Consumer attitudes and use of antibiotics.Recent antibiotic use is a risk factor for infection or colonization colonization, extension of political and economic control over an area by a state whose nationals have occupied the area and usually possess organizational or technological superiority over the native population. with resistant bacterial pathogens. Demand for antibiotics can be affected by consumers' knowledge, attitudes, and practices. In 1998-1999, the Foodborne Diseases Active Surveillance Network (FoodNet) conducted a population-based, random-digit dialing telephone survey, including questions regarding respondents' knowledge, attitudes, and practices of antibiotic use. Twelve percent had recently taken antibiotics; 27% believed that taking antibiotics when they had a cold made them better more quickly, 32% believed that taking antibiotics when they had a cold prevented more serious illness, and 48% expected a prescription for antibiotics when they were ill enough from a cold to seek medical attention. These misguided beliefs and expectations were associated with a lack of awareness of the dangers of antibiotic use; 58% of patients were not aware of the possible health dangers. National educational efforts are needed to address these issues if patient demand for antibiotics is to be reduced.
Antimicrobial antimicrobial /an·ti·mi·cro·bi·al/ (-mi-kro´be-al)
1. killing microorganisms or suppressing their multiplication or growth.
2. an agent with such effects. resistance is a rapidly increasing problem in the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. and worldwide. A well-documented risk factor for infection or colonization with resistant bacterial pathogens is recent antibiotic use, particularly within 4 weeks or 1 month before exposure (1-6). As a result, one of the primary strategies to prevent and control the emergence and spread of resistant organisms is to reduce the selective pressure of overuse overuse Health care The common use of a particular intervention even when the benefits of the intervention don't justify the potential harm or cost–eg, prescribing antibiotics for a probable viral URI. Cf Misuse, Underuse. and misuse of antibiotics in human medicine (7).
Several studies have identified and examined specific causes of the misuse of antibiotics, including unnecessary prescribing (8-14) and patient demand (15-17). Factors contributing to inappropriate prescribing practices have been elucidated. In particular, numerous studies of adults have shown that patients' expectations or physicians' perceptions of those expectations affect the physicians' prescribing behavior (10,13,16-24).
To solve the problem of antibiotic misuse, a more thorough understanding of what influences the development and expression of patients' expectations must be gained. Understanding patients' knowledge, attitude, and practices may facilitate more effective communication between the clinician and patient, as well as aid in the development of strategies to educate patients and the public (25). Several lines of evidence suggest educational interventions directed at patients and clinicians can increase patients' knowledge and awareness, as well as reduce the frequency with which clinicians prescribe antibiotics inappropriately (26-30).
Our investigation, an analysis of data from a national population-based cross-sectional survey, provides a glimpse of the current knowledge, attitudes, and practices regarding antibiotic use among patients. We also attempt to identify demographic characteristics associated with particular knowledge, attitude, and practices and to determine whether a person's attitudes toward and knowledge of risks associated with taking antibiotics are associated with recent antibiotic use. Identifying subgroups of the population with high levels of antibiotic use and with misconceptions about antibiotic use will help public health officials target and track the impact of interventions. Other information obtained from this population-based survey will provide further insight for the development and evaluation of health education and prevention strategies.
From February 2, 1998, through February 15, 1999, the Emerging Infections Program's Foodborne Diseases Active Surveillance Network (FoodNet) conducted a telephone-based population survey in Connecticut, Minnesota, and Oregon, and selected counties in California The U.S. state of California is divided into fifty-eight counties. Counties are responsible for all elections, property-tax collection, maintenance of public records such as deeds, and local-level courts within their borders, as well as providing law enforcement (through the county , Georgia, Maryland, and New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of (total population 29 million). Each month, approximately 150 residents in each state were interviewed. After screening to remove business and nonworking telephone numbers, an outside contractor outside contractor n → contratista m/f independiente contacted respondents by telephone using a random-digit-dialing, single-stage sampling method (31).
These contractors conducted the interviews using methods similar to those used in the Behavioral Risk Factor Surveillance System The Behavioral Risk Factor Surveillance System (BRFSS) is a United States national health survey that looks at behavioral risk factors. It is run by Centers for Disease Control and Prevention and conducted by the individual states. (32). All interviews were conducted in English. Using a standardized questionnaire, they asked one respondent per household about his or her knowledge, attitudes, and recent practices regarding antibiotic use. All members of the household were eligible for selection. Institutional review boards at the Centers for Disease Control and Prevention Centers for Disease Control and Prevention (CDC), agency of the U.S. Public Health Service since 1973, with headquarters in Atlanta; it was established in 1946 as the Communicable Disease Center. and all participating states approved the study.
Interviewers obtained verbal consent from all study participants before administering the questionnaire. They informed participants why the information was being collected, and how it would be used, and read them a statement informing them that their participation was voluntary before the start of the interview. No personal identifiers were included in this dataset.
Five items (two questions and three statements) addressing participants' knowledge, attitudes, and practices regarding antibiotic use were included in the survey. Recent antibiotic use referred to antibiotic use in the past 4 weeks. Respondents who took an antibiotic were asked whether the antibiotic was prescribed by their physician for a current illness or for a previous illness or if the antibiotic was prescribed for someone else. For the question, "Are you aware of any health dangers to yourself or other people associated with taking antibiotics?" respondents' knowledge of health dangers associated with taking antibiotics was classified into the following categories: emerging drug resistance, allergies/reactions, antibiotics may kill "friendly"/"good" microbes, it is unhealthy to take drugs/chemicals in general, misuse/overuse of antibiotics, multiple reasons, other, don't know Don't know (DK, DKed)
"Don't know the trade." A Street expression used whenever one party lacks knowledge of a trade or receives conflicting instructions from the other party. , or refused. Answers to survey items 1 and 5 were yes/no. For statements 2, 3, and 4, participants were asked to respond according to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. the following 5-point Likert scale Likert scale A subjective scoring system that allows a person being surveyed to quantify likes and preferences on a 5-point scale, with 1 being the least important, relevant, interesting, most ho-hum, or other, and 5 being most excellent, yeehah important, etc : 1=strongly agree, 2=agree somewhat, 3=unsure, 4=disagree somewhat, and 5=strongly disagree. We classified those who answered "strongly agree" or "agree somewhat" to the antibiotic knowledge questions as having agreed and those who answered "strongly disagree" or "disagree somewhat" as having disagreed. Those who refused to answer a question were not included in the analysis.
In addition to eliciting participants' responses to these questions, the survey also recorded demographic characteristics of the participants, including their sex, age, income level, education, race, state, and place of residence. Respondents' place of residence was categorized cat·e·go·rize
tr.v. cat·e·go·rized, cat·e·go·riz·ing, cat·e·go·riz·es
To put into a category or categories; classify.
cat as urban if they reported living in a city or town of [greater than or equal to] 50,000 residents. Presence of children in the household (yes/no), month of interview, and medical insurance status were also recorded. Respondents were classified as being "with insurance" if they reported any of the following as their type of insurance: health maintenance organization, preferred provider organization pre·ferred provider organization
Abbr. PPO A medical insurance plan in which members receive more coverage if they choose health care providers approved by or affiliated with the plan. , traditional indemnity insurance indemnity insurance Managed care A type of health insurance in which a Pt can choose the hospital and provider, and the insurer reimburses the Pt or provider for a set percentage of the cost, minus deductibles and co-payments , Medicaid, Medicare, or other. If respondents reported their type of insurance as "don't know" or if they refused to answer the question, they were not included in the analysis.
To simplify our analysis, we coded persons indicating Hispanic ethnicity as Hispanic, even if they also identified themselves by race (e.g., a white-Hispanic male would be coded for race as Hispanic). For our multivariable analysis, we grouped persons identified as Asian, Pacific Islander Pacific Islander
1. A native or inhabitant of any of the Polynesian, Micronesian, or Melanesian islands of Oceania.
2. A person of Polynesian, Micronesian, or Melanesian descent. See Usage Note at Asian. , American Indian American Indian
or Native American or Amerindian or indigenous American
Any member of the various aboriginal peoples of the Western Hemisphere, with the exception of the Eskimos (Inuit) and the Aleuts. , or Alaskan Native into the category called "other." We also added those who responded "don't know" or "unsure" to the attitude questions to the "agree" group to divide respondents into two groups: those who responded correctly (disagree) and those who did not (agree or don't know). For our multivariable logistic regression In statistics, logistic regression is a regression model for binomially distributed response/dependent variables. It is useful for modeling the probability of an event occurring as a function of other factors. , we grouped respondents who answered "don't know" to the question, "Are you aware of dangers associated with antibiotics?" with those who answered "no." Persons responding "don't know" to the question, "In the past 4 weeks, have you taken antibiotics?" were not included in the analysis. We evaluated respondents' education and income levels as continuous variables.
To compensate for respondents' unequal probability of selection and allow population estimates to be made, we weighted the data following procedures from the Behavioral Risk Factor Surveillance System (33) and based our weighting on the number of residential phone numbers, the number of people per household, and the 1998 postcensus estimates for the age- and sex-specific population of the FoodNet sites (B. Imhoff, pers. comm.). We did not include race in the poststratification weight since some site-sex-age-race groups contained <10 survey participants.
We analyzed the data using SUDAAN (SUrvey DAta ANalysis, v7.5.2, Research Triangle Institute The Research Triangle Institute (RTI) is a non-profit research organization based in the Research Triangle Park (RTP) of North Carolina. RTI is the oldest tenant of this major research park, and the sister organization to the Research Triangle Foundation. , Research Triangle Park Research Triangle Park, research, business, medical, and educational complex situated in central North Carolina. It has an area of 6,900 acres (2,795 hectares) and is 8 × 2 mi (13 × 3 km) in size. Named for the triangle formed by Duke Univ. NC), a specialized statistical procedure for analyzing complex sample survey data, and ran the analysis using SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System. (Statistical Analysis Software, v6.12) (SAS Institute SAS Institute Inc., headquartered in Cary, North Carolina, USA, has been a major producer of software since it was founded in 1976 by Anthony Barr, James Goodnight, John Sall and Jane Helwig. , Inc., Cary, NC) This software adjusts for the complexity of the sampling design (unequal weighting and clustering) and uses Taylor series linearization In mathematics and its applications, linearization refers to finding the linear approximation to a function at a given point. In the study of dynamical systems, linearization is a method for assessing the local stability of an equilibrium point of a system of nonlinear differential methods to estimate variances. Because the ratio of sample size to population size was small, we approximated the sample design by a "with-replacement" design for purposes of variance estimation in SUDAAN. Any bias resulting from such replacement sampling will be in the conservative direction.
We examined respondents' attitudes toward, and awareness of, antibiotic use by their age, sex, race, income level, education, state, place of residence, medical insurance status, presence of children in household, and month of the interview. We then tested the relationships between respondents' demographic characteristics and their responses to the questions and statements about antibiotics using chi-square tests chi-square test: see statistics. for independence. We used the results of the bivariate bi·var·i·ate
Mathematics Having two variables: bivariate binomial distribution.
Adj. 1. analyses to develop two multivariable logistic regression models: 1) a model assessing the effects of respondents' awareness of antibiotic dangers on their attitudes toward and expectations of antibiotics; and 2) a model assessing the influence of respondents' attitudes on their recent antibiotic use.
Because of the complexity of the analyses, we used only second-degree product terms to assess interaction effects. Results of the logistic regression models are reported as odds ratios (ORs) with 95% confidence intervals confidence interval,
n a statistical device used to determine the range within which an acceptable datum would fall. Confidence intervals are usually expressed in percentages, typically 95% or 99%. (CIs). The level of significance is p=0.05.
The sample consisted of 12,755 respondents: 7,254 females and 5,501 males. Of these 12,755, a total of 1,975 were <18 years old or of an unknown age and thus were excluded from the analysis (Table 1). Of the remaining 10,780 respondents, 12% reported taking antibiotics within the 4 weeks before the interview (Table 2). Those who took antibiotics within the prior 4 weeks were more likely to be female (13.9% overall, 65% of all who took antibiotics), have medical insurance (12.6%, p<0.01), and live in rural or farm areas (12.9% and 17.6%, respectively, p=0.02). In addition, antibiotic use varied by age group, with the highest use among persons 25-39 years old (13.2%) and those >60 (13.7%) (Figure). We found no significant differences in antibiotic use among groups defined by race, education level, income, state, month of interview, and having children in the household. Of those who took antibiotics (n=1,253), 91% reported using an antibiotic prescribed for a current infection, while 9% reported using an old prescription or someone else's. No demographic variable was significantly associated with whether respondents used antibiotics obtained to treat their own current illness.
Of the 10,780 respondents, 27% believed taking antibiotics when they had a cold prevented more serious illness (survey item 2, Table 2), 32% believed taking antibiotics when they had a cold made them recover more quickly (survey item 3), and 48% expected a prescription for antibiotics when they were ill enough from a cold to seek medical attention (survey item 4). Respondents agreeing with any one of these statements were significantly more likely (p<0.01) to be male, younger (18-24 years), nonwhite non·white
A person who is not white.
nonwhite adj. , not college educated, and earning <$30,000 per year (Figure). We also found significant differences by place of residence, with respondents living in rural or farm areas being more likely to agree with the statements. Respondents with children were more likely to agree with survey item 2 (28% vs. 26%), item 3 (34% vs. 31%), and item 4 (50% vs. 46%): all differences had p values <0.01. Responses varied among states (p<0.01), with residents of Maryland and Georgia consistently having higher levels of agreement than residents of the other study areas. (For item 2: 27% and 38% vs. 22%-26% [other states] item 3: 35% and 41% vs. 26%-31% [other states], and item 4: 50% and 56% vs. 40%-48% [other states]). Agreeing with the statement, "By the time I am sick enough to see a doctor because of a cold, I usually expect a prescription for antibiotics," did not vary significantly by month of interview or health insurance status. However, not having insurance was significantly associated with agreement to the statements, "When I get a cold, antibiotics help me to get better more quickly" (42% vs. 27%, p<0.01), and "When I have a cold, I should take antibiotics to prevent getting a more serous serous /se·rous/ (ser´us)
1. pertaining to or resembling serum.
2. producing or containing serum.
Containing, secreting, or resembling serum. illness" (40% vs. 25%, p<0.01). Being interviewed from September through January was also associated with agreeing with these statements (p< 0.05 and p<0.02, respectively).
Fifty-eight percent of respondents were not aware of health dangers associated with taking antibiotics (Table 2). Persons not aware of dangers associated with antibiotic use were significantly (p<0.01) more likely to be male and younger and to live in rural or farm areas. They were also significantly more likely to have less education, lower income, and no insurance (Figure). We found no association between awareness of the dangers of antibiotic use and the month of the interview or having children in the household. Of those aware of health dangers, 58% mentioned factors related to the emergence of drug resistance as a consequence of antibiotic use, 27% mentioned allergies/reactions, 9% recognized that antibiotics kill "good" microbes, and 5% agreed that "it is generally unhealthy to take antibiotics."
Associations between Attitude Statements and Awareness of Dangers
We constructed three independent models to assess the relationship between participants' knowledge of the dangers of antibiotics (and demographic characteristics) and each of the three different attitude statements as the outcome. Each of these relationships was significant in the univariate and multivariable analyses (Table 3).
Participants not aware of adverse effects of antibiotic use were 2.5 times more likely to agree with the statement, "When I have a cold, I should take antibiotics to prevent getting a more serious illness" (95% CI 2.14 to 2.92). In addition, the demographic variables of age, sex, race, income level, education level, and state were all significant predictors of agreement. We also found significant interactions between the awareness variable and race and education, as well as interactions between age and gender.
We also found a significant association between participants agreeing with the statement, "When I have a cold, antibiotics help me to get better more quickly," and their being aware of health dangers associated with indiscriminate use of antibiotics (OR 2.29, 95% CI 1.99 to 2.65). Those agreeing with this statement were more likely to be older (40-59 years old: OR 2.20, 95% CI 1.32 to 3.66; and >60 years old: OR 2.08, 95% CI 1.22 to 3.25).
Participants not aware of dangers were 1.96 times more likely to agree with the statement, "By the time I am sick enough to talk to or visit a doctor because of a cold, I usually expect a prescription for antibiotics" (95% CI 1.72 to 2.23). The other demographic variables in the model significantly associated with participants' responses to this statement were age, sex, income level, education level, insurance, state, and place of residence.
Association between Antibiotic Use and Attitude Statements and Awareness of Dangers
Using another multivariable model, we examined the association between respondents' taking antibiotics in the prior 4 weeks and their attitudes toward and knowledge of the adverse effects of antibiotic use (Table 4). The overall model was adjusted for participants' sex, age, education, race, household income, state, place of residence, child in the house, and insurance. After adjusting for these demographic variables, we found that only one attitude statement remained a predictor of recent antibiotic use. Participants agreeing with the statement, "When I have a cold, antibiotics help me to get better more quickly," were 1.50 times more likely to have recently taken an antibiotic.
Paradoxically, participants aware of dangers related to antibiotic use were 1.37 times more likely to have taken antibiotics in the previous 4 weeks (95% CI 1.11 to 1.69) even though awareness of these dangers was not a univariate predictor of antibiotic use (OR 0.99, 95% CI 0.49 to 1.98). Of note, only one attitude statement was significant in predicting antibiotic use, suggesting that all of the statements are measuring similar things (Table 4).
The results of this FoodNet survey showed that 12% of adult respondents had used antibiotics doing the prior month, most (91%) of which were prescribed for a current infection. Extrapolating from the survey data, we estimate that every adult in the United States in 1998 used antibiotics an average of 1.4 times and that approximately 1 in 10 adults who used antibiotics did so without seeing a physician.
The results also suggest that peoples' knowledge and attitudes regarding antibiotic use can be substantially improved and that improved knowledge may be important for efforts to reduce the misconceptions and misguided expectations contributing to inappropriate antibiotic use. Overall, 53% of respondents to this population-based survey reported at least one misconception that may put them at unnecessary risk for infection with resistant bacterial pathogens, and 58% were not aware of the health dangers associated with antibiotic use. Nearly half (48%) of the respondents indicated that they expected an antibiotic when they visit a doctor.
This survey identified persons in demographic groups who had both higher levels of misconceptions and lower levels of knowledge about the potential adverse impact of antibiotics. These groups included persons of lower socioeconomic status socioeconomic status,
n the position of an individual on a socio-economic scale that measures such factors as education, income, type of occupation, place of residence, and in some populations, ethnicity and religion. , lower educational status, males, those in younger age groups, and the elderly. Efforts to reach these groups must be a part of any educational efforts to change patient expectations and to reduce the corresponding pressure on providers to prescribe antibiotics inappropriately.
The results of this study did not show a consistent direct link between misguided expectations and higher levels of recent antibiotic use. In part, this lack may have been due to the design of the survey, which focused on collecting frequency data and did not aim to define the reasons for antibiotic use. In addition, in our analysis, we found that the three attitude statements were similar measures of a person's opinions on antibiotic use. The statements have the same demographic predictors and association with the knowledge variable and, in reality, they appear to measure the same thing (Table 3).
We did not find an association between recent antibiotic use and lower knowledge levels. Before the analysis, we assumed that persons lacking knowledge about the dangers associated with antibiotic use would be more likely to take antibiotics. However, we found that study participants aware of these health dangers were actually more likely to have taken antibiotics in the prior 4 weeks. Persons of higher socioeconomic status (higher education higher education
Study beyond the level of secondary education. Institutions of higher education include not only colleges and universities but also professional schools in such fields as law, theology, medicine, business, music, and art. and income) have better access to health care and are more likely to use antibiotics in general; we did find that people who took an antibiotic recently were more likely to have medical insurance. Another possible explanation is that those who recently took antibiotics may have learned about the adverse effects of antibiotic use from their physician or pharmacist or from their personal experience with antibiotic side effects Side effects
Effects of a proposed project on other parts of the firm. . Future epidemiologic studies epidemiologic study A study that compares 2 groups of people who are alike except for one factor, such as exposure to a chemical or the presence of a health effect; the investigators try to determine if any factor is associated with the health effect of antibiotic use in diverse populations should be designed to collect information on why participants use antibiotics to distinguish between appropriate and inappropriate antibiotic use.
This type of study has several other important limitations. A telephone survey creates the possibility of selection bias because it may not reflect the population being surveyed (32). In addition, the survey catchment catch·ment
1. A catching or collecting of water, especially rainwater.
a. A structure, such as a basin or reservoir, used for collecting or draining water.
b. population did not include persons who refused to participate, did not have a telephone, did not speak English, or could not respond because of physical or mental impairment. However, the weighting process adjusted for age- and sex-based differences in rates.
Another limitation is the cross-sectional nature of this study. Each participant was assessed only once, and the study was not designed to detect recent changes in opinion. Furthermore, the indicators used measured self-reported behavior not actual behavior. We did not attempt to validate responses on the basis of actual observation, and the survey did not determine whether the antibiotic use was appropriate.
Additionally, respondents may have misunderstood the statements about colds and antibiotics. For example, if they had previous experience with what they thought was a cold, and a physician diagnosed a bacterial ear infection, they may have responded that antibiotics help them get better more quickly when they have a cold (17). In addition, several studies have shown that patients often do not have accurate knowledge of antibiotics (15,34). Hong et al., for example, found that patients often could not identify whether a medication was an antibiotic or not and that many patients considered "antibiotics" to be any prescription medication (34).
This study focused only on antibiotic use among adults. Antibiotic use is, however, highest among children, as is the potential for its misuse. In fact, we found that respondents with children in the household were more likely to agree with the attitude statements, demonstrating that it is often parents who influence their children's perceptions of antibiotic use.
The results of this analysis demonstrate that population-based surveys can contribute to efforts to monitor and reduce inappropriate antibiotic use. The magnitude of recent antibiotic use among adults, as well as widespread lack of awareness about and inappropriate attitudes toward such use indicate that continued population-based surveys could be useful in efforts to monitor trends in antibiotic use. Furthermore, such surveys have the potential to effectively monitor antibiotic knowledge, attitudes, and practices among demographic subgroups of concern. Knowing the magnitude of the problem and the groups who misuse antibiotics most frequently will help public health officials develop and fund intervention efforts, including public information campaigns.
However, our findings also point out some important issues that need to be addressed if this surveillance tool is to be used to full effect. First, additional population-based studies are needed not only to measure antibiotic use but also to determine the reasons that people use them. Such studies should explore the motivations, expectations, and incentives that lead persons to use or not use antibiotics. Second, future studies should include more clearly defined measures of patients' knowledge. Better measures of knowledge may involve asking respondents to differentiate between antibiotics and other types of prescription medicine and to identify types of infections requiring antibiotics. A more thorough evaluation of respondents' attitudes may also be useful. To this end, focus groups may help develop questions that better monitor the general population's attitudes toward antibiotics. Finally, longitudinal tracking of these types of studies will provide important information for the assessment of public health programs.
Table 1. Demographic characteristics of participants in FoodNet population survey, 1998-1999 Demographic N=12,775 % (a) characteristics Sex Male 5,501 49.0 Female 7,254 51.0 Age (y) <18 1,817 25.4 18-24 1,005 8.9 25-39 3,239 23.5 40-59 4,105 26.1 60+ 2,431 15.4 Unknown 158 0.8 Race White 10,278 75.0 Black 1,152 11.2 Hispanic 675 7.6 Asian 339 3.6 American Indian 99 0.9 Other Race 80 0.9 Unknown 132 0.9 Education <High school or less 1,792 19.3 High school graduate 3,169 24.7 Some college 3,528 26.4 College graduate 2,556 18.3 Postgraduate 1,595 10.6 Unknown 115 0.8 Income [less than or equal to] $15,000 1,536 10.6 >$15,000 but 2,097 15.5 [less than or equal to] $30,000 >$30,000 but 3,444 26.1 [less than or equal to] $60,000 >$60,000 but 1,969 16.2 [less than or equal to] $100,000 >$100,000 947 7.8 Unknown 2,762 23.8 Residence City/urban 4,374 34.2 Suburban 4,338 33.3 Town/village 1,807 13.4 Rural (not farm) 1,672 14.4 Farm 493 4.3 Unknown 71 0.5 Insurance With medical insurance 10,561 79.6 Without medical insurance 990 8.3 Unknown 1,204 12.2 (a) Percentages are based on weighted population data. Table 2. Responses of 10,780 persons to survey items, FoodNet population survey, 1998-1999 Survey item Yes/agree No/disagree Unsure % yes 1. In the past 4 1,255 9,485 N/A 12.0 weeks, have you (has he/she) taken any antibiotic medicine? 2. When I have a 2,544 7,638 538 27.4 cold, I should take antibiotics to prevent getting a more serious illness. 3. When I get a cold, 3,053 6,758 896 32.2 antibiotics help me to get better more quickly. 4. By the time I 4,812 4.954 911 47.6 am sick enough to talk to or visit a doctor because of a cold, I usually expect a prescription for antibiotics. 5. Are you aware of 4,860 5,749 164 41.9 any health dangers to yourself or other people associated with taking antibiotics? (a) Values are numbers of persons who answered the questions or statements. Percentages are based on weighted population data. Table 3: Effect of knowledge on attitude statements, FoodNet population survey, 1998-1999 (a) 95% CI (d) Independent Adjusted models OR (b,c) Upper Lower 1. Agree that antibiotics 2.05 (d) 2.14 2.92 prevent serious illness 2. Agree that antibiotics 2.29 (d) 1.99 2.65 help me get better more quickly 3. Expect a prescription 1.96 (d) 1.72 2.23 for antibiotics (a) We constructed three independent models with the three attitude statements as the dependent variables and knowledge of the dangers of antibiotics and selected demographic characteristics as independent variables. (b) OR, odds ratio; CI, confidence interval. (c) Adjusted for sex, age, education, race, household income, state, place of residence, and insurance. (d) Values are significant (p<0.01) after adjusting for multiple comparisons. Table 4: Effect of attitude and awareness on antibiotic use, FoodNet population survey, 1998-1999 (a) 95% CI Adjusted Variable OR (b),(c) Upper Lower Agree that antibiotics 0.78 0.57 1.06 prevent serious illness Agree that antibiotics help 1.50 (d) 1.13 1.99 me get better more quickly Expect a prescription for 0.96 0.77 1.20 antibiotics Aware of antibiotic dangers 1.37 (d) 1.11 1.69 (a) We constructed a multivariable model to look at the association between respondents taking antibiotics in the previous 4 weeks and their attitudes toward and knowledge about the adverse effects of antibiotic use. (b) OR, odds ratio; CI, confidence interval. (c) Adjusted for sex, age, education, race, household income, state, place of residence, child in household, and insurance. (d) Values are significant (p<0.01) after adjusting for multiple comparisons.
We thank the Emerging infections Program's Foodborne Diseases Active Surveillance Network (FoodNet) for providing the data we used in this analysis and the many persons who contributed to the analysis.
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n the ability of certain strains of microorganisms to develop resistance to antibiotics.
antibiotic resistance among nasopharyngeal nasopharyngeal
pertaining to the nasal and pharyngeal cavities.
see nasopharyngeal meatus.
see reverse sneeze. isolates of Streptococcus pneumoniae Streptococcus pneu·mo·ni·ae
Streptococcus pneumoniae Microbiology A pathogenic streptococcus with 90 serotypes associated with pneumonia, bacteremia, meningitis Transmission Person to person Incidence and Haemophilus influenzae--Bangui, Central Africa Republic, 1995. MMWR MMWR Morbidity & Mortality Weekly Report Epidemiology A news bulletin published by the CDC, which provides epidemiologic data–eg, statistics on the incidence of AIDS, rabies, rubella, STDs and other communicable diseases, causes of mortality–eg, Morb Mortal Wkly Rep 1997;46:62-4.
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Pneumococcal pneumonia is a common but serious infection and inflammation of the lungs. It is caused by the bacterium Streptococcus pneumoniae. and bacteremia bacteremia: see septicemia.
Presence of bacteria in the blood. Short-term bacteremia follows dental or surgical procedures, especially if local infection or very high-risk surgery releases bacteria from isolated sites. among unvaccinated nursing home residents. N Engl J Med 1998;338:1861-8.
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(6.) Magee JT, Pritchard EL, Fitzgerald KA, Dunstan FDJ FDJ Francaise des Jeux (French Games - Tour de France cycling sponsor)
FDJ Freie Deutsche Jugend (East German communist youth organization)
FDJ Full Devil Jacket (band) Howard AJ. Antibiotic prescribing and antibiotic resistance in community practice: retrospective study retrospective study,
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(9.) Kunin CM. Resistance to antimicrobial drugs--a worldwide calamity. Ann intern intern /in·tern/ (in´tern) a medical graduate serving in a hospital preparatory to being licensed to practice medicine.
in·tern or in·terne
n. Med 1993; 118:557-61.
(10.) Barden LS, Dowell SF, Schwartz B, Lackey C. Current attitudes regarding use of antimicrobial agents Antimicrobial agents
Chemical compounds biosynthetically or synthetically produced which either destroy or usefully suppress the growth or metabolism of a variety of microscopic or submicroscopic forms of life. : results from physician's and parents' focus group discussions. Clot clot (klot)
1. coagulum; a semisolid mass, as of blood or lymph.
agony clot a type of antemortem clot formed in the process of dying. Pediatr 1998;37:665-71.
(11.) Gonzales R, Steiner JF, Sande MA. Antibiotic prescribing for adults with colds, upper respiratory tract infections upper respiratory tract infection URI Infectious disease A nonspecific term used to describe acute infections involving the nose, paranasal sinuses, pharynx, and larynx, the prototypic URI is the common cold; flu/influenza is a systemic illness involving the URT , and bronchitis bronchitis (brŏnkī`tĭs), inflammation of the mucous membrane of the bronchial tubes. It can be caused by viral or bacterial infections or by allergic reactions to irritants such as tobacco smoke. by ambulatory care ambulatory care
Medical care provided to outpatients.
n the health services provided on an outpatient basis to those who can visit a health care facility and return home the same day. physicians. JAMA 1997;278:901-4.
(12.) Nyquist AC, Gonzales R, Steiner JF, Sande MA. Antibiotic prescribing for children with colds, upper respiratory tract infections, and bronchitis. JAMA 1998;279:875-7.
(13.) Schwartz RH, Freij BJ, Ziai M, Sheridan MJ. Antimicrobial prescribing for acute purulent rhinitis purulent rhinitis
Chronic rhinitis in which pus formation is excessive. in children: a survey of pediatricians and family practitioners family practitioner
n. Abbr. FP
See family physician. . Pediatr Infect Dis J 1997;16:185-90.
(14.) Paluck E, Katzenstein D, Frankish CJ, Herbert CP, Milner R, Speert D, et al. Prescribing practices and attitudes toward giving children antibiotics. Can Fam Physician 2001;47:521-7.
(15.) Chretien JH, McGarvey M, deStwolinski A, Esswein JG. Abuse of antibiotics. A study of patients attending a university clinic. Arch Intent Med 1975;135:1063-5.
(16.) Bauchner H, Pelton SI, Klein JO. Parents, physicians, and antibiotic use. Pediatrics 1999;103:395-401.
(17.) Palmer DA, Bauchner H. Parents' and physicians' views on antibiotics. Pediatrics 1997;99:E6.
(18.) Cockburn J, Pit S. Prescribing behaviour in clinical practice: patients' expectations and doctors' perceptions of patients' expectations--a questionnaire study. BMJ 1997;315:520-3.
(19.) Vinson DC, Lutz LJ. The effect of parental expectations on treatment of children with a cough: a report from ASPN ASPN ActiveState Programmer Network
ASPN American Society of Pediatric Neurosurgeons
ASPN American Society of Pediatric Nephrology
ASPN Active State Programmer Network
ASPN Active Server Pages Network . J Fam Pract 1993;37:23-7.
(20.) Gonzales R, Steiner JF, Lum n. 1. A chimney.
2. A ventilating chimney over the shaft of a mine.
3. A woody valley; also, a deep pool. A, Barrett PH, Jr. Decreasing antibiotic use in ambulatory practice: impact of a multidimensional mul·ti·di·men·sion·al
Of, relating to, or having several dimensions.
multi·di·men intervention on the treatment of uncomplicated acute bronchitis acute bronchitis Pulmonology A lower RTI–up to 95% of which are viral–that causes reversible bronchial inflammation Clinical Cough, fever, sputum, wheezing, rhonchi DiffDx Asthma, aspergillosis, occupational exposure, chronic bronchitis, sinusitis, in adults. JAMA 1999;281:1512-9.
(21.) Butler CC, Rollnick S, Pill R, Maggs-Rapport F, Stott N. Understanding the culture of prescribing: qualitative study of general practitioners' and patients' perceptions of antibiotics for sore throats. BMJ 1998;317:637-42.
(22.) Hamm RM, Hicks Hicks , Edward 1780-1849.
American painter of primitive works, notably The Peaceable Kingdom, of which nearly 100 versions exist. RJ, Bemben DA. Antibiotics and respiratory infections: are patients more satisfied when expectations are met? J Fam Pract 1996;43:56-62.
(23.) Macfarlane MacFarlane or Macfarlane is a surname shared by:
lung - either of two saclike respiratory organs in the chest of vertebrates; serves to remove carbon dioxide and provide oxygen to the blood illness in general practice: questionnaire study. BMJ 1997;315:1211-4.
(24.) Mangione-Smith R, McGlynn EA, Elliott MN, Krogslad P, Brook RH. The relationship between perceived parental expectations and pediatrician antimicrobial prescribing behavior. Pediatrics 1999;103:711-8.
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A genus of spherical-shaped anaerobic bacteria occurring in pairs or chains. Sydenham's chorea is considered a complication of a streptococcal throat infection. in Finland. N Engl J Med 1997;337:441-6.
(28.) Trepka MJ, Belongia EA, Chyou PH, Davis JP, Schwartz B. The effect of a community intervention trial on parental knowledge and awareness of antibiotic resistance and appropriate antibiotic use in children. Pediatrics 2001;107:E6.
(29.) Finkelstein JA, Davis RL, Dowell SF, Metlay JP, Soumerai SB, Rifas-Shinnan SL, et al. Reducing antibiotic use in children: a randomized ran·dom·ize
tr.v. ran·dom·ized, ran·dom·iz·ing, ran·dom·iz·es
To make random in arrangement, especially in order to control the variables in an experiment. trial in 12 practices. Pediatrics 2001;108:1-7.
(30.) Belongia EA, Sullivan BJ, Chyou PH, Madagame E, Reed KD, Schwartz B. A community intervention trial to promote judicious antibiotic use and reduce penicillin-resistant Streptococcus pneumoniae carriage in children. Pediatrics 2001;108:575-83.
(31.) Dayton JJ. Proposal to conduct statewide BRFSS BRFSS Behavioral Risk Factor Surveillance System for Pennsylvania. Work plan. Burlington (VT): Macro International, Inc.; 1996.
(32.) Remington PL, Smith MY, Williamson DF, Anda RF, Gentry EM, Hogelin GC. Design, characteristics, and usefulness of state-based behavioral risk factor surveillance: 1981-87. Public Health Rep 1988;103:366-75.
(33.) Gentry EM, Kalsbeek WD, Hogelin GC, Jones JT, Gaines KL, Forman MR, et al. The behavioral risk factor surveys: II. Design, methods, and estimates from combined state data. Am J Prev Med 1985;1:9-14.
(34.) Hong JS, Philbrick JT, Schorling JB. Treatment of upper respiratory infections Noun 1. upper respiratory infection - infection of the upper respiratory tract
respiratory infection, respiratory tract infection - any infection of the respiratory tract : do patients really want antibiotics? Am J Med 1999;107:511-5.
Ms. Vanden Eng is a master's degree master's degree
An academic degree conferred by a college or university upon those who complete at least one year of prescribed study beyond the bachelor's degree.
Noun 1. candidate in biostatistics biostatistics /bio·sta·tis·tics/ (-stah-tis´tiks) biometry.
The science of statistics applied to the analysis of biological or medical data. at the University of Michigan (body, education) University of Michigan - A large cosmopolitan university in the Midwest USA. Over 50000 students are enrolled at the University of Michigan's three campuses. The students come from 50 states and over 100 foreign countries. , Ann Arbor, Michigan
“Ann Arbor” redirects here. For other uses, see Ann Arbor (disambiguation).
Ann Arbor is a city in the U.S. state of Michigan and the county seat of Washtenaw County. . She conducted this study while working on a master's of public health degree in infectious disease Infectious disease
A pathological condition spread among biological species. Infectious diseases, although varied in their effects, are always associated with viruses, bacteria, fungi, protozoa, multicellular parasites and aberrant proteins known as prions. epidemiology at Yale University Yale University, at New Haven, Conn.; coeducational. Chartered as a collegiate school for men in 1701 largely as a result of the efforts of James Pierpont, it opened at Killingworth (now Clinton) in 1702, moved (1707) to Saybrook (now Old Saybrook), and in 1716 was School of Public Health, New Haven New Haven, city (1990 pop. 130,474), New Haven co., S Conn., a port of entry where the Quinnipiac and other small rivers enter Long Island Sound; inc. 1784. Firearms and ammunition, clocks and watches, tools, rubber and paper products, and textiles are among the many , Connecticut.
Address for correspondence: Ruthanne Marcus, Connecticut Emerging Infectious Program, One Church Street, 7th Floor, New Haven, CT 06510 USA; fax: 203-764-4357; email: email@example.com
Jodi Vanden Eng, * Ruthanne Marcus, * James L. Hadler, ([dagger]) Beth Imhoff, ([double dagger double dagger
A reference mark () used in printing and writing. Also called diesis.
Noun 1. ]) Duc J. Vugia, ([section]) Paul R. Cieslak, ([paragraph]) Elizabeth Zell, ([double dagger]) Valerie Deneen, (#) Katherine Gibbs McCombs, ** Shelley M. Zansky, ([dagger][dagger]) Marguerite A. Hawkins, ([double dagger][double dagger]) and Richard E. Bessert ([double dagger])
* Connecticut Emerging Infections Program, New Haven, Connecticut, USA, ([dagger]) Connecticut Department of Public Health, Hartford, Connecticut “Hartford” redirects here. For other uses, see Hartford (disambiguation).
Hartford is the capital of the State of Connecticut. It is located in Hartford County on the Connecticut River, north of the center of the state. , USA; ([double dagger]) Centers for Disease Control and Prevention, Atlanta, Georgia, USA; ([section]) California Department of Health Services Department of Health Services may refer to:
Minneapolis (pronounced IPA: /ˌmɪniˈæpəlɪs/) is the largest city in the U.S. , USA; ** Georgia Division of Public Health, Atlanta, Georgia, USA, ([dagger][dagger]) New York State Department of Health, Albany, New York For other uses, see Albany.
Albany is the capital of the State of New York and the county seat of Albany County. Albany lies 136 miles (219 km) north of New York City, and slightly to the south of the juncture of the Mohawk and Hudson Rivers. , USA, and ([double dagger][double dagger]) University of Maryland University of Maryland can refer to:
Baltimore is an independent city located in the state of Maryland in the United States. , USA