Background We sought to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. We also evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.
Background
We sought to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. We also evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.Methods Subjects were parents of children aged 3 months to 18 years. The sample was derived from the General Panel, Mommies Sub-Panel, and Chronic Illness Sub-Panel of Harris Interactive. Participants rated health scenarios for atopic dermatitis, asthma, and eyeglasses on a visual analog scale, imagining a child was experiencing the described state.
Methods
Subjects were parents of children aged 3 months to 18 years. The sample was derived from the General Panel, Mommies Sub-Panel, and Chronic Illness Sub-Panel of Harris Interactive. Participants rated health scenarios for atopic dermatitis, asthma, and eyeglasses on a visual analog scale, imagining a child was experiencing the described state.Results A total of 3539 parents completed the survey. Twenty-nine percent had a child with a history of atopic dermatitis. Mean preference scores for atopic dermatitis were as follows: mild, 91 (95% confidence interval [CI], 90.7 to 91.5); mild/moderate, 84 (95%CI, 83.5 to 84.4); moderate, 73 (95%CI, 72.5 to 73.6); moderate/severe, 61 (95%CI, 60.6 to 61.8); severe, 49 (95% CI, 48.7 to 50.1); asthma, 58 (95%CI, 57.4 to 58.8); and eyeglasses, 87(95%CI, 86.3 to 87.4).
Results
A total of 3539 parents completed the survey. Twenty-nine percent had a child with a history of atopic dermatitis. Mean preference scores for atopic dermatitis were as follows: mild, 91 (95% confidence interval [CI], 90.7 to 91.5); mild/moderate, 84 (95%CI, 83.5 to 84.4); moderate, 73 (95%CI, 72.5 to 73.6); moderate/severe, 61 (95%CI, 60.6 to 61.8); severe, 49 (95% CI, 48.7 to 50.1); asthma, 58 (95%CI, 57.4 to 58.8); and eyeglasses, 87(95%CI, 86.3 to 87.4).Conclusions Parents perceive that atopic dermatitis has a negative effect on quality of life that increases with disease severity. Estimates of parents' preferences can provide physicians with insight into the value that parents place on their children's treatment and can be used to evaluate new medical therapies for atopic dermatitis.
Conclusions
Parents perceive that atopic dermatitis has a negative effect on quality of life that increases with disease severity. Estimates of parents' preferences can provide physicians with insight into the value that parents place on their children's treatment and can be used to evaluate new medical therapies for atopic dermatitis.

Background Atopic dermatitis is the most common of childhood skin diseases, with a lifetime prevalence in children of 10% to 20% [ 1 ]. The disease is most common in industrialized countries and among Caucasians and Asians [ 2 ]. Annual total costs of treatment are estimated to range from $0.9 to 3.8 billion in the United States [ 3 ]. Atopic dermatitis can have a negative impact on quality of life by affecting psychosocial adjustment in children. Lapidus and Kerr [ 4 ] report that atopic dermatitis can cause embarrassment, disrupt sporting activities in older children, and interfere with employment opportunities among adults. The disease can also have a negative impact on families. Parents report feelings of guilt, exhaustion, frustration, and helplessness [ 4 - 7 ]. Atopic dermatitis can disrupt sleep in patients and their family members, and parents can miss work or avoid outside work altogether to care for a child with the disease [ 4 , 8 ]. Fivenson et al [ 9 ] found that 50% of the total burden of illness of atopic dermatitis is associated with lost productivity. Specifically, they found that days lost from work and nights of sleep lost were high among parents of young children with atopic dermatitis [ 9 ]. These stresses take additional tolls on familial relationships and are confounded in low-income families, who often have minimal access to social support mechanisms [ 4 ]. As the need to control increasing medical expenditures continues to mount, formal economic evaluations are taking on a prominent role in assessing the value of new medical therapies. To properly evaluate the impact of new therapies for atopic dermatitis, patients' health-related quality of life (HRQOL) must be considered. Although a number of quality-of-life evaluations have been conducted for adults and children affected with atopic dermatitis [ 4 , 7 , 9 - 12 ], quality-of-life adjustments in cost-utility analyses must be performed using preference weights. Preference weights represent summary measures of HRQOL associated with individual health states and are necessary to calculate quality-adjusted life-years (QALYs) for use in cost-utility analyses. Although the prevalence of atopic dermatitis is highest in children, the existing literature on patient preferences for atopic dermatitis is limited to the adult population [ 13 , 14 ]. However, eliciting preferences from children may not be possible, because they lack the necessary language skills and cognitive abilities to interpret and respond to questions used to evaluate preferences. Evidence suggests that proxy reports by parents may provide valid estimates of HRQOL in children [ 15 ]. Therefore, our primary objective was to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. In addition, we evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.
Background
Atopic dermatitis is the most common of childhood skin diseases, with a lifetime prevalence in children of 10% to 20% [ 1 ]. The disease is most common in industrialized countries and among Caucasians and Asians [ 2 ]. Annual total costs of treatment are estimated to range from $0.9 to 3.8 billion in the United States [ 3 ].Atopic dermatitis can have a negative impact on quality of life by affecting psychosocial adjustment in children. Lapidus and Kerr [ 4 ] report that atopic dermatitis can cause embarrassment, disrupt sporting activities in older children, and interfere with employment opportunities among adults. The disease can also have a negative impact on families. Parents report feelings of guilt, exhaustion, frustration, and helplessness [ 4 - 7 ]. Atopic dermatitis can disrupt sleep in patients and their family members, and parents can miss work or avoid outside work altogether to care for a child with the disease [ 4 , 8 ]. Fivenson et al [ 9 ] found that 50% of the total burden of illness of atopic dermatitis is associated with lost productivity. Specifically, they found that days lost from work and nights of sleep lost were high among parents of young children with atopic dermatitis [ 9 ]. These stresses take additional tolls on familial relationships and are confounded in low-income families, who often have minimal access to social support mechanisms [ 4 ].As the need to control increasing medical expenditures continues to mount, formal economic evaluations are taking on a prominent role in assessing the value of new medical therapies. To properly evaluate the impact of new therapies for atopic dermatitis, patients' health-related quality of life (HRQOL) must be considered. Although a number of quality-of-life evaluations have been conducted for adults and children affected with atopic dermatitis [ 4 , 7 , 9 - 12 ], quality-of-life adjustments in cost-utility analyses must be performed using preference weights. Preference weights represent summary measures of HRQOL associated with individual health states and are necessary to calculate quality-adjusted life-years (QALYs) for use in cost-utility analyses.Although the prevalence of atopic dermatitis is highest in children, the existing literature on patient preferences for atopic dermatitis is limited to the adult population [ 13 , 14 ]. However, eliciting preferences from children may not be possible, because they lack the necessary language skills and cognitive abilities to interpret and respond to questions used to evaluate preferences. Evidence suggests that proxy reports by parents may provide valid estimates of HRQOL in children [ 15 ]. Therefore, our primary objective was to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. In addition, we evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.Methods Preference assessment instrument Patient preferences can be elicited using standard gamble or time-tradeoff or direct rating methods such as a visual analog scale. Because both the standard gamble and the time-tradeoff exercises involve choices between two alternatives that involve a chance of death or longevity, we believed it was unethical to ask parents to participate in such exercises when children were the subjects. Therefore, our choice for eliciting preferences was the rating scale. We developed case scenarios for 5 levels of atopic dermatitis severity – mild, mild/moderate, moderate, moderate/severe, and severe. These severity levels were created by combining the characteristics of an Investigator Global Assessment (IGA) and the Eczema Area and Severity Index (EASI) score [ 16 ]. Each scenario included descriptions of erythema, infiltration and/or papulation, excoriation, and lichenification, as well as location of body area affected (Table  1 ). Efforts were made to ensure that the scenarios were descriptive, explicit, nonjudgmental, and targeted to an eighth-grade reading level. A medical artist developed images to depict the descriptions of atopic dermatitis. We also included 2 additional scenarios – one that described wearing glasses and another that described suffering from asthma – to compare the preferences for atopic dermatitis health states to nondermatological health states. A pediatrician and a pediatric dermatologist reviewed the descriptions and illustrations to ensure their validity and realism. The scenarios were revised based on their comments. Table 1 Scenario descriptions Severity Scenario Mild • The area looks like a light pink or white, dusty rash. • It is affecting the cheeks. • It is rarely itchy and your child scratches it only a few (about 3) times a day. • There are only a few (about 3) slightly bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is rarely disrupted by itching. Mild/Moderate • The area looks like a light pink or white, dusty rash. • It is affecting the cheeks and the chin. • It is somewhat itchy and your child scratches it about 5 times a day. • There are about 5 slightly bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is somewhat disrupted by itching. Your child loses about 15 minutes of sleep each night because of scratching. Moderate • The area looks like a dark pink rash. • It is affecting the cheeks, the chin and the inside of the elbows. • It is moderately itchy and your child scratches it often (about 15 times) during the day. • There are about 7 moderately bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is disrupted by itching. Your child loses about 1 hour of sleep each night because of scratching. Moderate/Severe • The area looks like a dark pink rash. • It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees. • It is itchy and your child scratches it often (about 30 times) during the day. • There are about 10 moderately bumpy areas. • There is some light oozing or crusting in one area. • The skin is not dry or leathery. • Sleep is disrupted by itching. Your child loses about 2 hours of sleep each night because of scratching. Severe • The area looks like a red rash. • It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees, and the trunk of the body. • It is very itchy and your child scratches it scratching continuously throughout the day. • There are numerous bumpy areas. • There is oozing or crusting in some areas. • The skin is dry and leathery in some areas. • Sleep is disrupted by itching. Your child loses about 3 hours of sleep each night because of scratching. Using cognitive interview techniques, we pilot-tested the preference assessment instrument in a convenience sample of 20 parents of children who were being evaluated at a local children's primary care clinic to assess patients' understanding of the instrument and its instructions. The instrument was further revised based on the results of the pilot test. Survey administration A health state preference assessment asks subjects to make judgments regarding the value of particular health states [ 9 ]. Preferences for health states can be elicited from patients with disease (or their family members), from patients at risk for disease, or from the general public [ 17 , 18 ]. We elected to develop preferences from family members of children who currently had atopic dermatitis or were at risk for the disease. To obtain responses from a broad range of respondents in an efficient manner, we recruited participants over the Internet. The sample was derived from the General Panel, the Mommies Sub-Panel, and the Chronic Illness Sub-Panel of Harris Interactive (Rochester, NY), an international market research and consulting firm. The General Panel is a multimillion-member panel of respondents who register to participate in The Harris Poll online panel. The Mommies Sub-Panel is a subpanel of respondents with children aged up to 2 years. The Chronic Illness Sub-Panel identifies respondents (or household members) who have been diagnosed with at least 1 of more than 44 chronic medical conditions, including skin conditions. (The Mommies and Chronic Illness Sub-Panels are part of The Harris Poll online panel. Aside from parental and health status, their members do not differ systematically from members of the General Panel.). Subjects were invited to participate in the survey from February 12 through 14, 2002, and were asked to register at a specific survey site. After consent was obtained online, subjects completed the survey. Respondents were offered the incentive of a chance to win one of five $100 prizes. Respondents had to be adults with children aged 3 months to 18 years in order to be included in the study. The study was approved by the institutional review board of Duke University Medical Center. Clinical data were based on self-report and included information on diagnosis history and severity level. Specifically, respondents were asked if they had a child between the ages of 3 months and 18 years who had ever been diagnosed by a medical professional with atopic dermatitis. If they responded "yes," they were then asked to describe the child's atopic dermatitis at its worst point by selecting from the following response options: mild, mild/moderate, moderate, moderate/severe, severe. For the preference assessment, each respondent was given the scenarios in the same order – mild through severe atopic dermatitis, asthma, and glasses. Subjects were instructed to indicate on a scale ranging from 100 (perfect health) to 0 (death) how good or bad they believed it would be to be a child experiencing the scenario depicted. Respondents recorded their values using a movable pointer on the scale. Respondents whose Internet browser software did not support the movable pointer entered their numerical responses manually into a required field. All 7 values were summarized at the end of the survey to allow respondents to review and, if desired, revise their ratings. Data analysis Descriptive statistics were used to describe the sample. Because subjects provided responses across severity levels, a repeated-measures analysis of variance was conducted using polynomial contrasts for the within-subjects (severity) effect. A  P  value of ≤ .05 was used as the criterion for statistical significance.
Methods
Preference assessment instrument Patient preferences can be elicited using standard gamble or time-tradeoff or direct rating methods such as a visual analog scale. Because both the standard gamble and the time-tradeoff exercises involve choices between two alternatives that involve a chance of death or longevity, we believed it was unethical to ask parents to participate in such exercises when children were the subjects. Therefore, our choice for eliciting preferences was the rating scale. We developed case scenarios for 5 levels of atopic dermatitis severity – mild, mild/moderate, moderate, moderate/severe, and severe. These severity levels were created by combining the characteristics of an Investigator Global Assessment (IGA) and the Eczema Area and Severity Index (EASI) score [ 16 ]. Each scenario included descriptions of erythema, infiltration and/or papulation, excoriation, and lichenification, as well as location of body area affected (Table  1 ). Efforts were made to ensure that the scenarios were descriptive, explicit, nonjudgmental, and targeted to an eighth-grade reading level. A medical artist developed images to depict the descriptions of atopic dermatitis. We also included 2 additional scenarios – one that described wearing glasses and another that described suffering from asthma – to compare the preferences for atopic dermatitis health states to nondermatological health states. A pediatrician and a pediatric dermatologist reviewed the descriptions and illustrations to ensure their validity and realism. The scenarios were revised based on their comments. Table 1 Scenario descriptions Severity Scenario Mild • The area looks like a light pink or white, dusty rash. • It is affecting the cheeks. • It is rarely itchy and your child scratches it only a few (about 3) times a day. • There are only a few (about 3) slightly bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is rarely disrupted by itching. Mild/Moderate • The area looks like a light pink or white, dusty rash. • It is affecting the cheeks and the chin. • It is somewhat itchy and your child scratches it about 5 times a day. • There are about 5 slightly bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is somewhat disrupted by itching. Your child loses about 15 minutes of sleep each night because of scratching. Moderate • The area looks like a dark pink rash. • It is affecting the cheeks, the chin and the inside of the elbows. • It is moderately itchy and your child scratches it often (about 15 times) during the day. • There are about 7 moderately bumpy areas. • There is no oozing or crusting. • The skin is not dry or leathery. • Sleep is disrupted by itching. Your child loses about 1 hour of sleep each night because of scratching. Moderate/Severe • The area looks like a dark pink rash. • It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees. • It is itchy and your child scratches it often (about 30 times) during the day. • There are about 10 moderately bumpy areas. • There is some light oozing or crusting in one area. • The skin is not dry or leathery. • Sleep is disrupted by itching. Your child loses about 2 hours of sleep each night because of scratching. Severe • The area looks like a red rash. • It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees, and the trunk of the body. • It is very itchy and your child scratches it scratching continuously throughout the day. • There are numerous bumpy areas. • There is oozing or crusting in some areas. • The skin is dry and leathery in some areas. • Sleep is disrupted by itching. Your child loses about 3 hours of sleep each night because of scratching. Using cognitive interview techniques, we pilot-tested the preference assessment instrument in a convenience sample of 20 parents of children who were being evaluated at a local children's primary care clinic to assess patients' understanding of the instrument and its instructions. The instrument was further revised based on the results of the pilot test.
Preference assessment instrument
Patient preferences can be elicited using standard gamble or time-tradeoff or direct rating methods such as a visual analog scale. Because both the standard gamble and the time-tradeoff exercises involve choices between two alternatives that involve a chance of death or longevity, we believed it was unethical to ask parents to participate in such exercises when children were the subjects. Therefore, our choice for eliciting preferences was the rating scale.We developed case scenarios for 5 levels of atopic dermatitis severity – mild, mild/moderate, moderate, moderate/severe, and severe. These severity levels were created by combining the characteristics of an Investigator Global Assessment (IGA) and the Eczema Area and Severity Index (EASI) score [ 16 ]. Each scenario included descriptions of erythema, infiltration and/or papulation, excoriation, and lichenification, as well as location of body area affected (Table  1 ). Efforts were made to ensure that the scenarios were descriptive, explicit, nonjudgmental, and targeted to an eighth-grade reading level. A medical artist developed images to depict the descriptions of atopic dermatitis. We also included 2 additional scenarios – one that described wearing glasses and another that described suffering from asthma – to compare the preferences for atopic dermatitis health states to nondermatological health states. A pediatrician and a pediatric dermatologist reviewed the descriptions and illustrations to ensure their validity and realism. The scenarios were revised based on their comments.Scenario descriptionsUsing cognitive interview techniques, we pilot-tested the preference assessment instrument in a convenience sample of 20 parents of children who were being evaluated at a local children's primary care clinic to assess patients' understanding of the instrument and its instructions. The instrument was further revised based on the results of the pilot test.Survey administration A health state preference assessment asks subjects to make judgments regarding the value of particular health states [ 9 ]. Preferences for health states can be elicited from patients with disease (or their family members), from patients at risk for disease, or from the general public [ 17 , 18 ]. We elected to develop preferences from family members of children who currently had atopic dermatitis or were at risk for the disease. To obtain responses from a broad range of respondents in an efficient manner, we recruited participants over the Internet. The sample was derived from the General Panel, the Mommies Sub-Panel, and the Chronic Illness Sub-Panel of Harris Interactive (Rochester, NY), an international market research and consulting firm. The General Panel is a multimillion-member panel of respondents who register to participate in The Harris Poll online panel. The Mommies Sub-Panel is a subpanel of respondents with children aged up to 2 years. The Chronic Illness Sub-Panel identifies respondents (or household members) who have been diagnosed with at least 1 of more than 44 chronic medical conditions, including skin conditions. (The Mommies and Chronic Illness Sub-Panels are part of The Harris Poll online panel. Aside from parental and health status, their members do not differ systematically from members of the General Panel.). Subjects were invited to participate in the survey from February 12 through 14, 2002, and were asked to register at a specific survey site. After consent was obtained online, subjects completed the survey. Respondents were offered the incentive of a chance to win one of five $100 prizes. Respondents had to be adults with children aged 3 months to 18 years in order to be included in the study. The study was approved by the institutional review board of Duke University Medical Center. Clinical data were based on self-report and included information on diagnosis history and severity level. Specifically, respondents were asked if they had a child between the ages of 3 months and 18 years who had ever been diagnosed by a medical professional with atopic dermatitis. If they responded "yes," they were then asked to describe the child's atopic dermatitis at its worst point by selecting from the following response options: mild, mild/moderate, moderate, moderate/severe, severe. For the preference assessment, each respondent was given the scenarios in the same order – mild through severe atopic dermatitis, asthma, and glasses. Subjects were instructed to indicate on a scale ranging from 100 (perfect health) to 0 (death) how good or bad they believed it would be to be a child experiencing the scenario depicted. Respondents recorded their values using a movable pointer on the scale. Respondents whose Internet browser software did not support the movable pointer entered their numerical responses manually into a required field. All 7 values were summarized at the end of the survey to allow respondents to review and, if desired, revise their ratings.
Survey administration
A health state preference assessment asks subjects to make judgments regarding the value of particular health states [ 9 ]. Preferences for health states can be elicited from patients with disease (or their family members), from patients at risk for disease, or from the general public [ 17 , 18 ]. We elected to develop preferences from family members of children who currently had atopic dermatitis or were at risk for the disease. To obtain responses from a broad range of respondents in an efficient manner, we recruited participants over the Internet. The sample was derived from the General Panel, the Mommies Sub-Panel, and the Chronic Illness Sub-Panel of Harris Interactive (Rochester, NY), an international market research and consulting firm. The General Panel is a multimillion-member panel of respondents who register to participate in The Harris Poll online panel. The Mommies Sub-Panel is a subpanel of respondents with children aged up to 2 years. The Chronic Illness Sub-Panel identifies respondents (or household members) who have been diagnosed with at least 1 of more than 44 chronic medical conditions, including skin conditions. (The Mommies and Chronic Illness Sub-Panels are part of The Harris Poll online panel. Aside from parental and health status, their members do not differ systematically from members of the General Panel.).Subjects were invited to participate in the survey from February 12 through 14, 2002, and were asked to register at a specific survey site. After consent was obtained online, subjects completed the survey. Respondents were offered the incentive of a chance to win one of five $100 prizes. Respondents had to be adults with children aged 3 months to 18 years in order to be included in the study. The study was approved by the institutional review board of Duke University Medical Center.Clinical data were based on self-report and included information on diagnosis history and severity level. Specifically, respondents were asked if they had a child between the ages of 3 months and 18 years who had ever been diagnosed by a medical professional with atopic dermatitis. If they responded "yes," they were then asked to describe the child's atopic dermatitis at its worst point by selecting from the following response options: mild, mild/moderate, moderate, moderate/severe, severe.For the preference assessment, each respondent was given the scenarios in the same order – mild through severe atopic dermatitis, asthma, and glasses. Subjects were instructed to indicate on a scale ranging from 100 (perfect health) to 0 (death) how good or bad they believed it would be to be a child experiencing the scenario depicted. Respondents recorded their values using a movable pointer on the scale. Respondents whose Internet browser software did not support the movable pointer entered their numerical responses manually into a required field. All 7 values were summarized at the end of the survey to allow respondents to review and, if desired, revise their ratings.Data analysis Descriptive statistics were used to describe the sample. Because subjects provided responses across severity levels, a repeated-measures analysis of variance was conducted using polynomial contrasts for the within-subjects (severity) effect. A  P  value of ≤ .05 was used as the criterion for statistical significance.
Data analysis
Descriptive statistics were used to describe the sample. Because subjects provided responses across severity levels, a repeated-measures analysis of variance was conducted using polynomial contrasts for the within-subjects (severity) effect. A  P  value of ≤ .05 was used as the criterion for statistical significance.PResults Of the 28105 subjects contacted, 6131 (22%) responded. Of the 6131 respondents, 3539 (57.7%) met the inclusion criteria, consented to participate in the study, and completed the survey (Table  2 ). The mean age was 41 years; 93% of the subjects were women; and 90% were white. Nonresponders were similar to responders with respect to age, sex, and race/ethnicity. More than 98% of the sample had at least a high school education, with 83% of respondents completing at least some college courses. Overall, the sample reflected moderate- to high-income families, with 74% having an annual household income of at least $35000. Thirty percent of the parents had a child with atopic dermatitis, 55% had a child with asthma, and 46% had a child who wore glasses. Table 2 Subject characteristics Characteristic Responders (n = 3539) Nonresponders (n = 21974) Age  Mean (SD) 40.6 (6.2) 41.0 (6.4)  Range 18–64 19–76 Female sex 3298 (93.2) 20263 (92.2) Race/ethnicity*  White 3045 (89.7) 18507 (84.2)  Black/African-American 136 (4.0) 1098 (5.0)  Hispanic 79 (2.3) 703 (3.2)  Asian/Pacific Islander 21 (0.6) 171 (0.8)  Native American 38 (1.1) 316 (1.4)  Mixed/other 77 (2.3) 520 (2.4)  Unknown 0 559 (2.5)  Declined to answer 0 100 (0.5) Education level†  Did not complete high school 55 (1.6) 706 (3.2)  High school degree 555 (15.7) 4366 (19.9)  Some college 1391 (39.3) 9074 (41.3)  College degree 991 (28.0) 5179 (23.6)  Some graduate school or degree 543 (15.4) 2568 (11.7)  Unknown 0 81 (0.4) Annual household income‡  <$15000 107 (3.5) 868 (3.9)  $15000$24999 262 (8.6) 1983 (9.0)  $25000–$34999 410 (13.4) 2945 (13.4)  $35000–$49999 665 (21.7) 4177 (19.0)  $50000–$74999 806 (26.3) 5065 (23.0)  $75000–$99999 406 (13.3) 2003 (9.1)  $100000–$149999 220 (7.2) 1442 (6.6)  $150000–$199999 85 (2.8) 273 (1.2)  $200000–$249999 61 (2.0) 117 (0.5)  ≥ $250000 16 (0.5) 103 (0.5)  Declined to answer 24 (0.8) 1 (0.0)  Unknown 0 2997 (13.6) Employment status §  Employed full-time 1766 (80.0) 9070 (42.2)  Employed part-time 554 (15.7) 2355 (11.0)  Self-employed 281 (7.9) 1588 (7.4)  Not employed but looking for work 119 (3.4) 852 (4.0)  Not employed and not looking for work 70 (2.0) 588 (2.7)  Retired 40 (1.1) 258 (1.2)  Student 149 (4.2) 3071 (14.3)  Homemaker 974 (27.5) 3566 (16.6)  Disabled 0 138 (0.6)  Not sure 4 (0.1) 0  Declined to answer 4 (0.1) 0  Not applicable 0 0 Number of children in household  Mean (SD) 1.9 (1.0) 1.9 (1.2)  Range 0–9 0–15 Country of residence  Australia 2 (0.1) --  Canada 2 (0.1) --  United States 3535 (99.9) -- Values are reported as number (percentage) unless otherwise indicated. *There were 143 missing responses for this variable. † There were 4 missing responses for this variable. ‡ There were 477 missing responses for this variable. § Responses sum to more than 100% because respondents could select more than one answer. Nonresponders did not have this option. There were 488 missing responses for this variable. Table  3  displays the characteristics of the children's atopic dermatitis (n = 1017). Seventy-eight percent (n = 806) of children were diagnosed more than a year ago. Sixteen percent (n = 160) of the sample described their child's atopic dermatitis as mild and 8% (n = 78) described their child's atopic dermatitis as severe. Thirty-five percent (n = 357) of the sample reported their child's atopic dermatitis under limited control or uncontrolled. Table 3 Disease characteristics for children with atopic dermatitis Characteristic Subjects (n = 1017) Time of diagnosis  ≤ 1 month ago 119 (11.7)  7 months to <12 months ago 95 (9.3)  1 year to 5 years ago 467 (45.9)  > 5 years ago 336 (33.0) Disease severity  Mild 160 (15.7)  Mild to Moderate 236 (23.2)  Moderate 277 (27.2)  Moderate to Severe 266 (26.2)  Severe 78 (7.7) How well controlled?  Complete 206 (20.3)  Good control 409 (40.2)  Limited control 330 (32.4)  Uncontrolled 27 (2.6)  No treatment 45 (4.4) Values are reported as number (percentage) unless otherwise indicated. The mean values for all participants are presented in Table  4 . Among the atopic dermatitis health states, there was a progressive decline in respondents' preferences, with the mildest state receiving the highest mean preference score and the severe state receiving the lowest mean preference score. On average, preferences for asthma were higher than for severe atopic dermatitis but lower than moderate/severe atopic dermatitis. Not surprisingly, wearing glasses received a higher preference value than suffering from asthma. Average preference values for the glasses health state were ranked between the mild and mild/moderate atopic dermatitis health states. Table 4 Average health state preference values Health State Mean Median 95% Confidence Interval Mild atopic dermatitis 91.1 95.0 90.7–91.5 Mild/moderate atopic dermatitis 83.9 88.0 83.5–84.4 Moderate atopic dermatitis 73.1 76.0 72.5–73.6 Moderate/severe atopic dermatitis 61.2 63.0 60.6–61.8 Severe atopic dermatitis 49.4 50.0 48.7–50.1 Asthma 58.1 60.0 57.4–58.8 Wearing eyeglasses 86.8 94.0 86.3–87.4 There was a significant effect of severity (F 4,3391  = 3065.66;  P  = .0001). The linear effect of severity (F 1,3394  = 11454.90;  P  < .0001) indicated that preference ratings significantly decreased as the severity of the health states increased (Figure  1 ). Furthermore, there was a significant main effect for preferences reported by parents of children with atopic dermatitis as compared to parents of children without atopic dermatitis (F 1,3394  = 8.10;  P  = .0045). Across all health states, parents of children with atopic dermatitis gave a slightly higher mean preference (72.85 [SD, 13.50]) compared to parents whose children did not have atopic dermatitis (71.34 [SD, 14.19]) (Figure  1 ). Figure 1 Comparison of overall ratings, stratified by children with atopic dermatitis and children without atopic dermatitis There was no significant severity by parent group interaction (F 4,3391  = 1.21;  P  = .31), indicating that the differences across health states were the same for both parent groups.
Results
Of the 28105 subjects contacted, 6131 (22%) responded. Of the 6131 respondents, 3539 (57.7%) met the inclusion criteria, consented to participate in the study, and completed the survey (Table  2 ). The mean age was 41 years; 93% of the subjects were women; and 90% were white. Nonresponders were similar to responders with respect to age, sex, and race/ethnicity. More than 98% of the sample had at least a high school education, with 83% of respondents completing at least some college courses. Overall, the sample reflected moderate- to high-income families, with 74% having an annual household income of at least $35000. Thirty percent of the parents had a child with atopic dermatitis, 55% had a child with asthma, and 46% had a child who wore glasses.Subject characteristicsValues are reported as number (percentage) unless otherwise indicated.*There were 143 missing responses for this variable.† There were 4 missing responses for this variable.‡ There were 477 missing responses for this variable.§ Responses sum to more than 100% because respondents could select more than one answer. Nonresponders did not have this option. There were 488 missing responses for this variable.Table  3  displays the characteristics of the children's atopic dermatitis (n = 1017). Seventy-eight percent (n = 806) of children were diagnosed more than a year ago. Sixteen percent (n = 160) of the sample described their child's atopic dermatitis as mild and 8% (n = 78) described their child's atopic dermatitis as severe. Thirty-five percent (n = 357) of the sample reported their child's atopic dermatitis under limited control or uncontrolled.Disease characteristics for children with atopic dermatitisValues are reported as number (percentage) unless otherwise indicated.The mean values for all participants are presented in Table  4 . Among the atopic dermatitis health states, there was a progressive decline in respondents' preferences, with the mildest state receiving the highest mean preference score and the severe state receiving the lowest mean preference score. On average, preferences for asthma were higher than for severe atopic dermatitis but lower than moderate/severe atopic dermatitis. Not surprisingly, wearing glasses received a higher preference value than suffering from asthma. Average preference values for the glasses health state were ranked between the mild and mild/moderate atopic dermatitis health states.Average health state preference valuesThere was a significant effect of severity (F 4,3391  = 3065.66;  P  = .0001). The linear effect of severity (F 1,3394  = 11454.90;  P  < .0001) indicated that preference ratings significantly decreased as the severity of the health states increased (Figure  1 ). Furthermore, there was a significant main effect for preferences reported by parents of children with atopic dermatitis as compared to parents of children without atopic dermatitis (F 1,3394  = 8.10;  P  = .0045). Across all health states, parents of children with atopic dermatitis gave a slightly higher mean preference (72.85 [SD, 13.50]) compared to parents whose children did not have atopic dermatitis (71.34 [SD, 14.19]) (Figure  1 ).PPPThere was no significant severity by parent group interaction (F 4,3391  = 1.21;  P  = .31), indicating that the differences across health states were the same for both parent groups.PDiscussion Our study evaluated preferences for 5 health states for atopic dermatitis. The aggregate values for each health state may be used for computing QALYs for new therapies that treat atopic dermatitis or can be used to help physicians make more informed decisions by considering parents' perceptions of atopic dermatitis measured on a continuum from perfect health to death. The differences in average values across the 5 health states were generally consistent, with mild at 91, mild/moderate at 84, moderate at 73, moderate/severe at 61, and severe at 49. Lundberg and colleagues [ 13 ] found that the mean health-state utility using a rating scale for patients with atopic dermatitis was 77, a value only slightly higher than our average health-state utility of 73. This small discrepancy could be explained by several factors. First, Lundberg et al [ 13 ] asked adult patients to provide ratings for themselves, whereas our study asked parents to provide ratings for children. Parents might feel that a given health state is worse for their children than it would be for themselves. Second, Lundberg et al [ 13 ] asked patients to provide a rating for their current health state, whereas our study asked parents to assign utilities to 5 varying levels of severity of atopic dermatitis. The average severity level of Lundberg et al's [ 13 ] sample might have been slightly lower than the average severity among our 5 health states, making the mean preference value slightly higher. By obtaining preferences for varying levels of severity, our results have greater applicability in various types of models for decision making. Although preference ratings were systematically higher among parents of children with atopic dermatitis than among parents whose children did not have atopic dermatitis, the magnitude of the difference was small (difference = 1.5). Research on adults who rate the health states of other adults has suggested that people who have experienced a particular health state are more likely to assign a higher value to it than others who are asked to imagine the health state [ 19 - 21 ]. The lack of a larger difference between the parent groups in our study could indicate that parents evaluate health states of children the same, regardless of whose children they are, because of a general concern for all children. Regardless of the reason, these results have important implications for the use of community-based preference weights, as opposed to patient-based (or parent-based) weights, in preference-weighted decision analyses. For this limited therapeutic domain, our study shows that parents in the general community would supply approximately the same preferences as parents whose children suffer the condition under study. Future research should consider whether this consistency holds for other serious childhood diseases, such as pediatric cancer. Our study has several limitations. First, by using the Internet, respondents did not have an opportunity to ask questions if they did not understand what they were being asked to do. However, we feel that this limitation was negligible, because the instrument was pilot-tested using in-person interviews, and the very large majority of responses were ranked appropriately (eg, mild ranked higher than severe). Secondly, there may be a sample bias in using the Harris Interactive database. Our sample reflected a predominately white, female cohort from a high socioeconomic class and may present generalizability issues. However, we received responses from 351 (14%) nonwhite respondents and 779 (22%) responses from participants with an annual household income of less than $35000, providing a sufficient number of responses to test for differences by race/ethnicity and income level. Further research will be needed to corroborate our findings using a population-based sample. Third, since these data are self-reported, some parents may have misclassified their children as having or not having atopic dermatitis, potentially biasing our results. Fourth, it is unclear whether physician assessment of severity would correspond with the severity levels that we assigned to the health states. However, physicians could evaluate the descriptions provided to judge whether their assessments are consistent. Finally, people enrolled in the Harris Interactive database are computer users who may be more motivated to participate in a survey than the general population. It is unclear how or in what direction these sample biases might affect the results of our analyses. We had an apparent response rate of 22%. Harris Interactive generally achieves a 15% to 20% response rate when using the Chronic Illness Sub-Panel. While our response rate was higher than Harris' average response rate, possible reasons for why the response was low could be attributed to the nature of the study design. First, because subjects were contacted by e-mail, it is possible that some subjects did not open the e-mail message until after the survey deadline. Second, Harris Interactive panel members agree to be notified about survey opportunities, but do not agree to participate in each survey. Since the characteristics of responders and nonresponders did not differ, we have no reason to believe that nonresponse bias is exerting a substantial influence on our results.
Discussion
Our study evaluated preferences for 5 health states for atopic dermatitis. The aggregate values for each health state may be used for computing QALYs for new therapies that treat atopic dermatitis or can be used to help physicians make more informed decisions by considering parents' perceptions of atopic dermatitis measured on a continuum from perfect health to death. The differences in average values across the 5 health states were generally consistent, with mild at 91, mild/moderate at 84, moderate at 73, moderate/severe at 61, and severe at 49.Lundberg and colleagues [ 13 ] found that the mean health-state utility using a rating scale for patients with atopic dermatitis was 77, a value only slightly higher than our average health-state utility of 73. This small discrepancy could be explained by several factors. First, Lundberg et al [ 13 ] asked adult patients to provide ratings for themselves, whereas our study asked parents to provide ratings for children. Parents might feel that a given health state is worse for their children than it would be for themselves. Second, Lundberg et al [ 13 ] asked patients to provide a rating for their current health state, whereas our study asked parents to assign utilities to 5 varying levels of severity of atopic dermatitis. The average severity level of Lundberg et al's [ 13 ] sample might have been slightly lower than the average severity among our 5 health states, making the mean preference value slightly higher. By obtaining preferences for varying levels of severity, our results have greater applicability in various types of models for decision making.Although preference ratings were systematically higher among parents of children with atopic dermatitis than among parents whose children did not have atopic dermatitis, the magnitude of the difference was small (difference = 1.5). Research on adults who rate the health states of other adults has suggested that people who have experienced a particular health state are more likely to assign a higher value to it than others who are asked to imagine the health state [ 19 - 21 ]. The lack of a larger difference between the parent groups in our study could indicate that parents evaluate health states of children the same, regardless of whose children they are, because of a general concern for all children. Regardless of the reason, these results have important implications for the use of community-based preference weights, as opposed to patient-based (or parent-based) weights, in preference-weighted decision analyses. For this limited therapeutic domain, our study shows that parents in the general community would supply approximately the same preferences as parents whose children suffer the condition under study. Future research should consider whether this consistency holds for other serious childhood diseases, such as pediatric cancer.Our study has several limitations. First, by using the Internet, respondents did not have an opportunity to ask questions if they did not understand what they were being asked to do. However, we feel that this limitation was negligible, because the instrument was pilot-tested using in-person interviews, and the very large majority of responses were ranked appropriately (eg, mild ranked higher than severe). Secondly, there may be a sample bias in using the Harris Interactive database. Our sample reflected a predominately white, female cohort from a high socioeconomic class and may present generalizability issues. However, we received responses from 351 (14%) nonwhite respondents and 779 (22%) responses from participants with an annual household income of less than $35000, providing a sufficient number of responses to test for differences by race/ethnicity and income level. Further research will be needed to corroborate our findings using a population-based sample. Third, since these data are self-reported, some parents may have misclassified their children as having or not having atopic dermatitis, potentially biasing our results. Fourth, it is unclear whether physician assessment of severity would correspond with the severity levels that we assigned to the health states. However, physicians could evaluate the descriptions provided to judge whether their assessments are consistent. Finally, people enrolled in the Harris Interactive database are computer users who may be more motivated to participate in a survey than the general population. It is unclear how or in what direction these sample biases might affect the results of our analyses.We had an apparent response rate of 22%. Harris Interactive generally achieves a 15% to 20% response rate when using the Chronic Illness Sub-Panel. While our response rate was higher than Harris' average response rate, possible reasons for why the response was low could be attributed to the nature of the study design. First, because subjects were contacted by e-mail, it is possible that some subjects did not open the e-mail message until after the survey deadline. Second, Harris Interactive panel members agree to be notified about survey opportunities, but do not agree to participate in each survey. Since the characteristics of responders and nonresponders did not differ, we have no reason to believe that nonresponse bias is exerting a substantial influence on our results.Conclusions The results of this analysis clarify the values that parents of children with atopic dermatitis assign to different atopic dermatitis health states. These assigned values, relative to the comparison states, clearly demonstrate the perceived burden of atopic dermatitis by parents of children suffering from the disease. Understanding the preferences for atopic dermatitis can provide physicians insight into the value that parents place on treatments for their child's disease and in evaluating the cost-effectiveness of therapies for atopic dermatitis.
Conclusions
The results of this analysis clarify the values that parents of children with atopic dermatitis assign to different atopic dermatitis health states. These assigned values, relative to the comparison states, clearly demonstrate the perceived burden of atopic dermatitis by parents of children suffering from the disease. Understanding the preferences for atopic dermatitis can provide physicians insight into the value that parents place on treatments for their child's disease and in evaluating the cost-effectiveness of therapies for atopic dermatitis.Competing interests This study was supported by a research agreement between Duke University Medical Center and the Novartis Pharmaceuticals Corporation, East Hanover, NJ, which manufactures a cytokine inhibitor for the treatment of atopic dermatitis. KPW and KAS have received monetary compensation for consultancies, and EBW and KAS have received research grants, from Novartis. KHK is an employee of Novartis.
Competing interests
This study was supported by a research agreement between Duke University Medical Center and the Novartis Pharmaceuticals Corporation, East Hanover, NJ, which manufactures a cytokine inhibitor for the treatment of atopic dermatitis.KPW and KAS have received monetary compensation for consultancies, and EBW and KAS have received research grants, from Novartis. KHK is an employee of Novartis.Authors' contributions JYF conceived of and designed the study, performed the statistical analysis, interpreted the data, and drafted the manuscript. SDR conceived of and designed the study and assisted in interpretation of the data and drafting of the manuscript. KPW assisted in interpretation of the data and drafting of the manuscript. EBW and KHK conceived of and designed the study and assisted in drafting of the manuscript. KAS conceived of and designed the study, assisted in drafting of the manuscript, and obtained funding. All authors read and approved the final manuscript.
Authors' contributions
JYF conceived of and designed the study, performed the statistical analysis, interpreted the data, and drafted the manuscript. SDR conceived of and designed the study and assisted in interpretation of the data and drafting of the manuscript. KPW assisted in interpretation of the data and drafting of the manuscript. EBW and KHK conceived of and designed the study and assisted in drafting of the manuscript. KAS conceived of and designed the study, assisted in drafting of the manuscript, and obtained funding. All authors read and approved the final manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Background A number of approaches have been used to grade levels of evidence and the strength of recommendations. The use of many different approaches detracts from one of the main reasons for having explicit approaches: to concisely characterise and communicate this information so that it can easily be understood and thereby help people make well-informed decisions. Our objective was to critically appraise six prominent systems for grading levels of evidence and the strength of recommendations as a basis for agreeing on characteristics of a common, sensible approach to grading levels of evidence and the strength of recommendations.
Background
A number of approaches have been used to grade levels of evidence and the strength of recommendations. The use of many different approaches detracts from one of the main reasons for having explicit approaches: to concisely characterise and communicate this information so that it can easily be understood and thereby help people make well-informed decisions. Our objective was to critically appraise six prominent systems for grading levels of evidence and the strength of recommendations as a basis for agreeing on characteristics of a common, sensible approach to grading levels of evidence and the strength of recommendations.Methods Six prominent systems for grading levels of evidence and strength of recommendations were selected and someone familiar with each system prepared a description of each of these. Twelve assessors independently evaluated each system based on twelve criteria to assess the sensibility of the different approaches. Systems used by 51 organisations were compared with these six approaches.
Methods
Six prominent systems for grading levels of evidence and strength of recommendations were selected and someone familiar with each system prepared a description of each of these. Twelve assessors independently evaluated each system based on twelve criteria to assess the sensibility of the different approaches. Systems used by 51 organisations were compared with these six approaches.Results There was poor agreement about the sensibility of the six systems. Only one of the systems was suitable for all four types of questions we considered (effectiveness, harm, diagnosis and prognosis). None of the systems was considered usable for all of the target groups we considered (professionals, patients and policy makers). The raters found low reproducibility of judgements made using all six systems. Systems used by 51 organisations that sponsor clinical practice guidelines included a number of minor variations of the six systems that we critically appraised.
Results
There was poor agreement about the sensibility of the six systems. Only one of the systems was suitable for all four types of questions we considered (effectiveness, harm, diagnosis and prognosis). None of the systems was considered usable for all of the target groups we considered (professionals, patients and policy makers). The raters found low reproducibility of judgements made using all six systems. Systems used by 51 organisations that sponsor clinical practice guidelines included a number of minor variations of the six systems that we critically appraised.Conclusions All of the currently used approaches to grading levels of evidence and the strength of recommendations have important shortcomings.
Conclusions
All of the currently used approaches to grading levels of evidence and the strength of recommendations have important shortcomings.

Background In 1979 the Canadian task Force on the Periodic Health Examination published one of the first efforts to explicitly characterise the level of evidence underlying healthcare recommendations and the strength of recommendations [ 1 ]. Since then a number of alternative approaches has been proposed and used to classify clinical practice guidelines [ 2 - 28 ]. The original approach used by the Canadian Task Force was based on study design alone, with randomised controlled trials (RCTs) being classified as good (level I) evidence, cohort and case control studies being classified as fair (level II) evidence and expert opinion being classified as poor (level III) evidence. The strength of recommendation was based on the level of evidence with direct correspondence between the two; e.g. a strong recommendation (A) corresponded to there being good evidence. A strength of the original Canadian Task Force approach was that it was simple; the main weakness was that it was too simple. Because of its simplicity, it was easy to understand, apply and present. However, because it was so simple there were many implicit judgements, including judgements about the quality of RCTs, conflicting results of RCTs, and convincing results from non-experimental studies. For example: • Should a small, poorly designed RCT be considered level I evidence? • Should RCTs with conflicting results still be considered level I evidence? • Should observational studies always be considered level II evidence, regardless of how convincing they are? The original approach by the Canadian Task Force also did not include explicit judgements about the strength of recommendations, such as how trade-offs between the expected benefits, harms and costs were weighed and taken account of in going from an assessment of how good the evidence is to determining the implications of the results for practice. The GRADE Working Group is an informal collaboration of people with an interest in addressing shortcomings such as these in systems for grading evidence and recommendations. We describe here a critical appraisal of six prominent systems and the results of the critical appraisal.
Background
In 1979 the Canadian task Force on the Periodic Health Examination published one of the first efforts to explicitly characterise the level of evidence underlying healthcare recommendations and the strength of recommendations [ 1 ]. Since then a number of alternative approaches has been proposed and used to classify clinical practice guidelines [ 2 - 28 ].The original approach used by the Canadian Task Force was based on study design alone, with randomised controlled trials (RCTs) being classified as good (level I) evidence, cohort and case control studies being classified as fair (level II) evidence and expert opinion being classified as poor (level III) evidence. The strength of recommendation was based on the level of evidence with direct correspondence between the two; e.g. a strong recommendation (A) corresponded to there being good evidence. A strength of the original Canadian Task Force approach was that it was simple; the main weakness was that it was too simple. Because of its simplicity, it was easy to understand, apply and present. However, because it was so simple there were many implicit judgements, including judgements about the quality of RCTs, conflicting results of RCTs, and convincing results from non-experimental studies.For example:• Should a small, poorly designed RCT be considered level I evidence?• Should RCTs with conflicting results still be considered level I evidence?• Should observational studies always be considered level II evidence, regardless of how convincing they are?The original approach by the Canadian Task Force also did not include explicit judgements about the strength of recommendations, such as how trade-offs between the expected benefits, harms and costs were weighed and taken account of in going from an assessment of how good the evidence is to determining the implications of the results for practice.The GRADE Working Group is an informal collaboration of people with an interest in addressing shortcomings such as these in systems for grading evidence and recommendations. We describe here a critical appraisal of six prominent systems and the results of the critical appraisal.Methods We selected systems for grading the level of evidence and the strength of recommendations that we considered prominent and that included features not captured by other prominent systems. These were selected based on the experience and knowledge of the authors through informal discussion. A description of the most recent version (as of summer 2000) of each of these systems (Appendix 1 to 6), was prepared by one of the authors familiar with the system, and used in this exercise. The following six systems were appraised: the American College of Chest Physicians (ACCP, [see  Additional file 1 ]) [ 21 ], Australian National Health and Medical Research Council (ANHMRC, [see  Additional file 2 ]) [ 17 ], Oxford Centre for Evidence-Based Medicine (OCEBM, [see  Additional file 3 ]) [ 16 ], Scottish Intercollegiate Guidelines Network (SIGN, [see  Additional file 4 ]) [ 18 ], US Preventive Services Task Force (USPSTF, [see  Additional file 5 ]) [ 22 ], US Task Force on Community Preventive Services (USTFCPS, [see  Additional file 6 ]) [ 25 ]. These descriptions of the systems were given to the twelve people who independently appraised the six systems, all of the authors minus GEV appraised the six systems, three of the authors (DH, SH and DO'C) appraised as a group and reported as one (see contributions). The 12 assessors all had experience with at least one system and most had helped to develop one of the six included systems. Twelve criteria described by Feinstein [ 29 ] provided the basis for assessing the sensibility of the six systems. Criteria used to assess the sensibility of systems for grading evidence and recommendations 1. To what extent is the approach applicable to different types of questions? -effectiveness, harm, diagnosis and prognosis (No, Not sure, Yes) 2. To what extent can the system be used with different audiences? -patients, professionals and policy makers (Little extent, Some extent, Large extent) 3. How clear and simple is the system? (Not very clear, Somewhat clear, Very clear) 4. How often will information not usually available be necessary? (Often, Sometimes, Seldom) 5. To what extent are subjective decisions needed? (Often, Sometimes, Seldom) 6. Are dimensions included that are not within the construct (level of evidence or strength of recommendation)? (Yes, Partially, No) 7. Are there important dimensions that should have been included and are not? (No, Partially, Yes) 8. Is the way in which the included dimensions are aggregated clear and simple? (No, Partially, Yes) 9. Is the way in which the included dimensions are aggregated appropriate? (No. Partially, Yes) 10. Are the categories sufficient to discriminate between different levels of evidence and strengths of recommendations? (No, Partially, Yes) 11. How likely is the system to be successful in discriminating between high and low levels of evidence or strong and weak recommendations? (Not very likely, Somewhat likely, Highly likely) 12. Are assessments reproducible? (Probably not, Not sure, Probably) No training was provided and we did not discuss the 12 criteria prior to applying them to the six systems. Our independent appraisal of the six systems were summarised and discussed. The discussion focused on differences in the interpretation of the criteria, disagreement about the judgements that we made and sources of these disagreements, the strengths and weaknesses of the six systems, and inferences based on the appraisals and subsequent discussion. In order to identify important systems that we might have overlooked following our appraisal of these six systems we also searched the US Agency for Health Care Research and Quality (AHRQ) National Guidelines Clearing House for organisations that have graded two or more guidelines in the Clearing House using an explicit system [ 30 ]. These systems were compared with the six systems that we critically appraised.
Methods
We selected systems for grading the level of evidence and the strength of recommendations that we considered prominent and that included features not captured by other prominent systems. These were selected based on the experience and knowledge of the authors through informal discussion. A description of the most recent version (as of summer 2000) of each of these systems (Appendix 1 to 6), was prepared by one of the authors familiar with the system, and used in this exercise. The following six systems were appraised: the American College of Chest Physicians (ACCP, [see  Additional file 1 ]) [ 21 ], Australian National Health and Medical Research Council (ANHMRC, [see  Additional file 2 ]) [ 17 ], Oxford Centre for Evidence-Based Medicine (OCEBM, [see  Additional file 3 ]) [ 16 ], Scottish Intercollegiate Guidelines Network (SIGN, [see  Additional file 4 ]) [ 18 ], US Preventive Services Task Force (USPSTF, [see  Additional file 5 ]) [ 22 ], US Task Force on Community Preventive Services (USTFCPS, [see  Additional file 6 ]) [ 25 ].These descriptions of the systems were given to the twelve people who independently appraised the six systems, all of the authors minus GEV appraised the six systems, three of the authors (DH, SH and DO'C) appraised as a group and reported as one (see contributions). The 12 assessors all had experience with at least one system and most had helped to develop one of the six included systems. Twelve criteria described by Feinstein [ 29 ] provided the basis for assessing the sensibility of the six systems.Criteria used to assess the sensibility of systems for grading evidence and recommendations 1. To what extent is the approach applicable to different types of questions? -effectiveness, harm, diagnosis and prognosis (No, Not sure, Yes) 2. To what extent can the system be used with different audiences? -patients, professionals and policy makers (Little extent, Some extent, Large extent) 3. How clear and simple is the system? (Not very clear, Somewhat clear, Very clear) 4. How often will information not usually available be necessary? (Often, Sometimes, Seldom) 5. To what extent are subjective decisions needed? (Often, Sometimes, Seldom) 6. Are dimensions included that are not within the construct (level of evidence or strength of recommendation)? (Yes, Partially, No) 7. Are there important dimensions that should have been included and are not? (No, Partially, Yes) 8. Is the way in which the included dimensions are aggregated clear and simple? (No, Partially, Yes) 9. Is the way in which the included dimensions are aggregated appropriate? (No. Partially, Yes) 10. Are the categories sufficient to discriminate between different levels of evidence and strengths of recommendations? (No, Partially, Yes) 11. How likely is the system to be successful in discriminating between high and low levels of evidence or strong and weak recommendations? (Not very likely, Somewhat likely, Highly likely) 12. Are assessments reproducible? (Probably not, Not sure, Probably) No training was provided and we did not discuss the 12 criteria prior to applying them to the six systems. Our independent appraisal of the six systems were summarised and discussed. The discussion focused on differences in the interpretation of the criteria, disagreement about the judgements that we made and sources of these disagreements, the strengths and weaknesses of the six systems, and inferences based on the appraisals and subsequent discussion. In order to identify important systems that we might have overlooked following our appraisal of these six systems we also searched the US Agency for Health Care Research and Quality (AHRQ) National Guidelines Clearing House for organisations that have graded two or more guidelines in the Clearing House using an explicit system [ 30 ]. These systems were compared with the six systems that we critically appraised.
Criteria used to assess the sensibility of systems for grading evidence and recommendations
1. To what extent is the approach applicable to different types of questions? -effectiveness, harm, diagnosis and prognosis (No, Not sure, Yes)2. To what extent can the system be used with different audiences? -patients, professionals and policy makers (Little extent, Some extent, Large extent)3. How clear and simple is the system? (Not very clear, Somewhat clear, Very clear)4. How often will information not usually available be necessary? (Often, Sometimes, Seldom)5. To what extent are subjective decisions needed? (Often, Sometimes, Seldom)6. Are dimensions included that are not within the construct (level of evidence or strength of recommendation)? (Yes, Partially, No)7. Are there important dimensions that should have been included and are not? (No, Partially, Yes)8. Is the way in which the included dimensions are aggregated clear and simple? (No, Partially, Yes)9. Is the way in which the included dimensions are aggregated appropriate? (No. Partially, Yes)10. Are the categories sufficient to discriminate between different levels of evidence and strengths of recommendations? (No, Partially, Yes)11. How likely is the system to be successful in discriminating between high and low levels of evidence or strong and weak recommendations? (Not very likely, Somewhat likely, Highly likely)12. Are assessments reproducible? (Probably not, Not sure, Probably)No training was provided and we did not discuss the 12 criteria prior to applying them to the six systems.Our independent appraisal of the six systems were summarised and discussed. The discussion focused on differences in the interpretation of the criteria, disagreement about the judgements that we made and sources of these disagreements, the strengths and weaknesses of the six systems, and inferences based on the appraisals and subsequent discussion.In order to identify important systems that we might have overlooked following our appraisal of these six systems we also searched the US Agency for Health Care Research and Quality (AHRQ) National Guidelines Clearing House for organisations that have graded two or more guidelines in the Clearing House using an explicit system [ 30 ]. These systems were compared with the six systems that we critically appraised.Results There was poor agreement among the 12 assessors who independently assessed the six systems. A summary of the assessments of the sensibility of the six approaches to rating levels of evidence and strength of recommendation is shown in Table  1 .
Results
There was poor agreement among the 12 assessors who independently assessed the six systems. A summary of the assessments of the sensibility of the six approaches to rating levels of evidence and strength of recommendation is shown in Table  1 .Discussion The poor agreement among the assessors likely reflects several factors. Some of us had practical experience using one of the systems or used additional background information related to one or more grading systems, and we may have been biased in favour of the system with which we were most familiar. Each criterion was applied to grading both evidence and recommendations. Some systems were better for one of these constructs than the other and we may have handled these discrepancies differently. In addition each criterion may have been assessed relative to different judgements about the evidence, such as an assessment of the overall quality of evidence for an important outcome (across studies) versus the quality of an individual study. Some of the criteria were not clear and were interpreted or applied inconsistently. For example, a system might be clear and not simple or visa versa. We likely differed in how stringently we applied the criteria. Finally, there was true disagreement. There was agreement that the OCEBM system works well for all four types of questions. There was disagreement about the extent to which the other systems work well for questions other than effectiveness. It was noted that some systems are not intended to address other types of questions and it is not clear that it is important that a system should address all four types of questions that we considered (effectiveness, harm, diagnosis, prognosis), although criteria for assessing individual studies must take this into account [ 31 , 32 ]. Most of us did not find that any of the systems are likely to be suitable for use by patients. Almost all agreed that the ACCP system was suitable for professionals and most considered that the USPSTF system was suitable for professionals. There was not much agreement about the suitability of any of the other systems for professionals or about the suitability of any of the systems for policy makers, although most assessed the USTFCPS system to be suitable for policy makers. There was no agreement that any of the systems are clear and simple, although USPSTF, ACCP and SIGN systems were generally assessed more favourably in this regard. It was generally agreed that the clearer a system was the less simple it was; e.g. the OCEBM system is clear but not simple for categorising the level of evidence. There was some confusion regarding whether we were assessing how clear and simple the system was to guideline developers (as some interpreted this criterion) or how clear and simple the outcome of applying the system was to guideline users (as others interpreted this criterion). Either way, the simpler a system is the less clear it is likely to be. Most of us judged that for most of the systems necessary information would not be available at least sometimes. The OCEBM system came out somewhat better than the other systems and lack of availability of necessary information was considered to be less of a problem for the USTFCPS system. However, the OCEBM and USTFCPS systems were considered by most to be missing dimensions which may, in part, explain why missing information was considered to be less of a problem. This would be the case to the extent the missing dimensions were the ones for which information would often or sometimes not be available. The dimension for which we considered that information would most often be missing was trade-offs; i.e. knowledge of the preferences or utility values of those affected. Additional problems were identified in relationship to complex interventions and counselling, particularly with the USTFCPS and USPSTF systems. It was pointed out that the USTFCPS system addressed this problem by including availability of information about the intervention as part of its assessment of the quality of evidence. Most of the systems were assessed to require subjective decisions at least to some extent. The OCEBM system again stood out as being assessed more favourably, although it may be related to omission of dimensions that require more subjective decisions. Judgement is clearly needed with any system. The aim should be to make judgements transparent and to try to protect against bias in the judgements that are made by being systematic and explicit. Inclusion of dimensions that are not within the constructs being graded was not considered a problem for most of the systems by most of us. Several people considered that it might be a problem for the USTFCPS and USPSTF systems. On the other hand, all of the systems were evaluated to be missing at least one important dimension by at least one person. The challenge of missing dimensions were considered less of a problem for the ACCP and ANHMRC systems. There was not agreement about any of the systems having a clear and simple approach to aggregating the dimensions, although this was considered to be less of a problem for the ACCP, SIGN and USTFCPS systems. There was also not agreement on the appropriateness of how the dimensions were aggregated. This was considered to be more of a problem for the ANHMRC and USTFCPS systems than the other four systems, all of which were considered to have taken an approach to aggregating the dimensions that was at least partially inappropriate by more than half of us. Most of us considered that most of the systems had sufficient categories, with the exception of the ANHMRC system. There was almost agreement that the USPSTF system has sufficient categories. We agreed that it is possible to have too many categories as well as too few, the OCEBM system being an example of having too many categories. There was not agreement that any of the systems are likely to discriminate successfully, although everyone thought that the ACCP, SIGN and USPSTF systems are somewhat to highly likely to discriminate. Lastly, we largely agreed that we were not sure how reproducible assessments are using any of the systems, although half of us considered that assessments using the ANHMRC system are unlikely to be reproducible and about 1/3 considered that assessments using the OCEBM and ACCP systems are likely to be reproducible. We identified 22 additional organisations that have produced 10 or more practice guidelines using an explicit approach to grade the level of evidence or strength of recommendations. Another 29 have produced between two and nine guidelines using an explicit approach. These systems include a number of minor variations of the six systems that we appraised in detail. There was generally poor agreement between the individual assessors about the scoring of the six approaches using the 12 criteria. However, there was general agreement that none of these six prominent approaches to grading the levels of evidence and strength of recommendations adequately addressed all of the important concepts and dimensions that we thought should be considered. Although we limited our appraisal to six systems all of the additional approaches to grading levels of evidence and strength of recommendations that we identified were, in essence, variations of the six approaches that we had critically appraised. Therefore we are confident that we did not miss any important grading systems available at the time when these assessments were undertaken. Based on discussions following the critical appraisal of these six approaches, we agreed on some conclusions: • Separate assessments should be presented for judgements about the quality of the evidence and judgements about the balance of benefits and harms. • Evidence for harms should be assessed in the same way as evidence for benefits, although different evidence may be considered relevant for harms than for benefits; e.g. local evidence of complication rates may be considered more relevant than evidence of complication rates from trials for endarterectomy. • Judgements about the quality of evidence should be based on a systematic review of the relevant research. • Systematic reviews should not be included in a hierarchy of evidence (i.e. as a level or category of evidence). The availability of a well-done systematic review does not correspond to high quality evidence, since a well-done review might include anything from no studies to poor quality studies with inconsistent results to high quality studies with consistent results. • Baseline risk should be taken into consideration in defining the population to whom a recommendation applies. Baseline risk should also be used transparently in making judgements about the balance of benefits and harms. When a recommendation varies in relationship to baseline risk, the evidence for determining baseline risk should be assessed appropriately and explicitly. • Recommendations should not vary in relationship to baseline risk if there is not adequate evidence to guide reliable determinations of baseline risk.
Discussion
The poor agreement among the assessors likely reflects several factors. Some of us had practical experience using one of the systems or used additional background information related to one or more grading systems, and we may have been biased in favour of the system with which we were most familiar. Each criterion was applied to grading both evidence and recommendations. Some systems were better for one of these constructs than the other and we may have handled these discrepancies differently. In addition each criterion may have been assessed relative to different judgements about the evidence, such as an assessment of the overall quality of evidence for an important outcome (across studies) versus the quality of an individual study. Some of the criteria were not clear and were interpreted or applied inconsistently. For example, a system might be clear and not simple or visa versa. We likely differed in how stringently we applied the criteria. Finally, there was true disagreement.There was agreement that the OCEBM system works well for all four types of questions. There was disagreement about the extent to which the other systems work well for questions other than effectiveness. It was noted that some systems are not intended to address other types of questions and it is not clear that it is important that a system should address all four types of questions that we considered (effectiveness, harm, diagnosis, prognosis), although criteria for assessing individual studies must take this into account [ 31 , 32 ].Most of us did not find that any of the systems are likely to be suitable for use by patients. Almost all agreed that the ACCP system was suitable for professionals and most considered that the USPSTF system was suitable for professionals. There was not much agreement about the suitability of any of the other systems for professionals or about the suitability of any of the systems for policy makers, although most assessed the USTFCPS system to be suitable for policy makers.There was no agreement that any of the systems are clear and simple, although USPSTF, ACCP and SIGN systems were generally assessed more favourably in this regard. It was generally agreed that the clearer a system was the less simple it was; e.g. the OCEBM system is clear but not simple for categorising the level of evidence. There was some confusion regarding whether we were assessing how clear and simple the system was to guideline developers (as some interpreted this criterion) or how clear and simple the outcome of applying the system was to guideline users (as others interpreted this criterion). Either way, the simpler a system is the less clear it is likely to be.Most of us judged that for most of the systems necessary information would not be available at least sometimes. The OCEBM system came out somewhat better than the other systems and lack of availability of necessary information was considered to be less of a problem for the USTFCPS system. However, the OCEBM and USTFCPS systems were considered by most to be missing dimensions which may, in part, explain why missing information was considered to be less of a problem. This would be the case to the extent the missing dimensions were the ones for which information would often or sometimes not be available. The dimension for which we considered that information would most often be missing was trade-offs; i.e. knowledge of the preferences or utility values of those affected. Additional problems were identified in relationship to complex interventions and counselling, particularly with the USTFCPS and USPSTF systems. It was pointed out that the USTFCPS system addressed this problem by including availability of information about the intervention as part of its assessment of the quality of evidence.Most of the systems were assessed to require subjective decisions at least to some extent. The OCEBM system again stood out as being assessed more favourably, although it may be related to omission of dimensions that require more subjective decisions. Judgement is clearly needed with any system. The aim should be to make judgements transparent and to try to protect against bias in the judgements that are made by being systematic and explicit.Inclusion of dimensions that are not within the constructs being graded was not considered a problem for most of the systems by most of us. Several people considered that it might be a problem for the USTFCPS and USPSTF systems. On the other hand, all of the systems were evaluated to be missing at least one important dimension by at least one person. The challenge of missing dimensions were considered less of a problem for the ACCP and ANHMRC systems. There was not agreement about any of the systems having a clear and simple approach to aggregating the dimensions, although this was considered to be less of a problem for the ACCP, SIGN and USTFCPS systems.There was also not agreement on the appropriateness of how the dimensions were aggregated. This was considered to be more of a problem for the ANHMRC and USTFCPS systems than the other four systems, all of which were considered to have taken an approach to aggregating the dimensions that was at least partially inappropriate by more than half of us.Most of us considered that most of the systems had sufficient categories, with the exception of the ANHMRC system. There was almost agreement that the USPSTF system has sufficient categories. We agreed that it is possible to have too many categories as well as too few, the OCEBM system being an example of having too many categories.There was not agreement that any of the systems are likely to discriminate successfully, although everyone thought that the ACCP, SIGN and USPSTF systems are somewhat to highly likely to discriminate. Lastly, we largely agreed that we were not sure how reproducible assessments are using any of the systems, although half of us considered that assessments using the ANHMRC system are unlikely to be reproducible and about 1/3 considered that assessments using the OCEBM and ACCP systems are likely to be reproducible.We identified 22 additional organisations that have produced 10 or more practice guidelines using an explicit approach to grade the level of evidence or strength of recommendations. Another 29 have produced between two and nine guidelines using an explicit approach. These systems include a number of minor variations of the six systems that we appraised in detail.There was generally poor agreement between the individual assessors about the scoring of the six approaches using the 12 criteria. However, there was general agreement that none of these six prominent approaches to grading the levels of evidence and strength of recommendations adequately addressed all of the important concepts and dimensions that we thought should be considered. Although we limited our appraisal to six systems all of the additional approaches to grading levels of evidence and strength of recommendations that we identified were, in essence, variations of the six approaches that we had critically appraised. Therefore we are confident that we did not miss any important grading systems available at the time when these assessments were undertaken.Based on discussions following the critical appraisal of these six approaches, we agreed on some conclusions:• Separate assessments should be presented for judgements about the quality of the evidence and judgements about the balance of benefits and harms.• Evidence for harms should be assessed in the same way as evidence for benefits, although different evidence may be considered relevant for harms than for benefits; e.g. local evidence of complication rates may be considered more relevant than evidence of complication rates from trials for endarterectomy.• Judgements about the quality of evidence should be based on a systematic review of the relevant research.• Systematic reviews should not be included in a hierarchy of evidence (i.e. as a level or category of evidence). The availability of a well-done systematic review does not correspond to high quality evidence, since a well-done review might include anything from no studies to poor quality studies with inconsistent results to high quality studies with consistent results.• Baseline risk should be taken into consideration in defining the population to whom a recommendation applies. Baseline risk should also be used transparently in making judgements about the balance of benefits and harms. When a recommendation varies in relationship to baseline risk, the evidence for determining baseline risk should be assessed appropriately and explicitly.• Recommendations should not vary in relationship to baseline risk if there is not adequate evidence to guide reliable determinations of baseline risk.Conclusions Based on discussions of the strengths and limitations of current approaches to grading levels of evidence and the strength of recommendations, we agreed to develop an approach that addresses the major limitations that we identified. The approach that the GRADE Working Group has developed is based on the discussions following the critical appraisal reported here and a pilot study of the GRADE approach [ 33 ]. Based on the pilot testing and the discussions following the pilot, the GRADE Working Group has further developed the GRADE system to its present format [ 34 ]. The GRADE Working Group has continued to grow as an informal collaboration that meets one or two times per year. The group maintains web pages   and a discussion list.
Conclusions
Based on discussions of the strengths and limitations of current approaches to grading levels of evidence and the strength of recommendations, we agreed to develop an approach that addresses the major limitations that we identified. The approach that the GRADE Working Group has developed is based on the discussions following the critical appraisal reported here and a pilot study of the GRADE approach [ 33 ]. Based on the pilot testing and the discussions following the pilot, the GRADE Working Group has further developed the GRADE system to its present format [ 34 ].The GRADE Working Group has continued to grow as an informal collaboration that meets one or two times per year. The group maintains web pages   and a discussion list.Competing interests DA has competing interests with the US Preventive Services Task Force (USPSTF), PAB has a competing interest with the US Task Force on Community Preventive Services (USTFCPS), GHG and HS have competing interests with the American College of Chest Physicians (ACCP), DH, SH and DO'C have competing interests with the Australian National Health and Medical Research Council (ANHMRC), BP has competing interests with the Oxford Centre for Evidence-Based Medicine (OCEBM). Most of the other members of the GRADE Working Group have experience with the use of one or more systems of grading evidence and recommendations.
Competing interests
DA has competing interests with the US Preventive Services Task Force (USPSTF), PAB has a competing interest with the US Task Force on Community Preventive Services (USTFCPS), GHG and HS have competing interests with the American College of Chest Physicians (ACCP), DH, SH and DO'C have competing interests with the Australian National Health and Medical Research Council (ANHMRC), BP has competing interests with the Oxford Centre for Evidence-Based Medicine (OCEBM). Most of the other members of the GRADE Working Group have experience with the use of one or more systems of grading evidence and recommendations.Contributions DA, PAB, ME, SF, GHG, DH, SH, AL, DO'C, ADO, BP, HS, TTTE, GEV & JWW Jr as members of the GRADE Working Group have contributed to the preparation of this manuscript and the development of the ideas contained herein, participated in the critical assessment, and read and commented on drafts of this article. GHG and ADO have led the process. GEV has had primary responsibility for coordinating the process.
Contributions
DA, PAB, ME, SF, GHG, DH, SH, AL, DO'C, ADO, BP, HS, TTTE, GEV & JWW Jr as members of the GRADE Working Group have contributed to the preparation of this manuscript and the development of the ideas contained herein, participated in the critical assessment, and read and commented on drafts of this article. GHG and ADO have led the process. GEV has had primary responsibility for coordinating the process.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:Supplementary Material Additional File 1 American College of Chest Physicians (ACCP), a brief description of the ACCP approach. Click here for file Additional File 2 Australian National Health and Medical Research Council (ANHMRC), a brief description of the ANHMRC approach. Click here for file Additional File 3 Oxford Centre for Evidence-based Medicine (OCEBM), a brief description of the OCEBM approach. Click here for file Additional File 4 Scottish Intercollegiate Guidelines (SIGN), a brief description of the SIGN approach. Click here for file Additional File 5 U.S. Preventive Services Task Force (USPSTF), a brief description of the USPSTF approach. Click here for file Additional File 6 U.S. Task Force on Community Preventive Services (USTFCPS), a brief description of the USTFCPS approach. Click here for file
Supplementary Material
Additional File 1
American College of Chest Physicians (ACCP), a brief description of the ACCP approach.Click here for fileAdditional File 2
Australian National Health and Medical Research Council (ANHMRC), a brief description of the ANHMRC approach.Click here for fileAdditional File 3
Oxford Centre for Evidence-based Medicine (OCEBM), a brief description of the OCEBM approach.Click here for fileAdditional File 4
Scottish Intercollegiate Guidelines (SIGN), a brief description of the SIGN approach.Click here for fileAdditional File 5
U.S. Preventive Services Task Force (USPSTF), a brief description of the USPSTF approach.Click here for fileAdditional File 6
U.S. Task Force on Community Preventive Services (USTFCPS), a brief description of the USTFCPS approach.Click here for file


Acidosis is a major cause of death in patients with malaria, although what causes acidosis is still unclear. One possibility is that hypovolemia contributes to the problem, and that rehydration therapy could be of benefit. Now, Sanjeev Krishna and colleagues have shown that in children with severe malaria dehydration is not severe and is not correlated with other measures of disease severity. “The optimum resuscitation approach in severe childhood malaria remains to be defined,” says Nick White (Mahidol University, Thailand), the academic editor of the paper. “The relative advantages of blood, colloids, and crystalloids need to be characterized.”Every year around 200 million people worldwide contract malaria, of whom over a million die. The vast majority of those who die are children under five years, mostly in Africa, since young children have had little chance to acquire any immunity. Fluid resuscitation is generally considered to be a cornerstone of treatment—but how much fluid should be given? Some researchers believe that surrogate signs of fluid depletion—such as tachycardia, reduced capillary refill time, and reduced urine excretion—suggest that there is substantial volume depletion. The reason that the amount of fluid given matters so much is that giving too much, especially of hypotonic solutions, can lead to electrolyte imbalance, especially hyponatremia and hypokalemia.Research efforts have been hampered by not having an easy way to assess in patients the fluid depletion in different compartments of the body, i.e., total body water and extracellular and intracellular water volume. Krishna and colleagues used heavy-water distribution to calculate the total body water and bromide distribution to determine the extracellular volume in 19 children with moderately severe malaria and 16 with severe malaria in Gabon. By subtracting extracellular volume from total body water, they were able to calculate intracellular volume for each child. They also used a less invasive and more rapid method of determining water volumes based on using bioelectrical impedance to calculate the volume.None of the children were severely dehydrated (defined as more than 100 ml/kg depletion), and only three of the children with severe anemia had fluid depletion, which was moderate (60–90 ml/kg depletion). “This challenges the view that dehydration is a major contributor to the pathology of this frequently lethal disease,” says White.So based on these data, obtained from a carefully studied, albeit small group of children, what should people who treat children with malaria do? The authors' first recommendation is that clinicians should think again about how vigorously they rehydrate children, and if they have access to ways of assessing fluid volume more precisely, they should do so (not a trivial undertaking in many hospitals where these children are treated). And certainly the methods used by Krishna and colleagues should undergo wider testing in larger groups of children to confirm their usefulness. Until the worldwide efforts to prevent malaria come to fruition, refining the management of infected children will remain a cornerstone of the efforts against this devastating disease.