The Influence of Primary Care and Hospital Supply on
Ambulatory Care–Sensitive Hospitalizations Among
Adults in Brazil, 1999–2007
James Macinko, PhD, Veneza B. de Oliveira, MD, PhD, Maria A. Turci, MPH, Frederico C. Guanais, PhD, Palmira F. Bonolo, MD, PhD,
and Maria F. Lima-Costa, MD, PhD
Hospitalizations for ambulatory care–sensitive
(ACS) conditions have been used to monitor
health system performance in the United States
and in several European countries.
1– 3
The idea
behind the indicator is that hospitalizations for
certain health problems represent a failure of the
health system to provide access to good quality
primary care, which should have detected the
condition early in its progression, reduced its
severity, or prevented the appearance of com-
plications, thus obviating the need for hospitali-
zation. Ambulatory care–sensitive hospitaliza-
tion rates have been associated with primary care
access and quality in several countries, including
Australia, Canada, Spain, and the United States.
4–9
However, these measures have rarely been
used to study health system performance in low-
and middle-income countries.
Several reasons exist for seeking a tool to
assess primary care effectiveness at this time in
Brazil. These reasons include the ongoing
process of developing the national health sys-
tem, which has been taking place since 1988.
10
In addition, since1994 the country has rolled out
the Family Health Program (FHP) as a new,
robust model of community-based primary
health care explicitly designed to provide acces-
sible, first-contact, comprehensive, and whole-
person care that is coordinated with other health
and social services and takes place within the
context of families and communities. Each FHP
team is multiprofessional and contains at least 1
physician, 1 nurse, 1 medical assistant, and 4 to
6 community health agents. Teams are orga-
nized by geographic regions and with a specific
territory containing approximately 3500 people
per team. The program enrolls the local popula-
tion and uses local health data to plan health
services and prevention efforts.
11
All services and
some medications are free of charge. By 2007,
FHP access expanded in nearly every munici-
pality and now reaches nearly 93 million people.
Concomitant changes have occurred in the
Brazilian hospital sector. The government pays
for about 80% of all hospitalizations, which
consume nearly 70% of all national health
expenditures.
12
Hospital care in the national
health system takes place in government-owned
and operated facilities (about 36% of all hospi-
talizations),aswellasinprivate(about37%)and
nonprofit (about 27%) hospitals that have been
contracted by the federal government.
13
Main
reforms have included a decrease in the private
or nonprofit to public sector hospital bed ratio,
changes to the payment system, and introduction
of new procedures covered by the national
health system.
Our objective was to assess factors associ-
ated with ACS hospitalization rates in Brazil.
Our main hypothesis was that the rapid scale-
up of the FHP over the past decade in Brazil
should have resulted in improved capacity for
primary care to resolve common population
health problems. There is some evidence of the
impact of the program on mortality—especially
among children.
14 ,15
FHP effects on adult
morbidityshouldbereflectedinadecreasein
ACS hospitalization rates, a hypothesis for which
there is preliminary evidence, at least for some
conditions.
16
However, to our knowledge, no
previous studies have assessed the role of the
FHP in relation to other factors related to ACS
hospitalization rates in the country.
METHODS
We performed an ecological cross-sectional,
time-series study, which pooled together 9
years (1999–2007) of cross-sections composed
of all 558 Brazilian microregions for each year,
for a maximum sample size of 5502 observa-
tions. Each microregion contains several of
Brazil’s 5564 municipalities (Brazil’s smallest
administrative unit) that have been grouped
Objectives. We assessed the influence of changes in primary care and hospital
supply on rates of ambulatory care–sensitive (ACS) hospitalizations among
adults in Brazil.
Methods. We aggregated data on nearly 60 million public sector hospitaliza-
tions between 1999 and 2007 to Brazil’s 558 microregions. We modeled adult
ACS hospitalization rates as a function of area-level socioeconomic factors,
health services supply, Family Health Program (FHP) availability, and health
needs by using dynamic panel estimation techniques to control for endogenous
explanatory variables.
Results. The ACS hospitalization rates declined by more than 5% annually.
When we controlled for other factors, FHP availability was associated with lower
ACS hospitalization rates, whereas private or nonprofit hospital beds were
associated with higher rates. Areas with highest predicted ACS hospitalization
rates were those with the highest private or nonprofit hospital bed supply and
with low (< 25%) FHP coverage. The lowest predicted rates were seen for areas
with high (>75%) FHP coverage and very few private or nonprofit hospital beds.
Conclusions. These results highlight the contribution of the FHP to improved
health system performance and reflect the complexity of the health reform
processes under way in Brazil. ( Am J Public Health. 2011;101:1963–1970. doi:10.
2105/AJPH.2010.198887)
RESEARCH AND PRACTICE
October 2011, Vol 101, No. 10 | American Journal of Public Health Macinko et al. | Peer Reviewed | Research and Practice | 1963
together to be geographically contiguous and
homogeneous in terms of demography, agri-
culture, and transportation. Microregions were
originally designed to contain at least1 hospital
within their border and to have a larger pop-
ulation than do individual municipalities,
allowing for construction of more stable hos-
pitalization rates over time.
17
Data Source
Our principal data source was the hospital-
ization information system, a national admin-
istrative database used to register inpatient
data in the Brazilian health system. These data
include the specific condition at discharge (In-
ternational Classification of Diseases, Tenth Re-
vision [ICD-10]
18
codes), patient information
(age, sex, and municipality of residence), type of
hospital, length of stay, and specific medical
procedures performed. Each medical procedure
was associated with a specific monetary amount,
based on a national list that is used to pay
hospitals on a prospective basis. We were thus
able to link nearly 60 million hospitalizations
since 1999 to information on Brazil9smunici-
palities and their respective microregions.
19
As per other studies on ACS hospitalizations,
we excluded hospitalizations related to birth,
because they apply only to women, are not
associated with illness, and have increased
because of public policies promoting in-hospital
births.
20
We further limited our analysis to
hospitalizations among adults, defined here as
aged 20 years or older because they represent
the largest proportion of hospitalizations in the
country.
21
We set a maximum age limit of 79
years because after a certain age it is difficult to
determine whether any hospitalization was pre-
ventable and because identifying the underlying
cause of the hospitalization becomes increasingly
difficult with older patients.
Our earlier work defined and validated a list
of ACS hospitalization conditions relevant to
the epidemiological and health services envi-
ronment in Brazil through systematic literature
reviews, expert meetings, consultations with
primary care professional organizations, and
a period of open public comment on the pro-
posed set of conditions.
19
The final list (available
as a supplement to the online version of this
article at http://www.ajph.org) is similar to many
international lists, but differs in its emphasis on
conditions that can be managed in primary care
(as opposed to any ambulatory care setting) and
its inclusion of several infectious diseases not
present on lists developed in richer countries.
The main exposure variables were the pro-
portion, by year, of the population in the
microregion with access to the FHP, and public
and private or nonprofit hospital beds per
10000 inhabitants. Confounding variables in-
cluded inflation-adjusted per capita income,
socioeconomic conditions (i.e., proportion of
the population older than 15 years who were
illiterate, proportion of households with access
to clean [indoor] water and adequate sanita-
tion), health service access (i.e., annual mean
number of doctor visits per capita), and the
proportion of individuals with private health
insurance. We also adjusted for population
health status by including a measure of pre-
mature mortality (all-cause mortality before the
age of 65 years) divided into quintiles.
Income and socioeconomic data were derived
from the national census and from national
population surveys.
22–24
Health services and
mortality data were from the Brazilian Ministry
of Health’s online data information systems.
25,26
We based population data on recent intercensal
estimates.
27
Some independent variable data were miss-
ing for some years. We imputed missing data
by using nonlinear interpolation methods that
modeled within-municipality changes as
a function of previous values at the municipal
level and contemporaneous values at the state
level. These techniques are described else-
where.
10
We then summed up all municipal-level
values to the microregional level.
Data Analysis
The available data were present for each
year from 1999 (the year the Brazilian gov-
ernment switched to the ICD-10 coding
scheme) to 2007. The model to be estimated
was as follows:
ð1Þ Y
it
¼ B
1
Y
it1
1 B
2
FHP
it
1 B
2
SES
it
1
B
4
health care
it
1 a
i
1 k
t
1 u
it
where Y
it
is the ACS hospitalization rate for
microregion i in year t, Y
it-1
is a lagged de-
pendent variable reflecting the fact that the
previous year’s hospitalization rate is a signifi-
cant predictor of contemporary rates, FHP is
the percentage of the population with access to
the FHP, SES is the socioeconomic conditions
in each microregion in each year, health care
represents the supply of health services, and u
it
is the error. The time-specific effect, k
t
,is
equivalent to a dummy variable for each year
and captures national-level policy changes, and
other technologic and economic trends that
affect all microregions. The fixed effect, a
i,
captures all unobserved, time-invariant factors,
such as persistent geographical and historical
differences between microregions, that might
affect hospitalization rates.
28
We used a linear dynamic panel data
method to estimate the model.
29,30
This ap-
proach was based on first-differencing the pre-
viously mentioned regression equation and used
laggeddependentvariablesaswellaspast,
present, and future values of independent vari-
ables as instruments for the lagged dependent
variable on the right-hand side.
31, 32
The tech-
nique also allowed us to address the problem of
endogeneity of the FHP and other independent
variables by using the appropriate lags as in-
struments for the FHP and other independent
variables in the same way that the model
estimated lagged dependent variables.
29
This
dynamic panel model method therefore allowed
a means of obtaining consistent parameter esti-
mates while controlling for unobserved time-
invariant factors, autocorrelation, and endoge-
nous explanatory variables.
33
To develop a valid model, the total number
of instruments was limited to the shortest
number of lags possible and the validity of
instruments was tested with a Sargan test of
over-identifying restrictions.
34
Then we tested
first- and second-order serial correlation in the
first-differenced residuals by using the Arellano-
Bond m1 and m2 statistics, respectively. We
based the final choice of models on the signifi-
cance of the coefficients for the lagged dependent
variables, the Arellano-Bond tests, and the Sar-
gan test. The most appropriate model treated all
control variables, except income per capita, as
endogenous. Dependent variables with 1-, 2-, or
3-year lags were included in each model and
varied by outcome, on the basis of their statistical
significance and the results of the tests described
previously.
Finally, we compared results of our final
models for ACS hospitalization rates with the
sum of hospitalizations for all other conditions
(non–ACS hospitalization). We predicted that
RESEARCH AND PRACTICE
1964 | Research and Practice | Peer Reviewed | Macinko et al. American Journal of Public Health | October 2011, Vol 101, No. 10
primary care supply should not be associated
with these outcomes, but that measures of
hospital supply should be, if the mechanisms
driving hospital decision-making regarding ad-
missions processes are similar regardless of
type of condition requiring hospital admission.
RESULTS
Table 1 shows the number of hospitaliza-
tions and government expenditures on these
hospitalizations from 1999 to 2007. The total
number of hospitalizations increased by about
2%, and hospitalizations for ACS conditions
decreased by nearly 17%. Hospitalizations for
all other (non–ACS) conditions increased by
nearly 10% during this time, resulting in a 5%
decrease in the share of all hospitalizations that
were considered ACS to about a quarter of
all hospitalizations in 2007.
Inflation-adjusted expenditures for all hos-
pitalizations increased by 43% to a total of 4.1
billion Brazilian reais (slightly less than US $2
billion) during this period. Total expenditures
for ACS conditions increased by about a quar-
ter, which was less than the increase for other
conditions (49%), and which resulted in
a 2.8% reduction in the share of expenditures
going to ACS hospitalizations in 2007. The
average expenditure per ACS hospitalization
increased by about 50% to 512 Brazilian reais,
and the average expenditure per non–ACS
hospitalization increased by 36% to 660 Bra-
zilian reais.
Table 2 presents descriptive data on study
variables. Mean income per capita rose slightly
over time, and other improvements in markers
of living conditions included increased per-
centage of houses with indoor water and re-
duced illiteracy rates. The FHP expanded
coverage from 13% to about 64% of the
Brazilian population, and the average yearly
number of medical consultations per capita
increased nearly 6-fold. The total number of
hospital beds shrank overall, primarily because
of a large reduction in the private–nonprofit
sector, accompanied by an 11% increase of
hospital beds in the public sector. The number
of families with private health insurance also
increased.
The bottom panel of Table 2 presents data
on hospitalization rates. The ACS hospitaliza-
tion rates declined by about a third, with an
average yearly reduction of 4.5%. Rates for
women were slightly higher than were those
for men, although this is mostly attributable to
differences in age distributions between the 2
groups. The ACS hospitalization rates for the
oldest population were nearly 7 times higher
than were those for the youngest age group.
Non–ACS hospitalization rates were generally
higher than were ACS hospitalization rates for
each sex and age group and declined more
slowly—about 10% overall with a yearly mean
percentage change of 1.4%.
Table 3 presents results from regression
models explaining changes in ACS hospitaliza-
tion rates over time. The first column contains
results for all hospitalizations. The model
shows a negative relationship between the
highest levels of FHP coverage and ACS hos-
pitalization rates. This pattern is repeated for
the male-only model in column 2. For women,
both the middle and high levels of FHP cov-
erage were significant and revealed a dose–
response relationship. In the age-stratified
models, both the middle and highest levels of
FHP coverage were significant for the oldest
groups showing a dose–response relationship
similar to that seen in sex-stratified models. The
magnitude of the FHP terms increased with
each age group and in the oldest group was
nearly 10 times higher than in the group aged
20 to 59 years. In all models, private hospital
beds were statistically significant, positive, and
of a similar magnitude; the 95% confidence
intervals overlapped in all but the age 20 to 59
years model. All models, except that for ages
20 to 59 years, met all the assumptions of the
dynamic model (m1 test was significant; m2
test was not significant; and the Sargan test was
not significant).
Table 4 presents analyses of non–ACS
hospitalization rates. In all models, the FHP
variables were not statistically significant,
whereas the coefficient for the private or non-
profit hospitals was significant and positive.
Note that only the models for the oldest age
groups, those aged 60 to 69 years and 70 to 79
years, met all the specification tests for dynamic
panel models. Nevertheless, each model pre-
sented similarly consistent results.
Figure 1 shows predicted ACS hospitaliza-
tion rates adjusted for all variables contained in
model 1 of Table 3. Predicted ACS hospitali-
zation rates were highest (about 160/10000)
for a microregion with a private or nonprofit
hospital bed ratio of 100 per10 000 population
and with less than 25% FHP coverage. The
predicted ACS hospitalization rates dropped by
about 10% for a high private or nonprofit
hospital microregion with high (more than
75%) FHP coverage, although this proportion
narrowed over time. In contrast, microregions
with very few (less than 10/10000) private
or nonprofit hospital beds and low FHP cov-
erage had about 35% lower predicted ACS
hospitalization rates, and the lowest rates were
found for low private or nonprofit hospital,
high FHP microregions (about 70/10 000).
Thus, in areas of both high and low private or
nonprofit hospital supply, higher FHP avail-
ability was associated with substantially lower
ACS hospitalization rates.
DISCUSSION
This study has shown that ACS hospitaliza-
tion rates have declined sharply in Brazil over
the past decade. Some of this decline may be
attributed to the expansion of the FHP, an
integrated primary care network that has sub-
stantially increased access to basic medical
services throughout the country. At the same
time, hospital-level factors such as the propor-
tion of contracted (private or nonprofit) hospi-
tals were associated with higher ACS hospital-
ization rates, even when other factors were
controlled.
Several possible explanations exist for the
observed results. First, the rapid expansion of
the FHP may have indeed resulted in improved
adult health, reducing the need for hospital
admission through better diagnosis, treatment,
or management of the chronic diseases that
make up the bulk of the ACS hospitalization
list. There is evidence that the FHP is associ-
ated with better management of some chronic
conditions, and since 2004 a systematic effort
to develop clinical guidelines for identification,
diagnosis, and treatment of such diseases, in-
cluding provision of essential drugs (free to the
consumer) for control of hypertension and
diabetes.
35,36
Further, the FHP uses community
health agents to actively screen populations (in
their homes) for risk factors such as smoking and
hypertension; to refer high-risk individuals to the
health center; to develop group interventions
to aid in smoking cessation, improve physical
RESEARCH AND PRACTICE
October 2011, Vol 101, No. 10 | American Journal of Public Health Macinko et al. | Peer Reviewed | Research and Practice | 1965
activity, and manage diabetes; and to develop
community-based health education programs.
35
Although the quality and intensity of these
activities varies by location, this study could
serve as an indication that they may reduce the
need for hospitalization for at least some ACS
conditions.
37,38
At the same time, the consistent association
of ACS hospitalization rates with the supply
of private or nonprofit hospital beds questions
the simple interpretation of attributing the
magnitude of ACS hospitalization rates to
access and quality of primary care. In Brazil,
hospitals vary enormously in terms of their
ownership, administration, and receptivity to
market pressures.
39
There has been some evi-
dence of induced demand in Brazil for certain
procedures among private and nonprofit hospi-
tals, and the fact that both ACS and non–ACS
conditions were positively associated with pri-
vate or nonprofit but not public sector hospital
supply strengthens the case for induced de-
mand.
40
Alternatively, a greater supply of private
and nonprofit sector hospital beds may be
present where the FHP itself is weaker. Although
thegrowthinFHPaccesshasoccurred
throughout the entire country, it has been
functioning longer primarily in smaller, more
rural municipalities, even though an explicit
policy to expand the program to large urban
areas has been in place since 2004.
41
Previous
studies have shown that it takes time before the
FHP becomes consolidated within a municipality
and this learning curve has been associated
with poorer outcomes.
10 , 42
Changes in the relative prices associated
with different hospital procedures may also
explain some of the decline in ACS hospitali-
zation rates, as government payments for pro-
cedures associated with lower-complexity con-
ditions (several of which are on the ACS
hospitalization list) are less than are those
associated with more complex conditions.
43
Although hospitals may have favored admissions
for conditions that might be more lucrative, this
fact would not explain why private or nonprofit
hospitals were found to be associated with higher
ACS hospitalization rates than public hospitals,
because both are paid according to the same
government rates.
Finally, declining ACS hospitalization rates
may also be related to a shifting of tasks from
hospitals to ambulatory specialist care. This
TABLE 1—Ambulatory Care–Sensitive Hospitalizations in Relation to Other Hospitalizations and Related Expenditures for Adults Aged 20 to 79 Years: Brazil, 1999–2007
Public-Sector Hospitalizations
a
Government Expenditures on Hospitalizations
b
Year
Total No.,
Millions
No. of ACS
Hospitalizations,
Millions
No. of
Non-ACS
Hospitalizations,
Millions
ACS
Hospitalizations
per Total, %
Total Hospitalizations,
Millions of
Reais
a
ACS
Hospitalizations,
Millions of Reais
Non-ACS
Hospitalizations,
Millions of Reais
ACS
Hospitalizations
per Total, %
ACS
Hospitalization
Average
Expenditure, Reais
Non-ACS
Hospitalization
Average
Expenditure, Reais
1999 6.52 1.92 4.60 29.47 2886.95 654.03 2232.92 22.65 340.64 485.42
2000 6.66 1.90 4.76 28.53 2690.82 609.60 2081.22 22.65 320.84 437.23
2001 6.59 1.87 4.72 28.35 2897.75 656.67 2241.09 22.66 351.16 474.81
2002 6.79 1.83 4.96 27.01 2893.58 617.23 2276.35 21.33 337.29 458.94
2003 6.80 1.78 5.02 26.19 3330.93 677.35 2653.58 20.34 380.53 528.60
2004 6.74 1.75 5.00 25.89 3424.33 704.64 2719.69 20.58 402.65 543.94
2005 6.62 1.67 4.95 25.19 4033.20 838.28 3194.91 20.78 501.96 645.44
2006 6.62 1.64 4.98 24.83 3895.80 785.27 3110.54 20.16 478.82 624.61
2007 6.63 1.60 5.03 24.09 4141.95 820.17 3321.78 19.80 512.61 660.39
Difference 1999 to 2007 0.12 –0.32 0.44 –5.38 1255.00 166.143 1088.86 –2.85 171.97 174.98
Change, % 1.8 –16.8 9.57 ... 43.47 25.40 48.76 ... 50.48 36.05
Note. ACS = ambulatory care–sensitive. Ellipses indicate that data were not available.
a
Excluded hospitalizations related to births. Included all hospitalizations in public and private and nonprofit contracted hospitals reimbursed by the federal government.
b
Inflation-adjusted and expressed in 1999 constant Brazilian reais.
RESEARCH AND PRACTICE
1966 | Research and Practice | Peer Reviewed | Macinko et al. American Journal of Public Health | October 2011, Vol 101, No. 10
phenomenon could be interpreted as improved
management for such conditions if they mean
avoiding unnecessary hospital-based care. Al-
though considerable work has been conducted
in examining primary care and the hospital
sector in Brazil, few studies have examined
providers of secondary care, so little evidence
exists on which to test this hypothesis.
Study Strengths and Limitations
Strengths of this study include the fact that
all public sector hospitalizations for adults
were included in the analysis. The length of
follow-up and use of microregions allowed for
calculation of stable hospitalization rates over
nearly a decade. Statistical analyses adjusted for
the endogenous nature of the main exposure
variable (FHP coverage) and other independent
variables. Although the results of these models
may be more conservative than traditional fixed-
effects models, they are more robust to a number
of biases inherent in many previous studies of
the FHP and its effects on health outcomes.
The main weakness of the study is the
ecological nature of its design; we could not
distinguish between those hospitalizations that
occurred among individuals who were served
by the FHP and those who were not. In fact,
even at the individual level, there is currently
no standard way to reliably develop a user
profile within the national health system, al-
though efforts are under way to implement
a national health identification card. We were
not able to test all possible factors associated
with the need for hospitalization, nor were we
able to adjust at the individual level for case
mix. Instead, we assessed the overall public
health impact of ACS hospitalization rates
on the national level as a whole and used
TABLE 2—Variables in Study of the Influence of Primary Care and Hospital Supply on Ambulatory Care–Sensitive Hospitalizations
Among Adults, Mean Values and Changes Over Time: Brazilian Microregions, 1999–2007
Variable 1999, Mean (SD) 2007, Mean (SD)
Change 1999–2007,
Difference
(Total % Change)
Mean Annual %
Change
1999–2007
a
Income per capita, reais 109.67 (140.60) 206.10 (276.49) 96.43*** (87.93) ...
Clean water, % of households 10.11 (8.83) 11.25 (9.26) 1.14*** (11.28) ...
Illiteracy, % of population > 15 y 3.39 (3.65) 2.47 (2.55) –0.91*** (–26.84) ...
Family Health Program, % of population 12.97 (15.73) 64.50 (25.03) 51.53*** (397.30) ...
Hospital beds, per 10 000
Public hospital 7.69 (8.87) 8.55 (7.57) 0.86** (11.18) ...
Private or nonprofit hospital 21.57 (18.86) 7.77 (9.65) –13.80*** (–63.98) ...
Private health insurance, % of population 7.00 (9.20) 8.46 (9.60) 1.46*** (20.86) ...
Medical consultations, per capita 0.25 (0.10) 1.72 (0.64) 1.47*** (588.00) ...
Premature mortality,
b
per 100 000
Male 57.14 (19.02) 51.72 (12.93) –5.42*** (–9.49) ...
Female 31.93 (11.26) 26.84 (8.02) –5.09*** (–15.94) ...
ACS hospitalizations
c
Total 224.19 (100.83) 151.33 (65.04) –72.85*** (–32.49) –4.51
Male 204.84 (91.56) 138.73 (57.93) –66.11*** (–32.27) –4.30
Female 243.69 (113.50) 164.19 (74.74) –79.49*** (–32.62) –4.70
Ages 20–59 y 142.07 (67.23) 93.22 (41.41) –48.84*** (–34.38) –4.89
Ages 60–69 y 595.62 (282.45) 383.49 (160.42) –212.12*** (–35.61) –5.11
Ages 70–79 y 993.99 (480.67) 724.64 (297.97) –296.35*** (–29.81) –3.20
Non–ACS hospitalizations
b
Total 495.99 (165.40) 449.50 (125.77) –46.48*** (–9.37) –1.39
Male 457.65 (195.24) 417.14 (148.07) –40.51*** (–8.85) –1.40
Female 534.68 (165.23) 482.37 (120.75) –52.32*** (–9.79) –1.38
Ages 20–59 y 451.94 (152.05) 403.44 (111.06) –48.49*** (–10.73) –1.55
Ages 60–69 y 704.26 (272.18) 648.15 (215.71) –56.10*** (–7.97) –1.30
Ages 70–79 y 889.85 (335.86) 880.18 (292.82) –9.66 (–1.09) –0.30
Note. ACS = ambulatory care—sensitive. Ellipses indicate that data were not available.
a
Results from Poisson regression of the form log(# of hospitalizations) = B
0
+B
1
(year), where year is a linear term ranging from 0 (1999) to 9 (2007), and the mean annual percentage change = 100
[exp(B
1
)-1]. Population size (by sex or age group) is used as an offset.
b
Premature mortality is defined as < 65 y.
c
All government and contracted hospitals, per 10 000.
*P < .05; **P < .01; ***P < .001.
RESEARCH AND PRACTICE
October 2011, Vol 101, No. 10 | American Journal of Public Health Macinko et al. | Peer Reviewed | Research and Practice | 1967
