Center for the Study of
Healthcare Provider Behavior
Impact of Practice Structure on the Quality
of Care among Women Veterans
Elizabeth M. Yano, PhD, MSPH,1,2
Bevanne Bean-Mayberry, MD,1,3 Michael Mitchell, PhD,1
Ismelda Canelo, MPA,1 Andrew B. Lanto, MA,1
Danielle Rose, PhD,1,2 Donna L. Washington, MD, MPH1,3
1VA
Greater Los Angeles HSR&D Center of Excellence
2UCLA Schools of Public Health and 3Medicine
Annual Research Meeting of AcademyHealth
Chicago, IL • June 2009
1
Women and War
Women have always participated in the military
– By WWII, female nurses’ role firmly established
Congress lifted 2% cap on their participation
towards end of Vietnam War  exponential ↑
Source: Murdoch, et al., Women and war: What physicians need to know. JGIM, 21:S5-10, 2006.
Women in the Military
Women now represent:
– 15% active duty
– 20% new recruits
– 17% reserve/National Guard
Equalization of hardships/risks
– 71% w/1+ combat exposure
Different sociodemographics
– >60% under age 45
– ↑ minority representation
Source: Murdoch, et al., Women and war: What physicians need to know. JGIM, 21:S5-10, 2006.
3
Women Veterans and VA
Women have been a numerical minority in VA
(5-7% of users)
Women now among fastest growing segments
of new VA users
– Legislative changes opened military careers to
women
– Increased efforts to enroll returning veterans from
Iraq and Afghanistan into VA care
– General declines in insurance availability nationwide
 anticipated doubling of numbers of women
veterans within a few short years
4
Women Veterans in VA
Significant comorbid physical and mental illness
–
–
–
–
Higher disease burden than female non-vets
Comparable disease burden to male vets
Greater mental health burden compared to male vets
High rates of sexual harassment, abuse, assault
Numerical minority creates challenges for VA
– Historical predominance of men in VA settings
Gaps in safety and privacy for women (GAO)
Limited gender-specific service availability (GAO)
VA providers with little/no exposure to women
5
Objective
To evaluate on a national scale the
impact of different practice structures on
the quality of care women veterans
receive
– Clinic types (women’s clinics vs. general
primary care clinics)
– Clinic features (e.g., staffing, resources,
clinical arrangements, service availability)
6
Study Design & Samples
Key informant practice structure survey
– Fielded to VAs serving 300+ women veterans
– Clinic types and clinic features (2001)
– 82% response rate (n=136 facilities)
Linked to patient-level quality indicators
– Random samples of outpatients per VA facility
– Chronic disease and prevention quality
indicators (2001-2003) (n=3300 to 11,000)
– Patient ratings of care (2002) (n=5,712)
7
Data Sources & Measures:
Practice Structure Survey
VA Survey of Women Veterans Health
Programs & Practices
– Clinic types (general PC clinic, women’s clinic)
– Clinic features
Staffing level and mix (e.g., GIM, Gyn, NP, RN)
Space/privacy (e.g., exclusive use exam rooms)
Resource sufficiency (e.g., WH expertise, samegender providers, female chaperones)
Service availability/care arrangements
8
Data Sources & Measures
Quality of Care
External Peer Review Program
– Chart-based HEDIS-like quality indicators
– Chronic disease care (e.g., diabetics’ foot exams,
blood pressure control)
– Preventive care
Gender-specific (e.g., cervical cancer screening)
Gender-neutral (e.g., flu shots, CRC screening
Survey of Healthcare Experiences for Patients
– Validated measures of accessibility, continuity,
coordination
Statistical Analysis
Random intercepts logistic regression
– Evaluated main effects and interactions
Clinic type (women’s clinic vs. general PC clinic)
Clinic features (e.g., staffing)
– Adjusted for patient clustering w/in practices
Calculated predicted probabilities of
achieving individual quality indicators
– Adjusted for patient-level covariates
Age, race-ethnicity, education, marital status,
insurance, income
VA Primary Care Practice Structure
Variations (n=136 facilities)
Separate
Women’s
Clinics
General
Primary
Care Clinics
No separate
women’s
clinics
(in addition
to general
PC clinics)
Varying scopes
of practice from:
54.4%
• comprehensive
• gender-specific exams only
45.6%
• All PCPs see women OR
• Designate a WH provider
Diabetes Care
Diabetic HbA1c tested (past year)
– No difference by clinic type or features
Diabetic foot sensation exams
– General PC outperforms women’s clinics**
Regardless of clinic features (more or less
comprehensive)
Diabetic lipid screening done (past 2 years)
– No difference by clinic type
– More comprehensive women’s clinics lower*
Adjusted for patient age, race-ethnicity, insurance status and income.
*p<.05; **p<.01; ***p<.0001
Blood Pressure Control
General PC
WC w/o feature
WC w/feature
Specialist MDs
RN staffing
p<.01
Admin staffing
p<.01
Separate exam
rooms
p<.05
0%
10%
20%
30%
40%
50%
60%
Predicted Probabilities of Having Blood Pressure Under Control (<130/85)
(adjusted for patient age, race-ethnicity, insurance and income)
Gender-Neutral Prevention
Colorectal cancer screening
– No difference by clinic type
– No difference by clinic features
Influenza immunizations
– No difference by clinic type
– Women seen in facilities that had hired more
staff to deliver women’s health care in past 2
years* were more likely to get flu shots
Adjusted for patient age, race-ethnicity, insurance status and income.
*p<.05; **p<.01; ***p<.0001
Gender-Specific Prevention
Cervical cancer screening
– Better in VAs with women’s clinics with
Comprehensive staffmix (gynecologists*, other
MDs*, social workers*)
Sufficient administrative support staff*, properly
equipped exam rooms*, female chaperones***
– Better in VA’s that had hired more staff to
deliver women’s health care in past 2 years*
Adjusted for patient age, race-ethnicity, insurance status and income.
*p<.05; **p<.01; ***p<.0001
Gender-Specific Prevention
Breast cancer screening
– Better in VA’s with women’s clinics overall***
– Better in VA’s with onsite screening* and
diagnostic* mammography services
– Worse in VA’s with women’s clinics staffed
with MD trainees*
Adjusted for patient age, race-ethnicity, insurance status and income.
*p<.05; **p<.01; ***p<.0001
Access to Care
Better in facilities with:
–
–
–
–
–
Separate women’s clinics***
Separate WH budget control**
Active WH quality improvement program*
Onsite bone scan equipment, endometrial biopsy, IUD*
Local clinical experts in WH*
Worse in facilities with:
– Designated WH providers in general PC*
– All/almost all women seen in general PC for care*
Adjusted for patient age, race-ethnicity, insurance status and income.
*p<.05; **p<.01; ***p<.0001
Coordination
Better in facilities with:
–
–
–
–
Separate women’s clinics*
Higher WH resources (clinical expertise, staffing)*
Active WH quality improvement program*
Greater authority over WH staffing arrangements*
Worse in facilities
– Having to refer women to another VA for WH
specialty care (e.g., non-surgical breast cancer
treatment)*
Adjusted for patient age, race-ethnicity, education, marital status, insurance, income
*p<.05
Limitations
Observational study with limited degrees
of freedom for evaluating all structural
variations of interest
– Yet one of largest studies of its kind to date
Absent small community clinics where
numbers are on the rise
Does not account for primary care and
other services obtained outside the VA
– VA counts outside performance if in record
– Adjusted for insurance status
Conclusions & Implications
Separate women’s clinics provide focused
attention and resources
– Concentrate women to a few providers
– Yielding better ratings of care and genderspecific prevention
– But women’s clinic “comprehensiveness” not
covering gender-neutral primary care basics
– A matter of resolving “who’s on first for what”?
Need reconciliation of clinic scope of practice
between general primary care and women’s clinics
Conclusions & Implications
General primary care clinics that spread
women to all providers or consolidate
them to a few perform below par
– Provider proficiencies need to be addressed
– Consider care management, care navigation
for women with complex needs
Better understanding of organizational
determinants of gender disparities may
inform policy and practice interventions