Trends in Preventable Hospitalization Patterns among the Adults: A Small Area Analysis of US States
among the Adults: A Small Area Analysis of US States
Jayasree Basu, PhD
Background

Access to primary care was largely explored using hospitalizations due Access
to primary care was largely explored using hospitalizations due
to Ambulatory Care Sensitive Conditions (ACSC) or preventable hospitalizations (PH) 
TTrends in PH rates in vulnerable patient subgroups over a longer time d i PH
i
l
bl
i
b
l
i
period remains understudied

Such a study provides an overview of how access to primary care has Such
a study provides an overview of how access to primary care has
changed in the nation

The issue is particularly important for the adults since previous research found barriers to access to be the highest among the young hf
db i
b h hi h
h
adult and middle aged populations Study Objectives
Study Objectives
1
1.
To analyze how Ambulatory Care Sensitive Condition (ACSC) rates To
analyze how Ambulatory Care Sensitive Condition (ACSC) rates
changed over time (1995‐2005): a period marking the beginning and end of several legislative and policy initiatives causing significant changes in the health care market.
the health care market. 2.
To analyze how ACSC rates varied across small market areas and whether the spatial distribution patterns changed over time
3.
To analyze how patient level and contextual factors explain variation in ACSC rates across small areas; and 4.
To assess the independent effects of insurance status, supply of primary care, as well as socio‐demographic factors on PH rates over time.
Study Design
Study Design
 The unit of analysis = PCSA The unit of analysis = PCSA
–
A PCSA is the smallest geographic area that can be considered a discrete service area for primary care
–
Defined on FFS Medicare patient flows to physician offices, updated by HRSA
–
A PCSA includes a zip code tabulation Area (ZCTA) with one or more primary care providers, and any additional contiguous ZCTAs, whose populations seek their
providers, and any additional contiguous ZCTAs, whose populations seek their plurality of primary care from the same providers
 Census data was aggregated to the PCSA unit: Sociodemographic conditions of the PCSA were computed
conditions of the PCSA were computed
 Compositional factors describing the hospitalized populations in each PCSA (e.g. proportion in PCSA with each type of insurer, proportion by age group mean disease severity race/ethnicity ) were derived
age group, mean disease severity, race/ethnicity ) were derived
Data
STEP 1
Patient‐level hospital discharge data was pulled for 8 states (AZ, CA, OR, WA, MA, MD, NJ, g
and NY) for adults (age 18 ‐
64) Files Used
1995: HCUP SID hospital discharge files (8 states)
2005: HCUP SID hospital discharge files (8 states)
STEP 2
Data was aggregated to PCSA level (using patients’ residence zip code)
zip code)
1995 and 2005: RTI developed zip code to ZCTA and ZCTA to PCSA cross‐walk files
STEP 3
PCSA‐level contextual factors were derived for all 8 states
were derived for all 8 states
1995: 1990 Census zip code level data
2005: 2000 census zip code level data
2000 census zip code level data
STEP 4
PCSA‐level medical provider d t
data were derived
d i d
1995: 1990‐1995 ARF data at the county level
2005: 2000‐2005 ARF data at the county level
2000 2005 ARF data at the county level
STEP 5
All PCSA‐level variables derived in the earlier steps were merged by matching the PCSAs
County level
County level data was interpolated to the PCSA level
Analysis

Descriptive: Hospitalization rates were computed by small areas (i.e. PCSAs) and the mean geographic variations in rate between 1995 and 2005 were examined
–
PH Rate (Unadjusted) = # PH in a PCSA / # All hospitalizations for patients living in that PCSA
–
PH Rate (Age‐adjusted) = ∑ (Weight i) * (# PH in PCSA for age i/#All PH
R t (A
dj t d) ∑ (W i ht i) * (# PH i PCSA f
i/#All
hospitalizations for age i)); Weights being the proportion of patients of the specific age group living in a PCSA

Multivariate: Multivariable models were fitted, one for each year, and M
lti i t M lti i bl
d l
fitt d
f
h
d
pooled over two years, to account for relative importance of determinants of preventable admission rates 
In particular, independent effects of supply of primary care, insurance status, as well as sociodemographic factors on PH rates over time were assessed
Descriptive Analysis
Descriptive Analysis
RESULTS
PH Rates: Adults
PH Rates: Adults
100
90
80
70
60
50
40
30
20
10
0
1995
2005
AZ
CA
OR
WA
MA
MD
NJ
NY
All
states
• Slight decline in PH rates between 1995‐2005 in Northeast (except NJ) • PH rates increased in OR and WA between 1995 and 2005 leading to PH rates increased in OR and WA between 1995 and 2005 leading to
an overall increase in PH rates among the Western states
No change in PH rates overall
• No change in PH rates overall
Coefficient of Variation in PH Rates across PCSA: Adults
across PCSA: Adults
04
0.4
0.3
1995
0.2
2005
0.1
0
AZ
CA
OR
WA
MA
MD
NJ
NY
• Small area variation declined between 1995 and 2005 (0.33 to 0.30): • Variation in PH rates HIGHER among adults than elderly
Variation in PH rates HIGHER among adults than elderly
• Western states had greater variation than Eastern states
• CA had greatest % decline between 1995 ‐
CA had greatest % decline between 1995 ‐ 2005
Condition Specific Rates: Adults
Condition Specific Rates: Adults
ACSC
N (1995)
N (2005)
Rate of Change
Unadjusted (age‐adjusted)
Immunization‐related
191
231
(
)
35.05 (20.02)
Grand mal status/other epileptic
8681
9901
‐1.39 (0.90)
Convulsion
21318
30523
28.18 (25.87)
Severe ENT infection
4972
5049
‐15.01 (‐12.65)
Pulmonary/oth tuberculosis
4047
2248
‐44.71 (‐45.20)
A h
Asthma
64318
57407
‐17.65 (‐20.90)
17 65 ( 20 90)
CHF
53154
72180
13.61 (‐1.03)
Hypertension
10118
14924
18.10 (10.60)
18.10 (10.60)
Angina
37593
13257
‐67.98 (‐71.74)
Cellulitis
42623
70999
55.50 (52.45))
Condition Specific Rates: Adults
Condition Specific Rates: Adults
ACSC
N (1995)
N (2005)
Rate of Change
Unadjusted (age‐adjusted)
Diabetes A
20493
29324
(
)
29.05 (35.85)
Diabetes B
13167 21205
42.34 (27.46)
Diabetes C
11730 12570
‐12.12 (‐16.76)
Hypoglycemia
686
556
‐21.76 (‐22.90)
Gastroenteritis
17102 18520
‐8.01 (‐8.89)
Kidney/Urinary infection
/
33932
41326
5.40 (3.92)
(
)
Pelvic inflammatory disease
14808
7960
‐53.71 (‐49.79)
Dental
2944
3709
16 35 (18 56)
16.35 (18.56)
Bacterial pneumonia
72832
83165
3.33 (‐5.87)
All ACSC
434709
495054
‐0.13 Highlights: Adults
Highlights: Adults
 Overall Trends
Overall Trends
o PH rates among adults did not decline overall between 1995‐2005
o
Western states had lower PH rate but higher increase than Northeast
Western states had lower PH rate but higher increase than Northeast
o
Small area variation declined between 1995 and 2005  Condition‐Specific Trends
o
Highest frequency (1) bacterial pneumonia (2) asthma (3) CHF (4) Hi
h f
(1) b
i l
i (2) h
(3) CHF (4)
cellulitis
o
Highest increase in rates (1) cellulitis (2) diabetes A/B (3) CHF
o
Highest decline (1) Angina (2) asthma (3) pelvic inflammatory disease.
Multivariate Analysis
Multivariate Analysis
RESULTS
Regression Methods
Regression Methods

Ordinary Least Squares (OLS) regression analysis

Adjustment for heteroscedasticity

Cross sectional and time series analysis

Study population: Adults age 18‐64
Study population: Adults age 18‐64

Dependent variable: Unadjusted all ACSC rate

Independent variables: Age (18‐44 yrs; 45‐64 yrs), race (White, AA, Hispanic, other), insurance (Medicaid FFS; Medicaid HMO; Private FFS; Private HMO; Self‐ pay; All other payers), population density, income, average distance to hospitals, rural/urban, and supply factors (like primary care physician capacity, hospital inpatient capacity)
Sample Means
Sample Means
Variable
2005
(n=953)
Mean
1995
(n=935)
Mean
PH admission rates , adults
Proportion of adult, 18 to 44 years
Proportion of adult, 45 to 64 years
0.087
0.53*
0.47* 0.088
0.61
0.39
Proportion of adult Caucasian discharges
Proportion of adult AA discharges
Proportion of adult Hispanic discharges
Proportion of adult other race discharges
0.69*
0.074
0.15*
0.092
0.72
0.072
0.12
0.088
Proportion of adult, Medicaid FFS
Proportion of adult, Medicaid HMO
f d l
d d
Proportion of adult, private FFS
Proportion of adult, Private HMO
Proportion of adult, self‐pay
Proportion of adult, self
pay
Proportion of adult, all other payers
0.100*
0.05**
0.30*
0.22*
0.05 *
0.05 0.29*
0.21
0.002
0.35
0.19
0.08
0.17
* Shows significant changes between 1995‐2005 (p<0.05)
Sample Means
Sample Means
2005 (n=953)
Variable
1995 (n=934)
Mean
51957 *
38265 Hospital inpatient capacity per Hospital
inpatient capacity per
1000 population
3688.09 *
890 39
890.39 Average distance from patient zip code to hospital zip code
24.38 24.07
3560.90 3953.36
Primary care physician capacity Primary
care physician capacity
per 1000 population
0.72**
0.51
Proportion of PCSA that is Rural
0.28 *
0.34
Average income Population density
Highlights
Factors likely to reduce PH rates:

Primary care physician capacity: increased by 41% between 1995 and 2005 with most increase in northeastern states (64%).

HMO enrollment: Significant increase in commercial as well as in Medicaid HMO enrollment occurred over the 10 year period.

The Northeastern states showed higher growth rates than the West in private as well as Medicaid HMO enrollments. 
Socioeconomic: Average household income increased by 36% overall.
Factors likely to increase PH rates:

Uninsured: proportions of uninsured dropped from 8% to 5% .

Medicaid FFS enrollment: declined sharply from 21% to 10%, while Private FFS enrollment declined moderately from 35% to 30%. 
Hospital inpatient capacity: increased with most increase observed in western states
Hospital inpatient capacity: increased with most increase observed in western states.

Demographic: Age group 45‐64 grew while age group 18‐44 declined and % minorities (e.g. Hispanics) significantly increased. 1995 – 2005 Multivariate Analysis
1995 2005 Multivariate Analysis
Variable
1995 (n=934), β
2005 (n=953), β
Adults ages 45 to 64 years
0.06889 §
0.08601 §
Adults Medicaid HMO ‐0.01159 0.03372 §
Adults Medicaid FFS
0.05685 §
0.04627 §
Adults private HMO
‐0.0432 §
‐0.011441 Adults uninsured
0.06936 §
0.14987 §
Adults other pay
Adults other pay
0.03719 §
0.03719
0.04793 §
0.04793
Adults African American
0.06658 §
0.06614 §
Adults Hispanic discharges
‐0.02730§
‐0.000457
Adults other race
Adults other race
‐0.01727
0 01727
0 015207
0.015207
Average income ‐0.00000339 §
‐0.000000234 §
Hospital inpatient capacity /1000 pop
0.000000641 0.0000000143
Average distance to hospital
d
h
l
0.00000463 ‐0.0.0000666
Population density
0.000000162 0.0000000922
PCP capacity /1000 pop
‐0.017871 §
‐0.0062358 §
Proportion of rural
0.0045181 ‐0.006585 §
Adjusted R2
0.4345
0.4786
1995 – 2005 Multivariate Analysis
WITH STATE FIXED EFFECTS
WITH STATE FIXED EFFECTS
Variable
1995 (n=934), β
2005 (n=953), β
California vs New York
‐0.0178 §
‐0.0087 §
Arizona vs
A
i
N Y k
New York
New Jersey vs New York
Massachusetts vs New York
‐0.0084
0 0084
0.0145 §
‐0.0019
‐0.0186 0 0186 §
0.0189 §
‐0.0015
g
y
Adults ages 45 to 64 years
0.0705 §
0.0890 §
Adults Medicaid HMO
Adults Medicaid FFS
Adults private HMO
Adults uninsured
Adults uninsured
Adults other pay
‐0.1061 0.1024 §
‐0.0163 0 0234
0.0234 0.0597 §
0.0345 §
0.0803 §
‐0.0122 0 0374
0.0374
0.0609 §
Adults African American
0.0487 §
0.0602 §
Adults Hispanic discharges
Adults
Hispanic discharges
Adults other race
‐0
0.0213 0213 §
‐0.0278 §
0.0058
0
0058
0.0193 §
‐0.000000516 §
‐0.000000265 §
Hospital inpatient capacity /1000 pop
p
p
p y/
p p
0.000000421 0.0000000510 §
Average distance to hospital
0.0000378 §
‐0.0000141
Population density
0.000000482 0.000000033
PCP capacity /1000 pop
PCP capacity /1000 pop
‐0.0067
0 0067
‐0.0068 0 0068 §
Proportion of rural
0.0017 ‐0.0078 §
Adjusted R2
0.4921
Average income 0.5114
1995 – 2005 Multivariate Analysis
with Panel data
with Panel data

Analysis of cross sectional time series panel data using a first l
f
l
ld
f
difference approach shows that the following variables were significantly associated with PH rates 1995‐2005
Variable
Time‐series β
Probability
Adults uninsured 0.0315 0.039
Hispanic discharges
0.0299
0 .028
Average income
5.57‐07 0.001
African‐American discharges
0.0990 0.000
Inpatient days
Inpatient days
7.01‐08
7.01
08 0.002
Adjusted R2
0.0675
Test for Heteroskedasticity
0.0013
Multivariate Analysis Summary: 1995‐2005
Summary: 1995
2005
Factors associated with lower PH rates

PCP capacity: increased density but contributions to PH rate significantly dropped in the 10‐year period 
Private HMO insurance : private HMO shares remained stable and the P
i t HMO i
i t HMO h
i d t bl
d th
contributions to PH rates significantly dropped in the 10‐year period 
Medicaid HMO enrollment increased but did not contribute to declining PH admissions (positive association in 2005)
admissions (positive association in 2005)

Although average income increased, the effect was smaller over time
Factors associated with higher PH rates
Factors associated with higher PH rates

Although proportions of uninsured declined, their contribution to increased PH rates became stronger over the 10‐year interval, particularly in Northeast 
Medicaid FFS: significant decline in % enrollment and non‐significant decline in per unit contributions.

Minority subgroups also increased in relative size and contributions leading to Minority
subgroups also increased in relative size and contributions leading to
increased PH admissions over time. 
Adults 45‐64 age‐group increased more than 18‐44 age group, leading to increased PH rates over time
Conclusions

The adult PH rates did not decline between 1995‐2005 despite policies h d l
dd
d l b
d
l
to improve access to care

This slower than expected decline could be explained by:
p
p
y
– stronger influence of minority and uninsured status,
relatively stable contribution of managed care enrollment in the
– aa relatively stable contribution of managed care enrollment in the commercial market, – weaker association of Medicaid managed care with PH rate reduction.
reduction

The PCP density had stronger association with reduction in PH rates for adults in 1995 than in 2005 
Although average per capita income increased, its per unit contributions to PH reduction did not increase.
Acknowledgments
Vennela Thumula, U. Mississippi L M bl Ph D R
Lee Mobley, Ph.D., Research Triangle Institute h T i l I tit t