This document summarizes research on how public health delivery systems in New York State responded to changes in cancer screening policies and the implementation of the Affordable Care Act. The research analyzed network data from 36 organizations delivering cancer screenings to the uninsured over 4 time periods from 2015 to 2017. Key findings include: - Partners and relationships decreased initially after the ACA but then stabilized, indicating a period of adjustment. Clinical partners and information sharing increased while community partners decreased. - Referrals within partnerships declined significantly over time, suggesting relationships took time to develop or were not maintained. - Changes indicate the delivery systems experienced churn as organizations adjusted their partnerships in response to policy changes, but the systems did not reach a new

1. Partner or Perish?
Public Health Delivery Systems
& Cancer Screenings for the
Uninsured under the ACA
Jennie R. Law, MPA
PhD Candidate
Social Networks & Health Fellow
May 22, 2017

2. Broad research interest
How do public health delivery systems respond
to external shocks, and how is response related
to service delivery capacity?

3. Broad research interest
How do public health delivery systems respond
to external shocks, and how is response related
to service delivery capacity?

4. Broad research interest
How do public health delivery systems respond
to external shocks, and how is response related
to service delivery capacity?

5. Public health delivery systems &
public health
Source:
https://www.cdc.gov/nphpsp/essentialservices.html

6. Service delivery capacity
1995 2017
Tanjasiri et.
al., 2007
Provan &
Milward, 1995
Issett et. al,
2009
Cohen-Cole &
Fletcher, 2008
Schoen et.
al., 2014
Rothenberg
et al., 1998
Hunter et
al., 2015
Harris et al.,
2011
Increased knowledge about how public health delivery systems are
organized, and how this organization is related to measurable health
outcomes (Mays et. al., 2010).

7. Capacity & Structure
Public health delivery system capacity to
deliver services
Structure (members and configurations of
relationships among them)
Provan et. al., 2003; Retrum, Chapman, & Varda, 2013

8. Structure & Capacity
Structures that are likely to have the
largest effects on population health
are the most difficult to develop
Mays & Scutchfield, 2010a

9. External shocks
Limited evidence about how public health delivery
systems respond to large changes (Mays et. al., 2010)
Policy changes (ACA)
Budget cuts
Natural disasters
(hurricanes, floods)
New public health threats
(zika, SARS)

10. External shocks
These events increase the likelihood that the system will
fail (Carboni & Milward 2012), in turn harming the
public’s health
Policy changes (ACA)
Budget cuts
Natural disasters
(hurricanes, floods)
New public health threats
(zika, SARS)

11. Case Study: Cancer Services Program
• Evaluate 36 public health delivery systems in
NYS that deliver breast, cervical, and
colorectal cancer screenings to uninsured
• Use organizational partnerships to identify,
educate, screen & treat eligible populations
• After ACA, organizational partnerships not
working

12. How do 36 public health delivery systems
delivering services to the uninsured respond to
changes following the ACA?
• Collect longitudinal egocentric network data from
contractors implementing program
– Survey instrument:
https://www.surveymonkey.com/r/evalcopy
• Evaluate changes over time
– Partners (Wilcoxon rank-sum)
– Relationships (z-test for proportions)
– Diversity of relationships across partners (z-test for
proportions)

13. Project timeline
2015 2017
Key informant
interviews
June 2015-November
2015
1st network
questionnaire
distributed
July 2015
2nd network
questionnaire
distributed
February 2016
3rd network
questionnaire
distributed
August 2016
4th network
questionnaire
distributed
March 2017
Network data
validated with
participants
August 2016-
September 2016
Key informant
interpretation
member-checked
November 2015

14. Network data

15. Network questionnaire response rate
Respondent characteristics:
Hospital (20)
Local health department (12)
Community-based (4)
Total (36)
Excluded organizations:
Hospital (6)
Local health department (1)
Survey period Response rate
July 2015 100%
February 2016 89%
August 2016 97%
March 2017 89%

16. Changes in partners
487
420
494 474
97
60 71
68
62 124
BASELINE WAVE 2 WAVE 3 WAVE 4
Continuing New Dropped

17. Clinical
Community-
based Government Private
Faith-
based Total
Baseline
(N=487)
Median 5.00 5.00 3.00 0.00 0.00 18.00
IQR 3.00 4.00 1.00 1.00 1.00 3.00
Wave 2
(N=517)
Median 7.00 4.00 3.00 0.00 0.00 18.00
IQR 2.00 4.00 2.00 1.00 1.00 5.00
z statistic -2.77 1.07 -0.19 -0.47 -1.49 -0.91
(p-value) (0.01) (0.01) (0.85) (0.64) (0.14) (0.36)
Wave 3
(N=553)
Median 6.00 4.00 4.00 1.00 0.00 17.00
IQR 4.00 4.00 1.00 1.00 1.00 6.00
z statistic -0.92 -0.74 -1.89 -3.30 0.46 -1.85
(p-value) (0.36) (0.46) (0.06) (>0.001) (0.64) (0.06)
Wave 4
(N=543)
Median 7.00 3.00 4.00 1.00 0.00 18.00
IQR 3.00 5.00 1.00 1.00 1.00 5.00
z statistic -0.68 -0.62 0.93 0.62 -0.15 -1.08
(p-value) (0.50) (0.53) (0.35) (0.53) (0.88) (0.28)
Table 1: Median number of partners by type
reported by public health delivery system

18. Changes in relationships
455 479 522 500
311
479 332 500
329
302
293
250
376
313
306
301
BASELINE WAVE 2 WAVE 3 WAVE 4
Sharing information Sharing resources
Sending referrals Receiving referrals

19. Table 2: Average number of partnerships by type
among public health delivery system partners
Share information
Share
resources
Send
referrals
Receive
referrals
Baseline
(N=487)
Proportion 0.93 0.64 0.68 0.77
(SE) (0.01) (0.02) (0.02) (0.02)
Wave 2
(N=517)
Proportion 0.90 0.51 0.53 0.59
(SE) (0.01) (0.02) (0.02) (0.02)
z statistic 1.79 3.97 4.71 6.30
(p-value) (0.07) (>0.001) (>0.001) (>0.001)
Wave 3
(N=553)
Proportion 0.93 0.60 0.52 0.55
(SE) (0.01) (0.02) (0.02) (0.02)
z statistic (1.56) (2.73) 0.21 1.35
(p-value) (0.12) (0.01) (0.83) (0.18)
Wave 4
(N=543)
Proportion 0.90 0.40 0.50 0.57
(SE) (0.02) (0.02) (0.02) (0.02)
z statistic 1.91 6.42 0.69 (0.79)
(p-value) (0.06) (>0.001) (0.49) (0.43)

20. Changes in Relationship Diversity:
July 2015-March 2017
0
0.2
0.4
0.6
0.8
1
Baseline Wave 2 Wave 3 Wave 4
Share information Share resources
Send referrals Receive referrals

21. Table 3: Changes in the median diversity of partnerships
within public health delivery systems over 24 months
Share
information Share resources
Send
referrals
Receive
referrals
Baseline
(N=487)
Median 0.74 0.59 0.56 0.63
IQR 0.06 0.23 0.23 0.19
Wave 2
(N=517)
Median 0.79 0.66 0.47 0.54
IQR 0.07 0.17 0.26 0.22
z statistic -2.28 -1.22 1.01 2.50
(p-value) (0.02) (0.22) (0.31) (0.01)
Wave 3
(N=553)
Median 0.81 0.66 0.47 0.63
IQR 0.07 (0.19) (0.33) (0.18)
z statistic -2.53 -0.72 -0.73 -0.93
(p-value) (0.01) (0.47) (0.47) (0.35)
Wave 4
(N=543)
Median 0.80 0.71 0.57 0.56
IQR 0.06 0.12 0.15 0.21
z statistic 0.06 -0.98 -0.44 1.17
(p-value) (0.95) (0.33) (0.66) (0.24)

22. Discussion

23. Public health delivery systems response to
policy change
• Changes in partners:
– More clinical partners & relationships among them
• Different kinds of clinical partners?
• Return to traditional model?
– Fewer community-based partners
• Low value partners?
• Difficult to establish relationships?
– Delayed increase in government partners
• Low value partner?
• Difficult to establish relationship?

24. Changes in relationships & diversity of
relationships
• Fewer relationships over all, Baseline to Wave 2
– Sharing information, sharing resources, sharing referrals
– Initial excitement about partnering, then losing interest?
• Fewer referrals received
– Increases in clinical partnerships (traditional partners),
– Decreases in community-based partners
• Fewer referrals sent
– Consistent decreases in referral sending
– Fewer clients to refer?
– Burned bridges?

25. Churn: Learning or failing?
• Changes in relationships & partners—a learning process or
stuck in a rut?
• Churn
– Dropping (community-based) partners who had previously
provided referrals
– Adding new partners (clinical)
– Adding new partners (government)
– Adding new partners (private)
– Relationships take time to develop…is this why we see
higher levels of sharing information and sharing resources?
– Referral sharing relationships—a mystery.

26. Other big questions
• To what extent are environmental and
organizational factors related to these responses?
– How do they affect performance?
– Part 2: Multilevel latent class models
• How are changes in delivery system structure
related to changes in referring behavior?
– How do these features co-evolve?
– Part 3: Stochastic actor-oriented models for ego-nets

27. Implications for practitioners & policy-
makers
• For practitioners
– Important to understand what the function of organizational
partnerships are prior to policy changes
– Important to be able to interact with a variety of organizations
(open versus closed networks)
• For policy-makers
– Contracting for public health services is common practice,
increases the vulnerability of delivery systems to these changes
• Contractors produce one part of a larger whole
• Contracts measure success in a particular way that may not be relevant
after a larger policy change/disaster/etc.
– Designing better contracts responsive to policy changes or other
external shocks

28. Implications for researchers
• Studying how public health delivery systems
respond to change is an open field
• Translating findings for practitioners & policy
makers is vital
– But, complicated by intensive nature of data collection
& uncertain policy implementation timelines
• Important to consider
– Adaptation is a lengthy process: no evidence of
equilibrium after 24 months
– Multiple networks
– Validation is important (and time consuming)

29. Limitations
• Not a pre/post analysis
• Analysis is descriptive and not causal
• Data from seven respondents was not included
in the final analysis
• Potential for recall bias

30. Questions
Jennie Law
jlaw@albany.edu
845-863-5045

31. Institute of Medicine (IOM)
Bellwether
• “Crossing the Quality Chasm: A New Health
System for the 21st Century” (2001)
– Health systems do not provide full complement of
services to serve people with common chronic
conditions
– Chasm between what we now have, and healthcare we
could have
– Need evidence to improve health delivery systems
• Who is involved
• How they work together
• How these elements affect service delivery

32. Policy changes and cancer screening in
NYS: 1994-2015
21,758
25,717
30,415
38,950
43,746
45,662
46,644
49,453
46,548
54,989
59,120
67,129
76,131
81,320
75,182
38,540
47,831
46,483
48,345
38,281
30,346
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
1994-
1995
1995-
1996
1996-
1997
1997-
1998
1998-
1999
1999-
2000
2000-
2001
2001-
2002
2002-
2003
2003-
2004
2004-
2005
2005-
2006
2006-
2007
2007-
2008
2008-
2009
2009-
2010
2010-
2011
2011-
2012
2012-
2013
2013-
2014
2014-
2015
NumberofClients
Program Year

33. New Yorkers Screened: 1994-2015
21,758
25,717
30,415
38,950
43,746
45,662
46,644
49,453
46,548
54,989
59,120
67,129
76,131
81,320
75,182
38,540
47,831
46,483
48,345
38,281
30,346
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
1994-
1995
1995-
1996
1996-
1997
1997-
1998
1998-
1999
1999-
2000
2000-
2001
2001-
2002
2002-
2003
2003-
2004
2004-
2005
2005-
2006
2006-
2007
2007-
2008
2008-
2009
2009-
2010
2010-
2011
2011-
2012
2012-
2013
2013-
2014
2014-
2015
NumberofClients
Program Year
Change in eligibility
for cervical cancer
screening
ACA, Medicaid
expansion,
NYSoH

34. How is response related to how
uninsured New Yorkers receive
cancer screenings?
• Key informant interviews (N=56)
• Validate of interview interpretation with
participants
• Validate of network data and interpretation
with participants
• Develop conceptual model of partnering &
service delivery capacity

35. How is response related to service
delivery capacity?
35
Uninsured
New Yorkers
Cancer
Service
Programs
Organizational
partners
ClinicalEngaged in
Healthcare

36. How is response related to service
delivery capacity?
36
Uninsured
New Yorkers
Cancer
Service
Programs
Organizational
partners
“working poor”
immigrants
moral objection
religious objection
Clinical
New partners
Dropped partners
Engaged in
Healthcare
Disengaged from
healthcare

37. Areas for further investigation
Evaluate relationship between changing partners
and partnerships in public health delivery
systems, and public health outcomes
– Latent class multilevel models
– Unit of analysis dyad
– DV referrals received OR cancer screening rates
– IV geography, contractor type, partner type,
partner type*new partner, partner
type*continuing partner, partner type*dropped
partner

38. Public health delivery systems &
systemic risk
• Systemic risk
– In interdependent networks of organizations, problems
within organizations increase systemic risk (Carboni
and Milward, 2012)
– External shocks to interdependent systems increase the
likelihood of systemic crisis (ibid)
– The more centralized a network is, the less resilient the
network will be to systemic shocks (ibid)
– As uncertainty about the potential shocks increases, a
network will be less resilient to shocks (ibid)

39. Understanding public health delivery
systems: network analysis
Harris et al., 2011
Tanjasiri et. al., 2007
Cohen-Cole & Fletcher, 2008
Schoen et. al., 2014
Hunter et al., 2015
Rothenberg et al., 1998
Isett et. al., 2009
Provan & Milward, 1995

40. Discussion (under construction)
• Evidence of adaptations in partners and partnerships in public health delivery systems across New
York State
• Number and type of partners included in different public health delivery systems
– Increase in partners across all types
– Changes between Baseline and Wave 2 indicative of rapid growth
– Changes between Wave 2 and Wave 3 could demonstrate
• Number of all relationships decreased between Baseline and Wave 1
– Could indicate a learning process, linked to results of number of partners
– Could indicate that it takes time to establish all types of relationships with partners
– Increase in resource sharing between Waves 2 and 3 could support this theory
• Diversity of relationships increased for information and resource sharing
– Could indicate that it takes less time to establish these particular types of relationships with partners
• Diversity of relationships decrease for referral sharing
– Could indicate a learning process, linked to results of number of partners & relationships
– Could indicate that it takes time to establish these particular types of relationships with partners
– Potentially supported by non significant increases in these areas between Wave 2 and Wave 3
– Could also indicate that new partnerships are less effective
– Or that lead organizations are less effective at establishing referral relationships with the new partners
introduced

41. New York State Department of Health
Cancer Services Program
• Department of Health contracts with hospital,
local health departments, and community-based
organizations to provide free breast, cervical, &
colorectal cancer screenings to uninsured men &
women between 40-64
• Contractors use partnerships to identify, educate,
and provide services to eligible populations
Organizational partnerships identify people
eligible for program (referrals)

42. Measuring change: instrument
• Electronic survey distributed to program contractors (ego net
design) N=36
– Four waves of data collection
– Identified partners routinely engaged in partnerships
– Identified relationships among partners:
• sharing information,
• receiving referrals,
• sending referrals, and
• sharing resources
• Organizational partner type was coded after data collection: clinical,
community-based, government, private, and faith-based