Abstract
Adequate information about characteristics of asthmatic patients in large health maintenance organizations (HMOs) is still lacking. As part of an ongoing longitudinal study, baseline data were collected on 914 individuals aged 3 to 55 yr with physician-diagnosed asthma within a large HMO, Kaiser Permanente, NW Region. There were no significant differences between men and women in post-bronchodilator FEV1 when expressed as percent (%) predicted yet women with asthma reported more daytime and nocturnal symptoms than men (p = 0.002), and worse quality of life in all but three of 14 subscales in two asthma quality of life instruments. Specifically, women in the 35–55 yr age group uniformly reported worse physical functioning on the SF-36 quality of life scale (71 ± 23 versus 85 ± 18; p = 0.001), social functioning (73 ± 21 versus 77 ± 20; p = 0.016), and bodily pain (63 ± 27 versus 72 ± 24; p < 0.001). Also these women reported use of more health care (p = 0.002) and more medications for asthma than men (p < 0.01). Our data suggest that men and women respond differently to their asthma, and observed gender differences in various measures of asthma such as hospital admissions, quality of life, and use of metered dose inhalers (MDIs), may be related to this difference in response to disease, rather than to real differences in the disease between men and women. Understanding gender related differences in response to a chronic disease such as asthma is important in tailoring an education and management plan to each individual patient.
Current data suggest there are age and gender differences in asthma. These differences have been demonstrated specifically for hospital admissions, quality of life, and use of metered dose inhalers (MDIs). Skobeloff and coworkers have demonstrated that hospital admission rates are higher for pre-pubertal boys than girls, yet higher in adult females than males (1). Using the SF-36 questionnaire, Bousquet and colleagues have observed that males report significantly higher (better) QOL scores than females (2). A recent study addressing use of MDIs concluded that females of all ages are more likely to have improper MDI technique than males. Such gender differences may reflect differences in biology, physiology, or exposures, or alternatively reflect behavioral differences or differences in asthma management (3). Still missing, therefore, are adequate data about the natural history of asthma and other important characteristics of asthmatic patients, such as patterns of medication use, health care utilization, quality-of-life, symptom profiles, and how these characteristics vary by age and gender. Such information will expand our understanding of this common and costly disease and provide information that is especially useful to those responsible for planning health care delivery within the managed care setting.
We report on data from 914 patients with asthma within a large health maintenance organization (HMO), Kaiser Permanente, NW Region who are participating in a longitudinal study of the risk factors for hospitalization. This paper presents descriptive data on characteristics of this population, including characteristics of asthma such as self-reported patterns of medication use, symptoms, exposure to aeroallergens, health care utilization, severity, and quality of life; as well as objective measures of lung function, skin test responsiveness, and observed use of metered dose inhalers (MDIs). We also contrast the distribution of these characteristics by age and sex.
Participants were members of a large health care maintenance organization (HMO), Kaiser Permanente, Northwest Region (KP), who were either hospitalized for asthma during the two years prior to recruitment or had at least two dispensings of anti-asthma medications in the year prior to recruitment. At the time of recruitment, all participants reported having physician-diagnosed asthma and indicated that they experienced ongoing symptoms consistent with asthma. By design, participants ranged from 3–55 yr of age. Our goal was to recruit approximately equal numbers of males and females in each of the three age groups and a total of approximately 266 in each age group. A total of 914 participants enrolled in the study.
We report on baseline, cross-sectional data collected as part of a cohort study to characterize risk factors for episodes of hospital-based asthma care within an HMO. The baseline assessment included questionnaires to obtain information about characteristics of the participants and their asthma, skin prick testing to inhalant allergens, spirometry before and five minutes after administration of two puffs of isoproterenol, and observation of MDI use. Not everyone completed all aspects of the protocol. For example, we did not conduct spirometry on children under the age of six, or skin prick tests on children whose parents declined the skin prick tests for them.
We developed two questionnaires: one for the 3–14 age group (completed by parents) and one for those aged 15–55 yr. The questionnaires included data on: respiratory symptoms, characteristics of asthma, demographic factors, tobacco use, allergen exposures, and medication use. Much of this information was obtained by incorporating relevant sections of the ATS-DLD 1978 respiratory symptom questionnaire (4) and the IUATLD bronchial symptoms questionnaire (5). Questions about symptoms were adapted from the National Asthma Education Program (NAEP) Expert Panel Guidelines (6).
Each questionnaire included both a generic and a disease-specific measure of health status. For children, the pediatric questionnaire included the Rand Child Health Status scale (7) and Stein's Functional Status II-R scale (8). The Rand scale is a seven-item scale that measures general well-being; the Stein scale asks about functional status in a general sense, but relates limitations to specific diseases. For those aged 15–55 yr, we used the SF-36 health status questionnaire (9) and the asthma quality of life questionnaire developed by Marks and coworkers (10). Scores from the SF-36 questionnaire, a well-validated instrument for general health measures, have been shown to significantly correlate with severity of asthma, to have a high internal reliability (alpha = 0.91), and can be used to examine quality of life in asthma (2).
Spirometry was performed using standardized methods with equipment that met or exceeded American Thoracic Society (ATS) requirements (11, 12). The best one-second forced expiratory volume (FEV1) was chosen for analysis and expressed as percent predicted FEV1 (FEV1%) using the prediction equations of Knudson and coworkers (13). MDI use was observed and technique was evaluated according to a ten point scale developed by Manzella and colleagues (14). If a participant did not use an MDI the observation was not done.
We conducted skin prick testing using 13 inhalant allergens appropriate for the Pacific Northwest: alder; birch; juniper; grass; western weed; cat; dog; mite (Dermatophagoides pteronyssinus and Dermatophagoides farinae); alternaria; cladosporium; aspergillus; and penicillium (Hollister-Stier). We also included a positive control (histamine) and two negative controls (saline and a “dry” prick control). The allergens were applied using standard prick test techniques (15). Twenty minutes after the last prick, the technician carefully circled each wheel with an ink pen, placed cellotape over the mark, removed the tape, and placed it onto the data entry form. Two diameters were measured from the tape record: the widest diameter and the diameter at a right angle to the widest diameter.
Of those for whom skin prick test data are available, 99.5% had positive reactions (at least one diameter > 3 mm) to the histamine control. Only one percent of subjects responded to the dry prick control, which was applied following the final allergen (western weed mix). Responses to the saline control varied. Initially this was applied immediately following the dust mite (D. pteronyssinus) allergen. Following unusually high response rates, a second saline was added following the western weed mix. Response rates to these two saline controls were 27% and 8%, respectively. An analysis based on the difference of wheal-saline wheal gave the same results. Using a method patterned after that of Barbee and colleagues (16), we computed a continuous skin test score.
All analyses were performed using the Statistical Analysis System (SAS) statistical software package. We used standard methods for analyzing contingency tables. p Values are based on the Pearson chi-square statistic and, for tests of trend, on the Mantel-Haenszel chi-square statistic (17). Maximum likelihood methods for log-linear and logistic models were used to evaluate associations while controlling for other factors. We used analysis of covariance to examine the joint effects of multiple variables on the MDI use and quality-of-life scores. Unless otherwise stated, all p values are two-sided and the term “significant” indicates a p value < 0.05.
Table 1 demonstrates the demographics of the sample. There were approximately 60% males in the youngest age group (3– 14 yr) and 60% females in the other two age groups. In all age groups, the percentage of nonwhites was small (6–13%), reflecting the low prevalence of minorities in the community. Compared to the population from which we attempted to recruit, study participants were older (28.7 versus 25.8 mean age p < 0.0001), but were similar in terms of gender or self-reported airflow obstruction (chronic bronchitis, emphysema, or chronic obstructive pulmonary disease). Response rates were 31% in the 3–14 age group, 30% in the 15–34 age group, and 43% in the 35–55 age group.
| Age Group (years) | ||||||
|---|---|---|---|---|---|---|
| 3–14 (n = 271) | 15–34 (n = 226) | 35–55 (n = 417) | ||||
| Gender | ||||||
| Male, % | 58 | 39 | 41 | |||
| Female, % | 42 | 61 | 59 | |||
| Household income | ||||||
| < 30,000, % | 23 | 20 | 21 | |||
| 30–39,999, % | 26 | 28 | 19 | |||
| 40–49,999, % | 17 | 21 | 18 | |||
| 50–59,999, % | 19 | 20 | 21 | |||
| 60+, % | 14 | 12 | 21 | |||
| Race | ||||||
| White, % | 87 | 92 | 94 | |||
| Other, % | 13 | 8 | 6 | |||
Table 2 presents the distribution of smoking status for subjects aged 15–55 yr. As expected, the prevalence of ever having smoked increases with age, although the overall prevalence of current smoking (11%) was similar for subjects aged 15–34 yr and those aged 35–55 yr. After adjusting for age, reported smoking patterns differed significantly for males and females with current smoking twice as frequent in females as in males.
Table 3 shows characteristics of asthma including prevalence of symptoms, pulmonary function and skin test responsiveness. All differed significantly by age and sex. For self-reported prevalences of daytime and/or nocturnal symptoms, younger participants reported fewer symptom days than older participants and males reported fewer symptom days than females. About half of the younger participants (3–14 yr) reported 0–1 symptom d/wk, whereas at least two-thirds of women aged 15– 55 yr and men aged 35–55 reported 2 or more days with symptoms per week. One third of the men aged 35–55 reported daily symptoms whereas almost half the women did. Specifically, men 15–34 yr reported fewer symptoms than women (p < 0.001) and, a similar trend was seen in the 35–55 yr old group, but did not reach significance (p = 0.083).
| Age Group (years) | p Value* for Sex Effect | p Value* for Age Effect | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3–14 | 15–34 | 35–55 | ||||||||||||||
| M | F | M | F | M | F | |||||||||||
| Prevalence of daytime and/or nocturnal symptoms in last month | (n = 156) | (n = 115) | (n = 89) | (n = 137) | (n = 171) | (n = 246) | ||||||||||
| ⩽ 1/wk, % | 56 | 48 | 48 | 27 | 32 | 27 | 0.002 | < 0.001 | ||||||||
| 2–6/wk, % | 31 | 37 | 36 | 42 | 32 | 28 | ||||||||||
| Daily | 13 | 16 | 16 | 31 | 36 | 45 | ||||||||||
| Pulmonary function | (n = 120) | (n = 85) | (n = 88) | (n = 137) | (n = 161) | (n = 240) | ||||||||||
| Pre-bronchodilator | ||||||||||||||||
| FEV1, % ⩾ 80% | 80 | 89 | 85 | 81 | 51 | 63 | 0.034 | 0.001 | ||||||||
| FEV1, % 60–80% | 16 | 11 | 11 | 15 | 25 | 18 | ||||||||||
| FEV1, % < 60% | 4 | 0 | 3 | 4 | 24 | 19 | ||||||||||
| Post-bronchodilator | (n = 126) | (n = 82) | (n = 86) | (n = 133) | (n = 157) | (n = 237) | ||||||||||
| % change in FEV1, mean ± SD | 7.5 ± 10.6 | 6.9 ± 7.1 | 6.7 ± 7.0 | 8.2 ± 9.1 | 11.4 ± 14.0 | 13.7 ± 19.9 | 0.306 | < 0.001 | ||||||||
| Skin prick test responsiveness | (n = 131) | (n = 94) | (n = 88) | (n = 136) | (n = 165) | (n = 237) | ||||||||||
| Skin test score | ||||||||||||||||
| 0–2, % | 21 | 34 | 14 | 20 | 19 | 32 | 0.011 | 0.010 | ||||||||
| 3–10, % | 37 | 30 | 27 | 21 | 19 | 22 | ||||||||||
| 11–22, % | 26 | 21 | 31 | 26 | 25 | 22 | ||||||||||
| 23+, % | 16 | 15 | 28 | 32 | 36 | 25 | ||||||||||
| % positive skin test | 86 | 76 | 88 | 85 | 90 | 79 | 0.002 | 0.255 | ||||||||
For pulmonary function, expressed as prebronchodilator FEV1, % predicted, and bronchodilator response, both the percent of participants with an FEV1 < 80% predicted and the bronchodilator response increased with age. Less than 15% of participants under 35 yr had an FEV1 < 80%. There were differences between men and women in FEV1% predicted, with men having a lower FEV1% predicted than women. After bronchodilator, however, there were no differences between the two.
Finally, approximately 80% of the subjects had at least one positive skin test (> 3 mm). The skin test score, a measure of the size of the reaction, increased with age in both males and females (p < 0.01). However, the increase in males was significantly greater (p < 0.011) than that in females such that in the 35–55 yr range, more than one third of males comprised the upper quartile (wheal size) as compared to one fourth of females.
Table 4 presents data on medications used for asthma within the last 12 mo and self-reported health care utilization (HCU). Virtually all individuals reported using beta agonist medications. Significantly more older participants (about 10%) reported using > 8 puffs daily. Of interest, there were wide variations in the use of non–beta agonist medication. About two thirds of participants were using anti-inflammatory agents, although the type of anti-inflammatory agent varied with age. Younger participants were significantly more likely to use cromolyn (p < 0.001), whereas older participants were more likely to use inhaled corticosteroids, (p < 0.001) particularly older females. These differences persisted even after adjusting for smoking status (never versus current/ex) (p < 0.05). “Oral steroids” refers primarily to the use of corticosteroids for exacerbations of asthma. Only 11 participants reported taking daily oral steroids. Very few individuals reported taking anti-cholinergic medication for asthma.
| Age Group (years) | p Value for Sex Effect* | p Value for Age Effect* | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3–14 | 15–34 | 35–55 | ||||||||||||||
| M | F | M | F | M | F | |||||||||||
| Medication in last 12 mo | (n = 156) | (n = 115) | (n = 89) | (n = 137) | (n = 171) | (n = 246) | ||||||||||
| β-agonist, % | 97 | 97 | 97 | 97 | 87 | 95 | 0.025 | < 0.001 | ||||||||
| > 8 puffs/d of MDI β-agonist, % | 4 | 2 | 2 | 6 | 8 | 13 | 0.123 | < 0.001 | ||||||||
| Cromolyn, % | 19 | 15 | 17 | 14 | 9 | 8 | 0.365 | < 0.001 | ||||||||
| Inhaled corticosteroids, % | 20 | 19 | 24 | 36 | 50 | 67 | 0.002 | < 0.001 | ||||||||
| Oral steroids, % | 30 | 27 | 20 | 25 | 15 | 30 | 0.020 | 0.194 | ||||||||
| Anticholinergic, % | 0 | 1 | 0 | 1 | 4 | 7 | 0.088 | < 0.001 | ||||||||
| Theophylline, % | 6 | 4 | 24 | 18 | 21 | 30 | 0.330 | < 0.001 | ||||||||
| Health Care Utilization | (n = 156) | (n = 115) | (n = 89) | (n = 137) | (n = 171) | (n = 246) | ||||||||||
| Ever treated in ER, % | 54 | 50 | 56 | 56 | 47 | 61 | 0.112 | 0.944 | ||||||||
| Ever hospitalized for asthma, % | 26 | 19 | 31 | 24 | 19 | 34 | 0.336 | 0.136 | ||||||||
| Hospitalized in last year, % | 5 | 3 | 2 | 3 | 1 | 2 | 0.695 | 0.163 | ||||||||
Over 50% of participants reported ever having had emergency room treatment for asthma, and up to one-third reported a previous asthma-related hospitalization, although less than 5% reported being hospitalized within the last year. In general, these patterns did not differ based on age or gender. In the 35–55 yr old age group, however, significantly more females than males reported emergency room visits (p < 0.007).
Table 5 demonstrates quality-of-life reported by adult participants in the study according to both the Asthma Quality-of-Life score (AQLS) and the SF-36 score. The AQLS ranges from 1–10, with 1 being the best quality of life score, whereas the SF-36 score ranges from 0–100 with 100 being the best quality of life score. On both scales, younger individuals (15– 34 yr) reported a better quality of life than those who were older (35–55 yr), and a better quality of life was reported by males than females for both age ranges. The gender differences were seen in all four subscales of the AQLS (p values ranging from < 0.001 to 0.006). The SF-36 score showed similar results. Men report significantly better quality of life than women on all but the “role emotional,” “mental health,” and “change in health” scales. For the latter two scales the trend was in the same direction. In children (3–14 yr), in contrast, two quality of life assessment instruments did not reveal significant gender differences (data not shown).
| Age Group (years) | p Value* for Sex Effect | p Value* for Age Effect | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Males | Females | |||||||||||
| 15–34 (n = 80) | 35–55 (n = 171) | 15–34 (n = 129) | 35–55 (n = 246) | |||||||||
| Asthma Specific QOL Scores: (lower value indicates quality of life) | ||||||||||||
| Breathlessness | 1.8 ± 1.6 | 2.0 ± 1.6 | 2.6 ± 1.8 | 3.0 ± 2.0 | < 0.001 | 0.011 | ||||||
| Mood | 1.9 ± 1.4 | 1.9 ± 1.8 | 2.2 ± 1.9 | 2.4 ± 2.0 | 0.005 | 0.258 | ||||||
| Social | 1.1 ± 1.4 | 1.2 ± 1.6 | 1.5 ± 1.8 | 1.6 ± 1.9 | 0.003 | 0.140 | ||||||
| Concerns | 1.2 ± 1.2 | 1.5 ± 1.5 | 1.6 ± 1.6 | 1.9 ± 1.9 | 0.006 | 0.015 | ||||||
| Total | 1.5 ± 1.2 | 1.7 ± 1.4 | 2.1 ± 1.5 | 2.3 ± 1.7 | < 0.001 | 0.026 | ||||||
| SF-36 Scores: (higher value indicates better quality of life) | ||||||||||||
| Physical functioning | 90 ± 12 | 85 ± 18 | 77 ± 16 | 71 ± 23 | < 0.001 | < 0.001 | ||||||
| Social functioning | 78 ± 15 | 77 ± 20 | 75 ± 18 | 73 ± 21 | 0.016 | 0.562 | ||||||
| Role physical | 78 ± 32 | 69 ± 39 | 70 ± 38 | 61 ± 41 | 0.015 | 0.008 | ||||||
| Role emotional | 79 ± 34 | 75 ± 38 | 74 ± 37 | 75 ± 37 | 0.483 | 0.523 | ||||||
| Mental health | 75 ± 15 | 76 ± 18 | 71 ± 18 | 74 ± 18 | 0.083 | 0.288 | ||||||
| Vitality | 61 ± 16 | 58 ± 22 | 53 ± 21 | 51 ± 22 | < 0.001 | 0.020 | ||||||
| Bodily pain | 76 ± 18 | 72 ± 24 | 68 ± 23 | 63 ± 27 | < 0.001 | 0.007 | ||||||
| General health | 70 ± 15 | 66 ± 22 | 59 ± 22 | 60 ± 22 | < 0.001 | 0.631 | ||||||
| Change in health | 62 ± 23 | 55 ± 19 | 63 ± 24 | 58 ± 23 | 0.148 | 0.001 | ||||||
MDI use scores were compared according to age and sex. The potential range of scores is 0–10. The overall score for use of the MDI suggests that about 71% of the activities required for successful use were done properly. There was no difference in use across the three age ranges 3–14, 15–34, and 35–55 (data not shown). The gender differences were very small, with lower scores among males (p < 0.005).
We have described 914 patients with asthma within a large HMO, a predominantly Caucasian middle class population recruited through a pharmacy database. The most important findings are that women with asthma report more symptoms and poorer quality-of-life than do men, although measures of airflow obstruction are comparable. It is important to point out that these findings are generalizable specifically to patients with asthma who require anti-asthma medications. It is very likely that the patients in our study have more severe asthma than patients who do not require asthma medications. We chose this group deliberately, recognizing that very little information is available on characteristics of such patients, yet they require significant health care resources (18).
Since interpretation of these findings could be limited by generalizability of the data, we specifically have addressed several potential limitations. First, we compared the study participants to all potential participants on anti-asthma medications with doctor-diagnosed asthma who refused to participate in the study. Compared with all potential participants, study participants were older, but were similar in terms of gender or self-reported airflow obstruction. Second, although we deliberately tried to recruit equal numbers of participants in each age-sex subgroup, differential response rates resulted in substantially more participants in the 35–55 yr old group and an unequal gender distribution across the three age groups with more males in the 3–14 age range, and more females in the 15–55 age range. This gender distribution, however, is consistent with the distribution of asthma prevalence in the U.S.: in children, asthma is more prevalent in boys than in girls with the gender ratio changing in the adult years. Since several hundred participants were recruited in each age category, we remain confident that the data are generalizable.
We acknowledge that our findings in the older group may be limited by the overlap between asthma and chronic obstructive pulmonary disease (COPD). Asthma in the older adults is a poorly understood condition that seems, in many, to combine the classic features of asthma (reversible airflow obstruction) with features more common to chronic obstructive pulmonary disease (irreversible airflow obstruction) (19). Consequently, following asthma is difficult in the elderly because of the misclassification of smoking-related COPD. In a previous study, we performed a chart review of asthma contacts within this HMO and found that COPD/asthma accounted for about 12% of all charts listing asthma as a diagnosis (20). To minimize overlap in the current study, we required all participants to have physician-diagnosed asthma (21). We felt this definition of asthma was well-accepted and appropriate for a cross-sectional analysis of patients with asthma within this HMO.
Our results indicate that women report more symptoms, and experience poorer quality-of-life than do men. One possible interpretation for these results is that women have more severe disease than males. However, several pieces of evidence suggest this is not the case. First, males had a lower prebronchodilator FEV1% predicted than females; there was no difference between men and women in post-bronchodilator FEV1. Although the FEV1 is a single snapshot in time, we would have expected to see a lower baseline FEV1% predicted in females than males if women had more severe disease than males. Second, self-reported symptoms did not correlate with either FEV1% predicted or use of “burst” oral steroids, both accepted measures of asthma severity.
Another interpretation is that the gender difference is due to a questionnaire bias. Since there is no gold standard for symptom scores or quality of life in asthma, we have only indirect ways to address this. First the symptom questionnaires are standard and identical for men and women. Also, there is internal consistency in the quality of life data in that the SF-36 showed a pattern of impaired health between males and females similar to the disease-specific instrument.
Yet another interpretation of these results is that for the same level of airflow obstruction women seek medical attention more frequently than do men. Consistent with this hypothesis, women tend to make more office visits, receive greater numbers of medical tests, and are prescribed more medications than are men across all diseases (24). Also, adult women tend to be more frequently hospitalized than men across all diseases (25), and for asthma (1, 3). If women seek more medical attention than men for the same level of air flow obstruction, one explanation is that women actually experience greater discomfort and as a consequence report more symptoms, take more anti-asthma medication, and seek more health care than men. The fact that women report poorer quality of life than men across several dimensions of the RAND SF-36 suggests that women perceive the same level of airflow obstruction differently than men, and that this impacts their daily living. Although less likely, it is possible that men perceive their asthma similarly to women, yet do not seek medical attention for it with the frequency that women do, nor report worse quality-of-life.
Our data demonstrated that older asthmatics, particularly females, reported using inhaled corticosteroids and beta agonists more than younger asthmatics. The fact that 9% of all participants aged 3–55 reported using more than 8 puffs/day of MDI beta agonists raises concern abut possible patient misuse of medications and/or over prescription.
It is possible that the increased medication use by females reflects the inability to correctly use an MDI as compared to males. However, females were observed to use MDIs at least marginally better than males according a 10 point observational scale developed by Manzella and colleagues (14). Although, Goodman and coworkers have demonstrated poorer MDI technique by females than males (26), their study population which included patients with COPD was different from ours, and their methodology was different, perhaps more accurate. Goodman and associates used a miniature sensing system to define an acceptable maneuver by four components: (1) inspiratory flow at actuation, (2) actuation during early inspiration, (3) adequate breath holding time, and (4) a deep inhalation. We simply do not have enough information to distinguish between these possibilities.
Our data demonstrated that skin test responsiveness increased with age. These data are consistent with the concept that asthma is more severe and is correlated with greater allergic responsiveness in older patients compared to younger patients. It is not clear why skin prick test responsiveness should increase with age, although it may simply reflect increasing lifetime exposure to environmental antigens. It is well established that skin test responsiveness wanes later in life (> 55 yr) as has been corroborated by large epidemiological studies in this country and abroad (27, 28).
Although our data do not address the best ways to minimize symptoms and improve quality of life, they do suggest several important implications for practice guidelines. The lack of agreement between symptoms and objective measures of severity, particularly in females, underscores the importance of clearly documenting both symptoms and the extent of airflow obstruction in asthma. The use of a peak flowmeter or an asthma diary might be particularly helpful in women who report frequent symptoms as a way of allowing them to monitor their asthma and adjust their medication, thereby allowing them to feel more in control. Second, the overuse of beta-agonists could be used to identify a subgroup of patients in which asthma is under-treated with anti-inflammatory agents. Third, it is essential to insure that all patients use MDIs correctly, since this might also decrease medication usage. Fourth, allergen avoidance should be routinely discussed with patients and their specific allergen sensitivities should be targeted. Fifth, specific action plans to manage changes in asthma symptoms and signs should be introduced to decrease unscheduled health care utilization.
Although there are no data confirming that focusing asthma education on women decreases health care utilization, it seems a reasonable first step. Also, it may be appropriate to consider use of peak flow meters in patients whose symptoms do not seem to correlate with the degree of airflow obstruction. Finally, asthma education programs might be particularly helpful in women. Guidelines are being developed for such programs (6, 29). A recent study of patients with moderate to severe asthma demonstrated that self-management education programs were associated with significant improvements in control of asthma symptoms, MDI technique, and environmental control practices (30). In summary, we have demonstrated important differences in the characteristics of asthma among men and women of a large health maintenance organization. These differences may have implications for asthma management in a managed care setting.
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This project supported by NIH Grant #HL 48237.
