Pediatric aspects are nowadays integrated early in the development process of a new drug. The stronger enforcement to obtain pediatric information by the regulatory agencies in recent years resulted in an increased number of trials in children. Specific guidelines and requirements from, in particular, the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) form the regulatory framework. This review summarizes the regulatory requirements and strategies for pediatric drug development from an industry perspective. It covers pediatric study planning and conduct, considerations for first dose in children, appropriate sampling strategies, and different methods for data generation and analysis to generate knowledge about the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug in children. The role of Modeling and Simulation (M&S) in pediatrics is highlighted—including the regulatory basis—and examples of the use of M&S are illustrated to support pediatric drug development. Over the past decade, regulatory legislations for drug development in pediatric patients were passed worldwide, dramatically increasing the number of drugs tested in and labeled for children. Both, the Food and Drug Administration (FDA) in the United States (U.S.), and the European Medicines Agency (EMA) in the European Union (E.U.), established approaches that have been successful in generating important new information about the safety and efficacy of drugs used by children ,2. TransThe progress was achieved by combining requirements for pediatric drug development with incentives for the pharmaceutical industry to (at least partly) cover the additional investment for testing drugs in children. There was and still is effort needed to harmonize the regulatory framework for pediatric drug development, but as of today pharmaceutical companies are still facing the problem that the regulatory requirements differ between FDA and EMA and that the development of a new drug in the pediatric population has to be in line with requirements from both authorities. Enforced by the authorities—in particular the EMA—pediatric aspects have to be integrated early in the development process of a new drug and the general strategy has to be part of the overall development program.Drug development for pediatric patients is accompanied by various challenges for pharmaceutical companies. Clinical studies in children are different from studies in adults and the planning and conduct of a pediatric study needs special attention since the patient population is more vulnerable. Practical and ethical considerations are prominent and the latter receives particular attention in the regulatory guidance documents. As a consequence, the main challenges are to define the first dose in children, to find appropriate sampling strategies, to choose the right methods for data collection and analysis, to generate knowledge about safety, efficacy, pharmacokinetics, and pharmacodynamics of a drug in children, and ultimately to determine the right dose and dosing regimen.To comply with regulatory requirements and to optimize pediatric drug development—in terms of ethical, operational, and scientific aspects—pharmaceutical companies have to develop new and appropriate strategies for clinical studies in children. Modeling and Simulation (M&S) techniques are beneficial tools for optimization of the design, increasing the knowledge gained from pediatric studies. Since several regulatory guidance documents refer explicitly to M&S methodology, there is a clear regulatory basis and a need for using M&S in pediatric drug development.This review gives an overview of the regulatory aspects for pediatric drug development and their influence on practical and scientific considerations when conducting clinical studies in children. M&S as an efficient means to extract knowledge from the data will be discussed subsequently, rounding off the process of pediatric study planning, conduct, and evaluation.Historically, only a small fraction of all marketed drugs have had clinical trials performed in pediatric patients and a majority of marketed drugs were not labeled for use in pediatric patients. Accordingly, many drugs were administered to children in an off-label fashion without adequate understanding of appropriate dose, safety, or efficacy .The first initiative took place in 1994 when the Pediatric Labeling Rule was issued . The PedPediatric Rule [Since the approach under the Pediatric Labeling Rule was entirely voluntary, and did not substantially increase the number of products with adequate pediatric labeling, the FDA proposed 1997) and finalized 1998) the ric Rule ,7. The r and fina the ric Also in 1997, the Food and Drug Administration Modernization Act (FDAMA) introduced a process in which the FDA would develop a list of drugs for which additional pediatric information might be beneficial, agree on necessary studies, and issue to sponsors a Written Request (WR) for pediatric studies . Many drIn 2001, the FDA's Report to Congress identified some drawbacks, for example, that the incentive legislation was only applicable to some drugs . These dIn 2003, the Pediatric Research Equity Act (PREA) was enacted, putting into legislation most components of the Pediatric Rule. It required pediatric assessment for certain applications unless waived or deferred and a pediatric plan that outlines the pediatric assessment (including timelines) and addressed development of an age-appropriate formulation ,14.In summary, there are two separate legislations for pediatric drug development in the U.S.: the PREA defining the requirements and the BPCA defining the incentives. The PREA covers drugs and biologics and the studies are mandatory , whereas the BPCA covers only drugs and the studies are voluntary, relate to entire moiety, and might expand indications (including orphan indications). PREA and BPCA request pediatric studies to be labeled and pediatric safety data to be presented publicly to an advisory committee one year after study conduct. Both acts are clearly designed to encourage more pediatric research and more development of pediatric medicines.In 2007, within the scope of the Food and Drug Administration Amendments Act (FDAAA), the PREA and the BPCA were amended and reauthorized . The reaSimilar to the U.S., the European Medicines Agency (EMA) perceived the need for legal obligations for pharmaceutical companies to perform pediatric studies to obtain pediatric information for medicines used in children. In 1997, the European Commission organized a round table of experts to discuss pediatric medicines at the EMA. The experts identified the need to strengthen the legislation, in particular by introducing a system of incentives [In 1998, the Commission supported the need for international discussion on the conduct of clinical trials in children in the context of the International Conference of Harmonisation (ICH) .In December 2000, the European Health Council requested the Commission to take specific action to remedy the problem of usage of unauthorized medicinal products in the pediatric population . One of In subsequent years, these proposals were assessed and resulted in a new legislation governing development and authorization of medicines for pediatric use. It was introduced in the European Union (E.U.) in December 2006 and entered into force January 2007 [In general, the objective of the E.U. regulation is to improve quality and ethical research into medicines for children, increase the availability of authorized medicines for children, and to increase available information on medicines for children without unnecessary studies in children and without delaying authorization for adults ,22. To aPediatric Committee (PDCO) ,24:The PDCO (speak: "pedco") is the counterpart to the PeRC in the U.S. It is a committee of experts with competence in development and assessment of all aspects of pediatric medicinal products: five members  of the Committee for Medicinal Products for Human Use (CHMP), one member  from each Member State not represented via CHMP membership, and six members  appointed by the European Commission representing healthcare professionals and patients’ organizations. The main responsibility of the PDCO is to assess the content of submitted Pediatric Investigation Plans (PIP) and adopt opinions on them in accordance with the E.U. pediatric regulation. This includes the assessment of applications for a full or partial waiver and for deferrals. Other tasks of the PDCO include assessing data generated in accordance with the PIP, advising and supporting the EMA on creation of a European pediatric network, and establishing and regularly updating an inventory of pediatric medicinal needs.Pediatric Investigation Plan (PIP) : A PIP is the basis for development and authorization of a medicinal product for the pediatric population subsets . The PIP has to be agreed upon and/or amended by the PDCO and is binding for the company. If new information becomes available during the development, it is possible to apply to the PDCO for a modification of the agreed-upon PIP.The PIP includes details of the timing and the measures proposed to demonstrate quality, safety, and efficacy in the pediatric population and should cover all ages from birth to adolescence. It reflects the development plan in clinical, non-clinical, and technical aspects including timelines and covers all existing or planned (adult) indications and dosage forms (including specific age-appropriate pediatric formulations or routes of administration if necessary). The PIP clearly defines timing of studies in children relative to adults, including deferrals until completion of studies in adults to ensure that studies in children are conducted only when it is safe and ethical to do so. A waiver can be granted for some or all age groups if the drug is likely to be ineffective or unsafe in part or all of the pediatric population, if it is intended for conditions that occur only in adult populations , or if it does not represent a significant therapeutic benefit over existing treatments for pediatric patients.. Information about EMA decisions on PIPs (and waivers) is made public after deletion of commercially confidential data.The EMA provides on its homepage detailed information about the PIP procedure including a guideline on format and content of PIPs . InformaRewards and incentives ,21: It is mandatory to submit pediatric data in accordance to an agreed PIP for all regulatory submissions for new products and for products still on patent in case of line extension requests . Once authorization is obtained in all E.U. Member States and study results are included in the product information, also if results are negative, the medicine is eligible for six months of patent extension. Orphan-designated medicinal products are subject to the same requirements and benefit from two years of market exclusivity in addition to the 10-year exclusivity rewarded under the E.U. Orphan Regulation. A prerequisite for getting any incentive (and to receive marketing authorization) is a compliance check with the agreed PIP.Other tools for information, transparency, and stimulation of research ,21: To improve the information available for medicines used in children, the EMA established an inventory of pediatric needs in the different therapeutic areas where research and development of medicinal products for children is needed [s needed . As a trs needed .For any pediatric development question, the EMA offers free scientific advice or protocol assistance (for orphan or rare diseases). The advice can be requested before submitting a PIP or after PDCO decision. The scientific advice is not binding for any PDCO decision .Another objective of the E.U. regulation is to foster high quality ethical research on medicinal products to be used in children. To meet this objective, the establishment of a European Pediatric Research Network (EnprEMA) of existing national and European networks is required. As an outcome of the second workshop of the EnprEMA in March 2010, the structure of operation was defined by establishing a coordination group and recognition criteria for existing networks to become a member of the EnprEMA .The primary goal of the E.U. and U.S. legislations is identical: to improve children's health through advancements in research and to provide a framework for evaluation of efficacy and safety in the pediatric population. The U.S. and the E.U. legislation show substantial differences though.The E.U. legislation unifies the incentives and requirements under one legislation and the changes occurred in a shorter time frame: since July 2008 (18 months after entry into force), all applications for new marketing authorization must contain results of studies conducted in compliance with an agreed PIP unless a waiver or deferral was granted; since January 2009 (24 months after entry into force) all applications for new indications, new routes of administration, or new pharmaceutical forms must contain results of studies in compliance with the PIP unless a waiver or deferral was granted.i.e., companies have to submit a PIP upon availability of adult PK studies. The PeRC and the PDCO have similar responsibilities, mainly to review the WR and the PIP, respectively. The PDCO has more authority though since its decisions are binding. Moreover, the E.U. legislation is leading to more profound changes. In the E.U., pediatric development is mandatory for all new medicinal products under development unless a waiver is granted, and the pediatric product development is discussed earlier in the regulatory process compared to the U.S., The WR and the PIP differ as well. The WR is voluntary and issued by the FDA, usually following a proposed pediatric study request (PPSR) from the sponsor. The PIP is mandatory and proposed by the sponsor. The PIP addresses non-clinical requirements, complete product quality including age‑appropriate formulation, and includes waiver and deferral requests, whereas the WR includes age‑appropriate formulations statements, might include non-clinical studies, and does not include a waiver or deferral ,22.These differences are largely due to the two legislations in the U.S.: the PIP in the E.U. covers both the requirements and the incentives, whereas in the U.S., the incentives are covered by the WR (under the BPCA) and the requirements by the pediatric plan (under PREA).Because of these differences, the FDA and the EMA developed a pediatric collaboration with exchange of information to avoid exposing children to unnecessary trials, to enhance the science and decrease the risk to children during pediatric drug development . This coWith Japan as part of the ICH, the tripartite harmonized ICH E11 guideline ''Clinical investigation of medicinal products in the pediatric population"  is the pIt is of public interest to trace the pediatric legislation and its impact for the pharmaceutical industry. Therefore, the FDA and the EMA regularly evaluate their pediatric legislations and make the results publicly available. The most recent EMA results are published in the "Report to the European Commission: Companies and products that have benefited from any of the rewards and incentives in the paediatric regulation and the companies that have failed to comply with any of the obligations in this regulation covering the years 2007 to 2009."  .Between August 2007 and December 2009, the PDCO received 629 validated PIP applications  of which 156 (25%) included requests for a full waiver for all conditions and all subsets of the pediatric population.Since its first meeting in July 2007, the PDCO adopted 125 positive opinions on product-specific waivers (36%), 205 positive opinions on a PIP (59%), including deferrals and/or partial waivers, and 17 negative opinions (5%). Details of PDCO decisions are published regularly on the EMA webpage .For the EMA, it is anticipated that the number of requests for modifications of an agreed PIP will increase exponentially. As (pediatric) drug development is a dynamic process depending on results of ongoing studies, it is anticipated that three to five modifications will be submitted per agreed-upon PIP. As of December 2009, the PDCO adopted 59 positive opinions on modifications of an agreed‑upon PIP .The FDA publishes statistics about pediatric exclusivity and pediatric studies on the FDA webpage . The “PeBetween September 2007 and June 2010, 273 studies were completed under the BPCA (51), the PREA (151), or both (71) pursuant to the FDAAA. Efficacy and safety studies (178) and PK and safety studies (49) were most frequent . The totAlthough these results are based on different legislations and thus difficult to compare, both evaluations show clearly that the pediatric legislations overall have been very successful and will further increase the number of drugs tested and labeled for children.Several specific regulatory guidelines for pediatric drug development have been released by the FDA and the EMA in recent years. These guidelines form the regulatory framework for the pharmaceutical industry when studying drugs in children. Clinical studies in children differ from studies in adults in many respects. The diversity of children in different age groups, the consent and recruitment process or the ethical implications are only some examples to explain why the planning and conduct of a pediatric study needs particular attention.Does the disease affect children?Is the disease / disease progression in children similar to that in adults?Is the outcome of therapy likely to be similar to that in the adult form of the disease?As soon as the decision is taken to develop a drug for a disease in adults that may have applicability in pediatrics, the drug development team should outline its strategy for pediatrics, answering the following three key questions :Does thIf all of these three questions are answered by yes, pharmacokinetic data in children (together with appropriate safety data) may be sufficient to support an application for pediatric labeling without the need for extensive efficacy trials. In many cases though, these conditions are not met or sufficient information about a new drug is not available to answer these questions and clinical efficacy trials may be required. The same applies if the relationship between concentration and response is anticipated to be different in children compared to adults.It is desirable to make maximum use of extrapolation to children of available adult efficacy data. The approach is acceptable if similarity in indication, mechanism or course of disease and outcome of therapy  is sufficient. The FDA describes this approach in the pediatric study decision tree: at minimum a PK / safety study would be sufficient to bridge between children and adults .For most new drugs, however, it is challenging to predict if the concentration-response relationship in pediatric patients is similar to adults and to have a validated and accepted PD variable to predict efficacy. In practice, a PK bridging study is possible, but for a new compound it is often not realistic and PK, PD, and safety / efficacy information should be collected.On outlining the general pediatric strategy, there are various other aspects a company has to consider before planning pediatric studies .First of all, the type of disease being treated has to be a relevant pediatric disease and the appropriate risk/benefit assessment of engaging in pediatric trials has to take place, including considerations of efficacy and safety of alternative treatments. After defining the type and timing of pediatric studies, the assessment of the need for pediatric formulations for all appropriate age ranges will be an important step . If no bIn order to design a clinical study in the pediatric population, preclinical aspects such as preclinical safety assessment and preclinical drug metabolism and pharmacokinetics are important. Data from rodent and non-rodent species can identify target organ toxicity and provide appropriate margins of safety between therapeutic exposure and those that produce adversity in non-clinical studies. Juvenile animal studies are an extension to this paradigm in providing a comparison between adults and immature forms of the animal species ,49.For planning and performing pediatric clinical trials, it is important to realize that such trials might take longer since recruitment may be much more difficult. In addition, clinical centers with pediatric expertise are needed for study conduct. Advice on design and conduct can be given by the EMA (free of charge) upon request prior to submission of a PIP or at a later stage .The proposed age categories  modeling can provide substantial support to determine the pediatric dose. When defining a dose for the pediatric population, in particular the dose to be administered initially, one should always recall that children are not small adults. The use of pharmacology/physiology knowledge together with the PK/PD relationship in adults and knowledge from drugs of the same pharmacological class is essential for selecting the first dose in pediatrics. The aim is to calculate the dose that provides the same systemic exposure as in adults taking into consideration the higher inter‑patient pharmacokinetic variability observed in children compared to adults , assuminFurthermore, to retrieve the starting dose, the predicted therapeutic dose should be scaled by an appropriate safety factor. An approach could be to start dosing a small cohort  and analyze safety, PK, and/or PD results. If this first dose is safe, a PK- and/or PD-guided dose escalation could be performed to reach target exposure and response. Once the target is reached, the cohort sample size can be increased to collect more data on safety, PK, and PD and/or efficacy.With increased age, children become more similar to adults. Thus, if several studies with different age ranges are planned in the pediatric population, it is good advice to start with the older age groups and use the collected data adaptively to possibly modify the doses for the subsequent studies in the younger age groups.Dependent on the drug, an individualization of therapy following initial dosing and careful observation of response or safety may be useful. A therapeutic drug monitoring of plasma concentrations should be considered if an exposure-response relationship is to be characterized.The EMA guideline "Ethical considerations for clinical trials on medicinal products with the paediatric population"  clearly The FDA provides no guidance on blood volume but describes that volume and frequency of blood sampling can be minimized by using micro-volume drug assays and sparse sampling techniques, respectively. In addition, non-invasive sampling procedures such as urine and saliva collection may suffice if the correlation with blood and/or plasma levels has been documented .The ICH guideline E11 gives additional practical considerations with respect to PK blood sampling. Besides using a sensitive assay to decrease blood volume per sample , the use of experienced laboratories that can handle small blood volumes for PK and laboratory safety samples is recommended. Whenever possible, PK and laboratory safety samples should be collected at the same time to avoid repeat procedures. To minimize distress, the use of indwelling catheters rather than repeated venipunctures for blood sampling is recommended .For optimized evaluation, several guidelines recommend the use of population PK modeling and sparse sampling to minimize the number of samples obtained from each patient see . This inThe oral route of administration is commonly used for dosing to children and, therefore, many medicines should be available in both, liquid and solid oral dosage forms, in order to target a wide age range. Liquid formulations, for instance, are most appropriate for younger children who are unable to swallow capsules or tablets. Parenteral formulations are commonly used in neonates and extra care should be taken with respect to drug concentrations and choice of excipients.The EMA requires that the PIP describes any measures to adapt the formulation of the medicinal product to make its use more acceptable, easier, safer, or more effective for different subsets of the pediatric population. The EMA reflection paper "Formulations of choice for the paediatric population"  is not iThe regulatory guidance documents cover various practical and ethical considerations for pediatric studies, but nevertheless, performing clinical trials in children remains a challenge and many special requirements and considerations have to be addressed up-front. Obviously, specialized tools that can help in designing and analyzing pediatric studies need to be considered and explored. The use of M&S methods is one possibility to support pediatric study design and to retrieve as much information as possible from data derived from trials in children.Besides the general applicability of M&S as a beneficial methodology in drug development , there iThe EMA "Workshop on modelling in paediatric medicines" (April 2008)  clearly Clinical trials in children are usually conduced with fewer subjects than in adults and the investigated population is a vulnerable group. Therefore, particular attention should be paid to shield them from undue risk and to minimize distress, pain, and fear. Every attempt should be made to minimize the number of participants and procedures. Therefore, the amount of information available for each subject is smaller and there is a need to be more efficient with the information/subjects at hand. Model-based drug development is of help in aiming at efficiency. M&S can help in different ways to reach this goal see .The use of M&S in pediatric drug development combines two different perspectives. On the one hand, M&S tools can be applied before any pediatric data from clinical studies becomes available. Scaling approaches, PBPK modeling, Bayesian approaches making use of prior knowledge, and simulations can be used to support the pediatric strategy, e.g., for the PIP, by developing dosing recommendations and designs for pediatric studies. On the other hand, M&S techniques can be used to analyze pediatric data during and after a clinical trial. This includes sparse sampling and population PK analyses to reduce the number of blood samples and to minimize distress as well as to maximize knowledge generation and optimize further pediatric drug development with PK/PD modeling.Since the EMA requests the PIP early in drug development, important information such as on the exposure-response relationship in adults is not yet available but early phase data can already give substantial input for M&S to support the PIP. Generally, it is almost always possible to first provide a general M&S strategy that can be later updated and/or fine-tuned with further details and results in anticipated PIP amendments.et al. published a case study with an anti-hypertensive drug to show an example on how industry and FDA scientists can collaborate in designing pediatric trials using clinical trial simulations [et al. proposed a pragmatic approach for the practical implementation of M&S in pediatrics within the current E.U. regulatory framework [The regulatory assessment of M&S results might differ between the U.S. and the E.U. and there is no guideline defining the requirements and acceptability. The feedback pharmaceutical companies receive after submission of PIPs or PPSRs clearly indicate that M&S techniques are endorsed or even mandatory. In addition, the establishment of the FDA Division of Pharmacometrics increased the acceptance and promotion of M&S in drug development including pediatrics. Jadhav ulations . Manolisramework .Population PK and PK/PD modeling, optimal design, and PBPK modeling are prominent examples of M&S techniques with an added value in pediatric drug development.etc.) and forms the scientific basis for study/trial simulations, dose adjustments or labeling extensions in other populations. Population PK and PK/PD modeling are explicitly recommended in several regulatory guidelines and should be the primary analysis method in pediatric drug development see . In partThe influence of patient-specific factors  can be identified and tested statistically to decide whether the factor enters the population model as predictive covariate. Once a qualified model is available, it can be used for simulations, e.g., to support the selection of dosing regimen and study design. In addition, one can extrapolate from adults and older children and—if applicable—use PK information to extrapolate efficacy . DependiPopulation PK/PD models can be developed based on data from previous clinical studies or based on pooled data from different studies. This modeling technique can be combined with other information such as exposure-response relationship and disease characteristics from adults to bridge the gap between adults and children . Dosing The evaluation and optimization of the study design becomes more and more important due to the limited number of studies, the low number of individuals and samples leading to the need of optimized and thus most informative sampling times. Prior information  can be employed to evaluate a sparse sampling strategy. The D-optimality sampling method allows finding a set of sampling times that yield the smallest error in the estimate of the parameters by maximizing the Fisher information matrix with respect to the sampling times. Afterwards, a large number of trials following the suggested sampling times can be simulated and the parameters can be estimated by population PK methods. These estimates can be compared to the "true" parameters used for the simulation. The comparison provides information about bias and precision to be expected in the parameter estimates if an actual study was conducted with a sampling strategy identified using the D-optimality sampling method ,81. An iin vitro predictions for distribution and elimination are utilized.PBPK models are based on physiological considerations and more comprehensive than empirical or semi-mechanistic models . A PBPK After validating a PBPK model with adult PK data, differences in growth and maturation can be accounted for to predict drug exposure in children. Since dose scaling approaches should only be used in children above two years of age, PBPK modeling can provide substantial support for pediatric dose finding in neonates and infants. Growth and maturation can affect all aspects of drug PK as well as PD and adverse effects. Physiological models for children have been established that incorporate the available knowledge and facilitate predictions of the effect of age on certain aspects of PK and PD. The performance of PBPK in describing and predicting PK in children has been proven ,84 and sMore and more pharmaceutical companies are submitting PBPK modeling reports to the EMA and the FDA and it is well possible that regulatory agencies ask for PBPK modeling approaches to support pediatric drug development in neonates and infants.In summary, M&S can optimize the generation of knowledge in a pediatric setting, support the development of best pediatric study designs and dosing recommendations, and minimize distress, pain, and fear in pediatric patients participating in a pediatric clinical study.The health authorities in the U.S. and the E.U. show a strong commitment to promote better medicines for children. The pediatric legislations have built a complex framework for pediatric drug development and the pharmaceutical industry has to deal with different requirements and special obligations to receive the incentives. The preparation of the PIP is a major task for each clinical development team and pediatric aspects have to be integrated early in development. The regulatory authorities reviewed a substantial number of pediatric evaluations in recent years and pharmaceutical companies become familiar with the pediatric regulations.Numerous regulatory documents are available to guide pharmaceutical companies through the specific procedures and to answer specific scientific questions regarding study design and conduct. Since pediatric drug development is a very complex area, many questions remain open, and close collaboration and communication between industry and health authorities is essential.Surprisingly, the number of companies using the free pediatric scientific advice is low compared to the number of submitted PIPs. Although the pediatric scientific advice is not binding, an open discussion about the pediatric strategy up-front can improve information exchange and reduce the time for the entire PIP procedure. Even though the guidelines cover various important aspects, the pediatric strategy is highly dependent on the properties of the drug, on the disease, and on the pediatric population and has to be defined carefully for each drug and indication. Similarly, pediatric studies vary widely and many procedural and scientific considerations  are indispensable and an extraordinary challenge for each study team.Recommended by many regulatory guidelines, M&S techniques are essential and well-established in pediatric drug development to support the pediatric strategy including dose finding, study design, and minimizing distress, pain, and fear. Recently, Gobburu  presente


A variety of systems have been developed to grade evidence and develop recommendations based on the available evidence. However, development of guidelines for medical tests is especially challenging given the typical indirectness of the evidence; direct evidence of the effects of testing on patient important outcomes is usually absent. We compared grading systems for medical tests on how they use evidence in guideline development.We used a systematic strategy to look for grading systems specific to medical tests in PubMed, professional guideline websites, via personal correspondence, and handsearching back references of key articles. Using the Appraisal of Guidelines for Research and Evaluation (AGREE) instrument as a starting point, we defined two sets of characteristics to describe these systems: methodological and process ones. Methodological characteristics are features relating to how evidence is gathered, appraised, and used in recommendations. Process characteristics are those relating to the guideline development process. Data were extracted in duplicate and differences resolved through discussion.Twelve grading systems could be included. All varied in the degree to which methodological and process characteristics were addressed. Having a clinical scenario, identifying the care pathway and/or developing an analytical framework, having explicit criteria for appraising and linking indirect evidence, and having explicit methodologies for translating evidence into recommendations were least frequently addressed. Five systems at most addressed these, to varying degrees of explicitness and completeness. Process wise, features most frequently addressed included involvement of relevant professional groups (8/12), external peer review of completed guidelines (9/12), and recommendations on methods for dissemination (8/12). Characteristics least often addressed were whether the system was piloted (3/12) and funder information (3/12).Five systems for grading evidence about medical tests in guideline development addressed to differing degrees of explicitness the need for and appraisal of different bodies of evidence, the linking of such evidence, and its translation into recommendations. At present, no one system addressed the full complexity of gathering, assessing and linking different bodies of evidence. Guideline panels aim to develop recommendations based on the best available evidence. In this process, panels often grade the quality of the available evidence . Such anFor intervention studies, the highest available evidence comes from high quality systematic reviews of randomized controlled trials that document the effect on patient important outcomes, such as mortality, morbidity, or functional health ,4. GuideHence, when developing recommendations for a medical test, guideline developers need to go beyond the analytical performance and the accuracy of a test to consider consequences of testing on patient outcomes, and to balance benefits versus harms of testing, taking into account patient values and preferences and resource implications .In addition, tests are never administered in isolation but as part of a testing strategy. Guideline panels therefore need to consider the evidence in context of the most likely testing strategy that includes the test that is being evaluated, and from there make judgments on the downstream patient consequences for further testing and treatment . All of Grading systems generally describe a subset of steps that are a part of the overall guideline development process. These are aspects concerned with formulating a key question, systematically searching and gathering the related evidence, and assessing its potential for bias, level of precision, and other elements of quality. The quality of the evidence, in combination with a number of other factors, guides the strength of the recommendation a guideline panel should provide. Some evidence-grading systems can go beyond these aspects and provide guidance on steps that are part of the overall guideline development process, such as those relating to stakeholder representation, dissemination and communication, and conflicts of interest. It has previously been reported that a lack of standardization in grading evidence and guideline development methodology can lead to varying quality of guideline recommendations ,11. A syOur aims were: first, to establish a comprehensive inventory of evidence-grading systems for medical tests. We wanted to compare the methods in each of these systems, with a focus on those aspects related to appraising evidence, linking different bodies of evidence, and translating evidence into recommendations; as a secondary aim, we compared the extent to which more general guideline development processes were included in these grading systems.For the purposes of this review, we defined an evidence-grading system to be a subset of the overall guideline development process. We defined a guideline development process to be one that begins by defining a topic of interest, then conducting a scoping of the literature in order to assess feasibility of the chosen topic. Once feasibility is ascertained, specific key questions that the guideline will address should be explicitly defined. This is followed by systematically gathering the available evidence and evaluating its quality; then formulating recommendations based on a number of factors such as the quality of the gathered evidence, applicability, resource issues, benefits versus harm, etc. We defined a grading system to be the subset of this series of processes: those that address aspects concerned with formulating a key question, systematically searching and gathering the related evidence, then assessing its quality and formulating recommendations Figure .Because grading systems are often described and used within the setting of a guideline development body, such as the National Institute for Health and Clinical Excellence (NICE), and are not always published in peer-reviewed journals, we felt it necessary to used a multi-staged approach that involved searching various information platforms up to March 2013  to assess the quality of the evidence and stopped there were excluded. Articles reporting a specific group process in developing a guideline, rather than a grading system that could be used as a tool for developing guidelines in general, were excluded.To be included in our review, the description of a grading system must explicitly state that it can be used for grading evidence and making recommendations for medical tests. The system must include a ‘levels of evidence’ and ‘strength of recommendations’ table. Systems that were non-English or employed only a checklist or other quality assessment tool  instrument , a tool Methodological characteristics are focused on the grading system within a guideline development process. These were defined based on how explicit a grading system was in describing how the evidence is gathered, its quality assessed and recommendations derived. Here, we defined six categories and a total of 23 subcategories Table . ProcessWe tested the applicability of each category and subcategory by extracting relevant information from a random selection of grading systems. Data extraction was done in duplicate and differences resolved through discussion between two reviewers. A third reviewer was involved when consensus could not be reached. A review protocol detailing the above methods was created by the first author , which each addressed five out of the seven subcategories listed Figure . The subHaving an explicit search methodology as part of evidence gathering was addressed in seven out of 12 systems, to varying degrees of detail. Less than half of the grading systems were explicit on the number and type of databases the search should include (5/12) Figure .The European League Against Rheumatism (EULAR), European Society of Cardiology (ESC), Institute for Clinical Systems Improvement (ICSI) and the Oxford Centre for Evidence-based Medicine (OCEMB) were not explicit on the types of evidence that needed to be collected. Half of the systems (6/12) specified the need to collect patient important outcome data of which only three  explicitly required other types of evidence to be gathered such as costs, quality of life, contextual implications of the test such as ethical, legal, and social implications and information on resource use.All systems except one (ESC) required users to grade the quality of the evidence at the individual study level, nine of which outlined specific criteria to be used. Nine systems also explicitly required the evidence to be graded collectively as a ‘body of evidence,’ and seven provided specific criteria by which this could be done. Only NICE DAP and the USPSTF provided explicit guidance on appraising different bodies of evidence. The EGAPP acknowledges in its grading system the need to link and appraise different types of evidence in the appraisal and guideline development for medical tests, given that accuracy alone is indirect evidence on patient important outcomes. However, it does not provide an explicit method by which this can be done.Methods for making recommendations were explicitly provided in most systems (9/12). Only six systems reported the inclusion of patient important data when making recommendations. Explicit methods for translating accuracy evidence to recommendations were provided in only three of the 12 systems ).Current publications on applying GRADE to diagnostic tests describe the use of a considered judgment process where the guideline panel makes an informed judgment based on presumed patient important outcomes for patients with true and false positive test results, and true and false negative test results ,18. OtheThe NHMRC’s process of moving from evidence to making recommendations is the least explicit, in comparison to the GRADE and NICE DAP processes. The guideline panel rates the evidence according to five factors: evidence base (as determined by the levels of evidence hierarchy with systematic reviews having the highest level), consistency, clinical impact, generalizability, and applicability Table . With thCharacteristics most likely to be addressed in the 12 grading systems were the need for involvement of relevant professional groups during guideline development (8/12), the need for external peer review of completed guidelines (9/12), and the inclusion of recommendations on methods for dissemination of completed guidelines to target audiences (8/12) Figure . The oveOther characteristics commonly addressed included the presence of a clear content layout and organization (7/12), the presence of a procedure for updating guidelines (7/12), and the need for guideline developers to address conflicts of interest in a guideline (7/12).Piloting of the grading system and information on funding were the least likely process characteristics reported, with only three of the 12 systems Figure  fulfilliAll 12 grading systems varied in the extent to which they addressed process and methodological characteristics. On the whole, we found a wide variation in terminologies used. Methodologically, out of the 23 subcategories defined, the EGAPP (19/23) followed by the USPSTF (18/23) and NICE (17/23) were the most comprehensive Figure a. The ScIn this review, we identified 12 eligible evidence-grading systems that could be used by guideline developers to develop guidelines for medical tests. The EGAPP, USPSTF, NICE, GRADE, and NHMRC systems addressed, to differing degrees of explicitness, the need for and appraisal of different bodies of evidence, the linking of such evidence and its translation into recommendations. However, no one system adequately addressed the complexity of gathering, assessing, and linking different bodies of evidence, which is one of the challenges guideline developers of medical tests face. All 12 varied in basic guideline development features.We have been able to include a number of grading systems available to guideline developers ,33. WhilTo describe the process characteristics, we felt adapting the domains from the AGREE instrument was the most appropriate, given that this is an instrument developed to assess the quality of clinical guidelines. However, AGREE was developed to assess the quality of the final product, whereas we looked at the guideline development process and thus we adapted the domains to fit this aim.For the methodological characteristics relating to how evidence is gathered, assessed, and recommendations derived, the AGREE instrument does not address these aspects in a manner that was specific to the issues in medical test guideline development. The domain on ‘Rigor of Development’ contains eight items relating to how the evidence is gathered, assessed, and how recommendations are formulated . While sThe objective of this review was not to make an analytical appraisal of the different grading systems available. To address that objective adequately is beyond the scope of this review. As such, the tables describing the systems in this review may not always appropriately discriminate the individual strengths and weaknesses of each system. It may also be worthwhile to note that not all of the systems included in this review may have been meant to be used independently, which may explain why they may have addressed process features poorly.et al.[Two other prominent reviews of evidence-grading systems have been reported in the literature ,2 A numbet al. did not Only three of the grading systems included in this review ,23,26 haet al. and Grilli et al. who report that guidelines produced by specialty societies tended to be of lower methodological quality compared to those produced by major guideline bodies [When it came to grading systems developed by specialty societies , these tended to be more comprehensive in addressing process characteristics related to guideline development, but were less thorough in covering features important for systematically gathering, assessing and making recommendations Figure  Figure a. For exe bodies ,36.e.g., primary care, specialty settings), how the test will be applied , and who will be tested . Both the EGAPP and USPSTF go one step further to put the clinical scenario in question into the context of an analytical framework, which essentially is an overarching key question on clinical utility, i.e., whether there is direct evidence that using the test leads to clinically meaningful improvement in outcomes.Given the complexities in medical test guideline development, we defined a number of methodological characteristics as being particularly pertinent to medical tests and different to the methods for guideline development for interventions. The definition of clearly defined key questions is a first step in any guideline development. Because medical tests are often part of a test/treatment strategy, a PICO (Patient-Intervention-Comparison-Outcome)  styled kWhile the NICE system does not contain an analytical framework, it defines a care pathway within which the clinical scenario is covered. The care pathway addresses a broader view on the test-treatment pathway that includes all aspects related to the application of the test and treatment sequences that may follow such as monitoring, retreatment, treatment for side effects and complications that may be experienced by the patient as a result of the test.‘Which of the above preparatory steps is best?’ is a difficult question to answer. The NICE, EGAPP, and USPSTF obviously go beyond the typical PICO-styled key question definition, and would make for a more comprehensive assessment of a medical test than a grading system that does not include such components in its preparatory phase. The inclusion of features such as the clinical scenario, analytical framework, and/or care pathway helps to address the point reiterated many times that an evaluation of a test in context of its test-treatment pathway is more informative than the evaluation of its accuracy alone ,38,39.Rating the quality of evidence is another aspect in grading systems that is challenging for medical tests compared to interventions. Because of the indirectness of evidence about tests relative to patient important outcomes, different bodies of evidence may need to be gathered and assessed that are relevant to the patient outcomes being considered by the guideline panel. Often these data are either lacking or when available maybe in the form of observational studies that, in the level of hierarchy of studies, is classified as low quality .Grading systems therefore need to provide explicit guidance to guideline panels on the types of evidence that need to be gathered in the absence of evidence of the effects on patient outcomes. Guidance is also needed on explicit methods on how these data can be assessed and linked to other bodies of evidence. Less than one-half of the grading systems in this review were explicit on the need for patient important data. Even fewer (three systems) mentioned the need to gather other types of data such as costs and resource utilization. EGAPP and NICE systems, both specific for medical tests, were the only two systems that were explicit on the need to gather such data, although they do not provide clear criteria on how such data should be evaluated. While we did not find this to be explicit in the current publications on GRADE for diagnostics -18, we aA considered judgment process was the most commonly used method to move from evidence to making recommendations. GRADE, NHMRC, and NICE were the only three systems that provided clear criteria that should be considered when making this transition. Although GRADE’s process was the simplest and clearest amongst the three, none of the three systems involved an explicit method by which the different bodies of evidence could be translated into recommendations. NICE’s economic modeling, which formed one aspect of the process of moving from evidence to recommendations, was the only aspect that involved an explicit, structured methodology with clear cut-off points and guidance on the different recommendations to make based on the model thresholds .Clear guidance for evaluating, linking, and making recommendations based on different bodies of evidence beyond test accuracy is lacking in grading systems available today. The EGAPP, USPSTF, NICE, GRADE, and NHMRC systems address these aspects to varying degrees of comprehensiveness. There is a clear need for closer analysis of these features in each of these systems and for more targeted user testing among medical test guideline developers.Existing grading systems for medical tests vary considerably in the extent to which they cover basic guideline quality requirements. The guideline development community can benefit from consensus and standardization regarding the basic features a grading system should cover. This could help to achieve consistency, improve transparency and, ultimately lead to the production of better quality guidelines.AGREE: Appraisal of guidelines for research and evaluation; AHAPG: American heart association practice guidelines; EGAPP: Evaluation of genomic applications in practice and prevention initiative; ESC: European society of cardiology; EULAR: European league against rheumatism; GRADE: Grading of recommendations assessment, development and evaluation; ICSI: Institute for clinical systems improvement; NHMRC: National health and medical research council; NICE: National institute for health and clinical excellence; NICE DAP: NICE Diagnostic assessment programme; OCEMB: Oxford centre for evidence-based medicine; PICO: Patient intervention comparison outcome; PIO: Patient important outcomes; QUADAS: Quality assessment of diagnostic accuracy studies; SIGN: Scottish intercollegiate guidelines network; SORT: Strength of recommendation taxonomy; USPSTF: U.S. Preventive services task force.The authors declare that they have no competing interests.GG defined the search strategy, conducted the literature search, data extraction and drafted the manuscript. MWL and MMGL carried out the data extraction each in duplicate with GG. PMMB initiated the original research idea. MWL, MMGL and PMMB participated in the definition of the data tables and figures. All authors contributed to and approved the final manuscript.Full electronic search strategy used in Pubmed.Click here for fileList of websites searched.Click here for fileReview protocol.Click here for file


In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009.We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs.This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 . Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%.This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic. On the 20th of August 2008, an outbreak of 118 cases was declared at St. Mary’s and Zenenga wards of Chitungwiza, a large urban centre on the outskirts of Harare .Following this initial outbreak in Chitungwiza, a second wave of infections was reported a few months later with numerous wards being affected and a rapid transmission of the infections to the whole city of Harare. This is one of the largest and most extensive outbreaks of cholera yet recorded in Zimbabwe affecting rural and urban areas -4.In developing countries, cholera is closely related to poor environmental status and lack of basic infrastructure. In this respect, high population densities and poor access to safe water and proper sanitation, along with other environmental conditions, contribute to the spread of cholera in Africa -7. ThereIn 1852, Farr hypothesized a causal relation between the cholera cases in the London epidemic and the elevation, indicating that there was an underlying “natural law” correlating infection with cholera inversely to elevation above high water. Farr stated that the elevation of the soil in London had a more constant relation with mortality from cholera than any other known element. In the same way, his contemporaneous colleague Snow, by mapping cholera deaths by elevation, found a correlation between higher elevations and reduced risk for cholera death ,17.Since 1852 there is some evidence about the association between cholera and elevation, although there is no empirical data explaining this pattern . CholeraWe developed an ecological study using secondary data.Data were drawn from the registry of cholera treatment centers (CTCs) and oral rehydration points (ORP) functioning during the cholera epidemic in Harare and Chitungwiza. Medecins Sans Frontieres, in collaboration with the Department of City Health of the Ministry of Health & Child Welfare, implemented and managed three CTCs, in the Budiriro Polyclinic, in the Beatrice Road Infectious Diseases Hospital and in the city of Chitungwiza. Ten ORPs were functioning in Harare city and one in Chitungwiza.Data used in this study were the aggregated number of cholera cases by suburb of residence, topographic average elevation of each suburb in meters, and the distance in meters from suburbs’ geometric centre to Chitungwiza (the epicenter of the epidemic).Population figures by suburb were calculated from the official census of Harare and Chitungwiza, completed in 2002. To estimate the populations’ figures at the time of the epidemic, we employed an average constant annual growth rate of 3%, as estimated by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat .During the epidemic, a cholera case was defined as any patient presenting 3 or more liquid stools and/or vomiting for the last 24 hours .We described the elevation by suburbs and distance of each suburb to the epicenter (Chitungwiza) in meters. Geographical information was integrated into the GIS database as vector polygons. Using ArcGIS ‘zonal statistics’ module, we measured for each polygon average ground elevation and the distance between each suburb centroid (geometric center of the polygon) to Chitungwiza.To describe the outbreak, we calculated attack rates for each suburb, including 95% exact confidence intervals (CIs). We assumed that the attack rates follow a Poisson distribution ,25. We tTo describe the relation between the cholera cases and elevation, the Pearson’s correlation coefficient (R) between the cholera rates and the mean elevation of each suburb was calculated. Then, a scatter plot was built to show the relation between the observed rates and the average elevation by suburb.A generalized linear mixed model (under the assumption of a Poisson distribution) was used to model the relation between the risk of cholera by suburb and the elevation in meters of Harare: The model assumed a random intercept, with the number of cholera cases as the dependent variable, the log of the population figures by suburb as the offset, and the suburb’s average elevation as the independent variable ,26.Q, based on the quintiles (Q1−Q5) of the distance of each suburb to Chitungwiza. Hence, our final model looks as follows: We use prior knowledge about the dynamic and spread of the epidemic to allow for spatial correlation between neighboring suburbs based on the distance in meters to the epicenter (Chitungwiza) . Therefowhich corresponds to The derived risk ratio of the model was interpreted as the cholera risk per 100 meters of increase in the elevation, taking into account overdispersion and spatial correlation of the data.Finally, the predicted rates of cholera by suburb were derived from the model using an Empirical Bayesian estimation. This Empirical Bayesian approach consists of computing a weighted average between the raw rate for each suburb and the regional average, with weights proportional to the underlying population at risk . EstimatA sensitivity analysis was developed to confirm the consistency of our findings, consisting of an iterative random selection of suburbs to build the cluster variable for the estimation of the random effect. The aim of this process was to estimate the variability of the coefficient of the elevation, taking our first estimation as reference and proving its consistency ,30.For the GIS component of the analysis (mean elevation and distance to the epicenter), a recent (2008) high resolution satellite image of Harare was used. In order to assure a good integration with field surveys, the satellite image was geometrically corrected to fit a set of control points surveyed with a GPS. This image was used as a spatial reference for all GIS analysis. Information from the Harare’s topographic map , was digitized on top of the satellite image using Arcview software. The elevation information was extracted from the SRTM database . In this database, ground elevation is provided for each 90x90 meter cell of the model. The extent of each urban district was integrated to the GIS database as vector polygons. Using ArcGIS ‘zonal statistics’ module, average ground elevation and the distance between each suburb centroid (geometric center of the polygon) to Chitungwiza were measured for each polygon.The statistical software used for this study was Stata v.11.2 and the module ArcMAP of ArcGIS  software for spatial and geographical representation.During this cholera outbreak, CTCs and ORPs in Harare and Chitungwiza registered and cared for 19,422 persons who met the case definition. The epidemiological description of the outbreak including the number of cases, attack rates and rate ratios by suburb is presented in Table R=−0.5; p-value < 0.001).This pattern of the spatial distribution of cholera cases correlates positively with the average elevation of Harare suburbs. The computed Pearson’s correlation coefficient between the cholera observed rates and the mean elevation by suburb shows a significant negative relationship, which means that at higher elevation, the number of cholera cases was lower , with higher rate ratios for the suburbs that are located nearest to the cholera epicenter . Even though this methodological approach is not the classic way to account for spatial correlation, the standard errors of our estimations allow for suburbs correlation within each of the five groups , relaxing the usual requirement that the observations are independent.Our study focuses only on topographic elevation, but we have to highlight that there were other historical, geographical, social, and environmental risk factors previously published, that explained the spatial pattern of the Harare epidemic. Specifically, the fact that Tafara, a high elevated suburb, had high cholera rates, could be explained by the dynamics of population movement because there is a big concentration of bus stops and informal markets offering service to the travelers moving to Mozambique. Bus stops and informal markets, residential housing, water supplies and sewage disposal system were previously identified as risk factors related with the cholera epidemic in Harare, 2008-2009 . FinallyDespite that fact, and to our knowledge, this study could be the first attempt to model the relationship between cholera risk and elevation. Furthermore, the sensitivity analysis developed reinforces the validity of our final model confirming the inverse relationship between cholera risk and elevation.In conclusion, it has been shown that the distribution of cholera cases by suburb in Harare during the outbreak of 2008-2009 followed an identifiable spatial pattern characterized by a lower cholera risk when the average elevation in meters by suburb increased by 100 meters. During the rainy season, in high populated urban areas, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns. This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Further studies are needed to explore the link between elevation, water supplies and sewage disposal systems. The identification of this spatial pattern could guide public health action to orientate further preparedness interventions in Harare, aiming to prevent a new cholera epidemic.The authors declare that they have no competing interests.MALF and PM developed the concept and design of the study; JF, YB, MALF and PM acquired the data; MALF and MS developed analyses; MALF carried out the analyses, and all authors interpreted the data; MALF and MS wrote the manuscript. All authors drafted the manuscript, revised critically the content, and gave technical support and conceptual advice. Finally, all authors read and approved the final manuscript. MALF is the guarantor of the paper.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/442/prepub


The spread of infectious disease epidemics is mediated by human travel. Yet human mobility patterns vary substantially between countries and regions. Quantifying the frequency of travel and length of journeys in well-defined population is therefore critical for predicting the likely speed and pattern of spread of emerging infectious diseases, such as a new influenza pandemic. Here we present the results of a large population survey undertaken in 2007 in two areas of China: Shenzhen city in Guangdong province, and Huangshan city in Anhui province. In each area, 10,000 randomly selected individuals were interviewed, and data on regular and occasional journeys collected. Travel behaviour was examined as a function of age, sex, economic status and home location. Women and children were generally found to travel shorter distances than men. Travel patterns in the economically developed Shenzhen region are shown to resemble those in developed and economically advanced middle income countries with a significant fraction of the population commuting over distances in excess of 50 km. Conversely, in the less developed rural region of Anhui, travel was much more local, with very few journeys over 30 km. Travel patterns in both populations were well-fitted by a gravity model with a lognormal kernel function. The results provide the first quantitative information on human travel patterns in modern China, and suggest that a pandemic emerging in a less developed area of rural China might spread geographically sufficiently slowly for containment to be feasible, while spatial spread in the more economically developed areas might be expected to be much more rapid, making containment more difficult. Worldwide urbanisation and increased human mobility create conditions favourable to the spread of emerging pathogens, such as the SARS epidemic st century.Increasing volumes of travel data for developed countries is becoming available, including origin-destination matrices for commuting obtained from census data, data from surveys and data from mobile phones A survey of commuting and travelling behaviour of 20,000 people from two different regions in China was conducted. Half of the study participants lived in Huangshan city at the southern end of Anhui province, whereas the other half come from in Shenzhen city in Guangdong province, bordering Hong Kong Special Administrative Region (SAR), China see .2.Anhui is a mainly agricultural and relatively poor province. Huangshan city has a population of 1,470,000, comprising both urban and rural areas. The population density overall is 180 people/km2. Shenzhen is a major manufacturing centre in China, but industries also include finance and high-tech enterprises. The population can be divided into local residents who tend to be better educated, and lower-skilled migrant workers. The latter typically originate from other provinces and come into Shenzhen for at least 6 months of the year, living in dormitories provided by their companies.By contrast, Guangdong province has a rapidly growing population and is now the largest province by population; it is also the richest province. Shenzhen city is a Special Economic Region, meaning it is more open to trade and commerce than other parts of China. Since it was established in the early 1980s, it has grown to a population officially recorded as 8.6 million at the end of 2007 with an average population density of 4200 people/kmThe Institutional Review Board of China CDC granted ethical permission for the study. Oral informed consent was obtained from all adult participants and parental consent for minors at the time of interview.10,000 individuals were selected each from the administrative areas of Huangshan city in Anhui province and Shenzhen city in Guangdong province.Huangshan city consists of 3 urban districts and 4 rural counties. Study participants were selected from one urban district, Tunxi district, and one rural county, Xiuning county, in proportion reflecting the overall proportions or urban and rural citizens within Huangshan city. Within Tunxi district, households were sampled randomly from all 11 communities, whereas within Xiuning county, households were sampled randomly from 30 of the 259 villages in the county.Shenzhen city consists of 6 urban districts with a total of 639 communities. A substantial proportion of the population are migrant workers who live in company provided dormitories. From 30 communities within Shenzhen city, households and migrant workers were recruited for the study in numbers reflecting the proportion of migrant workers within the population.All members of the selected households were administered a questionnaire, provided in We analysed the demographic data, commuting distances and travel behaviour. In order to find predictors of the commuting distances, we fitted linear regression models and characterised the remaining variability within the categories defined by the best fitting regression model by fitting functional distributional forms to the observed distributions of distances travelled to school or work. To model the probability of an individual travelling we fitted logistic regression models to the data. In order to describe the distances of the occasional journeys we fitted functional forms to the overall distributions of distances travelled for the different cities using a gravity model approach. Due to the large number of possible origins and destinations within the country, we used a gravity model integrated over individual destinations that only retained information on the distances travelled.In order to find predictors of the distance travelled to school or workplace we fitted linear regression models to the data, adjusting the confidence intervals of the estimated parameters for the clustering by households in the dataset. We used the logarithm of the distance to the school or workplace as the independent variable. For those individuals with a reported distance of 0 km we used ln) instead. Potential independent variables are age, sex, family size, registration status, frequency of travel outside the study area and whether or not they live in a rural area (Huangshan) or are migrant workers (Shenzhen). For students, age was categorised by type of school, whereas for employees we used 10-year age bands, with those below 20 and those over 60 merged. Family size was classified into small (< = 3) and large (>3) families. To decide which variables and interactions to include into the final model, we fitted models with all possible variable combinations, and up to two interaction terms, as well as all possible combinations of interaction terms for all combinations of up to 4 variables. The model with the lowest value of the Bayesian information criterion µ and σ are the mean and standard deviation of the log distance of the fitted distribution.We wished to characterise the variation between individuals in the typical distances travelled to school or work. To this end we categorised observed log distances into bins of uniform width, with the first category containing all distances up to We also included an additional parameter To find predictors of those who had travelled outside the study area within the past week, we fitted logistic regression models to the data analogously to the way described above for the school and workplace distances. Here we did not perform a separate analysis for students and employees, so the occupational status was included as an additional potential independent variable. We fitted models with all possible variable combinations and up to one interaction term, and all possible combinations of interaction terms, if up to 3 variables were included.For the occasional journeys we fitted a simplified gravity model, where we assumed that the number of journeys from each study area to destinations a certain distance Gravity models assume that the frequency of journeys As spatial kernel 95% confidence intervals for the parameter estimates of the fitted distributions to both the commuting and occasional journey distances were obtained by varying all parameters around the maximum likelihood estimate and identifying the part of the parameter space, where twice the negative log likelihood differs less than st July and 8th September 2007, in Shenzhen between 9th and 30th August 2007. Some descriptive statistics of the study populations are shown in The selected study participants in Huangshan were interviewed between 21There are interesting differences in occupational status by sex and age, see The distance distributions for both students and employees from Huangshan city fall off very steeply between 20 and 30 km, whereas the distance distributions from Shenzhen city show a longer tail of up to 300 km . We exam2-values of all these fits were low to moderate for students but very low for employees , indicating substantial residual variation in distances within categories. The observed distance distributions by category are summarised in For Shenzhen, the best model for students included age and registration status; the best model for employees included age, sex, registration status and whether an individual was a local resident or migrant worker. Fitting the local population and migrant workers separately yielded the same included variables for the local population, but the preferred model for the migrant population was the null model, indicating a fairly homogeneous composition of this subpopulation. The ROverall, age is the most important predictor for students with older students travelling further to school. In Huangshan, students from rural areas have longer distances than those from urban areas, whereas in Shenzhen, those not registered travel longer distances than those registered.For employees, men tend to have a longer commuting distance than women. Here, the commuting distance tends to decrease with age, but teenage employees in the Shenzhen local resident population travel fairly short distances. In contrast to the situation for students, local registration is associated with longer commuting distances in the Shenzhen local employees. Further details about the regression models can be found in We fitted lognormal distributions to the distance distributions stratified by study area and employment status, and further into the categories determined by the linear regression. Cumulative observed and fitted lognormal distance distributions are shown in We also fitted several other distributional forms, including exponential, power law and Weibull distributions, to the commuting distance distributions, however, the lognormal tended to give the best fit for the majority of strata.−8, p = 6·10−5, p = 0.015, for urban Huangshan, rural Huangshan and Shenzhen, respectively), indicating inhomogeneity in travelling behaviour – i.e. the majority of travelling is done by a minority of the population  to work or school travelled more, and those who were unemployed travelled less than either students or employees. All included variables are highly significant, apart from the lack of differentiation between students and employees.For Shenzhen city, the best fit logistic regression model included registration status, distance to work or school, subpopulation  and family size. Those registered, with a long distance to work/school, from the local population and from smaller families travelled most, with all variables being highly significant. Odds ratios for these models are shown in We fitted a simplified gravity model with a lognormal kernel function to the observed distances travelled. The lognormal distribution yielded a better fit than other distributions, namely exponential, power law and Weibull. We have analysed a unique dataset of commuting and travel behaviour in two very different parts of China. The commuting and travel patterns show interesting differences between these two study sites, indicating that there is substantial heterogeneity in China. This means that it is not easy to generalise our results to the whole country, although the overall demographics of China resemble more those in Huangshan, whereas the strongly peaked age distribution found in Shenzhen that reflects the large number of migrant workers appears to be fairly unique see .Study participants were chosen by household. In Huangshan, households were selected from one of the urban districts and one of the rural counties to match the overall proportion of urban and rural residents, whereas in Shenzhen, households were chosen from the local residents population and the dormitories housing the migrant workers. This selection process should give a fair representation of the overall populations in Huangshan and Shenzhen, respectively, as long as these are the major subpopulations. However, should there be substantial differences between the different urban districts and rural counties in Huangshan or any other important population groups in Shenzhen that are not covered in the selection of households, it is possible that the study populations might not reflect the cities well.In China, the hukou system of residency permit Migrant workers move from rural areas to the cities in order to take up jobs in manufacturing for at least 6 months of the year, living in company provided dormitories. The fact that around 17% of Shenzhen's population is made up of these migrant workers points to the rapid growth of the local economy and its need for labour as well as the differential in wages between Shenzhen and other areas of China. The dormitories provide very crowded accommodation with typically 8–12 people sleeping in the same room and many more assemble in the communal areas for eating and other pursuits The regression modelling has identified several predictors of the distance to school or work, but the best fitting models explain only a small part of the observed variability, pointing to probably a large number of factors that are not well understood influencing the complex choices of places to live and work. However, the covariates included in the models do indicate that different mechanisms determine the distance to school or work for students and employees.For students, the most important factor is student age, with older students travelling further as the number of schools decreases from primary to secondary schools. As the population density is lower in the rural areas, students in rural Huangshan tend to have to traverse longer distances to get to their school than those in the urban area. In Shenzhen, students without residency permit tend to have longer distances to school, possibly reflecting the more difficult access to education for the unregistered population For employees, it is tempting to surmise that a long commuting distance is associated with a higher socio-economic status, as has been reported for other places such as Seoul These mobility determinants might interact in interesting ways with other characteristics important for disease spread, such as, for instance, an interaction between the age-dependence of commuting distance with the age-dependence of contact patterns or biological susceptibility to disease.Travelling behaviour does not differ substantially between students and employees, but otherwise the predictors of travelling are similar to those for long commuting distances, with a long commuting distance being explicitly included in the logistic regression models for travelling.Viboud et al. While the patterns of occasional journeys observed might have a smaller impact on disease spread than commuting distances, due to the lower frequency of journeys, infrequent but very long distance trips are likely to play an important role in spreading diseases between regions, particularly in areas like Huangshan, where commuting is limited to very short distances.This paper has presented the first known data on human travel patterns within China. The striking differences between travel distances seen in Huangshan and Shenzhen point to substantial heterogeneity in travel behaviour within China at its current state of development. It is important to take these regional differences into account when modelling the likely speed of geographic spread of an infectious disease outbreak and in planning for containment or control of such outbreaks. Ongoing modelling work is using these data to examine the feasibility of containment of a lethal influenza pandemic in different areas of China.Text S1Questionnaires.(PDF)Click here for additional data file.Text S2Additional Detail of the Analyses.(PDF)Click here for additional data file.