AN INVESTIGATION OF PEDIGREES OF 110 PATIENTS WITH GRAVES' DISEASE AND THE CLINICAL SIGNIFICANCE OF DETERMINATIONS OF ANTITHYROID ANTIBODIES OF THEIR FIRST-DEGREE RELATIVES

Eight hundred and ten pedigree members of 110 patients with Graves' disease were studied. In 700 first-egree relatives, inquiry of medical history, physical examination (including eyes, thyroid, heart rate, etc), thyroid function tests (serum T3, T4 and TSH levels), determinations of thyroglobulin antibodies (TgAb) and thyroid microsomal antibodies (TmAb) were performed. For male (female) probands, the incidence of Graves' disease in male (female) first-degree relatives were investigated and their serum TgAb and TmAb were analysed. The incidence of these two kinds of autoantibodies in the male (female) first-egree relatives of familial and nonfamilial Graves' disease were analysed. Eighteen persons with positive TgAb and TmAb from 5 pedigrees had been followed up one year after initial determinations. Our results suggest that the positive rates of TgAb and TmAb in the first-egree relatives of Graves' disease were coincident with the incidence of Graves' disease, and the positive results of TgAb and TmAb in the first-egree relatives of Graves' disease may be an indicator of pre-raves' disease or pre-utoimmune thyroid diseases.

A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases-application to sexually transmitted infections

Background:A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention.In the United States,sexually transmitted infections(STIs)are a huge economic burden,with a large fraction of the burden attributed to HIV.Data also show interactions between HIV and other sexually transmitted infections(STIs),such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without.However,given the wide range in prevalence and incidence burdens of STIs,current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling.Further,causal factors for higher risk of coinfection could be both behavioral(i.e.,compounding effects of individual behaviors,network structures,and care behaviors)and biological(i.e.,presence of one disease can biologically increase the risk of another).However,the data on the fraction attributed to each are limited.Methods:We present a new mixed agent-based compartmental(MAC)framework for jointly modeling STIs.It uses a combination of a new agent-based evolving network modeling(ABENM)technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases.As a demonstration,we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2,using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs.We simulated sexual transmissions among heterosexual males,heterosexual females,and men who have sex with men(men only and men and women).Setting the biological risk of co-infection to zero,we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics.Results:The contribution of behavioral factors to risk of coinfection was sensitive to disease burden,care access,and population heterogeneity and mixing.The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here,suggesting potential role of biological factors,that should be investigated further specific to an STI.Conclusions:The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features.The numerical analysis serves as proof-of-concept for the application to STIs.Interactions between diseases are influenced by behavioral factors,are sensitive to care access and population features,and are likely exacerbated by biological factors.Social and economic conditions are among key drivers of behaviors that increase STI transmission,and thus,structural interventions are a key part of behavioral in-terventions.Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention.The new simulation framework is especially suited to simulate behavior as a function of social determinants,and further,to identify optimal combinations of common structural and disease-specific interventions.