Crucial connections between sexual network structure and the distribution of HIV remain inadequately understood, especially in regard to the role of concurrency and age disparity in relationships, and how these network characteristics correlate with each other and other risk factors. Social desirability bias and inaccurate recall are obstacles to obtaining valid, detailed information about sexual behaviour and relationship histories. Therefore, this study aims to use novel research methods in order to determine whether HIV status is associated with age-disparity and sexual connectedness as well as establish the primary behavioural and socio-demographic predictors of the egocentric and community sexual network structures.We will conduct a cross-sectional survey that uses a questionnaire exploring one-year sexual histories, with a focus on timing and age disparity of relationships, as well as other risk factors such as unprotected intercourse and the use of alcohol and recreational drugs. The questionnaire will be administered in a safe and confidential mobile interview space, using audio computer-assisted self-interview (ACASI) technology on touch screen computers. The ACASI features a choice of languages and visual feedback of temporal information. The survey will be administered in three peri-urban disadvantaged communities in the greater Cape Town area with a high burden of HIV. The study communities participated in a previous TB/HIV study, from which HIV test results will be anonymously linked to the survey dataset. Statistical analyses of the data will include descriptive statistics, linear mixed-effects models for the inter- and intra-subject variability in the age difference between sexual partners, survival analysis for correlated event times to model concurrency patterns, and logistic regression for association of HIV status with age disparity and sexual connectedness.This study design is intended to facilitate more accurate recall of sensitive sexual history data and has the potential to provide substantial insights into the relationship between key sexual network attributes and additional risk factors for HIV infection. This will help to inform the design of context-specific HIV prevention programmes. In South Africa, 16.9% of the 15 to 49 years old population is infected with HIV -4. FurthFor young women in South Africa, an older partner is more likely to expose them to the virus, since the HIV prevalence in men increases with age, peaking in their early thirties . MoreoveIn concert with age-disparate relationships, concurrent relationships - overlapping sexual partnerships where sexual intercourse with one partner occurs between 2 acts of intercourse with another partner - have been purported by several authors to facilitate the spread of HIV -18. ContTo this end, our study will be located in three disadvantaged communities in Cape Town, South Africa. The primary objective is to determine whether HIV status is associated with age-disparity and sexual connectedness. The secondary objectives of the study are to ascertain behavioural and socio-demographic predictors of the egocentric and community sexual network structures. To accomplish these objectives we will implement novel methods that will address many of the typical challenges of obtaining unbiased sexual behaviour information.We will undertake a cross-sectional, sexual behaviour survey. This study is registered with and approved by the Stellenbosch University Health Research Ethics Committee (N11/03/093) and it is a subsidiary study to the Zambia South Africa TB and AIDS Reduction study (ZAMSTAR) (ISRCTN36729271), a community randomised trial aimed at reducing the prevalence of tuberculosis in communities with a high burden of TB and HIV, by novel public health interventions.The study outlined here will take place in three peri-urban, disadvantaged communities in the greater Cape Town area with a high burden of HIV. These communities represent one predominantly black community and two racially diverse communities. They were chosen in order to gather a representative sample of the different races most affected by HIV in South Africa.The final primary outcome measurements of the ZAMSTAR study were collected through an HIV and TB prevalence survey, conducted in 2010. In this prevalence survey, participants provided written informed consent to complete a questionnaire on basic demographic information and medical history, to provide sputum for TB culture, and to have repeated visits by the research team. Through an additional consent procedure, participants gave consent for HIV counselling and testing. Our study will randomly select 500 previous ZAMSTAR participants in each of the three communities. In the ZAMSTAR survey, some participants chose not to have HIV testing done. For this reason, 20% of our sample per study site (100/500) will consist of participants who did not consent for HIV testing. By sampling both participants who did, and who did not, consent to an HIV test, we may be able to detect selection bias.Study participants will be included if they are between the ages of 18 and 64 at the time of the ZAMSTAR survey, able to give informed consent, and agree to complete the study questionnaire. Individuals who do not speak English, Afrikaans or isiXhosa, or who are enrolled in any other ongoing study, will be excluded.A touch screen questionnaire, utilizing an audio computer-assisted self-interviewing (ACASI) application has been developed to facilitate the collection of sexual history data. The respondents will wear headphones to hear questions in their choice of English, Afrikaans, or isiXhosa, and simultaneously be able to read the question and select their answers on a 22-inch touch screen monitor. This question format was derived from a UNAIDS "Best Practice" questionnaire of sexual partnership data , as wellThe questionnaire is set up to ask questions along a temporal trajectory. It will begin by asking the participant basic demographic information and then proceed to ask if the participant had a main sexual partner one year ago and if that relationship is still ongoing. The participant will be able to indicate, on a touch screen timeline, the periods they were in this relationship. For each relationship, we ask whether or not the participant or his/her partner used drugs or alcohol at their first intercourse. To gauge the level of spatial connectedness, we ask how long the participant usually travelled to their partner and his/her degree of proximity. For each distinct period that the respondent engaged in sexual activity with a particular partner, measured in weeks, additional questions will be asked about the frequency of intercourse and condom use. This series of questions will be asked for each main sexual partner, as well as the casual sexual partners. The onset, dissolution, and duration of each relationship will be displayed on the touch screen timeline for the participant to see, using different colours for each partner. At the end of the questionnaire, participants will be asked how many partners they have ever had. In addition to the sexual behaviour questionnaire we added three questions to assess the ease of use, perceived confidentiality and self-reported truthfulness when answering questions in this survey. Finally, we ask them to indicate their preferred medium of answering questions about sexual behaviour: touch screen computer with ACASI, researcher-administered verbal questionnaire, self-administered written questionnaire, or telephonic survey.All questions were developed and refined after conducting cognitive interviews with six people who were representatives of the study communities. These interviews assessed the clarity, comprehensibility, and cultural sensitivity of the proposed questions, allowing us to improve the phrasing of questions, incorporate meaningful slang, and define suitable categorical answer options that maximize the precision and accuracy of responses. Furthermore, the cognitive interviews allowed us to gauge the anticipated community response to a survey utilizing ACASI. Our interlocutors responded positively to the idea of answering questions about their sexual histories in a completely anonymous way and articulated that the communities would embrace a survey that could accomplish this.The senior data manager will have access to ZAMSTAR study participant information and will therefore be able to obtain names and addresses for each individual. Consent forms will be printed that include a new unique barcode - different from but linked to the barcode allocated in the ZAMSTAR study - along with names and addresses of the individuals. The barcodes from the ZAMSTAR study will not be printed on these consent forms. Therefore, none of the study staff, except the data manger, will be able to link the HIV test results obtained in the ZAMSTAR survey to our survey data.Residents of the community, who were confirmed with names, addresses and dates of birth to be previous ZAMSTAR paricipants, will be asked if they consent to participation after reading an information sheet and listening to verbal information given by the research assistants in their home language. The research assistants will make it known that the anonymised HIV test results from the ZAMSTAR study will be accessed and used in the analysis. If the participant consents to the survey and signs the consent form, he/she will be escorted to a camper van - located in a safe part of the neighbourhood - where the survey will commence. The camper van has been refurbished to provide a private and safe office space, containing a partition, in which two participants can take the survey simultaneously at their own desk, chair and computer. Inside the van, the research assistant will scan the barcode on the consent form into the touch screen computer.All subsequent answers to the touch screen questionnaire will be answered in private by the participant only. No one will be able to see their responses, including the research assistants who will be waiting outside of the van or in the driver's cab. They will remain close by at all times in the event that a participant requests their assistance. While two participants take the survey, two additional research assistants will be out recruiting the next two participants from the same community. The participant's name will not be asked in the questionnaire, hence the senior data manager will be the only person who can link the name from the consent form via the unique barcode. The touch screen questionnaire data will be uploaded from the laptop onto the central database designed for this study. All data will be backed up onto a second server. The consent forms will be stored in locked cabinets at the study centre.To ensure the quality of the data collection process, the fieldwork coordinator will randomly select 5% of the completed consent forms every week and do a home visit to verify that the participant was in fact enrolled in the study and to confirm the signature on the consent form.The HIV status of participants was determined in the 2010 ZAMSTAR Tuberculosis Prevalence Survey. In that study, for those who consented to HIV testing, Abbott Determine HIV-1/2 screening tests were used, and a second, confirmatory test was conducted for those who tested positive. The data manager of this study is the only person to have access to the ZAMSTAR HIV test results and after the completion of our survey, he will link these test results to the new survey data.In an initial descriptive analysis, the prevalence of HIV infection and the point prevalence of concurrent relationships and age-disparate relationships will be calculated, along with the average number of partners per year, the average frequency of sexual intercourse, the frequency of condom use for each of the reported relationships, condom use at last sexual intercourse with each of the reported partners, the average age difference between individuals and their partners and the variance of these age differences in the study population. Since many individuals are likely to report on more than one relationship, linear mixed-effects models will be used to analyze the inter- and intra-subject variability in the age difference between sexual partners, and covariates associated with large age differences. To take into account the fact that multiple sexual partnerships may co-exist, and the timing of partnership initiation and dissolution are correlated, or dependent on each other, survival analysis for correlated event times will be conducted to model concurrency patterns, partnership durations and rates of partnership initiation and termination. This type of survival analysis also accommodates right-censored observations where current partnership durations are only known up to the time of the survey. Marginal survival models for correlated event times will be used to enable comparisons between genders or other covariate groupings.Next, logistic regression models for clustered data  will be fit to the data to determine whether HIV status is associated with age-disparity and sexual connectedness. Age-disparity and sexual connectedness will be operationalised using the following individual and community characteristics: (a) the mean difference between the age of an individual and the age of his/her partners; (b) the variability in age difference between an individual and his/her partners; (c) being engaged in a concurrent relationship; (d) having ever engaged in concurrent relationships; (e) the number of past and present concurrent relationships; (f) the cumulative overlapping time engaged in concurrent relationships; (g) the number of lifetime sexual partners; (h) spatial proximity to sexual partners; (i) the population mean age differences between individuals and their partners; (j) the population variability in age difference between individuals and their partners; (k) point prevalence of concurrency; (l) prevalence of having ever engaged in concurrent relationships; (m) population mean number of past and present concurrent relationships; (n) the population per capita cumulative overlapping time engaged in concurrent relationships; (o) population mean number of lifetime partners; (p) population spatial assortativeness (i.e. choosing partners from one's own community).In a supplementary analysis, socio-demographic and behavioural predictors of egocentric and community sexual network structures will be ascertained. Potential risk factors under consideration are: race, gender, proximity to city centre, education level, socio-economic status, age, religion, and alcohol and drug use at first intercourse. Confirmed risk factors will be added to the models as confounders in the primary analyses and adjusted associations for age-disparity, concurrency and HIV status at the individual and cluster level will be calculated.For an alpha level of 5%, a design effect of 2.0 (effect of clustered data rather than independent random sample) and a prevalence of HIV infection of 15%, with the width of the 95% confidence interval at 6% (i.e. +/-3%), 1089 study participants with HIV test results are needed. Building a 10% margin for inconsistent and incomplete data, we will aim to administer the survey to 1200 study participants for whom HIV test results are available. Additionally, we will include 300 study participants  who did not opt for HIV testing, to investigate whether non-universal consent for HIV testing may have introduced selection bias.This study will describe age-disparity and sexual connectedness in disadvantaged communities around Cape Town, South Africa. These aspects of sexual networks appear to be important for the spread of HIV, yet their complex patterns and the way in which they are driving HIV transmission are insufficiently understood. One of the fundamental challenges to understanding the role of sexual network structure in the spread of HIV is obtaining valid sexual history data. We intend to address this challenge by using a survey design that attempts to minimize social desirability bias and maximize confidentiality.Specifically, we created a mobile office space where participants are free to answer questions away from their homes, where they often encounter many familial disruptions and potential eavesdroppers that could influence how they answer questions about their sexual behaviours. Secondly, we decided to use ACASI methods, which have successfully been used in previous sexual behaviour surveys to reduce social desirability bias. This bias is introduced when participants are intimidated by the researchers or fieldwork assistants and experience pressure to provide socially accepted answers, not necessarily in line with the truth. Studies comparing ACASI methods with face-to-face interviews, suggest that participants are more likely to report high-risk sexual behaviours while using the ACASI -31.In addition to this, our study will also attempt to curb recall bias about certain sexual partners and behaviours. Events History Calendar (EHC) methods have the potential to unlock autobiographical memory better than traditional survey methods  because This survey will expand the body of knowledge on sexual and behavioural determinants of HIV infection, with special focus on the role of age disparity and sexual connectedness. Since the population impact of an HIV prevention method does not only depend on its intrinsic efficacy, but also on the structure of the sexual network in which it is introduced, our study hopes to contribute to the development of appropriate HIV preventative strategies that take the local sexual network structure into account.The authors declare that they have no competing interests.WD, AW, ED, NB and MT were responsible for the conceptual design of the study. All authors participated in revisions to the study design and approved the final study design. RB and WD were involved in drafting of the manuscript, all other authors were involved in overall revision of the manuscript. All authors are involved in the implementation of the project, and have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/616/prepub


The malaria test positivity rate (TPR) is increasingly used as an indicator of malaria morbidity because TPR is based on laboratory-confirmed cases and is simple to incorporate into existing surveillance systems. However, temporal trends in TPR may reflect changes in factors associated with malaria rather than true changes in malaria morbidity. This study examines the effects of age, area of residence and diagnostic test on TPR at two health facilities in regions of Uganda with differing malaria endemicity.The analysis included data from diagnostic blood smears performed at health facilities in Walukuba and Aduku between January 2009 and December 2010. The associations between age and time and between age and TPR were evaluated independently to determine the potential for age to confound temporal trends in TPR. Subsequently, differences between observed TPR and TPR adjusted for age were compared to determine if confounding was present. A similar analysis was performed for area of residence. Temporal trends in observed TPR were compared to trends in TPR expected using rapid diagnostic tests, which were modelled based upon sensitivity and specificity in prior studies.Age was independently associated with both TPR and time at both sites. At Aduku, age-adjusted TPR increased relative to observed TPR due to the association between younger age and TPR and the gradual increase in age distribution. At Walukuba, there were no clear differences between observed and age-adjusted TPR. Area of residence was independently associated with both TPR and time at both sites, though there were no clear differences in temporal trends in area of residence-adjusted TPR and observed TPR at either site. Expected TPR with pLDH- and HRP-2-based rapid diagnostic tests (RDTs) was higher than observed TPR at all time points at both sites.Adjusting for potential confounders such as age and area of residence can ensure that temporal trends in TPR due to confounding are not mistakenly ascribed to true changes in malaria morbidity. The potentially large effect of diagnostic test on TPR can be accounted for by calculating and adjusting for the sensitivity and specificity of the test used. As malaria control efforts intensify, there is a vital need to accurately measure changes in the burden of disease and to evaluate the impact of control interventions through improved surveillance . MalariaThe malaria test positivity rate (TPR), defined as the proportion of diagnostic tests that are positive for malaria, is an alternate indicator of malaria morbidity . TPR is As the WHO now recommends laboratory confirmation for all patients suspected of having malaria before treating , the TPRThe Uganda Malaria Surveillance Project (UMSP) in collaboration with the Uganda National Malaria Control Programme (NMCP) established a health facility-based malaria surveillance system at six sentinel sites between September 2006 and January 2007. Detailed descriptions of study sites and data collection have been published previously . BrieflyAge data were collected as months and years up to age 5, and as years only after age five. Age was classified as less than 5, 5 to 15, and greater than 15 years in analyses of the relationships between age and TPR and between age and calendar time. In adjusting for age, visits at each site were separately categorized into 20 equivalent age quantiles, each containing 5% of visits at that site. Area of residence categories were determined based upon the reported parish of residence. Parishes that contributed to < 1% of cases that underwent diagnostic testing were combined into an “other” category. There were 24 categories of calendar time based upon the month and year. Suspected malaria was defined as all patients referred for malaria laboratory testing plus all patients not referred for a malaria laboratory test, but given a clinical diagnosis of malaria. TPR was defined as the proportion of tests  positive for malaria.exp) for an RDT was calculated from the observed TPR (TPRobs) as follows:Expected TPR . Overall TPR for the entire study period was higher at Aduku compared to Walukuba .The characteristics of outpatient visits at the two sites are summarized in Table Changes in TPR over time could be confounded by age, which has a well known association with malaria infection and morbidity. As expected, TPR varied significantly by age group in Aduku. For visits by patients under five years of age, five to 15 and over 15, TPR was 71%, 64%, and 30%, respectively (p < 0.001). In Walukuba, the association between TPR and age was less dramatic , but remained statistically significant (p < 0.001).For age to confound temporal trends in malaria TPR, it must also be associated with calendar time, which was the case at both sites in this study. In Aduku, the proportion of visits by patients under age five varied from a high of 56% in September 2009 to a low of 24% in December 2010 (p < 0.001). There was an upward trend in age at Aduku throughout the study period with the proportion of visits by patients under age five ranging between 44% and 55% in the first six months of the study period, and between 24% and 40% in the final six months. Walukuba demonstrated less variation in the distribution of age over time, and did not exhibit any consistent trend, but differences remained statistically significant. The proportion of patients under age five varied from a high of 41% in April 2009 to a low of 23% in June 2010 (p < 0.001).Given the significant associations between both age and calendar time, and age and TPR, the potential for confounding was present at both sites. To determine the degree to which confounding actually occurred, temporal trends in the observed TPR and TPR adjusted for age were compared as shown in FigureThe association between area of residence and TPR is also well known, and was of interest in this study. Visits were categorized based on the parish where the patient lived, and visits from parishes contributing fewer than 1% of the cases for the entire study period were grouped into a category labelled “Other”. This created 8 regions surrounding Walukuba and 18 surrounding Aduku. Table Although there were significant associations between area of residence and TPR at both sites, the distribution of area of residence among patients undergoing diagnostic testing would need to vary significantly over time for the potential for confounding to be present, and this was the case at both sites. For example, the proportion of patients who underwent diagnostic testing who were from the Masese Parish ranged from 30% in October 2009 to 39% in December 2010 at Walukuba and patients who were from the Ongoceng Parish ranged from 17% in August 2009 to 25% in October 2009 at Aduku (p < 0.001 in both cases).Given the associations with both TPR and calendar time, area of residence was a potential confounder. As was done for age, temporal trends in observed TPR and TPR adjusted for area of residence were compared, however, they differed only slightly and without any clear pattern Figure. The larFigureTPR is increasingly used as an indicator of temporal trends in malaria morbidity. Ideally, changes in TPR over time will reflect true changes in malaria incidence for a population of interest. However, several factors including potential confounders such as age and area of residence, proportion of cases subjected to testing, care-seeking and utilization trends, and choice of diagnostic test may cause changes in TPR independent of true changes in the incidence of malaria. In this study, the effects of age, area of residence, and diagnostic test on TPR were investigated at two sites with different transmission intensity in Uganda. Age and area of residence demonstrated the potential to be important confounders at both sites given their independent associations with both time and TPR. However, controlling for each of them had only a small effect on the trends in TPR at the two sites. The directions of trends in the expected TPR using pLDH- and HRP-2-based RDTs were similar to trends in observed TPR, but there were differences between the values of observed and expected TPR at all time points. These differences were more pronounced at Aduku, the higher transmission site, and more pronounced using the HRP-2-based RDT.Potential confounders are an important consideration in observational studies assessing any association, including temporal trends, which represent associations between time and an indicator of interest, in this case TPR. Any factor associated with both the exposure of interest  and the outcome of interest (TPR) has the potential to confound temporal trends in TPR. Numerous factors may be associated with both time and malaria incidence such as weather, precipitation patterns, proportion of patients tested, care-seeking and utilization and home construction. Age and area of residence were chosen for analysis because they are well known to be associated with malaria incidence, and they can both be easily measured.In the case of age, the expected association between younger age and higher TPR was observed. The age distribution of patients also differed significantly by calendar time, particularly at Aduku where the population receiving a malaria blood smear became gradually older over time. The comparison of temporal trends in observed TPR and age-adjusted TPR in Aduku provides a subtle demonstration of confounding in which age-adjusted TPR increased over time relative to the observed TPR due to the gradual increase in age over time and the lower TPR in older patients. Although confounding by age only had a modest effect on temporal trends in TPR in this study, the effect could be larger in other circumstances, for example a large increase in paediatric capacity at the clinic where surveillance is being conducted.TPR was also associated with area of residence, though there were no clear patterns relative to distance from the clinic. In the case of Aduku, the TPR was lower outside the catchment area compared to near the clinic, whereas the opposite was true in Walukuba. As with the age distribution, the distribution of area of residence for patients receiving a blood smear also varied over time at both sites. However, there was no noticeable confounding of temporal trends in TPR by area of residence at either site. Nonetheless, it is easy to imagine circumstances in which confounding of temporal trends in TPR by area of residence may be important such as changes in the availability of transportation to the clinic from one area relative to another with a substantially different malaria burden. Controlling for factors such as age and area of residence with methods such as direct standardization or stratification can assure that changes in TPR over time that are due to confounding by these factors are not mistakenly ascribed to changes in malaria morbidity.via comparison with a gold standard at the health facilities of interest. Using those results, the TPR can be adjusted accordingly based on the equation shown earlier.The choice of diagnostic test can also affect the interpretation of trends in TPR in two important ways. First, even when the proportion of patients with true infections stays the same, a change from one diagnostic test to another could cause a change in TPR that is exclusively due to a change in the proportion of true positive and false positive tests. Separately reporting the TPR for microscopy and RDTs, as is done in the World Malaria Report , partialThis study has several important limitations. First, the sensitivity and specificity of RDTs at these sites using microscopy as a gold standard may have changed between the time of the study referenced for those values  and thisTPR is a key malaria surveillance indicator in resource-limited settings with medium to high transmission. It is easily integrated into HMIS reporting, and its reliability will depend less upon stable clinic attendance as do estimates of malaria incidence based on clinical or laboratory diagnosis, so long as a consistently high and representative proportion of clients access health facilities and are offered a diagnostic test. A thorough understanding of both the limitations of TPR and methods for improving its accuracy is important for monitoring the effectiveness of malaria control interventions. Indeed, improved methods to quantify and compare changes in TPR in different settings could be used to provide more practice-based evidence on the relative effectiveness of malaria control interventions. The urgency of the overall burden of malaria, the increasing availability of new tools to fight malaria, and the tremendous resources required for controlled experiments of public health interventions demand creative techniques such as health facility-based surveillance with indicators like TPR to efficiently test, refine, and deploy the next generation of strategies for malaria control.The authors declare that they have no competing interests.AG, RK, SPK, SN, MRK and GD contributed to study design and oversight. DF, AG, and GD contributed to methodology, data analysis, interpretation of results, and drafting of the manuscript. All authors read and approved the final manuscript.


The lack of conclusive results and the scarce use of the Millon Clinical Multiaxial Inventory-III (MCMI-III) in the study of the relationship between smoking and personality are the reasons that motivated the study reported here. The aim of the present study was to analyze the influence of personality patterns, assessed with the MCMI-III, and of nicotine dependence on treatment outcomes at the end of the treatment and at 12 months follow-up in men and women smokers receiving cognitive-behavioral treatment for smoking cessation.The sample was made up of 288 smokers who received cognitive-behavioral treatment for smoking cessation. Personality patterns were assessed with the Millon Clinical Multiaxial Inventory-III (MCMI-III). Abstinence at the end of the treatment and at 12-month follow-up was validated with the test for carbon monoxide in expired air.The results showed significant differences by personality patterns that predict nicotine dependence . At the end of the treatment it is more likely that quit smoking males with a Compulsive pattern and less likely in those scoring high in Depressive, Antisocial, Sadistic, Negativistic, Masochistic, Schizotypal and Borderline. In women, it is less likely that quit smoking those with the Schizoid pattern. At 12 months follow-up it is more likely that continue abstinent those males with a high score in the Compulsive pattern. Furthermore, nicotine dependence was an important variable for predicting outcome at the end of the treatment and smoking status at 12 months follow-up in both men and women.We found substantial differences by gender in some personality patterns in a sample of smokers who received cognitive-behavioral treatment for smoking cessation. We should consider the existence of different personality patterns in men and women who seek treatment for smoking cessation. In recent years, a large number of publications and studies have focused on the analysis of the relationship between personality traits, from different theoretical models, and cigarette smoking. A part of such research has concentrated on describing which personality traits are most frequently found in smokers -3. For eOther studies have assessed the extent to which certain personality traits are related to success or failure in smoking cessation and maintaining abstinence ,6. For eOther research, based on Millon’s personality model, has tried to identify which personality patterns are most common among smokers. Millon defines the personality patterns as a set of attributes that are neither a category nor a dimension, but rather a synthesis of both of them. Patterns that make up the personality of the individual are the results of the inter-relation between contextual and constitutional factors, and are considered to be dynamic styles on a continuum from normality to pathology, which makes possible intervention and change . Normal Thus, Fernández del Río et al. , using tTherefore, on the basis of Millon’s model, research suggests that certain personality patterns are associated with greater difficulty for smoking cessation and with higher likelihood of relapse, while others would be associated with the maintenance of abstinence. However, the results are still somewhat inconclusive and further research is necessary.The lack of conclusive results and the scarce use of the MCMI-III  in the sThe aim of this study is to analyze the influence of the personality patterns, assessed with the MCMI-III, and of nicotine dependence on treatment outcomes at the end of the treatment and at 12 months follow-up in men and women smokers receiving cognitive-behavioral treatment for smoking cessation.The sample was made up of smokers seeking smoking cessation treatment at the Smoking Cessation Unit of the Faculty of Psychology at the University of Santiago de Compostela (Spain) during the period January 2009 to June 2011. Participants were recruited through the media, by GPs’ recommendation and by word of mouth from people who had taken the treatment previously. Inclusion criteria were: age 18 or over; a wish to participate in the treatment program; and smoking a minimum of 10 cigarettes a day. Exclusion criteria were: a diagnosis of severe mental disorder (bipolar disorder and/or psychotic disorder); concurrent dependence on other substances ; having participated in the same or similar treatment over the previous year; having received another type of effective smoking cessation treatment  in the past year; suffering from a physical pathology with a high life risk which would require immediate individual intervention ; smoking a type of tobacco other than cigarettes ; refusing to be video-recorded during the sessions; and failing to attend the first treatment session.SD = 10.55).From an initial sample of 332 smokers, 42 were excluded for having fulfilled one of the exclusion criteria and two because their MCMI-III score was invalid . Therefore the final sample was made up of 288 smokers . Their average age was 41.49 years (Cuestionario sobre el hábito de fumar.Regarding smoking status at 12 months follow-up we only found significant differences in the men’s group: those who were abstinent scored higher in the Compulsive pattern [Mean = 61.23  separated according to gender. The criterion variable was “abstinence” at the end of the treatment and at the 12-month follow-up .At the end of the treatment, in women none of the 14 personality patterns predicted abstinence, and only nicotine dependence (FTND) appears as a predictor variable. In men, the Masochistic personality pattern and FTND were associated with abstinence at the end of the treatment. Men and women with higher nicotine dependence were less likely to give up smoking. In the case of men, we also found that those who score higher in the Masochistic pattern were less likely to achieve abstinence  in a sample of 288 men and women smokers receiving cognitive-behavioral treatment for smoking cessation. The results indicated differences between men and women in the personality patterns analyzed in the present study, and that nicotine dependence plays a relevant role in treatment outcomes.When assessing the personality patterns of smokers seeking smoking cessation treatment, we found that women score significantly higher in the Histrionic and Compulsive patterns, whilst in men the Narcissistic pattern is the most prevalent. This result is in line with those of other studies, such as that of Berlin et al. , who conWe did find differences regarding to which patterns predict such dependence. In men, nicotine dependence was predicted by a high score in the Antisocial pattern and a low score in the Narcissistic pattern, and in women it was predicted by a high score in the Schizoid personality pattern. Nieva et al.  had alreOn the other hand, we did find differences by gender in relation to the patterns most strongly related to smoking abstinence at the end of the treatment. Men with a high score in the Compulsive pattern were likely to stop smoking, but high scores in the Depressive, Antisocial, Sadistic, Negativistic, Masochistic, Schizotypal and Borderline personality patterns were associated with lower percentages of abstinence at the end of the treatment. The Antisocial and Borderline patterns have in common the Impulsiveness trait, which according to Nieva et al.  is relatLong-term outcomes were also different according to personality patterns. In men, a high score in the Compulsive pattern was related to continuous abstinence at 12-months follow-up, while in women no pattern was significant. Previous studies carried out from Millon’s personality model ,39.Concerning the regression analysis, nicotine dependence is the determining variable for smoking status in both short and long term, regardless of gender. In men only, the Masochistic pattern partly explains poor outcomes at the end of the treatment. Nicotine dependence is considered the key variable for explaining the results of smoking cessation interventions according to previous studies ,40,41, wHowever, the results also indicated that certain personality patterns predict nicotine dependence and that there were significant differences according to gender. On the one hand, the Schizoid pattern has been linked to smoking at the end of the treatment in women, and on the other hand, higher scores in this pattern was associated with higher nicotine dependence. The Antisocial personality pattern has been linked to lower percentages of abstinence, and has been a pattern that contributes to explain the variance of FTND in men. Thus, we know that certain personality patterns, which differ according to gender , are related to greater dependence, which in turn predicts poorer results both at the end of the treatment and at the 12-month follow-up.Some limitations in this study should be noted. First, the sample is not representative of the total population of smokers, since it includes only those who sought smoking cessation treatment, and we know that this type of population has different characteristics from those of smokers from the general population. Therefore, we could expect that the patterns and the differences found by gender could vary in the profile of smokers who stop smoking without following a specific treatment, so that our results could be extrapolated only to similar clinical samples. As Hughes et al.  and Le SIn summary, as far as we know, this is the first study which examines the relationship between Millon’s personality patterns and smoking status in a clinical sample taking into account the influence of gender. Our results show that the personality patterns of men and women who seek treatment to quit smoking are different, and also that there are significant differences between personality patterns and nicotine dependence and their influence on treatment outcomes by gender. However, only nicotine dependence had a significant role in predicting continuous abstinence at the 12-month follow-up. But as Marqueta et al.  indicateThe authors declare that they have no competing interests.BP, EB designed the study and wrote the protocol. BP, ALD, EFR, UM conducted literature searches and provided summaries of previous research studies. BP, ALD, EFR, UM conducted the statistical analysis. BP, ALD, EFR wrote the first draft of the manuscript, and all authors contributed to and have approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/306/prepub


A large phase III placebo-controlled, randomized efficacy trial of an investigational 11-valent pneumococcal conjugate vaccine against pneumonia in children less than 2 years of age was conducted in the Philippines from July 2000 to December 2004. Clinical data from 12,194 children who were given either study vaccine or placebo was collected from birth up to two years of age for the occurrence of radiologically proven pneumonia as the primary endpoint, and for clinical pneumonia and invasive pneumococcal disease as the secondary endpoints. Several tertiary endpoints were also explored. Along the core trial, several satellite studies on herd immunity, cost-effectiveness of the study vaccine, acute otitis media, and wheezing were conducted.We describe here in detail how the relevant clinical records were managed and how quality control procedures were implemented to ensure that valid data were obtained respectively for the core trial and for the satellite studies. We discuss how the task was achieved, what the challenges were and what might have been done differently.There were several factors that made the task of data management doable and efficient. First, a pre-trial data management system was available. Secondly, local committed statisticians, programmers and support staff were available and partly familiar to clinical trials. Thirdly, the personnel had undergone training during trial and grew with the task they were supposed to do. Thus the knowledge needed to develop and operate clinical data system was fully transferred to local staff.Current Controlled Trials ISRCTN62323832 Streptococcus pneumoniae causes high morbidity and mortality from pneumonia and invasive disease among young children particularly in low income countries  comprised of those who were recruited and enrolled in the efficacy trial of the 11PCV against pneumonia N = 12,194). A subset of the RCT group of 1,111 children was enrolled into the nested immunogenicity study according to their place of residence . This study assessed the effect on the siblings of the nasopharyngeal pneumococcal carriage of a younger sibling who had been vaccinated with the 11PCV. Nasopharyngeal swabs for detection of pneumococcal carriage were collected at the enrolment (6 weeks of age) and at the measles vaccination visits (9 months of age).Starting in January 2002, the field nurses recruited subjects for the herd immunity study. There were 1,843 infants enrolled in the efficacy trial with elder sibling who enrolled in the herd immunity study. The herd immunity study was conducted to assess the possible herd immunity effects of the 11PCV  until they reached the age of 5 years.The AOM and wheezing studies started in February 2002 and children enrolled were diagnosed with pneumonia and/or AOM. The study subjects were less than 5 years old. The pertussis study started in July 2001 with a study population that included children less than 13 years old. This study included older children (not necessary enrolled in the RCT) who qualified the criteria for pertussis. Data for AOM, wheezing and pertussis were collected until December 2004. In the AOM and wheezing studies, 2,018 children with one or more than episodes of hospitalizations and consultations were enrolled. The AOM and wheezing studies aimed at determining the presence of AOM and wheezing in children less than 5 years who were hospitalized at BRH or consulted at BRH-OPD. The data collected included presence of cough, duration of cough, ear pain, irritability, vomiting, methods of ear examination, diagnosis of ear examination, laboratory samples collected and episodes of wheezing. In the pertussis study, 219 children with one or more than episodes of hospitalizations and consultations were enrolled. The data collected included duration of cough, presence of paroxysmal cough, vomiting (induced by cough), whooping cough, and suspicion of pertussis in the patient and/or among the household members.All births occurring since February 2000 in the six study municipalities were followed up for possible recruitment in the core trial. Basic demographic information and the intention of the parents/guardians to have their child participate in the trial were collected. Information on the place of residence and the first DTP vaccination were obtained from the parents/guardians. This was usually done during home visits by the study nurse when she talked with the parents/guardians about the trial. These activities were undertaken by the study nurses to facilitate recruitment and to give time to the parents/guardians to make decision before enrolment.During the first vaccination visit, the study nurse or study physician ascertained that the infant fulfilled all the inclusion criteria, did not present with the exclusion criteria, and that the parent/guardian signed the informed consent form. The study nurse filled in an infant data information sheet containing basic demographic data of the child and the mother, and assigned unique child identification (Child_ID) number. Based on the randomization list, which was produced by the pharmaceutical partner of the trial, the nurse allocated the child to either receive the study vaccine (11PCV) or the saline placebo. The infant data information sheet constituted the primary record of the child. During the first visit, the study nurse also issued to the parent/guardian a health card (commonly known as the “yellow card”) with a Child_ID sticker pasted on the card. The vaccinations received, among other information, were recorded into this card. To identify the child’s enrolment in the trial, the parent/guardian was requested to always bring the child’s yellow card with them whenever the child visited the health centre or hospital.After enrolment, the child received the first dose of the trial vaccine or placebo at the minimum of 6 weeks of age. The second dose was scheduled to be given 4 weeks after the first dose and the third dose 4 weeks after the second dose. Administration of the study vaccine and concomitant vaccines  were recorded on the vaccination case report form (CRF).Those not enrolled in the trial received routine Expanded Program of Immunization (EPI) vaccines  available from the health centre supply. This data was collected and recorded on a shorter version of the vaccination CRF.Before vaccination, the child was assessed for the presence of any permanent or temporary contraindications. The study nurse had a list of questions to assist her in assessing the eligibility of the child to receive the vaccination. The presence or absence of contraindications was recorded on the contraindication CRF. If there was a temporary contraindication, the vaccination was postponed to the next scheduled health centre vaccination visit (~4 weeks later). If there was an absolute contraindication, the child was excluded from the trial.Hospitalizations due to pneumonia at BRH and the 3 private hospitals were monitored daily for children less than 5 years. Consultations in the out-patient department of the BRH (BRH-OPD) were monitored only on working days. Each hospitalization and consultation of the child was given a unique identification  number. A child could have several hospitalizations during the trial. Hospitalization from BRH was recorded using a different CRF, because besides pneumonia, also initial diagnoses of meningitis and sepsis were recorded. For hospitalizations in the private hospitals and the BRH-OPD only pneumonia cases were recorded.Any hospitalization in the BRH due to pneumonia in children not enrolled in the core trial and aged 6 weeks to 59 months was collected for the epidemiology study. Also consultations of pneumonia in the BRH-OPD and hospitalizations due to pneumonia in the 3 private hospitals for children aged 6 weeks to 23 months were included in the epidemiology study. Initial diagnoses of meningitis and sepsis in the BRH were also included in the epidemiology study.th patient. These were recorded using a laboratory CRF and indexed with the same hospitalization identification number compared to the hospitalization or consultation.Several samples  for laboratory tests were collected during hospitalization due to pneumonia, meningitis and/or sepsis in the BRH. For OPD consultations nasopharyngeal aspirate was collected using systematic sampling of every 5An SAE was defined as any untoward medical occurrence after immunization that resulted in death, was life threatening, required in-patient hospitalization or prolonged existing hospitalization, or resulted in persistent or significant disability/incapacity. SAEs were monitored in eight hospitals in the study area as well as in the 48 barangay health stations. Information on hospitalizations of study infants from 6 weeks to two years of age belonging to the RCT group was identified, followed up and recorded by using a screening log form. During vaccination days, study nurses questioned barangay health stations’ staff about any SAEs and deaths in the area. SAEs were reported to the safety monitoring team of local and Finnish physicians who will review and classify the SAE for periodic or immediate reporting to the vaccine manufacturer, Sanofi Pasteur. Periodically reported SAEs are reported monthly while immediately reported SAEs are reported within 48 h from the time the event become known to the investigators.Data collection and information system requirements originated from the need of regular, effective and timely monitoring and reporting during the trial. This was also essential for the statistical analyses once the trial was completed. The primary goal of the clinical data management system (CDMS) was to allow these reporting requirements to be met, while ensuring the integrity and validity of the data being collected. It was necessary that multiple users were simultaneously able to make modifications to the data and to generate reports from real time data in the database . A decisThe scope of the CDMS was defined by the “data dictionaries”  containing metadata of CRFs and by other general reporting or informational requirements. The DDs contained description of forms and other information sources of each study and contained short description of the data: variable names, short explanation, code lists, legal values, format and length. The DDs were maintained by the data manager (DMG) and were defined in conjunction with the trial investigators. The DDs were created alongside the paper forms, such as CRFs. The CRFs were used to initially collect the data by trained nurses, technicians and doctors before entry into the CDMS. To facilitate entry, the data entry screens were one-to-one matches for the paper forms (i.e. the data entry screen looked similar to the paper format). The database structure, tables and definitions of fields were developed from DDs. The database was not normalized (i.e. more like wide form in statistics datasets) [A provisional database had to be implemented in the start of the core trial, because the data management system contracted from a third party  was not yet complete and in order to report in time the progress of the trial. The provisional database was used only for 2 months and the final version of the Microsoft SQL server database was installed in Bohol Data Centre (BDC) in October 2000. Data entered first in the provisional database was re-entered in the final database due to the differences in the system and definitions.Reporting was accomplished by exporting the relevant data tables from the Microsoft SQL Server database to a Microsoft Access database, which were then transformed and used by SAS scripts to generate reports in HTML format. The reports were generated monthly and delivered to the relevant members of the trial consortium for monitoring, informational purposes and other requirements.The Relational Database Management System (RDMS) consisted of Microsoft SQL Server 7 and a Visual Basic 6 front end combined with various Microsoft Access 2000 client fields. The operating system was Windows NT Server 4.0 for the database server and Windows NT 4 Workstation for the clients.There was also an application for checking the edits, which was created using Microsoft Access 2000. This application contained miscellaneous reporting tools, such as the “Vaccination Schedules” generator. Most importantly, it contained a module for double entry validation that was for checking of discrepancies between the first and second entry data.To keep track of changes in the database and to restrict privileges, each user had an account and changes were audited with the track including the following information: the site where the data was created, the date when the record was created, the person who modified the record and the date of modification.The BDC was composed of a senior data manager, assistant data managers, system administrator, and data entry persons. In general, the staff performed often more than just a one major function. For example the data managers also performed SAS programming and data analyses. This was deemed advantageous because knowledge of the different studies and in-depth understanding of contents, history, restrictions and the relationships of the data were necessary to perform these functions. The trial had a high retention of its personnel due to good working conditions and training provided. Hiring of additional personnel was necessary during the peak period of the data entry.The personnel were continuously trained to use the CDMS, to handle data management tasks, to do SAS programming and to administrate the database system. Close monitoring of the UQ counterpart was necessary to maintain the high performance of the data management unit.The BDC was under the direct supervision of the UQ and the RITM. The BDC data manager liaised with the international and local consultants. Quarterly visits in the BDC were conducted by the UQ personnel to monitor the activities, to directly train personnel and eventually to transfer the responsibility of managing the data and maintaining the database system. The RITM investigators conducted monthly visits in the BDC to monitor the activities and to resolve any administrative issues. In the middle of 2003, most of the trial data management was handled by the BDC. The UQ remained as a consultant and was regularly updated on the activities at the study site.The CRFs were in triplicate paper copies, which served as the backup copies for the consortium members Table . After tSubmission of vaccination CRFs was done by the centre visits per child rather than separately by forms. For the hospitalizations, submission of CRF was also per hospitalization and included laboratory and bacteriology CRFs, if required. Usually a certain schedule was followed with the submissions of the CRFs to give time for data entry, cleaning and validation before the production of monthly reports.All CRFs from the different studies  were entered twice by two independent data entry officers. For the remaining subjects, any CRFs arising from participation in any of the other studies were single-entered in the database. The birth and death records, which were collected from the public documents  were single-entered. The data entry contained integrity checks, range checks and business rules of the trial.The core data were stored in the database server in two separate databases: the first entry and the second entry database. This process enabled separate entries, since the data entry was not dependent on whether the data were already entered in the primary database (which was the first entry for this study). For the remaining subjects, all data were entered in the primary database only.After the first 12 months of double data entry, an assessment was made about the continuation of double data entry in light of the very minor errors. The conclusions of the assessment were that it took six to seven months for error rates to stabilize and that there was considerable variation by the type of CRF. Particularly there were high error rates in the Infant Data Sheet, Serious Adverse Event and Admission CRFs. The results were presented to the Data Safety and Monitoring Board (DSMB) in July 2001 . It was The data validation method was done stepwise during entry, periodically in the database and with separate logic in SAS depending on the nature of errors traced . The valOn-site and monthly validation and cleaning of the database contributed advantageously to the project, because the volume of CRFs to be corrected was manageable for the data managers, and for the field and hospital personnel who collected the data. It was also easier for the field and hospital personnel to recall the recent data or go back to the source, when required. An independent data monitoring team (IDMT) which was formulated on the recommendation of the DSMB randomly checked a certain percentage of case report forms monthly.Corrections of the CRF entries were performed directly on the paper CRF following a standard operating procedure (SOP). In order to keep tract of any corrections and movement of the CRF, a query sheet was designed to record this information. The query sheet was attached to the CRF and issued by the data manager. The query sheet was a preformatted form and filled-in with information on the data to be corrected, reason for its error, name of the CRF and name of the study nurse who completed the CRF. The query sheet was given to the personnel who made the correction. The information on the query sheet was stored in the database and it was also used as a tool to evaluate the performance of the study nurses.There were 4,508 data queries that were issued from December 2000 to February 2005. The most common errors were wrong entries. Other errors were inconsistent entries within the form and/or compared to other forms. Quality assurance on the filling of the BDC copies of the CRFs was performed on a quarterly basis. Ten percent of the forms filed at the BDC office were manually checked by the data managers for completeness and consistency.Monthly trial progress summary and monthly total numbers of birth, recruitment, vaccination, SAE, pneumonia admission and consultation, completion and termination were reported. Other reports produced were quarterly summaries of the data safety monitoring board (DSMB) and operational reports. At the start of the trial, the reports were sent out to the consortium via email in a secure encrypted form. Later on, when the ARIVAC website was constructed, the reports were posted in the secure area.When analysing the data, sessions of code breaking are organized to maintain and keep the trial vaccine/placebo codes blinded. The goal of blinded analysis is to ensure that any person involved in developing analyses is not at any stage exposed to the true values of the trial vaccine/placebo codes. The 11PCV/placebo was randomized using letter codes from A to F (3 letters for 11PCV and placebo). The vaccines bearing these letter codes were allocated to the children according to a scratchable randomization list provided by Sanofi Pasteur.Analysis scripts were developed using the scrambled version of the Master database, where the key variable of 11PCV/placebo was replaced with randomly allocated letter. After the analysis scripts were tested, the scripts were run over the complete data with real 11PCV/placebo codes. A third person external to the project was hired to make this codebreak and ensure that the data remained blinded.This person opened and used the 11PCV/placebo codes to run the analysis, and ensured that the 11PCV/placebo codes were secured after the analysis was complete. Outputs from the unblinded analysis were distributed to the ARIVAC consortium. To protect the integrity of the primary objective of the trial, the manuscript on the primary results was first published before unblinded data were given to researchers.Clinical data management of this 11PCV efficacy trial and related satellite studies was challenging, because of the magnitude of data collection and the corresponding complexities presented by the simultaneously conducted studies. One of the challenging tasks was to keep track of children with multiple clinical events, i.e. all the different outcomes studied. These multiple clinical events and all information from the different studies had to be linked accurately. It was laborious to maintain the quality of the data, and the linkage of various clinical data records to establish relationships. However, due to careful trial preparation and conscious efforts to follow SOPs for data collection and management, the data management team cleaned, corrected and properly joined data from various sources.Despite all the trial preparation activities, the approach taken during the initial design of the data entry application did not take into account that major changes would need to be implemented in the CDMS during the course of the trial. The data management group attempted to reduce the effort necessary to implement new modules in the system, and these efforts, combined with the original design, formed the final architecture.Given the extensive data entry validation requirements, the approach taken with low level of database normalisation and the database design was, initially, to leave validation-related code in separate application. This greatly simplified the database design and coding. Reporting was done with external system in SAS using data exported from database. This made data entry and database design less dependent from reporting. It could have been possible to follow all standard database design techniques instead of using straight forward copies of paper forms in database design; this design complicated implementing changes in database. This is a question about which tools the personnel is already familiar with and how much already existing code can be reused. For some institutions in the ARIVAC consortium SAS as a major statistics programming environment was a natural choice for many tasks in data validation and reporting. Problems arose in meeting deadlines for the verbatim report for the SAE prepared by the local safety monitoring team. Lack of coordination among the data managers and the manual computation of the figures to be included in the report were some of the causes for the delay.The trial recruited local statisticians who learned how to manipulate data management and statistical software through regular trainings provided by the UQ data management experts. Although the CDMS evolved, the locally hired data management team became skilled in operating the system. There was considerable turnover in the data management team, but new staff quickly and efficiently gained knowledge of the system after training sessions conducted by the UQ.Data collection officially ended December 2004. Some field trial staff, i.e. study nurses and physicians remained until end of 2005 to answer questions arising from the data processing. Data management team personnel, particularly the statisticians, were still available part time until 2009, when the main trial results were published . An addiAt the time of this trial, the data collection in the developing country settings had to rely on paper CRFs and recent development towards Electronic Data Capture (EDC) and/or web based distributed data collection and management was not considered . At the We have hereby shown that in this complex, randomized, controlled trial to determine the efficacy of an investigational pneumococcal conjugate vaccine against childhood pneumonia (conducted in a developing country setting and involving concurrent satellite studies) data management needs/requirements were successfully met. There were several factors that made the task of data management doable and efficient. First, a pre-trial data management system established during the preparatory hospital based and immunogenicity studies in the population to obtain important background information about pneumonia, gave the trial a head start to conduct the data management tasks. Secondly, enough human resources like statisticians, programmers and support staff were recruited to help in data collection and management. Thirdly, the personnel had undergone training by experienced senior statisticians and programmers. Moreover, updating training was conducted locally and internationally throughout the study period. Fourthly, there was a health care data infrastructure utilizing the English language in the health records, which contributed to the ease of clinical data collection in this international project. Finally, a centralized data entry system combined with a semi-automated quality control resulted in a fairly fast feedback of corrections and high quality data with low error rates.Although data management was successfully conducted, some problems arise during the trial. Trusting the functionality and reliability of the data management system building up the final database for data analyses, a full-time statistical expert from Australian collaborators was not hired for the entire duration of the trial to supervise the local data analyses . We therInternational research in developing country with many counterparts is challenging. To be successful one needs to deliver resources correctly to different places, to different tasks and on right time. It is important to find out what kind of resources, especially different professionals are available and where. This demand can be fulfilled by training new professionals, hiring existing ones or outsourcing services. Language and cultural barriers needs to be crossed to make such a large project working. In this case we did all these things keeping in mind sustainable capacity building in local institutions in the Philippines.AOM, Acute otitis media; ARIVAC, Acute Respiratory Infection Vaccine Consortium; BCG, Bacillus Calmette Guérin vaccine; BHS, Barangay health station; BRH, Bohol Regional Hospital; CDMS, Clinical data management systems; CRF, Case report form; CSF, Cerebrospinal fluid; CU, University of Colorado, USA; DD, Data dictionary; DMG, Data manager; DSMB, Data safety monitoring board; DTP, Diphtheria, Tetanus, Pertussis combined vaccine; DTP/PRP ~ T, Diphtheria, Tetanus, Pertussis combined vaccine reconstituted with Haemophilus influenzae type b vaccine; EDC, Electronic data capture; EPI, Expanded program of immunization; HBV, Hepatitis B plasma derived vaccine; ICL, Imperial College London, UK; LAN, Local area network; LGU, Local government unit; OPD, Outpatient department; OPV, Oral poliomyelitis vaccine; PATH, Program for Appropriate Technology in Health; PCV, Pneumococcal conjugate vaccine; RCT, Randomized controlled trial; RDMS, Relational database management system; RITM, Research Institute for Tropical Medicine; SAE, Serious adverse event; SMS, Short message service; SOP, Standard operating procedure; THL, National Institute for Health and Welfare ; UQ, University of Queensland; WHO, World health organization.The authors declare that they have no competing interests.DHMS was the data manager of the project and wrote the initial draft of the manuscript. MGL, HN, VLT, and AT participated in drafting the manuscript, contributed ideas and provided comments to the manuscript. LTN participated in drafting the manuscript, and incorporated the revisions, comments and recommendations of the authors. GW was the biostatistician of the project who supervised the design and implementation of the data management systems, extensive quality control, as well the construction of the data warehouse for analysis and provided comments to the manuscript. All authors read and approved the final manuscript. ARIVAC Consortium consists of Joan Adamson; Adam Chester; Simon Forsyth; Elja Herva; Helena Mäkelä, MD; Antti Nissinen; Taneli Puumalainen, MD; Petri Ruutu, MD; Socorro Lupisan, MD; Beatriz Quiambao, MD; Ian Riley, MD and Eric Simões, MD.ARIVAC Consortium consists of Joan Adamson; Adam Chester; Simon Forsyth; Elja Herva; Helena Mäkelä, MD; Antti Nissinen; Taneli Puumalainen, MD; Petri Ruutu, MD; Socorro Lupisan, MD; Beatriz Quiambao, MD; Ian Riley, MD and Eric Simões, MD


Individuals who are sick and unable to work may receive wage replacement benefits from an insurer. For these provisions, a disability evaluation is required. This disability evaluation is criticised for lack of standardisation and transparency. The International Classification of Functioning, Disability and Health (ICF) was developed to express the situation of people with disability. We discuss potential benefits of the ICF to structure and phrase disability evaluation in the field of social insurance. We describe core features of disability evaluation of the ICF across countries. We address how and to what extent the ICF may be applied in disability evaluation.The medical reports in disability evaluation contain the following core features: health condition, functional capacity, socio-medical history, feasibility of interventions and prognosis of work disability. Reports also address consistency, causal relations according to legal requirements, and ability to work. The ICF consists of a conceptual framework of functioning, disability and health, definitions referring to functioning, disability and health, and a hierarchical classification of these definitions. The ICF component ’activities and participation’ is suited to capture functional capacity. Interventions can be described as environmental factors but these would need an additional qualifier to indicate feasibility. The components ‘participation’ and ‘environmental factors’ are suited to capture work requirements. The socio-medical history, the prognosis, and legal requirements are problematic to capture with both the ICF framework and classification.The ICF framework reflects modern thinking in disability evaluation. It allows for the medical expert to describe work disability as a bio-psycho-social concept, and what components are of importance in disability evaluation for the medical expert. The ICF definitions for body functions, structures, activity and participation, and environmental factors cover essential parts of disability evaluation. The ICF framework and definitions are however limited with respect to comprehensive descriptions of work disability. Individuals who are unable to work because of sickness or injury can receive support for return to work and/or wage replacement benefits if they are unable to return to work. The legal rules require these individuals to file a claim and undergo a medical evaluation of work disability in the field of social insurance . The concept of ‘disability evaluation in social insurance’ itself is equivocal. Literature defines disability evaluation in different ways -4. One wCritics across Europe have pointed to the lack of quality and transparency of disability evaluation -14, and In this article, we will first describe the core features of disability evaluation and the core features of the ICF. Then we address how and to what extent the ICF might be applicable in disability evaluation. We concentrate on the medical reports, as these are better documented than the processes of disability evaluation.Despite the wide variation of social insurance systems across Europe and country- specific organization of disability evaluation and differences in structure and size of medical reports, we identified 4 core features of work disability for medical experts : 1) the Professional guidances on disability evaluation advise the medical expert to draft a holistic picture of the claimant ,20,21.The medical report must also follow legal requirements, such as to establish a causal relation between a claimant’s health condition and his/her functional capacity. Lack of motivation or lack of opportunity to find work ,22 does In 2001 the World Health Organisation (WHO) adopted the International Classification of Functioning and Disability and Health as a means to describe health, functioning and disability for populations and individuals within health related domains , such asThe ICF framework reflects a bio-psycho-social approach to depict health and disability in different components: health condition, body structure and body function, activity, participation, environmental factors, and personal factors  but body structures and body functions are taken apart and activity and participation are taken together. The components, with the exception of personal factors, are subdivided into 1424 categories  .In this section, we discuss as to what degree the current ICF framework, the definitions, and the classification may capture the core features in the reports on work disability  . It alsoDisability is a process rather than a state. Disability refers to the past, present, and future outcome of a person’s interaction with his/her physical, social, cultural and legislative environment . The ICFIn several countries such as such as France , GermanyAs stated above, the ICF classification contains 1122 explicit definitions . The definitions can serve to standardize and harmonise the evaluation reports, and avoid ambiguity and variation in the presentation and interpretation of the findings. Our question is if the ICF definitions capture the core features of disability evaluation.The core features functional capacity, health state, and the ability to participate in working life can be described with the components ‘body structure/function’ and ‘activity and participation’. As the ICF has not been specifically developed for work disability, it stands to be tested if the present set of definitions is comprehensive in this field.Aspects of the socio-medical history and prognosis can be depicted with the definitions, but it is not practicable to line up the content in a chronological sequence. Like the framework, the definitions, do not describe the dynamic development of disability. Therefore, socio-medical history, and prognosis are not easily covered in the ICF definitions.Interventions can be described as facilitating environmental factors. In disability evaluation, we need to qualify some interventions as feasible. Such qualifiers do not exist currently, which stresses the need to develop them within the ICF concept of environmental factors.Further, disability evaluation gives a judgment on the claimant's situation. This can be given from two different viewpoints: the (self-) perception of the claimant and the perception of the medical expert. Medical experts usually integrate both perceptions in their reports. Applying the ICF would make it necessary to keep the two systematically apart. Although it is no difficult to separate the two and it can be considered beneficial to do so, it is not a common practice.Restricting the cause why a person is not able to work is an important statement in disability evaluation. The ICF definitions cannot describe causal relation because the current ICF definitions cannot be put together.Finally, medical examiners must also provide a general statement about work ability. Percentage, degree of disability or in working hours cannot be described with ICF definitionsThe classification organises categories and definitions in a hierarchical system. The applicability of the classification goes as far as the application of the definitions goes. The refined coding system of the ICF classification can be useful in research, or for documentation, or in the statistics of a social insurance administration. For these purposes core sets have been published in the field of disability evaluation as well. These core sets facilitate the description of functional capacity ,43. For Overall, we feel that using the ICF for development of disability evaluation does hold promises but it also has its limitations. The ICF framework fits modern thinking about disability evaluation. It helps medical experts to describe work disability as a bio-psycho-social phenomenon rather than as biomedical phenomenon only. The framework illustrates the connections between the different components in the disability evaluation that the medical expert has to address. The ICF definitions for body functions, structures, activity and participation, and environmental factors cover essential parts of the disability evaluation. Empirical testing is needed to establish if the definitions are useful and sufficiently detailed. Clear and broadly accepted definitions will support the understanding of the medical reports for professionals and administration and allow the development of instruments.The ICF framework and definitions are limited in the following aspects: the dynamism of development of disability, definitions for personal factors and, causality and consistency. An explicit time dimension could supplement the present ICF framework. Describing “history and prognosis” in words may overcome the lack of dynamic time perspective. For feasibility of interventions qualifiers could be developed.Empirical research would be needed to test our considerations in practice. Several studies are underway. In one study, we are testing the consensus-based 20-item core set for functional capacity suggested by the European Union of Medicine in Assurance and Social Security (EUMASS)  for applIn another study, Kirschneck et al. translated concepts of disability evaluation to ICF categories by linking medical reports from claimants with low back pain and chronic widespread pain and compared them with the existing core set of these conditions . The preIn a third study, we tested the potential of applicability the ICF core sets of low back pain and chronic pain in disability evaluation in Switzerland . We studIn a fourth study, Linden et al. have tested an ICF-based instrument to assess functional incapacity in patients with mental health problems . The insWe determined how and to what extent the ICF could capture the medical reports of disability evaluation by defining the key aspects of the disability evaluation and relating them to the framework and the definitions of the ICF.When evaluating work disability, the medical expert describes the claimant‘s health condition and functional limitations, socio-medical history, feasible interventions and prognosis and relates his/her findings to the requirements of the social insurance scheme. The ICF framework seems to reflect the view of the modern medical expert, especially with regard to functional capacity. However, the framework does not incorporate certain critical elements of a disability evaluation such as the dynamic time perspective or the restricted causal connection between functional capacity and the health condition. The ICF definitions enable the medical expert to report systematically about health aspects and actual functional capacity, and to a lesser extent, work characteristics. The ICF might provide useful concepts and definitions, especially in countries where medical examiners do not describe functional capacity in a structured manner .Before advancing with applied research around the optimal use of the ICF in disability evaluation, the professional community needs to specify its expectations: in what way should the ICF framework and the classification be used to express a claimant’s functional capacity? How could the application of the ICF improve the medical report? What additional benefit would an ICF-based functional capacity assessment provide to the professionals who perform the disability evaluation, to the administrators in the social insurances who use the results, and to researchers who want to support disability evaluation with evidence? On-going research indicates the potential of the ICF to express functional capacity in disability evaluation.The authors declare that they have no competing interests.Jessica Anner and Wout de Boer prepared the first draft of this paper. The other authors have made substantial comments on the content of this manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/470/prepub


The immune system is critical for protection and health maintenance and is likely required for a long lifespan. Yet, despite its importance for health, the ability to assess its quality of function has been poor, nor is much known on its variation between individuals. Hence direct assessment of immune health has largely been missing from medicine, and metrics of immune health are not well defined, especially in non-extreme states. This is chiefly due to the high complexity of the immune system. Recently emerging technologies now enable broad surveying of many immune system components at high resolution, setting forth a transformation of immunology and, through it, medicine. Such technologies enable, for the first time, high-resolution monitoring of an individual’s immune system. The resulting information can be used for diagnostic and prognostic purposes, as well as to provide a quantitative, global view of the immune system, i.e. “systems immunology.” This is especially relevant in the context of aging, in which the immune system exhibits profound alterations in state and function. Immunity to infectious diseases is orchestrated by a highly complex system of specialized cells and organs that flourishes on diversity and is in a constant interplay with its environment. Today, roughly 50 years since the inception of modern immunology, the immune system is considered a rich and complex system whose basic mechanisms are largely understood. As in other fields, a reductionist approach has been the predominant research strategy in immunology research for many years. Immunologists working in various subfields have made huge advances toward understanding particular segments of the immune system. Medical interventions have arisen from this basic science work, beginning with vaccines for infectious diseases, to the more recent development of monoclonal antibodies used to treat a variety of diseases. The total impact of immunological interventions, vaccinations in particular, on human health has been great, especially when considering molecular-based health interventions.Despite these medical successes and a good understanding of basic mechanisms of immunity, it appears as though we have only scratched the surface. As technological breakthroughs have enabled ever more sophisticated research tools, additional layers of complexity have been revealed. For example, new immune cell subtypes  have been discovered such that the estimates for the total number of distinct cell subtypes now number in the hundreds. As in other large complex systems, cooperativity and cross-talk abound in the immune system, and these likely play a key role, as protective immunity is ultimately an emergent phenomenon whose functionality is greater than the sum of its parts. A view with this richness in mind would suggest that our understanding of how the immune system functions as a whole is very limited.At the clinical level, despite its importance for general health, many aspects of the immune system are mostly ignored, and little is known about the variation in immune system components and their functions. The standard “complete blood count” (CBC), one of the most commonly prescribed tests by physicians, is indicative of a recent infection or extreme disease cases such as drastic reduction in cell counts . First uThis paper describes recent technological and methodological advances in measuring immune system function. In stark contrast to other critical organ systems, such as the cardiovascular system, the question “Doc, how is my immune system doing?” has no good answer at present, especially for those individuals with no cases of extreme disease. The new advances in measuring the immune system may for the first time provide deeper understanding of the role of immunity in health and the development of immune health metrics.Cells are the quanta of the immune system, and their identity and function can be understood by the degree to which they express proteins on the cell surface or intracellularly. Traditionally, the work-horse tool of immunology has been the flow cytometer, which optically measures the return fluorescence from cells stained with fluorophore-labeled antibodies bound to proteins. It is through functional characterization of cells expressing specific combinations of proteins that much of our understanding of immune system functionality has been gained and the many different cell types, each with their own functionality, have been delineated. Thus, a drive towards being able to measure an increasing number of fluorophores on single cells was present from the early days of flow cytometry development.X = 2n−1, where X is the number of combinations and n is the number of labels). Of note, repeated runs of a flow cytometer on the same sample, each time measuring a different combination of antibodies, will not have equivalent coverage to the mass cytometer as the data will not be obtained on the same single cell. Thus there is much greater resolving power with the number of labels possible with this new technology, enabling simultaneous quantification of the majority of known immune cell types from an individual sample, including rare cell subsets  that measures the abundance of heavy metal isotope labels on antibodies and other tags (such as peptide-MHC tetramers for labeling specific T cells) on single cells using mass spectroscopy. subsets .For primary tissue, original applications of flow cytometry allowed only the quantification of cell surface markers. However, developments over the past decade, primarily in the lab of Garry Nolan at Stanford University, have enabled the quantification of intracellular proteins, and of particular interest, phosphorylated epitopes.The immune system is very much dependent on the interactions of various white blood cells with each other, either in synaptic contacts, or at a distance using secreted proteins such as cytokines or chemokines for regulation and response. Cytokine levels in body fluids, such as serum or cerebrospinal fluid, can be quantitated, and due to their importance for immunity their measurement has always been of interest. Yet, the family of cytokines, chemokines, and more generally secreted factors involved in immunity is large, including many redundancies in its effects on cellular response and regulation, with many cytokines exhibiting multiple functionality that is context-dependent. Until recently only a handful of cytokines could be measured simultaneously in a study, and the resultant partial capture of this complex milieu did not yield high clinical utility.In recent years, several technological platforms enabling simultaneous measurement of serum protein in multiplex have become available. Most popular amongst them are the bead array multiplex assays based on the Luminex technology . These can measure up to 500 analytes from very small blood volumes, for almost 100 samples at a time. Kits geared for immune-phenotyping of up to 51 serum proteins in one assay well are already available from several vendors. Hence, it is a new day for attempts to identify predictive signatures of disease associations from body fluid-detectable proteins.8 different combinations can be created by VDJ recombination, and repertoire diversity is thought to be critical for protective immunity. With an estimated cell count of 1011 different B and T cells in an individual human being, it is assumed that this mechanism generates a sufficiently large repertoire for immune system antigen recognition. However, until recently, surveying even a small fraction of an individual’s repertoire was considered an impossible task.The adaptive arm of immunity tailors responses for any encountered antigen. To do so, B and T cells generate an enormous repertoire of structural diversity in antigen-recognizing proteins, including antibodies and T cell receptors (TCR), through a gene segment rearrangement process which combines variable, diverse, and joining gene segments, known as VDJ recombination. An allelic exclusion mechanism generally allows only a single VDJ combination to be expressed in a given cell, despite the additional chromosomal copies, and a separate mechanism-activated post-antigen recognition assures high specificity of the receptor/antibody to the antigen through hyper-mutation and selection. As many as 106 for TCR in humans, though still negligible compared with the upper theoretical limit of 1011), and a significant overlap of sequence repertoires between any two individuals can be observed.Next-generation DNA sequencing now offers the opportunity of starting to explore the basic principles of repertoire selection as well as its relation to disease. Through the design of primers flanking regions of interest, in-depth sequencing of a representative sampling of repertoire diversity may be achieved. First studies performing deep sequencing of antibody and TCR sequences have all reported that the VDJ recombination is biased.en masse single cell data. Hence, it is often the case that multiple cell types are analyzed as a single tissue, such as happens for example when gene expression is analyzed in bulk from whole blood, and the resulting analysis to a large degree describes the heterogeneity of the tissue, rather than the underlying biological changes in condition that are of interest.Much of our knowledge in immunology comes from bulk measurements of many cells together. Due to problems of averaging and noise, the behavior of cells as inferred from average measurements often drowns cell-to-cell differences and may not reflect the behavior of any single cell.Recent methodological and technological innovations now elevate some of these difficulties and enable the in-depth exploration of several biological species in many cell types or single cells. These include computational methodologies to infer cell type-specific information from heterogeneous tissue data,In a highly interconnected system such as immunity, it would be expected that changes in one component of the immune “network” will affect other connected components.At present, the high-bandwidth technologies available and discussed here measure distinct components of the immune system: cells types, their communication with one another, functionality, and specificity. Although these parts are rich in novel information, a more sophisticated level of analysis would integrate multiple components to glean a full view of immunity in man . The intFrom these findings, a profiling methodology arises which calls for one-stop shop immune monitoring.Historically, the ability to dissect biological phenomena with increased resolution has been closely tied not only to new discovery but to increased understanding of disease heterogeneity leading to improved detection and treatment outcomes. For medicine, the above-described technological innovations will primarily be judged by their ability to deliver clinically actionable information for improved diagnosis and treatment. The leap in resolution these technologies provide for each of the parts of the immune system surveyed is orders of magnitude higher than any technological or methodological progress to date. This is revealing striking variation even between antigen-specific single cells previously thought to be identical.Some of the first published studies using these advanced technologies suggest that clinically valuable information may be learned from increased resolution. The direct relevance of insights gained varies by assay type, the analyses performed with the generated data, and the appropriateness of the assay for probing the disease studied. Particularly strong evidence for clinical relevance has come from the results of phospho-flow, a technique first applied close to a decade ago. Here studies have illustrated an ability to identify hyper-responsive cell subsets negatively prognostic of tumor progression,11In gene expression, novel methodologies for analyzing heterogeneous tissue microarray data to yield cell type-specific expression have been used on whole blood samples from pediatric renal transplant patients to identify monocyte-specific differences between acute rejection and stable patients, undetectable otherwise,The chances of an elderly individual contracting an infectious disease and developing complications are significantly higher than those of a younger person. Protective vaccination of these older adults is less than half as effective as that of younger individuals.46Due to the complexity of the immune system, studies of immunosenescence published to date have focused on different aspects of immunological function, and an understanding of how the different alterations observed in immunity with aging has been missing. Moreover, there are unresolved disagreements in the literature, such as whether IL-6 levels in blood increase with age.Indeed, gene expression studies of longevity in humans, comparing tissues from young and older adults, commonly identify an age association or differential expression of many immune-related genes. However, due to increased disease and inflammation with age, tissue from older adults often contains a higher proportion of lymphocytes, making the reliable identification of age-associated immune system genes and pathways difficult.The critical nature of the immune system for health and the high variation observed among older adults would suggest that differences in immune system parameters (immune states) between individuals have a significant role in determining disease susceptibility and outcome. The logic here is simple: The immune system is known to be important in a large variety of diseases and is suspected to be involved in many more. Thus if variation exists in the immune system, especially if that variation is large and is temporally stable, one would expect it to have an effect on the health of an individual and how he/she combats disease. High-resolution immune monitoring, as described here, appears to be perfectly poised to address the complexity of the immune system in general and the high variability observed in the elderly in particular. The strength comes from the comprehensive and integrated manner in which each individual is profiled and the high resolution each assay provides, coupled to standard clinical information. This enables us to observe within single individuals the relationship between multiple factors and in effect spear-heads a new direction for personalized medicine, one that is much closer to health and disease than mere genetic information.