Review of Size Estimation Methods

Baneshi, Mohammad Reza; Rastegari, Azam; Haghdoost, Ali Akbar

doi:10.1007/978-3-030-75464-8_1

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…Key population surveys rely on respondent-driven or network sampling methods 4,5 because KPs are hard to reach in household survey sampling frames due to small population size, high mobility and transient living arrangements, non-disclosure of risk behaviour, and societal marginalisation. [6][7][8] Surveys are commonly restricted to or disproportionately conducted in urban areas and studies are conducted infrequently.…”

Section: Introductionmentioning

confidence: 99%

“…Key population surveys often rely on respondent-driven and venue-based sampling methods 5,6 whose interpretation as population-representative relies upon strong assumptions that can be difficult to meet in practice. Household survey sampling frames are inappropriate for surveying migratory populations that are unlikely to disclose risk behaviour due to societal marginalisation and discrimination.…”

Section: Introductionmentioning

confidence: 99%

“…Key population survey data are sparse and less representative of their target population than common general population sampling approaches such as national household surveys or sentinel surveillance. Key population surveys often rely on respondent-driven and venue-based sampling methods 5,6 which require strong assumptions to interpret as population-representative. Household survey sampling frames are inappropriate for reaching populations that are mobile and unlikely to disclose risk behaviour due to societal marginalisation and discrimination.…”

Section: Introductionmentioning

confidence: 99%

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Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023

Stevens

Sabin

Garcia

et al. 2022

Preprint

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Background: HIV programmes in sub-Saharan Africa (SSA) require information about HIV among key populations to ensure equitable and equal access to HIV prevention and treatment. Surveillance has been conducted among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender populations, but is not systematically included in national HIV estimates. We consolidated existing KP surveys to create national-level estimates of key population size, HIV prevalence, and ART coverage for mainland SSA. Methods: Key population size estimates (KPSE), HIV prevalence, and ART coverage data from 38 countries from 2010-2021 were collated from existing databases, deduplicated, and verified against primary sources. We used Bayesian mixed-effects regression to spatially smooth KPSE, and regressed subnational key population HIV prevalence and ART coverage against age/sex/year/province-matched total population estimates. Findings: We extracted 1449 unique KPSE datapoints, 1181 HIV prevalence datapoints, and 242 ART coverage datapoints. Countries had data for a median of five of the twelve population/outcome stratifications. Across countries, a median of 1.44% of urban women were FSW (interquartile range [IQR] 0.83-1.89%); 0.60% of urban men were MSM; and 0.16% of urban adults injected drugs (IQR 0.14-0.24%). HIV prevalence in all key populations was higher than matched total population prevalence. ART coverage was correlated with, but lower than, total population ART coverage. Across SSA, key populations were estimated as 1.1% (95%CI 0.7-1.9%) of the population but 5.1% (95%CI 3.2-10.3%) of all PLHIV aged 15-49 years. Interpretation: Key populations in sub-Saharan experience disproportionate HIV burden and somewhat lower ART coverage, underscoring need for focused prevention and treatment services. However, large heterogeneity and incomplete data availability limit precise estimates for programming and monitoring trends. Future efforts should focus on integrating and strengthening key population surveys and routine data within national HIV strategic information systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023

Stevens

Sabin

Garcia

et al. 2022

Preprint

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“…The sample providing an estimate of B is referred to as the “benchmark” sample with the marginal count known; 1/ p is referred to as the “multiplier.” Several assumptions differentiate this method from capture–recapture: (1) p is the probability of observing a target subject, conditional on the specific characteristics ( B ); (2) the method requires completeness of the benchmark sample with respect to the characteristics ( B ) 8 ; and (3) the method can be applied when only two data sources are available; although it does require that data sets overlap or be related predictably, the individual linkage is not required for estimation. 8 For example, a benchmark population can be counted from a registry, such as substance use treatment program enrollment. The multiplier can be estimated from a separate survey by asking the participants about their treatment history.…”

mentioning

confidence: 99%

Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture–Recapture Methods

Wang,

Doogan,

Thompson

et al. 2023

Epidemiology

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Background: The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructure-related issues. Methods: We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture–recapture analysis under log–linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD. Results: Our estimates for OUD prevalence among Massachusetts residents (aged 18–64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture–recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates. Conclusion: The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates. Trial registration: ClinicalTrials.gov Identifier: NCT04111939.

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Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23

Stevens,

Sabin,

Anderson

et al. 2024

The Lancet Global Health

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Review of Size Estimation Methods

Cited by 3 publications

References 25 publications

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023

Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture–Recapture Methods

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23

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