Undiagnosed prediabetes in Mexican adolescents under poverty in contexts affected by collective violence: A clinical comparison among health services users and hidden population.

Introduction: The epidemiological pattern of prediabetes in adolescents is understudied. In Mexico, adolescents are exposed to social adversity conditions, including poverty and violence. Therefore, understanding their clinical profiles and how the social determinants of health impose barriers to access to health services is important to address detection, in those who, by their vulnerability, remain a hidden population. Aim: This study aimed to describe undiagnosed prediabetes in Mexican adolescents under poverty in violent contexts and to compare the clinical features among health services users and hidden population. Methods: This cross-sectional study included 371 adolescents from difficult access locations in violent contexts. Poverty, lack of health services access, and perceived vulnerability were determined in all samples. Endocrine markers (BMI, HOMA-IR, HbA1c, and cortisol) were measured in those with high violence perception. Results: A total of 61.7% of the adolescents had a suburban grid and urban cluster residence, and 77.7-85.7% of them belonged to locations where 35-50% of their population lived below the poverty line. In total, 40-75% had a lack of 10-20% access to health services, and 18.8% had a high perceived vulnerability due to collective violence and were screened. Overall, 61.9% of respondents were newly diagnosed with prediabetes and showed the worst HbA1c (p = 0.001) compared to the health services subsample, which showed the highest BMI (p = 0.031) and insulin resistance (p = 0.025). Conclusion: There is a prediabetes hidden population living in violent contexts under poverty. These social determinants promote poor outcomes in perceived vulnerability and endocrine response and represent barriers to access to health services.

Estimating hidden populations by transferring knowledge from geographically misaligned levels.

The estimation of hidden sub-populations is a hard task that appears in many fields. For example, public health planning in Brazil depends crucially of the number of people who holds a private health insurance plan and hence rarely uses the public services. Different sources of information about these sub-populations may be available at different geographical levels. The available information can be transferred between these different geographic levels to improve the estimation of the hidden population size. In this study, we propose a model that use individual level information to learn about the dependence between the response variable and explanatory variables by proposing a family of link functions with asymptotes that are flexible enough to represent the real aspects of the data and robust to departures from the model. We use the fitted model to estimate the size of the sub-population at any desired level. We illustrate our methodology estimating the sub-population that uses the public health system in each neighborhood of large cities in Brazil.