The presence of multiple chronic conditions is associated with lower health related quality of life (HRQOL). Disease severity also influences HRQOL. To analyse the effects of all possible combinations of single diseases along with their severity on HRQOL seems cumbersome. Grouping diseases and their severity in specific organ domains may facilitate the study of the complex relationship between multiple chronic conditions and HRQOL. The goal of this study was to analyse impaired organ domains that affect the most HRQOL of patients with multiple chronic conditions in primary care and their possible interactions.We analysed data from 238 patients recruited from the clientele of 21 family physicians. We classified all chronic conditions along with the measure of their severity into the 14 organ domains of the Cumulative Illness Rating Scale (CIRS). Patients also completed the 36-item Medical Outcomes Study questionnaire (SF-36). One-way analyses of variance were performed to study the relationship between the severity score for each CIRS domain and both physical component summary (PCS) and mental component summary (MCS) of HRQOL. Two-way analyses of variance were conducted to investigate the significance of possible organ domains interactions. Variables involved in significant bivariate relationships or interactions were candidates for inclusion in a multivariate model. Five additional variables were included in the multivariate model because of their possible confounding effect: perceived social support, age, education, perceived economic status and residual CIRS.Significant differences in the PCS (p < 0.01) were found in 12 of the 14 CIRS organ domains. A significant difference in MCS was found only in the Psychiatric domain. In the multivariate analysis for the PCS, the CIRS domains Musculoskeletal, Neurological, and Psychiatric, had an independent direct impact on PCS while the Upper gastrointestinal, Vascular, Cardiac and Respiratory domains were involved in interactions. A multivariate model was not necessary for the mental component.Vascular, Upper gastrointestinal and Musculoskeletal systems have strong negative effects on HRQOL. Among combinations of systems, the respiratory and cardiac combination is of particular concern because of a synergistic negative effect. This study paves the way for a future study with a bigger sample that could yield a model of wider generalizability. Goals of caring for patients with a chronic condition are to enhance their functional status, minimize symptoms, control pain, reduce disability, and prolong life through secondary prevention . PatientHealth-related quality of life (HRQOL) provides a multidimensional perspective that encompasses a patient's physical, emotional, and social functioning . In geneDisease interactions known to affect HRQOL include, among others, diabetes with hypertension, heart disease, arthritis, obesity and respiratory problems; arthritis with heart disease, hypertension, pulmonary diseases and obesity -10. To mThe goal of this study was therefore to analyse which impaired organ domains affect the most HRQOL of patients with multiple chronic conditions in primary care and their possible interactions.We used data collected in a group of 238 adult primary care patients (age 18 or older) recruited from the clientele of 21 family physicians in the Saguenay region, Canada, who participated in a study on HRQOL . DetailsFrom an exhaustive chart review, we extracted a comprehensive list of diagnoses of all chronic conditions for every patient after informed consent. For this review, the information contained in the medical chart was considered valid and complete. We then scored the Cumulative Illness Rating Scale (CIRS) .Each domain of the CIRS is scored from 0 to 4 as follows: 0, no problem; 1, minor current problem or significant history; 2, morbidity or moderate discomfort, requiring treatment; 3, severe problem, constant significant discomfort, chronic problem difficult to control; 4, extremely severe problem, requiring immediate treatment, organ failure or severe functional impairment. The theoretical score ranges from 0 to 56, but scores close to the highest value are incompatible with life. The CIRS has been shown to be valid and reliable for its use in primary care .Patients completed the French version of the self-administered 36-item short form of the Medical Outcomes Study questionnaire (SF-36) that is well known and validated to assess HRQOL . The SF-Data for potential confounders  were also collected. Self-perceived social support was measured with the Social Provisions Scale .The research ethics board of the Centre de santé et de services sociaux de Chicoutimi approved this study.Characteristics of the participants were first summarized using means and standard deviations or percentages. We then examined the proportion of subjects falling into each severity level for the 14 domains covered by the CIRS. Severity levels with low prevalence (less than 10 subjects) were combined with the adjacent level for subsequent analyses.For each domain of the CIRS, one-way analyses of variance (ANOVA) were first performed to study the relationship between severity and both physical (PCS) and mental (MCS) components of quality of life, ignoring the effect of other domains. Severity levels with non significant differences in quality of life were combined. Two-way analyses of variance were conducted to investigate the significance of possible interactions based on disease interactions previously identified in the literature -10 or inFive additional variables were included in the multivariate model because of their possible confounding effect: perceived social support, age, education, perceived economic status and residual CIRS . The latCharacteristics of the 238 participants are reported elsewhere . On averResults from the bivariate analyses of the 14 domains of the CIRS with the PCS are shown in Table In the analyses of interactions, only 2 were significant: the Cardiac-Respiratory and the Vascular-Upper gastrointestinal. Details are shown in Table 2 = 6.54%). Therefore, a multivariate model was not necessary for the mental component.In the bivariate analyses of the 14 domains of the CIRS with the MCS, a significant difference in MCS was found only between 2 severity subgroups in the Psychiatric domain: no problem/mild/moderate vs. severe  and all chronic conditions – grouped by body systems – of patients with multimorbidity, while taking into account the degree of severity of the health impairments evaluated with the CIRS. Although small in size, this is the first study evaluating the impact of systems involvement and their combination on HRQOL.The summary scales PCS and MCS were chosen to represent HRQOL in this study because they have been shown to be among the most valid SF-36 scales for measuring physical health and mental health, respectively . Also, cIn our multivariate model, all variables have a different weight on the resultant PCS. Impairments in all the CIRS domains that remained in the multivariate model have a negative coefficient Table . This meApart from being additive (the sum of the independent effect of each condition approximates their combined effect), the combined influence of pairs of chronic conditions on the HRQOL can also be synergistic (the combined effect is greater than the sum of the independent effect of each condition) or subtractive  -10,24.et al.[We found a synergistic effect between respiratory and cardiac systems which replicates results previously reported by Rijken et al.. They foet al.. Our datWe found a subtractive interaction between moderate or severe Upper gastrointestinal problem and mild, moderate or severe Vascular problem. The independent effect of each condition is quite important in reducing physical functioning, but their combined effect is smaller than the sum of the independent effects Figure . We cannThe literature reports other additive and synergistic interactions of pairs of diseases such as diabetes-hypertension (Endocrine-Vascular domains in the CIRS), diabetes-heart disease (Endocrine-Heart), diabetes-arthritis  and arthritis-respiratory disease  that were not found to be significant in our analysis of interactions by CIRS domains -10,24. Cet al.[It is worth noting that the Psychiatric domain of the CIRS remained significant in the multivariate model for the physical functioning. Whereas this domain is frequently excluded from studies relating comorbidity or multimorbidity with HRQOL , our reset al..et al. also assessed the joint effects of a limited number of somatic chronic diseases and found that, in general, the MCS was not related to any of the diseases evaluated with the exception of a negative effect of thyroid dysfunction [Rijken function . In our Knowledge of the estimates of the relative impact of individual chronic diseases on HRQOL is important for decision-makers to better plan and allocate resources for research, training and health care . For theThis study was based on the analysis of an existing database, and its main limitation is the relatively small sample of subjects. This limitation is directly related to the effect of the intentional sampling process to recruit patients that represented five CIRS groups, each with different levels of multimorbidity. This sampling process by itself was not expected to affect the relationships in our analyses but produced its effect through the size of the sample we obtained. The number of subjects per severity level varied considerably. This forced us to group subjects with problems of different levels of severity. Also, a number of diseases within the CIRS domains may not be represented. A bigger sample would have given more power to the statistical analyses and any application of the multivariate model at its present state should be made with caution. Another limitation of the study is that the list of diagnosis was based on chart review of all diagnoses already made by the primary care physician and no attempt was made to assess whether the diagnoses were correct. This method has the limitation that conditions frequently misdiagnosed such as asthma and chronic obstructive pulmonary disease, or frequently undiagnosed such as mental disorders were not verified. Due to all the limitations listed above, we consider that any generalizability of the model at its present state should be made with caution. It might be applicable only to the primary care clientele of our region.Unlike previous studies, we did not analyze the combined effects from a pre-determined list of diseases; instead, we included in the corresponding CIRS domains all the chronic conditions found in the subjects. This approach is pragmatic and directly in line with clinical practice. Nevertheless, the combined influence of more than two chronic conditions pertaining to more than two systems on the HRQOL can be expected to be more complex than that of a pair of diseases and consequently, caution is mandatory in the interpretation of the study results.With this study we have demonstrated the feasibility of developing a multivariate model for the physical functioning aspect of the HRQOL applicable to patients with multimorbidity that takes into account all the chronic diseases (grouped by organ domains) and their level of severity, while controlling for age, education, perceived social support and economic status. Simple disease count as a measure of multimorbidity has a weak correlation with HRQOL; grouping diseases by organ domains which take into account disease severity is a practical alternative to consider individually each condition and its severity. Vascular, Upper gastrointestinal and Musculoskeletal systems have strong negative effects on HRQOL. Among combinations of systems, the respiratory and cardiac combination is of particular concern because of a synergistic negative effect. This study paves the way for a future study with a bigger sample that could yield a model of wider generalizability.The author(s) declare that they have no competing interests.MF participated in the conception and design of the study, supervised data collection and analysis and drafted the manuscript. M-FD participated in the conception and design of the study, performed the statistical analysis and helped draft the manuscript. CH participated in the design of the study critically reviewed the manuscript. HS participated in data analysis and critically reviewed the manuscript. JA participated in the data analysis and helped draft the manuscript. All authors gave their final approval of the version of the manuscript submitted for publication.


Alteration of natural or historical aquatic flows can have unintended consequences for regions where waterborne diseases are endemic and where the epidemiologic implications of such change are poorly understood. The implementation of flood protection measures for a portion of an intensely monitored population in Matlab, Bangladesh, allows us to examine whether cholera outcomes respond positively or negatively to measures designed to control river flooding.Using a zero inflated negative binomial model, we examine how selected covariates can simultaneously account for household clusters reporting no cholera from those with positive counts as well as distinguishing residential areas with low counts from areas with high cholera counts. Our goal is to examine how residence within or outside a flood protected area interacts with the probability of cholera presence and the effect of flood protection on the magnitude of cholera prevalence.In Matlab, living in a household that is protected from annual monsoon flooding appears to have no significant effect on whether the household experiences cholera, net of other covariates. However, counter-intuitively, among households where cholera is reported, living within the flood protected region significantly increases the number of cholera cases.The construction of dams or other water impoundment strategies for economic or social motives can have profound and unanticipated consequences for waterborne disease. Our results indicate that the construction of a flood control structure in rural Bangladesh is correlated with an increase in cholera cases for residents protected from annual monsoon flooding. Such a finding requires attention from both the health community and from governments and non-governmental organizations involved in ongoing water management schemes. Several empirical studies have examined the impact of water construction projects on communicable disease rates. With the exception of onchocerciasis, whose vector habitat is fast moving streams, the rates of nearly every disease whose host or vector depend on standing water sources increase when water resource management programs are implemented by governments or NGOs -8. HugheSince the 1950s, flood control and water management have been central issues for the Bangladeshi government . Starting with the 20-year Water Master Plan in 1964, and continuing through today, heavy emphasis was placed on the construction of embankments around the country . Yet, th2 [Among the thousands of kilometers of embankments that have been constructed in Bangladesh is the Meghna-Dhonagoda Irrigation Project (MDIP), built in 1988-1989 and located in Matlab Figure . Running2 .The population of Matlab is over 200,000 persons, with a population density of approximately 1000 persons per square kilometer . Health Thus, in Matlab there exists a unique opportunity to conduct a longitudinal analysis comparing disease pre- and post-implementation of a flood control program. In the present analysis we examine whether residence within the flood protected area of Matlab is associated with increased or decreased cholera prevalence. Previous studies have described a positive association between residence in the flood control area and cholera, but over much shorter time periods ,12. AnalData from ICDDR,B's health and demographic surveillance system is joined to geographic information in a Geographic Information System (GIS) for Matlab. From the ICDDR,B health records, we have data on 8,500 laboratory-confirmed cholera cases between January 1, 1983 and December 31, 2003. This 21 year time frame spans the seven years prior to completion of the MDIP and the 14 years following. Cholera cases are assigned to the bari location where they occurred, and the total cholera count for Matlab's 7,490 baris is summed for each of the two time periods, 1983-1989 and 1990-2003. From ICDDR,B demographic surveillance we also calculate the mid-year bari population, and then sum the population counts for each bari across the 1983-1989 and 1990-2003 time periods to create a total background population at risk during each portion of the study period. Mid-year populations are appropriate to use for longitudinal studies such as this, since they approximately balance out the number of births and deaths that a bari experiences during each year. Summing these annual mid-year population counts also controls for the possibility of changing population sizes in baris across the duration of the study period, such that population growth or decline at the bari level is factored into individual baris' cholera risk. A dichotomous variable (0 indicating 1983-1989 and 1 indicating 1990-2003) is assigned to differentiate between the bari-level cholera and population counts for the pre- and post-flood protection period.The original data set derived from ICDDR,B records had 14,980 entries: two for each of 7,490 baris, representing the cholera counts and population size before and after the flood control embankment project. We then removed baris with zero population counts in either period. Zero population records were more common in the first period because 1,609 baris were established only after 1989. This elimination process yielded a rectangular data set with 5,390 baris in each period.The period cholera and population data for each bari are subsequently linked to a specific geographic location in the Matlab GIS. The GIS is accurate within 10 m and includes geographic features such as bari locations, the Dhonagoda River, the MDIP embankment and the location of the ICCDR,B hospital . Using tTwo environmental variables have been shown to be correlated with cholera in Matlab: distance to the ICDDR,B hospital and distance to the Dhonagoda River ,3,12. StCount data, such as the bari cholera counts examined in the present study, often are characterized by overdispersion and excess zeros -17. ZeroBaris in the Matlab study area reporting zero cholera cases are viewed as consisting of two subpopulations. The first subpopulation, producing only zeros, corresponds to baris that are free of cholera due to a presumed combination of positive local environmental or infrastructure factors . Zero cholera counts for these baris may be considered "true" zeros. The second subpopulation consists of baris where cholera is actually prevalent but not reported, "false" zeroes. This situation can arise when an infected individual fails to seek medical assistance because, for example, the infection is relatively mild or when self-treatment with oral rehydration solution (ORS) is elected. A ZINB model can accommodate these excess "true" and "false" zeros, and also overdispersion (extra heterogeneity) among the positive outcomes that render a ZIP approach non-optimal. The parameter estimates in the count model, in our case a negative binomial formulation, test for correlation between variables and increasing counts. The zero-inflated parameter estimates, in contrast, represent correlation between the variables and a zero count. Thus, the parameter estimates for the count model and the zero-inflated models are typically of opposite signs.pscl package [The distribution of cholera counts in the Matlab region during the period of study is overdispersed with respect to a Poisson distribution  and has an excess of zeros (70.2% of observations), suggesting the appropriateness of a ZINB regression model. The markedly skewed histogram in Figure  package -24.The results of fitting the ZINB regression model to the Matlab cholera counts using the covariates discussed in Table Cholera incidence is directly related to the river distance variable in the count model net of the other covariates. Cholera counts increase with increasing distance to the Dhonagoda. In the zero-inflation portion of the model the positive coefficient suggests, however, that as distance increases the frequency of zeros goes up, though this relationship is not statistically significant. These results suggest that the influence of proximity to the Dhonagoda River has complex interactions with cholera outcomes, as we explore in the following section.The other distance variable in the model similarly behaves in an inconsistent fashion. Cholera counts decline with increasing distance to the ICDDR,B regional hospital. The most probable reason for this finding relates to difficulty of access, a consideration we develop more fully in the following Discussion section. However, the zero-inflation portion of the model further suggests that as distance to the regional hospital increases, the count of baris reporting no cholera cases decreases.The two key independent variables in this analysis provide the most interesting findings from the study. Cholera counts in Matlab declined overall in the second portion of the study period and the frequency of zero reports increased, net of the other covariates, as evidenced by the signs of the coefficients of the Period variable. We interpret this as a general decline in cholera incidence in the region after 1990 Figure  and deveThe background population of a bari is a large factor driving both cholera counts and the division between baris with cholera and those without. The total population of a bari represents the background risk, the number of opportunities for a cholera case to exist. Thus, as the population of a bari increases the number of potentially infected individuals increases. Among baris in the negative binomial count model, population is highly statistically significant and had the largest coefficient. For baris that experienced cholera, higher numbers of residents acted as a driver of higher cholera counts, there were more people be exposed and infected by the cholera bacteria. Among baris with no cholera, population had a strong negative effect, with a coefficient of -14.08, the largest in either model. As the population of a bari increases in the zero-inflation model, the chance of baris remaining cholera-free greatly decreases. Among baris in Matlab, the total population of a residence has the largest effect on both any cholera cases occurring and of higher numbers of cases.The period variable (where 1 represents the 1990-2003 post-flood control period) was significantly associated both with baris that experienced cholera during the study period and with baris that did not, signaling a global decline of cholera in the study between the pre- and post-flood control infrastructure Figure . Among bAlthough travel to and treatment at the ICDDR,B hospital is free for Matlab's residents, there still exists a distance decay effect across baris. Residents who live further away from the hospital, which is located in the southwestern portion of the study area Figure , appear The relationship between cholera and proximity to the Dhonagoda River is similarly paradoxical. Baris that are further from the river either reported more cholera cases or more frequently reported zero cholera. We had hypothesized that there would be a negative relationship between cholera and river proximity, believing that the flooding of the Dhonagoda River constituted a primary source of potentially cholera-contaminated water. Perhaps, however, the opposite is true, that baris distant from the river are more dependent on stagnant or backwater sources of drinking, cooking and bathing water, sources that might have higher levels of bacterial contamination. The river also acts as a primary transportation route through Matlab, so it could be that baris further from the river are less likely to travel to the hospital, and thus more frequently report zero cholera. While we are unsure of the precise relationship that our model indicates, it is clear that the Dhonagoda River has complex and spatially diverse effects on cholera outcomes.Residence in the flood protected area of Matlab, when all the other environmental variables are controlled for, has a statistically significant positive effect in the cholera count model. It has no effect in the zero-inflation model. Thus, while the implementation of flood protection appears to have had no effect on whether a bari experiences cholera, it does act to increase the number of cholera cases among baris with cholera. While the positive relationship between flood protection and cholera may seem counterintuitive, it helps to think of monsoon flooding as a natural part of the aquatic cycle in Matlab. Monsoon flood waters act as a system reset, flushing out depleted and potentially cholera infected water supplies. We suggest that baris protected from this flooding have greater continued exposure to cholera bacteria in the environment, either through natural occurrence or through contamination of water supplies by human waste. Alternately, rather than an environmental explanation, we conjecture that there could be a behavioral change responsible for the relationship between flood protection and increased cholera counts: if people believe themselves to be better-protected from choleric water supplies they could relax their guard against interacting with contaminated water.The process by which an individual resident of Matlab is exposed to and becomes infected with the cholera bacterium is dependent on the dynamic interactions of a wide array of population and environmental factors. Whether that individual goes on to infect others is further dependent on the timing and occurrence of these variables. While we have examined only a few of the many factors believed to be important to cholera outcomes, it is useful to examine the broad patterns by which the residential environment of Matlab contributes both to the presence of cholera in a bari and to the propagation of the disease among certain baris over time. Our results illustrate that many factors important to the degree to which a bari experiences cholera are also important in determining whether a bari experiences cholera at all. The alteration of Matlab's traditional aquatic cycle for a portion of the study population via the construction of the MDIP embankment, however, appears to act as a driver of higher cholera counts, and does not confer any significant protection against a bari ever experiencing cholera.Ongoing construction of flood protection structures in Bangladesh, and the continued alteration of water ecosystems for economic development in other countries, will likely continue to have adverse effects on human health until these mechanisms are better understood. While those in favor of water management schemes can list increased agricultural productivity and other benefits, the fact remains that the planning, implementation and monitoring stages of such projects need to explicitly account for increased occurrence of endemic waterborne diseases. Our longitudinal analysis of highly detailed and rigorously collected health data confirms previous studies that the introduction of flood protection in Matlab amplified cholera occurrence, even as the overall incidence of cholera in Matlab was on the decline. These unintended "side effects" must be considered by health officials in Matlab and elsewhere as water systems are modified. Such considerations, when jointly made by environmental, agricultural, development and public health officials, should help public agencies work together meet mutual goals, among them direct public health mitigation efforts.FP: Flood Protection; GIS: Geographic Information System; ICDDR, B: International Centre for Diarrhoeal Disease Research, Bangladesh; MDIP: Meghna-Dhonagoda Irrigation Project; NGO: Non-Governmental Organization; ORS: Oral Rehydration Solution; ZI: Zero-Inflated; ZINB: Zero-Inflated Negative Binomial; ZIP: Zero-Inflated Poisson.The authors declare that they have no competing interests.MC conceived of the study, carried out the data preparation, participated in statistical analysis and drafted the manuscript. PV conducted statistical analysis and helped to draft the manuscript. PKS and MY oversaw the design and collection of the dataset and helped interpret findings in the context of the study area. ME conceived of the study and participated in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.


Smoking is associated with disadvantage. As people with lower social status reside in less privileged areas, the extent of contextual influences for smoking remains unclear. The aims were to examine the spatial patterning of daily smoking within the city of Helsinki, to analyse whether contextual variation can be observed and which spatial factors associate with current daily smoking in the employed female population.Data from a cross-sectional questionnaire were collected for municipal employees of Helsinki (aged 40–60 years). The response rate was 69%. As almost 4/5 of the employees are females, the analyses were restricted to women (n = 5028). Measures included smoking status, individual level socio-demographic characteristics  and statistical data describing areas in terms of social structure  and social cohesion (proportions of single parents and single households). Logistic multilevel analysis was used to analyse data.After adjusting for the individual-level composition, smoking was significantly more prevalent according to all social structural and social cohesion indicators apart from the proportion of manual workers. For example, high unemployment in the area of domicile increased the risk of smoking by almost a half. The largest observed area difference in smoking – 8 percentage points – was found according to the proportion of single households.The large variation in smoking rates between areas appears mainly to result from variation in the characteristics of residents within areas. Yet, living in an area with a high level of unemployment appears to be an additional risk for smoking that cannot be fully accounted for by individual level characteristics even in a cohort of female municipal employees. Smoking is increasingly associated with many markers of deprivation at the individual level. Such markers include single parenthood, material poverty, poor socioeconomic status  and poor housing conditions -3. SmokiRegional differences in smoking have been well described -20. StudAn Indian study found significant contextual variation in smoking according to local area, district and even state . Indeed,To take into account the fact that the area effects may be sensitive to the context , population group  or area unit  we have limited our study to females employed by the City of Helsinki and residing within Helsinki, the capital of Finland. Due to active city planning and housing policies Helsinki can be spatially characterised with a relatively small degree of gentrification but in terms of reputation, ethnicity and socio-economic distribution, neighbourhoods can be clearly distinguished .By choosing a more limited focus we aim to assess whether the contextual effects suggested by earlier studies result mainly from patterns typical of the less privileged population groups. For example, as poverty and unemployment are likely to hinder a person's options for daily life due to more limited mobility; they may also be more subject to the influence of characteristics of localities. Further, earlier studies suggest that contextual patterns of smoking may differ by gender, even though research focussing specifically on females is rare .The general aim of this study was to investigate whether the association between smoking and deprivation of the small local area can be found among employed females as well. A failure to establish the relationship would contribute to specifying the mechanisms through which spatial influences may operate.Specifically, we aim, first, to explore whether smoking varies by the area of residence, and, second, to find out the extent to which this variation results from the composition of the population in the area. Third, we examine whether the social and economic characteristics of the area – particularly indicators of social structure  and cohesion (proportion of single parents and of single households) can explain smoking in the target population.Currently, the theoretical understanding of the mechanisms explaining potential associations between area characteristics and smoking behaviour is limited. We assume that the main process connecting the social structure of the area with individuals' smoking operates via contagion ,38. In oThe data were derived from the Helsinki Health Study , which eWe used cross-sectional baseline data that were gathered in two separate surveys in 2000 and 2001. Employer records were used to identify all employees of the City of Helsinki. A self-administered questionnaire was sent to all employees who reached the age of 40, 45, 50, 55 or 60 in the year of the survey. The overall response rate among women was 69% in both years. The total number of respondents was 6243, of whom 80% (5028) were women, which is why the analyses were restricted to women. The non-response analysis showed that respondents correspond to the target population reasonably well, except for younger respondents and manual workers being slightly underrepresented . The queSmoking status was inquired with the question "Do you smoke cigarettes, cigars or a pipe daily?" with just two response alternatives, yes and no. One-fifth of women were daily smokers. Smokers were almost exclusively cigarette smokers, with only about 0.1% of women smoking a pipe or cigars.Individual level socio-demographic characteristics were age, occupational class, educational attainment, and family type. Age was obtained from register-based information for the sample frame, whereas other measures were based on self-reports. Educational attainment was measured by a question asking about completed general or vocational education. Education was divided into four groups that correspond to elementary (compulsory) education , vocational education (21%), secondary education  and higher education (university degree) .Occupational class consisted of four hierarchical groups: managers and professionals, semi-professionals, routine non-manuals, and manual workers. Manual workers and non-manual employees were separated using the socioeconomic classification of Statistics Finland and non-manual employees were further divided into four groups according to the occupational classification of the City of Helsinki. The routine non-manual class was the largest (43%) whereas only 11% came from the manual class. There were few female managers and they were merged with professionals (together 27%). The semi-professionals accounted for 19% of women.Family type was constructed using information on marital status and having dependent children in the household. Family type was categorised into four groups: single  (19%), single parent (8%), married, no dependent children (37%) and married with dependent children (36%).Contextual measures were retrieved from published official  statistics describing the 107 neighbourhoods of Helsinki. These areas are of the size that most residents could walk across them in 15–20 minutes and have an average population of 4000. Area-level indicators measured the social structure of the area  and social cohesion (proportions of single and single-parent households). These variables were divided into quartiles for the analyses  for 107 neighbourhoods in Helsinki was calculated in quartiles. The prevalence of daily smoking with 95% confidence intervals was also calculated in the corresponding quartiles of area indicators to assess the extent of area variation in smoking. Second, smoking prevalence was studied by the individual characteristics of social background by means of cross-tabulation and Pearson's χThe modelling part of the analysis was conducted using the GLIMMIX macro of SAS . The estIn the main stage of analysis, two-level logistic regression models were fitted stepwise. In the base model only the unadjusted neighbourhood variance was estimated. Age was controlled for in the first model, with family type in the second model, and socioeconomic status indicators  in the third model. The results of these analyses are presented by showing area effects  for daily smoking according to quartiles of area indicators in each of the three models. The area indicators were included separately, because a model combining all four area indicators would have suffered from multicollinearity.Substantial differences existed between the neighbourhoods in terms of social structure and cohesion Table . In the The variation in daily smoking by neighbourhood was weaker than the variation in the area indicators. Furthermore, daily smoking was patterned by area so that it was most prevalent in the second most deprived quartile according to all other indicators except for the proportion of manual workers. The least deprived areas consistently showed the lowest rates of smoking in the three other area indicators .The largest observed area difference in smoking – of 8.3 percentage points – was between the first and the third quartile of the proportion of single households. Smoking was also statistically significantly more prevalent in the third unemployment quartile than in the lowest and the second lowest quartiles. Similarly, according to single households and single parents, smoking was more frequent in the third quartile than in the lowest quartile. In contrast, smoking was more prevalent in the highest quartile of manual workers (the most deprived areas) than in the second quartile.Smoking was likewise associated with all individual-level sociodemographic characteristics Table . SmokingIn sum, the cross-tabulations showed that daily smoking varies both between individual level characteristics and between the areas. The area differences were, however, clearly smaller than those between individual characteristics, particularly age, educational and occupational class groups. Correlations between the area indicators were low (between 0.1 and 0.2) apart from the correlation between unemployment and manual workers (r = 0.5) and the correlation between single households and single-parent households (r = -0.5).Area-level differences were further studied by a two-level logistic regression analysis. The area-level variance was not statistically significant  even in the unadjusted base model. Every subsequent adjustment attenuated the variance further. We thus do not report the variances in the tables, but concentrate on the regression coefficients of the fixed part of the models.After adjusting for the individual level characteristics Table  the areaAdjusting for the social composition of the areas decreased the variance between areas on average by about one fifth. We further assessed the role of composition by controlling for housing tenure, perceived financial difficulties and financial satisfaction (data not shown) but this did not affect the association between area characteristics and smoking.There was considerable variation in female smoking by area, represented here by neighbourhoods in Helsinki. As middle-aged female smoking rates tend to be lower than in the general population and smoking is increasingly concentrated on the unemployed  population we expected not to find a small area effect in smoking in this population. Yet, we found that an additional risk of smoking can also be found among employed females residing in an area with a high level of unemployment, of single households and of single-parent families; adjusted smoking rates were between 21–35% higher in these areas.At the individual level, the highest frequency of smoking was found among 45-year-old women, among those with the lowest level of education, single mothers and manual workers. However, even when taking into account the differences in these individual-level characteristics, we did not remove the association between smoking and area-level deprivation, which is in line with many earlier studies of representative samples of the adult population -35,45-47Our sample consisted of employees of the City of Helsinki and we limited our focus to women, as they represent the great majority (four fifths) of the employees of the City of Helsinki. In other words, even though the study setting can be seen to compromise the representativeness to some extent, it still allowed us to explore area variation in smoking in a fully employed population. In this respect, the magnitude of the area variation observed in this study is noteworthy as one might suspect this quite homogenous population to lack significant lifestyle differences. This is especially surprising given the low level of spatial differentiation in deprivation in Helsinki in comparison with e.g. London .So far, the reasons for more prevalent smoking in socio-economically deprived and socially less cohesive areas are unclear. Obviously, a cross-sectional study does not allow causal inferences, which is why our interpretations must remain cautious. Yet, we may hypothesize that material smoking-related characteristics, such as the price of tobacco and the availability of cigarettes are unlikely to factor as they do not vary between urban neighbourhoods to any great extent. Instead, social norms and local cultural context may regulate smoking differently between areas. For example, more affluent areas may be less tolerant towards smoking in public places or in restaurants. In Finland, smoking outside designated areas in pubs and cafés has been banned since 2000. Furthermore, there may be more opportunities and less social control to smoke for example in the areas where single households form a major population group. As most Finns prefer not to smoke in their own home, then pubs, cafés and restaurants provide an environment for smoking. At the time of the survey, smoking was allowed in most public places. Currently smoking in pubs, cafés and restaurants is allowed only in designated areas and outdoors .This study contributes to existing research by specifying the way a small local area may affect smoking. The area variables were conceptualised to measure social structure and social cohesion of the area. We found some tendency of the strongest area effects on smoking not among residents of the most, but the second most deprived areas, which can also probably be explained by the lack of a non-employed population in the sample. Their influence, particularly the influence of the unemployed population, on local smoking culture and other area-related factors is likely to be strongest on the most deprived areas where their presence is notable.In part the associations may result from a local housing policy that blends population groups effectively. As council housing is available also in the more affluent neighbourhoods, the area-based segregation becomes less pronounced.The study suggests that even though smoking may be to some extent contagious in areas with a high prevalence of smoking, there appears to be a distinctive mechanism as well. Thus, in the most deprived areas the employed females may want to distinguish themselves from the predominantly unemployed smoking male population by ceasing to smoke to a larger degree and they may be less likely to be in relationships with them. If this is correct, in the second most deprived area a similar social distance would not be reasonable as smokers would comprise a more heterogeneous group.A similar process can be proposed with respect to low social cohesion. We expected cohesion to represent the quality of social life, which via norms would regulate the desirability of smoking. We did find evidence of increasing smoking with decreasing cohesion but again the area with second lowest cohesion featured the highest smoking rates. Clearly, the employed females are not affected by this type of normative climate to a similar degree in areas where smoking is, if not most desirable, at least, least un-desirable. Even though our data does not allow testing for the hypothesis we suspect this effect may be gender-specific so that the more permissive norms affect females less than males. This results from the fact that particularly in the most permissive areas male smoking is also most visible due to the public nature of smoking in Finland.Methodologically, an analysis of an employed population controls for a number of potential confounders between area and smoking. For example, as smoking has been shown to associate with poor economic conditions  observedThis study suggested a small significant area effect on smoking according to three variables measuring social structure and social cohesion of the area. The study probably produces quite a conservative estimate of the scale of the area effects. Even though the area differentiation in Helsinki city is considerable , some ofOur failure to detect statistically significant random variance (between neighbourhoods) with coexisting statistically significant area-level fixed effects is evidently due to the lack of statistical power within our empirical model. As proposed by Diez-Roux  the testOverall, our study showed social structural and social cohesive factors of the area to have a sizeable association with daily smoking among urban employed women. These effects were partly independent of individual socioeconomic characteristics and persisted after controlling for their compositional variation. In addition to a closer examination of the neighbourhood processes that induce smoking locally, the findings suggest that individual-based approaches to reducing smoking would benefit from community health promotion projects.The authors declare that they have no competing interests.SK conceived the study and the study design and drafted the manuscript. PS carried out the statistical analyses and contributed to their reporting. PM, OR and ML participated in designing the study and helped to draft the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


One of the most frequent uses of bioinformatics tools concerns functional characterization of a newly produced nucleotide sequence (a query sequence) by applying Blast or FASTA against a set of sequences (the subject sequences).However, in some specific contexts, it is useful to compare the query sequence against a cluster such as a MultiAlignment (MA). We present here the RegExpBlasting (REB) algorithm, which compares an unclassified sequence with a dataset of patterns defined by application of Regular Expression rules to a given-as-input MA datasets.The REB algorithm workflow consists ini. the definition of a dataset of multialignmentsii. the association of each MA to a pattern, defined by application of regular expression rules;iii. automatic characterization of a submitted biosequence according to the function of the sequences described by the pattern best matching the query sequence.An application of this algorithm is used in the "characterize your sequence" tool available in the PPNEMA resource. PPNEMA is a resource of Ribosomal Cistron sequences from various species, grouped according to nematode genera. It allows the retrieval of plant nematode multialigned sequences or the classification of new nematode rDNA sequences by applying REB. The same algorithm also supports automatic updating of the PPNEMA database. The present paper gives examples of the use of REB within PPNEMA.The use of REB in PPNEMA updating, the PPNEMA "characterize your sequence" option clearly demonstrates the power of the method. Using REB can also rapidly solve any other bioinformatics problem, where the addition of a new sequence to a pre-existing cluster is required. The statistical tests carried out here show the powerful flexibility of the method. The rationale of the OS/DnMA approach has been implemented in other programs such as HMMalign within PFAM [Plant-Parasitic Nematode bioinformatics resource, offers the scientific community a pre-processed archive of plant parasitic nematode sequences useful for nematologists: it is a tool for retrieving plant nematode multialigned sequences for phylogenetic studies or identifying a nematode by comparing its rDNA sequence with the PPNEMA available, genus specific MultiAlignments (MA). Classification of a new sequence by applying BLAST to all the sequences available through PPNEMA should have required an ab-initio procedure not considering already available multialigned groups. RegExpBlasting (REB), the algorithm proposed here, which compares a new sequence against a dataset of patterns derived from the application of Regular Expression coding to multialigned sequences, successfully solves this problem. In addition, as regards PPNEMA database updating, starting from the new rDNA cystronic phytoparasite nematode sequences extracted through the Entrez retrieval system [One of the most frequent needs a Bioinformatics end-user has is to characterize a newly produced sequence. In addition, the design and implementation of specialised bio-databases is a very frequent research activity, as demonstrated simply by accessing the NAR Database Issue compilation , but thehin PFAM  or HoSeqhin PFAM , but bothin PFAM  and the hin PFAM  both deshin PFAM ,8, we hal system , availabWhen phytoparasite nematologists produce a new sequence, they need to classify it as regards both function and species. The application of REB through the "characterize your sequence" option available in PPNEMA is of great help in this case. The algorithm searches for a perfect match between the query sequence and PPNEMA multi-alignments. An example is shown in Figure Anguina, coding for one or more elements of the RNA cistron and annotated after January 1 2007 and before January 1 2008. They are extracted, and a file containing their nucleotide sequences in FASTA format is submitted to "Normal Search", which is based on RegExpBlasting of each of the selected sequences against the PPNEMA MA dataset. Each new sequence is associated with a pool of matching patterns that we call "positives"; any unclassified sequences or negative results are marked as True Negatives (TN), i.e. new group-defining sequences, and False Negatives (FN). In the first application of the protocol described here, we obviously carried out many checks in the various steps, in order to verify the efficiency of the protocol as we have designed it, and to optimize it. The positives are submitted to an automatic annotator check, which establishes if the results are true positives (TP) or false positives (FP). TP entries, i.e. entries matching MA of the same genera and related to various elements of the same cistron, are annotated in PPNEMA. The FP are submitted to an Operator Check and distinguished into FP due to annotation errors, e.g. erroneous species attribution, or to short conserved among-genera sequences. In the latter case, the same sequence matches several genera; thus, to solve the problem, RegExpBlasting can modify the minimal length parameter (see Methods). By increasing the minimal length, the probability of random matches decreases, although some significant but short matches may be lost. Figure w, scanning step s and minimal length minl. Once fixed minl to 60, the results which show a cut-off (cf) value higher than 0.70 are selected (see Methods).As already emphasized in the Background, in bio-database management, the main problem is to keep the databases updated in the time. We present here a procedure, based on REB, which allows the PPNEMA database to be updated in an almost completely automatic process. Figure We tested the procedure using window values of 20, 30 and 40, and tested for each window steps 10, 15 and 20.Figure However in the end, we fixed the Scanning procedure parameters as follows: minl = 60, w = 20, s = 5 and cf = 0.85. Table It has been emphasized that REB is neither an exact matching method nor a heuristic one but, thanks to careful selection of minimal length and cut-off values, as well as scanning parameters (w and s), results mainly fitting true results can be obtained. What are the limitations? One of the most difficult problems that can be avoided concerns the quality of the MA: non-optimised MA can give rise to increased numbers of False results, both positive and negative. In addition, a poor-quality MA may introduce a great number of gaps, implying reduction of minimal length, elimination of the cluster to be analysed from the dataset, and hence reduction of informative data necessary to allow positives to grow. The solution is to lower the minl value but this, as shown in Figure The results here presented and the description of the algorithm clearly show that REB is a tool with a wide range of applicability. Besides the example shown here, we hypothesize that it may be used to definite advantage in Barcoding  procedurSimilarity searching algorithms are usually classified as exact or heuristic methods.Both methods compare an anonymous sequence against a dataset of sequences available in bio-databases. Exact methods search for the perfect match between an anonymous sequence and dataset sequences. Heuristic methods, i.e., FASTA and Blast, compare sequences by searching for matching substrings.However, biological sequences, due to their intrinsic features, the presence of SNPs, insertions and deletions, cannot be managed well with exact methods. And although heuristic methods provide many results, many of them may be approximate or false, so that end-users have to check all results, if possible discriminate false from true positives, and then select the result that gives the best match with the queried sequence from among the true positives.RegExpBlasting is a valid alternative to exact matching methods because, if positive results are obtained, they will be formed by a set of sequences grouped into an MA described by a pattern written through regular expression rules. Thus, RegExpBlasting is based on the widely used Regular Expression syntax, by which better than with the consensus definition, it is possible to describe the degeneration of a biological pattern defining a specific function.The starting point in the generation of the regular expression within biosequence management is a sequence MA.The algorithm is made of two phases: one which generates regular expressions starting from the MA, and the other based on the search of the MA whose regular expression pattern best describes the sequence to be characterized. The algorithm is organized in two phases. RegExpBlasting phase I produces the RegExp pattern describing each of the considered multialignments by extracting the regular expression which represents all the variations of the group of sequences available through their MA. RegExpBlasting phase II allows the classification of a new sequence by comparing it with each of the patterns defined in phase I and reports as output the matching MA to which the new sequence can easily be added.Regular expressions are tools used to represent every possible character variation in a sequence alignment, and are much more powerful than the classic consensus sequences which leave out a great quantity of information about sites such as possible SNPs, insertions and deletions. RegExpBlasting is an algorithm which can characterize a new sequence simply by comparing it with the regular expressed patterns defining the queried MA. Starting from each of the MA to be browsed, the nucleotide or aminoacid composition of each variant MA site is calculated, inclusive of gaps: this datum allows the site to be translated into regular expression annotation. The rule is: the entire site composition is associated with a character class, within square brackets; if gaps occur in the site, a question mark is added to the right of the character class. If the site is invariant, it is represented by the nucleotide followed by "?" if gaps occur in the site in question. If the MA is made of non-conserved sequences, displaying large gapped regions due to indel events among the MA composing sequences, the regular expression in the indel regions allows any character, i.e., a wild card. For this whole region, RegExpBlasting implements two different options: a) a number of wild cards less than or equal to the length of the indel region or b) a number of wild cards of any length. From a biological viewpoint, this implies that the indel regions, being so variant, have no importance in the function of the multialigned sequences in question.minl is the shortest sequence, which can produce a match, and is calculated as the difference between alignment length and number of gapped sites. It is closely correlated to the number of false positives, as will be demonstrated in the description of phase II. The number of gapped sites strongly influences the processing time of the algorithm: the higher the number of gapped sites, the longer the processing time.In this way, each MA is translated into a Regular Expression, representing every sequence composing that MA and thus generating the RegExp datasets against which "blasting" of the query sequence can be applied. In order to use RegExpBlasting to best effect, it is important for end-users to be aware of several aspects which lie at the basis of pattern generation. These are minimal length and the number of gapped sites. Minimal length w characters long and a step s characters long, thus generating n sub-patterns which can then be analyzed. Both w and s are parameters which can be modified by end-users. Thus, once cut-off cf  has been fixed, the algorithm selects the positive matches if the number of matched sub-patterns related to the total number of considered sub-patterns is equal or greater than the fixed cut-off. In the first application, several tests were carried out by changing the minimal length, window and step length; in order to evaluate parameter values that optimize the performance of the algorithm and define general criteria for end-users.In this phase, RegExpBlasting compares the query sequence against the RegExp dataset, and the resulting output is the MA associated with the best matching pattern. When no positive results are obtained, a further analysis can be performed through the Scan-RegExpBlasting procedure. This procedure scans each RegExp dataset-composing pattern, using a window The algorithm is written in python  but a WeMA: Multiple Alignments; REB: Regular Expression BlastingThe authors declare that they have no competing interests.FR has designed and implemented the algorithm with the advise of MA. MA has interpreted the results and wrote the manuscript.List of False Negative GenBank entries. List shows GenBank ID and species name of entries extracted through Entrez, submitted to PPNEMA RegExpBlasting "Normal Search" modality (see figure Click here for fileReport of the Scan-RegExpBlasting procedure applied to FN listed in Aditional file Click here for file


With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes. The advent of microarray technology has made it possible the study of the expression profiles of a huge number of genes across different experimental conditions or tissue samples simultaneously. This has significant impact on cancer research. Microarray technology is being utilized in cancer diagnosis through the classification of the tissue samples. When microarray datasets are organized as samples versus gene fashion, then they are very helpful for classification of different types of tissues and identification of those genes whose expression levels are good diagnostic indicators. The microarray datasets, where the tissue samples represent the samples from cancerous  and non-cancerous (benign) cells, the classification of them will result in binary cancer classification. On the other hand, if the samples are from different subtypes of cancer, then it becomes the problem of multi-class cancer classification. Multi-class cancer classification and detection of gene markers for each cancer subtype is a more challenging task compared to the binary classification.Most of the researches in the area of cancer diagnosis have focused on supervised classification of cancer datasets through training, validation and testing to classify the tumor samples as malignant or benign, or their subtypes a priori.A microarray gene expression dataset consisting of Genetic algorithms (GAs) Furthermore, the clustering solution produced by the proposed MOGASVM clustering technique has been used to identify the gene markers that are mostly responsible for distinguishing a particular tumor class from the remaining ones. Signal-to-Noise ratio (SNR) statistic-based gene ranking has been utilized for this purpose.The performance of the proposed MOGASVM clustering technique has been demonstrated on three publicly available benchmark cancer datasets, viz., SRBCT, Adult malignancy and Brain tumor. The superiority of the proposed technique, as compared to K-means clustering In many real world situations there may be several objectives that must be optimized simultaneously in order to solve a certain problem. This is in contrast to the problems tackled by conventional GAs, which involve optimization of just a single criterion. The main difficulty in considering multiobjective optimization is that there is no accepted definition of optimum in this case, and therefore it is difficult to compare one solution with another. In general, these problems admit multiple solutions, each of which is considered acceptable and equivalent when the relative importance of the objectives is unknown. The best solution is subjective and depends on the need of the designer or decision maker.Traditional search and optimization methods such as gradient descent search, and other unconventional ones such as simulated annealing are difficult to extend as it is to the multiobjective case, since their basic design precludes the consideration of multiple solutions. On the contrary, population based methods like evolutionary algorithms are well suited for handling such situations. The multiobjective optimization can be formally stated as Pareto-optimality is useful in the domain of multiobjective optimization. A formal definition of Pareto-optimality from the viewpoint of minimization problem may be given as follows. A decision vector weakly non-dominated and strongly non-dominated solutions are defined non-dominated solutions.The concept of There are different approaches for solving multiobjective optimization problems In this section, we have described the use of NSGA-II for evolving a set of near-Pareto-optimal clustering solutions In the NSGA-II based clustering, the chromosomes are made up of real numbers which represent the coordinates of the centers of the clusters. Suppose the size of the dataset is For computing the objective functions, first the centers The global compactness The second objective is cluster separation selection, crossover and mutation. The selection operation used here is the crowded binary tournament selection used in NSGA-II The popularly used genetic operations are The most characteristic part of NSGA-II is its elitism operation, where the parent and child populations are combined and the non-dominated solutions from the combined population are propagated to the next generation. For the details on the different genetic processes, the readers may refer to Support vector machine (SVM) classifiers are inspired by statistical learning theory and they perform structural risk minimization on a nested set structure of separating hyperplanes kernel function.Suppose a dataset consists of Kernel functions help to map the feature space into higher dimensional space. The kernel function may be linear or non-linear, like polynomial, sigmoidal, radial basis functions (RBF), etc. The four kernel functions used in this article are as follows:Linear: Polynomial: Sigmoidal: Radial Basis Function (RBF): The extended version of the two-class SVM that deals with multi-class classification problem by designing a number of one-against-all two-class SVMs As the multiobjective clustering produces a set of non-dominated solutions in the final generation, it is required to apply some technique to obtain the final clustering solution from this set. This section describes the proposed scheme for combining the NSGA-II-based multiobjective clustering algorithm with the SVM classifier for this purpose. In the combined approach, named MOGASVM, each non-dominated solution is given equal importance and a majority voting technique is applied. This is motivated by the fact that due to the presence of training points, supervised classification usually performs better than the unsupervised classification or clustering. Here we have exploited this advantage while selecting some training points using majority voting on the non-dominated solutions produced by the multiobjective clustering. The majority voting technique gives a set of points for which most of the non-dominated solutions assign the same class labels. Hence these points can be thought to be clustered properly and thus can be used as the training points of the SVM classifier. Subsequently, the remaining low-confidence points are classified using the trained classifier. The process is repeated for different kernel functions and the final clustering is obtained through majority voting among the cluster label vectors produced by the different kernel functions. The steps of MOGASVM are described below.Step 1: Execute MOGA clustering to obtain a set Step 2: Decode each solution Step 3: Reorganize the cluster label vectors to make them consistent, i.e., cluster Step 4: Mark the points which are given the same class label Step 5: Train the SVM classifier with some kernel function using the training points.Step 6: Generate the class labels for the remaining points using the trained SVM classifier.Step 7: Repeat Steps 5–6 for the four kernel functions considered here and obtain the four cluster label vectors.Step 8: Combine the four clustering label vectors through majority voting ensemble, i.e., each point is assigned a class label that obtains the maximum number of votes among the four clustering solutions. Ties are broken randomly.The sizes of the training and testing sets depend on the parameter For setting the number of clusters To select the number of clusters medoid of the cluster. A cluster medoid, unlike cluster mean, is an actual data point in the cluster from which the sum of the distances to the other points of the cluster is minimum. Since medoid is an actual data point, it is less influenced by the presence of outliers It is known that the presence of outliers can affect the performance of the clustering algorithms. The proposed MOGASVM clustering algorithm computes the means of the clusters during chromosome updation which is likely to be affected due to the presence of outliers in the dataset. To cope with this, we modified the proposed algorithm as follows. During the chromosome updation, instead of taking the means of the points in a cluster, we compute the Two performance measures, i.e., percentage Classification Accuracy (We define the percentage Classification Accuracy (The Adjusted Rand index (In this section we have demonstrated how the proposed MOGASVM clustering technique can be used to identify the gene markers that are mostly responsible for distinguishing the different classes of tissue samples. Here we have demonstrated the process for the SRBCT dataset (described in the next section). This has been done as follows.At first, MOGASVM is applied to cluster the samples of the preprocessed dataset into four classes corresponding to the tumor subtypes EWS, NB, BL and RMS, respectively. To obtain the gene markers for the EWS subtype, the clustering result is treated as two classes: one class corresponds to the EWS tumors and the other class corresponds to the remaining tumor types. Considering these two classes, for each of the genes, a statistic called Signal-to-Noise Ratio (SNR) It has been observed that the set of top 10 genes selected in different runs of MOGASVM varies slightly from one run to another. So while reporting the final gene markers for the SRBCT data, we have reported the most frequently selected 10 genes over all the runs. The frequencies of the selected genes have also been reported. Moreover, the clustering result obtained using the 40 marker genes for the SRBCT data (10 for each of the 4 cancer subtypes) is compared with the clustering results obtained using initially selected 200 genes to show the effectiveness of using only the marker genes for clustering.SRBCT, Adult malignancy and Brain tumor datasets have been used for experiments. The datasets are described in this section.In this article, three publicly available benchmark cancer datasets, viz., http://www.ailab.si/supp/bi-cancer/projections/info/SRBCT.htm.The small round blood cell tumors (SRBCT) are 4 different childhood tumors named so because of their similar appearance on routine histology http://algorithmics.molgen.mpg.de/Static/Supplements/CompCancer.This data consists of 190 tumor samples, spanning 14 common tumor types to oligonucleotide microarray http://algorithmics.molgen.mpg.de/Static/Supplements/CompCancer.Embryonal tumors of the central nervous system (CNS) represent a heterogeneous group of tumors Each dataset is subjected to the following preprocessing steps to find out the genes with most variability across the samples. At first, for each gene, we have calculated its variance across all the samples. Thereafter, the genes are sorted in descending order of their variances. Subsequently, from all the genes, the top 200 genes with the largest variance across the samples are selected. This is done with the expectation that the genes having larger variance across the samples are more effective in distinguishing different classes of tumor samples rather than the genes with small variance across the samples. Next, the expression values are log-transformed. Finally, each sample is normalized to have mean 0 and variance 1.The performance of the proposed MOGASVM clustering has been compared with that of K-means clustering The different parameters of MOGA and SGA are taken as follows: number of generations = 100, population size = 50, crossover probability = 0.8 and mutation probability = 0.01. The value of the parameters Firstly, in From the tables, it appears that MOGASVM also outperforms its single objective counterpart SGA that optimizes the combination of cluster compactness and cluster separation. On the other hand, MOGASVM optimizes both of these objectives simultaneously. As MOGASVM performs better in terms of both the performance scores, it indicates that optimizing multiple criteria simultaneously can yield better clustering rather than the case when the objectives are combined into one.For the purpose of illustration, All the algorithms have been implemented in Matlab and executed on an Intel Core 2 Duo 2.0 GHz Machine with 2 GB memory having Windows XP operating system. It should be noted that time requirement for the GA based clustering techniques is usually more because of the different genetic operations performed during the execution of the algorithms. On average, MOGASVM executes for 97.24 seconds for the SRBCT dataset, whereas the SGA and SiMM-TS clustering takes 75.93 and 161.37 seconds, respectively. The other algorithms execute only for a few seconds for this dataset. The execution times have been computed on the basis of the parameter settings discussed in the Input Parameters section. As expected, the execution time of MOGASVM is larger compared to the other single objective clustering methods because of some additional operations necessitated by its multiobjective nature. Only SiMM-TS takes more time than it, because SiMM-TS needs two stages of clustering. The iterative algorithms have always converged far before reaching the maximum number of iterations. However, as is evident from the results, the clustering performance of MOGASVM is the best among all the methods for all the datasets considered in this article. It is also found during experimentation that even if the other algorithms used for comparison are allowed to run for the time taken by MOGASVM, they are not able to improve their clustering results any further. The average execution times of MOGASVM for the Adult malignancy and the Brain tumor datasets are 212.76 and 81.28 seconds, respectively. The timing requirements of the proposed technique can be reduced further by using a stopping criterion based on some test of convergence of the multiobjective evolutionary process.To establish that MOGASVM is significantly superior to the other algorithms, a statistical significance test called t-test has been conducted at 5% significance level. Ten groups, corresponding to the ten algorithms  have been created for each dataset. Each group consists of the P-values produced by t-test for comparison of two groups  at a time. As a null hypothesis, it is assumed that there is no significant difference in the mean values of the two groups. Whereas, the alternative hypothesis is that there is significant difference in the mean values of the two groups. All the P-values reported in the table are less than 0.05 (5% significance level). This is a strong evidence against the null hypothesis, indicating that the better mean values of the As is evident from the In in vivo. In Among the above gene markers, many of those have already been validated to be associated with the respective cancer classes in different existing literature. For example, the gene PTPN13 has been shown to be over-expressed for EWS (thus has been treated as a marker for the EWS class) in http://babelomics.bioinfo.cipf.es/). The outcome of the study has been reported in To look into the biological relationship among the selected gene markers, gene ontology based study has been conducted using FatiGO (For the purpose of illustration, the


The comparative analysis shows that the algorithm can generate anatomy-based IMRT plans with about 50% reduction in number of MUs and 60% reduction in number of apertures, while producing dose distribution comparable to that of beamlet-based IMRT plans. Hence, it is clearly evident from the study that the proposed algorithm can be effectively used for clinical applications.This study aims to evaluate the performance of a new algorithm for optimization of beam weights in anatomy-based intensity modulated radiotherapy (IMRT). The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The distinct feature of the algorithm is that it takes only fraction of a second to optimize the beam weights, irrespective of the complexity of the given case. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI) option for convenient specification of dose constraints and penalties to different structures. We have tested the numerical and clinical capabilities of the proposed algorithm in several patient cases in comparison with KonRad In inverse planning for IMRT, the conventional practice is to define the constraints for tumor and normal tissues based on which the optimal fluence is calculated.2 The optSeveral techniques have been proposed to simplify IMRT plans. An alternative approach to BBIP is Aperture-based Inverse planning (ABIP), where only required numbers of deliverable apertures are generated and their weights get optimized. Also, plans with pre-defined anatomy-based MLC fields (anatomy-based ABIP), as proposed by many authors,–15 could22® inverse planning system.In this communication, we present an algorithm to optimize beam weights in anatomy-based IMRT. The distinct feature of the algorithm is that it takes only fraction of a second for optimizing the beam weights, irrespective of the complexity of the given case. The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI) option for convenient specification of dose constraints and penalties to different structures. We initially tested the performance of the algorithm in different patient cases in terms of convergence, consistency and optimization speed. We also tested the clinical capabilities of the proposed algorithm in several patient cases. To date, we have treated three patients planned using the algorithm in our clinic. In this article, we limited the clinical performance analysis to two patient cases, performed in comparison with beamlet-based IMRT plans generated using KonRadWe use a simple anatomy-based segmentation method–15 to maTo optimize beam weights in IMRT, the objective is to minimize the difference between the prescribed and calculated dose distributions. The optid(x)) is the cost function, where d(x) is a dose distribution among the voxels of the patient model and x is the vector of parameters to be selected (beam weights) and N is the no. of voxels. Di = Di(x) and pDi are the calculated and prescribed maximum dose in voxel i, respectively. Here the array Pi contains the penalties for different structures for violation of dose limits. For instance, a voxel in the target is penalized if it is overdosed (>Dmax) as well as underdosed (<Dmin). Likewise, a voxel in the normal structure get penalized if it receives a dose more than its Dmax.Here F treatment planning system. In this planning system the dose is calculated using a fast convolution superposition algorithm.[Our algorithm has been implemented in MATLAB. Patient contours are first generated in the CMS-XiOlgorithm. Also, thAs part of the work, a Graphical user Interface (GUI) was developed. The GUI was written in MATAB with DICOM compatibility for enabling communication between the planning system and the algorithm. The GUI contains a series of push buttons, checkboxes and pop-up menus to implement the following functions: (1) import: importing dose values obtained at sample points; (2) Optimization parameters: inputting dose constrains and penalties for different structures; (3) Optimization process: performing optimization and re-optimization; and (4) Output: display of the final beam weights and cost values.4 - 106 simultaneous equations). However, the speed and accuracy of Gaussian Elimination method for relatively small (E.g. 101 - 102 equations) and medium (E.g. 102 - 104 equations) scale optimization problems can be better than any iterative method. In order to verify the actual numerical capability of the algorithm in the context of beam weight optimization, an analysis was initially performed for a variety of patient cases, in which we evaluated the characteristics of the algorithm in terms of convergence, consistency and speed. To perform the analysis, different data sets  are generated that belong to different patient cases . Each data set is represented using a cost function, whose basic structure resembles the one shown in By far, applying of Gaussian Elimination in optimization of IMRT has not been attempted by any researchers, because the performance of such non-iterative algorithms may degrade for large scale optimizations problems (E.g. 103 (approximately) for which the cost function has roughly converged in most of the patient cases. For the sake of comparison, beamlet-based IMRT plans were also generated for the same cases presented, using the KonRad inverse planning system, which uses a pencil beam algorithm for dose calculation. We used a grid size of 3mm for the dose calculation in KonRad. Also, we used 3 mm CT slices for the study cases. The gantry, collimator and couch angles are kept same for both plans to make the comparison more realistic. We used an intensity level of 7 in the BBIP plans presented in this study. Moreover, the parameters of the objective function are also kept same for both plans, as the clinical objectives for both plans are obviously the same.We have studied the clinical performance of the proposed algorithm in eight patient cases  out of which three cases planed using the algorithm have been treated in our clinic. In this communication, we have presented a detailed account on the clinical performance analysis done for two patient cases (H&N and brain) planned using the algorithm. We used a sampling density (ρ) of 1 point/cmmax Strictly within 45 Gy. The treatment goals for this plan are summarized in The case is a typical concave H and N lesion of volume 379cc encircling spinal cord and surrounded by parotids on both sides. The difficulty in this case is to cover the target with a prescription dose of 50.4 Gy as homogeneously as possible and at the same time maintain the spinal cord Dmax of chiasm and brainstem to less than 50 Gy. The treatment goals for this plan are summarized in The case is a temporal brain lesion of volume 273cc surrounded by critical organs such as brainstem, chiasm, and optic nerves. The prescription for the PTV was 50 Gy along with tight constraints to the nearby OARs. The difficulty in this case is because of the close proximity of the target to optic chiasm, left optic nerve and brainstem. Our intension was to give uniform target coverage and reduce DIn our approach, further improvement in dose distribution is derived in successive trials by modifying the penalties of different sub-objectives according to the situation as usually performed in most of the optimization systems. Moreover, we also investigated the consistency of the Gaussian Elimination method by repeating the optimizations for multiple times. Our observation is that the variation in the results is less than 0.3% in almost all the data sets presented. The time taken for each optimization was of fractions of CPU seconds in a 1.8 GHz Pentium microprocessor. Therefore, it is evident from this analysis that the characteristics of the algorithm are numerically acceptable for a broad category of cases in the context of beam weight optimization in anatomy-based IMRT.The method described here looks attractive in many aspects. Since the algorithm optimizes beam weights in fractions of a second, the user is given an opportunity to repeat the optimization multiple times with different combinations of penalties by analyzing a plan in multiple clinical, physical and technical viewpoints in a shorter time. The method offers a GUI option for conveniently specifying different dose limits for the tumor and normal tissues as well as assigning individual penalties for reaching the specified objectives. Another advantage in our optimization method is that we did not employ a separate dose calculation algorithm for optimization. We use an existing treatment planning system (CMS XiO) to calculate dose at the user defined points for a set of uniform beam weights and use those dose values as a standard reference. Afterwards, the dose values resulting from each optimization are derived by just performing an inverse Gaussian Elimination operation over those pre-generated reference dose values. This approach reduces a significant amount of time during optimization and allows to directly utilizing the dose calculation accuracy of the existing planning system for the optimization.mean is about 4% higher in ABIP plan as compared to BBIP plan and the spinal card Dmax in ABIP is 5% higher in ABIP plan than BBIP plan. Likewise, in Case B, the Dmax of chiasm, brainstem and Lt. Optic nerve was about 4% higher in ABIP plan as compared to BBIP plan. However, all the parameters were stringently kept within the desired limits in ABIP plans for both the cases. As suggested in recent investigations,[It has been clearly demonstrated in the numerical analysis that the numerical performance of the algorithm in terms of convergence, consistency and speed are suitable to perform beam weight optimization in anatomy-based IMRT for a wide range of patient cases. The clinical performance analysis shows that the required concavities are produced in the isodose lines of interest so that it conforms to the tumor volume while sparing OARs effectively. Also it is evident from the study that the ABIP plans generated using the algorithm are comparable to BBIP plans in terms of target coverage, OAR sparing and spillage. However, it can be observed from the clinical results that in some parameters ABIP plans are ranking behind BBIP plans. For instance, in Case A, the PTV Digations,32 it wilThe number of MUs and apertures are significantly lesser in the ABIP plans as compared to their BBIP counter plans. For instance, in Case A, the reduction in the number of MUs and segments in ABIP plan was 53% (BBIP:768/ABIP:360) and 64% (BBIP:84/ABIP:30) respectively as compared to BBIP plan. Likewise, in Case B, the reduction in MU and no of segments in ABIP plan was 48% (BBIP:652/ ABIP:342) and 57% (BBIP:66/ABIP:28) respectively as compared to BBIP plan. The observed reduction in the number of MUs and apertures in the ABIP plans can significantly improve the treatment delivery efficiency and reduce the treatment verification burden.It is accepted that the optimization methodology mentioned in this study is designed only for quickly finding a local optimum instead of locating a global optimum. But, in the cases studied, we could not observe any particular problem in producing desired results by not getting the global optimum in the optimization. This demonstrates that locating a global optimum for the underlying non-convex optimization problem may not be necessarily of any significant importance, at least from a clinical point of view.An algorithm for fast optimization of beam weights in anatomy-based IMRT has been proposed. The numerical as well as clinical performance of the algorithm has been investigated in different patient cases. Our results show that one is able to generate anatomy-based IMRT plans using the proposed algorithm that are comparable to BBIP plans in terms of dose distribution and superior to the same in terms of monitor units and number of apertures. It is evident from the study that the proposed algorithm could effectively produce satisfactory plans meeting the clinical objectives, while the verification could remain simple.


Relative survival after cancer in Finland is at the highest level observed in Europe and has, in general, been on a steady increase. The aim of this study is to assess whether the high survival is equally shared by different population subgroups and to estimate the possible gains that might be achieved if equity prevailed.The educational level and occupation before the cancer diagnosis of patients diagnosed in Finland in 1971–2005 was derived from an antecedent population census. The cancers were divided into 27 site categories. Cancer (cause)-specific 5-year survival proportions were calculated for three patient categories based on the educational level and for an occupational group of potentially health-conscious patients . Proportions of avoidable deaths were derived by assuming that the patients from the two lower education categories would have the same mortality owing to cancer, as those from the highest educational category. Estimates were also made by additionally assuming that even the mortalities owing to other causes of death were all equal to those in the highest category.For almost all the sites considered, survival was consistently highest for patients with the highest education and lowest for those with only basic education. The potentially health-conscious patients had an even higher survival. The differences were, in part, attributable to less favourable distributions of tumour stages in the lower education categories. In 1996–2005, 4–7% of the deaths in Finnish cancer patients could have potentially been avoided during the first 5-year period after diagnosis, if all the patients had the same cancer mortality as the patients with the highest educational background. The proportion would have also been much higher, 8–11%, if, in addition, the mortality from other causes had been the same as that in the highest educational category.Even in a potentially equitable society with high health care standards, marked inequalities persist in cancer survival. Earlier cancer diagnosis and the ability to cope within the health care system may be a partly relevant explanation, but personal habits and lifestyles also have a role, particularly for the cancer patients’ mortality from other causes of death than cancer. Finland has one of the highest relative survival for cancer patients in Europe  to form a fourth group. Cancers were divided into 27 site categories . The patThe main outcome measure of the patients was the cancer (cause)-specific 5-year survival, where deaths owing to causes other than the cancer of the patients are counted as events censoring the follow-up time. The Cancer Registry corrects, when necessary, the official cause-of-death information, as it has more information than the person signing the death certificate . The RegThe relative survival ratio, another measure preferred by population based cancer registries in estimating the cause-specific survival without relying on the quality of death certificates, was used for comparison . First, the assumption was made for cancer-specific mortality only. Second, the mortality owing to the competing risks of death was, in addition, assumed to be on the level observed in the high educational category. The numbers of avoidable deaths for all sites combined were obtained by summing the site-specific numbers. The theory of competing risks of death  was usedThe educational level could be identified from the censuses for 99.2% of patients; those with unknown education were excluded. Altogether, 526 143 patients were included in the analyses . PatientThe group with a high educational level had a higher age-standardised cancer-specific 5-year survival than that with basic educational level, almost without exception . The ageThe relative survival analysis, where the baseline general mortality had been calculated specific to the educational category, mostly gave similar results (RER) to those (RR) of the cause-specific analyses (results not shown). Not using comparable baseline mortality with respect to educational category resulted in larger differences between the RERs and the RRs, which also held true when stage was taken into account in the model.If in the most recent period 1996–2005, the cancer-specific mortality of all male and female patients equalled that of the high education group, 8% of cancer deaths in patients diagnosed at ages 25–64 years during the first 5 years after diagnosis are theoretically avoidable . For ageThe avoidable cancer deaths will contribute to an increase in the number of persons exposed to competing causes of death, so, there will be negative numbers in the avoidable deaths owing to causes other than the cancer  test will increase the apparent survival for prostate cancer (The numbers and proportions of avoidable deaths have been used as a measure of what possibly could be achieved if cancer patients’ relative survival could be raised to an optimal level defined by a higher survival in other subgroups  or expecClear educational differences in cancer patient survival are still observed in Finland, despite the level of survival being internationally very high and regardless of the very strong improvement in survival over the past decades.