To examine the prevalence of major chronic diseases and their risk factors in different socioeconomic groups in the Australian population, in order to highlight the need for public policy initiatives to reduce socioeconomic inequalities in health.Data were provided by the Australian Bureau of Statistics (ABS) from the 2001 National Health Survey (NHS) for selected chronic diseases and associated risk factors. Conditions selected were those, which form the National Health Priority Area (NHPA) conditions ; plus other 'serious' chronic conditions, in line with the classification developed by Mathers; and for which sufficient cases were available for analysis by socioeconomic status. Indirectly age-standardised prevalence rates were calculated by broad age group for Australia and for five groups of socioeconomic status; rate ratios were calculated to show variations in prevalence between these groups.Significant socioeconomic inequalities were evident for many of the major chronic diseases; the largest was for diabetes mellitus (at ages 25 to 64 years); and for many diseases, there was also a strong, continuous socioeconomic gradient in the rates.Circulatory system diseases  and digestive system diseases also exhibited a strong differential in the 25 to 64 year age group.In the 65 years and over age group, the strongest inequalities were evident for mental and behavioural problems, diabetes (with a continuous socioeconomic gradient in rates) and respiratory system diseases.A number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight showed a striking association with socioeconomic status, in particular for people who were smokers and those who did not exercise.This analysis shows that the prevalence of chronic disease varies across the socioeconomic gradient for a number of specific diseases, as well as for important disease risk factors. Therefore, any policy interventions to address the impact of chronic disease, at a population level, need to take into account these socioeconomic inequalities. As in other developed countries, chronic diseases in Australia are major contributors to the extent of illness, disability and premature mortality in the population. They are estimated to make up the greatest proportion of the burden of disease, mental problems and injury for the population as a whole (about 80%), and for particular sub-population groups .Chronic diseases are exemplified by having multifactorial aetiologies, including common disease risk factors and determinants; significant latency periods and protracted clinical courses; and are seldom cured completely ,3. CausaThe inequalities in health observed across populations are many – some of them are inevitable and others, unnecessary and unfair. Those inequalities that are potentially avoidable are deemed 'inequitable' . DespiteThere have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate -10. An aHowever, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living  . One of The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) . MathersCurrent information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population.Information for a selection of chronic diseases is shown in Table The main findings are:The largest differential between those in the most well off and those in the most disadvantaged areas was for diabetes mellitus at ages 25 to 64 years, with the prevalence in the most disadvantaged areas being just over two and a quarter times (a rate ratio of 2.28) the prevalence for the least disadvantaged; there is also a strong, continuous gradient in the rates, with the rate ratios in each of the third to fifth quintiles statistically significant.There was a statistically significant differential of 67% at ages 25 to 64 years, with a strong, continuous gradient, in the prevalence of self-reported mental and behavioural problems across the socioeconomic gradient; differentials  in the 0 to 14 year and 65 years and over age groups were 52% and 56%, respectively.Circulatory system diseases  and digestive system diseases also exhibit a strong differential in the 25 to 64 year age group .In the 65 years and over age group, the strongest differentials were evident for mental and behavioural problems , diabetes  and respiratory system disease .Asthma accounted for almost two thirds of the rate of reporting of respiratory system disease in the 0 to 14 year age group, almost half in the 15 to 24 year age group, and for about a third of the rate in the 25 to 64 year age group.The NHS also included data on a number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight. A number of these risk factors show a striking association with socioeconomic status, in particular for people who are smokers and those who did not exercise, with continuous gradients and highly elevated rates of statistical significance Table . The difIt is important to note that the inequalities reported above relate to the health of those people living in a geographic area and to the overall level of socioeconomic disadvantage of that area. Most areas will contain varying levels of individual socioeconomic disadvantage and, to the extent that the poorer health is associated with individual economic circumstances and living conditions rather than communal environment, the inequalities will understate the true differences in health status according to socioeconomic disadvantage .Furthermore, there are limitations to the use of area-based measures of SES. Due to misclassification error , estimates of difference across the quintiles will be smaller than if data on individual-level measures of SES were used . Thus, cOur analysis indicates that socioeconomic inequalities in the prevalence of chronic diseases and their concomitant risk factors are evident across the Australian population. However, the diseases with substantial disparities across the socioeconomic quintiles are different, for different stages in the life course. Although these results cannot be directly compared with those of previous studies, because of definitional and methodological differences, the recurring finding of inequalities for chronic disease morbidity and risk factor prevalence across the socioeconomic gradient remains a significant concern.The burden in the Australian population attributable to socioeconomic inequality is large, and has far-reaching implications in terms of unnecessary disability and suffering, the loss of potentially economically productive members of society, and increased costs for the health and social care systems . DespiteWhat should we be doing differently? There is a growing body of knowledge that will help to provide direction for developing policies to reduce inequities across the population. The socioeconomic environment is a powerful and potentially modifiable factor, and public policy is a key instrument to improve this environment, particularly in areas such as housing, taxation and social security, work environments, urban design, pollution control, educational achievement, and early childhood development .However, attention must be paid to the nature of any action that is taken, to ensure that social and economic inequalities are not increased. Some programs, by their very success, can increase inequality by widening the gap between groups in the population; for example, such programs may be more attractive to those who are already healthier, or not as effective for certain groups with poorer health, less education or more stressful lives. In one smoking cessation initiative, it was found that the prevalence of smoking decreased predominately in those adults with higher education, thus increasing the existing difference with those who were more disadvantaged . While sIt is also evident that the ways in which systems such as education and health are delivered and structured can increase existing inequality. For example, schooling can be a way of addressing inequality and also a way of reproducing it. It has been suggested that there are two goals for a social justice program in education: to work to eliminate the contribution that the education system makes to the production over time of social inequality in general; and to maximise the positive contributions that the education system makes to reducing social inequality . TherefoPolicy-makers who wish to address socioeconomic inequalities in health may favour one of the following approaches. Some view the impact of socioeconomic disadvantage on those groups with the poorest health in the population, such as Aboriginal people and Torres Strait Islanders, as the priority policy goal. Others identify the gap between the health of those groups at the outer ends of the socioeconomic hierarchy , and see the narrowing of the gap as the goal. Others prefer to focus on the socioeconomic gradient in health that runs across the whole population .Graham has identified that the last approach widens the policy debate in three ways . FirstlyShe also observes that, "improving the health of poor groups and improving their position relative to other groups are necessary elements in a strategy to reduce the socioeconomic gradient. However, neither is sufficient on its own. To reduce the socioeconomic gradient, health in other socioeconomic groups also needs to improve at a faster rate than in the highest socioeconomic group. Thus, policies to ameliorate health disadvantage, to close health gaps and to reduce health gradients need to be pursued together, and not at the expense of each other" .There is also an urgent need to make health inequalities a research priority for each stage of the life course – not just to monitor the size and extent of the disparities but also to undertake research that will find preventive approaches and further policy interventions that will be effective in reducing them, and that are likely to be implemented by governments and communities.Clearly, any moves to address the impact of chronic disease at a population level must take into account socioeconomic inequalities in prevalence. More research is needed to determine which approaches are effective and why others have failed to have the desired impact, particularly for those who are from socioeconomically disadvantaged areas. Finally, although rates are generally highest at the oldest ages, the development of risk factors for many chronic diseases occurs early in life, and thus, it is essential those health inequities are addressed right across the life course.The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 . The NHSThe survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs . HoweverThe particular conditions for which data were requested from the ABS for this analysis were:the NHPA conditions ; plus other 'serious' chronic conditions, in line with the classification developed by Mathers ; and forThe risk factors used by the ABS were those identified for the NHPA conditions .The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases . The risGiven the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions  appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings  the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data.The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population.The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores  and Quintile 5 comprises the CDs with the lowest IRSD scores . Each quintile comprises approximately 20% of CDs.The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 . The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used , it may also reflect different response rates to the survey from different socioeconomic groups.The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health . The ageChronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups  were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 .The author(s) declare that they have no competing interests.JG conceived the study, and was responsible for its design and coordination. ST participated in the design of the study and performed the statistical analysis. DH participated in the drafting of the manuscript. All authors read and approved the final manuscript.


Diagnosing chronic heart failure is difficult, especially in mild cases or early in the course of the disease, and guidelines are not easily implemented in everyday practice. The aim of this study was to investigate general practitioners' diagnostic reasoning about patients with suspected chronic heart failure in comparison with recommendations in European guidelines.Think-aloud technique was used. Fifteen general practitioners reasoned about six case vignettes, representing authentic patients with suspected chronic heart failure. Information about each case was added successively in five steps. The general practitioners said their thoughts aloud while reasoning about the probability of the patient having chronic heart failure, and tried to decide about the diagnosis. Arguments for and against chronic heart failure were analysed and compared to recommendations in guidelines.Information about ejection fraction was the most frequent diagnostic argument, followed by information about cardiac enlargement or pulmonary congestion on chest X-ray. However, in a third of the judgement situations, no information about echocardiography was utilized in the general practitioners' diagnostic reasoning. Only three of the 15 doctors used information about a normal electrocardiography as an argument against chronic heart failure. Information about other cardio-vascular diseases was frequently used as a diagnostic argument.The clinical information was not utilized to the extent recommended in guidelines. Some implications of our study are that 1) general practitioners need more information about how to utilize echocardiography when diagnosing chronic heart failure, 2) guidelines ought to give more importance to information about other cardio-vascular diseases in the diagnostic reasoning, and 3) guidelines ought to treat the topic of diastolic heart failure in a clearer way. Chronic heart failure (CHF) is a major cause of morbidity and mortality and it has a considerable impact on the health care system . In a reThe European Society of Cardiology adopted guidelines for diagnosing CHF in 1995, and these were revised in 2001 -9. Swedia) one or more typical symptoms (at rest or during exercise), b) objective evidence of cardiac dysfunction (at rest), and c) response to treatment directed towards CHF (in cases where diagnosis is in doubt). Criteria a and b should be fulfilled in all cases. Echocardiography (ECHO) is mentioned as the single most effective tool in widespread clinical use for objective assessment of cardiac dysfunction. In the algorithm for the diagnosis of CHF, a sequence of investigations is recommended: suspect CHF because of symptoms and signs; assess presence of cardiac disease by electrocardiography, X-ray or Natriuretic peptides (where available); imaging by echocardiography; assess aetiology, degree, precipitating factors and type of cardiac dysfunction; additional diagnostic tests where appropriate; choose therapy. Table The full versions of the above-mentioned guidelines are comprehensive documents, covering epidemiology, aetiology, pathophysiology and diagnostic methods, but may be difficult to apply to specific diagnostic situations . HoweverRelatively few studies on how patients suspected of having CHF are diagnosed have been performed in primary health care settings, and most of them report over-diagnosis ,4,14-16.Process-tracing techniques are used to study the cognitive processes involved in decision-making such as, for example, how judgements change over time as new information is presented, and which decision rules are used . A methoThe value of conclusions reached in such studies depends on the validity of the think-aloud method, and on the reliability of the coding process. Thinking aloud while performing a task often lengthens the time for completing the task, but does not seem to change the accuracy of task fulfilment or the cognitive processes . In a ren = 61) were listed and contacted in a random order. The distance from central Stockholm was chosen for practical reasons. In each health care centre the GPs were contacted in a random order by one of the authors (YS). Only one GP at each centre was included in the study, and this person had to be a specialist in family medicine. We contacted the GPs during their regular telephone hour, during the period October 2001 to October 2002. Our goal was to include 15 GPs in the study.All health care centres in northern Stockholm within a distance of 20–30 km from the city centre  years, they were on average 52.7 (range 42–62) years of age, and six of them were men. The non-participating GPs were on average 52.7 (range 35–62) years of age, and seven of them were men.Six case vignettes (CV), based on authentic patients, were presented to the participants. The information presented in the case vignettes was obtained from the patient records and included information about relevant diseases , lifestyle factors , symptoms, signs, electrocardiography (ECG), chest X-ray findings, and ECHO. Chest X-ray and ECHO results were presented in the same format as in the patient records. ECHO results could contain information about ejection fraction (EF), valvular disorders and ventricular wall motility. The diagnoses made by the attending cardiologists  were used as a reference standard when assessing the participants' diagnostic accomplishments.The six cases were selected to represent patients with various types of potential diagnostic problems: A "prototypical" CHF patient (CV2), a patient with both CHF and chronic obstructive pulmonary disease (COPD) (CV6), a patient with CHF, tachycardia and mitral valve insufficiency (CV3), an obese non-CHF patient with normal ECG and EF (CV5), a non-CHF patient with COPD (CV4) and a non-CHF patient with alcohol abuse and a metabolic syndrome (CV1). For one of the cases (CV3) there was a disagreement between the diagnosis according to the cardiologists and the diagnosis that could be deduced from a simplistic interpretation of the guidelines. This patient had typical clinical findings including gallop rhythm, cardiomegaly, and pulmonary congestion, but normal left ventricular function according to ECHO. It could therefore be categorized as not CHF according to the definition given in the guidelines. However, this patient also had a mitral valve insufficiency, which can give a "false normal" ejection fraction value: the left ventricle is emptied both forward (cardiac output) and backward .Before the sessions the GPs had received written information about the aim of the study  and about the method , but not about the kind of medical problems that would be presented to them. The study was conducted at the GPs' offices. All visits and recordings were made by one of the authors (YS). The participants were instructed that six authentic patients, suspected by GPs to have CHF, would be presented, and that their task was to say aloud their thoughts about the case, and to try to decide whether the patient had CHF or not.The order of the cases was the same for all participants. The order in which the information was presented was arranged to be as realistic as possible in relation to clinical practice . Each vignette was presented on a computer screen in five successive steps using QA software . All preThe doctors first got a test case (not recorded) in order to get acquainted with the think-aloud method, and then continued with the six study cases. The only intervention from the researcher during the think-aloud session was that a participant who was silent for more than about 15 seconds was reminded to say his or her thoughts aloud about the information presented . All sesAll information in the case vignettes that was of relevance for the diagnosis and that could take on different values was considered to be variables. Fifty variables were defined: 19 of them were included in all six vignettes , six in five, one in four, six in three, one in two, and 17 in one vignette . For each case vignette, the presented variables were coded for content and value Table .2) as an argument against CHF. For each participant, a specific evaluation of each variable value was only counted once for each case vignette in order not to give more weight to thoughtful repetitions of an argument than to a single, firm statement. However, if a participant used the same variable value as an argument both for and against the diagnosis of CHF, both evaluations were coded. Ten percent of the 90 case vignette protocols were selected at random and coded independently by two of the authors  to estimate the interrater agreement of the coding process. The rest of the protocols were coded by one of the authors (YS).For each participant, every mention of a variable was coded for how the GP seemed to use it: as an argument for the diagnosis of CHF, as an argument against CHF, or as not being of any explicit use for the diagnosis (mentioned only). "He has basal rales. This guy has CHF!" is an example of a participant using the variable "rales"  as an argument for CHF. "So I'm not really sure that he has got CHF. Just a moderate cardiac enlargement, no, I wouldn't think so" is an example of a participant using the variable "relative heart volume"  in three categories: CHF or probably CHF; uncertainty about diagnosis; probably not CHF or not CHF.The participants were not forced to express their diagnostic judgements in a specific format, and their free verbal statements therefore had to be interpreted and coded. Two of the authors  independently classified all the diagnostic judgements (Stata 8.0 was used for the statistics. Cohen's kappa test (κ) was used to determine interrater agreement regarding the coding of the think-aloud protocols and the classification of the diagnostic judgements. Kappa values are classified as follows: <0, worse than chance; 0 to 0.2, poor; 0.21 to 0.4, fair; 0.41 to 0.6, moderate; 0.61 to 0.8, good; and >0.8, very good .The research ethics committee of Huddinge University Hospital approved the study.The randomly selected test protocols contained 322 segments of propositions, 36 of which were excluded since they contained variables that were not going to be investigated in this study (e.g. treatment suggestions). There was disagreement between the two coders about the content of variables in 14 of the remaining 286 segments (4.8%). The remaining 272 segments were then tested for interrater agreement on argument values , which was 95% (κ 0.85).The interrater agreement was 92% (κ 0.85). For the few diagnostic judgements where there was initial disagreement, it was possible to agree upon an interpretation.The information that was used most frequently in diagnostic arguments was the ejection fraction value on ECHO, pulmonary congestion, and cardiac volume Figure . The mosSymptoms and signs were not often used as arguments in the GPs' diagnostic reasoning Figure . SymptomAccording to the guidelines, a normal ECG opposes the diagnosis of CHF Table . Only onAccording to the guidelines, information about cardiac enlargement or pulmonary congestion on chest X-ray gives some support for CHF if there are any pathological findings, and is of some importance as an argument against CHF if the findings are normal Table . Chest XEach of the 15 GPs judged 6 case vignettes, which resulted in 90 judgement situations. In 48 of them, the EF value (or the information about left ventricular function in CV3) was used as an argument for or against CHF . In three judgement situations, the final diagnosis was in the same direction as the X-ray argument (three CHF). In seven judgement situations, the GP was uncertain about the diagnosis.In our study, the GPs used other diseases as an argument in a total of 70 judgement situations, mostly as arguments for CHF (91%). Atrial fibrillation, emphysema, history of myocardial infarction, and hypertension were the diagnoses most commonly used in this way.For certain variables, the same information value was used by some GPs as an argument for and by others as an argument against CHF. The presence of emphysema was sometimes seen as increasing the risk of CHF , or as an alternative explanation for symptoms .Diabetes could also be seen as increasing the risk of CHF , or as an alternative explanation for symptoms .Advanced age could be seen as increasing the probability of CHF , or as an alternative explanation for symptoms . Age was used as a diagnostic argument only for the two patients over 80 years of age.For relative cardiac volume, the reasoning could be compatible with GPs using different threshold values in their reasoning. The two lowest values were only used as arguments against CHF, the two highest values only as arguments for it, and the two intermediate values were used as arguments in both directions.There was total agreement among the GPs only for the prototypical CHF case; otherwise there was a large variation among GPs regarding diagnoses. Case vignettes representing CHF patients were more likely to be correctly diagnosed than those representing non-CHF patients Table .When comparing the GPs' diagnostic reasoning with guidelines, we found that the clinical information in the case vignettes was not used to the extent recommended in the guidelines. It is true that information about the ejection fraction value on ECHO was the single most frequent diagnostic argument, and it was the most common argument against CHF. This is in line with the guidelines, which emphasize the need for objective evidence of cardiac dysfunction. However, in more than one third of the judgement situations, the information about ECHO that was presented was not used as an argument. Over-diagnosis of CHF in primary health care has been demonstrated in a number of studies, with ECHO findings as the gold standard ,4,15. LiIn the diagnostic algorithm, symptoms and signs are the entry criteria. However, the GPs did not seem to use them consistently in this way, except when diagnosing the prototypical CHF and non-CHF cases. One reason for this might be that most symptoms and signs considered typical for CHF are fairly non-specific as regards the diagnosis CHF.Information about other relevant diseases, which was important in the GPs' diagnostic reasoning, is not included in the list of assessments to be performed routinely Table  9]. How. How9]. The case vignettes represented authentic patients referred by GPs to a cardiology department for problems related to heart failure. This may have led to a selection of more complicated patients than the "typical" heart failure patients in primary health care. The reason we chose this group of patients was that we wanted to include patients who were thoroughly investigated, with a well-founded clinical diagnosis, and for whom information about all variables of interest could be found in the patient records. Selecting GPs only from health care centres in, or relatively close to, the city centre may have biased the results, since differences in catchment areas, working conditions, and access to echocardiography may influence GPs' diagnostic habits. This could make it difficult to generalize the results to other GPs. Only 50% of the GPs who were contacted agreed to take part in the study, which could bias the results. However, since the age distribution was the same in the two groups, it seems unlikely that the drop-out group would differ from the study group regarding clinical experience.The full version of the guidelines is difficult to apply to individual diagnostic situations and it is also difficult to use it for assessment of diagnostic behaviour . In thisGPs' tendency to over-diagnose CHF has been explained by their relying on symptoms, signs and less specific investigations such as chest X-ray, and by limited access to ECHO in the primary health care. However, this study indicates that a substantial minority of GPs seem to be less familiar with the use of ECHO and EF. Thus, access to ECHO ought to be accompanied by education about how to integrate this information better in the diagnostic reasoning.Guidelines ought to include search of information about other cardio-vascular diseases in the list of assessments to be performed routinely Table  and in tThe information in the case vignettes was underused as arguments for and against the possibility of CHF as compared with the guidelines. Information about the EF value was the single most frequently used argument for or against CHF; nevertheless, in one third of the diagnostic judgements the GPs did not consider any information about the ECHO in their diagnostic reasoning. Information about symptoms and signs were not used to to the extent suggested in the guidelines. Information about other relevant diseases was frequently used in the GPs' diagnostic reasoning, indicating that they often relied on illness scripts. Some implications of our study are that 1) GPs should be taught how to use ECHO information better in their diagnostic reasoning, 2) guidelines ought to give more importance to information about other cardio-vascular diseases in the diagnostic reasoning, and 3) guidelines ought to treat the topic of diastolic heart failure in a clearer way.CHF Chronic heart failureCOPD Chronic obstructive pulmonary diseaseECHO EchocardiographyECG ElectrocardiographyEF Ejection fractionGP General practitionerThe author(s) declare that they have no competing interests.All authors conceived of the study and participated in the design. YS carried out the data collection, performed the statistical analyses and drafted the manuscript. All authors participated in the interpretation of the results and the discussions of the drafts. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Case vignette characteristics The additional file shows some important characteristics of the case vignettes. Format: Word-table.Click here for file


Low birth weight (LBW) is a public health problem linked to lack of equity in populations. Despite efforts to decrease the proportion of newborns with LBW, success has been quite limited. In recent years, studies focused on explaining how social factors influence this problem have shown that populations with greater inequities have a greater proportion of newborns with LBW.The objective was to describe socioeconomic factors related to LBW adjusted by demographic, reproductive and health services variables in Mexico City. A case-control study was carried out in three hospitals with gynaecological and obstetrics services in Mexico City during the first half of 1996. During the recruiting period all children with LBW (cases), defined as newborns weighing <2500 grams, were matched with children born on the same day to control for time of birth. Upon arrival at the hospital for delivery, women were interviewed to determine if they met our inclusion criteria. Women with a history of chronic conditions and those with twin or multiple pregnancies were excluded. Variables with clinical and statistical significance were included in a multivariate model (logistic regression).We found that low socioeconomic level was the most important risk factor for LBW and was independent of other factors, including those related to reproduction and nutrition, smoking, morbidity during pregnancy, accessibility to health services and prenatal care .We found that socioeconomic factors are relevant to LBW. However further research should be done in different population groups as well as developing precise ways of measuring socioeconomic factors and their role in reproductive health. On average, the worldwide incidence of low birthweight (LBW) is 17% per year, making LBW an important infant health problem in many populations . The LBWIn addition to its impact on infant mortality, LBW has been associated with higher probabilities of infection, malnutrition and handicapping conditions during childhood, including cerebral palsy, mental deficiencies and problems related to behavior and learning during childhood -6. Childin utero are related to the mother's physiology, including her nutrition (mother's weight before pregnancy and history of having newborns with LBW), exercise, infections, and consumption of tobacco, alcohol and other drugs [The biological processes that affect the fetus er drugs ,11.During the fetal phase, growth depends on the nutritional condition of the mother, indicating that pregnant women should not only increase their weight but also consume essential nutrients. For many women in the developing world, however, economic, social and cultural factors make it difficult for them to obtain the necessary food and healthcare, which are closely interrelated .Associations between poorer child health and poverty, inequity and social exclusion have been documented worldwide and have been shown to be independent of research methods, local culture, and available health care services . While tAmong the socio-economic factors are income, education, occupation, household leadership and gender differences related to roles within the family ,18. In MSeveral studies have shown different results on whether socioeconomic factors affect pregnancy outcomes and newborn conditions -21. The In this study, we have analyzed the socio-economic factors related to birthweight adjusted by others known factors in the urban population of Mexico in three hospitals located in three different geographic areas of Mexico City.A case-control study was carried out in three hospitals with gynaecological and obstetrics services in Mexico City during the first semester of 1996. We recruited 154 LBW newborns, defined as newborns weighing <2500 grams, from these three hospitals, as well as 474 controls chosen from births on the same day to control for time of birth. All newborns included in this study provided a statistical power of 80% when we assumed a = 0.05 (one-sided test), a case/control ratio 1:3, and a 3% difference among both groups in the low socioeconomic level .Upon arrival at the hospital for delivery, women were interviewed to determine if they met our inclusion criteria. Women with a history of previous chronic conditions or those with twin or multiple pregnancies were not included. Following delivery, and after obtaining informed consent, each woman was administered a questionnaire by a trained interviewer to obtain information about socio-economic, reproductive, and nutritional factors. Socioeconomic factors included age, level of education, civil status, occupation, income and owning certain goods; reproductive factors included parity, history of preterm delivery and LBW, which were classified as positive or negative for all previous pregnancies; and nutritional factors included calcium and iron supplementation, pre-gestational weight, prenatal care, morbidity during pregnancy and tobacco exposure. Clinical records were also reviewed to verify information about each newborn.To create a socioeconomic level index, we used two variables that have been considered as proxies . OwnershStatistical analysis was performed by describing sociodemographic, reproductive and prenatal care characteristics of the mother and newborn. Univariate analysis was used to evaluate the association between the independent variable (socioeconomic level) and covariates with LBW (outcome variable). Stratified Mantel-Haenszel analysis was performed to evaluate confounding and/or interaction (i.e. parity and age). To obtain the association magnitude of the socioeconomic level adjusted by the covariates, multivariate analysis was performed (logistic regression). Variables with clinical and statistical significance were included in multivariate modeling. We used three models to classify factors relevant to LBW into three groups as known, controversial and unknown risk factors for LBW.Model 1 included socioeconomic level index and maternal age, maternal education, marital status and accessibility to public services. In model 2 we included all variables of the model 1 besides, prenatal care, tobacco exposure, and morbidity during pregnancy were considered as adjusted variables. Finally, model 3 included all variables previously mentioned . However we thought that it will be a non differential error. Although we analyzed other proxies of socioeconomic level, we found that each of them was irrelevant. For example, although education has been used as a proxy of social status, we found that this indicator was not important in our population, perhaps because the women in our study were incorporated into the work force, making exposure to intermediate factors, such as occupational stress and load work, an influence on pregnancy outcomes. Family circumstances and biological processes may also be affected by a wider social context, including cultural and historic issues such as educational opportunities, parent's divorce, unemployment, risk of poverty risk, and risks factors for smoking and obesity.Although many socioeconomic factors related to LBW have been identified, the specific role of each of them is not known, limiting the ability to use preventive actions in exposed populations. Interventions aimed at reducing the number of LBW infants have had limited success on conditions of the newborn, although some showed benefits in pregnant women . To decrHeterogeneity among different populations makes findings related to interventions in one population not applicable to others. Thus, it is necessary to design studies that account for the geographic, racial, cultural, social and economic context of each country and specific group. In the case of Mexico, we believe that our results provide information representing a closer approach to the effect of socioeconomic status on LBW. Our results provide a starting point in the search for better indicators for evaluating socioeconomic status in Mexican populations with other social conditions, including suburban, rural, and indigenous populations.Another aspect to consider within the context of each country is the availability of information. In Mexico, there is no way to obtain uniform information about the socioeconomic characteristics of the population. Information about conditions of different populations is important in designing programs aimed at solving existing inequities. It is a challenge to create a socioeconomic index that will reflect the real living conditions of pregnant women in Mexico. This effort will require including other variables, as well as refining proxy variables.The author(s) declare that they have no competing interests.LPTA, PCC, SFH contributed in the conception and design of the study and statistical analysis. JPVB, ERM reviewed for important intellectual content. All authors participated in the interpretation of data and read and approved the final version to be published.The pre-publication history for this paper can be accessed here:


Working memory performance is important for maintaining functioning in cognitive, academic and social activities. Previous research suggests there are prevalent working memory deficits in children with attention deficit hyperactivity disorder (ADHD). There is now a growing body of literature characterizing working memory functioning according to ADHD subtypes in children. The expression of working memory deficits in adults with ADHD and how they vary according to subtype, however, remains to be more fully documented.This study assessed differences in working memory functioning between Normal Control (NC) adults (N = 18); patients with ADHD, Combined (ADHD-CT) Type ADHD (N = 17); and ADHD, Inattentive (ADHD-IA) Type (N = 16) using subtests from the Wechsler Adult Intelligence Scale-III and Wechsler Memory Scale-III and the Paced Auditory Serial Addition Task (PASAT).The ADHD groups displayed significant weaknesses in contrast to the NC group on working memory tests requiring rapid processing and active stimulus manipulation. This included the Letter-Number-Sequencing test of the Wechsler scales, PASAT omission errors and the longest sequence of consecutive correct answers on the PASAT. No overall ADHD group subtype differences emerged; however differences between the ADHD groups and the NC group varied depending on the measure and the gender of the participants. Gender differences in performance were evident on some measures of working memory, regardless of group, with males performing better than females.In general, the data support a dimensional interpretation of working memory deficits experienced by the ADHD-CT and ADHD-IA subtypes, rather than an absolute difference between subtypes. Future studies should test the effects of processing speed and load on subtype performance and how those variables interact with gender in adults with ADHD. Combined , Predominantly Inattentive (ADHD-IA), and least common, Predominantly Hyperactive-Impulsive (ADHD-HI). The psychiatric classification system for ADHD, with its various subtypes, has evolved substantially over the years. DSM-IV defined the disorder based on the best available research at the time, but there is debate as to whether the exiting subtypes provide an accurate description of the disorder. Some researchers [Attention deficit hyperactivity disorder (ADHD) may be the most common childhood DSM-IV diagnosis , with 30earchers -4 hypothearchers  is most earchers  that theearchers . Thus, oThe results for documenting neuropsychological differences according to DSM-IV nosology for ADHD subtypes have been ambiguous at best. Several pediatric studies found no or limited differences between ADHD-CT and ADHD-IA subtypes on a series of executive functioning measures ,6-8. HowWhile the findings regarding subtype differences in children are mixed, even less is known about the presence of neuropsychological differences between subtypes among ADHD adults. Gansler et al.  comparedMurphy and colleagues  examinedThe inclusion of WM subtests in the battery of neuropsychological tests in previous studies in ADHD is quite logical given the growing body of literature suggesting WM impairments associated with the disorder ,13-18. WWe expect that both ADHD subtypes will exhibit impaired processing speed given its relationship to inattention , but thaThis study also tested for gender differences in WM performance. The ADHD literature tends not to find significant differences between adult males and females -33 or boGender differences in WM paradigms are relatively sparse with most studies examining memory differences in relationship to gender, rather than explicitly studying WM performance. Recent brain imaging studies, however, have begun to explore gender differences in relationship to brain activation -41 and fThe objectives of this study are to test for differences in WM between adults with and without ADHD and between ADHD-CT and ADHD-IA adults. We hypothesize that the ADHD participants will exhibit WM deficits in contrast to healthy control (NC) participants, and that there will be subtype-specific WM errors. We hypothesize that gender will have a significant effect on WM tasks, particularly those that require a greater processing speed load. It is also expected that faster interstimulus interval (ISI) presentation rates on the PASAT will be more sensitive in detecting differences between the NC and ADHD groups due to demands in processing speed.The sample included a total of 51 participants: 17 Combined Types , 16 Inattentive Types ; and 18 Normal healthy Controls . See Table Participants were recruited from a university-based adult ADHD clinic and local advertisements. Following a description of the study and its associated risks, each participant gave written informed consent for a protocol approved by the Human Investigations Committee associated with the university at which the study was conducted.rd Edition (WAIS-III)[Each potential participant completed a personal information questionnaire packet (PIQ) that inquired about the volunteer's developmental, health, medication, alcohol use, employment, social, financial, and educational history. Potential participants also completed a computerized structured psychiatric interview (Mini-SCID for DSM-IV) and the WAIS-III) screenedVolunteers completed two self-report versions of the Adult ADHD DSM-IV Rating Scale , one revProspective participants were excluded for the following reasons: clinically significant medical conditions, mental retardation, clinically unstable psychiatric conditions , bipolar disorder, obsessive-compulsive disorder, current major depressive episode, drug or alcohol abuse or dependence within one year preceding the study, and/or current use of antipsychotic medication. In addition, healthy normal controls (NC) were excluded if they met DSM-IV criteria for ADHD, learning disability or any major psychiatric disorder. No participant was taking psychoactive medication that would impact attention at the time of testing. Participants taking stimulants took a "medication vacation" for at least 24 hours before testing.The investigators first screened prospective participants by phone to obtain a general understanding of the severity and chronicity of the volunteer's ADHD symptoms and presence of exclusionary criteria. Qualifying prospective participants then completed a research packet with screening measures to bring to the first visit. Next, a master's level clinician (R.B.H.) and/or a licensed, Ph.D.-level psychologist (J.B.S.) interviewed each volunteer. J.B.S. participated in 70% of the interviews of those accepted into the study and reviewed all interview forms and test records. Interviews included a review of the PIQ, the semi-structured interview based on the DSM-IV criteria for ADHD , and iteProcedurally, the first step toward a diagnosis for either subtype of ADHD required significant symptom reports on the ADHD self-rating scale with ratings of a 2 (frequent) or 3 (very frequent) on the appropriate scale(s) for either subtype for 6 or greater symptoms (see Table rd Edition (WMS-III). The PASAT [Participants completed Digit Span (DS), Arithmetic, and Letter-Number-Sequencing (LNS) subtests from the WAIS-III and the Spatial Span subtest of the Wechsler Memory Scale – 3he PASAT  further he PASAT . The Lonhe PASAT  has showp = .05. Effect sizes are presented for the comparisons using partial eta squared (ηp2) as the statistic. Partial eta-squared is the proportion of variance accounted for attributable to a given effect, partialling out all other factors in the model [An analysis of variance (ANOVA) tested for group differences between gender, NCs and the two ADHD groups (ADHD-CT & ADHD-IA subtypes) on the age-corrected LNS, Spatial Span, Arithmetic, and DS followed by least significant difference (LSD) post hoc pairwise comparisons for significant omnibus group effects. Covariates included age and education for the Wechsler subtests. A mixed design with between-group  and within-subject repeated (ISI presentation rates) factors tested for performance differences on the PASAT for each of the three dependent measures labeled Number Correct, Omission Errors, and Longest Correct Sequence. IQ subtests (Block Design and Vocabulary) unlikely to be influenced by attention also were specified as covariates of interest for the PASAT because performance on the PASAT may be associated with age and intelligence ,49. Posthe model .F = 5.33, p = 0.03, ηp2 = 0.11) on the LNS. Men  performed more trials correctly than women . Differences between men and women approached significance on Digit Span  = 3.67, p = 0.06, ηp2 = 0.08), once again with men  performing better than women . On the PASAT, significant main effects for Gender emerged on Number Correct,  = 5.96, p = 0.02, ηp2 = 0.13), on which men  answered significantly more items correctly than women . Significant main effects for Gender also emerged on the PASAT Omission Error variable  = 4.57, p = 0.04, ηp2 = 0.10). Women  omitted answers on significantly more PASAT items than men .Across all groups , males performed significantly better than females on some WM measures. There was a significant main effect for Gender,  = 4.32, p = 0.02, ηp2 = 0.13) (see Table p = 0.008) and the ADHD-CT group (p = 0.03) committed more errors than the NC group. A comparison between groups on the Digit Span test was not significant for a main effect  = 2.79, p = 0.07, ηp2 = 0.05) but showed the same pattern. Exploratory post hoc comparisons indicated that performance by the ADHD-IA group was worse than the NC group (p = 0.02), although there was no significant difference between the NC and ADHD-CT groups. There was no main effect for group on Arithmetic or Spatial Span tests. There were no significant group differences on the Wechsler tests between the two ADHD subtypes.An analysis of the WM measures from the Wechsler scales identified a main effect of group on WM performance on the LNS test  = 4.13, p = 0.02, ηp2 = 0.18). Post hoc pairwise comparisons suggested that the ADHD-CT group made more omission errors compared to the NC group (p = 0.008), however the difference in omission errors between the ADHD-IA and NC group was not statistically significant (p = 0.07). A significant main effect for Group emerged on the Longest Sequence variable  = 3.22, p = 0.05, ηp2 = 0.15). The post hoc pairwise comparisons indicated that the NC group generated significantly longer sequences of consecutive correct responses than the ADHD-CT group (p = 0.02), whereas there was no significant ADHD-CT versus ADHD-IA group difference.On the Number Correct variable for the PASAT the differences between groups failed to show a significant effect  = 3.24, p = 0.05, ηp2 = 0.15). There were no significant differences in omission errors between the groups in the female sample; the difference between groups in omission errors for the male sample approached significance  = 2.66, p = 0.09, ηp2 = 0.17). Exploratory analyses revealed that for the male group, pairwise comparisons showed that the ADHD-IA group generated significantly more omission errors than the ADHD-CT group (p = 0.02).The only group by gender interaction effect was on the PASAT Omission Error variable  = 2.55, p = 0.03, ηp2 = 0.12). This 3-way interaction suggested the need to explore the effect of ISI presentation rate and group within each gender. The ANOVA for the male group only showed a significant ISI by group interaction  = 3.96, p = 0.004, ηp2 = 0.24). The overall male group was affected by the length of the ISIs in a linear fashion with shorter ISIs resulting in more errors  = 4.06, p = 0.05, ηp2 = 0.14). There was also a significant linear ISI by group interaction  = 8.67, p = 0.001, ηp2 = 0.41). The data indicate that as the presentation rate quickened for the PASAT, male subjects in the ADHD groups demonstrated a shallower slope in response to the faster ISIs than the NC group. Responding in the ADHD-IA group demonstrated the steepest slope, showing a greater number of omission errors at each ISI change (beginning at 2.4s to 2.0 s), whereas the ADHD-CT group did not show an effect of ISI until the ISI changed from 2.0 s to 1.6 s. There were no significant findings for the female group.There were no main effects of ISI on any of the PASAT variables. However, there was a 3-way interaction of Group by ISI by Gender on the Omission Error variable  type of ADHD-IA individual  may be hWM deficits are particularly evident in tasks comparing normal control adults to adults with ADHD when tasks requiring significant processing speed are used. Differentiation in WM performance between adults with the Inattentive versus Combined Type of ADHD are less pronounced and may be obscured when groups of male and female subjects are combined. Males with the ADHD-IA subtype may experience greater WM deficits with higher processing speed loads. These data suggest future studies should explore the role of gender on WM functioning in both ADHD and normal control populations.The author(s) declare that they have no competing interests.RBH and JBS conceived of the study. All authors contributed to writing the manuscript. RBH collected the testing data; JBS collected a subset of the interviews and reviewed all of the interview and rating scale data. RBH performed the preliminary data analysis and JBS and DRM performed the final analyses. All authors read, provided feedback and approved the manuscript.


There is much evidence to suggest that risk for common clinical disorders begins in foetal life. Exposure to environmental risk factors however is often not random. Many commonly used indices of prenatal adversity  are influenced by maternal genes and genetically influenced maternal behaviour. As mother provides the baby with both genes and prenatal environment, associations between prenatal risk factors and offspring disease maybe attributable to true prenatal risk effects or to the "confounding" effects of genetic liability that are shared by mother and offspring. Cross-fostering designs, including those that involve embryo transfer have proved useful in animal studies. However disentangling these effects in humans poses significant problems for traditional genetic epidemiological research designs.We present a novel research strategy aimed at disentangling maternally provided pre-natal environmental and inherited genetic effects. Families of children aged 5 to 9 years born by assisted reproductive technologies, specifically homologous IVF, sperm donation, egg donation, embryo donation and gestational surrogacy were contacted through fertility clinics and mailed a package of questionnaires on health and mental health related risk factors and outcomes. Further data were obtained from antenatal records.To date 741 families from 18 fertility clinics have participated. The degree of association between maternally provided prenatal risk factor and child outcome in the group of families where the woman undergoing pregnancy and offspring are genetically related  is compared to association in the group where offspring are genetically unrelated to the woman who undergoes the pregnancy . These comparisons can be then examined to infer the extent to which prenatal effects are genetically and environmentally mediated.A study based on children born by IVF treatment and who differ in genetic relatedness to the woman undergoing the pregnancy is feasible. The present report outlines a novel experimental method that permits disaggregation of maternally provided inherited genetic and post-implantation prenatal effects. The causal risk factors and pathways leading to common clinical problems, such as cardiovascular disease, asthma, schizophrenia and depression remain largely unknown. There is consistent evidence demonstrating that inherited, genetic factors play an important role in such disorders ,2. AlthoOne important example of this complexity is the growing evidence that exposure to many important environmental risk factors for common disorders is not random. Specifically environmental risk factors such as exposure to early adversity are not independent of an individual's genetically influenced characteristics and behaviour or those of their parents . Thus, aExamples of such designs include early adoption studies of animals and humans where the postnatal environment is provided by genetically unrelated parents. These studies show that regardless of genetic liability, postnatal environmental factors have important effects on many different outcomes; for example stress susceptibility , renal rIn utero programming, whereby a stimulus or insult at a sensitive period of development has lasting effects, is thought to represent a key risk pathway by bringing about long-lasting changes to the structure and metabolism of the organism [The leading causes of global disease burden are complex disorders such as cardiovascular disease and depression. There has been increasing evidence over the last twenty years that many of these disorders and health-related problems have their origins in foetal life, with early intrauterine factors hypothesised to have long term effects on health and behaviour ,3. This organism . Replicaorganism , diabeteorganism , depressorganism  and earlorganism ; betweenorganism ; betweenorganism ; and betorganism . Most oforganism  and Chinorganism  with latorganism  and coroorganism .in utero, such as gestational stress and cigarette smoking in pregnancy, are also influenced by maternal characteristics, including those that are influenced by maternal genotype [in utero and disease outcomes could arise through maternally provided genetic factors and/or a 'true' environmentally mediated effect  and cardiovascular disease (CVD), which may share common antecedents [One example is the link between gestational stress and subsequent anxiety in offspring , where tecedents -27. MothPrenatal cross-fostering of animals allows disentanglement of these mechanisms and has been used in some instances to test the relative contributions of the prenatal and postnatal environment and genetic factors to different phenotypes in animals. For example, one such study demonstrated the contribution of maternally transmitted autoantibodies  to diabetes in offspring. This was achieved, in part, by prenatal cross fostering non-obese diabetic mice embryos to mothers of a non-autoimmune strain . The genin utero". In vitro fertilisation (IVF) is becoming an increasingly common means of conception. Current estimates suggest 1.3%–3.6% of European births are now due to IVF [It should be possible to distinguish between whether maternally provided prenatal risk effects are mediated environmentally or genetically in humans by studying offspring whose intrauterine environment is provided by a genetically unrelated mother; essentially an adoption study "e to IVF ; a propoe to IVF . HoweverOur aims were to 1) test the feasibility of identifying and recruiting a sample of children born by IVF from the 5 treatment groups, 2) establish a sample of children aged 5 to 9 years born by homologous IVF, sperm donation, egg donation, embryo donation and gestational surrogacy, 3) obtain questionnaire measures on a range of potential health and mental health related risk factors and outcomes, 4) obtain data from antenatal records for each child and 5) identify the extent to which families would be willing to engage in future research.in utero as well as genetic factors. The research protocol was approved by Wales Multicentre Research Ethics Committee.The questionnaire measures and antenatal data obtained were aimed at testing a range of hypotheses, specifically that associations between a) specific antenatal events , b) maternally perceived gestational stress, c) markers of prenatal-growth  and child behaviour and mental health outcomes are attributable to maternally provided environmentally mediated effects Over the past 3 years, in an ongoing study, 18 fertility centres have participated. Families with school aged children (aged 5 to 9 years) born following IVF treatment have been recruited. To date, 741 families have participated  although data collection is ongoing and the conception groups have been recruited in a sequential manner. The expected and current numbers in each conception group are shown in Table Among those who participated in this project, clinic staff and families reported a positive research experience, with the vast majority of families (655/741 = 88%) agreeing to receive information about future research studies. 80% (573/712) of mothers  agreed for researchers to access antenatal records to obtain detailed information about the pregnancy. The level of agreement between maternal report of pre-/peri-natal factors and information obtained from antenatal notes was excellent for a range of variables (e.g. birth weight (r = .991), smoking during pregnancy (kappa = .806), high blood pressure during pregnancy kappa = .716) . The onlTable Table These data show that this novel design is feasible and can be successfully employed in the U.K. to test the effects of maternally provided prenatal environment on offspring. As with all other research designs there are strengths and weakness. Issues that will need consideration are the effects of programming at the pre-implantation stage, the impact of differences in IVF methods and statistical power in relation to group and sub-group comparisons. Potential limitations include under-representation of some risk factors as well as health problems related to older maternal age and IVF. The objective of this design is not to obtain an epidemiologically representative sample but rather to test differences in the degree of association between risk factors and outcome across different conception groups. Thus it is important to consider whether specific associations differ in strength between the homologous IVF group and the general population. Given the current numbers of children born by IVF in the rare groups, this design will be most useful for examining trait measures or common conditions and risk factors such as emotional and behavioural symptoms, cognitive ability, asthma, blood pressure and BMI. For many outcomes, both clinical conditions and traits that appear later in life, the children will need to be followed up into adult life.Retrospectively obtaining information on pregnancy complications for the surrogacy group was difficult because commissioning parents were often not aware of details of the pregnancy and families were either no longer in touch with the surrogate or did not wish her to be contacted. Thus information was difficult to obtain from the surrogate who experienced the pregnancy. However, this is not crucial for the design as the egg donation and embryo donation groups also allows the researcher to separate the effects of genetic transmission and prenatal environmental exposure and here, pregnancy data are easily available.In summary there is evidence that many health problems have their origins in foetal life and are also genetically influenced. Associations between prenatal adversity and many health outcomes arise because of either true environmentally mediated effects or inherited genetic factors or both. There is a need for experimental research designs that enable disentanglement of inherited genetic from environmentally mediated risk effects. To date twin and adoption study designs of clinical disorders and behaviours have been used to separate the effects of postnatal and later environmental influences from inherited factors . HoweverIn conclusion, there is increasing evidence that prenatal risk factors may contribute to the aetiology of different disorders and traits. The observed associations between prenatal risk factors and specific outcomes could arise through genetic pathways. We present a novel method that appears to be feasible for testing whether associations between putative prenatal risk factors and different outcomes are attributable to an environmentally mediated effect.AT has received educational, non promotional grants from pharmaceutical companies. The other authors declare that they have no competing interests.AT and GH jointly contributed to the idea and scientific research design. XJG also contributed to the idea. FR has undertaken project management and supervision and generated the data. JB provided links to Fertility Centres, with JB and FR adding additional expertise relating to specific IVF subgroup considerations. DH and MVB are co-applicants on the grant and will be involved in analysis and measurement. All authors contributed to the writing and editing.The pre-publication history for this paper can be accessed here:


The purpose of this study is to provide insight into the determinants of low birth weight (LBW) in Italy.The study was carried out in a non-teaching hospital in Catanzaro . All LBW and very LBW newborns (200) were included in the study and a random sample of 400 newborns weighing ≥ 2500 g was selected. Data were collected from the delivery certificates during one year. Smoking activity of mother and familiar and/or social support during pregnancy was gathered through telephone interviews.Overall annual LBW rate was 11.8%. Among LBW newborn there were 125 preterm and 75 term. Younger mothers, those who smoked during pregnancy, and had fewer prenatal care visits were more likely to deliver a LBW child; moreover, preterm newborns, delivered by caesarean section, and twin or multiple birth were significantly more likely to have a LBW. The comparison of very LBW (<1500 g) to LBW newborns showed that a very LBW was significantly more likely in newborns delivered by less educated mothers, those who work outside the home, live in smaller towns, and had less echographies; moreover, as expected, very LBW newborns were more likely to be preterm.Several modifiable factors affect the risk of LBW, even when universal access to health care is freely available, but socio-economic status appears to correlate only to very LBW. Low birth weight (LBW), as a result of preterm birth or of intrauterine growth retardation, is the strongest single factor associated with perinatal and neonatal mortality and an established determinant of post-natal and infant mortality. Moreover, birth weight is related to health outcomes in childhood, such as neurological deficits and lower cognitive skills -3 as welLBW rates vary considerably among studies and countries, ranging from 3.1 to 13.3% -12 and USeveral determinants have been associated with LBW, related to intrauterine growth retardation, such as maternal smoking, poor diet, and low weight of the mother ,14-19, wIn Italy universal access to health care is provided by the National Health Service, however indicators of inequalities in health have been reported . Since bThe purpose of this study is to provide insight into the determinants of LBW in the context of universal access to health care, particularly in Italy.th week of gestation. Social support was measured according to the Neighbourhood Support Scale , marital status of mother , education of mother , occupation of mother , inhabitants in the place of residence , number of fetuses , number of prenatal care visits , number of echographies performed during pregnancy (continuous), amniocentesis , course of pregnancy , type of delivery , sex of newborn , birth order , gestational age , social support during pregnancy , number of cigarettes/day during pregnancy (continuous), congenital malformation . Adjusted odds ratio (OR) and 95% confidence intervals (CI) were calculated. The study was carried out in compliance with the principles of the Helsinki Declaration. The Ethics Committee of the "Mater Domini" Hospital of Catanzaro  approved the protocol of the study (Prot. E.C. no.8/2004).All analyses were performed by means of Stata version 8.1 software .During the study period a total of 1,700 newborns were delivered at the hospital, and of these 200 had a weight < 2500 g for an overall annual LBW rate of 11.8%. Among LBW newborn there were 125 preterm and 75 term. The distribution of maternal, pregnancy, prenatal care and newborn characteristics according to gestational age and birth weight is reported in Tables Delivering a LBW newborn was significantly related to maternal age, smoking habit during pregnancy, frequency of prenatal care visits, gestational age, type of delivery and number of fetuses. Indeed, the risk of LBW significantly increased with the number of cigarettes consumed by mothers during pregnancy , in newborns delivered by caesarean section , and twin or multiple fetuses , whereas a significantly reduced risk of LBW was associated to older age of mothers, with more prenatal care visits  and to term newborns  , caesarean section , multiple fetuses  and marital status  , a set of measures developed by the Agency for Healthcare Research and Quality (AHRQ) to identify Ambulatory Care Sensitive Conditions (ACSC) as rates of admission to the hospital, based on the assumption that high hospitalization rates for ACSC may result from poor access to primary care and can be prevented . Indeed,Many investigators have studied the impact of social and economic factors on the outcome of pregnancy, particularly on birth weight, revealing a significant risk of prematurity and intrauterine growth retardation in low socio-economic and in specific ethnic groups ,24,29,30Our present analysis has several limitations that need to be addressed, and the results may be biased in selection, misclassification, or confounding. The target population in our study consisted of all newborns in a tertiary care hospital, and an overestimation of "critical" pregnancies with LBW deliveries might have occurred. In this situation, we might have expected an increased LBW rate, and inference of this result to the overall population would not be valid, since our population was attending a tertiary care hospital and therefore was different from the overall population of pregnant mothers. However, selection bias would pertain to the comparison of LBW newborns compared to "controls – non LBW newborns" and it is well known that selection bias is likely to occur when "controls" do not provide an estimate of the exposure distribution in the source population from which the cases originate. Since our controls originated from newborns whose mothers decided to deliver in the hospital we chose, they belong to the same population of "cases – LBW newborn", thus reducing the risk of selection bias. Moreover, all women could be approached by telephone interview, and none refused to participate. Therefore, we believe that selection was not a substantial source of bias in the comparison of cases and controls. We gathered individual level data on the perceived social and/or familiar support of mothers during pregnancy; and in the presence of a perception measure, misclassification bias cannot be ruled out. In addition, the information on maternal smoking habits was self-reported, and underreporting of smoking during pregnancy, particularly by mothers who delivered LBW children, has been reported in previous studies ,33. HoweIn conclusion, several modifiable factors affect the risk of LBW, even when universal access to health care is freely available, but socio-economic status appears to correlate only to very LBW. In order to develop an effective prevention strategy to reduce LBW rates, research is needed to investigate reasons for low attendance of prenatal care.Low birth weight (LBW)The author(s) declare that they have no competing interests.All authors have made substantive contributions to the study, and all authors endorse the data and conclusions.CGAN has contributed in the study concept and design, in the acquisition of the data and in the analysis and interpretation of the data, and in the drafting of the manuscript.GR and CA have contributed in the acquisition of the data and in the analysis and interpretation of the data.MP has contributed in the study concept and design, in the acquisition of the data, in the analysis and interpretation of the data, in the drafting of the manuscript, in the critical revision of the manuscript for important intellectual content.The pre-publication history for this paper can be accessed here:


The results of the Swedish two-county study are analysed with respect to tumour size, nodal status and malignancy grade, and the relationship of these prognostic factors to screening and to survival. It is shown that these factors can account for much of the differences in survival between incidence screen detected, interval and control group cancers but to a lesser extent for cancers detected at the prevalence screen where length bias is greatest. Furthermore, examination of the relationships among the prognostic factors and mode of detection indicates that malignancy grade, as a measure of inherent malignant capacity, evolves as a tumour grows. The proportion of cancers with poor malignancy grade is several fold lower for cancers of diameter less than 15 cm than for cancers greater than 30 cm, independent of the length bias of screening. The implications of these findings for screening frequency are briefly discussed.


The behaviour of children with Attention-Deficit/Hyperactivity Disorder is characterized by low predictability of responding. Low behavioural predictability is one way of operationalizing intra-individual ADHD-related variability. ADHD-related variability may be caused by inefficient behavioural selection mechanisms linked to reinforcement and extinction, as suggested by the recently published dynamic developmental theory (DDT) of ADHD. DDT argues that ADHD is a basic neurobehavioural disorder, caused by dysfunctioning dopamine systems. For establishing ADHD as a neurobehavioural disorder, findings from studies conducted in Western countries should be replicated in other cultural populations. The present study replicated the study conducted in Norway, with children from the Limpopo province in the Republic of South Africa.th percentile were matched on gender and age and comprised the non-ADHD group. The children completed a computerized game-like task where mouse clicks on one of two squares on the screen resulted in delivery of a reinforcer according to a variable interval schedule of reinforcement. Reinforcers were cartoon pictures presented on the screen together with a sound. Predictability of response location and timing were measured in terms of explained variance.Boys and girls, aged 6–9 yr, from seven ethnic groups participated. Scores by teachers on the Disruptive Behavior Disorders rating scale defined participation in either ADHD-hyperactive/impulsive (-HI), ADHD-predominantly inattentive (-PI), or ADHD-combined (-C) groups. Children below the 86Overall, the results replicated findings from Norway. Specifically, the ADHD-C group showed significantly lower predictability of responding than the non-ADHD group, while the ADHD-HI and the ADHD-PI groups were in-between. In accordance with the previous study, response location, but not response timing, was a sensitive behavioural measure. There were no significant gender differences. Cartoon pictures were effective reinforcers as the non-ADHD group showed learning of the task. There was no relation between behavioural predictability and motor functions.The present study makes a strong case for ADHD as a basic, neurobehavioural disorder, not a cultural phenomenon, by replicating findings from a wealthy Western country in a poor province of a developing country. The results were, generally, in line with predictions from the dynamic developmental theory of ADHD by indicating that reinforcers were less efficient in the ADHD group than in the non-ADHD group. Finally, the results substantiated ADHD-related variability as an etiologically important characteristic of ADHD behaviour. Attention-deficit/hyperactivity disorder (ADHD)  is a behADHD is characterized by age-inappropriate hyperactivity, impulsiveness, and deficient sustained attention . Most clADHD-related variability is mainly observed at the behavioural level , so a thThe present study is a replication and extension of that study, with a larger sample including both genders. Further, the study is conducted in a developing country with a culturally less homogeneous population. However, clinical resources are sparse, and assessment methods developed and validated in Western countries may not be relevant or feasible. A recent prevalence study conducted in various language groups in South Africa showed that when using a teacher rating scale, similar figures for prevalence as in Western countries were obtained. Also, rather similar distributions across the three ADHD subtypes  were found, in addition to a similar gender distribution .The primary aim of the present study was to investigate if the results from the Norwegian study could be replicated in a different sample derived from various language groups in the Limpopo province of South Africa. If replicated, the combined results from the Norwegian and the South African studies would make a strong case for a basic, neurobiological mechanism differentiating ADHD from non-ADHD children, refuting the argument that ADHD is a cultural phenomenon resulting from Western way of life, or from Western conceptualization of psychiatric problems. In addition, the findings might lend support to the recently published dynamic developmental theory of ADHD  arguing The present sample made it possible to investigate potential differential effects of reinforcers on response patterns in the three subtypes of ADHD : the preADHD-related variability was defined as reduced predictability of consecutive responses. The task used was a computerized game where mouse clicks on either of two squares on the screen resulted in the presentation of a reinforcer. Reinforcers were cartoon pictures displayed on the screen for a short period accompanied by a sound. These were delivered according to variable interval (VI) schedules of reinforcement. VI schedules specify that responses result in a reinforcer after varying time intervals. Thus, reinforcers are presented at unpredictable times , avoidinChildren from seven ethnic groups of the Limpopo Province of South Africa  were recruited from a school-based population. The 179 children (128 boys and 51 girls) were recruited following screening of the general population of primary school children representative of all socio-economic levels for ADHD-like behaviour. The Disruptive Behavior Disorders (DBD) rating scale ,23 was sADHD group (95th percentile or above) was based on the results from the prevalence study .side response pattern, i.e., whether consecutive responses were on the left or right side of the screen. Highly predictable responding would typically be on the side where the correct target was positioned, and indicates good stimulus control. Likewise, low predictability implies that responses are unsystematically distributed on the two sides and means poor stimulus control. 2) The distance-to-nearest-centre measure was based on the distance from the pixel where the response was placed, to the centre of the selected square, whether it was the correct square or not. This measure indicates whether the children developed strategies of responding that was related to the borders of the squares, disregarding whether the square was correct or not. 3) The distance-to-correct-centre measure was based on the distance from where the response was placed to the centre of the correct square. Thus, this measure was a variant of the distance-to-nearest-centre measure, anchored to the centre of the correct square. Both distance scores (measure 2 and 3) were in terms of vertical and horizontal pixels, with 0,0 defining the centre of the square. 4) Timing response patterns were based on consecutive interresponse times (IRTs).The same behaviours were analyzed in the previous  and the The predictability of responses spatially and temporally was assessed by explained variance, i.e., autocorrelations squared. Explained variance is a better measure for overall predictability than autocorrelations by itself since the latter will have both positive and negative values cancelling each other when added. Autocorrelations  of each measure were correlations of consecutive response measures across five lags. Thus, correlations between the value of response n and of response n+1 is the first lag, between n and n+2 is the second lag, and so on up to correlations between response n and n+5 being the fifth lag. For programming reasons, autocorrelations were computed on consecutive responses through a full segment and not reset at reinforcer delivery because the number of responses was huge compared to the number of reinforcers. Changes in predictability of responding throughout the experiment could be observed in the explained variance for each child from segment to segment. An increase in autocorrelations over segments would indicate that performance became more and more predictable throughout the experiment, and thus be an indirect measure of learning. We hypothesised that compared to the non-ADHD group less of the behaviour of children with ADHD would be predictable, indicated by generally less explained variance. In addition, predictability of responses in the ADHD group should, to a larger extent than in the non-ADHD group, be restricted to the first lag, indicating shorter behavioural sequences.learning was measured as mean percent of all responses that were placed within the correct square. A high score on percent correct would indicate that the dark grey square exerted good stimulus control over the responding of the children, and is thus a measure of attention.In addition to the response sequences, Data were analyzed by means of SPSS 11.0 for Windows (SPSS) and Statistica 6.1  program Analyses relevant for the primary aim were performed first, with Group (2: ADHD and non-ADHD) as the between-group variable; and segment (5) and lag (5) as within-group variables. The ADHD group consisted of children with ADHD-C and ADHD-HI in order to replicate the Norwegian study. Then, as follow-up analyses, subtypes of ADHD  versus non-ADHD were analysed across segments and lags, in order to investigate potential differences between subtypes of ADHD. Results were followed up with post hoc Scheffé tests where relevant. Non-published results may be obtained from the first author upon request.Demographic and diagnostic measures of the ADHD-related subtypes and the non-ADHD comparison group are displayed in Table Generally, there was no effect of Gender, neither main effects nor interaction effects. Consequently, the reported findings combine boys and girls. Further, predictable responding was found for the three spatial behavioural measures, but not for the timing measure. The ADHD Combined group had the lowest explained variance of all groups on the spatial measures.This measure assessed whether the child's choice of side of the screen (left or right) was predictable irrespective of on which side the correct response target was displayed. Highly predictable responding across lags would imply that behaviour was orderly related to side of screen.2 > 0.17; median R2 = 0.22) compared to previously published results [Generally, predictability from one response to the immediate next (first lag) was in the low range  = 2.99; p < .001), and between Subtype, Segment, and Lag  = 1.62; p < .005). Only the three-way interaction was confirmed by the MANOVA. The interaction effects implied that predictability in responding for the three ADHD subtypes and the non-ADHD group changed differently across segments and lags. The ADHD-C group showed the least predictable responding across segments. No other interaction effects were statistically significant.This measure assessed to what degree the children responded in predictable patterns in terms of the distance from where the response was placed to the centre of the nearest square irrespective of whether it was the correct response target or not.2 > 0.12; median R2 = 0.17) show a rather low predictability from one response to the immediate next. As can be seen in Figure The generally low values of explained variance in the first lag  = 149.27; p < .001). In order to check if there were statistically significant differences between any two subtypes, a post hoc Scheffé test of Subtype and Lag was performed. The main results from this test indicate a larger decrease in explained variance from the first to the next lags in the non-ADHD group and in the ADHD-PI subtype compared to the other subtypes, and that there was a larger difference between the ADHD-C subtype and the non-ADHD group compared to any other combination of subtypes.2 > 0.19; median R2 = 0.25) compared to previous results [This measure assessed patterns in response placements in terms of distance from the centre of the correct square. Again, low explained variance indicated high variability in spatial responding. As can be seen in Figure  results . The ADHThe ANOVA showed a significant main effect of Group, in addition to the main effect of Lag Table . There wThe follow-up ANOVA of the three subtypes and the non-ADHD group only showed a significant interaction effect between Subtype and Lag  = 2.64; p < .002) in addition to a main effect of Lag  = 198.39; p < .001). The interaction effect indicated that the ADHD-C group showed less behavioural predictability than any of the other groups, while the ADHD-PI did not differ from the non-ADHD group. The interaction was not confirmed by the MANOVA.In order to check what contributed to the interaction effect, a post hoc Scheffé test was performed involving Subtype and Lag. The main results from this test confirmed some overlap between the non-ADHD and ADHD-PI subtypes, and that explained variance for these groups was significantly higher than that of the ADHD-C subtype.2 > 0.05). Besides a significant main effect of Lag, there were no other main or interaction effects (Table The development of patterns in response timing was investigated in terms of consecutive interresponse times (IRTs). Explained variance of the first lag was generally very low and not significantly different between the groups  was used as a measure of learning. Overall, the non-ADHD group had higher percent correct scores than the ADHD group (58.6% vs 46.2%). The non-ADHD group increased their scores with about 10% (from 52.8% to 62.5%) across segments, while the ADHD group only increased with less than 2%. The ANOVA of percent correct choice of square showed significant main effects of Group  = 16.142; p < .001) and of Segment  = 7.635; p < .001). The interaction effect between Group and Segment was also significant  = 4.297; p < .002); and was confirmed by the MANOVA  = 2.964; p < .03).The follow-up ANOVA of the three subtypes and the non-ADHD group showed a main effect of Subtype  = 6.14; p < .001) and of Segment  = 7.18; p < .001) . The ADHD-PI group was not significantly different from any of the other subtypes, and the -C and -HI subtypes were not significantly different from each other.A recent study of motor functions of the present South-African sample showed significant differences between children with ADHD-related symptoms and those without . Increased behavioural variability may be a result of motor coordination problems. This relation was tested by correlating first lag explained variance of all spatial measures with normalized scores on Grooved Pegboard test and Mazes (see Meyer & Sagvolden for details) for both dominant and non-dominant hand, for the ADHD-C group and the non-ADHD group separately. For the ADHD group, scores on response predictability was mainly negatively related to scores on motor tests , but the relation was weak (Pearson correlations .234 > r > -.352). For the non-ADHD group, the relation was even weaker (Pearson correlations .237 > r > -.175).The present study was designed to follow up the results of a study in a wealthy European country, Norway, which showed that response sequences in children with ADHD symptoms is less predictable than those of children without ADHD . The preThe most striking finding in the present study was that the lower predictability of consecutive responses of boys with ADHD compared to controls in the Norwegian study  was replAnother striking similarity between the two studies was the finding that predictability of consecutive responding were found for the spatial behavioural measures only and not for the temporal measure. We speculated that this finding might be related to the visuo-spatial nature of the task, alternatively that striatal dysfunction might explain deficient habit learning in ADHD . The facIt is important to notice that the cartoons acted as reinforcers in the non-ADHD children as shown by their learning curves Fig. . Learninth percentile. In addition, children in a developing country may not be as used to computers as Norwegian children. While most Norwegian children are acquainted with computer games, including clicking on items in order to bring up other items or happenings on the screen, the South African children may not be that familiar with computer games. This will result in more explorative and somewhat less systematic behaviour in the South African children. Further, an unintended procedural difference between the two studies might have influenced the results. In Norway, testers were instructed to add verbal feedback  when the cartoons were displayed on the screen. This was not done in South Africa. Thus the reinforcing effect might have been less, particularly for the non-ADHD children as this group deviated more from their Norwegian counterparts than the ADHD group.In general, the behaviour of all groups was less predictable in the present study than in the previous study , particuThe findings demonstrate, nonetheless, that the task could be run in just one session and with no extra tangible reinforcers. Hence, a quick , and easy task as the present actually showed basic behavioural differences between children with ADHD and children without symptoms.The present study found few statistically significant differences between the three subtypes. When differences were indicated, they generally showed that the ADHD-C group performed with lower predictability than the other groups, and often the results for the ADHD-HI group fell between the -C and the -PI groups. The most likely explanation of this result is that it reflects that the behavioural disturbances in the ADHD-PI and ADHD-HI groups are less severe than those of the ADHD-C group.There were no gender differences. This supports other findings with population-based samples, showing that non-referred subjects of both genders present with similar clinical and cognitive profiles . These aADHD-related variability has been demonstrated in a plethora of tasks, particularly response time tasks . These aIncreased variance may be a result of augmented motor difficulties in children with ADHD-related problems. In the Norwegian sample, scores on motor tests did not differentiate ADHD from non-ADHD groups . CorrelaThe present task provides an objective measure of basic behavioural processes that is not confounded with timing skills, motor functions, performance requirements , or the correct understanding of more or less complicated instructions. As such, the task may prove easy to carry out; both for testers and subjects; and results are interpretable within a theoretical framework. Future studies will show its predictability and diagnostic utility.Some obvious limitations to the present study should be mentioned. Group membership was decided solely on the basis of teacher scores on the Disruptive Behavior Disorders (DBD) rating scale ,23 and nThe present study makes a strong case for ADHD as a basic, neurobehavioural disorder, not a cultural phenomenon, by the overall replication of the results from Norway, a wealthy Western European country, in the very different, poor Limpopo province of South Africa and with a large majority of native African children.Overall, the results were in line with the predictions from the dynamic developmental theory of ADHD by indicating that reinforcers were less efficient in the ADHD group than in the non-ADHD group in establishing stimulus control and predictable responding, due to a shorter delay-of-reinforcement gradient. The results also substantiated ADHD-related variability as an etiologically important characteristic of ADHD.The present study did not find statistically significant differences between the non-ADHD group and the ADHD-HI or -PI groups. It is likely that this reflects less severe behavioural disturbances in these subtypes compared to the ADHD-C subtype.In spite of basically similar response patterns, behaviour could not be predicted to the same degree in the South African children as in the Norwegian children, particularly when comparing non-ADHD groups. Likely reasons are less computer experience in South African children, procedural differences related to reinforcement, and increased group heterogeneity in the present study since the Disruptive Behavior Disorders (DBD) rating scale is less sensitive than the standard comprehensive diagnosis performed in the Norwegian study. Still, the DBD proves to be a powerful instrument.Finally, there were no statistically significant gender differences. This supports other findings with population-based samples.The author(s) declare that they have no competing interests.HA participated in the development of study design, development of the reinforcement task used, participated in preparing the data, performed the statistical analyses, and wrote the manuscript. AM leaded the data collection, participated in preparing the data, participated in writing the manuscript, read and approved the final draft. TS participated in the development of study design, development of the reinforcement task, wrote the programs for statistical analyses, participated in data analyses, participated in writing the manuscript, read and approved the final draft.