Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database
Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database
Snigdha Jain 0 1
Rohan Khera 0
Eric M. Mortensen 0
Jonathan C. Weissler 0 1
0 Editor: Nan Liu, Duke-NUS Medical School , SINGAPORE
1 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, UT Southwestern Medical Center , Dallas, TX , United States of America, 2 Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center , Dallas, TX , United States of America, 3 Division of General Internal Medicine, University of Connecticut Health Center , Farmington, CT , United States of America
In the Nationwide Readmission Database for the years 2013 and 2014 we identified all
adults ( 18 years) discharged alive after a hospitalization with the primary diagnosis of
pneumonia, and examined rates of readmissions within 30-days of discharge. Using
covariates included in the Center for Medicare & Medicaid Services risk-adjustment model for
pneumonia readmissions in a multivariable regression model for survey data, we identified
predictors of 30-day readmission.
We identified 629,939 index pneumonia hospitalizations with a weighted estimate of
1,472,069 nationally. Overall, 16.2% of patients were readmitted within 30 days of their
hospitalization for pneumonia, with 30-day readmission rates of 12.4% in the 18±44 year
agegroup, 16.1% in the 45±64 year age-group, and 16.7% in the 65-year age-group. In
riskadjusted analyses, compared with elderly, middle-aged adults were more likely to be
readmitted (risk-adjusted OR 1.05, 95% CI 1.03±1.07). Mean cost per readmission was also
highest for this age group at $15,976.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
While 30-day readmissions following hospitalization for pneumonia have been well-studied
in the elderly, their burden in young adults remains poorly understood.
To study patterns of readmissions following hospitalization for pneumonia across age
groups and insurance payers.
Middle-aged adults experience substantial rates of 30-day readmission that are comparable
to those over 65 years of age, with a higher cost per readmission event. Future efforts are
needed to identify potential interventions to alleviate the high burden of pneumonia
readmissions in middle-aged adults.
Pneumonia is the most common medical cause of hospitalization in the United States.[
Among Medicare beneficiaries, one in five patients discharged after a hospital stay for
pneumonia is readmitted within 30 days.[
] While considerable effort has been targeted towards
identifying and reducing unplanned readmissions in the elderly, the risk of post-pneumonia
readmissions in patients under the age of 65 years is largely unknown.[3±5] A few,
hospital[6±12] and state-level[
] studies have characterized re-hospitalizations in young adults with
rates of readmission varying between 7.1% and 14.4%.[
6, 8, 13
] Furthermore, while prior
studies have reported advanced age, high comorbidity burden, and unemployment as potential
reasons increasing the risk of readmission in the elderly, patient demographic factors and
comorbid illnesses influencing readmission risk in young and middle-aged adults are not
4, 8, 14
] Moreover, since there are hospital and regional differences in patient
composition and management of pneumonia, a comprehensive assessment of the burden of
readmissions across all age groups is needed at a national-level to assess care priorities and potential
targets for health policy interventions.
To address this, we sought to examine the burden of pneumonia readmissions across all age
groups, and study patient characteristics that are associated with high readmission rates after
an index hospitalization for pneumonia using the Nationwide Readmissions Database (NRD)
for the years 2013 and 2014. We hypothesized that characteristics of patients readmitted after
hospital discharge for pneumonia would vary with age and would be influenced by
demographic and comorbidity characteristics.
We used the Agency of Healthcare Research and Quality's (AHRQ's) nationally representative,
all-payer database, the Nationwide Readmissions Database (NRD) for the years 2013 and
2014. The database has been described previously.[15±17] Briefly, the NRD is designed as an
annual dataset constructed from all hospitalizations captured in the State Inpatient Database
(SID) of geographically-dispersed participating states, 21 in the year 2013 and 22 in 2014. For
2013 and 2014, the NRD captured 49.3% and 51.2% of the total U.S. resident population, and
represented 49.1% and 49.3% respectively of all US hospitalizations during these years,
respectively. In the NRD, all patients with one or more inpatient hospitalizations are assigned a
verified de-identified patient linkage number allowing tracking of discharges for an individual
across hospitals within a state throughout a calendar year. While State Inpatient Databases
contain separate inpatient records of discharges and admissions that have the same date, in
order to accurately capture readmissions, in NRD, these pairs of records are combined into
one common record and ascribed to the institution where the final disposition occurred.
Further, NRD is accompanied by discharge weights to generate national estimates. These
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discharge weights are generated by dividing the number of discharges nationally (from
participating and non-participating states) by the number captured in the NRD within strata defined
by patient (gender, and age groups: 0, 1±17, 18±44, 45±64, 65 years) and hospital (census
region, urban/rural location, teaching status, size of the hospital defined by the number of
beds, and hospital control) characteristics.
We identified all adults aged 18 years who were hospitalized during 2013 and 2014 with a
primary diagnosis of pneumonia using the International Classification of Diseases-9th
Clinical Modification (ICD-9CM) codes for pneumonia (480.x, 481, 482.xx, 483.x, 485, 486,
487.0, and 488.11), as used by the Center of Medicare and Medicaid Services in reporting
pneumonia readmission measures.[
] We included all observations with a principal
diagnosis of pneumonia discharged alive between January 1, 2013ÐNovember 30, 2013 and
January 1, 2014- November 30, 2014 allowing for a 30-day follow up period for each year.
Patient discharged against medical advice were excluded. Since the NRD and its
recommended analytic approach do not exclude index events occurring within 30-days from
discharge from a preceding index event, in our analyses, a readmission event can serve as an
index event itself.
Study variables and outcomes
For each patient with an index event, we identified 30-day readmission as the first subsequent
hospitalization for any cause within 30 days of discharge following a primary pneumonia
hospitalization. We examined patient demographics (age and gender), insurance (Medicare,
Medicaid, private, other, uninsured), income status (median household income based on zip code
of residence, available as quartiles of income), admission status (elective or non-elective),
hospital charges, length of stay and comorbidities.
To assess comorbidities as predictors of readmission, we used clinical classifications
software (CCS) categories, a classification system which combines clinically meaningful ICD
codes into 259 broad categories.[
] The CCS categories are substantively similar to
clinically coherent condition categories (CCs) used by CMS in their risk-adjusted model for
] These included asthma, chronic obstructive pulmonary disease,
pleurisy, smoking, diabetes mellitus, hypertension, history of coronary artery bypass
grafting (CABG), chronic coronary artery disease, acute myocardial infarction, valvular heart
disease, cardiac arrhythmias, congestive heart failure, peripheral vascular disease, acute
stroke or transient ischemic attack, hepatobiliary disease, anemia and other blood disorders,
leukemia or metastatic malignancy, major solid cancers, disorders of fluid/ electrolyte/ acid
base, chronic kidney disease, acute kidney injury, end-stage renal disease/need for
hemodialysis, cardiorespiratory failure, protein calorie malnutrition, history of infection, chronic
skin ulcer, paralysis, history of pneumonia, urinary tract infection, drug or alcohol use,
sepsis, other gastrointestinal disorders, injuries, mood or anxiety disorders and other
psychiatric disorders. Furthermore, since our study cohort spans across all age groups, we
conducted a sensitivity analysis with the inclusion of 29 comorbidities developed by
Elixhauser et al for use in all payer databases and report the potential predictors of readmissions
from these models.
The primary outcome for the study was 30-day readmission, which was addressed for the
overall population as well as for pre-defined age groups, 18±44 years, 45±64 years and 65
years of age.
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We compared baseline demographic characteristics and prevalence of comorbidities for patients
within defined age groups of 18±44 years, 45±64 years and 65 years. We tested for differences
across age-groups using the Scott-Chi square test for categorical variables and for the change in
the distribution of continuous variables (age, length of stay and cost of hospitalization) across the
three age groups using linear regression. These tests explicitly accounted for the clustering and
stratification of data derived from a complex survey. Next, we obtained unadjusted estimates of
30-day readmission following an index hospitalization for pneumonia, both overall and for each
of these age groups, expressed as percentage of all discharges. We used discharge weights provided
in the NRD database to obtain national-level estimates of readmissions.[
In the analyses addressing predictors of readmission, we used a logistic regression model that
accounted for clustering for patients at hospitals, and for the clustering and stratification of data.
Our analyses were consistent with the approach outlined in a recent methodology paper by
members of our group for the use of data from the AHRQ.[
] In our risk-adjustment model, we
included variables previously validated for pneumonia 30-day readmission in the Medicare
population with comorbidities as detailed above. In addition to the above comorbidities, we adjusted
for gender as well as income status and examined the interaction of gender and income status
with the previously defined age groups to determine their effect on readmissions.
Further, to address whether predictors of readmission varied across age groups, we
constructed additional models where we examined the interaction of age group (above or below
65 years of age) with the all covariates included in the model. For these analyses, in order to
use the widest set of comorbidities that were not specific to any particular age group, we used
Elixhauser comorbidities as the patient-level covariates included in the model. We also
obtained estimates of hospitalization cost for both the index pneumonia hospitalization as well
as re-hospitalization by multiplying the claim charge reported in NRD for each hospitalization
with the cost-to-charge ratio for the respective hospitals.[
All patient-level analyses were performed in accordance with survey methodology
recommended by HCUP.[
] All analyses were performed using SAS 9.4 (Cary, NC). Statistical
significance was set as a two-tailed p-value 0.05. This project was reviewed by the Institutional
Review Board of the University of Texas Southwestern Medical Center and was determined to
be exempt from review.
We identified 629,939 patients who were discharged alive following an index hospitalization
for pneumonia between January to November for the years 2013 and 2014 with a weighted
estimate of 1,472,069 discharges nationally. With 532,481 estimated discharges, adults <65
years of age constituted more than one third of this cohort (36.1%). The overall mean age was
68.9 years and females represented more than 50% of discharges after an index stay for
pneumonia in all three age groups (18±44 years, 45±64 years and 65 years). Cardiovascular
comorbidities including coronary artery disease, history of CABG, arrhythmias, valvular heart
disease and acute myocardial infarction were significantly more frequent in older adults. Most
other comorbidities including COPD, tobacco smoking, malignancy, malnutrition and anemia
were also more commonly noted in the elderly while asthma was more common in younger
adults. Cardiorespiratory failure was more prevalent in older adults while sepsis was seen more
frequently in younger adults. Medicare was the primary payer for 69.7% of index hospital stays
overall and for 92.5% of patients 65 years of age. All patient characteristics are presented in
Table 1. Characteristics were comparable in the years 2013 and 2014 with no significant
differences (data not shown).
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PLOS ONE | https://doi.org/10.1371/journal.pone.0203375
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SDÐstandard deviation, SEMÐstandard error of mean, C.I.Ðconfidence interval. COPDÐChronic obstructive pulmonary disease, CADÐCoronary artery disease,
CABGÐcoronary artery bypass grafting, TIAÐtransient ischemic attack, CVA -Cerebrovascular accident, ECMOÐExtracorporeal membrane oxygenation. All
numbers represent percentages (standard errors), unless otherwise specified.
§Median household income quartiles based on patient zip code
||trend for `lowest quartile (0-25th percentile)' vs `others'
trend for Medicare vs others. Reporting of elective status may vary by states.
For further description of data elements, readers are encouraged to consult the AHRQ website at `https://www.hcup-us.ahrq.gov/db/nation/nrd/nrddde.jsp'.
The unadjusted rate of 30-day readmission was 16.2% for the entire cohort. Readmission
rate was highest in the elderly at 16.7%, substantial in the middle age group at 16.1% and
lowest in young adults at 12.4% (p-value for trend <0.0001). Compared to women, men were
more likely to be readmitted (OR 1.05, 95% CI 1.03±1.07) overall. After adjustment for
comorbidities, middle-aged adults had small but statistically significantly higher odds of readmission
compared to the elderly (risk adjusted OR 1.04, 95% CI 1.01±1.07). Patients in the lowest
income quartile were also more likely to be readmitted than those in the highest income
quartile (risk adjusted OR 1.09, 95% CI 1.04±1.13).
The current CMS risk-adjustment model for comorbidities performed modestly in our
allpayer population, which included patients below 65 years of age (model c-statistic 0.63).
Among comorbid conditions, end stage renal disease or being on dialysis was associated with
the highest odds of re-hospitalization (risk adjusted OR 1.88, 95% CI 1.79±1.96). Other
comorbid conditions including metastatic malignancy or leukemia (risk adjusted OR 1.59, 95% CI
1.57±1.61), chronic skin ulcer (risk adjusted OR 1.45, 95% CI 1.38±1.52), congestive heart
failure (risk adjusted OR 1.33, 95% C.I 1.29±1.36) and anemia (risk adjusted OR 1.28, 95% C.I
1.25±1.31) were also significant predictors of 30-day pneumonia readmission. A forest plot of
predictors affecting risk of readmission is shown in Fig 1 and odds ratios are detailed in S1
In the interaction analysis, after adjustment for comorbidities and income, men in middle
(risk adjusted OR 1.1, 95% CI 1.06, 1.15) and older age groups (risk adjusted OR 1.03, 95% CI
1.01, 1.06) had higher odds of being readmitted than women in the same age categories (P for
age gender interaction 0.020). There was no significant interaction between age or gender and
income quartile on readmission risk.
In sensitivity analyses, we used the 29 prognostically relevant comorbidities defined by
Elixhauser et al in a logistic regression model (S2 Table). We found that among comorbidities that
are not included in the CMS model but are defined as Elixhauser comorbidities, collagen
vascular diseases, liver disease, obesity, weight loss and pulmonary circulation diseases were
significant predictors of readmission. Hypertension, acquired immune deficiency syndrome and
hypothyroidism were also included as Elixhauser comorbidities but were not found to be
significant predictors of readmission in this cohort. Moreover, in our assessment of the
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Fig 1. Risk-adjusted odds ratios for readmission by age, gender, income and comorbidities.
interaction between sex and three age groups (18±44, 45±64, and >65 years), results from the
model based on Elixhauser comorbidities were similar to those based on the CMS model.
Notably, in both the CMS model and the one based on Elixhauser's comorbidities, males
had a higher risk-adjusted odds for readmission compared to females in middle aged (OR
1.12, 95% C.I. 1.09±1.15 in CMS model versus OR 1.10, 95% C.I. 1.06±1.15 in the one with
Elixhauser's comorbidities) and elderly patients with pneumonia (OR 1.05, 95% C.I. 1.03±1.07
in CMS versus OR 1.03, 95% C.I. 1.01±1.06 in the one with Elixhauser's comorbidities) in
these age groups when adjusted for the Elixhauser comorbidities.
Further, we assessed the differences in predictors for readmissions across patients aged 65
years and older compared with those less than 65 years of age. Using Elixhauser comorbidities
in the risk adjustment model, we found a significant interaction by age group (S3 Table).
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However, for most comorbid conditions, the odds of readmission risk varied in the same
direction for both young and elderly patients with a difference predominantly in the
magnitude of effect sizes. This was most prominent for solid tumors, lymphoma, metastatic cancers
and renal failure with these comorbidities predicting a higher risk of readmission in patients
less than 65 years of age compared to those 65 years of age or older. Notably, hypertension was
independently associated with higher odds for readmission risk in patients less than 65 years
of age whereas it was associated with lower readmission odds in those 65 years and older (risk
adjusted OR: 1.06, 95% C.I. 1.03±1.09 versus risk adjusted OR: 0.97, 95% C.I. 0.95±0.99, P for
Overall cost of index hospitalizations was $14.8 billion with mean cost per hospital stay of
$9,305.9 for the 18±44 year age group, $10,238.7 for 45±64 year age group and $10,163.9 for
patients 65 years (Table 2). Overall cost of readmissions was estimated at $3.5 billion for the
entire cohort. Notably, mean cost per hospital stay was significantly higher for middle-aged
adults at $15,976.3 compared to young adults ($15,539.2) and the elderly ($14,442.7).
In our study assessing 30-day pneumonia readmission in a nationally representative all-payer
sample of hospital discharges in the US, we made the following key observations. First,
although the risk of readmission after a hospital discharge for pneumonia is highest for the
elderly, it is substantial for middle-aged adults. This risk remains high after adjusting for
comorbidities. Second, men were at a higher risk of being readmitted compared to women in
the overall population, and on specific gender and age interaction analysis, this risk was found
to be significant for the middle and elderly age groups. Third, we identified multiple
comorbidities, most prominently, end stage renal disease, that increase the risk of patients being
readmitted after discharge even after adjusting for age. Finally, we found that readmissions
pose a substantial economic burden, with a higher cost of readmission for the middle-aged
population per hospital stay compared to both young adults and the elderly.
With the largest number of hospitalizations for a medical diagnosis, pneumonia accounts
for extensive hospital costs and preventing readmissions is a potential avenue to mitigate this
] However, most data regarding the burden of pneumonia readmissions is
derived from Medicare patients with analysis of readmission patterns in younger age groups
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limited to individual hospital and state level studies. To our knowledge, this is the first
description of readmission patterns following pneumonia in a nationally representative sample in the
United States that includes patients from all ages and all payers across a span of two years.
Our study elucidates that although the risk of readmission increases with advancing age,
the burden of readmission among middle-aged adults is substantial. Krumholz et al reported
similar results from the California State Inpatient Database and confirmation of this burden at
a national level is an important finding that deserves consideration.[
] Recognition of the
significant number of middle-aged adults who are readmitted after hospital discharge for
pneumonia is the first step at identifying them as an ªat riskº group prompting further investigation
into factors that influence it and strategies to mitigate it.
The impact of gender on outcomes following a hospitalization for pneumonia is less well
studied. Mongardon et al described increased mortality in males admitted to the ICU with
severe pneumococcal pneumonia whereas Caceres et al did not find gender differences in a
similar setting. [
] In a prospective cohort study of adults older than 65 years, males were
found to be at higher risk of readmission after hospitalization for community acquired
pneumonia. We found males in middle and older age groups to be at a slightly higher risk of
readmission compared to females. Further studies focusing on severity of illness, adherence to
treatment, and compliance with follow up are needed to understand the reasons for these
gender differences in readmission risk.
Similar to readmission after discharge for acute myocardial infarction and heart failure,
readmission after pneumonia has also been found to be associated with comorbid illnesses in
3, 6, 8
] We found that patients with end stage renal disease had twice the odds of
being re-hospitalized even after adjusting for age and other comorbidities. Prior studies have
noted chronic kidney disease or the need for renal replacement therapy to be independent
predictors of both re-hospitalization and mortality. [
2, 7, 24, 26
] Possible reasons for this could
include special considerations regarding antibiotic dosing in patients with poor kidney
function, underlying immune dysfunction and dialysis needs.
Another significant finding in our study is the substantial cost burden of pneumonia
re-hospitalization and the strikingly higher cost per hospital stay for middle-aged adults
compared to both young and elderly patients. With 16.1% of adults in this age group
being readmitted following discharge from a hospital stay for pneumonia, the cost burden
of readmissions is significant enough to warrant attention. Reasons for this are unclear
however a possible factor could be severity of illness. CURB-65, one of the most
commonly used admission criteria for pneumonia by emergency departments is more readily
met by definition in patients older than 65 years whereas younger patients must exhibit at
least two features such as confusion, uremia, tachypnea or hypotension marking a higher
severity of illness. The NRD does not allow an assessment of disease severity, therefore,
further studies are needed to evaluate this and assess if this population warrants closer
attention at discharge with regards to in-hospital functional decline, medication
adherence and follow up visits to prevent readmission.
Our study has a few limitations. First, data on readmissions were only available for the
years 2013 and 2014 at the time of analysis. It is, therefore, unknown if these findings are
consistent over time. Second, NRD is constructed from State Inpatient Databases and
therefore readmissions occurring in another state are not captured. However in large
sensitivity analysis reported by AHRQ less than 5% of readmission estimates are affected by
readmissions across state borders.[
] Third, information on race and ethnicity is not
available and therefore the impact of these on readmissions cannot be assessed. Fourth,
given the design of the NRD, we were unable to adequately pursue hospital-level analyses
or those that span multiple years. Fifth, although conceptually similar, our risk adjustment
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model is not the same as that adopted by CMS for pneumonia readmission since we used
CCS codes rather than CCs in accordance with the AHRQ recommendations for NRD.
However, this approach has been used in prior studies addressing readmissions for other
] and the performance of the CMS model for risk-adjustment used by
national policymakers was comparable in our study (c-statistic 0.63) to that in the original
fee-for-service Medicare population (c-statistic 0.63). Furthermore, we performed a
sensitivity analysis using the 29 comorbidities developed by Elixhauser et al specifically for use
in all payer databases and found that potential predictors of readmissions were
comparable in these models. Finally, diagnosis of pneumonia and comorbidities used in the
riskadjustment model are derived from administrative claims codes, and important clinical
information on disease severity and therapeutic strategies is not available. However, these
are known challenges in working with administrative data. [
] We therefore relied on
the comorbidities identified in prior models for identifying predictors of readmission
In conclusion, our results highlight the burden of 30-day readmission in young and
middle-aged pneumonia survivors and underscore the need for further studies focusing on this
population to understand gender, disease severity, comorbidity and treatment related factors
that may influence this risk.
S1 Table. Risk- adjusted odds ratios for readmission within 30 days after discharge
following a hospitalization for pneumonia using the risk adjustment model adopted by Centers
for Medicare and Medicaid Services.
S2 Table. Risk- adjusted odds ratios for readmission within 30 days after discharge
following a hospitalization for pneumonia using the Elixhauser model.
S3 Table. Risk- adjusted odds ratios for readmission within 30 days after discharge
following a hospitalization for pneumonia using the Elixhauser model in patients less than 65
years of age versus those 65 and older. P value presented for interaction analysis between age
and each characteristic.
Conceptualization: Snigdha Jain, Rohan Khera.
Data curation: Snigdha Jain.
Formal analysis: Snigdha Jain, Rohan Khera.
Investigation: Eric M. Mortensen, Jonathan C. Weissler.
Methodology: Rohan Khera, Eric M. Mortensen.
Supervision: Snigdha Jain, Eric M. Mortensen, Jonathan C. Weissler.
Validation: Snigdha Jain, Rohan Khera, Eric M. Mortensen, Jonathan C. Weissler.
Writing ± original draft: Snigdha Jain.
Writing ± review & editing: Rohan Khera, Eric M. Mortensen, Jonathan C. Weissler.
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