Supplemental Material, appendix - Generalized Inflated Discrete Models: A Strategy to Work with Multimodal Discrete Distributions

<p>Supplemental Material, appendix for Generalized Inflated Discrete Models: A Strategy to Work with Multimodal Discrete Distributions by Tianji Cai, Yiwei Xia, and Yisu Zhou in Sociological Methods & Research</p

Behavior and background measures: definition, construction, mean(sample size) at each Add Health wave, and overall mean(SD)(sample size) for white males.

<p>Behavior and background measures: definition, construction, mean(sample size) at each Add Health wave, and overall mean(SD)(sample size) for white males.</p

Background gap between the DAT1*9R/9R and the DAT1*Any10R genotypes among white males.

<p>Background gap between the DAT1*9R/9R and the DAT1*Any10R genotypes among white males.</p

Behavior gap between the DAT1*9R/9R and the DAT1*Any10R genotypes among white males: ten risky behaviors.

<p>Behavior gap between the DAT1*9R/9R and the DAT1*Any10R genotypes among white males: ten risky behaviors.</p

Behavior differences between individuals with the <i>DAT1</i>^*<i>9R/9R</i> and <i>DAT1</i>^*<i>Any10R</i> genotypes, white males, and Add Health Waves I–III: Age-Gene Interaction Models.

<p>“*” indicates a statistically significant result at the level of 0.05.</p><p>“+” indicates a statistically significant result at the level of 0.10.</p

The protective effect of the DAT1*9R/9R genotype relative to the DAT1*Any10R genotype depends on age in adolescence and young adulthood: Parts 1–9.

<p>The protective effect of the DAT1*9R/9R genotype relative to the DAT1*Any10R genotype depends on age in adolescence and young adulthood: Parts 1–9.</p

Behavior and background differences between individuals with the <i>DAT1</i>^*<i>9R/9R</i> and <i>DAT1</i>^*<i>Any10R</i> genotypes, white males, and Add Health Waves I–III: Main Effects Models.

<p>“*” indicates a statistically significant result at the level of 0.05.</p><p>“+” indicates a statistically significant result at the level of 0.10.</p><p>^“†” indicates that the coefficient is exponentiated ().</p><p>“Lin, Poi, Olog, and Log” indicate a linear regression, Poisson regression, ordered logit regression, and logit regression models, respectively.</p

Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly.</p