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Caffeine is the most widely consumed central nervous system stimulant in the world.1 Caffeine is present in a

variety of foods and beverages, as well as some medicines. Many cold remedies, pain relievers, and fatigue restoratives include caffeine for its antihypnotic effects or to enhance the main effects of these drugs.2 Although consumers frequently self-medicate with these medicines, few consumers are aware of the presence of caffeine as an ingredient.

There have been many studies devoted to caffeine in an attempt to determine its effects. Because the primary source of caffeine for many people is coffee, most caffeine studies have focused on the relationship between coffee consumption and the risk of disease. Although still controversial, increas-ing evidence supports the hypothesis that coffee consumption does not increase the risk of hemorrhagic stroke (HS).3–7 In addition, a recent meta-analysis reported that moderate cof-fee consumption may be weakly inversely associated with risk of all types of stroke.8 A few studies have addressed

tea consumption, which might reduce the risk of ischemic stroke.9,10 The association of caffeine in pharmaceutical prod-ucts and aneurysmal subarachnoid hemorrhage (SAH) or intracerebral hemorrhage (ICH) warrants further study.11,12 Therefore, a nationwide, multicenter, matched case–control study was performed to evaluate the association between caffeine-containing medicines (CCMs) and the risk of HS, including SAH and ICH in those aged 30 to 84 years.

MethodsStudy Design and SettingThe multicenter, matched case–control study data collected for the acute brain bleeding analysis were used.13 Patients who had expe-rienced a HS, and who were aged 30 to 84 years and able to com-plete an interview, were recruited sequentially from 33 hospitals in Korea between 2002 and 2004. This study was approved by the Seoul National University Hospital/Seoul National University College of Medicine institutional review board, and all study participants pro-vided written informed consent to participate.

Background and Purpose—Research on the relationship between caffeine-containing medicines (CCMs) and the risk of hemorrhagic stroke (HS) is sparse. The aim of this study is to evaluate the association between CCMs and the risk of HS.

Methods—We performed a multicenter case–control study in South Korea, from 2002 to 2004. A total of 940 patients with nontraumatic acute HS, aged 30 to 84 years without a history of stroke, 940 community, and 940 hospital controls, age and sex matched to each case, were included. We obtained information on all medications taken in the 14 days before the date (index date) and time of stroke onset (zero-time) for case subjects or matched zero-time for control subjects. Exposure to CCMs was defined by use on the index date before zero-time or during the preceding 3 days. The adjusted odds ratios and their 95% confidence intervals (CIs) were estimated by conditional logistic regression.

Results—The adjusted odds ratio for the association between the use of CCM and risk for HS was 2.23 (95% CI, 1.41–3.69) for all HS, 2.24 (95% CI, 1.08–4.66) for subarachnoid hemorrhage, and 2.49 (95% CI, 1.29–4.80) for intracerebral hemorrhage. Stratified by daily coffee intake, adjusted odds ratio of CCMs for HS was 2.95 (95% CI, 1.45–5.98) for those who did not drink coffee on a daily basis.

Conclusions—These results suggest that use of CCMs is associated with increased risk of HS, both subarachnoid hemorrhage and intracerebral hemorrhage. (Stroke. 2013;44:2139-2143.)

Key Words: caffeine ■ case-control studies ■ cerebral hemorrhage ■ subarachnoid hemorrhage

Caffeine-Containing Medicines Increase the Risk of Hemorrhagic Stroke

Seung-Mi Lee, PhD*; Nam-Kyong Choi, PhD*; Byung-Chul Lee, MD, PhD; Ki-Hyun Cho, MD, PhD; Byung-Woo Yoon, MD, PhD; Byung-Joo Park, MD, PhD

Received August 24, 2012; final revision received April 23, 2013; accepted May 2, 2013.From the Chung-Ang University College of Pharmacy, Seoul, South Korea (S.-M.L.); Medical Research Collaborating Center, Seoul National University

Hospital (N.-K.C., B.-J.P.), Department of Neurology (B.-W.Y.), and Department of Preventive Medicine (B.-J.P.), Seoul National University College of Medicine, Seoul, South Korea; Institute of Environmental Medicine (N.-K.C.), Neuroscience Research Institute (B.-W.Y.), Seoul National University Medical Research Center, Seoul, South Korea; Department of Neurology, Hallym University College of Medicine, Anyang, Gyeonggi-do, South Korea (B.-C.L.); and Department of Neurology, Chonnam National University Hospital, Gwangju, South Korea (K.-H.C.).

*Drs Lee and Choi contributed equally to this article.The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.

111.674077/-/DC1.Correspondence to Byung-Joo Park, MD, PhD, Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehakno,

Chongno-Gu, Seoul 110-799, South Korea. E-mail bjpark@snu.ac.kr© 2013 American Heart Association, Inc.

Stroke is available at http://stroke.ahajournals.org DOI: 10.1161/STROKEAHA.111.674077

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2140 Stroke August 2013

Identification of CasesWe screened all of the patients with HS admitted to the participat-ing hospitals. HS was defined as either SAH or ICH. The diagnosis of SAH was based on clinical symptoms plus either a brain image

(computed tomography [CT], MRI) or evidence of xanthochromia on a lumbar puncture. ICH was diagnosed on the basis of clinical symp-toms and detection of blood in the brain parenchyma or ventricles by CT or MRI. Study eligibility criteria included ages ranging from 30 to 84 years, absence of a history of stroke or hemorrhage-prone brain le-sions, no causal relationship of the stroke to trauma, and the ability to communicate and complete an interview within 30 days after the onset of the stroke. For each patient, we identified the zero-time as the cal-endar day (ie, index date) and the time of day that marked the onset of symptoms that were plausibly related to hemorrhage and that caused the patient to seek medical attention.14 We defined the zero-time after considering the symptoms of hemorrhage, including paralysis, vertigo, disorders of memory, epilepsy, numbness, diplopia, impaired vision, dysphagia, disorders of consciousness, dysarthria, sentinel headache, dysuria, syncope, aphasia, nausea, vomiting, and motor ataxia. The neurologist at participating hospitals reviewed each case and estab-lished the zero-time. All of the collected medical and image informa-tion was then sent to the central coordinating center according to the standard operating procedure agreed in advance. A second and final check on eligibility and reconfirmation of the diagnoses were com-pleted by 1 neurologist at central coordinating center who was kept unaware of medication exposures. Any ambiguities were resolved by consensus with the neurologist at participating hospitals. The purpose of the multilevel review was to ensure uniform standards for docu-mentation and eligibility across all participating hospitals. According to the process, missing clinical data were obtained. Finally, among the 2294 patients who were eligible for this study, a total of 5 patients were excluded after adjudication, who were not actually cases of HS.

Selection of ControlsEach case was matched to 2 controls (hospital and community) by age (±5 years) and sex. Hospital controls were selected among pa-tients who were hospitalized in the same institution for diseases other than stroke, but who were not in the hospital when the stroke of the matched case occurred and had been hospitalized after the date of the stroke event. The eligible hospital controls were screened from the order communication system or electronic medical record system in each participating hospital using a daily update of the list of cases. If there were multiple subjects, the hospital controls were preferably selected among the hospitalized patients from otolaryngology, ortho-pedic, ophthalmology, neurology, and neurosurgery departments. The closest subject to the patient with respect to date of birth was the first chosen from the list of eligible controls. After confirming the interview availability by considering the subject’s conditions with an attending physician of the subject, the potential control was asked to sign a consent form to participate in the study.

The eligibility criteria for the community controls included ab-sence of a history of stroke, absence of dementia or other neurologi-cal diseases, and the ability to communicate. The community control was recruited from siblings, friends, or neighbors of the patient, in

2,710 patients aged 25 years or more and hospitalized with hemorrhagic stroke were screened.416 patients* were excluded because;

306 patients had a history of stroke, 106 were less than 30 years or more than 84 years, 56 had a history of brain lesion to increase risk for hemorrhagic stroke, and 13 had a brain hemorrhage caused by trauma.

2,294 patients were eligible for this study except the ability to communicate & complete interview. 1298 patients* did not complete the interview because;

1,081 were not able to communicate within 30 days after stroke, 150 refused to participate in this study, 31 were discharged before the arrangement of interview, 5 were diagnosed as no hemorrhagic stroke, 3 were older than 84 years old at the day of interview 2 were 30 days had passed from the index date at the day of interview and 67 withdrew consent during interview.

996 patients completed the interview. 968 were matched to hospital control. 943 were matched to community control.

940 patients were completely matched to 1880 control subjects without violation.

Figure 1. Identification and enrollment of study subjects. *The summation of the number of patients below is not equal to this number because some of the patients have >1 reason.

Table 1. Characteristics of Cases and Control Subjects

CharacteristicsCases

n=940 (%)Controls

n=1880 (%) P Value*

Men 469 (49.9) 938 (49.9) 1.000

Age (mean±SD), y 54.1±11.4 53.6±11.6 0.220

Body mass index (mean±SD), kg/m2 23.6±3.0 23.6±2.9 0.718

High-school graduation or more 447 (47.6) 957 (50.9) 0.093

Family history of stroke 224 (23.8) 281 (14.9) <0.001

History of hypertension 442 (47.0) 381 (20.3) <0.001

History of diabetes mellitus 69 (7.3) 172 (9.1) 0.105

History of hyperlipidemia 31 (3.3) 96 (5.1) 0.029

History of heart disease 27 (2.9) 68 (3.6) 0.302

Upper respiratory tract infections within 30 days from zero-time

143 (15.2) 235 (12.5) 0.046

Smoking <0.001

None 512 (54.5) 1073 (57.1)

Past smoker 119 (12.7) 301 (16.0)

Current smoker 307 (32.7) 486 (25.9)

Unknown 2 (0.2) 20 (1.1)

Alcohol intake <0.001

None 366 (38.9) 841 (44.7)

Past drinker 40 (4.3) 159 (8.5)

Current drinker 529 (56.3) 861 (45.8)

Unknown 5 (0.5) 19 (1.0)

Frequent spicy food intake 423 (45.0) 711 (37.8) <0.001

High salt intake 378 (40.2) 599 (31.9) <0.001

Phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days

10 (1.1) 4 (0.2) 0.002

Daily coffee intake 527 (56.1) 1061 (56.4) 0.851

Daily tea intake 155 (16.5) 394 (21.0) 0.005

Laborious work ≥7 h/wk 280 (29.8) 411 (21.9) <0.001

*P values were calculated by Pearson χ2 test, Fisher exact test, or a Student t test, where appropriate.

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descending order of preference. The recruited patients offered names and contact information of potential controls in order (eg, first: sib-ling 1, second: sibling 2, third: friend 1, fourth: neighbor 1, fifth: neighbor 2, and sixth: neighbor 3). Therefore, the sequence of po-tential controls was established by the case. Trained nurse interview-ers called the suggested potential controls according to the order. We selected the controls in sequence when they met the inclusion criteria and received consent to participate in the study.

Every interview with control subject had to be completed within 30 days after zero-time of the matched case and within 7 days when the matched case’s interview was completed.

Data CollectionStructured questionnaires evaluated for feasibility by an expert panel were administered by trained nurse interviewers to all participants. Each case and their matched controls were questioned on the basis of zero-time of the matched case. To avoid interviewer bias, the interviewers and subjects were kept blind to the hypothesis of this study. They were informed that the objective of this study was to investigate the effect of lifestyles and medications on the risk of HS. Information with regard to the following basic characteristics was obtained from the interview for both cases and controls: age, sex, height, weight, socioeconomic factors, lifestyle factors, medical history, family history of stroke, and all medi-cations taken in the 14 days before zero-time. Dietary habits were ex-amined according to intake of spicy or high salt food stuffs. Participants were asked to indicate how many cups of coffee or tea per day or per week they had consumed during the past year. The questionnaire did not inquire about the type of coffee consumed because consumption of de-caffeinated coffee in the Korean population was very low. We obtained information on physical activity performed in the past 6 months from the quantitative history of physical activity surveys. We ascertained a history of exposure to medication for all prescribed and nonprescribed drugs and collected information related to drug brand names, generic names, indications, the first date of administration, dose, and duration.13

Definition of Caffeine-Containing MedicinesA list was made of all the CCMs marketed in Korea during the study period. The brand name, indication, and caffeine content per dose were collected for these medications (Table I in the online-only Data Supplement). To compare the risk of CCMs with caffeine-free medi-cines (CFMs), drugs with the same indication as CCMs (pain reliev-ers, cold remedies, and fatigue restoratives) were included as CFMs. The exposure windows of CCMs and CFMs were defined as the index day before zero-time and the preceding 3 calendar days (eg, if zero-time was 15:00 [24-hour clock] on July 10, the exposure window was from 0:00 [12 midnight] on July 7 to 15:00 on July 10). We defined a 3-day exposure window on the basis of pharmacokinetic effect of caffeine,15 considering previous study results.11,12

Statistical AnalysisWe compared demographic, clinical, and behavioral features of the case and control subjects using Pearson χ2 test, Fisher exact test, or the Student t test where appropriate. Variables whose P values were <0.1 and with clinical importance were selected as potential confounding variables. We then selected variables with statistically significant dif-ferences between exposed and unexposed persons (P<0.1) with clinical importance among them. Finally, the model included age, family his-tory of stroke, history of hypertension, history of diabetes mellitus, up-per respiratory tract infection within 30 days from zero-time, high salt intake, current smoking, daily tea intake, and phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days as the adjusting variables. Odds ratios (ORs) and 95% confidence intervals (CIs) of HS associated with CCMs and CFMs were estimated using a conditional logistic regression model. Subgroup analysis was conducted with the different types of HS and control groups.

We calculated the individual caffeine dose per day from medica-tion, and tested trends against daily dose amounts. The CCM dose per day was calculated as follows: CCM dose/d (mg)=the caffeine content per dosage (mg/U)×the number of dosages per intake×the number of intake per day.

The risk of HS according to the daily intake of caffeine from medication was estimated. The daily intake dose of caffeine from CCMs was split into 2 groups at 35 mg/d which was the median daily intake dose of caffeine in the CCM-exposed control subjects. Additional stratified analyses were performed by the amount of coffee intake, using unconditional logistic regression. The amount of daily coffee intake was categorized into 3 groups: <1 cup, ≥1 cup and <3 cups, and ≥3 cups. All analyses were performed with Statistical Analysis System version 9.2 (SAS Institute, Inc).

ResultsA total of 2710 patients with hemorrhagic stroke were screened for their eligibility. Among them, 416 patients were excluded at the time of screening. In addition, 1294 patients were excluded because of not completing the interview. Among 996 patients enrolled and interviewed, 56 were matched to no con-trol subjects or matched to only 1 control subject (Figure 1).

In total, 940 HS patients were matched to 1880 control subjects. Of the cases, 442 (59.0% women; mean age±SD, 50.8±10.6 years) had a SAH and 498 (42.2% women; mean age, 57.1±11.3 years) had an ICH. The cases and controls were generally similar with regard to baseline characteristics, including age, sex, body mass index, history of diabetes melli-tus, history of heart disease, and daily coffee intake (Table 1).

A total of 46 patients (4.9%) with HS were CCM users, compared with 44 (2.3%) of the controls, providing a crude OR of 2.21 (95% CI, 1.44–3.41). The adjusted odds ratio (aOR) of CCMs for HS was 2.28 (95% CI, 1.41–3.69), after adjusting for potential confounders, compared with nonusers. However, the aOR of CFMs was 0.71 (95% CI, 0.39–1.28; Table 2). The aOR for subjects with SAH was 2.24 (95% CI, 1.08–4.66), and ICH was 2.49 (95% CI, 1.29–4.80). The results of the subgroup analysis according to type of control are summarized in Table 3. The aOR for subjects with hospital control was 2.14 (95% CI, 1.20–3.83), and community control was 2.64 (95% CI, 1.36–5.12).

Table 4 shows the risk of HS according to daily intake of caffeine from medications. We did not observe a quantitatively significant trend in the association between daily dose of caf-feine and risk for hemorrhagic stroke. According to daily coffee intake per group, aORs of CCMs for HS were 2.95 (95% CI, 1.45–5.98) for <1 cup, 1.59 (95% CI, 0.78–3.23) for ≥1 cup and <3 cups, and 1.63 (95% CI, 0.51–5.26) for ≥3 cups (Table 5).

Table 2. Association of CCMs and CFMs With the Risk of HS

Type of Medicines

Cases n=940 (%)

Controls n=1880 (%)

Crude OR (95% CI)

Adjusted OR (95% CI)*

CCMs

No exposure 894 (95.1) 1836 (97.7) 1.00 1.00

Exposure to CCMs

46 (4.9) 44 (2.3) 2.21 (1.44–3.41)

2.28 (1.41–3.69)

CFMs

No exposure 916 (97.4) 1830 (97.3) 1.00 1.00

Exposure to CFMs

25 (2.7) 50 (2.7) 0.98 (0.61–1.58)

0.71 (0.39–1.28)

CCM indicates caffeine-containing medicines; CFMs, caffeine-free medicines; HS, hemorrhagic stroke; CI, confidence interval; and OR, odds ratio.

*ORs and 95% CIs were calculated by conditional logistic regression, adjusted for age, a family history of stroke, a history of hypertension, a history of diabetes mellitus, upper respiratory tract infection within 30 days from zero-time, high salt intake, current smoking, daily tea intake, and phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days.

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2142 Stroke August 2013

DiscussionThe results of this case–control study indicate that the use of CCMs was associated with an increased risk of HS, both SAH and ICH.

Few studies have addressed the association between caf-feine and risk of HS. In a recent, small clinical trial, caffeine caused nearly identical blood pressure elevation in men and women, and women responded to caffeine with an increase in cardiac output.3 In addition, the interaction between caf-feine and other ingredients within medications may further increase the risk of HS. Some researchers have reported that taking caffeine with phenylpropanolamine raises the blood pressure and the level of caffeine in the plasma because phen-ylpropanolamine increases caffeine absorption and inhibits its removal.16,17 Taking caffeine with Mahuang, which includes ephedrine or pseudoephedrine, has been reported to induce arrhythmias, high blood pressure, and intracranial hemor-rhage, by increasing sympathomimetic action.18–20 Cold reme-dies or appetite suppressants containing phenylpropanolamine have also been suggested to increase the risk of HS.13,21–23

In the stratified analysis according to coffee intake, the group that did not drink coffee daily had a significantly higher risk of HS compared with those who frequently drank coffee. The finding of a higher OR among persons with a lower daily intake of caffeine makes sense because users develop tachy-phylaxis to caffeine. In addition, recently a meta-analysis of data from prospective studies to assess the relation quanti-tatively between coffee consumption and risk of stroke was published.8 Findings from this meta-analysis indicate that moderate consumption of coffee (1–3 cups of coffee per day) may be weakly inversely associated with risk of stroke. These results supported the hypothesis that components in coffee other than caffeine may lower the risk of stroke, although the association was modest and the biological mechanism remains unclear.8 In this context, our results suggest that CCMs increased the risk of HS consistently. In addition, people who

do not drink coffee daily may avoid coffee because of previous feelings of sensitivity or intolerance to caffeine. Those who did not drink coffee daily made up 44% of both the case and control groups. Therefore, our estimates of ORs do not seem to have been affected by coffee consumption itself.

This study has several strengths. First, the diagnostic accuracy was high because all cases of HS were confirmed by CT or MRI brain image. No SAH patients presented with a normal CT scan but positive LP finding. Furthermore, the neurologists were blinded to the drug exposure information when they evaluated potential cases according to predefined criteria.13,24 Second, we collected drug exposure data directly from our study participants. Most of the CCMs are available at pharmacies without a prescription, whereas only a few needs a physician’s prescription; therefore, we were able to collect information on prescribed and nonprescribed CCMs.

However, our results must be interpreted in the context of the following study limitations. First, there was no choice but to use personal interviews to obtain information on whether subjects took CCMs. Therefore, we included patients who were hospitalized and had the mental capacity to respond to a direct interview to avoid information bias from proxy interviewers. For this reason, we included those patients who experienced milder strokes, and in doing so excluded patients with severe neurological deficits or those who died before they had reached a hospital. Thus, the study participants did not represent the whole spectrum of HS patients; and, therefore, our findings do not apply to all HS patients, especially those with severe or fatal neurological status.24 In addition, the selection of nonpopulation-based controls from sources, such as hospital admissions or discharges, the same primary care provider, friends, or relatives, risks the possibility that the selection may be related to some of the factors under study.25 In our study, consistency among results across a series of control groups26 suggests that bias by control selection may not be a substantial weakness of this study.

Second, we collected our information on potential con-founders and drug exposure after the onset of HS. Recall bias at this point may have affected the results; therefore, to mini-mize this possibility, we kept the interviewers and participants blind to the major study hypothesis. The information on the use of nonprescription medication from pharmacies depended on the participants’ memories.

Table 3. Association Between CCMs and the Risk of HS by Control Type

Type of Controls

Cases n=940

(%)

Controls n=940

(%)

Crude OR (95%

CI)Adjusted OR

(95% CI)*

Cases vs hospital controls

No exposure 894 (95.0) 915 (97.3) 1.00 1.00

Exposure to CCMs

46 (4.9) 25 (2.7) 1.91 (1.16–3.17)

2.14 (1.20–3.83)

Cases vs community controls

No exposure 894 (95.0) 921 (98.0) 1.00 1.00

Exposure to CCMs

46 (4.9) 19 (2.0) 2.59 (1.48–4.53)

2.64 (1.36–5.12)

CCM indicates caffeine-containing medicines; HS, hemorrhagic stroke; CI, confidence interval; and OR, odds ratio.

*ORs and 95% CIs were calculated by conditional logistic regression, adjusted for age, a family history of stroke, a history of hypertension, a history of diabetes mellitus, upper respiratory tract infection within 30 days from zero-time, high salt intake, current smoking, daily tea intake, and phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days.

Table 4. Risk of HS by the Daily Intake of Caffeine From Medicine

Daily Caffeine Intake

Cases n=940

(%)

Controls n=1880

(%)Crude OR (95% CI)

Adjusted OR (95% CI)*

No intake 894 (95.1) 1836 (97.7) 1.00 1.00

<35 mg/d 19 (2.0) 22 (1.2) 1.77 (0.95–3.27)

2.25 (1.18–4.52)

≥35 mg/d 27 (2.9) 22 (1.2) 2.74 (1.50–5.01)

2.31 (1.20–4.45)

HS indicates hemorrhagic stroke; CI, confidence interval; and OR, odds ratio.*ORs and 95% CIs were calculated by unconditional logistic regression,

adjusted for age, a family history of stroke, a history of hypertension, a history of diabetes mellitus, upper respiratory tract infection within 30 days from zero-time, high salt intake, current smoking, daily tea intake, and phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days.

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Third, temporal-precedence bias might have affected our study. The bias refers to a systematic error in which an exposure is counted, although the exposure occurs after the onset of the disease under study, often in response to disease symptoms.27 Therefore, reverse causality occurs when the probability of the outcome is causally related to the exposure being studied. The most common scenario for use of CCMs was for treatment of headache, which is often a presenting and important symptom of brain hemorrhage. Moreover, CCMs are used as self-medicating drugs to relieve pain or cold symptoms, or to recover from fatigue. Individuals who frequently experience colds or fatigue may be in a relatively unhealthy condition. Although this study cannot assuredly avoid such bias, we compared the use of CCMs with CFMs with the same indication and found no increased risk of HS among CFM users.

ConclusionsResults from this nationwide multicenter case–control study suggest that caffeine in pharmaceutical products may increase the risk of HS, including both SAH and ICH. The increased risk was especially evident in the rarely drinks coffee group. Although these results are suggestive, further analysis should be performed to confirm the association.

DisclosuresNone.

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24. Choi NK, Park BJ, Jeong SW, Yu KH, Yoon BW. Nonaspirin nonste-roidal anti-inflammatory drugs and hemorrhagic stroke risk: the Acute Brain Bleeding Analysis study. Stroke. 2008;39:845–849.

25. Kalton G, Piesse A. Survey research methods in evaluation and case-control studies. Stat Med. 2007;26:1675–1687.

26. Choi NK, Hahn S, Yoon BW, Park BJ. Potential bias caused by con-trol selection in secondary data analysis: nonaspirin nonsteroidal anti-inflammatory drugs and hemorrhagic stroke. Pharmacoepidemiol Drug Saf. 2010;19:604–609.

27. Horwitz RI, Feinstein AR, Harvey MR. Case-control research. Temporal precedence and other problems of the exposure-disease relationship. Arch Intern Med. 1984;144:1257–1259.

Table 5. Association Between CCMs and the Risk of HS by Daily Coffee Intake

Daily Coffee Intake

Cases n=940 (%)

Controls n=1880 (%)

Crude OR (95% CI)

Adjusted OR (95% CI)*

<1 cup

No exposure 387 (94.6) 799 (98.0) 1.00 1.00

Exposure to CCMs

22 (5.4) 16 (2.0) 2.84 (1.47–5.46)

2.95 (1.45–5.98)

>1 cup and <3 cups

No exposure 320 (95.2) 706 (97.1) 1.00 1.00

Exposure to CCMs

16 (4.8) 21 (2.9) 1.68 (0.87–3.27)

1.59 (0.78–3.23)

≥3 cups

No exposure 187 (95.9) 331 (97.9) 1.00 1.00

Exposure to CCMs

8 (4.1) 7 (2.1) 2.02 (0.72–5.67)

1.63 (0.51–5.26)

CCM indicates caffeine-containing medicines; CI, confidence interval; HS, hemorrhagic stroke; and OR, odds ratio.

*ORs and 95% CIs were calculated by unconditional logistic regression, adjusted for age, a family history of stroke, a history of hypertension, a history of diabetes mellitus, upper respiratory tract infection within 30 days from zero-time, high salt intake, current smoking, daily tea intake, and phenylpropanolamine intake on the index day before zero-time or during the preceding 3 days.

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Caffeine-Containing Medicines Increase the Risk of Hemorrhagic Stroke

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1

"SUPPLEMENTAL MATERIAL"

Caffeine Containing Medicines Increase the Risk of Hemorrhagic Stroke

Seung-Mi Lee, PhD,1 Nam-Kyong Choi, PhD,2,3 Byung-Chul Lee, MD, PhD,4 Ki-Hyun Cho,

MD, PhD,5 Byung-Woo Yoon, MD, PhD,6 Byung-Joo Park, MD, PhD 2,7

1 Chung-Ang University College of Pharmacy; 2 Medical Research Collaborating Center,

Seoul National University Hospital, Seoul National University College of Medicine; 3

Institute of Environmental Medicine, Seoul National University Medical Research Center; 4

Department of Neurology, Hallym University College of Medicine; 5 Department of

Neurology, Chonnam National University Hospital; 6 Department of Neurology, Seoul

National University College of Medicine; 7 Department of Preventive Medicine, Seoul

National University College of Medicine, Republic of Korea

Seung-Mi Lee and Nam-Kyong Choi contributed equally to this paper.

Corresponding author: Byung-Joo Park MD, PhD

2

Supplemental Methods

Selection of Controls

The ABBA study was originally designed to investigate the risk of HS in relation to

PPA exposure in decongestants used as cold remedies. Thus, those departments’ patients were

considered as optimal hospital control groups that were not related to the use of cold

remedies to address the primary research question. Accordingly, a majority of the hospital

controls were selected from neurology (34.8%), orthopedic (30.4%), neurosurgery (5.7%),

and otolaryngology departments (5.5%).1,2

Community controls were selected from siblings, friends, or neighbors of each case

in descending order of preference. The community controls were selected from among

siblings (8.4%), friends (11.4%), and neighbors (78.6%).

Data Collection

We ascertained an exposure history to medication before the index date for all

prescribed and non-prescribed drugs and collected information related to drug brand names,

generic names, indications, and the first date of administration, dose, and duration. We also

tried to obtain the doctor’s prescriptions from all study subjects. When prescriptions were

unavailable or medications were purchased without prescriptions, the subjects were asked to

bring in the medication packages. If these packages were unavailable, we asked the subjects

to provide the exact name of the drug and manufacturer, and select their medications from a

set of real samples of drugs and a sample book of photographs of drug packages. If the

subjects could not select their products from the sample book, we asked their pharmacists by

3

telephone. We particularly included most of the pain relievers and cold remedies, regardless

of caffeine content. Only patients who had verified exposures to medication were counted in

the analysis.

Definition of Caffeine Containing Medicines

Most of the CCMs contained certain active ingredients according to their indication.

The pain relieving, cold remedy, and fatigue restorative CCMs contained acetaminophen,

sympathomimetic agents, and taurine, respectively. Therefore, to improve comparability, the

CFMs for cold remediation were defined as drugs containing sympathomimetic agents

without caffeine; those for treating fatigue were defined as drugs containing taurine without

caffeine; and those for pain relief were defined as drugs containing acetaminophen without

caffeine.

Supplemental Results

Compared to the control subjects, case patients were more likely to have a family

history of stroke, a history of hypertension, an upper respiratory infection within 30 days

prior to the index date, be current smokers, current alcohol drinkers, frequent spicy food

intake, have a high salt intake, work ≥ eight hours a day, and perform laborious work ≥ seven

hours a day; however, they were less likely to have a history of hyperlipidemia and daily tea

intake (Table I).

Supplemental Discussion

Recall bias at this point may have affected the results; therefore, to minimize this

4

possibility, we kept the interviewers and participants blind to the major study hypothesis. The

information on use of non-prescription medication from pharmacies depended on the

participants’ memories. Packages of major non-prescription drugs were used to aid recall of

brand names, but failed to cover all drugs, including CCMs, on the market. However, we

included the packages of many frequently used non-prescription drugs, and the effect of

participant use of missing drugs is likely negligible within our study.

The selection of non-population-based controls from sources such as hospital

admissions or discharges, the same primary care provider, friends or relatives, risks the

possibility that the selection may be related to some of the factors under study.3 However,

population-based controls are often identified by telephone surveys using random digit

dialing (RDD) sampling methods, which are now experiencing serious problems with

response rates.4 A recent alternative approach involved selecting controls from frames such as

driver license lists that contain valuable demographic information for use in matching.

Moreover, the screening costs can be particularly high when the controls are selected by a

face-to-face interview survey based on an area probability sample design.3 To that end, some

researchers have suggested choosing more than one control group and in such studies,

controls can be selected from non-hospitalized persons living in the community or from

hospitalized patients admitted for diseases other than that for which the cases were admitted.5

In this study, in-depth face-to-face interviews had to be performed within the time period.

Therefore, we were forced to choose between hospital controls and those from among friends

or relatives. When performing secondary analysis, extra care should be taken to note whether

the results are consistent across control groups.2

5

Supplemental References

1. Yoon BW, Bae HJ, Hong KS, Lee SM, Park BJ, Yu KH, et al. Phenylpropanolamine

contained in cold remedies and risk of hemorrhagic stroke. Neurology 2007;68:146-149.

2. Choi NK, Hahn S, Yoon BW, Park BJ. Potential bias caused by control selection in

secondary data analysis: nonaspirin nonsteroidal anti-inflammatory drugs and

hemorrhagic stroke. Pharmacoepidemiol Drug Saf. 2010;19:604-609.

3. Kalton G, Piesse A. Survey research methods in evaluation and case-control studies.

Stat Med. 2007;26:1675-1687

4. Parfrey PS. Validity of random-digit-dialing in recruiting controls in a case-control

study. Am J Health Behav. 2009;33:513-520.

5. Ibrahim MA, Spitzer WO. The case control study: the problem and the prospect. J

Chronic Dis. 1979;32:139-144.

6

Supplemental Table I. The list of caffeine containing medicines that the study subjects took

within three days from the index date

Brand name Indication Formula Caffeine content

(mg/unit) Maximum daily dose

(mg/day) Codemil Cold Tablet 10 60 Facol Cold Capsule 10 60 Neomedicough Cold Capsule 10 60 Socoltoben Cold Capsule 10 60 Muscol Cold Capsule 15 90 Rapicol Cold Capsule 15 90 Tapcol F Cold Capsule 15 90 Tobecol Cold Capsule 15 90 Codenong Cold Tablet 20 120 Pancold A Cold Capsule 20 120 Panpyrin F Cold Capsule 20 120 Sincol Cold Capsule 20 120 Whituben Cold Capsule 20 120 Maparam Cold Powder 30 90 Pancold A Cold Solution 30 90 Panpyrin F Cold Tablet 30 90 Panpyrin F Cold Solution 30 90 Panto A Cold Solution 30 90 Sinoca Cold Capsule 30 90 Golgen Cold Capsule 40 120 Bacchus F Fatigue Solution 30 30 Noesin Pain Powder 30 90 Amcilon Pain Tablet 50 150 Geworin Pain Tablet 50 150 Neusun Pain Powder 50 150 Penzal Pain Tablet 50 150 Saridon Pain Tablet 50 150