Treatment for Rheumatoid Arthritis and the Risk of Hospitalization for Pneumonia

Treatment for Rheumatoid Arthritis

and the Risk of Hospitalization for Pneumonia

Associations With Prednisone, Disease-Modifying Antirheumatic Drugs, and

Anti–Tumor Necrosis Factor Therapy

Frederick Wolfe,

_{Liron Caplan,}

_{and Kaleb Michaud}

Objective.

Pneumonia is a major cause of

mortal-ity and morbidmortal-ity in rheumatoid arthritis (RA). This

study was undertaken to determine the rate and

predic-tors of hospitalization for pneumonia and the extent to

which specific RA treatments increase pneumonia risk.

Methods

. RA patients (n

ⴝ

16,788) were assessed

semiannually for 3.5 years. Pneumonia was confirmed

by medical records or detailed patient interview.

Covari-ates included RA severity measures, diabetes,

pulmo-nary disease, and myocardial infarction. Cox

propor-tional hazards regression was used to determine the

multivariable risk associated with RA treatments.

Results

. After adjustment for covariates,

pred-nisone use increased the risk of pneumonia

hospitaliza-tion (hazard ratio [HR] 1.7 [95% confidence interval

1.5–2.0]), including a dose-related increase in risk

(

<

5mg/day HR 1.4 [95% confidence interval 1.1–1.6],

>5–10 mg/day HR 2.1 [95% confidence interval 1.7–

2.7], >10 mg/day HR 2.3 [95% confidence interval

1.6–3.2]). Leflunomide also increased the risk (HR 1.2

[95% confidence interval 1.0–1.5]). HRs for etanercept

(0.8 [95% confidence interval 0.6–110]) and

sulfasala-zine (0.7 [95% confidence interval 0.5–1.0]) did not

reflect an increased risk of pneumonia. HRs for

inflix-imab, adalimumab, and methotrexate were not

signifi-cantly different from zero.

Conclusion

. There is a dose-related relationship

between prednisone use and pneumonia risk in RA. No

increase in risk was found for anti–tumor necrosis

factor therapy or methotrexate. These data call into

question the belief that low-dose prednisone is safe.

Because corticosteroid use is common in RA, the results

of this study suggest that prednisone exposure may have

important public health consequences.

Pneumonia due to infection is the leading cause

of hospitalization in the US, excluding childbirth and

psychosis (1). It is also one of the major causes of

mortality in patients with rheumatoid arthritis (RA) (2).

However, most of the interest in the pulmonary diseases

associated with RA has been directed toward

uncom-mon entities, including interstitial lung disease

second-ary to RA (3) and adverse pulmonsecond-ary effects of specific

treatments. Prior reports have putatively implicated

injectable gold (4), penicillamine (5), sulfasalazine (6,7),

methotrexate (8–10), infliximab (11), and leflunomide

(12,13). Thus, while bacterial and viral causes of

pneu-monia predominate, they are rarely reported in research

studies.

The use of immunomodulatory drugs has sparked

an interest in the infections that might result from

treatment interventions. Methotrexate is suspected of

conferring susceptibility to infectious pathogens (14–

20), although most reports concern methotrexate

pneu-monitis, a hypersensitivity reaction (21). Low-dose

pred-nisone is another commonly used treatment for RA, but

there are no reported studies determining whether it is a

risk factor for pneumonia in RA. Anti–tumor necrosis

Bank for Rheumatic Diseases has received support from Abbott, Amgen, Bristol-Meyers-Squibb, Centocor, Merck, and TAP.

1_{Frederick Wolfe, MD: National Data Bank for Rheumatic}

Diseases and University of Kansas School of Medicine, Wichita, Kansas;2_{Liron Caplan, MD: Washington University School of}

Medi-cine, St. Louis, Missouri;3_{Kaleb Michaud, MS: National Data Bank}

for Rheumatic Diseases, Wichita, Kansas, and Stanford University, Stanford, California.

Address correspondence and reprint requests to Frederick Wolfe, MD, National Data Bank for Rheumatic Diseases, Arthritis Research Center Foundation, 1035 N. Emporia, Suite 230, Wichita, KS 67214. E-mail: fwolfe@arthritis-research.org.

Submitted for publication February 27, 2005; accepted in revised form October 25, 2005.

factor (anti-TNF) therapy has raised concern about a

general risk of infection (22,23), but there is little

evidence from clinical trials suggesting a real or

substan-tial risk (24,25).

Surprisingly, there have been no previous

pub-lished studies of RA and pneumonia. We therefore

undertook this study to 1) determine the rate of

pneu-monia in RA, 2) determine whether RA treatments

increase the risk of hospitalization for

infection-associated pneumonia, and 3) estimate the degree of risk

associated with each treatment.

PATIENTS AND METHODS

Patients and data collection.Patients in this study were participants in the National Data Bank for Rheumatic Dis-eases (NDB) longitudinal study of RA outcomes. NDB partic-ipants are recruited from the practices of US rheumatologists and are followed up prospectively with semiannual detailed, 28-page questionnaires, as previously described (26–28). This report concerns the status of 16,788 adult RA patients who completed questionnaires regarding their illness for up to 3.5 years beginning with events that occurred in first 6 months of 2001 and ending with events that occurred in the first 6 months of 2004, resulting in a mean⫾SD followup time of 2.2⫾1.2 years (median 2.5). Of these patients, 5,317 were enrolled as part of an infliximab safety registry and 1,852 as part of a leflunomide safety registry.

Recorded demographic variables included sex, age, ethnic origin, education level, current marital status, and medical insurance type. Functional disability in this report was assessed by the Health Assessment Questionnaire (HAQ) (29,30). Information on pain, global disease severity, and fatigue was determined by visual analog scale (31), as well as the Rheumatoid Arthritis Disease Activity Index (RADAI) (32). Patients reported all medications, including dosage and frequency of use. Prednisone use was further characterized as mean daily dosage. The effect of comorbidity was assessed by a comorbidity score, which is the sum of 11 present or past comorbid conditions reported by the patient. Conditions in-clude cancer, stroke, fracture, and renal, neurologic, endo-crine, gastrointestinal, cardiovascular, pulmonary, genitouri-nary, and psychiatric problems. In addition, rates of specific conditions, including preexisting pulmonary disease, myocar-dial infarction, and diabetes, were determined.

Patients report all hospitalizations. Hospitalizations for pneumonia were identified from patient descriptive re-ports, hospital records, physician rere-ports, and mortality records. In hospital and mortality reports, pneumonia was confirmed by the presence of International Classification of Diseases, Ninth Revision codes 480–486, 136.9, and 997.3. If records could not be obtained, we contacted the patient’s physician and/or interviewed the patient with a structured, preplanned interview designed to address the reported condi-tion. Comparison of patient reports with medical records indicated agreement in⬎94% of cases. Pneumonia cases also included deaths in which pneumonia was listed as a major cause on death records, provided that the death occurred in

the 6-month period following the last NDB survey. We ob-tained information on deaths from family members and phy-sicians, and also systematically screened the National Death Index (33,34) at yearly intervals for death information and causes of death.

Statistical analysis.Predictors of first hospitalization for pneumonia were examined using Cox proportional hazards regression; all analyses satisfied the proportional hazard as-sumption based on Schoenfeld residuals. Patients reported events and treatments that occurred during the previous 6 months. Predictor treatment variables represented treatments being used at the start of the 6-month period in which pneumonia was assessed, while covariate values were those Table 1. Characteristics of the 16,788 RA patients*

Demographics

Age, mean⫾SD years 62.0⫾13.3

Male sex 22.8

Ethnic origin

White, not of Hispanic origin 89.7

Black, not of Hispanic origin 4.8

Asian or Pacific Islander 1.0

American Indian or Alaskan native 1.1

Hispanic 3.0 Other 0.5 Education, years 0–8 2.6 ⬎8–11 7.9 12 39.2 13–15 24.8 ⱖ16 25.6 RA characteristics

Disease duration, mean⫾SD years 16.3⫾11.3 Lifetime no. of DMARDs or biologic

agents, mean⫾SD

3.3⫾2.1

HAQ (0–3), mean⫾SD 1.1⫾0.7

Semiannual direct medical costs, median dollars 7,024 Treatment

Prednisone, all dosages 38.1

Daily dosage among prednisone users, mg

ⱕ5 66.9 ⬎5–10 23.4 ⬎10 9.8 Methotrexate 54.5 Hydroxychloroquine 17.7 Leflunomide 14.4 Sulfasalazine 5.7 Infliximab 36.9 Etanercept 12.8 Adalimumab 4.3 Comorbidity Smoking (ever) 54.2 Diabetes 10.1

Pulmonary disease (ever) 17.0

Myocardial infarction (ever) 8.2

Comorbidity index (0–11), mean⫾SD 2.5⫾2.0 Pneumonia-associated hospitalization 3.7

Pneumonia-associated death 0.4

* Except where indicated otherwise, data refer to values at last study observation. Except where indicated otherwise, values are the percent of patients. RA ⫽ rheumatoid arthritis; DMARDs ⫽ disease-modifying antirheumatic drugs; HAQ⫽Health Assessment Question-naire.

determined at the close of the previous 6 month period. In the event of death, treatment variables were those in the 6-month period prior to the death. Approximately 8% of the fields coding previous history of pulmonary problems and previous history of myocardial infarction were missing; 0.6% were missing for diabetes history. To allow for all variables to be used in the multivariable analyses, we used the method of multiple imputation by chained equations to impute data to 5 data sets (35), according to the implementation method of Royston (36,37). We calculated incidence rates for the first hospitalization due to pneumonia beginning with each pa-tient’s enrollment in the NDB, using all patients. So that rates would be generalizable to community arthritis patients, we also calculated detailed incidence rates after excluding patients who were enrolled as part of pharmaceutical company–sponsored safety registries. Cox regression analyses also included safety registry status as a dummy variable. However, in these multiple covariate analyses, safety registry status was not found to be a significant term. Analyses were performed using the Stata SE statistical package, version 8.2 (2003) (Stata, College Station, TX). P values less than 0.05 were considered significant. Ninety-five percent confidence intervals (95% CIs) were cal-culated, and all tests were 2-tailed.

RESULTS

At the time of the last assessment, prednisone

and methotrexate were the most common treatments in

this cohort during the previous 6 months (38.1% and

54.5%, respectively), followed by infliximab (36.9%),

hydroxychloroquine (17.7%), and etanercept (12.8%)

(Table 1). Among prednisone users, 66.9% received

ⱕ

5

mg/day, 23.4% received

⬎

5–10 mg/day, and 9.8%

re-ceived

⬎

10 mg/day. The mean and median daily dosage

of prednisone in the study cohort were 7.4 mg and 5.0

mg. On average, patients had been exposed to 3.3

disease-modifying antirheumatic drugs (DMARDs) or

biologic agents, and the HAQ score was 1.1. Preexisting

(pre-pneumonia) pulmonary disease had occurred in

17.0% of subjects. Diabetes was noted in 10.1%, and

myocardial infarction in 8.2%.

There were 749 hospitalizations for pneumonia

that occurred in 644 patients in the full study cohort. The

incidence density of pneumonia was 17 per 1,000

patient-years (95% CI 16.4–19.1) for all patients, 19.2

per 1,000 patient-years for men (95% CI 16.3–22.5), and

17.3 per 1,000 patient-years for women (95% CI 15.8–

18.9). For greater generalizability, we also conducted

detailed incidence rate analyses of the 9,619 patients

who were not part of the safety registries (Table 2). The

incidence rate for pneumonia in this group was slightly

lower than in the full cohort (14.7 per 1,000 patient-years

rheumatoid arthritis

Infections

Exposure, years

Incidence rate per 1,000 patient-years 95% confidence interval All patients 305 20,744 14.7 13.1–16.4 Sex Female 222 16,114 13.8 12.0–15.7 Male 83 4,630 17.9 14.2–22.1 Age, years ⬍20 0 1 0.0 0.0–3,688.9 20–34 2 443 5.1 0.8–17.2 35–44 13 1,332 9.6 5.1–16.4 45–54 32 3,546 9.0 6.2–12.8 55–64 63 5,501 11.5 8.8–14.7 65–74 115 5,869 19.6 16.2–23.5 75–84 74 3,525 21.0 16.5–26.3 ⱖ85 5 526 9.9 3.3–27.7

Table 3. Univariable nontreatment predictors of pneumonia hospitalization*

Variable Hazard ratio _P 95% CI

Demographics

Age (per 10 years) 1.3 ⬍0.001 1.3–1.4

Sex (male) 1.1 0.253 0.9–1.3

Education level (years) 0.9 0.001 0.9–1.0

Non-Hispanic white† 1.1 0.357 0.9–1.5

Comorbidity

Smoking (ever) 1.3 0.001 1.1–1.5

Diabetes 2.0 ⬍0.001 1.6–2.5

Pulmonary disease (ever) 3.8 ⬍0.001 3.2–4.4 Myocardial infarction (ever) 2.1 ⬍0.001 1.7–2.6 Comorbidity score (0–11) 1.3 ⬍0.001 1.2–1.3 RA characteristics

RA duration (per 10 years) 1.1 0.009 1.0–1.2 No. of previous DMARDs or

biologic agents

1.1 ⬍0.001 1.1–1.2

HAQ (0–3) 2.0 ⬍0.001 1.8–2.2

* 95% CI⫽95% confidence interval (see Table 1 for other defini-tions).

[95% CI 13.1–16.4]). The rate was greater in men (17.9

per 1,000 patient-years [95% CI 14.2–22.1]) than in

women (13.8 per 1,000 patient-years [95% CI 12.0–

15.7]). The rate increased with age (Table 2). In the age

group 75–84 years, it reached its maximum (21.0 per

1,000 patient-years). Only 3% of pneumonia

hospitaliza-tions were attributed directly to nonviral, nonbacterial

opportunistic infections.

A number of demographic and clinical variables

were associated with the risk of pneumonia, as seen in

the covariate-specific disease associations presented in

Table 3. For example, each 10-year increase in age was

associated with a 30% increase in pneumonia risk.

Pneumonia was also more common in those with less

education. Comorbidity status predicted future

pneumo-nia among those who had ever smoked (hazard ratio

[HR] 1.3 [95% CI 1.1–1.5]), were diabetic (HR 2.0 [95%

CI 1.6–2.5]), had a past myocardial infarction (HR 2.1

[95% CI 1.7–2.6]), or had prior pulmonary disease (HR

3.8 [95% CI 3.2–4.4]). Certain RA features were also

associated with the risk of pneumonia. Risk was

in-creased with increasing duration of RA (HR 1.1 per 10

years increase in RA duration [95% CI 1.0–1.2]) and

with each additional prior DMARD or biologic agent

(HR 1.1 [95% CI 1.1–1.2]). Among the most powerful

predictors of hospitalization for pneumonia was

func-tional status: a 1-unit increase in HAQ score had an HR

of 2.0 (95% CI 1.8–2.2).

The effect of treatment variables on the risk of

pneumonia varied according to the covariates in the

model. Table 4 presents univariable associations

unad-justed for covariates and associations adunad-justed for

co-variates. The addition of covariates mitigated the risk

associated with treatment variables. In the adjusted

analyses, use of prednisone increased the risk of

pneu-monia by 70% (HR 1.7 [95% CI 1.5–2.1]) and

lefluno-mide increased the risk by 30% (HR 1.3 [95% CI

1.0–1.5],

P

⫽

0.036), while persons receiving

sulfasala-zine had a reduced HR (0.7 [95% CI 0.4–1.0],

P

⫽

Variable

Unadjusted Adjusted†

Hazard ratio P 95% CI Hazard ratio P 95% CI

Prednisone, all dosages 2.3 ⬍0.001 1.9–2.7 1.7 ⬍0.001 1.5–2.1

No prednisone 1.0 1.0 Prednisoneⱕ5 mg/day 1.7 ⬍0.001 1.4–2.1 1.4 ⬍0.001 1.1–1.6 Prednisone⬎5–10 mg/day 2.9 ⬍0.001 2.3–2.7 2.1 ⬍0.001 1.7–2.7 Prednisone⬎10 mg/day 3.1 ⬍0.001 2.2–4.3 2.3 ⬍0.001 1.6–3.2 Methotrexate 1.0 0.951 0.9–1.2 1.0 0.884 0.8–1.2 Hydroxychloroquine 0.8 0.011 0.6–0.9 0.9 0.331 0.7–1.1 Leflunomide 1.3 0.003 1.1–1.6 1.3 0.036 1.0–1.5 Sulfasalazine 0.6 0.027 0.4–1.0 0.7 0.053 0.4–1.0 Infliximab 1.5 ⬍0.001 1.3–1.7 1.2 0.182 0.9–1.4 Etanercept 0.7 0.013 0.5–0.8 0.8 0.051 0.6–1.0 Adalimumab 1.4 0.257 0.8–2.3 1.1 0.816 0.6–1.8

* 95% CI⫽95% confidence interval (see Table 1 for other definitions).

† Adjusted for age, sex, and for lagged variables of HAQ, pulmonary disease, diabetes, myocardial infarction, number of DMARDs or biologic agents, RA duration, smoking ever, education categories, safety registry membership, and prednisone usage (for nonprednisone variables).

Table 5. Multivariable predictors of pneumonia hospitalization*

Variable Hazard ratio P 95% CI

Treatment Prednisone 1.7 ⬍0.001 1.5–2.0 Leflunomide 1.2 0.062 1.0–1.5 Infliximab 1.1 0.322 0.9–1.4 Adalimumab 1.1 0.747 0.6–1.9 Methotrexate 1.0 0.927 0.8–1.2 Hydroxychloroquine 0.9 0.481 0.7–1.2 Etanercept 0.8 0.107 0.6–1.1 Sulfasalazine 0.7 0.072 0.5–1.0 Demographics†

Age (per 10 years) 1.3 ⬍0.001 1.2–1.4

Sex (male⫽1) 1.1 0.425 0.9–1.1

Smoking (ever) 1.1 0.161 1.0–1.3

Comorbidity

Pulmonary disease (ever) 2.9 ⬍0.001 2.3–3.4 Myocardial infarction (ever) 1.4 0.092 1.1–1.8

Diabetes 1.5 ⬍0.001 1.2–1.9

RA characteristics

HAQ (0–3) 1.5 ⬍0.001 1.3–1.7

No. of previous DMARDs or biologic agents

1.1 0.020 1.0–1.1

Duration of RA (years) 1.0 0.687 1.0–1.0

* 95% CI⫽95% confidence interval (see Table 1 for other defini-tions).

† Also adjusted for categories of educational attainment and safety registry enrollment.

0.053). Etanercept use in this model was also associated

with a marginally reduced HR (0.8 [95% CI 0.6–1.0],

P

⫽

0.051). In addition to a simple association of

pred-nisone use with pneumonia, we also found a

dose-related increase. Even dosages of

ⱕ

5mg/day were

asso-ciated with pneumonia risk, in both the unadjusted

model (HR 1.7 [95% CI 1.4–2.1]) and the adjusted

model (HR 1.4 [95% CI 1.1–1.6]).

Because drugs are often used concurrently, Table

5 recapitulates the adjusted analyses shown in Table 4,

but includes all of the treatment variables

simulta-neously. In this model, treatment effects were weakened

for all drugs except prednisone (HR 1.7 [95% CI 1.5–

2.0]). Of interest, most demographic, comorbidity, and

RA-related variables remained significant in this

multi-variable model.

DISCUSSION

Despite the fact that infectious pneumonia ranks

among the most common causes of death in RA, it has

not yet been studied specifically in RA patients (2).

Instead, most studies address noninfectious pulmonary

complications of RA (such as interstitial lung disease),

drug-related complications (such as hypersensitivity

pneumonitis), or opportunistic pulmonary infections

caused by unusual organisms. In our analyses, we

con-firmed that these types of infections were indeed rare,

contributing only 3% of cases.

Many of the pneumonia risk factors described in

our model are consistent with findings of previous

investigations conducted in non-RA populations and

may reflect underlying biologic phenomena. Among

these factors is preexisting pulmonary disease, which has

been shown in more than 20 studies to greatly increase

the risk of pneumonia (38–41). The pathophysiologic

explanation for this finding is, however, incompletely

understood. A number of studies have also established

smoking history as a risk factor for pneumonia (42–

45).The relevant mechanisms that have been proposed

include decreased ciliary and respiratory epithelial

func-tion, as well as defects in cellular and humoral immunity

(44,46). Interestingly, smoking entered the univariate

model (Table 3), but was not predictive by multivariate

analysis (Table 5) because of its correlation with

preex-isting pulmonary disease (which it may have caused).

The proinflammatory cytokine TNF

␣

mediates

the early response of mononuclear phagocytes to

bacte-rial infections and may play an important role in lung

disease. In particular, in endotoxin-reliant animal

mod-els of pneumonia, TNF

␣

production has been shown to

be stimulated, which in turn contributes to inflammatory

cell recruitment (47). Furthermore, bronchoalveolar

fluid TNF

␣

levels may be higher in infected pulmonary

lobes compared with the uninvolved lobes of patients

with community-acquired pneumonia (48) (or may show

a trend toward increased levels [49]). But despite the

theoretical role of TNF

␣

in pneumonia, our study failed

to demonstrate an increase in risk associated with any of

the anti-TNF

␣

therapies (Table 5).

Steroid exposure has rarely been addressed as a

potential risk factor for pneumonia in the general

pop-ulation (38–41,44). To our knowledge, glucocorticoid

dosage has not been demonstrated to be predictive of

pneumonia risk in any previous study. However, we

found a dose-related association between prednisone

and pneumonia hospitalization in patients with RA

(Tables 4 and 5). This relationship was evident even with

average daily dosages of

ⱕ

5mg, and the association was

robust to covariate control.

The immunomodulatory effect of prednisone is

facilitated through both genomic and nongenomic

path-ways. These actions have been recently summarized and

involve an extensive cascade of transcriptional/

translational events, along with more rapid

glucocorti-coid receptor–dependent and –independent means (50).

Given the broad spectrum of cellular mechanisms

pro-voked by glucocorticoids (in relative contrast to the

relatively specific actions of other DMARDs), it is

perhaps not surprising that prednisone exhibited the

strongest association with subsequent pneumonia.

Prednisone use is common in RA and is therefore

a potentially important health risk. In our study cohort,

a substantial minority (38.1%) of patients were receiving

prednisone. This prevalence would be reduced to 30.9%

if the infliximab and leflunomide safety registry patients

were excluded. Likewise, the removal of subjects from

these 2 registries would reduce the prevalence of

anti-TNF exposure to 32.3%.

If the results of this study are correct, they may

undermine the current belief that low-dose prednisone is

safe. Given the prevalence of prednisone use, the

find-ings of this investigation suggest a potentially important

public health problem. Our data do not, and cannot,

address the issue of net benefit. It is possible that

discontinuing prednisone or not using prednisone in the

first place might provoke equally undesirable adverse

effects.

It is noteworthy that we found no evidence of

increased rates of pneumonia associated with

metho-trexate (Tables 4 and 5), which challenges the

percep-tion from earlier reports (14,15). We found a very slight

increased risk with leflunomide (HR 1.2 [95% CI 1.0–

1.5]) (Table 5). This compound, an inhibitor of de novo

pyrimidine synthesis, retards the enzymatic activity of

dihydroorotate dehydrogenase, thereby blocking T cell

expansion. In addition, other pathways have recently

been described, including inhibition of TNF-dependent

NF-

␬

B activation of T cells (51,52). The relevance of

these actions on the development of pneumonia has yet

to be articulated. Finally, our results rule out the notion

of an increased risk of pneumonia associated with

sulfasalazine (HR 0.7 [95% CI 0.5–1.0]). This marginally

significant effect may or may not relate to sulfasalazine’s

known mechanisms of action (53,54).

Although observational studies reflect actual

clinical practice in the community, nonrandom

assign-ment to therapy can confound the association between

treatment and pneumonia unless all important

covari-ates are controlled for. In the current study, we adjusted

for differences in severity by the use of lagged covariates

(Tables 4 and 5). The HAQ is usually thought of as the

best predictor of hospitalization and long-term

out-comes (28,55), and we included this variable in the

model. We also included a count of the lifetime number

of DMARDs or biologic agents used by patient, since

the number of drugs is a measure of the lack of control

of RA; furthermore, we included the duration of RA.

HAQ disability and number of therapeutic agents were

both found to be predictive of pneumonia risk. It is

interesting to speculate whether known defects in

immu-nity that parallel RA disease severity (such as

mannose-binding lectin concentration) (56) might mediate this

elevated risk.

Although not included in the models whose

re-sults are shown in Tables 4 and 5, we examined a series

of other covariates, including the RADAI, pain scores,

and global severity. In the presence of the study

covari-ates, these factors were not statistically significant and

did not change the results of the analyses; therefore they

were excluded from the analyses presented in Tables 4

and 5. In addition to direct estimates of arthritis severity,

the inclusion of biologic agents in the analysis in Table 5

serves as a further adjustment for arthritis severity, since

anti-TNF therapy is prescribed to patients whose

arthri-tis is more severe. As demonstrated in Tables 3 and 5,

demographic factors and the presence of pulmonary

disease, cardiovascular disease, and diabetes also

con-tribute to the risk of pneumonia, and we controlled for

these factors. Even so, we may not have accounted for all

covariate effects and we suggest that, ideally, a

random-ized controlled trial should be undertaken to confirm the

findings of this study, particularly given the public health

implications of our findings.

In summary, our findings demonstrate a

dose-related relationship between prednisone treatment and

the risk of pneumonia in RA patients in the community.

No increased risk was found with anti-TNF therapy or

methotrexate. A slight increase in risk was found with

leflunomide. Diabetes, prior myocardial infarction, and

prior pulmonary disease also increase the risk of

pneu-monia. Because corticosteroid use is common in RA, the

results of this study suggest that prednisone use may

have important public health consequences.

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