Research
Sentinel Surveillance:
A Reliable Way To Track Antibiotic Resistance in Communities?
Stephanie J. Schrag,* Elizabeth R. Zell,* Anne Schuchat,* and
Cynthia G. Whitney*
*Centers for Disease Control and Prevention, Atlanta, Georgia,
USA
We used population-based
data to evaluate how often groups of randomly selected clinical
laboratories accurately estimated the prevalence of resistant
pneumococci and captured trends in resistance over time. Surveillance
for invasive pneumococcal disease was conducted in eight states
from 1996 to 1998. Within each surveillance area, we evaluated
the proportion of all groups of three, four, and five laboratories
that estimated the prevalence of penicillin-nonsusceptible pneumococci
(%PNSP) and the change in %PNSP over time. We assessed whether
sentinel groups detected emerging fluoroquinolone resistance.
Groups of five performed best. Sentinel groups accurately predicted
%PNSP in five states; states where they performed poorly had high
between-laboratory variation in %PNSP. Sentinel groups detected
large changes in prevalence of nonsusceptibility over time but
rarely detected emerging fluoroquinolone resistance. Characteristics
of hospital-affiliated laboratories were not useful predictors
of a laboratory’s %PNSP. Sentinel surveillance for resistant pneumococci
can detect important trends over time but rarely detects newly
emerging resistance profiles.
Antibiotic-resistant infections are an emerging problem in community
as well as nosocomial settings. Streptococcus pneumoniae infections
are a leading cause of community-acquired respiratory illness in
young children, the elderly, and persons with chronic medical conditions.
Pneumococcal infections range from otitis media and bacteremia to
pneumonia and meningitis. Although penicillin has traditionally
been an effective treatment for pneumococcal infections, in recent
years the increasing prevalence of drug-resistant pneumococci threatens
the effectiveness of antibiotic therapy (1,2).
Surveillance for resistant pneumococci is an essential component
of public health efforts to prevent the spread of drug resistance.
In addition to increasing awareness of the public and health-care
providers about resistance, surveillance data can be used to target
high-prevalence areas for judicious use of antibiotics, pneumococcal
vaccination campaigns, or both; identify newly emerging strains
and resistance profiles; and assess trends in resistance. At the
national level, surveillance data can contribute to the development
of clinical guidelines for managing pneumococcal disease (3,4).
Local surveillance data can in some instances guide patient care
(4).
The prevalence of drug-resistant pneumococci varies geographically.
Because national trends may not reflect trends within specific regions,
local and state-specific data can motivate prevention efforts (5).
Although invasive disease due to drug-resistant pneumococci was
added to the National Notifiable Diseases List in 1994, mandatory
reporting remains low (53% of states and territories in 1999) (6),
in part because collecting antimicrobial susceptibility data can
be difficult. Active, population-based surveillance for resistant
pneumococci based on laboratory-confirmed invasive disease may be
considered the most accurate method of estimating rates of drug-resistant
pneumococcal disease in a defined area. Such systems, however, are
often costly and labor-intensive for state or local health departments
to maintain.
Sentinel surveillance, a system that collects information on drug-resistant
pneumococci from a limited sample of hospital, clinic, and/or private
laboratories, has been suggested as a feasible alternative method
of collecting regional data, and some states are adopting this approach
(7). Although sentinel systems are useful for monitoring
trends in a number of diseases (8-10) and a sentinel
hospital surveillance system in the 1980s first detected increases
in the prevalence of penicillin-resistant pneumococci in the United
States (11), observations that the prevalence
of resistant pneumococcal isolates can vary dramatically from laboratory
to laboratory within a state or area (12) raise
the question of whether sentinel laboratories can accurately reflect
an area’s prevalence of pneumococcal resistance.
For pneumococcus, the most common approach to sentinel surveillance
is to select a small number of clinical laboratories within an area
and collect information on susceptibility of all invasive pneumococcal
isolates at those facilities as a way of estimating the prevalence
of resistance in the area as a whole. To evaluate the validity of
this sentinel approach, we assessed how often small groups of laboratories
in a given area accurately estimated the area’s proportion of resistant
invasive pneumococcal isolates, using population-based surveillance
as the standard. We also evaluated whether such sentinel groups
of laboratories accurately tracked changes in the proportion of
drug-resistant pneumococci over time, and whether they could detect
newly emerging resistance profiles. Finally, we explored whether
hospital characteristics could be used to guide selection of hospital
laboratories for inclusion in sentinel systems, in order to increase
the system’s representativeness and reliability.
Methods
Population-Based Data
Invasive pneumococcal surveillance was conducted from 1996 to 1998
as part of the Active Bacterial Core Surveillance/ Emerging Infections
Program Network (ABCs) using previously described methods (1).
Briefly, project personnel communicated at least twice each month
with contacts in all participating microbiology laboratories serving
acute-care hospitals in San Francisco County, California; Connecticut;
eight counties in Georgia (Cobb, Clayton, De Kalb, Douglas, Fulton,
Gwinnett, Newton, and Rockdale) with 12 additional Atlanta-area
counties starting in 1997; six counties in Maryland (Anne Arundel,
Baltimore, Baltimore City, Carroll, Harford, and Howard); seven
counties in Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey,
Scott, and Washington); seven counties in New York starting in 1997
(Genesee, Livingston, Monroe, Ontario, Orleans, Wayne, and Yates);
three counties in Oregon (Clackamas, Multnomah, and Washington);
and five counties in Tennessee (Davidson, Hamilton, Knox, Shelby,
and Williamson).
A case was defined as the isolation of Streptococcus pneumoniae
from a normally sterile site (e.g., blood or cerebrospinal fluid)
from a resident of a surveillance area. Periodic audits were conducted
in each area. Any cases newly identified by audits were included
in the surveillance database.
All isolates were sent to one of two centralized laboratories for
susceptibility testing by broth microdilution, with a panel of drugs
that included (in 1998) penicillin, amoxicillin, cefotaxime, cefuroxime,
meropenem, erythromycin, clindamycin, chloramphenicol, vancomycin,
rifampin, levofloxacin, trovafloxacin, and quinupristin‑dalfopristin
(Synercid7). Nonsusceptibility (resistance and intermediate susceptibility)
was determined according to criteria of the National Committee for
Clinical Laboratory Standards (13).
Ability of Sentinel
Laboratory Groups To Estimate Proportion of Resistant Isolates
In each surveillance area for 1998, we generated all possible simple
random samples of three, four, and five laboratories, excluding
laboratories with <10 isolates. We limited our selection to up
to five laboratories because a central objective of sentinel surveillance
is to reduce required resources by reducing the number of facilities
participating in the surveillance system. We refer to these simple
random samples as sentinel groups of laboratories. We then calculated
the percent of penicillin-nonsusceptible (MIC >0.1 µg/mL)
pneumococci (%PNSP) among isolates in each of these sentinel groups
and compared these percentages to the area’s actual %PNSP, as measured
by ABCs. The %PNSP in sentinel groups was considered to be accurate
if it was within 5 percentage points of the area’s actual %PNSP.
We chose this interval because variation in the %PNSP within this
range is unlikely to influence public health decisions (12).
We used a finite population correction based on the total number
of isolates in each surveillance area to assess the number of randomly
sampled isolates that would be needed to estimate an area’s actual
%PNSP within 5 percentage points (14). We compared
that number with the number of isolates in sentinel groups in each
area.
Ability of Sentinel
Groups To Track Changes in Prevalence of Drug-Resistant Pneumococci
over Time
In each surveillance area, we subtracted the %PNSP in each possible
group of five laboratories in 1996 from that measured for the group
of five laboratories in 1998. We included only laboratories with
>10 isolates in each of the 2-year periods. We then measured
how often the change in %PNSP in sentinel groups was within 5 percentage
points of the area’s actual change in %PNSP during the same time
periods, based on ABCs data. We performed a similar analysis using
the percentage of erythromycin-nonsusceptible (MIC >0.5
µg/mL) isolates as the outcome measure.
Ability of Sentinel
Goups To Detect Emerging Fluoroquinolone Resistance
Using data from 1998, we measured the proportion of all possible
groups of five sentinel laboratories within each surveillance area
that captured any pneumococcal isolates with fluoroquinolone (levofloxacin
or trovafloxacin) nonsusceptibility. We then compared that proportion
with area-specific data on the presence of pneumococcal fluoroquinolone
resistance from ABCs in 1998.
Evaluation of Hospital
Predictors of %PNSP
We merged ABCs data from 1997 and 1998 with purchased data on hospital
characteristics collected by the American Hospital Association (AHA)
as part of the AHA Annual Survey of Registered American Hospitals
in 1997. We categorized each hospital that matched between the two
datasets into the following PNSP classes: >5 percentage
points above the surveillance area proportion PNSP (high PNSP),
<5 percentage points above or below the surveillance area PNSP
(average PNSP), or >5 percentage points below the surveillance
area PNSP (low PNSP). We used logistic regression to perform univariate
analyses. We compared hospital characteristics in the high group
with those in the average group, separately comparing hospital characteristics
in the low group with those in the average group. We categorized
continuous variables according to their quartiles or medians based
on their distributions. We limited our analysis to hospital characteristics
that might plausibly influence a hospital’s %PNSP based on findings
of previous studies (15,16).
Results
Population-Based Data
The %PNSP across surveillance areas in 1998 varied from 15 (California
and New York) to 35 (Tennessee) (Table 1).
The number of laboratories that isolated invasive pneumococci and
the total number of invasive pneumococcal isolates also varied by
surveillance area (Table 1). Consistent with
previous observations (12), each surveillance
area had striking variation across laboratories in the %PNSP in
invasive pneumococcal isolates (Figure).
Ability of Sentinel
Laboratory Groups To Estimate %PNSP
In New York, California, and Oregon (areas with a relatively small
number of laboratories with >10 invasive pneumococcal
isolates), sentinel groups of three, four, or five laboratories
all did well at estimating the area’s actual %PNSP (Table
1). In the remaining areas, increasing the number of laboratories
included in sentinel groups from three to five increased the probability
that the sentinel %PNSP approached the area’s actual %PNSP. However,
in Georgia and Tennessee, the two areas with the highest actual
%PNSP, sentinel groups of five laboratories still poorly estimated
the area’s actual percentage (Table 1).
In surveillance areas where most sentinel groups had an adequate
sample size to estimate %PNSP accurately (i.e., the number of isolates
met the sample size requirement), sentinel groups performed well
compared with population-based surveillance (Table
2). In contrast, in Georgia and Tennessee, where sentinel groups
performed poorly, a smaller proportion of sentinel groups met the
minimum sample size requirements. However, in some states that failed
to meet sample size requirements (e.g., Connecticut), sentinel groups
performed well.
Ability of Sentinel
Groups To Detect Trends in Prevalence of Nonsusceptible Pneumococci
The actual change in %PNSP in 1998 compared with that in 1996 varied
across areas, ranging from Georgia’s 2% decline to Maryland’s 7%
increase (Table 3). Because sentinel groups
of five were the most accurate at predicting an area’s actual %PNSP,
we focused strictly on groups of five for this analysis. Laboratories
participating in ABCs in 1998 were often not the same as those participating
in 1996 because of hospital or laboratory mergers, closing or opening
of microbiology facilities in the surveillance areas, and expansion
of areas under surveillance. Consequently, only a subset of all
possible sentinel groups in 1998 matched those in 1996.
Over two thirds of each area’s sentinel groups of five accurately
estimated changes in %PNSP, except in Tennessee, where only 45%
correctly estimated a <5 percentage point change (Table
3). In the three areas with large changes in %PNSP (>3
percentage points), >90% of sentinel groups in each area predicted
the direction of the change (increases in each case).
Trends in the proportion of isolates that were erythromycin nonsusceptible
also varied by area, and three areas showed large increases from
1996 to 1998 (Table 3). Similar to trends
observed for penicillin nonsusceptibility, sentinel groups had a
high probability of detecting these increases in erythromycin nonsusceptibility
(Table 3).
Ability of Sentinel
Groups To Detect Emerging Fluoroquinolone Resistance
In 1998, seven isolates submitted to ABCs were nonsusceptible to
levofloxacin; five of these were also nonsusceptible to trovafloxacin.
The isolates came from seven different hospitals, located in five
of the eight surveillance areas (California, Connecticut, Maryland,
Minnesota, and Oregon). One of these hospitals, the only hospital
from Oregon, had only five invasive pneumococcal isolates in 1998
and thus was excluded from our analysis of sentinel groups. Approximately
40% of sentinel groups of five laboratories in these areas (range
37% in Connecticut to 45% in Maryland) included a laboratory with
a fluoroquinolone-nonsusceptible isolate, except in California,
where there was only one possible sentinel group of five laboratories
and this group included the fluoroquinolone-nonsusceptible isolate.
Evaluation of Hospital
Predictors of %PNSP
The merged dataset of ABCs and AHA hospitals contained 104 hospitals:
24 (23%) were in the high PNSP category, 52 (50%) were in the average
PNSP category; and 28 (27%) were in the low PNSP category. Hospitals
that admitted only children (four hospitals that matched between
the two datasets) were significantly more likely to be in the high
PNSP group than in the average group (all four hospitals fell in
the high category; Fisher’s exact test, p=0.008). Larger hospitals
(measured by adjusted inpatient days, total beds, or total beds
set up and staffed) were more likely to fall in the average category,
but this trend was not consistent for all indicators capturing hospital
size (Table 4). Additional variables tested
by univariate analysis were not predictive of falling in the high
or low category (Table 4). When we performed
similar analyses using the percent of erythromycin-nonsusceptible
isolates or of isolates with resistance to more than one drug class
as the primary outcome measure, no additional predictors were identified.
In areas where sentinel surveillance did not accurately estimate
the %PSNP (Georgia and Tennessee), can hospital predictors be used
to improve performance? When we limited sentinel groups of five
to the laboratories with the largest number of isolates, the range
in %PNSP narrowed, but accuracy was not guaranteed (range in Georgia
29%-34%; range in Tennessee 36%-44%). Additionally, consistent with
the analysis above, hand-picking sentinel hospitals to include those
with a high proportion of pediatric isolates was likely to overestimate
the actual %PNSP; in Georgia the children’s hospital had a %PNSP
of 61%, whereas the area’s true %PNSP was 33% (Table
1).
Discussion
As the incidence of drug-resistant pneumococcal disease continues
to increase, the need for local and state-specific data on the emergence
of drug-resistant invasive pneumococcal strains also grows. Although
active, population-based surveillance provides highly accurate data
for tracking pneumococcal resistance trends, few states can afford
to implement such labor-intensive and costly systems. Moreover,
states may have a variety of objectives for their surveillance systems,
ranging from increasing awareness of resistance in local communities
and promoting appropriate antibiotic use activities to estimating
directly the drug-resistant isolates and trends in drug resistance;
some of these objectives require more accurate surveillance systems
than others.
Our evaluation of the performance of sentinel laboratory groups
suggests that sentinel surveillance is a viable alternative to population-based
surveillance in situations where a high degree of accuracy is not
required. In some cases, sentinel surveillance may also be useful
when accurate estimates of %PNSP trends are a primary objective.
Sentinel laboratory groups were most reliable at detecting large
increases or decreases in the proportion of nonsusceptible invasive
isolates; the groups varied in their ability to predict an area’s
actual %PNSP; and they were poor at detecting newly emerging fluoroquinolone
resistance. As a result, areas considering sentinel surveillance
should design systems and interpret data with caution.
Baseline information on isolates processed annually per laboratory
and between-laboratory variability in %PNSP can be used to predict
how well sentinel systems will perform at estimating this percentage
in a given area. Such information can often be collected retrospectively
or prospectively from microbiology laboratories. Authorities in
areas with high between-laboratory variability or with few isolates
per laboratory may want to consider alternatives or complements
to sentinel systems.
Reasons for high between-laboratory variability in the proportion
of nonsusceptible invasive pneumococcal isolates, such as we observed
in Tennessee (Figure), remain unclear. This
variability likely reflected differences in the risk for nonsusceptible
pneumococcal infections in communities served by different laboratories.
Because health insurance policies in the United States often determine
the hospitals and laboratories that patients use, these facilities
rarely serve populations that are representative of the community
as a whole or even the neighborhood where the hospital is located.
Characterizing risk factors for nonsusceptible invasive pneumococcal
disease in a hospital’s patient population is difficult. Readily
obtainable hospital characteristics such as those collected by AHA
did not explain the between-laboratory variation we observed. Unfortunately,
some known predictors of resistance in health-care settings, such
as suburban middle- and upper-class patient populations (15,16),
were not available to link to our surveillance data.
Although most basic hospital characteristics were not a reliable
guide to selecting laboratories to be included in sentinel systems,
pediatric hospitals were significantly more likely than other hospitals
in an area to have a high %PNSP. Because children are a primary
reservoir of S. pneumoniae and the incidence of invasive
pneumococcal disease is elevated in children and the elderly (1),
states may sometimes choose to include children’s hospitals in sentinel
surveillance systems to increase their likelihood of identifying
resistance problems. However, to track trends in resistance to drugs
such as fluoroquinolones that are not indicated for use in children,
children’s hospitals may not be reliable indicators.
For states wishing to increase the reliability of sentinel systems,
increasing the overall number of laboratories participating in sentinel
systems improved the accuracy of systems, particularly in areas
where the %PNSP approaches 50%. However, in areas with high between-laboratory
variation in %PNSP, accuracy is difficult to achieve without including
most laboratories in the system.
For states or regions with a primary objective of detecting rare,
newly emerging resistance profiles, more than one surveillance approach
may be necessary. For example, sentinel surveillance combined with
universal reporting of fluoroquinolone- or vancomycin-resistant
pneumococci will help detect important new strains before they become
widespread. Additionally, authorities in such areas may consider
collecting the isolates captured by sentinel facilities and conducting
susceptibility testing by using a more diverse drug panel than is
typically used in most clinical microbiology laboratories.
If used and interpreted appropriately, sentinel laboratory surveillance
helps document pneumococcal resistance and improve prevention efforts.
Evaluation of alternative surveillance methods such as analysis
of hospital antibiograms (17) or direct electronic
reporting of susceptibility results from hospital laboratories to
a central network (M. Soriano-Gabarro, unpub. data) will further
contribute to identifying low-cost, feasible methods of documenting
trends in pneumococcal resistance.
Acknowledgments
We acknowledge B. Barnes, N. Barrett, W. Baughman, N. Bennett,
J. Besser, P. Cieslak, A. Craig, P. Daily, B. Damaske, R. Facklam,
M. Farley, L. Gelling, J. Hadler, L Harrison, T. Hilger, J. Jorgensen,
L. Lefkowitz, C. Lexau, R. Lynfield, M. Pass, A Reingold, K. Robinson,
G. Rothrock, K. Stefonek, C. Wright, and S. Zansky for collecting
population-based surveillance for invasive pneumococcal disease.
We are grateful to J.T. Weber and E. Brink for comments on the manuscript.
Dr. Schrag is an epidemiologist in the Respiratory Diseases Branch,
Division of Bacterial and Mycotic Diseases at the Centers for Disease
Control and Prevention. Her research focuses on methods of monitoring
and preventing the spread of pneumococcal resistance and on prevention
of neonatal sepsis.
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Table
1. Ability of sentinel groups of three, four, and five laboratories
to estimate accurately %PNSP, 1998a |
|
Area |
Labs with >10 isolates
(total labs)
|
Actual %PNSP
|
Total isolates
|
Percent of sentinel groups
within 5 percentage points of actual %PNSP
|
|
3 labs (no. of groups; overall
range in %PNSP)
|
4 labs (no. of groups; overall
range in %PNSP)
|
5 labs (no. of groups; overall
range in %PNSP)
|
|
CA
|
5 (9)
|
15
|
181
|
100 (10; 12-17)
|
100 (5; 13-16)
|
100 (1; NA)
|
CT
|
25 (32)
|
18
|
681
|
73 (2,300; 2-31)
|
81 (12,650;4-30)
|
87 (53,130;6-30)
|
GA
|
18 (34)
|
33
|
860
|
45 (816; 19-51)
|
52 (3,060; 20-49)
|
58 (8,568; 21-48)
|
MD
|
20 (26)
|
22
|
579
|
60 (1,140; 8-40)
|
68 (4,845; 9-38)
|
74 (15,504; 10-37)
|
MN
|
12 (24)
|
20
|
470
|
78 (220; 11-30)
|
88 (495; 12-29)
|
94 (792; 14-28)
|
NY
|
5 (19)
|
15
|
191
|
80 (10; 9-15)
|
100 (5; 10-14)
|
100 (1; NA)
|
OR
|
6 (13)
|
21
|
228
|
80 (20; 14-25)
|
93 (15; 14-23)
|
100 (6; 17-21)
|
TN
|
20 (30)
|
35
|
419
|
37 (1,140; 11-62)
|
40 (4,845; 13-59)
|
44 (15,504;14-57)
|
|
aIn Active Bacterial Core surveillance
areas.
%PNSP, percent of penicillin-nonsusceptible invasive pneumococcal
isolates.
|
Table
2. Number of isolates required to estimate accurately %PNSP
in a given area and percentage of sentinel laboratory groups
that met sample size requirements |
|
Area
|
Actual %PNSP (target range)
|
No. of isolates needed to estimate
%PNSPa
|
% of sentinel groups of 5 laboratories
with
> no. of required isolates
|
|
CA
|
15 (10-20)
|
94
|
100
|
CT
|
18 (13-23)
|
172
|
3
|
GA
|
33 (28-38)
|
243
|
40
|
MD
|
22 (17-27)
|
183
|
12
|
MN
|
20 (15-25)
|
163
|
70
|
NY
|
15 (10-20)
|
97
|
100
|
OR
|
21 (16-26)
|
120
|
100
|
TN
|
35 (30-40)
|
191
|
0
|
|
aNo. of isolates, n, required to
estimate the area’s actual %PNSP (P) within 5 percentage points
(d=0.05) with 95% confidence (Z=1.96) is: n= (Z2
P(1-P))/d2, where d is the range of accepted variation
around the actual %PNSP, and Z is the Z-score range within
which values must fall. Because the total no. of isolates
per area, N, was small, we corrected this estimate for finite
population size: n=n/[1+(n-1)/N]. There is no power associated
with this estimate (14).
%PNSP, percent of penicillin-nonsusceptible pneumocooccal
isolates.
|
Table
3. Ability of sentinel groups of five laboratories to estimate
an area’s change in %PNSP and erythromycin-nonsusceptible pneumococci,
1996–1998 |
|
Outcome measure
|
Areaa
|
Actual change in % NS pneumococci
|
% sentinel groups within 5
percentage points of the area’s actual change in % NS pneumococci
|
Percent of sentinel groups
detecting an increase or decrease in the actual % NS pneumococcic
|
|
Penicillin NS
|
CA
|
+3
|
100 (1)
|
100
|
CT
|
+1
|
67 (15,504)
|
|
GA
|
-2
|
76 (2,002)
|
|
MD
|
+7
|
70 (15,504)
|
93
|
MN
|
+6
|
97 (252)
|
99
|
TN
|
0
|
45 (462)
|
|
Erythromycin NS
|
CA
|
-2
|
100 (1)
|
|
CT
|
+2
|
95 (15,504)
|
|
GA
|
+6
|
80 (2,002)
|
86
|
MD
|
+6
|
97 (15,504)
|
99
|
MN
|
+7
|
83 (252)
|
99.6
|
TN
|
+2.5
|
51 (462)
|
--
|
|
aNY joined ABCs in 1997; the only
group of 5 laboratories in OR in 1996 did not match any of
the groups in 1998.
bGroups that merged between the 2 years.
cWe limited this analysis to areas with >3%
change in either direction.
%PNSP, percent penicillin-nonsusceptible pneumococci; NS,
nonsusceptible.
|
Table
4. Univariate analysis of characteristics of hospitals with
a high or low %PNSP compared with hospitals with an average
%PNSPa |
|
Hospital characteristic |
High vs. average
%PNSP
|
Low vs. average %PNSP
|
|
|
No.
|
Odds ratio
|
p value
|
No.
|
Odds ratio
|
p value
|
|
|
High
|
Avg
|
Low
|
Avg
|
|
Adjusted inpatient daysb
|
0.02
|
|
|
|
0.06
|
|
11
|
7
|
Refc
|
|
8
|
7
|
Ref
|
|
66,453-104,771
|
5
|
11
|
0.29
|
0.09
|
10
|
11
|
0.80
|
0.73
|
104,772-146,879
|
6
|
17
|
0.23
|
0.03
|
3
|
17
|
0.15
|
0.02
|
>146,879
|
3
|
16
|
0.12
|
0.007
|
7
|
16
|
0.38
|
0.17
|
Total beds set up and staffed
|
|
|
|
0.04
|
|
|
|
0.25
|
0-173
|
11
|
8
|
Ref
|
|
7
|
8
|
Ref
|
|
174-300
|
6
|
11
|
0.40
|
0.18
|
10
|
11
|
1.04
|
0.96
|
301-413
|
4
|
16
|
0.19
|
0.02
|
5
|
16
|
0.36
|
0.16
|
>414
|
4
|
16
|
0.19
|
0.02
|
6
|
16
|
0.43
|
0.23
|
Adult medical/surgical and ICU beds
|
0-16
|
15
|
19
|
Ref
|
|
17
|
19
|
Ref
|
|
>16
|
7
|
26
|
0.31
|
0.05
|
9
|
29
|
0.39
|
0.06
|
Pediatric medical/surgical and ICU beds
|
0-10
|
13
|
20
|
Ref
|
|
16
|
20
|
Ref
|
|
>11
|
9
|
25
|
0.55
|
0.26
|
10
|
25
|
0.50
|
0.17
|
Hospital with a pediatric ICU
|
No
|
18
|
35
|
Ref
|
|
22
|
35
|
Ref
|
|
Yes
|
4
|
10
|
0.78
|
0.70
|
4
|
10
|
0.64
|
0.49
|
Medicaid inpatient days
|
0.10
|
|
|
|
0.36
|
0-3,730
|
9
|
10
|
Ref
|
|
7
|
10
|
Ref
|
|
3,731-8,797
|
7
|
10
|
0.78
|
|
9
|
10
|
1.3
|
0.71
|
8,798-19,477
|
7
|
15
|
0.52
|
|
4
|
15
|
0.38
|
0.20
|
>19,477
|
2
|
16
|
0.14
|
|
8
|
16
|
0.71
|
0.61
|
Medicare inpatient days
|
0.04
|
|
|
|
0.02
|
0-18,246
|
10
|
6
|
Ref
|
|
10
|
6
|
Ref
|
|
18,247-29,026
|
5
|
12
|
0.25
|
0.06
|
9
|
12
|
0.45
|
0.24
|
29,027-45,471
|
5
|
18
|
0.17
|
0.01
|
3
|
18
|
0.10
|
0.005
|
>45,471
|
5
|
15
|
0.20
|
0.03
|
6
|
15
|
0.24
|
0.04
|
Metropolitan statistical area size
|
<1 million population
|
5
|
10
|
Ref
|
|
5
|
10
|
Ref
|
|
>l million population
|
20
|
41
|
0.98
|
0.97
|
23
|
41
|
1.12
|
0.84
|
|
aHigh %PNSP was defined as >5
percentage points above the surveillance area % of penicillin-nonsusceptible
pneumococci (PNSP) ; low as >5 percentage points
below the surveillance area %PNSP; average as <5 percentage
points above or below the surveillance area %PNSP.
bAdjusted inpatient days were calculated as Inpatient
Days + (Inpatient Days * [Outpatient Revenue/Inpatient Revenue]).
cRef=Referent group.
ICU, intensive-care unit.
|
|
|
|
|
|
|
|
|
|
|
|