5%)
Example using AUD in India (p = 0.046):
n = (1.96)² × 0.046 × 0.954 / (0.05)² = ~68 minimum per stratum
Using "any SUD" p = 0.25:
n = (1.96)² × 0.25 × 0.75 / (0.05)² = ~288 minimum
For hospital-based Pathways to Care studies, if you are recruiting from treatment settings, the prevalence is effectively 100% (all are SUD cases) - in that case, sample size should be powered for proportions of pathway types or scores on BACE, not the prevalence of SUD itself.
Barriers to Treatment - India/Nepal Context
A 2024 cross-sectional study from South India (PMID: 37830122, Veena et al., J Addict Dis) using a Barriers Questionnaire in AUD males found:
- 73.3% were in the Low-Barrier group but significant high-barrier individuals reported: denial of alcoholism, stigma, cost, fear of job loss, and social embarrassment.
- These align well with BACE v3 domains.
A systematic review (PMC9434658) summarizes structural barriers relevant to LMICs like Nepal and India: long waiting times, unaffordable costs, lack of trained providers, stigma, and absence of gender-appropriate services.
Recommended Citations for Your Protocol
- Gururaj G et al. National Mental Health Survey of India 2015-16. NIMHANS, Bengaluru 2016. PMID: PMC5419008
- Ministry of Social Justice & Empowerment, Govt. of India. Magnitude of Substance Use in India, 2019. (WHO ASSIST validated tool used)
- Jha AK, Ojha SP, Dahal S et al. Prevalence of Mental Disorders in Nepal: Findings from the Pilot Study. J Nepal Health Res Counc 2019. PMID 31455924
- Veena M et al. Barriers to seeking treatment for alcohol use disorders among males in a tertiary care center in South India. J Addict Dis 2024. PMID 37830122
- Treatment gap context: NMHS India reports 86.3% treatment gap for AUD - important justification for your barriers study.
Key Notes for Your Protocol
- Nepal lacks a national-level SUD survey comparable to India's NMHS/MoSJE surveys; the pilot NMHS (Jha 2019) is the best available community data. The full national survey may now be available from MoHP Nepal (check nhrc.org.np).
- For WHO Encounter Form data collection, the first contact delay (DUP/DOT) and prior consultation types are your pathway variables - Indian data shows median 2.5 months for depression but longer for SUD.
- If recruiting from treatment centers (de-addiction wards, OPD), your p for sample size may be best estimated from the proportion expected to show long pathways (e.g., > 3 prior consultations before reaching specialist) - typically 50-60% in South Asian settings.
- Consider design effect (DEFF) of 1.5-2.0 if using cluster sampling, multiply your calculated n accordingly.
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