CALIFORNIA TEACHERS STUDY
Request to conduct a data analysis project
Date:
November 10, 2015
PI:
Sue Kim
Institution:
University of Southern California
e-mail:
sueekim@usc.edu
(Co-PI Michael Cousineau)
CTS Steering Committee Sponsor: Dennis Deapen
Title of project: Variation in health care use among California Teachers Study participants
Key words: chronic disease, geographic variation in health care use, health care
delivery, preventable hospitalization
Start date:
October 2015
Is funding available? |_x_| yes
End date:
October 2016
|__| no
Funding source (include grant # if applicable)? PI time covered by general
departmental research fund
ABSTRACT
Specific Aims, Rationale, Significance (1.5 page maximum):
Background/significance
Past studies have shown that there are regional variations in health care utilization, such as
physician visits, hospitalization, and surgical procedures.1,2 These variations are often associated
with higher health care spending without the benefit of better health outcomes.3,4 The variations
are only partially explained by patient population, provider practice patterns, and the availability of
1
Rodriguez F, Wang Y, Naderi S, Johnson CE, Foody JM. Community-level cardiovascular risk factors impact geographic
variation in cardiovascular disease hospitalizations for women. Journal of Community Health. 2013 Jun;38(3):451-7.
2 Talbott EO, Rager JR, Brink LL, Benson SM, Bilonick RA, Wu WC, Han YY. Trends in acute myocardial infarction hospitalization
rates for US States in the CDC tracking network. PLoS One. 2013 May 22;8(5):e64457.
3Reschovsky JD, Hadley J, O'Malley AJ, Landon BE. Geographic variations in the cost of treating condition-specific episodes of
care among Medicare patients. Health Services Research. 2014 Feb;49(1):32-51.
4 Franzini L, White C, Taychakhoonavudh S, Parikh R, Zezza M, Mikhail O. Variation in inpatient hospital prices and outpatient
service quantities drive geographic differences in private spending in Texas. Health Services Research. 2014 Dec;49(6):1944-63.
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health care resources in the region. Understanding the regional variation in health care use will help
tailor the interventions to better focus on risk profiles and high needs regions.
The use of California Teachers Study (CTS) survey data and the Office of Statewide Health Planning
and Development (OSHPD) hospital discharge data will provide a unique opportunity to examine
patient and hospital characteristics that are associated with regional variation in health care use.
Such information can help policy makers to formulate appropriate policies for the region and
improve the care of patients.5
Identifying the areas of variation and overutilization can have a major implication on the cost of
care. Given that an average hospital admission cost is more than $9,7006, reducing admissions by
even a small percentage means potential savings of millions of dollars. It could be a source for
potential long-term savings if areas of high utilization can be brought to at least the average cost
areas. Preventable hospitalization is one area of health care that can lead to unnecessary use of
resources. By identifying hospitalization that could have been prevented, along with an
understanding of the patients’ characteristics that are associated with these preventable
hospitalizations, it may be possible to recommend changes for the future. The public health impact
is that the analysis will show that focusing more on prevention is beneficial for the patient as well as
lowering the cost of care.
The results of this study can be used to explore the effects from transforming health care delivery
to a more integrated system. With the implementation of the Affordable Care Act (ACA), there is
impetus for health care organizations to become more integrated and changing how medical care is
reimbursed. There is more emphasis on quality, tying payments to health outcomes. If this study
shows that there are significant differences by region or county, additional analyses can examine
the organizational characteristics of the health care systems or hospitals for those particular
regions. The comparison of health care systems with different levels of integration may provide
information about the effects of ACA implementation on the structure of the health care system
and in turn variation in health care use. As more health care is delivered by integrated systems, the
expectation is that there will be less variation, more efficiency, and better patient management and
outcomes.
Study Objectives
This study aims to examine whether people’s characteristics or where they receive care affect
health care utilization for similar diagnosis. The matching of CTS participants to OSHPD data will
provide an opportunity to examine whether people with similar diagnosis from different regions of
California receive same care. The participant characteristics in the CTS data will be used to predict
hospitalization, length of stay, preventable hospitalization, common medical procedures, and
emergency department use (Table 1, p.3). The analyses will provide information about whether
some regions have overutilization of care. This study will help to answer the question of who or
what is responsible for the different patterns of health care use. Previous studies lack detailed
5
Newhouse JP, Garber AM. Geographic variation in Medicare services. New England Journal of Medicine. 2013 Apr
18;368(16):1465-8.
6 HCUP Statistical Brief #416, January 2013. Costs for hospital stays in the US, 2010. http://www.hcup-
us.ahrq.gov/reports/statbriefs/sb146.pdf
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information about the patients. Information in the CTS data will help to determine what, if any,
patient characteristics explain regional variations.
This study will also examine if there were preventable hospital stays and the different rates by
region. To examine preventable hospitalization, preventable quality indicator (PQI), developed by
Agency for Health Research and Quality7, will be used. PQI are set of measures that can be used to
identify hospitalizations that could potentially have been prevented by good outpatient or early
intervention to prevent complications. This analysis will examine what proportion of the hospital
admissions could have been prevented and estimate the potential savings that would result from
avoiding such unnecessary admissions. PQI uses discharges with certain principal diagnosis codes
and population in the metropolitan area or county to examine the rates.
Table 1. Summary Table
Study Question







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What is the rate of
hospitalization for CTS
population by region?
What are the participant
characteristics that predict
hospitalization?
What is the average length of
stay for the hospitalization by
region?
What are the participant
characteristics that predict LOS?
What is the rate of preventable
hospitalization by region? What
are the participant
characteristics that predict
preventable hospitalization?
What is the rate of common
procedures by region? What are
the participant characteristics
that predict the procedures?
What is the rate of emergency
department use by region?
What are the participant
characteristics that predict use
of emergency department?
Outcome variable (OSHPD
variables)
Hospitalizationadmissions (ADMTDATE,
DSCHDATE, VISITNO)
Hospital length of stay
(LOS)
Preventable Quality
Indicators
(DIAG_P, ODIAG1ODIAG24, MSDRG)
Common procedures
(PROC_P, OPROC1OPROC20)
Explanatory variables (CTS
data)
Demographics: age,
race/ethnicity, income,
education, marital status,
city/zipcode/county;
General health status: diet,
nutrition, physical activity,
BMI, aging test, smoking,
second hand smoke, social
support, emotional health;
Medical diagnosis: asthma,
diabetes, heart disease,
respiratory (COPD), cancer,
or other diagnosis;
Preventive: mammogram,
colonoscopy, medical
imaging, medication;
Type of health plans
Emergency department
visits (DX_PRIN, FAC_ID,
admission/discharge
dates)
AHRQ. Preventional Quality Indicators Overview. http://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx
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Design and Analysis Plan (be sure to include subject eligibility criteria and power
calculations):
Subject eligibility: all CTS sample
Hospital discharge data is available from OSHPD for years 1991 to 2012. Emergency department
and ambulatory surgery data are available for years 2010 to 2012.
Design and Analysis plan
Bivariate and multivariate analyses will be conducted based on the variables listed in Table 1. For
example, for the rate of hospital admissions, a bivariate analysis will determine the number of
hospitalization by diagnosis. If there is sufficient number of cases for analysis, the following
diagnosis will be considered: asthma, diabetes, heart disease, respiratory disease (COPD),
pneumonia, and breast and colon cancer. Second, multivariate logistic regression models will
identify significant variables that are associated with hospital admissions, length of stay,
preventable hospitalization, and emergency department visits.
Sample size and power calculation
According to the literature and previous studies on hospital admissions, the rate of hospital
admissions in California is 85 per 1,000 population.8 It is estimated that there could be 5-15%
difference in the admission rate across different geographic regions in California.
The sample analysis is based on logistic regression with multiple predictors. The sample size would
differ depending on the number of covariates and correlation between those variables. Table 2
shows sample sizes needed to detect significance at two different levels of correlation among the
covariates (R-square of 0.2 and 0.4).
Table 2 also shows the sample sizes needed to detect 5, 10, or 15 percent difference in hospital
admission rates. The sample size calculations detect significance when alpha is 0.05 and 0.01, and at
power levels of 80% and 90%.
Table 2: Estimated sample size needed for logistic regression model: Hospital admissions
Assume lower correlation among
Assume higher correlation among
predictors (R-square=0.2)
predictors (R-square=0.4)
α=0.05
α=0.01
α=0.05
α=0.01
90% power analysis
To detect 5% difference
51,579
78,409
68,772
104,545
in admissions
To detect 10% difference
13,451
20,455
17,935
27,273
in admissions
To detect 15% difference
6,225
9,471
8,300
12,628
in admissions
8 Kaiser Family Foundation. 2013 State Health Facts:Hospital admissions per 1,000 population by ownership type.
http://kff.org/other/state-indicator/admissions-by-ownership/#table
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80% power analysis
To detect 5% difference
in admissions
To detect 10% difference
in admissions
To detect 15% difference
in admissions
37,242
60,461
49,656
80,614
9,716
15,778
12,955
21,038
4,499
7,310
5,999
9,746
List of variables needed -Primary exposures: NA
Covariates: Please see Table 1
Genetic data: NA
Outcomes: Please see Table 1
BIOSPECIMENS
Are biospecimens required? |__| yes |_X| no
Biospecimens needed:
Special processing /
Biospecimen
specifics *
Selection criteria **
Amount needed
DNA
serum
plasma
urine
other
* i.e., fasting sample, 24-hr urine, etc.
** if the same as specified in the design section, state this; if different, specify
Plans for processing biospecimens (where processing will take place, plans for unused
sample, processing methods, specific data that will be obtained, etc.):
AGREEMENT
By submitting this concept proposal, I agree that I will abide by the CTS publication
guidelines, including (but not limited to) co-authorship by interested CTS
investigators, submission of the final manuscript to the CTS Publications Committee
for approval prior to journal submission, and inclusion of the CTS acknowledgement
statement in the manuscript. In addition, I agree that any new data variables
generated from the analysis of data or biospecimens will be provided to the CTS at
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the time of publication and no later than 12 months following the end date of the
project. At the end of the project, I will return (or destroy, as mutually agreed upon
by me and the CTS) all data files and biospecimens to the CTS. The data or specimens
will not be used for any purposes other than stated in this proposal (or subsequent
modifications of this proposal as agreed upon by me and the CTS in writing).
__________________________________________________
Signature
9/29/2015
_________________
Date
FOR CTS USE ONLY:
Date of review:
_____________________________________
Decision: |__| approved |__| additional information requested
|__| rejected
Writing team:
Comments:
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