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Using Network Analysis to Understand Public Health Delivery Systems & Population Health Improvement Glen Mays, PhD, MPH University of Kentucky glen.mays@uky.edu systemsforaction.org AcademyHealth Annual Research Meeting Boston, MA

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Using Network Analysis to Understand Public Health Delivery Systems & Population Health Improvement

Glen Mays, PhD, MPH University of Kentucky

glen.mays@uky.edu systemsforaction.org

AcademyHealth Annual Research Meeting • Boston, MA • 26 June 2015

N a t i o n a l C o o r d i n a t i n g C e n t e r

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SLIDE 2

Acknowledgements

Funded by the Robert Wood Johnson Foundation through the Systems for Action National Coordinating Center Collaborators include Cezar Mamaril, Lava Timsina, Rachel Hogg, Rick Ingram

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Using networks for population health improvement strategies

Designed to achieve large-scale health improvement: neighborhood, city/county, region Target fundamental and often multiple determinants of health Mobilize the collective actions of multiple stakeholders in government & private sector

Mays GP. Governmental public health and the economics of adaptation to population health

  • strategies. IOM Population Health Roundtable Discussion Paper. February 2014.
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SLIDE 4

Incentive compatibility → public goods Concentrated costs & diffuse benefits Time lags: costs vs. improvements Uncertainties about what works Asymmetry in information Difficulties measuring progress Weak and variable institutions & infrastructure Imbalance: resources vs. needs Stability & sustainability of funding

Using networks to overcome collective action problems

Ostrom E. 1994

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SLIDE 5

Research questions of interest

Which organizations contribute to the implementation of population health activities in local communities? How do these contributions change over time? Recession, recovery, ACA implementation? How do patterns of interaction in population health activities influence quantity, quality, cost & health

  • utcomes?

− Complementarities/Synergies − Substitutions/Cannibalization

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http://www.rwjf.org/en/culture-of-health/2015/11/measuring_what_matte.html

Guided by Culture of Health Action Framework

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Comprehensive Public Health Systems

One of RWJF’s Culture of Health National Metrics

http://www.cultureofhealth.org/en/integrated-systems/access.html

Broad scope of population health activities Dense network of multi-sector relationships Central actors to coordinate actions

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SLIDE 8

Data: networks for population health

National Longitudinal Survey of Public Health Systems Cohort of 360 communities with at least 100,000 residents Followed over time: 1998, 2006, 2012, 2014**, 2006 Local public health officials report: – Scope: availability of 20 recommended population health activities – Network: types of organizations contributing to each activity – Effort: contributed by designated local public health agency – Quality: perceived effectiveness

  • f each activity

** Stratified sample of 500 communities<100,000 added in 2014 wave

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SLIDE 9

Assess needs & risks Recommend actions Engage stakeholders

Develop plans & policies

Mobilize multi- sector implementation

Monitor, evaluate, feed back

Foundational Capabilities for Population Health

National Academy of Sciences Institute of Medicine: For the Public’s Health: Investing in a Healthier Future. Washington, DC: National Academies Press; 2012.

Measures of population health activities

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SLIDE 10

Cluster and network analysis to identify “system capital”

Cluster analysis is used to classify communities into one of 7 categories of population health system capital based on: Scope of activities contributed by each type of organization Density of connections among organizations jointly producing activities Degree centrality of the local public health agency and other organizational contributors

Mays GP et al. Understanding the organization of public health delivery systems: an empirical typology. Milbank Q. 2010;88(1):81–111.

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SLIDE 11

Network analytic approach

Two-mode networks (organization types X activities) transformed to one-mode networks with tie strength indicated by number of activities jointly produced

Organization Type Activities 1 2 3 4 5 6 7 ...20

Local public health agency

X X X X

State public health agency

X X X X

Hospitals

X X X X

Physician practices

X X

CHCs

X X X

Insurers

X X X

Employers Social service organizations

X X X

Schools

X X X

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Estimating network effects

Dependent variables: Scope: Percent of population activities performed Quality: Perceived effectiveness of activities Resource use: Local governmental expenditures for public health activities Health outcomes: premature mortality(<75), infant mortality, death rates for heart disease, diabetes, cancer, influenza Independent variables: Contribution scores: percent of activities contributed by each type of organization Network characteristics: network density, organizational degree centrality, betweenness centrality Composite network measure: comprehensive system capital

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Estimating network effects

Estimation:

Log-transformed Generalized Linear Latent and Mixed Models Account for repeated measures and clustering of public health jurisdictions within states Instrumental variables address endogeneity of network structures

All models control for type of jurisdiction, population size and density, metropolitan area designation, income per capita, unemployment, racial composition, age distribution, educational attainment, and physician availability.

Ln(Networkz,ijt) = ∑ αzGovernance ijt+ β1Agencyijt+β2Communityijt+ j+t+ijt Ln(Quantity/Quality/Costijt) = ∑ αzLn(Networkz) ijt+ β1Agencyijt+β2Communityijt+ j+t+ijt ^

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Prevalence of Public Health System Configurations, 1998-2014

% of recommended activities performed Scope High High High Mod Mod Low Low Centrality Mod Low High High Low High Low Density High High Mod Mod Mod Low Mod

Comprehensive Conventional Limited

(High System Capital)

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Average public health network structure in 2014

Node size = degree centrality Line size = % activities jointly contributed (tie strength)

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Variation in network structure

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Network Density Network Centerality Hospital Dcentrality Employer Dcentrality Lowest 10% Median Highest 10%

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Organizational contributions to population health activities, 1998-2014

% of recommended activities performed

Type of Organization 1998 2014 Percent Change Local public health agencies 60.7% 67.5% 11.1% Other local government agencies 31.8% 33.2% 4.4% State public health agencies 46.0% 34.3%

  • 25.4%

Other state government agencies 17.2% 12.3%

  • 28.8%

Federal government agencies 7.0% 7.2% 3.7% Hospitals 37.3% 46.6% 24.7% Physician practices 20.2% 18.0%

  • 10.6%

Community health centers 12.4% 29.0% 134.6% Health insurers 8.6% 10.6% 23.0% Employers/businesses 16.9% 15.3%

  • 9.6%

Schools 30.7% 25.2%

  • 17.9%

Universities/colleges 15.6% 22.6% 44.7% Faith-based organizations 19.2% 17.5%

  • 9.1%

Other nonprofit organizations 31.9% 32.5% 2.0% Other 8.5% 5.2%

  • 38.4%
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Bridging capital in public health delivery systems Trends in betweenness centrality

* * * * * * * *

* Change from prior years is statistically significant at p<0.05 2014

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Changes in tie strength: 1998-2014

S t a t e g

  • v

e r n m e n t L

  • c

a l g

  • v

e r n m e n t F e d e r a l g

  • v

e r n m e n t P h y s i c i a n s H

  • s

p i t a l s C H C s F a i t h

  • b

a s e d O t h e r n

  • n

p r

  • f

i t s H e a l t h I n s u r e r s E m p l

  • y

e r s S c h

  • l

s U n i v e r s i t i e s Local public health

  • 4.9%

4.6%

  • 3.4%
  • 13.0%

24.1% 130.6%

  • 12.8%

9.2% 22.0%

  • 13.8%

83.8% 47.4% State government

  • 14.8%

2.3%

  • 19.8%

2.6% 81.8%

  • 26.5%
  • 11.2%

8.6%

  • 31.2%

81.0% 18.0% Local government 5.6%

  • 11.0%

13.8% 117.8%

  • 16.5%

7.1% 17.2%

  • 16.6%

136.4% 51.3% Federal government

  • 11.7%

2.4% 82.4%

  • 38.1%

2.4% 24.2%

  • 47.6%

126.7%

  • 0.8%

Physicians

  • 8.8%

57.9%

  • 21.2%
  • 12.8%

5.1%

  • 22.6%

122.1% 35.3% Hospitals 142.4%

  • 10.1%

11.3% 29.5%

  • 10.4%

141.5% 55.4% CHCs

  • 10.7%

115.8% 103.7%

  • 8.4%

411.0% 172.5% Faith-based organizations

  • 12.4%
  • 8.8%
  • 8.0%
  • 7.7%

0.4% Other nonprofits 17.6%

  • 9.2%

148.0% 53.8% Health insurers

  • 4.6%

240.1% 57.7% Employers

  • 15.7%
  • 6.7%

Schools 288.0%

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Network density and scope of activities

0% 20% 40% 60% 80% 0% 20% 40% 60% 80% 100% Proportion of Activities Contributed 1998 2014

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Changes in system capital prevalence and coverage

System Capital Measures 1998 2006 2012 2014 2014 (<100k) Comprehensive systems % of communities 24.2% 36.9% 31.1% 32.7% 25.7% % of population 25.0% 50.8% 47.7% 47.2% 36.6% Conventional systems % of communities 50.1% 33.9% 49.0% 40.1% 57.6% % of population 46.9% 25.8% 36.3% 32.5% 47.3% Limited systems % of communities 25.6% 29.2% 19.9% 20.6% 16.7% % of population 28.1% 23.4% 16.0% 19.6% 16.1%

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Determinants of system structure

Probit Estimates of Factors Influencing the Probability of Comprehensive System Capital

Models also control for racial composition, unemployment, health insurance coverage, educational attainment, age composition, and state and year fixed effects. N=779 community-years **p<0.05 *p<0.10 Marginal Effect on Probability

  • f System Capital

IVs

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Health effects attributable to system structures

Models also control for racial composition, unemployment, health insurance coverage, educational attainment, age composition, and state and year fixed effects. N=1019 community-years

Fixed effects IV Estimates on Mortality, 1998-2014

100 200 300 400 500 600 700 800 900 1000 All-cause Heart disease Diabetes Cancer Influenza Residual Deaths per 100,000 residents County Death Rates Without Comprehensive System Capital With Comprehensive System Capital

–7.1%, p=0.08 –24.2%, p<0.01 –22.4%, p<0.05 –14.4%, p=0.07 –35.2%, p<0.05 +4.3%, p=0.55

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Comprehensive systems do more with less

Type of delivery system Expenditures per capita % of recommended activities performed

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Do other organizations complement or substitute for local public health agency centrality?

Results from Multivariate GLLAMM Models

  • 0.3
  • 0.2
  • 0.1

0.1 0.2 0.3 0.4 0.5

Hospitals Insurers Employers Physicians CHCs

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SLIDE 26

How does organizational centrality affect the total supply of public health activities?

Results from Multivariate GLLAMM Models

  • 0.3
  • 0.2
  • 0.1

0.1 0.2 0.3 0.4 0.5 0.6 Hospitals Insurers Employers Physicians CHCs

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Some conclusions

Population health activities are produced through highly inter-organizational and multi-sectoral efforts (62% outside public health sector). Structure of population health networks varies widely and changes over time Structure appears closely related to performance & outcomes Network structure is endogenous – ignoring this can lead to biased estimates of impact Single reliable respondents allows for feasible data collection

  • n large numbers of networks over time

Sectors vs. organizations in measurement and analysis

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For More Information

Glen P. Mays, Ph.D., M.P.H. glen.mays@uky.edu @GlenMays

Supported by The Robert Wood Johnson Foundation

Email: systemsforaction@uky.edu Web: www.systemsforaction.org www.publichealthsystems.org Journal: www.FrontiersinPHSSR.org Archive: works.bepress.com/glen_mays Blog: publichealtheconomics.org

N a t i o n a l C o o r d i n a t i n g C e n t e r