Background

The multiplier method is one of the most frequently used population size estimation (PSE) methods for key populations, yet estimates from this method are often inconsistent with each other, other PSE methods and local knowledge. We developed a novel Bayesian approach, the 'Anchored Multiplier', which synthesizes estimates from multipliers coupled to an a priori estimate to arrive at a single consensus estimate and credible range.

Methods

Data for size estimation were collected from three cross-sectional bio-behavioural surveillance studies of people who inject drugs (PWID) in San Francisco, CA, USA (2005, 2009 and 2012). We demonstrate the application of the Anchored Multiplier and a Variance Adjusted-Anchored Multiplier using PSE produced by multipliers in the three surveys and the literature for the USA. Size estimates were compared with estimates from other available PSE methods.

Results

Using the Anchored Multiplier, we estimated the PWID population made up 2.41% [95% credible interval (CI): 1.9-2.85] of the adult population in 2005, 2.1% (95% CI: 1.8-2.48) in 2009 and 2.3% (95% CI: 2.03-2.61) in 2012. The Variance Adjusted-Anchored Multiplier calculated similar point estimates, with wider 95% credible intervals. Credible intervals from both approaches were substantially narrower than from other standard PSE methods and, unlike other methods, indicated that the prevalence of PWID was stable over time.

Conclusions

The Anchored Multiplier is a promising new approach to size estimation, which generates a single estimate to inform programmatic strategies to counter the HIV epidemic, and provides a robust denominator to quantify the burden of disease for key populations.

Estimating the number of people in hidden populations is needed for public health research, yet available methods produce highly variable and uncertain results. The Anchored Multiplier calculator uses a Bayesian framework to synthesize multiple population size estimates to generate a consensus estimate. Users submit point estimates and lower/upper bounds which are converted to beta probability distributions and combined to form a single posterior probability distribution. The Anchored Multiplier calculator is available as a web browser-based application. The software allows for unlimited empirical population size estimates to be submitted and combined according to Bayes Theorem to form a single estimate. The software returns output as a forest plot (to visually compare data inputs and the final Anchored Multiplier estimate) and a table that displays results as population percentages and counts. The web application 'Anchored Multiplier Calculator' is free software and is available at [http://globalhealthsciences.ucsf.edu/resources/tools] or directly at [http://anchoredmultiplier.ucsf.edu/].

Inequitable gender norms and beliefs contribute to increased sexual risk behavior, and, among adolescent girls and young women (AGYW), risk of HIV acquisition. We investigated the longitudinal measurement properties of the Gender Equitable Men's Scale (GEMS) when applied to a cohort of AGYW in rural South Africa (2011-2015). We used item response theory [Person-Item maps, Differential Item Functioning (DIF)] and measurement invariance confirmatory factor analysis models to assess the validity and reliability of the GEMS instrument. Item difficulty and endorsement of gender equitable beliefs both shifted over time. DIF analysis identified item bias for over half of the items; influenced by age, pregnancy, sexual debut, and intimate partner violence. Measurement invariance models revealed strong longitudinal invariance properties. GEMS is a reliable longitudinal measurement of gender equitable beliefs, with notable bias for specific items when administered to subgroups. Additional items specific to the adolescent experience are warranted for a more stable assessment of gender equitable beliefs in a population facing shifting norms as they mature.

Introduction

COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy.

Methods

Here we present a method called COVIDNearTerm to "forecast" hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT).

Results

We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%.

Conclusion

COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.