About 10 to 20% of children in West European countries have atopic dermatitis (AD), often as part of the atopic syndrome. The full atopic syndrome also consists of allergic asthma, allergic rhinitis and food allergy. Treatment approaches for atopic dermatitis and asthma include intermittent anti-inflammatory therapy with corticosteroids, health education and self-management training. However, symptoms persist in a subgroup of patients. Several observational studies have shown significant improvement in clinical symptoms in children and adults with atopic dermatitis or asthma after treatment at high altitude, but evidence on the efficacy when compared to treatment at sea level is still lacking.This study is a pragmatic randomized controlled trial for children with moderate to severe AD within the atopic syndrome. Patients are eligible for enrolment in the study if they are: diagnosed with moderate to severe AD within the atopic syndrome, aged between 8 and 18 years, fluent in the Dutch language, have internet access at home, able to use the digital patient system Digital Eczema Center Utrecht (DECU), willing and able to stay in Davos for a six week treatment period. All data are collected at the Wilhelmina Children’s Hospital and DECU. Patients are randomized over two groups. The first group receives multidisciplinary inpatient treatment during six weeks at the Dutch Asthma Center in Davos, Switzerland. The second group receives multidisciplinary treatment during six weeks at the outpatient clinic of the Wilhelmina Children’s Hospital, Utrecht, the Netherlands. The trial is not conducted as a blind trial. The trial is designed with three components: psychosocial, clinical and translational. Primary outcomes are coping with itch, quality of life and disease activity. Secondary outcomes include asthma control, medication use, parental quality of life, social and emotional wellbeing of the child and translational parameters.The results of this trial will provide evidence for the efficacy of high altitude treatment compared to treatment at sea level for children with moderate to severe AD.ISRCTN88136485.Current Controlled Trials  About 10 to 20% of children in West European countries are diagnosed with atopic dermatitis (AD) . The revHowever, symptoms persist in a subgroup of children with moderate to severe AD. Often these children have an extensive treatment history and are unable to gain sufficient control of their disease. It has been shown that a multidisciplinary approach to AD treatment is beneficial for children who do not respond well to regular treatment, and often results in sustained clinical improvement ,8. The mSince the 1950s Dutch children with asthma are referred to the Dutch Asthma Center in Davos, located at 1560 m altitude in the Swiss Alps, for ‘high altitude’ treatment . ImportaIn the DAVOS trial, we compare multidisciplinary treatment at high altitude and sea level in a pragmatic randomized design. The objective of this study is to find the most effective treatment strategy for children with difficult to treat AD. This paper describes the study design and the interventions.A pragmatic randomized controlled trial was designed in collaboration with the Department of Pediatric Dermatology/Allergology in the Wihelmina Children’s Hospital  and the high altitude clinic Dutch Asthma Center Davos in Switzerland. Dutch children with moderate to severe AD are either treated during six weeks at the Dutch Asthma Center Davos (inpatient treatment) or at the Wilhelmina Children’s Hospital (outpatient treatment). All children are expected to be included in the study within a time period of four years.The trial consists of three different components: psychosocial, clinical and translational. The children in the study will be extensively monitored on these three aspects at study inclusion and during the study period. The study period includes the actual enrollment in the study, a six week intervention period and follow-up measurements at six weeks and six months after the intervention Figure . EnrollmThis study follows the Dutch Medical Research Involving Human Subjects Act 1998 (WMO) and the Helsinki Declaration principles 2008, meaning that the legal representatives of all participants will sign a written informed consent, stating that participation can be withdrawn at any time without any negative consequences concerning their current or future medical treatment. All study procedures have been reviewed and approved by the Medical Ethics Committee of the Utrecht Medical Center, the Netherlands (reference 09-192/K).The DAVOS trial is designed for children in the Netherlands with difficult to treat AD who are unable to gain sufficient control of their disease with current treatment strategies. Children are eligible for enrolment in the study if they are: diagnosed with moderate to severe AD within the atopic syndrome; aged between 8 and 18 years; fluent in the Dutch language; have internet access at home; able to use the Digital Eczema Center Utrecht; and willing and able to stay in Davos for a six week treatment period.Children are not eligible for enrolment if they are currently participating in another study.The DAVOS trial is a national trial conducted in the Netherlands. Children with moderate to severe AD will be recruited from the outpatient clinic of the Wilhelmina Children’s Hospital and from other hospitals in the country. Dutch dermatologists, pediatricians, allergists, general practitioners and patient support groups were informed about the trial in journals, conferences and using online media.Children and/or their parents who are interested in the trial can register themselves directly in the DECU. A questionnaire with items regarding allergic complaints, use of  immunosuppressive therapy such as corticosteroids, and more specific questions regarding AD activity (including a self-administered eczema area and severity index (SA-EASI)), questions on nightly complaints of itch and sleeping behavior and, if relevant, questions on asthma, rhinitis and food allergy complaints has to be answered in order to register. Possible study participants are invited by the dermatologist to the outpatient clinic and assessed for eligibility. They are not required to stop or change their medication at this point. Children who fulfill the inclusion criteria are then informed about the study. If they are interested in participating in the trial, they are invited for a complete evaluation by the multidisciplinary team at the Wilhelmina Children’s Hospital, and informed consent is signed.et al. on the effect of educational programs in children and adolescents with AD [The sample size calculation for the psychosocial part is based on the primary outcome coping with itch, measured with the JUCKKI, children’s itching cognitions questionnaire . A diffe with AD . In thiset al., in which SCORAD was used to measure disease activity, a difference of 10 points between the groups and a SD of 15 for each group was expected [An additional sample size calculation was carried out for the clinical part of the trial. This was based on the primary outcome disease activity, measured with the SA-EASI . Based oexpected ,26.With a power of 80%, 36 children are needed in each group. Assuming a drop-out rate of 10%, 40 children will be assigned to each of the two study conditions.Children are randomly assigned to either intervention or control groups. Randomization is done in SAS  with a custom made program by an external data manager using a covariate-adaptive randomization method, controlled for age and diagnosis of asthma .The multidisciplinary team consists of a dermatologist, a pediatric allergist or pediatrician, psychologist and (dermatology/research) nurse. Weekly multidisciplinary evaluation during the intervention between the involved professional teams in Utrecht and Davos takes place using video conference. In this way, expertise is being combined and optimal care for all included patients is guaranteed. A short summary of discussed topics is provided in the patient record in the DECU.Before randomization and start of the intervention a multidisciplinary treatment plan is developed, with input from both multidisciplinary teams, to make sure the treatment goals can be achieved under either intervention and control conditions. Based on the intake , specific problem areas are identified and each healthcare professional writes down the treatment goals and strategy in a treatment plan. Psychosocial as well as clinical treatment goals are formulated. Examples of treatment goals are: adherence to treatment; influence and acceptance of AD in everyday life, including distinguished appearance; correct application of topical corticosteroids; coping with itch; sleep disturbances; and knowing what to do during an exacerbation. Planned interventions to achieve the goals are specified, as well as the responsible healthcare professional. There are no treatment restrictions during the trial. The treatment plan is discussed with the child and their parents and may be modified if needed. External professionals who are already involved with the child, for example the referring physician, nurses or social workers, are invited to participate during the study through the DECU, with permission of the child and their parents. In this way, optimal continuation of transmural treatment for the patient is made possible after the end of the study in the home region of the patient.Children are admitted to the Dutch Asthma Center Davos for a period of six weeks in groups of four children. The children attend a school integrated into the clinic. There are also social workers who accommodate the group, and each child has his/her own mentor.The treatment program consists of several fixed elements. Every day, a group physical activity is organized under supervision of a physiotherapist, such as fitness, swimming or outdoor activities. Weekly individual treatment sessions take place with the pediatrician, the psychologist alternating with the psychomotor therapist, and the physiotherapist (if needed). The nurse monitors correct application of topical treatment in individual sessions with each child twice daily. In these individual sessions health education is provided with a varying weekly theme. The content of the individual consultations depends on the problems described in the treatment plan. Children and their parents are separated during the six week treatment period, however parents usually visit once.Recently, a new multidisciplinary intervention has been developed for children with AD combining education, involvement of both dermatologists, pediatricians, psychologists and nurses, and the use of the DECU. Children are seen during half a day at the outpatient clinic on a weekly basis for a period of six weeks. During this period they have three consultations with the dermatologist, five consultations with the dermatological nurse, three consultations with the pediatric allergist and three consultations with the psychologist. The content of the individual consultations depends on the treatment goals described in the treatment plan.In the third week a group medical appointment  is scheduled for the children and their parents separately, about coping with AD and compliance . It is aA description of intervention and control conditions is provided in Table There is a six month follow-up period during which all children are assessed three times at the Wilhelmina Children’s Hospital: immediately after the intervention, six weeks after the intervention and six months after the intervention, which is the end of the follow-up period. During the entire follow-up period, children are encouraged to use the self-management tools in the DECU. In case of questions and/or exacerbations, they can contact the dermatology nurse with an e-consultation and, if needed, an appointment with the dermatologist is made. All children are encouraged to play sports, for example at FitKids, a Dutch organization that facilitates physical activity programs for children with a chronic disease under supervision of a children’s physiotherapist .The primary outcomes of this study are coping with itch and quality of life of the child for the psychosocial part and disease activity for the clinical part. Coping levels are measured with the JUCKKI questionnaire for children (aged 8 to 12 years) and the JUCKJU questionnaire for adolescents (aged 13 to 18 years) . QualitySecondary outcomes for the psychosocial part include social and emotional wellbeing of the child, feelings of autonomy and fear, quality of life and stress levels of the parents.Secondary outcomes for the clinical part include medication use, TARC (thymus and activation-regulated chemokine), total and specific IgE (Immunoglobulin E), asthma control parameters such as FeNO , ACQ (asthma control questionnaire), PAQLQ , BHR  and spirometric variables such as FEV1 (forced expiratory volume in 1 second), FVC  and their ratio (FEV1/FVC). During the follow-up period, the number of flares, the number of dermatologist/pediatric/psychologist appointments, e-consultations and emergency visits or hospital admissions are recorded.At each time point in the study, questionnaires are filled in by the child and his/her parents, blood samples are taken from the child, and lung function is assessed. A detailed overview of all parameters and measurements is provided in Table In this study two sources of electronic data are used. The DECU gives a detailed overview of the intervention and follow-up period and contains the data from the questionnaires, assessments and e-consultations. Additional patient data is extracted from the electronic patient file in the Wilhelmina Children’s Hospital. After data collection, all data will be combined into one SPSS  database.Data will be analyzed on an intention to treat basis. To examine whether there is a significant difference on the primary outcomes between the intervention and control group, a mixed-effects regression model will be used. Fixed effects for time, intervention group and the time*group interaction will be included in the model in order to examine the differences between the groups over time. To adjust for possible curvilinearity a squared term for time will be included in the model. A random intercept and random slope per child will be included in order to account for repeated measures within children. Baseline values of the outcome variables are included as fixed covariates to examine their possible impact on the observed treatment effect.Data relating to asthma outcomes will be analyzed separately for all children diagnosed with asthma. All analyses conform to a specified plan. There are no interim analyses or stopping rules.The DAVOS trial is a pragmatic randomized controlled trial that investigates the effect of multidisciplinary treatment at high altitude compared to sea level on coping, quality of life and disease activity in children with moderate to severe AD within the atopic syndrome. It is designed as a pragmatic trial, which fits best with our objective to find a new intervention with optimal treatment results for children with difficult to treat AD . In thisThere are several differences between the intervention and control conditions, such as the differences in setting: inpatient and outpatient, own environment and change to alpine environment. During the intervention in Davos, adherence to treatment is more likely to increase due to the clinical setting. Children are separated from their parents which can be perceived as an extra burden for the child. It may also be an advantage, especially when the coping strategy of the child is negatively influenced by the coping strategies of the family .All children in both interventions are discussed in weekly videoconferencing sessions with both multidisciplinary teams. AD treatment in both interventions is supervised by a single dermatologist in the Netherlands to minimize differences. Corticosteroid use is reduced or increased according to similar stepwise plans in both treatment arms. The educational plan is co-developed by both centers and consists of similar elements in both treatment arms. It is administered by the nurse and there is frequent contact between the nurses of both treatment arms.The pragmatic nature of our trial will not allow us to find out how or why the interventions work, or which elements of the interventions contribute most to the observed treatment effects. Therefore we will study effectiveness: the long-term benefit of either treatment in routine clinical practice .This study has several strengths. Children that participate come from all over the Netherlands. They often have an extensive treatment history and still experience an impaired quality of life, despite treatment according to current guidelines. In this study, children receive multidisciplinary treatment with individualized treatment goals either in their own environment or in an alpine clinical environment. The results of this trial will show which would be the most appropriate approach to provide treatment that results in sufficient long-term disease control for children with difficult to treat AD. The findings of our trial will be directly applicable in practice in the Netherlands.The study also has potential drawbacks. It is an intense period for the study participants in which they have to visit the Wilhelmina Children’s Hospital several times for the scheduled assessments. Each assessment means a missed day of work for the parents and a missed school day for the child. This may lead to a larger drop-out rate than anticipated and it may take a lot of effort to keep the study participants motivated for all assessments. In this study neither the study participants nor the healthcare professionals assessing outcomes were blinded to the received intervention. This may cause bias for the more subjective outcome measures, such as the SA-EASI.To the best of our knowledge, this is the first randomized trial that compares high altitude treatment with treatment at sea level in children with difficult to treat AD. Our study provides new and detailed information on the characteristics of these children. We hope to provide evidence for an intervention that gives the best results in terms of coping, quality of life and disease activity for this group of children.The first patient was included in the study in September 2010. At the moment patient recruitment and data collection is in progress. We expect to complete patient recruitment by May 2014.AA: Allergic asthma; ACQ: Asthma control questionnaire; AD: Atopic dermatitis; BHR: Bronchial hyperresponsiveness; CDQLI: Children’s dermatology quality of life index; DECU: Digital Eczema Center Utrecht; FeNO: Fraction of exhaled nitric oxide; FEV1: Forced expiratory volume in one second; FVC: Forced vital capacity; JUCKKI/JUCKJU: Itching cognitions questionnaire; PAQLQ: Pediatric asthma quality of life questionnaire; (SA)-EASI: (Self-administered) Eczema area and severity index; SAS: Statistical analysis software; SCORAD: Scoring atopic dermatitis; SD: Standard deviation; TARC: Thymus and activation-regulated chemokine.The authors declare that they have no competing interests.KF: conception and design, data collection and analysis, manuscript writing and final approval of the manuscript. WZ: conception and design, data collection and analysis, manuscript writing and final approval of the manuscript. HO: conception and design, critical revision and final approval of the manuscript. YM: data collection, critical revision, final approval of the manuscript. MUV: data collection, critical revision, final approval of the manuscript. LR: data collection, critical revision, final approval of the manuscript. MH: conception and design, critical revision, final approval of the manuscript. CBK: conception and design, critical revision and final approval of the manuscript. SP: conception and design, data collection, critical revision and final approval of the manuscript. All authors read and approved the final manuscript.


The IDARS/ISN satellite/KDARS meeting entitled "New Research Frontiers and Advances in Drug Addiction” was held on August 14-17, 2009 at the Grand Hyatt Hotel, Seoul, S. Korea. The atmosphere of the meeting especially the surroundings were just magnificent. Over 120 participants from 15 different countries attended the meeting. Owing to a relatively small number of participants, every one enjoyed the scientific exchange in a very relaxing and informal atmosphere. This conference was planned in a way that only one session was held at a time, so no-one missed anything. There was plenty of time allowed for questions and informal discussions.This volume contains a reviewed selection papers presented at the 2d-amphetamine, methamphetamine, and MDMA), alcohol, marijuana, nicotine, opiates, GHB and organic solvents. Separate sessions were devoted to the underlying mechanisms of drug addiction such as genes and drugs of abuse; gene behavior and psychostimulants; substituted amphetamine neurotoxicity; novel neurobiological targets for the treatment of alcoholism, psychostimulants and opiates addiction. Another feature of this conference was that each session included both clinical and basic research scientists working in the same area of research. Several scientists presented clinical and basic studies aimed at understanding the addictive effects of drugs of abuse as well as developing new strategies to treat drug addiction. Although a tremendous body of data has been gathered on the molecular genetics of abused drugs, the molecular mechanisms responsible for addiction and toxicity induced by exposure to these drugs remain to be fully elucidated. The informal atmosphere of the meeting allowed connections and potential collaborations to be established. This occurred especially during the poster session, which was extremely productive. At the end of the conference, there was a panel discussion and open forum, during which a summary of the conference was made and future recommendations voiced. The conference achieved its main goal of bringing together clinical and basic scientists from around the world in a multidisciplinary forum to exchange ideas and data relevant to the expanding field of drug addiction.The major goal of the conference was to understand the cellular and molecular mechanisms of drugs of abuse, such as cocaine, substituted amphetamines (Current Neuropharmacology for publishing these manuscripts. Last, but not least, we want to thank many of our colleagues who helped us in the organizing of this satellite meeting. The 3rd IDARS/ISN-sponsored satellite meeting dealing with the same topic will be held on August 23 – 26, 2011, at Grand Hyatt Hotel in Istanbul, Turkey.We express our gratitude to the organizations and the government agencies that supported this meeting. All papers included in this issue were reviewed by at least one referee, and we would like to thank them all for their valuable time and efforts. We also thank Professor Tom Salt, Editor-in-chief,


Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data.2.We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and RThis study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI.This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. Reports from the World Health Organization (WHO) indicate that obesity has reached epidemic proportions globally, with over a billion people said to be overweight Furthermore, longitudinal studies from the Framingham Heart study suggest that health related behavior from obesity These studies depend on self-reported data collected over prolonged periods of time, and may be subject to inaccuracies Gathering data on human interactions utilizing routine or generally accepted techniques, such as surveys, interviews, is constrained in spatial and time scales by technical difficulties and cost In this study, we used mobile phone sensing techniques to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, we avoided the limitations of self-reported social exposure data. To our knowledge, this is the first study that attempts to understand the effect of exposure on the change in BMI in a sensor delineated face-to-face network of individuals.This study was performed in a real-world setting of an American university undergraduate dormitory during the spring of 2009. The analysis included 42 subjects composed of freshmen, sophomores, juniors, seniors and graduate resident tutors responsible for each floor in the residence. The subjects ranged from 20–30 years of age with a median age of 22 years. There were 21 females and 21 males.All subjects in the study were given ‘socially-aware’ mobile phones that used Bluetooth sensors/transceivers to detect other proximate phones in the study. Once a phone detects another phone in proximity, it captures the other phone's identifier and records the length of time the phones were in proximity. A detailed description of the data collection platform and the technologies used is available elsewhere http://realitycommons.media.mit.edu/SocialEvolution.zip.Subjects completed a monthly health-related survey, which included information about dietary habits, physical exercise, weight, height, and stress level for the months of March, April and May 2009. Subjects also indicated their close friendships (binary responses). The dataset is included at the following URL: The study was approved by the Massachusetts Institute of Technology Institutional Review Board (IRB) called the Committee On the Use of Humans as Experimental Subjects (COUHES) and conducted under strict protocol guidelines. Participants provided their written informed consent to participate in this study.The hierarchy of the data used in this study is shown in The change in BMI is the difference of the BMI measured at the beginning and end of the study and represents the dependent variable. We identify subjects who gained weight as individuals with weight change above the threshold value of 1% change in weight and subjects who lost weight as individuals with weight change below the threshold value of −1% change in weight. A histogram showing change in BMI between March to May 2009 is shown in The healthy diet category was captured on the Likert scale of 1–7 . Happiness was captured on the scale of 1–7 . Ability to manage stress was captured on a 1–4 scale .Using the healthy diet category scale for each subject, we obtained the duration of interaction with and number of people classified as unhealthy  and healthy  that interacted with each subject. With the overall happiness for each subject, we identified the duration of interaction with and number of subjects who are unhappy  and happy  that interacted with each subject. We identified the duration of interactions with and number of subjects who are stressed  and are not stressed (response to ability to manage stress is greater than 2) that interacted with each subject.We identified two possible measures of exposure from the interaction information capture. These exposure measures are, together with personal choices and attributes, the independent variables for two distinct groups of multivariate exposure models, respectively. These exposure measures are (1) Scored duration of interaction and (2) Number of subjects exposed to.The first measure is scored duration of interaction which is based on the intuition that subjects interact in diverse patterns with varying duration levels. Therefore, it is imperative that duration of interactions with others is normalized across subjects. To do so, we implemented a function that maps the durations of interactions for each subject that occur in the first, second, third and fourth quartile to values 1, 2, 3, and 4 respectively. This quartile scoring approach retains the magnitude of durations of interaction as a factor in understanding change in BMI. Scored duration of interaction comprises variables that describe total score of the duration of interaction with both close friends and acquaintances. We considered interactions with close friends and acquaintances that gained weight, lost weight, did not change weight, eat healthy, eat unhealthy, are happy, are unhappy, are stressed and are not stressed. These exposure attributes are the sums of the scored durations of interaction by a subject with people that have a specific health-related attribute  or outcome such as gain in weight or loss of weight.The second measure is the number of subjects exposed to which consists of variables such as the numbers of close friends or numbers of acquaintances who gained weight, lost weight, did not change weight, eat healthy, eat unhealthy, are happy, are unhappy, are stressed and are not stressed. These exposure attributes are numbers of people with whom a subject interacts, who have specific health-related or weight change outcomes. All variables were available and included for each participant.We approached the statistical analysis of the data in two steps. The first step is variables selection, which searches for the exposure related parameters that can explain change in BMI. To implement variables selection, we used least absolute shrinkage and selection, LASSO p2 and Akaike Information Criterion (AIC). AIC was proposed by Akaike We took Mallows CAn optimal model that explains 68% (p<0.00001) of the variation in change in BMI was obtained. The model consists of statistically significant exposure terms such as “duration of interaction with acquaintances that gained weight”, “duration of interaction with acquaintances that are not stressed” and “duration of interaction with close friends who are not stressed”. The model also includes significant parameters representing individuals' personal choices of level 4 to level 6 of the healthy diet category and level 3 of the ability to manage stress. The resulting linear regression models are presented in Furthermore, when individual choices and attributes  were taken out of the optimal model, social exposure alone explains approximately 38% (p<0.001) of the variation in change in BMI, which is more than half of the 68% of the variation in change in BMI explained by the combination of exposure and personal choices. The personal attributes (gender) and health related choices, healthy diet category and ability to manage stress, only explain approximately 26% (p = 0.13) of change in BMI.The optimal model is based on parameters aggregated for the entire period of the study. A model based on weekday exposure only and a model based on weekend exposure explain approximately 65% (p<0.0001) and 57% (p<0.01) of the variation in change in BMI, respectively with independent variables similar to those in the optimal model  of the variation in change in BMI. The significant exposure parameters in this model include the “number of acquaintances that gained weight”, diet and stress see .pThe model based on weekday exposure (number of subjects) explains approximately 54% (p<0.01) of the variation in change in BMI. However, for exposure on the weekends, LASSO, at the minimum Mallows C2 and AIC values. Therefore, models based on duration of interactions explain variation in change in BMI better than the models on the numbers of subjects exposed to. The optimal model is a model based on duration of interactions aggregated for the entire period of the study. This model indicates that the entire period of interaction is more important than subsets of itself (weekdays and weekends) in explaining change in BMI.Overall, a comparison of the models based on duration of interactions, in Change in BMI showed a strong association with social exposure to acquaintances who gained weight. However social exposure to close friends who experienced a weight change did not show any significant correlation with change in BMI, which is different from the result by Christakis and Fowler In the model, exposure to acquaintances that gained weight has a negative relationship with change in BMI whereas exposure to acquaintances that are not stressed shows a positive relationship with change in BMI. In the western, world slenderness is associated with happiness, success, youthfulness, and social acceptability Interestingly, social exposure alone is more important in explaining variation in change in BMI than personal choices, since exposure explains more than half of the 68% of change in BMI within the optimal model, which is a combination of exposure and personal choices; and the personal attributes (gender) and health related choices, healthy diet category and ability to manage stress alone do not explain the change in BMI. This finding emphasizes the importance of holistic and accurate measures of social exposure. It shows that social interactions, which are overlooked in obesity and weight studies, are particularly useful in explaining change in BMI.Personal attribute and gender were included in the model, based on studies that indicate BMI is not independent of gender To our knowledge this is the first study that integrates both interactions with close friends and acquaintances  in combination with individual health-related information to explain change in BMI. We predict change in BMI by social exposure to people in a social network, both close friends and acquaintances, thereby eliminating any bias that may result from sampling only close friends or acquaintances. This study suggests that exposure is more important than individual choices in explaining change in weight, and highlights the importance of people that an individual is exposed to in their community. These findings further suggest that research work on obesity and weight change-related issues need to take cognizance of social exposure especially to acquaintances, since it may be as critical if not more important than the exposure to close friends. The ability to measure social interaction using embedded sensing techniques in the real world presents a new avenue to understand individual behavior and to achieve the ultimate goal of Public Health - guaranteeing every individual in the community a standard of living adequate for the maintenance of health.


Chronic diseases are an increasing threat to people’s health and to the sustainability of health organisations. Despite the need for routine monitoring systems to assess the impact of chronicity in the population and its evolution over time, currently no single source of information has been identified as suitable for this purpose. Our objective was to describe the prevalence of various chronic conditions estimated using routine data recorded by health professionals: diagnoses on hospital discharge abstracts, and primary care prescriptions and diagnoses.The ICD-9-CM codes for diagnoses and Anatomical Therapeutic Chemical (ATC) codes for prescriptions were collected for all patients in the Basque Country over 14 years of age  for a 12-month period. We employed a range of different inputs: hospital diagnoses, primary care diagnoses, primary care prescriptions and combinations thereof. Data were collapsed into the morbidity groups specified by the Johns Hopkins Adjusted Clinical Groups (ACGs) Case-Mix System. We estimated the prevalence of 12 chronic conditions, comparing the results obtained using the different data sources with each other and also with those of the Basque Health Interview Survey (ESCAV). Using the different combinations of inputs, Standardized Morbidity Ratios (SMRs) for the considered diseases were calculated for the list of patients of each general practitioner. The variances of the SMRs were used as a measure of the dispersion of the data and were compared using the Brown-Forsythe test.The prevalences calculated using prescription data were higher than those obtained from diagnoses and those from the ESCAV, with two exceptions: malignant neoplasm and migraine. The variances of the SMRs obtained from the combination of all the data sources  were significantly lower than those using only diagnoses.The estimated prevalence of chronic diseases varies considerably depending of the source(s) of information used. Given that administrative databases compile data registered for other purposes, the estimations obtained must be considered with caution. In a context of increasingly widespread computerisation of patient medical records, the complementary use of a range of sources may be a feasible option for the routine monitoring of the prevalence of chronic diseases. The chronification of certain diseases and growing life expectancy of populations of most countries are leading to an increase in the number of people living with one or more chronic diseases,2. ChronIt is widely accepted that there is no single source of information from which we can obtain all the data necessary for routine monitoring of chronic diseases. For thiThe present study focuses on analysing data from routine administrative healthcare databases. As in many industrialized countries, the Basque Country has several information systems that collect data related to the healthcare of chronic conditions. First, there is a hospital discharge data system (HDDS). In addition, since 2005 the Basque Health Service, Osakidetza, extracts information from the diagnoses recorded by primary care physicians on EHRs to use in population-based case-mix systems. These data are mainly used to identify trends in morbidity seen in primary care, assess differences between geographical areas and profile health professionals. As in other places, the quality of diagnostic reporting by primary care clinicians is uneven and there is a significant degree of variability between professionals-14. An aGiven the variety of data sources and the intrinsic limitations of each of them, the objective of this study was to describe the prevalence of a range of chronic diseases observed in patients under the care of the Basque health system as currently recorded in various types of administrative databases and to compare these values. To provide a context for our results from the health information systems of our region, we also report the prevalence estimated by the 2007 Basque Health Interview Survey. In addition, we observed the variations among practitioners, regarding the prevalence of disease in the populations they served, according to the different sources of information.This was a descriptive, cross-sectional study. The study population included all patients over 14 years of age who were assigned to a physician of the Basque Health Service (Osakidetza) for at least 6 months between 1st September 2007 and 31st August 2008 , regardless of whether they visited any doctor during this period or if at the end of the study period they maintained public insurance coverage or they had been removed for any reason .Osakidetza is a public healthcare organisation that provides universal coverage, funded through regional general taxation. Primary care clinicians work in teams and each person is included on the list of one physician , who acts as the gatekeeper to other levels of care. In 2008, there were 1,196 GP patient lists.For this study we obtained permission from the Basque Health Service, to use the database of the Basque Country population stratification program (PREST). PREST eOur EHRs were designed to facilitate the provision of individualized care to patients during their visit to a clinician. The computerisation of medical offices started in 1990 and all clinicians have had a computer in their office since 2005. The health problems of patients are organised by healthcare episodes is used The data on prescriptions were extracted from the specific forms completed in primary care EHRs and their coding, according to the Anatomical Therapeutic Chemical (ATC) Classification System, was perA total of 12 health problems were selected and the Adjusted Clinical Groups Case-Mix– Expanded Diagnosis Clusters (EDCs): 267 groupings generated by the aggregation of ICD-9-CM coded diagnoses, according to clinical criteria. They were designed in order to enable the identification of patients with specific diseases, decreasing the differences in coding behavior between practitioners.– Rx-Morbidity Groups (Rx-MGs): 64 groupings from a classification system based on the drugs prescribed (ATC coding system). These groups were designed according to four criteria: primary anatomical-physiological system, morbidity differentiation, expected duration, and severity of the health problem being acted by the medication.Since the objective was to compare the prevalence of various chronic diseases as estimated from these different data sources, we selected health problems for which it is easy to establish a relationship between Rx-MGs and the corresponding EDCs. Under Spanish law, the drugs used to treat these conditions always require a doctor's prescription and can not be sold directly to patients as over the counter drugs. For some conditions there is more than one classification group, so in order to simplify the analysis data were merged. Thus, for diabetes, the four corresponding EDCs were combined, as were the two Rx-MGs, in order to create a single group of patients diagnosed with diabetes and another single group of patients receiving anti-diabetic medication; similarly, the two EDCs related to the diagnosis of arterial hypertension were grouped together.The prevalence of the diseases in the general population was obtained from the various data sets: hospital diagnoses, primary care prescriptions and diagnoses separately and, subsequently, by combining the two sources of diagnoses (primary care and HDDS) and these with the prescription data. A patient was considered to have a medical condition if any of the diagnoses given or the drugs prescribed in the previous 12 months were included in the corresponding morbidity groups (EDCs or Rx-MGs). Since we used combined data on diagnoses and prescriptions, a patient could have both an EDC and an Rx-MG that indicate the same medical condition. Obviously, in such cases the patient was counted only once in the prevalence estimate.For the subpopulations assigned to each GP, prevalence rates were calculated based on three combinations of data: only primary care diagnoses, all the diagnoses  and the combination of prescription data with all the diagnoses. In order to compare the values for each GP, standardized morbidity ratios (SMRs) were calculated. The SMR is the ratio of the observed number of patients with a disease to the expected number obtained by indirectly adjusting for age and sex the overall prevalence data across Osakidetza. A value above 1.00  indicates that the number of patients diagnosed with a specific medical condition by a given doctor is higher than the average across all the GPs in the Basque Country ; the opposite being true for values under 1.00. A detailed description of this process for EDCs can be found in the literature.In order to evaluate the dispersion of the data, namely the spread of the three SMR values for each medical condition, the variances were calculated and their differences were assessed using the Brown-Forsythe test for homogeneity of variances.Of the population analysed, 51% were women  and 22%  were 65 or older. Overall, there were 188,051 admissions to hospital and 10,463,390 primary care visits. A total of 70% of patients  visited their GP at least once during the 12-month study period and 7%  required one or more admissions. From the HDDS we retrieved 664,185 diagnoses and from primary care EHRs 18,586,949 diagnoses and 22,518,451 prescriptions.Table Table Figure Administrative databases can be a very useful tool for epidemiological surveillance: they not only enable us to estimate the incidence and prevalence of medical conditions, but also to identify cohorts of patients with a given diagnosis and follow their progression over time including the outcomes of the care they have received. NeverthWhile there is no universally accepted gold standard to establish the exact prevalence of a medical condition in a given population, other studies have indicated that the detection of chronic diseases from administrative databases leads to an underestimate,27, espeIn our case, using different data sources we obtained inconsistent prevalence estimates. Specifically, in 11 of the 12 diseases studied prescription data indicated a larger number of chronic patients than primary care and hospital diagnoses. Further, except in the case of migraine, the combination of prescriptions and both sources of diagnoses produced higher estimates than those found in the health survey.The differences between prevalence estimates for GP lists were smaller taking into account the combination of diagnoses and prescriptions, than using only diagnosis data. Although the distribution of medical conditions between patient populations under the care of different doctors is not random, the difIn primary care, the completeness of data entry regarding diagnoses is influenced by a range of factors related to the organisation of the health service, the patients and the doctors themselves,30,31. IAs we have discussed, another option for detecting health problems is prescription data. RecordsThe prevalence estimates we obtained using prescription data could seem too high, being bigger than those derived from survey data. In contrast, in a recent study carried out in Italy, comparing results from a range of different data sources, prevalence estimates from surveys were higher than those from pharmaceuticals prescribed for 9 out of 12 conditions studied, though the method for classifying drugs was different to the one we used. In our Our study has certain limitations. First, although in a tax-financed insurance system that provides universal coverage, there are fewer barriers to healthcare access than in other models of care, such situation is not perfect. Thus, to the extent that some population subgroups might have a poorer access to services, their health problems would be less accurately registered. In this sense, it is not possible to rule out a certain selection bias, but this would have influenced all of data and it seems unlikely that it produced dissimilar effects on the values obtained from diagnoses, prescriptions or both sources. On the other hand, it was not possible to access the registers of Emergency Departments or specialist outpatient services; these would have provided valuable information, in particular, concerning patients who have the more severe forms of chronic conditions, or in the case of problems which are by their nature episodic, such as asthma, and others which need specialized medical care but do not require admission . In addition, we adopted relatively loose criteria, accepting that a patient presented a given condition even when there was only one recording of a diagnosis or associated prescription, so some errors due to mistakes in data entry by physicians could be overlooked. Further, the particular characteristics of our health service and the EHRs used make it difficult to extrapolate these results to other systems. Indeed, it is very difficult to compare our results with those of other studies, given substantial differences in methodology and, actually, there is not even a universally accepted definition of a chronic disease.We underline the need to be cautious when estimating the prevalence of medical conditions from data recorded for other purposes, as there may be remarkable biases in records. On the other hand, given the growing use of computers in medical offices, the complementary use of a range of data sources  may be a feasible option to improve the accuracy of such estimations.Encuesta de Salud de la CAPV); GP: General practitioner; HDDS: Hospital discarge data system; Rx-MG: Rx-morbidity groups; SMR: Standardized morbidity ratio.ACG: Johns Hopkins adjusted clinical groups case-mix system; ATC: Anatomical therapeutic chemical; EDC: Expanded diagnosis cluster; EHR: Electronic health records; ESCAV: Basque health interview survey (Roberto Nuño-Solinis Maider Mateos, Itziar Vergara, Gonzalo Grandes and Santiago Esnaola contributed equally to this workThe authors declare that they have no competing interests.All authors participated in the design of the study. JFO performed the validation of medical records. MM was responsible for statistical analyses. JFO wrote the draft of manuscript. All authors participate in interpretation of data, critically reviewed and gave final approval to the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/12/365/prepub


To assess the magnitude and pattern of socioeconomic inequality in current smoking in low and middle income countries.We used data from the World Health Survey [WHS] in 48 low-income and middle-income countries to estimate the crude prevalence of current smoking according to household wealth quintile. A Poisson regression model with a robust variance was used to generate the Relative Index of Inequality [RII] according to wealth within each of the countries studied.In males, smoking was disproportionately prevalent in the poor in the majority of countries. In numerous countries the poorest men were over 2.5 times more likely to smoke than the richest men. Socioeconomic inequality in women was more varied showing patterns of both pro-rich and pro-poor inequality. In 20 countries pro-rich relative socioeconomic inequality was statistically significant: the poorest women had a higher prevalence of smoking compared to the richest women. Conversely, in 9 countries women in the richest population groups had a statistically significant greater risk of smoking compared to the poorest groups.Both the pattern and magnitude of relative inequality may vary greatly between countries. Prevention measures should address the specific pattern of smoking inequality observed within a population. There is substantial evidence that poor people in resource rich countries carry the heaviest burden of tobacco related premature death and disability The WHO Framework Convention on Tobacco Control has given an added impetus to implement more effective demand reduction measures, and there has been a considerable reduction in tobacco use in high income countries http://unstats.un.org/unsd/default.htm) and also non-response, post-stratification corrections were made to sampling weights The World Health Survey (WHS) was conducted by the World Health Organization in 2002–04 to provide valid, reliable, representative and comparable population data on the health status of adults, aged 18 years and older. The survey was conducted in 70 countries from all regions of the world A total of 213,807 men and women from the 48 low and middle income countries were available for analyses . As mostConfounding indicators included participants' sex, age ; marital status (married/cohabiting vs. never married vs. divorced/separated/widowed); educational level (no education vs. incomplete primary vs. complete primary vs. secondary/high school vs. college completed or above); employment status (not employed vs. Employed); and area of residence .pro-rich inequality. Conversely, a RII value less than one indicates that smoking is more likely to be prevalent among those with higher wealth level: referred to as pro-poor inequalityThe overall proportion of current smokers, as well as the proportion by household wealth quintile, was calculated for each country and for men and women separately. These are crude estimations (i.e. not age-standardized) because our main aim was not to compare the smoking prevalence between countries but to compare the magnitude of within country variation in smoking according to wealth. Socioeconomic inequality in smoking prevalence was measured using the relative index of inequality (RII) that takes into account the distribution of smoking as well the distribution of the population across wealth quintiles All analyses were weighted accounting for the individual survey sample designs. The non-independence of observations within the survey clusters were also incorporated in the analysis. Stata 11 was used in all analyses.Informed consent was obtained in all surveys. A standard consent form approved by the ethics review committee was read to the respondent in the respondent's language. Once the respondent agreed to participate in the survey, if the respondent was literate the form was provided to the respondent to read over and sign and was countersigned by the interviewer. If the respondent was illiterate and gave consent to participate, the interviewer confirmed this consent and signed on the form that the respondent had been read the form, had understood the study and agreed to participate. This procedure was approved by the institutional review boards. The full list of collaborating partners in the 48 countries where the ethical procedure is reviewed and approved is provided in List S1.The estimated crude prevalence of current smoking for each of the 48 countries can be found in There was great variation in smoking within countries with respect to wealth, and in some cases extremely high rates were observed in certain groups. The smoking prevalence was above 70% in poorest men from Bangladesh, Bosnia and Herzegovina, China and Lao People's Democratic Republic, and in Hungary this figure was over 80%. Similarly, the prevalence of smoking in the poorest women in Hungary was among the highest observed in women (over 65%). High rates of smoking were not always confined to the poorest groups. For example, in Bosnia and Herzegovina, Georgia, Latvia, Philippines, Russian Federation and Ukraine, even in the richest population group smoking in men was more common than not .With regard to socioeconomic inequality in males, smoking was disproportionately prevalent in the poor in the majority of countries. In a number of the countries studied, the poorest men were over two and a half times more likely to smoke than the richest men . NeverthSocioeconomic inequality in women was much more mixed, showing types of both pro-rich and pro-poor inequalities. Pro-rich socioeconomic inequality was most extreme in Congo, Kenya and Lao People's Democratic Republic where the poorest women had more than 10 times more risk of smoking compared to the richest women. Conversely, in Ecuador, Georgia, Kazakhstan and Mexico women in the richest women in the population had over three and a half times the risk of smoking compared to the poorest groups. In fact, this type of pro-poor inequality in women was observed in 16 of the countries studied, and was statistically significant among 9 . There aAfter controlling for age, marital status, education, employment and urban/rural residence, pro-rich socioeconomic inequality in smoking was still statistically significant among men in nearly half of the countries studies. In nine countries from both low- and middle-income groups the poorest men were at least two times more likely to smoke than the richest ones even after controlling for these factors . The proThis study used data from 48 countries that took part in the world Health Surveys to analyse socioeconomic inequality in current daily or occasional tobacco smoking in men and women from low- or middle-income countries. We have shown that the magnitude and direction of socioeconomic inequality varies substantially between counties. It is conventional wisdom that smoking levels are highest in the poorest groups of the population, but this is not always the case as shown in our study. Particularly in women and in middle income countries, we observed a significant pattern of pro-poor inequality – risk of smoking was higher in the wealthiest populations groups. More research is required in order to see if this pattern persists over time, and to explore the situation in other middle-income countries that are not included in this study. Interestingly, pro-poor inequality in smoking according to education has also been observed in women in Southern Europe Apart from these examples of pro-poor inequality, we have shown that many countries exhibit pro-rich smoking inequality. This type of inequality is especially relevant because smoking in the poorest population groups can have additional effects that go beyond the direct health effects, further exacerbating health-related inequality. It has been shown that in low socioeconomic groups, already scarce income may be diverted away from things such as education, health care, housing, or quality food, in order to purchase cigarettes An individual's risk of smoking is likely to be influenced by a number of linked social factors as well as their cumulative effect over the life course. Here we assessed inequality by wealth, but it is clear that other factors such as education are also key determinants of inequality. Nevertheless, after we controlled for other factors such as age, marital status, education, rural/urban dwelling and employment, we showed that wealth remains a significant determinant of smoking risk in many countries. One policy implication of this finding is that interventions to reduce the burden of tobacco related ill health should also be directed at poverty itself and its associates. Similarly, some authors have suggested that anti-smoking policies should focus on improving the standard of living among smokers Apart from estimating the magnitude of inequality, it is also important to consider the pattern of inequality within the country. Different patterns of inequality have been described in childhood malnutrition Inequality in tobacco use translates into inequality in tobacco-related premature death and disease. In this regard, the disparities between the wealthiest members of society and the poorest members should be considered a health inequity, because they are unfair and preventable Table S1Study population  by country and sex, World Health Survey, 2002–2004.(DOC)Click here for additional data file.List S1List of in-country collaborating partners.(XLS)Click here for additional data file.


Feedback is potentially effective in improving the quality of care. However, merely sending reports is no guarantee that performance data are used as input for systematic quality improvement (QI). Therefore, we developed a multifaceted intervention tailored to prospectively analyzed barriers to using indicators: the Information Feedback on Quality Indicators (InFoQI) program. This program aims to promote the use of performance indicator data as input for local systematic QI. We will conduct a study to assess the impact of the InFoQI program on patient outcome and organizational process measures of care, and to gain insight into barriers and success factors that affected the program's impact. The study will be executed in the context of intensive care. This paper presents the study's protocol.We will conduct a cluster randomized controlled trial with intensive care units (ICUs) in the Netherlands. We will include ICUs that submit indicator data to the Dutch National Intensive Care Evaluation (NICE) quality registry and that agree to allocate at least one intensivist and one ICU nurse for implementation of the intervention. Eligible ICUs (clusters) will be randomized to receive basic NICE registry feedback (control arm) or to participate in the InFoQI program (intervention arm). The InFoQI program consists of comprehensive feedback, establishing a local, multidisciplinary QI team, and educational outreach visits. The primary outcome measures will be length of ICU stay and the proportion of shifts with a bed occupancy rate above 80%. We will also conduct a process evaluation involving ICUs in the intervention arm to investigate their actual exposure to and experiences with the InFoQI program.The results of this study will inform those involved in providing ICU care on the feasibility of a tailored multifaceted performance feedback intervention and its ability to accelerate systematic and local quality improvement. Although our study will be conducted within the domain of intensive care, we believe our conclusions will be generalizable to other settings that have a quality registry including an indicator set available.ISRCTN50542146Current Controlled Trials  To systematically monitor the quality of care and develop and evaluate successful improvement interventions, data on clinical performance are essential ,2. TheseAlso within the domain of intensive care, several indicator sets have been developed -9, and nBesides facilitating data collection and analyses, NICE-like most quality registries-also sends participants periodical feedback reports on their performance over time and in comparison with other groups of ICUs. Although feedback is potentially effective in improving the quality of care -16, meree.g., insufficient data quality, no acknowledgement of the room for improvement in current practice, or lack of resources to implement quality interventions [Information Feedback on Quality Indicators). InFoQI was developed and will be evaluated within the context of intensive care and the Dutch NICE registry. By targeting the potential barriers to using performance feedback as input for systematic QI activities at ICUs, the InFoQI program ultimately aims to improve the quality of intensive care.Previous systematic reviews reported potential barriers at different levels to using performance data for systematic improvement of health care, ventions ,16. The ventions . As suggventions ,19, we tThe study as proposed in this protocol aims to evaluate the effect of the tailored multifaceted feedback intervention on the use of performance indicator data for systematic QI at ICUs. Specific objectives include:1. To assess the impact of the InFoQI program on patient outcome and organizational process measures of ICU care.2. To gain insight into the barriers and success factors that affected the program's impact.3. The InFoQI program was designed to overcome the previously identified barriers to using performance indicator data as input for local QI activities. Based on this assumption we hypothesize that ICUs participating in the InFoQI program will improve the quality of their care significantly more than ICUs receiving basic feedback from the NICE registry.The results of this study will inform those involved in providing ICU care on the feasibility of the InFoQI program and its ability to accelerate systematic, local QI at ICUs. More in general, we believe that our results might be of interest to clinicians and organizations in any setting that use a quality registry including performance indicators to continuously monitor and improve the quality of care.We will execute a cluster randomized controlled trial to compare facilities participating in the InFoQI program (intervention arm) to facilities receiving basic feedback from the NICE registry (control arm). Because the InFoQI program will be implemented at the facility rather than individual level, a cluster randomized trial is the preferred design for the evaluation of the program's effectiveness . Like moi.e., units with the intensivist as the patient's primary attending physician. The units are a mixture of academic, teaching, and nonteaching settings in urban and nonurban hospitals. In 2005, 8.4 adult ICU beds per 100,000 population were available, and 466 patients per 100,000 population were admitted to the ICU that year [The setting of our study is Dutch intensive care. In the Netherlands, virtually all 94 ICUs are mixed medical-surgical closed-format units, hat year . CurrentAt the NICE coordination center, dedicated data managers, software engineers, and a coordinator are responsible for routine processing, storing, checking, and reporting of the data. Also, for the duration of the study, two researchers will be available to provide the InFoQI program to ICUs in the intervention arm. The availability of these resources is essential for the feasibility of our study.All 46 ICUs that participate in NICE and (are preparing to) submit data to the registry on the extended quality indicator set will be invited to participate in our study. They should be willing and able to allocate at least two staff members for an average of four hours per month to be involved in the study. The medical manager of the ICU must sign a consent form to formalize the organization's commitment.All patients admitted to participating ICUs during the study period will be included in the analyses. However, when evaluating the impact on patient outcomes, we will exclude admissions based on the Acute Physiology and Chronic Health Evaluation (APACHE) IV exclusion criteria , as wellThe ICUs allocated to the control arm will be treated as 'regular' NICE participants. This implies they will receive basic quarterly and annual feedback reports on the registry's core outcome indicators case-mix adjusted hospital mortality and length of ICU stay. In addition, they will be sent similar, but separate, basic quarterly and annual feedback reports containing data on the extended indicator set. Also, support by the NICE data managers is available and includes data quality audits, support with data collection, and additional data analyses on request. Furthermore, they are invited to a yearly discussion meeting where they can share experiences with other NICE participants.i.e., participating in the InFoQI program, will receive the same intervention as the control arm, but extended with more frequent and more comprehensive feedback, a local, multidisciplinary QI team, and two educational outreach visits  charts [e.g., a list of unexpected non-survivors . The table in 'Additional file From the prospective barriers analysis, it appeared that many barriers concerned the basic NICE feedback reports. To target the lack of case-mix correction and lack of information to initiate QI actions, the basic quarterly report will be replaced by an extended, comprehensive quarterly report that facilitates comparison of an ICU's performance with that of other ICUs, ) charts . To decrICUs in the intervention arm will establish a local QI team, creating a formal infrastructure at their department for systematic QI. This team must consist of at least one intensivist and one nurse; a management representative and a data manager are suggested as additional members. To target the lack of motivation to change, team members should be selected based on their affinity and experience with measuring and improving quality of care and their capability to convince their colleagues to be involved in QI activities. The team's main tasks are described in a protocol and include formulating a QI action plan, monitoring of performance using the feedback reports, and initiating and evaluating QI activities  to select outcome measures from the extended quality indicator set to evaluate the effectiveness of our intervention. To decrease the probability of finding positive results by chance as a result of multiple hypothesis testing , we limi centers .Primary outcome measures will be:1. Length of ICU stay (ICU LOS); this will be calculated as the difference in days between the time of ICU discharge and time of ICU admission. To account for patients being discharged too early, the length of stay of the first ICU admission will be prolonged with the length of stay of subsequent ICU readmissions within the same hospital admission.2. Proportion of shifts with a bed occupancy rate above 80%; this threshold is set by the NVIC in their national organizational guideline for ICUs . We willSecondary outcome measures will be all-cause, in-hospital mortality of ICU patients, duration of mechanical ventilation, proportion of glucose measurements outside the range of 2.2 to 8.0 mmol/L, and the proportion of shifts with a nurse-to-patient ratio below 0.5.e.g., their patient data management system. Each month, participants upload their data from the local, electronic database to the central, electronic registry database. ICUs in the intervention arm that have not submitted their data at the end of a month will be reminded by phone, and assisted if necessary. Quarterly reports are provided within ten weeks after the end of a period, and monthly reports within six weeks. The NICE registry uses a framework for data quality assurance [We will use the existing data collection methods as currently applied by the NICE registry . Most ICssurance , includiThe minimally required number of ICUs participating in the trial was based on analysis of the NICE registry 2008 data. First, ICUs were ranked by average ICU LOS of their patients. The anticipated improvement was defined as the difference in average ICU LOS of the 33% top ranked ICUs (1.28 days) and average ICU LOS among the remaining ICUs (2.11 days), and amounted to a reduction of 0.58 days per patient. A senior intensivist confirmed that this reduction is considered clinically relevant. Assuming an average number of 343 admissions per ICU per year, calculations based on the normal distribution showed that we will need at least 26 ICUs completing the trial to detect this difference with 80% power at a type I error risk (α) of 5%, taking an estimated intra-cluster correlation of 0.036 into account. With this number of ICUs, the study will also be sufficiently powered to detect a reduction in mechanical ventilation duration of 0.75 days per patient (from 2.96 to 1.75 days). We do not expect to be able to detect an effect of the intervention on ICU or hospital mortality.To determine the required sample size for bed occupancy, shifts with an occupancy exceeding 80% were counted. This occurred in 44% of all shifts in 2008. Following the same ranking procedure as described above, a reduction of 24% was anticipated, and considered clinically relevant. Power calculations based on the binomial distribution showed that we will need a minimum of 16 ICUs completing the trial to detect this difference, taking an estimated intra-cluster correlation of 0.278 into account.We will randomly allocate ICUs (clusters) to one of both study arms, stratified by the number of ventilated, non-cardiac surgery admissions , and involvement in a previous pilot study to evaluate feasibility of data collection of the NVIC indicator set  involve. A reseaFor ICUs in the intervention group, the time from randomization to the first outreach visit-with an expected duration of six to eight weeks-will be regarded as a baseline period. Follow-up will end three months after the last report has been sent, assuming this is the average time required for an ICU to read, discuss, and act on a feedback report. The expected duration for intervention ICUs will therefore be approximately fourteen months. Control ICUs will have a fixed baseline period of two months, and a follow-up of fourteen months.To assess the effect of the InFoQI program, the outcome values measured during the follow-up period will be compared between both study arms. To assess the effect of the program on length of stay, we will perform a survival analysis of time to alive ICU discharge with dying at the ICU as a competing risk , and adjTo assess the effect of the intervention on the proportion of shifts with a bed occupancy rate above 80%, shift-level occupancy data  will be analyzed with logistic regression analysis. In this case, generalized estimation equations with an autoregressive correlation structure will be used to account for the longitudinal nature of shift occupancy observations. The same procedure will be followed to analyze the proportion of shifts with a nurse-to-patient ratio below 0.5.To assess the effect on the proportion of out-of-range glucose measurements, multi-level logistic regression analysis will be performed where subsequent glucose measurements on the same patient are treated as time series data, and both patient-level and ICU-level intercept estimates are used to account for potential correlation of measurements within patients and within ICUs.We will complement the quantitative trial results with the results from a process evaluation to gain insight into the barriers and success factors that affected the program's impact . We willThe Institutional Review Board (IRB) of the Academic Medical Center  informed us that formal IRB approval and patient consent was not deemed necessary due to the focus of the InFoQI program on improving organizational processes; individual patients will not be directly involved. Additionally, in the Netherlands there is no need to obtain consent to use data from registries that do not contain patient-identifying information, as is the case in the NICE registry. The NICE foundation is officially registered according to the Dutch Personal Data Protection Act.This paper describes the protocol of a cluster randomized trial to evaluate the effect of the InFoQI program on the quality of ICU care and a qualitative process evaluation to gain insight into the barriers and success factors that affected the program's impact. The program-tailored to prospectively identified barriers and facilitators-consists of comprehensive feedback reports, establishing a local, multidisciplinary QI team, and educational outreach visits. We expect that this multifaceted intervention will improve the quality of ICU care by enabling ICUs to overcome known barriers to using performance data as input for local QI activities.In our study, we used the previously developed NVIC extended indicator set as the basis for our feedback intervention. Although the NVIC is the national organization representing the Dutch intensive care profession, some ICUs may still disagree with the relevancy of some of the indicators in the set. This would hinder the use of the feedback as input for local QI activities, potentially decreasing the effectiveness of the intervention. However, disagreement with the content of the indicator set was not identified as a barrier in our prospective barriers analysis. We will reassess this during the process evaluation.Building on an existing indicator set also results in a clear strength of our study, because we are able to use the data collection methods as currently applied by the NICE registry. This will increase the feasibility of the InFoQI program, because eligible ICUs already routinely collect the necessary data items as a result of their participation in NICE; participation in the InFoQI program does not require additional data collection activities. Furthermore, the data quality assurance framework as applied by NICE increases the reliability of the data ,38, and Unfortunately, the design of the study will not allow us to quantitatively evaluate the relative effectiveness of the individual components of the InFoQI program. We considered a factorial design  for a sei.e., the ICUs involved in the indicator pilot study [As for the participants in our study, only ICUs that participate in the NICE registry, are capable of submitting indicator data, and agree to allocate resources to establish a local QI team will be eligible for inclusion. These criteria may lead to the selection of a non-representative sample of ICUs, because eligible facilities are less likely to be understaffed and more likely to have information technology (IT) support to facilitate routine collection of NICE data. This will not affect the internal validity of our results, because both study arms will consist of these early adopters. Moreover, the 'earliest adopters'-ot study -should bThe effectiveness of feedback as a QI strategy has often been evaluated, as indicated by the large number of included studies in systematic reviews on this subject ,15. HowePrevious before-after studies found a moderate effect of performance feedback  and of mThe results of this study will inform ICU care providers and managers on the feasibility of a tailored multifaceted performance feedback intervention and its ability to accelerate systematic, local QI activities. However, the results will also be of interest to other settings where national quality registries including performance indicators are used for continuous monitoring and improving care. Furthermore, the quantitative effect measurement together with the qualitative data from the process evaluation will contribute to the knowledge on existing barriers to using indicators for improving the quality of care and how they can be effectively overcome.The authors declare that they have no competing interests.GW, KJ, MDV, NDK, PVDV, SVDV, and WG had the basic idea for this study and were involved in the developing the protocol. NP planned the statistical analysis. SVDV drafted the manuscript. All authors were involved in the critical revision of the paper for intellectual content and its final approval before submission.NDK is director of the NICE registry. NDK and PVDV are members of the NICE board. PVDV is chairing the Netherlands Society of Intensive Care committee on quality indicators.Barriers to using performance data and how they are targeted The prospectively identified barriers to using performance data and how they are targeted by the feedback interventionClick here for fileContent of the feedback reports Summary of the content of the quarterly and monthly InFoQI feedback reportsClick here for file


Globally, and in Africa, after adjusting for age, women are about 1.4 times more likely to be blind than men. While women generally live longer than men, the lack of accessibility to and use of services is likely the most important reason for excess blindness in women in Africa.We sought to review the literature on vision loss in Africa and summarize the findings related to gender equity.Information from across sub-Saharan Africa was collected on the evidence of gender inequity and reasons for this inequity. Finally, the results were used to generate suggestions on how gender equity could be improved.In all published surveys (except one), cataract surgical coverage among women was lower than cataract surgical coverage among men. Although data available are limited, similar findings appeared in the use of services for other disease conditions, notably, childhood cataract and glaucoma. Evidence suggests that a variety of approaches are needed to improve the use of eye care services. Three main strategies are needed to address gender inequity in vision loss in Africa. First, it is important to address transport needs. Second, counseling of patients and family members is required. Finally, programs need to put in place pricing systems that make the services affordable the population.VISION 2020 can be achieved in Africa, but investment is needed in a variety of strategies that will ensure that eye care services are affordable, accessible, and acceptable to women and girls. The WHO estimates that worldwide 37 million people are blind and an additional 124 million have some visual impairment.A meta-analysis of population-based surveys on blindness prevalence in Asia, Africa, and the industrialized countries in 2000 indicated that women bear approximately two-thirds of the burden of blindness in the world.The meta-analysis in 2000 showed that in Africa, specifically, the age-adjusted odds for women to be blind compared to men was 1.39 (95% CI: 1.20-1.61).Cataract is the major cause of blindness in Africa and Cataract Surgical Coverage (CSC) is one of the most meaningful measures of service delivery for cataract. A meta-analysis of recent (since the year 2000) surveys, including CSC, found that males were 1.7 times (95% CI: 1.48-1.97) more likely to have received cataract surgery than women.Lower cataract surgical coverage among women is one of the contributing factors to the higher prevalence of blindness in women in Africa. Lower coverage in women is due to various social and cultural factors. A qualitative study conducted in TanzaniaTrachoma, an important cause of blindness in some areas in Africa, is also more common in women than in men. A recent meta-analysis showed that women are 1.82 times (95% CI: 1.61-2.07) more likely to have trichiasis compared to men.11Cataract is one of the main avoidable causes of blindness in children, and it is estimated that worldwide, about 200,000 children suffer from blindness caused by cataract, with 20,000-40,000 born each year with congenital cataract.19In some African communities, when family resources are scarce, fathers and some mothers tend to give preference to boys as compared to girls.Glaucoma is an important cause of blindness in Africa, although in most African countries, it has not been prioritized in VISION 2020 plans. When considering glaucoma and gender, Primary Open Angle Glaucoma (POAG) must be addressed separately from Primary Angle Closure Glaucoma (PACG). The latter is known in Asia and Europe to have an incidence 2-3 times higher in females than in males. Limited data from Africa are available, but there is some indication that it may be more common in African women than men.21POAG is usually regarded as the most important of the glaucomas in Africa, and there is no evidence that it occurs more frequently in one sex than the other. There is, however, evidence that men receive surgery for glaucoma at twice the rate as women.A number of strategies seem to help in decreasing the gender imbalance in service utilization in Africa.Transportation in Africa is both expensive and difficult to use. The quality of roads and the dispersed nature of the population mean that people must often travel long distances to access care. Both long distances and poor quality roads result in transport costs often being too high for the rural poor to afford. This often affects women more because they have limited financial decision-making authority, limited funds of their own, and minimal experience in traveling outside of the village. The scenario applies for all major causes of blindness: cataract, glaucoma, trachomatous trichiasis, and childhood cataract.Outreach programs, while expensive, are often the only way to reach rural populations, who otherwise would not have access to eye care services. Outreach that includes transport to the hospital for surgically treatable conditions provides the rural elderly with the physical and social capacity to seek care. Such systems also avoid unnecessary trips from one referral level to the next, which can be costly and discouraging for elderly patients. There is good evidenceSuccessful approaches to improving uptake of trichiasis surgical services have often relied on the surgical team to visit communities to provide trichiasis surgery. This also enables women to have easier access to these services. Generally, trichiasis surgeons who only work in static settings  have low levels of productivity.24There are several social barriers that limit women from using eye care services, and approaches are needed to address each one of these. Social support, often lacking for women, can only be addressed by counseling of male members of the household—husbands and sons. One goal of counseling is to provide the family with the information necessary to make a decision that will benefit all concerned. A second goal of counseling is to ally fears that patients and family members may have regarding surgery.Female peers in the community may be used to counsel and encourage families and women with visual problems to attend services. Through counseling, families are convinced to go for eye care services and fear of surgery is reduced. It must be recognized, however, that in many settings in Africa, even when cataract surgery is offered free of charge and transport is provided, not all patients (particularly women) are willing to accept surgery.25Counseling is also important for parents of children who have congenital or developmental cataract. Research suggests that initial poor follow-up of girls who received surgery can be improved by provision of counseling (as well as cell phone contact) when the child is in the hospital.Requiring patients to contribute to the cost of eye care services is important for improving financial sustainability in Africa. Charging for services tends to encourage greater accountability of service providers and leads to improved quality of care. On the other hand, if the amounts charged (including both direct and indirect fees) is too high, the price of service becomes a barrier.Gender inequity in the use of eye care services remains a problem in Africa; however, understanding of the reasons for gender inequity have led some programs to explore ways to address the problem. While solutions will need to vary, country by country, setting by setting, there are some principles that apply across the continent. First, programs must address transport needs, particularly for the elderly (women) and children. For trichiasis surgery, outreach visits to communities to provide surgery may be required. Second, good counseling helps provide the information needed for better decision making and for education of the benefits of eye care (and follow-up). Third, setting prices for services within the capacity of the family to pay  will improve service use for both women and men.There is a need to understand “emerging” conditions in Africa—particularly glaucoma and diabetic retinopathy. The dearth of information on these two diseases has limited our understanding of how best to ensure equitable service delivery.Even with the changes that have occurred in the past 10 years, problems and challenges remain. Most women still have limited decision making authority regarding healthcare and finances. Social support for elderly women to access services remains a problem in many settings. Finally, very few eye care programs in Africa monitoring service are disaggregated by sex, and fewer still have adopted gender-sensitive strategies. In the next 10 years, we anticipate that much can be done to address these challenges.