Understanding and addressing geographic barriers to accessing TB services in a high-burden urban setting - SUMMARY Each year, over 10 million people become sick with tuberculosis (TB), and around 1.4 million die from the disease. Around 86% of people with TB live in middle-income countries, with a large proportion living in cities. To substantially decrease the global burden of TB, it is necessary to ensure that people are diagnosed quickly and treated successfully. Geographic barriers to accessing health services – such as living far from a health center – can lead to poor health outcomes. However, there is limited knowledge about how geographic access barriers impact TB diagnosis and treatment outcomes in middle-income country urban settings. Health facilities are generally present in these settings, but people with TB, who are often socially and economically disadvantaged, face barriers in accessing them. We also lack tools for designing and targeting interventions that address access barriers and thus improve TB diagnosis and treatment. This proposal seeks to address these knowledge gaps using the “5 A’s” conceptual framework, which describes five domains that drive health care access: availability, accessibility, accommodation, affordability, acceptability. To help understand how geographic accessibility barriers and other types of access barriers contribute to delayed TB diagnosis, we will apply structural equation modeling and simulation methods to data from TB patient surveys based on the 5 A’s framework. To help programs target interventions to communities that are most at risk for delayed TB diagnosis and incomplete TB treatment, we will create community-level risk scores that incorporate measures of geographic accessibility as well as socioeconomic and demographic census data. To help programs develop effective treatment support interventions, we will conduct a discrete choice experiment to identify optimal packages of interventions aimed at addressing different types of access barriers during treatment, assessing how preferences differ among different demographic groups. This proposal is significant because the results will help TB programs to identify interventions that would be most effective for improving TB diagnosis and treatment, and target these interventions to the individuals and communities that need them most. This proposal is innovative because prior quantitative TB research studies have not used a health care access conceptual framework, structural equation modeling or conjoint analysis to understand how to address access barriers, or community risk scores to target interventions. In the long term, this research will help TB programs reduce delays to diagnosis and incomplete treatment rates, thus reducing the global burden of TB morbidity and mortality.
