The economic consequences of decriminalizing sex work in Washington, DC

Background

Under repressive policies, sex workers are at disproportionate risk for violence, HIV, and sexually transmitted infections. The decriminalization of sex work between consenting adults provides increased social and health benefits to both sex workers and society at large. Proponents of decriminalization advocate for these added human rights; this is the first research that complements these arguments with a quantifiable economic impact of such a law and a model for future calculations. This research assesses the potential economic consequences of decriminalizing sex work in the District of Columbia (DC) in three areas: (1) income tax revenue, (2) criminal justice system savings, and (3) health sector savings (violence, HIV, gonorrhea, and herpes).

Methods

An economic model is developed and utilized based on a literature review and records from local and federal agencies.

Results

The decriminalization of sex work in DC will generate $5,191.61 per sex worker and $2.49 per client annually, plus an additional $20,118.17 in total criminal justice system savings a year. Per sex worker, $4,906.39 will be gained from income tax revenue, and $285.46 will be generated through health sector savings. Per client, decriminalization will generate $0.05, $2.28, and $0.16 from HIV, gonorrhea, and herpes respectively, or $8,311.67 annually after considering the total number of clients. Estimates are reported in 2019 US dollars.

Conclusions

The potential economic impact of decriminalizing sex work is widespread. In DC, this legislation should be implemented to not only promote the city's human rights but also economic growth. The presented model, in conjunction with a rights-based foundation, should urgently be used by advocates, sex workers, decision-makers, and other researchers.

Key messages

An economic analysis of a policy to decriminalize sex work in DC demonstrates its widespread economic impact across sectors. The economic model generated in this research should be utilized in other regions to strengthen human rights-based arguments in support of these policies.

Using AI for Mental Health Analysis and Prediction in School Surveys

Background

Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper-based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data-driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.

Methods

For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns.

Results

We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.

Conclusions

This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects.

Key messages

This model uses artificial intelligence to improve mental health surveillance and evaluation in school settings. Artificial intelligence can be applied more broadly in public health to harness the potential of predictive models.