Association Between Gender-Affirming Surgeries and Mental Health Outcomes

Missing data in medical research is a common problem that has long been recognised by statisticians and medical researchers alike. In general, if the effect of missing data is not taken into account the results of the statistical analyses will be biased and the amount of variability in the data will not be correctly estimated. There are three main types of missing data pattern: Missing Completely At Random (MCAR), Missing At Random (MAR) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins. The aim of this practice note is to describe these patterns of missing data and how they can occur, as well describing the methods of handling them. Simple and more complex methods are described, including the advantages and disadvantages of each method as well as their availability in routine software. It is good practice to perform a sensitivity analysis employing different missing data techniques in order to assess the robustness of the conclusions drawn from each approach.

Transgender people are a diverse population affected by a variety of negative health indicators across high, middle, and low income settings. Studies consistently document high prevalence of adverse health outcomes in this population, including HIV and other sexually transmitted infections (STIs), mental health distress, and substance use and abuse. However, many other health areas remain understudied, population-based representative samples and longitudinal studies are lacking, and routine surveillance efforts for transgender population health are scarce. The absence of survey items with which to identify transgender respondents in general surveys often limits availability of data to estimate the magnitude of health inequities and characterize transgender population-level health globally. Despite limitations, there are sufficient data highlighting the unique biological, behavioral, social, and structural contextual factors surrounding health risks and resiliencies for transgender people. To mitigate these risks and foster resilience, a comprehensive approach is needed that includes gender affirmation as a public health framework, improved health systems and access to healthcare informed by high quality data, and effectively partnering with local transgender communities to ensure responsiveness of and cultural specificity in programming. Transgender health underscores the need to explicitly consider sex and gender pathways in epidemiologic research and public health surveillance more broadly.