Our study provides further support to decrease the overweight epidemic, involving classroom teachers in a training program and making them dedicated interventionists. The purpose of this study was to assess the effects of a six-months’ nutrition program, delivered and taught by classroom teachers with in-service nutrition training, on the prevention of overweight and obesity among children in grades 1 to 4. In this randomized trial, four hundred and sixty four children from seven elementary schools were allocated to a nutrition educational program delivered by their own teachers. Intervened teachers had 12 sessions of three hours each with the researchers throughout six months, according to the topics nutrition and healthy eating, the importance of drinking water and healthy cooking activities. After each session, teachers were encouraged to develop activities in class focused on the learned topics. Sociodemographic, anthropometric, dietary, and physical activity assessments were performed at baseline and at the end of the intervention. In the intervention group the increase in Body Mass Index (BMI) z-score was significantly lower than in the control group ( The prevalence of obesity continues to increase  and is aAlthough the picture of how to intervene is far from complete, guidance developed by effective programs have led to an increase in obesity prevention research, but so far only yielded “best practice” recommendations. In addition, most studies have been developed in the United States, raising questions about their applicability in European countries given the different school system and school nutrition conditions, as well as differences in eating habits or obesity rates ,4. As a Interventions in childhood are considered to have larger effects than in older ages because children are more sensitive to outside influences and may consider interventions particularly attractive . MoreoveUntil now, the role of teachers in the delivery features of the interventions and its impact on child’s body composition is unclear ,8. In adThe purpose of the present research was to assess the impact of a six months nutrition program, delivered and taught by classroom teachers with in-service nutrition training, on the prevention of overweight and obesity among children in grades 1 to 4. During 2007/2008 seven out of eighty public elementary schools were randomly selected and invited to participate in this study. The number of schools involved was according to constraints of personnel for the assessment and implementation of the program. Schools were the unit of randomization and three were assigned into intervention, and four into control group . The disThe flow of the subjects during the study is presented in BMI and major sociodemographic characteristics did not differ significantly between the children who participated in the baseline and those not included in the final assessment.Attrition rates did not differ between intervention and control group . Major reasons for nonparticipation were school transfer (94.1%), parent refusal (4.1%) and absence from school (1.8%). Children and outcomes assessors were blinded to group assignment. A total of 257 parents of the children involved in the study provided data at baseline and 203 (79.0%) at post-intervention. Teachers from intervention schools were invited and agreed to participate in the program conducted between October 2008 and March 2009; fifteen teachers were involved. This program was based on the Health Promotion Model  and the The Health Promotion Model argues that actions and the health promotion behavior are a corollary of personal characteristics, prior experiences, perceived benefits and barriers for action as well as perceived self-efficacy . This prIn Portugal, in-service training is considered the basis for the efficacy of the Education system. Teachers have the right and the obligation to develop their training through an accumulation of credits. Therefore, the research team proposed the accreditation of the training sessions developed with the teachers to the Minister of Education, Scientific-Pedagogic Council for In-service Training . It was approved in the form of a “training workshop” with 72 h duration, distributed between active learning strategies (36 h with the researchers) and the delivery of the learnt contents to the children (36 h). Thus, the program was implemented over two terms with teachers training delivered by researchers and intervention delivered by trained teachers to children. Teachers of the intervention group had 12 sessions of three hours each with the researchers during six months, which included the following contents: session 1, how to promote health and prevent disease, lifestyle determinants of health, obesity—definitions and descriptions of the problem, risk factors and health problems; session 2, key concepts in food and nutrition; sessions 3 and 4, dietary guidelines (the Portuguese Food Wheel), healthy eating advice for children, covering the five main food groups, and interventions to help children and their families to consume healthy foods and plan well-balanced meals and snacks; session 5, teach children about the importance of water, and teaching strategies to replace consumption of sugar-sweetened beverages with water; sessions 6 and 7, appropriate physical activity levels and healthy eating behaviours such increasing fruit and vegetable intake and decreasing energy-dense micronutrient-poor foods; session 8, teaching strategies and learning theory in the classroom; session 9, strategies to reduce screen exposure time; session 10, global assessment of the training program; sessions 11 and 12, healthy cooking and strategies to get children and their families involved in healthy cooking.After each session, teachers delivered the learnt contents and developed creative and engaging classroom activities about the addressed topic. All the questions that arose during the implementation of classroom activities were addressed and resolved shortly with the researchers. Teachers were allowed to develop and refine the activities and the learning strategies that were proposed by the researchers. At the end of this period, the teachers delivered a critical report of activities focused on the intervention, evaluated by the research team. The autonomy of teachers in the training process was preserved and the researchers had a serious investment on professionalism, pragmatism and policy . RecogniThe implementation of the program occurred as planned. All the children of the intervention schools had contact with trained teachers. Teachers taught the components of the program as prescribed and the researchers were always available to answer any question. In addition, teachers reported they were enthusiastic about the training, and had a total attendance in the sessions with the researchers. 2. The prevalence of underweight, normal weight, overweight and obesity was calculated according to the International Obesity Task Force (IOTF) criteria . To account for these differences at baseline, these variables were controlled for in subsequent analyses.There were significant differences between groups with regard to mother and father education (versus 0.13 (0.04)]. After adjusting for gender, age, baseline total energy intake, baseline BMI z-score and parents’ education, the BMI z-score increased 0.176 units more in the control group than in the intervention group , not represented in the table.After intervention , the BMIp < 0.05). Furthermore, as expected, overweight children with a BMI closer to normal weight status were more likely to have remission of overweight than overweight children with a higher BMI (p = 0.018). Similarly, normal weight children on the threshold of being overweight were more likely to suffer of overweight compared with normal weight children with a lower BMI (p = 0.001), not represented in the table. More intervened children had remission of overweight compared to control group  although non-significantly. After intervention there were no significant differences in the prevalence, incidence or remission of obesity between groups of schools.Significantly fewer children in the intervention schools (5.6%) than in the control schools (18.4%) became overweight after the intervention (unadjusted means; Our study shows that a nutrition program, delivered and taught by in-service teachers trained in nutrition, is effective in lowering the increase in the incidence of overweight among schoolchildren. Intervened children have a significantly lower increase in BMI and a lower proportion of children become overweight. This is particularly important as it provides further evidence to the WHO recommendations for schools to include both dietary and physical activity components in the curriculum taught by trained teachers . Our study has some weaknesses that should be mentioned. We did not explore differences between schools selected and not selected given human and materials constraints, although the schools were from the same geographical area and to the best of our knowledge, no data is available reporting sociodemographic and income significant differences between schools from this particular area. In addition, to the best of our knowledge there were no other “anti-obesity” programs going on in intervention and control schools.It is possible that our sample size was not large enough to detect other significant differences than those reported. Furthermore, we failed to obtain identically equivalent groups after randomization, namely in parents education level and children’s height, mainly because we randomized by school and not by subjects, aiming to avoid cross contamination between intervention and control groups. Nevertheless, these differences were taken in account in all of the statistical models. Also, physical activity levels were obtained upon self-reported data making recall bias and overestimation possible. However, the questionnaire was validated for Portuguese adolescents  and we hOn the other hand, the present study has important strengths that also should be acknowledged. First, to the best of our knowledge this is the first study that included the program in the progression of teaching career. This probably allowed teachers to increase their motivation in the delivery of the intervention. It would be desirable that other similar programs could be recognized on the career progression of teachers in Portugal and other countries in order to engage and reward teachers for their efforts. Second, the long term teachers’ in-service training and the subsequent network developed between themselves, researchers and children. We know that in Portugal students of education courses do not have health promotion in their training curricula, neither consider its changes towards an increase in health content important . In addiWe observe that most children are in the sedentary or low activity group. These results are in line with previous pediatric studies .et al. [et al. [We further observe a very high prevalence of overweight and obesity in our subjects, emphasizing the epidemic state of overweight/obesity in Portuguese children and adolescents . Actuallet al. , Borys e [et al.  and Panu [et al. . The int [et al.  to five  [et al. , althoug [et al. . BesidesIn conclusion, our program targeting classroom teachers as dedicated interventionists has significant effects on overweight prevention. It is well recognized that public health efforts are crucial to avoid childhood overweight and obesity as well as later problems. Changes in dietary behavior may be brought about, not by direct modification of food habits, but by alteration or manipulation of education and culture . TherefoWe believe that future studies with larger samples and longer periods of follow-up should try to replicate these findings to assess if this approach could be disseminated to other school districts. In addition, future directions should center on other aspects like school environment, physical education classes or on the environment beyond the school, such as homes and stores. The already high prevalence of overweight in children with this age and the incidence of new cases of overweight and obesity, even in intervention schools, leads to the need to improve the effect, dose, and range of prevention programs directed to children younger than 6 years.


In psychiatry, most of the focus on patient aggression has been in adolescent and adult inpatient settings. This behaviour is also common in elderly people with mental illness, but little research has been conducted into this problem in old age psychiatry settings. The attitudes of clinical staff toward aggression may affect the way they manage this behaviour. The purpose of this study was to examine the attitudes of clinical staff toward the causes and management of aggression in acute old age psychiatry inpatient settings.A convenience sample of clinical staff were recruited from three locked acute old age psychiatry inpatient units in Melbourne, Australia. They completed the Management of Aggression and Violence Scale, which assessed the causes and managment of aggression in psychiatric settings.Eighty-five staff completed the questionnaire, comprising registered nurses , enrolled nurses  and medical and allied health staff . A range of causative factors contributed to aggression. The respondents had a tendency to disagree that factors directly related to the patient contributed to this behaviour. They agreed patients were aggressive because of the environment they were in, other people contributed to them becoming aggressive, and patients from certain cultural groups were prone to these behaviours. However, there were mixed views about whether patient aggression could be prevented, and this type of behaviour took place because staff did not listen to patients. There was agreement medication was a valuable approach for the management of aggression, negotiation could be used more effectively in such challenging behaviour, and seclusion and physical restraint were sometimes used more than necessary. However, there was disagreement about whether the practice of secluding patients should be discontinued.Aggression in acute old age psychiatry inpatient units occurs occasionally and is problematic. A range of causative factors contribute to the onset of this behaviour. Attitudes toward the management of aggression are complex and somewhat contradictory and can affect the way staff manage this behaviour; therefore, wide-ranging initiatives are needed to prevent and deal with this type of challenging behaviour. Aggression  ocThe causes of aggression in elderly patients with mental health problems are complex and interrelated. Aggression may be attributable to psychosocial-environmental factors or the complex interaction of patients, staff and inpatient unit culture influences . It may There is also some debate that aggression may be due to the elderly person having a pre-morbid personality trait of aggression. Various reviews and studies have concluded, however, that an inconclusive relationship exists between premorbid personality and this behaviour ,14. For There are also contrasting reports about a relationship between particular illnesses and aggression. On the one hand, there are claims that aggression is associated with the person’s illness . In partWhen confronted by aggression, clinical staff may use a range of person-centred  and containment  measures to manage this behaviour. Two containment approaches ─ restraint and seclusion ─ are examined because there is debate about their use in the literature. Restraint  and secInternational variation is evident in containment practises, and this is influenced by a range of factors such as concerns about efficacy and patient safety, and broader cultural values . For insResearch into the use of restraint in elderly people has been conducted in general hospitals  and nursThe attitudes of clinical staff toward aggression can influence the way they respond to this behaviour ,34. PosiLittle research has been carried out into the attitudes of clinical staff toward the causes and management of patient aggression in psychiatry in general ,34 and iA survey design was used, incorporating a structured questionnaire.A convenience sample of clinical staff was recruited from three locked acute old age psychiatry inpatient units and their associated community outreach teams, in Melbourne, Australia. The units are managed by the same public mental health service, but each is located in separate geographical locations. The units offer mainly single-room accommodation with en suite toilets, and all have common recreational and dining facilities and gardens. Patients aged 65 years and over are admitted directly to the units from the community or residential care for short-term management of an acute phase of mental illness, until they recover enough to be treated in a community-based setting, and are different, therefore, from nursing homes that cater specifically for people with dementia. Medical, nursing and allied health staff provide the care, and the staffing ratios are broadly similar across the units.The inclusion criterion for staff was clinical staff employees  working on day shifts in the respective units. The exclusion criterion was staff working at night and at weekends. Staff received written invitations to participate, and the project was explained to them either at staff meetings or individually. They were given the choice to complete the questionnaire and hand it back to the researcher, or to return it by mail. Almost all chose the former option.The Management of Aggression and Violence Scale (MAVAS) -39 was uThe internal consistency of the instrument has been established in several studies -40. The Ethical approval to carry out the study was obtained from Melbourne Health Mental Health Research and Ethics Committee. Researchers, who were not employees of the mental health service, undertook recruitment. Return of questionnaires was interpreted as consent.Data analyses were undertaken using the R environment for statistical computing and graphics . FrequenApproximately 90 clinical staff were invited to take part in the study, and of these, 85 completed the questionnaire, equivalent to 78% of the total number of staff in the three units. Unit 3 had the highest level of participation, followed by units 1 and 2 respectively. Almost two-thirds of respondents were female. The mean age of respondents was 43 years, ranging from 24 to 62 years. About half were born in Australia, just over 40% in Asian countries, and just under 10% in western European countries. Most respondents (88%) were registered and enrolled nurses, while the remainder were medical and allied health staff (Table The results are presented in two main sections: (i) causes of aggression, and (ii) management of this behaviour.The overall mean score for internal causes of aggression was 2.6 (SD = 0.4), suggesting respondents had a tendency to disagree that factors directly attributable to the patient contributed to this type of behaviour. Specifically, they perceived aggression was associated with mental illness, and particular types of patients were prone to aggression. They also indicated aggressive behaviour was preventable, but tended to disagree that such behaviour resolved of its own accord if the patient was left alone , indicating respondents tended to agree that environmental factors in the units were influential in causing aggression. Restrictive care environments, such as locked wards, were perceived as contributing to aggression. Likewise, respondents were in agreement that if the physical environment was better patients would be less likely to be aggressive , suggesting respondents were in agreement that factors in the immediate situation, including the way staff communicated with patients, contributed to this type of behaviour. In particular, the respondents were in agreement that patient aggression occurred because of the influence of others. Patients were also more likely to become aggressive because of poor patient-to-staff communication. However, there was agreement-to-disagreement that this form of challenging behaviour was attributable to staff failing to listen to patients  suggested respondents were in general agreement with the statements that cultural and gender influences contributed to the initiation of aggression in units. In particular, there was agreement patients’ cultural background and cultural miscommunications between patients and staff contributed to the onset of aggression. Likewise, there was agreement gender mix of staff was an important consideration in dealing with aggression , indicating respondents had a tendency to agree with the statements about how to respond to these challenging situations  influences contributed to the onset of aggression, including the use of restrictive environments such as locked wards. They also perceived if the physical environment was improved patients would be less prone to aggression. These findings can be explained by external and situational/interactional influences, such as restrictive environments, which increase the likelihood of frustration and the possibility of aggression . The finThere was agreement in the current study that poor staff-to-patient communication contributed to the onset of aggression. Whittington and Wykes , in a UnRegarding the management of patient aggression, the respondents were in agreement this behaviour could be managed more successfully, and a person-centred approach could be used more often as well as judicious use of medication. These findings can be explained, whereby external stimuli, such as person-centred and careful medication-use techniques ,11, coulThe findings of the present study also indicated restraint and seclusion were used more than necessary in the management of aggression, and there was disagreement about whether the practice of seclusion should be stopped. The preference by staff to retain the option to use seclusion has also been reported in United Kingdom studies by Duxbury and Whittington  and FostWhile a representative sample of 78% of clinical staff was obtained, the data may not accurately reflect the views of all such staff because those working at weekends and at night were not included. This could be addressed in a future study by recruiting respondents throughout the 24-hour, 7-day spectrum. Next, as the sample was derived from three inpatient units within a single mental health service, this limits the ability to infer from this sample to the general population of clinical staff working in other acute old age psychiatry inpatient units. Recruiting respondents from a wider range of mental health services could rectify this in a subsequent study. Finally, the sample only included the views of clinical staff; current and former inpatients were considered for inclusion but were not deemed well enough by clinical staff to provide consent.Our survey provides a valuable insight into the contentious issue of attitudes toward the causes and management of aggression, and contributes to the limited body of research about this issue in old age psychiatry inpatient settings. Consideration of attitudes is important because there is a direct relationship between attitudes and the types of measures that are used to manage this challenging behaviour. It can be inferred from the findings of this study that a broad approach to prevention and management of aggression — one that addresses socio-demographic and interpersonal as well as situational and contextual influences — needs to be adopted. They findings also show clinical staff possess contrasting attitudes about the most effective and acceptable ways to prevent and manage this challenging behaviour; in particular, there were mixed views about whether the practice of seclusion should cease.Therefore, measures taken to address prevention and management should also focus on these contrasting attitudes. Overall, strategies to prevent and manage aggression in acute old age psychiatry inpatient settings have implications for the overall quality of care; in particular, patient charactristics, inpatient environment, the way staff interact with patients, cultural sensitivity and gender mix of staff, and consideration of appropriate ways to manage aggression such as the adoption of person-centred alternatives to restraint and seclusion.The authors declare that they have no competing interests.TMcC had a major role in the design of the study, undertook some data collection, carried out the data analysis, and had a major role in writing the paper. JB had a major role in the design of the study, and had a key role in writing the paper. EMC had a major role in the design of the study, and had a major role in writing the paper. All authors have approved the final draft.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/14/80/prepub


With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society. The Internet constitutes a huge source of information. Since it became a widespread service, it has been used by individuals seeking medical information. However, with the advent of the Web2.0 paradigm, the Internet is now used not only as an information source but also to disseminate personal health information, experiences and knowledge http://www.twitter.com), for example, offers a micro-blogging platform that allows users to communicate through status updates limited to 140 characters, commonly referred to as “tweets”. It has over 200 million active users (https://twitter.com/twitter/status/281051652235087872), and around 400 million tweets are published daily. These large quantities of user generated content (UGC) can be used in different ways, representing great opportunities for data and text mining approaches in many fields of application. Mining these data provides an instantaneous snapshot of the public's opinions, and longitudinal tracking allows identification of changes in opinions Much of this health related information is shared through social media platforms such as Twitter and Facebook. Twitter  and in the analysis of four health states, namely flu, depression, pregnancy and eating disorders. We acquired more than 10 million tweets and performed a set of stages in order to obtain the final filtering and classification strategy. The proposed method is general and may be automatically applied (e.g. South America) or adapted for other regions and health conditions. Furthermore, each step of our method was implemented using freely available tools and is described in detail, facilitating the reproduction of the research work described in this paper The structure of this article is as follows. In Section 2 we comment on related works regarding the retrieval of health information from social media. In Section 3 we describe the proposed method, explaining the details of each of the method's stages, the datasets used and the experiments performed. In Section 4 we present the results and the improvements obtained in terms of efficacy and performance when using feature selection. Finally, Section 5 includes our conclusions and the possible future work in this field.https://www.influenzanet.eu/) Several works regarding the retrieval of health information from social media have already been published, with a major focus on measuring the incidence rate of influenza. In an early work, Chew and Eysenbach suggested a complementary infoveillance approach based on Twitter, and applied it to data obtained during the 2009 H1N1 pandemic. They performed content and sentiment analysis on 2 million tweets containing the keywords “swine flu”, “swineflu”, or “H1N1”. For this, they created a range of queries related to different content categories, and showed that the results of these queries correlated well with the results of manual coding, suggesting that near real-time content and sentiment analysis could be achieved, allowing monitoring large amounts of textual data over time Apart from analysing the incidence of flu and infectious diseases related events, the analysis of other health parameters using Twitter data has also been reported. Scanfeld et al., for example, applied content analysis to 1000 tweets to explore evidence of misunderstanding or misuse of antibiotics In this article we propose a method to extract a set of tweets that show the presence of certain health conditions in people, as a way to infer the incidence of such conditions in society. This section describes the procedures used to obtain the tweets related to these conditions.To acquire the tweets for this study, we developed an application that uses the Twitter search API After obtaining the datasets, the following stages were performed in order to obtain the final filtering and classification strategy:Define several regular expressions to extract only the tweets related to the studied diseases. The method can be applied to another group of diseases, requiring the creation of a different set of regular expressions.In order to filter those tweets that match with the regular expressions but that do not indicate the presence or absence of a disease in one person, we apply machine learning. For this, we tagged a set of tweets as being positive or negative for each health condition, and defined a set of features to be used to train the classifiers.Finally, since a large feature set is generated, we use feature selection in order to improve the results, the computational cost and the execution time.These steps are explained in detail in the following sections.To create these expressions, we initially obtained a set of tweets containing the name of each condition, removing tweet replies (re-tweets) and tweets that included links, and calculated the log-likelihood of the words that occurred within those datasets, obtaining an ordered list of words associated with each condition. Based on these lists, on manual content analysis and on general knowledge about the studied health states, we then defined the regular expressions for each specific case.Flu: regular expressions used are related to flu and its variants, and also to known symptoms of the disease.Pregnancy: for obtaining tweets related to pregnancy we used different variations of the word based on its lexeme. We also used words and short phrases identified in the manual analysis of the tweets, as for example, the words used to celebrate the birth of a child.Depression: we used words and symptoms related to depression.Eating disorders: we used the names of different eating disorders, and various phrases and words that are usually related to these. We have considered the following eating disorders: anorexia, bulimia, bigorexia, obesity and orthorexia.The use of regular expressions allowed obtaining large sets of tweets related to the specified diseases. However, among the obtained tweets, negative sentences that do not indicate the presence or absence of a disease in one person, such as “Hoping the flu does strike me again this winter”, may also occur. To solve this problem, we applied machine learning techniques on the datasets obtained using the regular expressions, in order to filter such cases. This allowed differentiating the tweets that only mention a given disease from those which actually indicate that the person has the disease.http://snowball.tartarus.org/) and word stemming In order to apply machine learning, each tweet has to be represented by a set of features. We used a simple bag-of-words (BOW) model after removal of stopwords , Naïve Bayes (NB), Decision Trees (DT) and Nearest Neighbour (kNN)) using the obtained features. To test these techniques, we used WEKA Before executing the experiments we tagged the different sets of tweets. Each tweet was considered positive, negative or undecided (ambiguous) if:Positive: the tweet content indicates the presence of one of the studied diseases/states in the person who has written the tweet.Negative: the tweet content indicates that the person who wrote it does not have any of the diseases/states studied.Undecided: the tweet does not fit into any of the above criteria.The tagging process was performed by two medical doctors from Portugal and three computer engineers from Spain. A simple computer program was created that iteratively showed a new tweet to the annotator, who read the text shown on the computer screen and pressed a key in the keyboard according to the desired classification, allowing the task to be performed in a very efficient manner. The task was performed within a window of fifteen days but in fact, although this was not measured, the total amount of time taken to annotate the whole dataset was much smaller. Still, there is always some amount of manpower required for creating these types of datasets. Therefore, for larger scale experiments this manual annotation step should serve as the initial input for self-training approaches.In the Portuguese data set, we only considered those tweets where agreement between the annotators was achieved, and the remaining were considered as undecided. For the Spanish data set a majority voting scheme was used, and tweets were tagged as undecided if no majority existed. For the classification process the tweets tagged as undecided were removed from the dataset, that is, a tweet was only used for the classification if it was labelled as positive or negative. Bag-of-words and character n-gram features usually contain several redundant or irrelevant features. So, in this step a subset of relevant features is selected and used for creating the classification model.The feature selection process provides the following advantages:It removes noisy or redundant features. This noise makes it more difficult to discover meaningful patterns.To discover good classification patterns, larger training datasets are required. However, in several data mining applications the training dataset is very small. In this case, the models based on a smaller set of features, obtained by applying feature selection, can be useful.It improves the efficacy of the classifiers.It reduces the training and executing times.There are two types of approaches for performing feature selection, described as wrapper and filter methods The feature selection process has two components: feature evaluator, the algorithm that determines the quality of the subset of features to discriminate the class label; and search method, the method used to search relevant features subsets. Since this problem grows with the number of features, there are several strategies to search the relevant subsets in an efficient way.The following feature selection algorithms were tested:Correlation-based feature selection (CFS): evaluates the relevance of a subset of attributes by considering the individual predictive capability of each feature, and the degree of redundancy between them. So, the subsets preferred are those that are highly correlated with the class label and the correlation between them is low. More details about this algorithm can be found in previous work Pearson correlation: this algorithm determines the relevance of a feature by measuring the Pearson correlation between it and the class.Gain Ratio: evaluates the worth of an attribute by measuring the gain ratio with respect to the class. The gain ratio of an attribute is obtained through the following equation, where Relief: evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the particular attribute for the nearest instance of the same and different class. That is, Relief evaluates the attributes according to how their values distinguish among instances that are near each other.Detailed descriptions of the Gain Ratio and Relief methods for feature selection can be found in the literature In this section, we present the results obtained with the proposed method to measure the incidence of different diseases, and compare the performance of the different classifiers tested. As can be seen from Precision: the proportion of cases classified as positive by an algorithm, which are actually positive;Recall: the proportion of positive cases that were correctly classified as positive;F-measure: the harmonic mean of precision and recall, that is, Area under the ROC curve (AUC): The ROC curve represents the rate of true positives versus false positives, at various threshold settings. The area under the curve gives an indication of the discriminatory value of the classifier at different operating points.We tested four classifiers, using all the features for each health state and each country: Naïve Bayes, SVM, Decision Trees and kNN. As can be seen from The cross-validation results obtained using the reduced subsets of features are shown in four tables, according to the feature selection algorithm applied. Statistically significant differences  collect an initial set of tweets that mention the health condition to be measured, using known names and synonyms; 2) use log-likelihood measures and manual content analysis to create regular expressions; 3) filter tweets using these regular expressions; 4) label the selected tweets as positive or negative; 5) extract n-gram features and apply correlation-based feature selection (CFS); 6) train  a classifier based on these data. In addition, the regular expressions and classifiers created can be applied to an incoming stream of Twitter data to identify tweets about specific health issues, allowing measuring and tracking the incidence of those health conditions almost in real-time. This approach is general and could in principle be easily adapted to other languages or geographical areas and to other health or social conditions, by creating new regular expressions following the same procedure and by training new classification models.The method was evaluated in a study centred in Spain and Portugal, based on the geocoded data and on the language of the tweets, and in four conditions: flu, depression, pregnancy, and eating disorders. To the best of our knowledge, there are no studies showing and comparing the application of similar techniques to data in two languages and covering different countries.We obtained classification results between 0.7 and 0.9 in AUC and also in terms of F-measure. The feature selection algorithms used  reduced the number of features by up to 90%, while at the same time improving the classification results by up to 18% in AUC and up to 7% in F-measure. Additionally, this reduced the time taken to generate the model and to make the classification by around 99% and 81%, respectively. The results obtained over these different health issues and languages indicate that the methodology can generalize to other cases and could be effectively and efficiently used for measuring and tracking the evolution of many health related aspects within the society.A possible limitation of this approach regarding its application to other languages and/or health conditions is the need for manually annotated training sets. Although this can be done efficiently, the possibility of using self-training techniques should be investigated, in order to reduce the amount of manpower required. Another possible way of improving and extending the method is by including other types of user-generated content, such as Internet searches or comments to news articles, which may also contain information related to some of these aspects. Thus, this data could be used to complement the data extracted from Twitter. Another interesting aspect would be to study the symptoms that appear in tweets related to each of the diseases discussed and compare these to the known symptoms today.


A paradigm shift in global health policy on user fees has been evident in the last decade with a growing consensus that user fees undermine equitable access to essential health care in many low and middle income countries. Changes to fees have major implications for human resources for health (HRH), though the linkages are rarely explicitly examined. This study aimed to examine the inter-linkages in Zimbabwe in order to generate lessons for HRH and fee policies, with particular respect to reproductive, maternal and newborn health (RMNH).The study used secondary data and small-scale qualitative fieldwork (key informant interview and focus group discussions) at national level and in one district in 2011.The past decades have seen a shift in the burden of payments onto households. Implementation of the complex rules on exemptions is patchy and confused. RMNH services are seen as hard for families to afford, even in the absence of complications. Human resources are constrained in managing current demand and any growth in demand by high external and internal migration, and low remuneration, amongst other factors. We find that nurses and midwives are evenly distributed across the country (at least in the public sector), though doctors are not. This means that for four provinces, there are not enough doctors to provide more complex care, and only three provinces could provide cover in the event of all deliveries taking place in facilities.This analysis suggests that there is a strong case for reducing the financial burden on clients of RMNH services and also a pressing need to improve the terms and conditions of key health staff. Numbers need to grow, and distribution is also a challenge, suggesting the need for differentiated policies in relation to rural areas, especially for doctors and specialists. The management of user fees should also be reviewed, particularly for non-Ministry facilities, which do not retain their revenues, and receive limited investment in return from the municipalities and district councils. Overall public investment in health needs to grow. A paradigm shift in global health policy regarding user fees has been evident in the last decade with a growing consensus that user fees are regressive and undermine equitable access to essential health services . In partThis article examines the case of Zimbabwe, which faces challenges in offering access to health services to its population, and also in retaining a skilled and well-distributed health workforce, after more than a decade of political conflict and economic collapse. Using existing data and studies, linked with small-scale qualitative fieldwork, this article examines current access to care, especially financial access, focussing on reproductive, maternal and newborn health services, and also the challenges facing HRH in Zimbabwe. The scope for increasing demand for services is investigated, and the implications for staffing analysed. This contributes to a wider study aiming to understand the implications of changing user fee regimes for human resources for health.The research is based on a mix of research methods which included secondary data and interviews with health officials. According to the Biomedical Research and Training Institute-Institutional Review Board, the study meets their criteria for exemption from ethical review.A thorough review of literature was undertaken on the themes of health financing policy, user fees, reproductive health and human resources for health in Zimbabwe. The literature review included searching peer-reviewed and grey literature in recognized electronic databases and websites. Sources for the literature included the Ministry of Health & Child Welfare (MoHCW), Department for International Development UK (DFID), United States Agency for International Development (USAID), World Health Organisation (WHO), health research institutions, mission umbrella organisations, the Centre for Disease Control (CDC) and professional associations. Key policy documents relating to user fees and HRH were also obtained and analysed.National data sets relating to staffing, staff remuneration and conditions, utilisation of services and other relevant indicators such as poverty and income levels were sought and analysed. Sources for these included the National Health Information System (NHIS), the database held by the Department of human resources (HR) in the MoHCW, and nationally published statistics produced by Zimbabwe National Statistics Agency (ZIMSTAT). Data from these sources were retrieved into an Excel spreadsheet, disaggregated to the lowest level permitted by the data.Key informant interviews (KII) were carried out with a selected number of experts and practitioners in Harare and one field site. The research district was selected because of its proximity to Harare and the fact that it contains a variety of communities, including mining and farming and new resettlement areas.A semi-structured questionnaire was drawn up for the KII. It covered the following topics: current policies on user fees; current policies on exemptions; plans to reform user fees; major constraints facing HRH; the interaction of fees and HRH; and any evidence on the impact of fees and the recent dollarisation on both users and staff. .The selection of the experts and practitioners was purposive. 13 individuals were interviewed – two at national level, with responsibility for human resources, and 11 at district level, in the district health office, the district hospital and health centres.Focus Group Discussions (FGDs) were held in three different types of area in the research district:1. Low income urban area2. Communal area3. New resettlement farming/resettlement communityThe key target groups were users of RMNH services and service providers. We conducted five FGDs as follows:1. Nurse trainees  (one group)2. User groups consisting of women at the community level  (three groups – one per area)3. One FGD with traditional birth attendants in a rural areaThe total number of participants was 43 .Topic guides were drawn up for the two groups. The topic guide for trainee nurse midwives asked about motivation for joining the profession; their views on user fees; and factors encouraging their retention and performance. The topic guide for the community level focussed on health seeking behaviour and views on quality of care; current payments for RMNH services and how affordable they are; and users’ views on fees.Quantitative data were collated and analysed using Excel. The WHO tool produced by the Making Pregnancy Safer (MPS) department was also used to project staffing needs and gaps.Qualitative data from the field were transcribed and analysed through categorisation of themes and content analysis. This was compared with findings from the literature review and policy analysis, as well as the secondary data, to reach overall conclusions and recommendations.Zimbabwe inherited the Rhodesian health care system at independence in 1980. Health services were divided along racial lines and distribution of available resources were highly skewed towards hospital service provision for the small white population at the expense of the indigenous population. There were profound imbalances in the allocation of physical, financial and human resources in the health sector before independence -10.After independence, the Government expressed the intention to focus its efforts on redressing the existing inequalities by investing especially in health services in rural areas. The government viewed health as an integral part of development and as a human right  and thisHealth for all Action Plan for the period 1991–95 maintained the focus of its predecessor on equity, the plan put more emphasis on quality of care, effective use of resources, value for money and appropriateness of services, including increased use of private sector facilities [Hit by low growth and an Economic and Social Adjustment Plan in the 1990s, the focus shifted from equity to cost recovery and greater efficiency. While the second cilities . This waThe World Bank  estimateWhilst the 1980s showed a general improvement in most of the major health indicators and service utilization, attributable to the expansion and improvements in the area of primary health care ,16,17, sThe health system is dominated by the public sector, which provides an estimated 65% of health care services in the country, although in rural areas the mission sector plays a major role . The pria. However, public health spending was reported to constitute less than one percent of GDP (an estimated 0.02%) in 2009. Per capita public spending was reported to be $5.77 in 2009 [Assessing trends in health financing is complicated by the years of hyperinflation in 2009 . This co in 2009 .User fees existed in Zimbabwe from independence. Those with incomes of less than Zimbabwe Dollar (ZWD) 150 per month qualified for free health care, however this covered a decreasing proportion of the population as inflation reduced the real value of the threshold . In 1991The economic collapse of the early 2000s led to a sharp reduction in public funding for health, and inevitably a shift to private (largely out of pocket) funding. Government health expenditures as a percentage of total health expenditures declined from 36.8% in 1999 to 9.8% in 2005 ,25. In tSpiralling inflation in the 1990s and the subsequent dollarisation will have affected the affordability of health care, through changes to incomes, prices of health care and the wider cost of living. In 2007, the ‘Access to health services’ study  found thAccounts of which services should be free vary in the written sources. According to one source, the following services and groups are exempt :•Antenatal care in rural and semi-rural areas•Referrals to the next highest level of facility for services that the lower-level facility cannot provide•Directly Observed Treatment Short course (DOTS) for tuberculosis (TB)•Family planning•Antiretroviral therapy (ART)•Emergency outbreak services (such as the recent cholera outbreak)•Health services for children under five, adults over 65, military veterans, health care providers, and individuals living below the poverty threshold (a designation that is very difficult to attain in practice)EQUINET states that the policy of free public sector care at rural clinics is still in force and that pregnant women, children under 5 and adults over 65 are exempt from fees up to district level . HoweverThe categories reported to be exempt during our fieldwork were under-fives and over-65s, and staff, but with local variations such as the chronically ill (psychiatric patients) and lepers being exempted. Certain donor-supported national programmes are also free (such as ART and TB treatment). It is not surprising that there is variation as there is a perception that charging policies can be locally determined.There is a Social Dimension Fund Scheme, run by the Ministry of Labour and Social Services, which applies its own criteria for exemption. Orphans, the elderly and the indigent are catered for through this fund. Patients who cannot pay cannot be turned away but can be referred to this fund. However, the money in the Fund is limited and so although the hospital presents claims for people who have been certified and treated for free, they are often not reimbursed as there are no funds.Consultation fees for adults, collected from a sample of facilities in 2009, increased according to level of care, with an average of $1 at rural health centres, $3 at mission hospitals, $4 at private health clinics, $4 at district hospitals, $5.5 at provincial hospitals and $9.75 at national hospitals .An official price list is produced by the Association of Health Care Standards of Zimbabwe. However, the prices are high, and facilities produce their own price lists. In the hospital visited, prices for those with Medical Assistance  were considerably higher than for those paying cash. The rural clinics used to be free but between 2009 and 2010 they started to charge. Municipal clinics charge more and always have done so.Prices for a sample of reproductive services are given below, based on the limited qualitative fieldwork undertaken Table .Delivery care in facilities, even in the absence of complications, was not seen as affordable by users or staff. This is likely to be one factor behind the high rate of home deliveries, even though these are discouraged  were nervous about speaking about their work as a result), and despite the fact that families have to bring newborn babies into health facilities to get a birth record.Our small-scale fieldwork confirms that users are being charged for primary care in rural areas, not least because under the policy of decentralisation the majority of clinics are managed by local authorities, which are able to set their own charging policies. In the area we visited, the rural and urban clinics collect fees but do not retain them. They were reported to receive very little back by way of financial support from district councils and municipalities. They therefore suffer a double disadvantage of higher barriers for users and a lack of funds to reinvest in services.Many try to avoid paying, so a variety of mechanisms are used to recoup health service costs . Upfront payments are taken for consultation. Patients’ relatives are encouraged to pay, when visiting. People can be allowed to pay in instalments. In some rural areas they pay in kind. Debtors are followed up after discharge. For women who have delivered, the birth record (which is needed for the birth certificate) is withheld until payments are made. In the hospital we visited, staff reported very poor relationships with clients over the issue of non-payment, with women being held in the hospital but in the corridor (as beds were needed) until they paid, or until they absconded.The general view based on KII and FGDs with selected health practitioners, user groups and experts was that user fees levied at government-owned health facilities are reasonable compared to those levied by local district council and the municipality. The most punitive aspect of the user fees regime at the council and municipality Family Child Health (FCH) centres, according to mothers who participated in FGDs and nurses in-charge at FCH centres, is the requirement to pay the prescribed fee for every Antenatal Care (ANC) visit. This is borne out by the fact that most mothers forego the routine ANC visits and only present for delivery. Late bookings are another manifestation of failure to afford payment of user fees and cases of booking at seven months were reported. In FGDs mothers reported that late booking was a mechanism to reduce costs that would ordinarily be incurred if one were to book at the recommended first trimester of pregnancy. Factors reported as encouraging home deliveries include long distances to health facilities, people’s inability to afford user fees and fear of being tested for HIV during ANC visits.Most mothers depend on regular income from formal employment or commercial agriculture to raise money to pay user fees for RMNH services. Not seeking medical attention due to inability to afford user fees is an option that mothers have to take sometimes, to avoid the humiliation that they meet if they present to health facilities without the required fees. Municipality FCH centres are strict and they do not entertain anyone without the requisite fees. The nurse in-charge at the municipality FCH acknowledged that as a result of the strict requirement for payment upfront at municipality clinics there are cases of home deliveries in urban areas.Home deliveries are common and more openly acknowledged in the rural, small scale and large scale commercial farming sectors than in the urban sector. Those who cannot afford the user fees at the FCH centre seek the help of TBAs for deliveries and are charged a fee of $5 and one bar of laundry soap per delivery. During FGDs with TBAs it emerged that even this lesser charge levied by TBAs was not always readily paid by mothers who were assisted during delivery. The TBAs observed that there were people who simply did not want to pay because they hold the view that TBAs are a community resource and hence should assist their neighbours for no charge.From the staff perspective, user fees were a necessary evil as non-payment would in the first instance promote wasteful utilisation of health services and eventually lead to the collapse of the system, given the current low levels of government funding reaching them. There is no direct benefit for staff – it is not legal for staff to receive any of the funds – but indirectly, they benefit through maintenance of services. Besides, some commented that ‘people should make a contribution’.However, staff in municipal facilities felt that the rates were too high for deliveries, and were putting women off coming there. They considered that they should be reduced, not least because many of the funded public health programmes such as Prevention of Mother to Child Transmission (PMTCT) rely on getting a high throughput of pregnant women in facilities. Others commented that staff should be involved in the setting of fee rates. Post Natal Care (PNC), for example, is charged at a flat rate of $20 at the district hospital, when the supplies involved are minimal (one pair of gloves).The MoHCW has been carrying out a review of user fees. Given the financial constraints facing the sector, there are concerns about the feasibility of reducing financial barriers for users. However, the inclusive government recognises that user fees are limiting mothers and children’s access to life-saving interventions and advocates for it to be scrapped .Changes to access to and utilisation of services have important implications for the health staff in Zimbabwe - if the user fee policy is revised to reduce or remove fees, which would translate into improved access and increased utilization of services, which would in turn increase pressure on staff. In this section, we consider the evidence relating to current stocks of health workers, any gaps in skills, and their distribution. Evidence on current workload is also assessed to establish whether there is scope for increasing the workload of existing staff. We also examine remuneration, which is an important factor behind retention and motivation of staff, and consider what would be the implication of increasing access to RMNH, in terms of the need for and cost of the additional staff.The health sector in Zimbabwe has been threatened by the exodus of critical medical skills through migration to countries like South Africa, Botswana, United Kingdom, and New Zealand . It is eAn assessment of maternal and neonatal health services conducted in 2004 found that there were serious shortages of nurses and midwives at the primary levels of care: 40% of primary facilities had no nurse and 50% no midwife at post . In ordeThe economic depression and hyperinflation experienced in the mid-2000s heavily affected the stability of health workers in the public sector. The Zimbabwe HRH profile  reports The numbers of health workers have been increasing in 2007–9, with the overall ratio of health workers to population reaching 2.25 per 1,000 population by 2009 . This isThe average vacancy rate for critical cadres (such as doctors) over 2005–9 was 50%, although for most groups the vacancy rates dropped over the period . It stooAlthough there are plans to re-attract health staff in the southern African diaspora, most key informants thought it too early for this to be effective, given the general conditions in Zimbabwe. However, some foreign medical staff continued to be recruited, including from Cuba and the Democratic Republic of Congo .As well as gaps in established posts, there are also disparities between posts in rural and urban areas, and proportions of posts filled. Within urban areas, Harare and Bulawayo absorb a large proportion of skilled staff . MissionWe analysed data on human resource distribution, drawn from the regular regional returns to the MoHCW and only including the public sector. District data were not available and the data did not separate midwives from other nursing staff. In 2010, there were 17,756 nurses, midwives, clinical officers and doctors working in the public health sector Table . OutsideDoctors were unevenly spread throughout the country. A concentration curve, and accompanying concentration index, for medical staff ordered by population density across the regions shows a strong pro-urban bias for doctors (0.53) and clinical officers (0.52) Figure . For nurHRH expenditure fell from 2005 through 2007, with a complete collapse in human resource spending in 2008, when human resources spending accounted for 0.3% of the public health budget . RealiziThe top-up payments, funded by partners, were higher than base salaries for some grades. For example, a District Nursing Officer in 2008 received $250 as monthly salary and $280 as retention payment. The differential for higher grades was even greater. Moreover, mission facilities and municipal facilities, while they share the same base salaries as the MoHCW facilities, are able to provide higher retention payments and allowances, leading to a new form of internal migration. The current City Health Council salaries and benefits are reported to be three times the MoHCW ones  . In geneAccording to the HSB Annual Report for 2010 , salarie•Uniform allowance: $15 per month•Residence: $150 per month•Stand-by/on call: 20% of salary•Transport allowance: minimum of $8 per month.Some loans are also available to help with housing and car purchase – however, the sums available were reported to be well below needs.The overall level of remuneration is therefore very low, and the retention payments  are being reduced by 25% each year, with the aim of phasing out in 2013. There are deep concerns about the effects which this may have on retention . However, expectations of a continuous improvement have not been met. Looking ahead, there is concern about ongoing economic and political problems and fears that things may even get worse – if, for example, the health workforce is ‘streamlined’.b.Table Health workers are paid above the much depleted average income level but not to the same extent as in other countries, and it is likely that the value of pay has fallen considerably since before the crisis. Doctors’ salaries are about 4-fold average GDP per capita.There were around 12 deliveries for each skilled health worker per year and 313 for each doctor across the country in 2010 Table . If all •how much of their time they devote to non-reproductive health services•their distribution within each region•the extent to which the skills of nurses currently qualify them to provide adequate delivery health services.Since most nurses lack midwifery skills it is likely that the number of medical workers considerably over -estimates the capacity to provide midwifery care to an adequate standard.c. Using 2010 as the baseline year, we have estimated the number of additional staff needed to reach near universal coverage (95%) by 2015.The MPS tool, produced by WHO, provides a simple and graphical analysis of the quantity and cost of skilled birth attendants and doctors as coverage is scaled upThe model is quantity driven. It assumes that the current level of staff is just sufficient to provide the current level of skilled deliveries. Any increase in coverage requires additional skilled birth attendants (SBAs) and doctors based on standard ratios: around 1,000 deliveries per doctor and (a user adjustable) 175 deliveries per SBA. The model permits users to specify levels of attrition for staff and salaries and assumed salary increases to permit cost projections. The number of births is based on demographic projections for the country. Midpoint annual salaries (2010) for registered nurses and doctors as computed above are used for the projection of recurrent cost and these are expected to increase by 3% per annum (in dollar terms) . Public investment in health care is low, and poverty is widespread.The past decades have seen a shift in the burden of payments onto households. Implementation of the complex rules on exemptions is patchy and confused. In addition, non-MoHCW facilities  have complete discretion on charging. Most staff and women see delivery care in facilities as hard for families to afford, even in the absence of complications. Moreover the fees generated are not retained in municipal and rural district-run facilities, thus creating a vicious cycle of low utilisation and low investment in the services. Central funding of non-salary recurrent costs, especially for district services, appears to be very low.Staff numbers have been reduced through emigration to other countries and also internal migration to other sectors, though this is less well documented. There is a particular shortage of midwives and of specialists, though for most groups in the past few years numbers have been increasing, while attrition rates have reduced. There are however discrepancies in vacancies across regions and sectors.Our analysis suggests that nurses and midwives are evenly distributed across the country , though doctors are not. This means that for some provinces , there are not enough doctors to provide more complex care, and only three provinces could provide cover in the event of all deliveries taking place in facilities. For midwives and nurses, there appear to be adequate numbers but the merger of categories means that assessing competence in obstetric care is hard and there are likely to be skills shortages for existing staff.Pay is recognised to be low – below the poverty line for an average family. There are also concerns about the effects of the ending of the emergency retention payments, which are due to be phased out shortly. Health workers are paid above the much depleted average income level but not to the same extent as in other countries, and it is likely that the value of pay has fallen considerably since before the crisis. Doctors’ salaries are about 4-fold average GDP per capita.This analysis suggests that there are a number of axes of challenge in Zimbabwe, all of which hinge on improved economic fortunes. There is a strong case for reducing the financial burden on clients of RMNH services and also a pressing need to improve the terms and conditions of key health staff. Numbers need to grow, and distribution is also a challenge, suggesting the need for differentiated policies in relation to rural areas, especially for doctors and specialists. The management of user fees should also be reviewed, particularly for non-MoHCW facilities which do not retain their revenues, and appear to receive limited investment in return from the municipalities and district councils. Overall public investment in the health system  needs to grow.aThe CPI index for health went from 100 in 2001 to 214 in 2002, 775 in 2003, 4,224 in 2005, 18,151 in 2005, and 595,497 in 2006, according to ZIMSTATS Quarterly Digest, July 2011. Later figures are not yet published.bAccessed 15th December 2011. Appropriate purchasing power parity (PPP) conversion factors that would allow for the estimation in international dollars are not available for Zimbabwe. The most recent PPP data are for 2009 and convert Zimbabwe $. They do not therefore relate to the current use of US$. However, it cannot be assumed that the purchasing power of 1US$ is the same in Zimbabwe and the US.cThe original model (http://mps.projection.free.fr/mdg5-hrsu.html) is available as an online and offline web-based. For the purposes of this study, the model was converted to Excel and adapted to permit variations in the base and target years and differences in attrition rates and salaries for doctors and nurses/midwives.The authors declare that they have no competing interests.YC led the research group, including participation in data collection, analysis and drafting of final article. SW led on overall study design, participated in data collection and led on analysis and drafting. MM participated in data gathering and analysis, focussing on secondary data. WM participated in data gathering and analysis, focussing on fieldwork. TE participated in study design and led on analysis of workload and need for HRH data. BM led on overall international study design and analysis of remuneration data. SM led the Zimbabwe team and contributed to study design. All authors have approved the final version of the article.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/13/197/prepub


As in many European countries, access to care is decreased for undocumented migrants in the Netherlands due to legislation. Studies on the health of undocumented migrants in Europe are scarce and focus on care-seeking migrants. Not much is known on those who do not seek care.This cross-sectional study includes both respondents who did and did not seek care, namely undocumented migrants who have been incarcerated in a detention centre while awaiting expulsion to their country of origin. A consecutive sample of all new arrivals was studied. Data were collected through structured interviews and reviews of medical records.Among the 224 male migrants who arrived at the detention centre between May and July 2008, 173 persons were interviewed. 122 respondents met inclusion criteria. Only half of the undocumented migrants in this study knew how to get access to medical care in the Netherlands if in need. Forty-six percent of respondents reported to have sought medical help during their stay in the Netherlands while having no health insurance (n = 57). Care was sought most frequently for injuries and dental problems. About 25% of these care seekers reported to have been denied care by a health care provider. Asian migrants were significantly less likely to seek care when compared to other ethnic groups, independent from age, chronic health problems and length of stay in the Netherlands.The study underlines the need for a better education of undocumented patients and providers concerning the opportunities for health care in the Netherlands. Moreover, there is a need to further clarify the reasons for the denial of care to undocumented patients, as well as the barriers to health care as perceived by undocumented migrants. Illegal migration is a fact of live in many European countries. In the Netherlands, the number of undocumented migrants was estimated 130,000 in 2005 [Koppelingswet ('linking act') of 1998 intends to discourage illegal residency in the Netherlands by linking the usage of public services, such as welfare, housing benefits, student grants and health insurance, to legal status. Health care providers, however, have the legal and moral duty to provide medical care to everyone, also to uninsured and/or undocumented residents. Therefore, funding was made available for the provision of 'medically necessary care' to these groups of patients. The term 'medically necessary care', however, is not well defined, causing much discussion among health care professionals on what kind of care can be provided [Dutch legislation plays a key role in many of the obstacles undocumented migrants face in health care provision. The provided . Moreoveprovided ,5,6. AltResearch on documented migrants in the Netherlands often focuses on the most prominent ethnic minority populations, that is, Turkish and Moroccan labour migrants. From this research, it is known that both groups are well represented in general practice, whereas outpatient and mental health care are underutilized when compared to ethnic Dutch ,8. Not sA recent review of the international literature on primary care use of migrant groups living in 7 different countries  failed tDocumented and undocumented migrants share the same motives for migration: they leave their countries due to poverty and political instability. Legal migrants, however, are given the opportunity to build up a life in the host country and enjoy access to health care and social security, undocumented migrants are threatened by expulsion to their home country. To conclude, their socioeconomic position can be considered worse than that of documented migrants.Not surprisingly, undocumented migrants are underrepresented in the research literature on health care utilization, especially in European countries. One of the underlying reasons probably is that research on the health of undocumented residents is difficult to perform since they belong to a hidden population. Due to mistrust in official institutions, undocumented migrants are less likely to respond to formal surveys. Not surprisingly, existing research in Europe concentrated on those who consulted health care services, providing an indication of the medical consumption and type of health problems of undocumented migrants who seek care, sometimes with contradicting results -22.Unlike previous studies, the current study includes both respondents who did and did not seek care, namely undocumented migrants who have been incarcerated in a detention centre while awaiting expulsion to their country of origin. This setting provides the opportunity to gather basic epidemiological data on the health and health care utilization of both care and non care-seeking undocumented migrants.The research questions posed are:1. Which proportion of the respondents knows how to get access to medical care in the Netherlands?2. Which proportion of the respondents has sought medical care in the Netherlands ('care seekers')?3. In case of health problems, what type of health care provider did they consult and which health problems did they present?4. Which proportion of the respondents reports to have been denied care by a health care provider during their stay in the Netherlands?5. What are determinants of care-seeking in undocumented migrants?A cross-sectional study was performed among undocumented migrants who were incarcerated on one of the two detention platforms located in Zaandam, a city 15 km north of Amsterdam.Zaandam's platforms can house a maximum of 576 male detainees at two men to a cell . Their oThe study aimed to include all persons who newly arrived at the detention centre between May 15 and July 17, 2008 (consecutive sample). For this purpose, the detention centre's administration department provided the research team on a regular basis with a list of names and cell numbers of all new arrivals. These persons were then approached by the interviewer. Before the interview, the interviewer introduced herself as an employee of the Amsterdam Public Health Service and provided information on the content and duration of the interview. It was emphasized that the interviewer was not related to the Ministry of Justice and its staff, and that information provided during the interview would not be shared with others. Hereafter, since the interview was on a voluntary basis, oral informed consent for participation was obtained in Dutch, English or French by the interviewer. If the respondent was unable to communicate in one of these languages, a telephone interpretation service was contacted for assistance. During the interviews, only the interviewer and the respondent were present in order to guarantee confidentiality. The first interviews took place on May 28, the last interviews on July 17, 2008. Interviews were carried out one to two times a week, depending on the number of new arrivals. If the participant was temporarily unavailable (e.g. participant was on transport to embassy or receiving visitors), a new attempt was made on the following occasion. Since the study protocol was in accordance with the Dutch Medical Research involving Human Subjects Act (WMO), it was not subjected to an ethics committee.As a conceptual model, we used an adaption of Andersens' behavioral model of health services use as a framework to study health care seeking . The modIn this study, 'need' was operationalized as suffering from chronic disease. Second, higher age was considered as a factor influencing ill-health and thus the 'need' determinant. As 'enabling factor', we measured knowledge of where to turn in case of health problems. Next to this, region of origin was included. Region of origin as an predisposing factor was considered because it can be assumed that migrants who belong to one of the larger migrant groups in the Netherlands are more likely to find their way in the Dutch health care system as these migrants are part of a larger group which is well acquainted with the host country, in contrast to members belonging to smaller, marginal migrant groups who lack sources of informal support and advice in the case of sickness.Furthermore, care seekers were defined as persons seeking care with officially registered care providers while not having health insurance at that point in time. This definition excludes care seeking with informal care providers such as traditional healers, as the study concentrates on health care seeking within the formal health care system.For the purpose of the study, two sources of information were used. First, a structured questionnaire was developed  were used to describe the basic features of the data. Differences between care seeking and non care seeking respondents were tested using t-tests and chi-square tests. Multivariate logistic regression was applied to study determinants of care seeking in the study population. In the analysis, we included age, chronic health problems, duration of stay in the Netherlands and country of origin as determinants. All analyses were carried out using SPSS 17 for Windows.3-Q1 = 6). 34% of the respondents originated from sub-Saharan Africa, 26% from North Africa, 17% from Asia, 13% from the Middle East, 9% from Latin America and 1% from Eastern Europe . On average, the interview took place nine days after arrival (SD = 6). Non-participation in the study was predominantly due to respondents being released from custody before the interview could take place (n = 45). Another four potential participants were unwilling to contribute to the interviews. Two other persons could not be interviewed because the situation was not considered to be safe for the interviewer. Respondents who reported to have spent less than three months in freedom in the Netherlands (36 out of 173) were excluded from the analyses, as well as those who reported to have been insured for medical expenses (n = 15), resulting in a sample of 122 respondents. Respondents in the final sample were aged 18 to 57 years  and had lived in the Netherlands for 8.2 years on average . Thirteen percent indicated that they did not know how to get access to medical care (n = 16). Fifty-seven respondents stated that they had sought medical help during their stay in the Netherlands although they had been uninsured ('care seekers'). In table Next to the interviews, the medical files of the respondents were checked for chronic diseases  , past suTwenty percent of these 95 individuals reported that they were currently using medications, with care-seekers using medications significantly more often than non-care seekers (p = 0.007). Other differences between the two groups did not reach statistical significance table . FinallyThe data presented here provide a glimpse into the background, types of medical complaints and health care seeking behaviour of undocumented male migrants in the Netherlands. Since respondents were interviewed in a detention setting, in contrast to previous studies, information on undocumented migrants who did not seek care could be collected as well.Univariate analyses demonstrated that undocumented migrants differed from care-seeking migrants with respect to the time being in the country, but not with respect to age and chronic health problems. Moreover, non-care seekers more often originated from Asian countries when compared to those who had sought care. Forty-six percent actually consulted a health care provider during their stay in the Netherlands. If care was sought, hospitals were most often consulted, followed by general practitioners and clinics for underserved populations such as the homeless and uninsured. Injuries and dental problems were the leading reasons for seeking care. About 25% of the care-seeking undocumented migrants reported to have been refused treatment by a care provider while in the Netherlands. According to medical files, 15% of both care and non-care seekers suffered from chronic health problems, 11% reported past suicidal thoughts and 20% were using medication.A strength of the study is that the participation rate for the interviews amounted to 77%. Non-participation was predominantly due to external factors which were largely unrelated to the study outcomes, meaning that the studied sample can be considered representative for the detention centre's population. Another major strength is the inclusion of both care and non-care seeking undocumented migrants. To our knowledge, there are no studies so far systematically comparing these two groups. Usually, reference groups for undocumented migrants consist of regular Dutch patients e.g. in primary care or emergency rooms. The current study is unique in this respect, since it makes use of a particular environment in which both care and non-care seeking individuals can be found.Performing research in this setting has advantages, but also its limitations. First, the small sample size can be considered as a weakness of the study, as this decreases the power of the study and increases the susceptibility to sampling error. A related issue concerns the representativeness of the study sample for the undocumented migrant population in the Netherlands. It cannot be assumed that our sample is a random selection of male undocumented migrants in general since it was performed among those who caught the attention of the police, leading to identification and subsequent incarceration. This could mean that the results of our study are not representative for the population of undocumented aliens in the Netherlands, with delinquent individuals being overrepresented. Nevertheless, it has to be taken into account that the Netherlands gradually introduced harsher immigration measures in the last decade. As a result, an increasing number of undocumented migrants are held in special detention units while awaiting expulsion to their country of origin. It is important to keep in mind that the large majority of individuals enclosed in our study population has not been incarcerated because they are suspects of crime or proven offenders, but for residing illegally in the Netherlands .It is hard to judge how representative the population encountered in the detention centre is for the population of undocumented migrants in the Netherlands, as the size and characteristics of this hidden population can only be estimated. According to the most recent estimates, approximately 130,000 undocumented migrants live in the Netherlands ,29. ThisAnother point of discussion concerns whether the findings of our study also might apply to asylum seekers and refugees who newly arrived in the Netherlands. These groups face similar problems, that is, the initial lack of knowledge on the Dutch health care system, language problems and difficulties arising in the communication with health care providers who have a different cultural background than the patient. These barriers to health care provision and access have been extensively described in the literature ,31. UndoIn addition, our study results may have been influenced by information bias. In order to establish the medical history of the respondents, the medical files of the participants were used as a source of information. As a standard procedure, nurses see those who arrive at the detention centre for an intake procedure. The aim of this procedure is to gain information on the health needs of detainees. As with any research based on medical files, the absence of written information does not necessarily imply the absence of a health problem. It cannot be excluded that certain conditions are underdiagnosed, especially in those who have not sought care in the past. Information bias thus could decrease the differences observed between care seekers and non-care seekers regarding the prevalence of chronic health problems. Furthermore, detainees with stigmatizing conditions such as HIV, other sexually transmitted diseases or mental health problems might have underreported these problems during the interview with the nurse. Mental health problems in particular can be difficult to assess due to different cultural backgrounds of patients and care providers . It can Finally, it should be noted that the used questionnaire was conceptualized in a rather broad manner, addressing several aspects of care seeking within a short interview performed in a quite restrictive setting. One might criticize that certain important aspects such as the denial of care should have been explored more in-depth. For example, the study cannot answer the question whether a doctor, nurse or administrator refused care to the patient and whether the patients were able to overcome access barriers by contacting another provider. Further research on this issue should therefore collect information on these aspects.After having described the strengths and limitations of this study, implications for researchers and policy makers should be discussed. This study demonstrates that less than half of the study sample utilizes medical care in the Netherlands. Although our study sample is relatively small, it gives a rough indication of the proportion of the population which remained out of sight in earlier studies. Moreover, our study demonstrated that Asian migrants in particular were significantly less likely to seek care in the Netherlands when compared to undocumented migrants from other regions of origin. Although there is a considerable body of literature on the traditional health care practices of Chinese migrants , it shouAnother major result concerns the fact that only half of the undocumented migrants in this study knew how to get access to medical care in the Netherlands if in need. This finding reflects a substantial lack of knowledge on treatment opportunities in the host country which could be improved by a better education of undocumented patients and health care personnel. Since current public policies are directed at discouraging illegal migration, the education of undocumented patients about their rights and opportunities for treatment mainly is in the hands of non-governmental organizations (NGOs). Our research demonstrated that undocumented migrants often consult clinics run by NGOs. A recent cross-national study included 835 undocumented migrants in 7 European countries who consulted free clinics . In thisst 2009, new legislation regarding health care for undocumented migrants has been put in place in the Netherlands. In the new system, hospitals can be reimbursed for their income losses as well if the patient is unable or unwilling to pay. General practitioners will receive compensation for 80% of their earning losses and midwives 100%. Although the new regulation has many positive aspects, it is feared that the 80% rule can lead to the refusal of patients by care providers. It therefore is important to critically evaluate the developments in the future [Of course, explanations for the denial of care must be sought on both the patient and the provider side. Sad enough, there are several case reports concerning the denial of care to undocumented migrants in critical situations . Reasonse future .The authors declare that they have no competing interests.JB, MJT, MC and TD carried out the interviews. KdK assisted with data collection concerning the medical files. TD performed the statistical analysis. MC, MB, and KD were involved in designing the study, the interpretation of data and helped to draft the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/190/prepubThis file includes the questionnaire used for the interviews performed in this study.Click here for file