social science challenge health connectivity: identifying social relations underlying health and...
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Social Science ChallengeHealth Connectivity: Identifying social relations
underlying health and health disparities
James Moody1,2, Joseph Lucas,3 Laura Sheble1
1Duke Network Analysis Center2 Dept. of Sociology
3 Department of Statistics
Social Determinants of Health“…social determinants of health refers to the complex, integrated, and overlapping social structures and economic systems that include social and physical environments and health services.” (CDC, 2010)
WHO Commission on Social Determinants of Health Conceptual Framework
Social Determinants of HealthSocial factors matter
RWJ, Health Affairs (2014) “The relative contributions of multiple determinants to health outcomes”
Iwashyna, Theodore, J., Jason D. Christie, James Moody, Jeremy M. Kahn, David A. Asch. “The Structure of Critical Care Networks.” Medical Care 47:787-793.
Hospital Transfer Networks: ICU
The Movement of Carbapenem-Resistant Klebsiella pneumoniae among Healthcare Facilities: A Network AnalysisD van Duin, F Perez, E Cober, SS Richter, RC Kalayjian, RA Salata, N Scalera, R Watkins, Y Doi, S Evans, VG Fowler Jr, KS Kaye, SD Rudin, KM Hujer, AM Hujer, RA Bonomo, and J Moody for the Antibacterial Resistance Leadership Group
Medical Care, 2015. 53:534-541
Data: Duke Electronic Medical Records
• Working on Joe’s server through his IRB• A 5-year data pull including all patients from 2007-2011 (inclusive) that have
addresses in Durham county.
Census Age distribution, Durham County.
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Sample limited to adults, includes multiple encounters, extreme outliers removed
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Sample limited to adults, includes multiple encounters, extreme outliers removed
Underweight
Obese
Normal
Overweight
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Normal
Pre Hyper
Stage 1
Stage 2
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Normal
Stage 2
1% random sample of adults, BMI outliers removed
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Females
Males
Probability of BP in Hypertension Range
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Black Males
White Female
Black Female
White Male
Logistic regression model, sample limited to adults, outliers removed, fit at age=mean
Probability of BP in Hypertension Range
Data: Duke Electronic Medical RecordsSome basic data descriptives…
Black Males
White Female
Black Female
White Male
Logistic regression model, sample limited to adults, outliers removed, fit at bmi=mean
Probability of BP in Hypertension Range
Sketch of Future Projects
Sick
Project 1: Variability effects on diagnostic cut points
Preliminary bits
Project 1: Variability effects on diagnostic cut points
True Score
Obs
erve
d Sc
ore
r=0.9
Preliminary bits
Project 1: Variability effects on diagnostic cut points
True Score
Obs
erve
d Sc
ore
r=0.9
Healthy people misdiagnosed as sick
Sick people misdiagnosed as Healthy
Preliminary bits
Project 1: Variability effects on diagnostic cut points
'1% random sample of patients with LDL-C measures, stratified by statin status’
Preliminary bits
Project 2: : Health Connectivity: Identifying social relations underlying health and health disparities
Main thrust of our work: Goal is to capture the multiple ways that patients are socially & medically connected to identify social sources of health & health disparities.
Theoretically, these include:- Family connections- Social care provider connections (who do you call in an emergency)- Co-worker context- Healthcare provider networks
We think we can get some of these from intake forms (place of employment, emergency contact) others from demographic & address (same place/time info).
Most direct is connection through healthcare provider – doctors who share patients.(note there is ancillary potential value here in helping administrators identify care provider communities)
• Focus on particular patient population (diabetics)– 19610 diabetics
• Examine only visits that occur on or after first diagnosis– 6398 providers
• One “provider” has seen > 105 encounters (dropped)
Project 2: : Health Connectivity: Identifying social relations underlying health and health disparities
# patients seen by each provider
# providers seen by each patient
Project 2: : Health Connectivity: Identifying social relations underlying health and health disparities
Project 2: : Health Connectivity: Identifying social relations underlying health and health disparities
Narrow the scope:• Focus on January and February of 2010• Examine only patients who have diabetes
diagnosis (19K)• 2000 physicians interacted with this population
in those 2 months• 177 of those had >40 interactions with diabetic
patients
Project 2: Health Connectivity: Identifying social relations underlying health and health disparities
Nodes=Physicians, size proportional to degree. Edges=n of shared patients (logged), connections of fewer than 3 patients removed for clarity. Node colors indicate the two largest graph-based groups observed in the network