analyzing healthcare sequences in social networks
TRANSCRIPT
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Analyzing healthcare sequences in social networks
Jeff Lienert, M.S.
ESRC Research Methods Festival
3 July, 2018
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Background
• When a person becomes infected and notices symptoms, they can take multiple actions
• Depending on the outcome, they can follow-up with other actions
• Which actions they take are dependent on many things
• Actions they take affect their outcome
• May also affect others
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Background
Step 1
•Self-care / rest
•At home
•3 days
•Called relative to talk about discomfort
Step 2
• Informal healer
•At home
•8 days
•Paracetamol (1 daily, for 3 days)
Step 3
•Public county hospital
• > 2 hours from home
• 14 days
• Called taxi
Step 4
•Private doctor
•10 minutes from home
•1 day
•Phenoxymethyl-penicillin (half dose daily, for 1 day)
Step 5
•Self-care / rest
•At home
•7 days
•Phenoxymethyl-penicillin (half dose daily, for 7 days)
Process Data
Symptoms | Diagnosis | Self-perceived severity | Total duration | Start date | Current status | People involved
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Background
• Actions can be analyzed in a number of ways• Counts
• Event sequence
• Network
• Each gives different information
• Here, we focus on the network approach, and what it offers over the others
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Participant demographics
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Making the network
Data Network
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Making the network
Data Network
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Making the network
Data Network
1
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Making the network
Data Network
1
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Making the network
Data Network
1
2
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Making the network
Data Network
1
2
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Making the network
Data Network
1
2
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Making the network
Data Network
1
2
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Making the network
Data Network
1
2
3
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Making the network
Data Network
1
2
3
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Making the network
Data Network
1
2
3
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Making the network
Data Network
1
2
3
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All steps
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Step 1
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Step 2
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Step 3
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Step 4
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Step 5
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Step 6
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Step 7
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Step 8
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Step 9
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Centrality measures
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Centrality measures
• Common first steps• Doing nothing
• Pharmacist
• Primary care
• Common last steps• Self-care
• Care from family and friends
• Common intermediate steps• Self-care
• Care from family and friends
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Correlation of centrality and stage
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Conclusions
• Network analysis provides important information about event history of infection healthcare-seeking behaviors
• Network centrality parameters independent of evet sequence parameters
• The small number of those seeking traditional healers have unique trajectory
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Next steps
• Dynamic network modeling (SAOMs)
• Multilevel network modeling to understand how social network influences event sequence and vice-versa
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AcknowledgementsPI: Marco J Haenssgen a, b, c
Proochista Ariana a
Caroline OH Jones f
Rachel Greer e
Yoel Lubell c
Mayfong Mayxay f
Paul N Newton f, g
Felix Reed-Tsochas b, h, i
Heiman FL Wertheim j, k
Giacomo Zanello l, m
a. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; b. CABDyNComplexity Centre, Saïd Business School, University of Oxford, Oxford, UK; c. Economics and Translational Research Group(ETRG), Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, Bangkok,Thailand; d. Department of Health System and Research Ethics, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya; e.
Chiangrai Clinical Research Unit, Chiangrai Prachanukroh Hospital, Chiang Rai, Thailand; f. Lao Oxford Mahosot Wellcome TrustResearch Unit (LOMWRU), Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, MahidolUniversity, Bangkok, Thailand; g. Faculty of Postgraduate Studies, University of Health Sciences, Vientiane, Lao PDR; h. Institutefor New Economic Thinking, Oxford Martin School, University of Oxford, Oxford, UK; i. Department of Sociology, University ofOxford, Oxford, UK; j. Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam; k. Medical MicrobiologyDepartment, Radboudumc, Nijmegen, Netherlands; l. School of Agriculture, Policy and Development, University of Reading,Reading, UK; m. Leverhulme Centre for Integrative Research on Agriculture and Health, London, UK
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References
• Allison, P. D. (2014). Event History and Survival Analysis. Thousand Oaks, CA: Sage.
• DuBois, C., Butts, C. T., McFarland, D., & Smyth, P. (2013). Hierarchical models for relational event sequences. Journal of Mathematical Psychology, 57(6), 297-309. doi: 10.1016/j.jmp.2013.04.001
• Haenssgen, M. J., & Ariana, P. (2017). Healthcare access: a sequence-sensitive approach. SSM -Population Health, 3, 37-47. doi: 10.1016/j.ssmph.2016.11.008