Exploring Disease Associations and Country of Origin: An In-Depth Analysis Using SAS

In this assignment, we delve into the intricate world of disease associations and the fascinating role of an individual's country of origin, brought to life through in-depth analysis using SAS. We journey through the exploration of Extrapulmonary Tuberculosis (EPTB) and Tuberculosis (TB) diagnosis in prison, unraveling the key factors influencing these conditions. Our analysis not only uncovers significant findings but also sheds light on the methodologies employed, such as Directed Acyclic Graph (DAG) modeling, and the impact of critical assumptions. Discover a compelling narrative that bridges data science and public health, offering valuable insights into disease epidemiology.

Problem Description

This SAS assignment explores the associations between diseases and country of origin, using data analysis techniques. We investigate two distinct medical conditions, Extrapulmonary Tuberculosis (EPTB) and Tuberculosis (TB) diagnosis in prison. The study aims to identify key factors influencing these conditions and understand the impact of various covariates.

Table 1: Sites of EPTB Association with Covariates

Key Findings:

• The analysis explores the association between EPTB sites and covariates, emphasizing the impact of factors like drug use, diabetes mellitus, and ARV treatment.
• Notably, drug use has the strongest association with EPTB sites, with an odds ratio of 0.946.
• Diabetes mellitus has the lowest association, suggesting a protective effect on EPTB.

SAS CODE:

proc freq data=WORK.TABLE1 order=data;
tables (LOS DrugUse DM ARV HIV Homeless)*XPSITE/nopercent norow nocol oddsratio;
run;
proc means data=WORK.TABLE1 chartype median vardef=df clm alpha=0.05 q1 q3 qmethod=os;
var LOS ARV DM Homeless HIV DrugUse;
class XPSITE;
run; 

Table 2: TB Diagnosis in Prison and Association with Covariates

Key Findings:

• This analysis focuses on the association between TB diagnosis in prison and various covariates.
• It highlights the impact of factors like drug use, alcohol abuse, and previous TB history.
• Notably, individuals born in the United States are more likely to have TB diagnosis in prison.

SAS CODE:

proc freq data=WORK.TABLE2 order=data;
tables (DrugUse AlcoholAbuse CD4ADM PreviousTB COUNTRY)*PRISON/nopercent norow nocol oddsratio;
run;
proc means data=WORK.TABLE1 chartype median vardef=df clm alpha=0.05 q1 q3 qmethod=os;
var DrugUse AlcoholAbuse CD4ADM PreviousTB COUNTRY;
class PRISON;
run; 

Table 3: DAG Model Analysis - HIV and Other Covariates

Key Findings:

• The study analyzes Directed Acyclic Graph (DAG) models to understand the relationship between HIV status and other covariates.
• It discusses the presence of colliders and their impact on the association between HIV and EPTB.
• Notably, the analysis indicates a weak and nonsignificant association between EPTB and HIV.

SAS CODE:

proc causalgraph
model “HIV”
HIV ==> ARV LOS DrugUse DM Homeless XPSITE,
LOS ==> DM Homeless,
ARV ==> HIV;
unmeasured AGE AlcoholAbuse CD4ADM COPU COUNTRY DEATH;
identify HIV ==> XPSITE;
run; 

Table 4: DAG Model Analysis - Prison and Country of Origin

Key Findings:

• The analysis continues to use DAG models to study the association between prison and country of origin.
• It discusses strategies for model building and the impact of assumptions on the results.

SAS CODE:

proc causalgraph
model “PRISON”
PRISON ==> COUNTRY DrugUse CD4ADM,
DrugUSE ==> PRISON AlcoholAbuse;
unmeasured AGE ARV LOS DrugUse DM Homeless XPSITE;
identify HIV ==> XPSITE;
run; 

This assignment exemplifies the use of SAS for in-depth data analysis and presents key findings related to disease associations and the impact of covariates on the studied outcomes.