COVID-19 and the epistemology of epidemiological models at the dawn of AI.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: 'model organisms' chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through 'causal inference'), and the (past/future) value of unmeasured variables (through 'classification/prediction'); and a range of modelling techniques to predict beyond the available data (through 'extrapolation'), compare different hypothetical scenarios (through 'simulation'), and estimate key features of dynamic processes (through 'projection'). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: 'selection-collider bias', 'unadjusted confounding bias' and 'inferential mediator adjustment bias' - all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined.1 Selection-collider bias occurs when these two variables independently cause a third (the 'collider'), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or 'mediators') fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at 'mediation analysis'). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to 'data-driven' modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

The Association Between Childhood Leukemia and Population Mixing: An Artifact of Focusing on Clusters?

BACKGROUND: Studies investigating the population-mixing hypothesis in childhood leukemia principally use two analytical approaches: (1) nonrandom selection of areas according to specific characteristics, followed by comparisons of their incidence of childhood leukemia with that expected based on the national average; and (2) regression analyses of region-wide data to identify characteristics associated with the incidence of childhood leukemia. These approaches have generated contradictory results. We compare these approaches using observed and simulated data. METHODS: We generated 10,000 simulated regions using the correlation structure and distributions from a United Kingdom dataset. We simulated cases using a Poisson distribution with the incidence rate set to the national average assuming the null hypothesis that only population size drives the number of cases. Selection of areas within each simulated region was based on characteristics considered responsible for elevated infection rates (population density and inward migration) and/or elevated leukemia rates. We calculated effect estimates for 10,000 simulations and compared results to corresponding observed data analyses. RESULTS: When the selection of areas for analysis is based on apparent clusters of childhood leukemia, biased assessments occur; the estimated 5-year incidence of childhood leukemia ranged between zero and eight per 10,000 children in contrast to the simulated two cases per 10,000 children, similar to the observed data. Performing analyses on region-wide data avoids these biases. CONCLUSIONS: Studies using nonrandom selection to investigate the association between childhood leukemia and population mixing are likely to have generated biased findings. Future studies can avoid such bias using a region-wide analytical strategy. See video abstract at, http://links.lww.com/EDE/B431.

Poverty, disability and self-reported health amongst residents and migrants in Gauteng, South Africa.

BACKGROUND AND AIM: The aim of the present study was to assess the relative importance of individual- and household-level indicators of poverty to the self-reported health of residents and recent migrants in South Africa's most urbanised province (Gauteng). SUBJECTS AND METHODS: Univariate and multivariable statistical analyses were undertaken on data from the 2014 Quality of Life household survey undertaken by the Gauteng City Regional Observatory. The survey generated data on a representative sample of n = 27 490 respondents. RESULTS: At the individual-level the odds for disability or health-limiting work/social activities was significantly lower amongst younger, better educated and employed respondents, and amongst both transnational and internal migrants. At the household-level, the absence of some basic services and household assets (particularly mains electricity, telecommunications and a television) were significantly associated with a lower odds of health-limiting work/social activities. CONCLUSIONS: Variation in sociodemographic and economic predictors of self-reported health at the individual- and household-level partly explain the lower odds of disability and health-limiting work/social activities of migrants, since migrants were less likely to be disabled and tended to be younger, with higher educational attainment and better employment status than residents, yet were also more likely to be living in households with fewer services and assets.

Improving maternal health in Pakistan: toward a deeper understanding of the social determinants of poor women's access to maternal health services.

Evidence suggests national- and community-level interventions are not reaching women living at the economic and social margins of society in Pakistan. We conducted a 10-month qualitative study (May 2010-February 2011) in a village in Punjab, Pakistan. Data were collected using 94 in-depth interviews, 11 focus group discussions, 134 observational sessions, and 5 maternal death case studies. Despite awareness of birth complications and treatment options, poverty and dependence on richer, higher-caste people for cash transfers or loans prevented women from accessing required care. There is a need to end the invisibility of low-caste groups in Pakistani health care policy. Technical improvements in maternal health care services should be supported to counter social and economic marginalization so progress can be made toward Millennium Development Goal 5 in Pakistan.

Prospects for progress on health inequalities in England in the post-primary care trust era: professional views on challenges, risks and opportunities.

BACKGROUND: Addressing health inequalities remains a prominent policy objective of the current UK government, but current NHS reforms involve a significant shift in roles and responsibilities. Clinicians are now placed at the heart of healthcare commissioning through which significant inequalities in access, uptake and impact of healthcare services must be addressed. Questions arise as to whether these new arrangements will help or hinder progress on health inequalities. This paper explores the perspectives of experienced healthcare professionals working within the commissioning arena; many of whom are likely to remain key actors in this unfolding scenario. METHODS: Semi-structured interviews were conducted with 42 professionals involved with health and social care commissioning at national and local levels. These included representatives from the Department of Health, Primary Care Trusts, Strategic Health Authorities, Local Authorities, and third sector organisations. RESULTS: In general, respondents lamented the lack of progress on health inequalities during the PCT commissioning era, where strong policy had not resulted in measurable improvements. However, there was concern that GP-led commissioning will fare little better, particularly in a time of reduced spending. Specific concerns centred on: reduced commitment to a health inequalities agenda; inadequate skills and loss of expertise; and weakened partnership working and engagement. There were more mixed opinions as to whether GP commissioners would be better able than their predecessors to challenge large provider trusts and shift spend towards prevention and early intervention, and whether GPs' clinical experience would support commissioning action on inequalities. Though largely pessimistic, respondents highlighted some opportunities, including the potential for greater accountability of healthcare commissioners to the public and more influential needs assessments via emergent Health & Wellbeing Boards. CONCLUSIONS: There is doubt about the ability of GP commissioners to take clearer action on health inequalities than PCTs have historically achieved. Key actors expect the contribution from commissioning to address health inequalities to become even more piecemeal in the new arrangements, as it will be dependent upon the interest and agency of particular individuals within the new commissioning groups to engage and influence a wider range of stakeholders.

Caste in Muslim Pakistan: a structural determinant of inequities in the uptake of maternal health services.

The failure to reduce maternal mortality rates in high-burden countries has led to calls for a greater understanding of structural determinants of inequities in access to maternal health services. Caste is a socially constructed identity that imposes structural disadvantages on subordinate groups. Although a South Asian construct, the existence of caste as a structural social stratifier is actively rejected in Muslim Pakistan as a regressive symbol of Hinduism. In this inimical context, the possibility of caste as a driver of maternal health care inequities is not acknowledged and has, therefore, remained unexplored in Pakistan. The objective of the present study is to quantitatively assess the variation in the use of maternity services across different caste groups in Pakistan. The research also contributes to methodological innovation in modelling relationships between caste, mediating and/or confounding socio-economic factors and maternal health service indicators. A clustered, stratified survey sampled 1457 mothers in districts Jhelum and Layyah. Multivariable, multi-level (confounder-adjusted) logistic regression analysis showed "Low" caste mothers had higher odds of landlessness, no education, working in unskilled occupations, asset poverty, no antenatal care and a home-based birth with an unskilled attendant compared to "High" or "Middling" caste individuals. Despite the important role of caste in patterning socio-economic disadvantage, its indirect causal effect on maternal health care was predominantly mediated through mothers' education and household assets. Our findings suggest a need for group-specific policies, including constructing schools in low-caste dominant settlements, affirmative action with job quotas, redistributing agricultural lands and promoting industrial development in the poorer districts.

Analyses of 'change scores' do not estimate causal effects in observational data.

BACKGROUND: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this approach can produce misleading causal-effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation for why change scores do not estimate causal effects in observational data. METHODS: Data were simulated to match three general scenarios in which the outcome variable at baseline was a (i) 'competing exposure' (i.e. a cause of the outcome that is neither caused by nor causes the exposure), (ii) confounder or (iii) mediator for the total causal effect of the exposure variable at baseline on the outcome variable at follow-up. Regression coefficients were compared between change-score analyses and the appropriate estimator(s) for the total and/or direct causal effect(s). RESULTS: Change-score analyses do not provide meaningful causal-effect estimates unless the baseline outcome variable is a 'competing exposure' for the effect of the exposure on the outcome at follow-up. Where the baseline outcome is a confounder or mediator, change-score analyses evaluate obscure estimands, which may diverge substantially in magnitude and direction from the total and direct causal effects. CONCLUSION: Future observational studies that seek causal-effect estimates should avoid analysing change scores and adopt alternative analytical strategies.

Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients.

The present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using real world demographic and clinical data from 1,802 heart failure patients enrolled in the UK-HEART2 cohort, the study found that univariable GLMs using LCA-generated subgroup/class membership as the sole candidate predictor of survival were inferior to standard multivariable GLMs using the same four covariates as those used in the LCA. The inclusion of the LCA subgroup/class membership together with these four covariates as candidate predictors in a multivariable GLM showed no improvement in predictive acuity. In contrast, LCR modelling resulted in a 18-22% improvement in predictive acuity and provided a range of alternative models from which it would be possible to balance predictive acuity against entropy to select models that were optimally suited to improve the efficient allocation of clinical resources to address the differential risk of the outcome (in this instance, survival). These findings provide proof-of-principle that LCR modelling can improve the predictive acuity of GLMs and enhance the clinical utility of their predictions. These improvements warrant further attention and exploration, including the use of alternative techniques (including machine learning algorithms) that are also capable of generating latent class structure while determining outcome predictions, particularly for use with large and routinely collected clinical datasets, and with binary, count and continuous variables.

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations.

BACKGROUND: Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. METHODS: Original health research articles published during 1999-2017 mentioning 'directed acyclic graphs' (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article's largest DAG. RESULTS: A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9-16, range: 3-28] and 29 arcs (IQR: 19-42, range: 3-99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31-67, range: 12-100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included 'super-nodes' (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). CONCLUSION: There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

Ratio index variables or ANCOVA? Fisher's cats revisited.

Over 60 years ago Ronald Fisher demonstrated a number of potential pitfalls with statistical analyses using ratio variables. Nonetheless, these pitfalls are largely overlooked in contemporary clinical and epidemiological research, which routinely uses ratio variables in statistical analyses. This article aims to demonstrate how very different findings can be generated as a result of less than perfect correlations among the data used to generate ratio variables. These imperfect correlations result from measurement error and random biological variation. While the former can often be reduced by improvements in measurement, random biological variation is difficult to estimate and eliminate in observational studies. Moreover, wherever the underlying biological relationships among epidemiological variables are unclear, and hence the choice of statistical model is also unclear, the different findings generated by different analytical strategies can lead to contradictory conclusions. Caution is therefore required when interpreting analyses of ratio variables whenever the underlying biological relationships among the variables involved are unspecified or unclear.

Cholesterol levels in later life amongst UK Channel Islanders exposed to the 1940-45 German occupation as children, adolescents and young adults.

BACKGROUND: To clarify the nature of the relationship between: food deprivation and undernutrition during pre- and postnatal development; and cholesterol levels in later life, this study examined the relationship between birth weight (as a marker of prenatal nutrition) and cholesterol levels among 396 Guernsey islanders (born in 1923-1937), 87 of whom (22%) had been exposed to food deprivation as children, adolescents or young adults (i.e. to postnatal undernutrition) during the 1940-45 German occupation of the Channel Islands, and 309 of whom (78%) had left or been evacuated from the islands before the occupation began. METHODS: Three sets of multiple regression models were used to investigate: Model A - the relationship between birth weight and cholesterol levels; Model B - the relationship between postnatal exposure to the occupation and cholesterol levels; and Model C - any interaction between birth weight, postnatal exposure to the occupation and cholesterol levels. Model A and Model B also tested for any interactions between: birth weight/occupation exposure and sex; and birth weight/occupation exposure and parish of residence at birth (as a marker of parish of residence during the occupation and related variation in the severity of food deprivation). RESULTS: Before (and after) adjusting for potential confounders, no statistically significant relationships were observed between either birth weight (before adjustment: 0.09 mmol/l per kg increase, 95% CI: -0.30, 0.16; after adjustment: 0.08 mmol/l per kg increase, 95%CI: -0.17, 0.34) or exposure to the occupation (before adjustment: 0.01 mmol/l for exposed group, 95%CI: -0.24, 0.27; after adjustment: 0.04 mmol/l for exposed group, 95%CI: -0.26, 0.33) and cholesterol levels in later life. There was also little evidence of significant relationships between birth weight, exposure to the occupation and cholesterol levels in later life when Model A and Model B were stratified by sex or parish of residence at birth, although there was a significant positive relationship between birth weight and cholesterol levels in women (0.44 mmol/l per kg increase, 95%CI: 0.07, 0.81). CONCLUSIONS: These analyses provide little support for the theory that birth weight is inversely related to cholesterol levels in later life. and do not offer any evidence in support of a relationship between undernutrition in childhood, adolescence and early adulthood and cholesterol levels in later life. However, further research may determine whether undernutrition at different stages of the life-course may influence cholesterol levels in later life.

Cardiovascular disease in a cohort exposed to the 1940-45 Channel Islands occupation.

BACKGROUND: To clarify the nature of the relationship between food deprivation/undernutrition during pre- and postnatal development and cardiovascular disease (CVD) in later life, this study examined the relationship between birth weight (as a marker of prenatal nutrition) and the incidence of hospital admissions for CVD from 1997-2005 amongst 873 Guernsey islanders (born in 1923-1937), 225 of whom had been exposed to food deprivation as children, adolescents or young adults (i.e. postnatal undernutrition) during the 1940-45 German occupation of the Channel Islands, and 648 of whom had left or been evacuated from the islands before the occupation began. METHODS: Three sets of Cox regression models were used to investigate (A) the relationship between birth weight and CVD, (B) the relationship between postnatal exposure to the occupation and CVD and (C) any interaction between birth weight, postnatal exposure to the occupation and CVD. These models also tested for any interactions between birth weight and sex, and postnatal exposure to the occupation and parish of residence at birth (as a marker of parish residence during the occupation and related variation in the severity of food deprivation). RESULTS: The first set of models (A) found no relationship between birth weight and CVD even after adjustment for potential confounders (hazard ratio (HR) per kg increase in birth weight: 1.12; 95% confidence intervals (CI): 0.70-1.78), and there was no significant interaction between birth weight and sex (p=0.60). The second set of models (B) found a significant relationship between postnatal exposure to the occupation and CVD after adjustment for potential confounders (HR for exposed vs. unexposed group: 2.52; 95% CI: 1.54-4.13), as well as a significant interaction between postnatal exposure to the occupation and parish of residence at birth (p=0.01), such that those born in urban parishes (where food deprivation was worst) had a greater HR for CVD than those born in rural parishes. The third model (C) found no interaction between birth weight and exposure to the occupation (p=0.43). CONCLUSION: These findings suggest that the levels of postnatal undernutrition experienced by children, adolescents and young adults exposed to food deprivation during the 1940-45 occupation of the Channel Islands were a more important determinant of CVD in later life than the levels of prenatal undernutrition experienced in utero prior to the occupation.

Genotyping the future: scientists' expectations about race/ ethnicity after BiDil.

The ongoing debate about the FDA approval of BiDil in 2005 demonstrates how the first racially/ethnically licensed drug is entangled in both Utopian and dystopian future visions about the continued saliency of race/ethnicity in science and medicine. Drawing on the sociology of expectations, this paper analyzes how scientists in the field of pharmacogenetics are constructing certain visions of the future with respect to the use of social categories of race/ethnicity and the impact of high-throughput genotyping technologies that promise to transform scientific practices.

Flaws in the U.S. Food and Drug Administration's rationale for supporting the development and approval of BiDil as a treatment for heart failure only in black patients.

The U.S. Food and Drug Administration's (FDA) rationale for supporting the development and approval of BiDil (a combination of hydralazine hydrochloride and isosorbide dinitrate; H-I) for heart failure specifically in black patients was based on under-powered, post hoc subgroup analyses of two relatively old trials (V-HeFT I and II), which were further complicated by substantial covariate imbalances between racial groups. Indeed, the only statistically significant difference observed between black and white patients was found without any adjustment for potential confounders in samples that were unlikely to have been adequately randomized. Meanwhile, because the accepted baseline therapy for heart failure has substantially improved since these trials took place, their results cannot be combined with data from the more recent trial (A-HeFT) amongst black patients alone. There is therefore little scientific evidence to support the approval of BiDil only for use in black patients, and the FDA's rationale fails to consider the ethical consequences of recognizing racial categories as valid markers of innate biological difference, and permitting the development of group-specific therapies that are subject to commercial incentives rather than scientific evidence or therapeutic imperatives. This paper reviews the limitations in the scientific evidence used to support the approval of BiDil only for use in black patients; calls for further analysis of the V-HeFT I and II data which might clarify whether responses to H-I vary by race; and evaluates the consequences of commercial incentives to develop racialized medicines. We recommend that the FDA revise the procedures they use to examine applications for race-based therapies to ensure that these are based on robust scientific claims and do not undermine the aims of the 1992 Revitalization Act.

A qualitative exploration of the production of Hospital Episode Statistics in a Guernsey hospital: implications for regional comparisons of UK health data.

Hospital Episode Statistics (HES) are widely used in the UK for audit and research. This often includes comparisons between different geographical regions, but there is some doubt as to the reliability of comparisons made using these data. Previous studies have focused on the quantitative accuracy of the coding, but not the full data chain. This study is a qualitative examination of the production of HES in a small hospital in Guernsey, which is not part of the NHS. Interviews were conducted with key participants in the production of HES to determine how the data were created, and the strengths and weaknesses of this system. We found that face-to-face communication between administrative and clinical staff was felt to contribute to the accuracy of the HES codes, and that a lack of detail in the case-notes was felt to be more problematic than the accuracy of the coders themselves. These findings have implications for the comparison of HES between NHS Trusts, since the processes involved in producing the data will be different in larger hospitals with less contact between coders and clinicians. It is therefore important to bear this in mind when making comparisons between such diverse sources of HES.

The standardization of race and ethnicity in biomedical science editorials and UK biobanks.

As the search for human genetic variation has become a priority for biomedical science, debates have resurfaced about the use of race and ethnicity as scientific classifications. In this paper we consider the relationship between race, ethnicity and genetics, using insights from science and technology studies (STS) about processes of classification and standardization. We examine how leading biomedical science journals attempted to standardize the classifications of race and ethnicity, and analyse how a sample of UK genetic scientists used the concepts in their research. Our content analysis of 11 editorials and related guidelines reveals variations in the guidance on offer, and it appears that there has been a shift from defining the concepts to prescribing methodological processes for classification. In qualitative interviews with 17 scientists, the majority reported that they had adopted socio-political classification schemes from state bureaucracy (for example, the UK Census) for practical reasons, although some scientists used alternative classifications that they justified on apparently methodological grounds. The different responses evident in the editorials and interviews can be understood as reflecting the balance of flexibility and stability that motivate standardization processes. We argue that, although a genetic concept of race and ethnicity is unlikely to wholly supplant a socio-political one, the adoption of census classifications into biomedical research is an alignment of state bureaucracy and science that could have significant consequences.

Health, wealth and IQ in sub-Saharan Africa: challenges facing the 'Savanna Principle' as an explanation for global inequalities in health.

Kanazawa's (2006) national- and individual-level analyses seem to support his hypothesis that: IQ scores are better predictors of health than wealth or inequality, except in 'evolutionarily familiar' sub-Saharan Africa which offers limited selection for intelligence (British Journal of Health Psychology 11: 623-42). However, the present paper exposes the flawed assumptions, questionable data, inappropriate analyses and biased interpretations on which this thesis was based. It questions the assumptions that: IQ scores are markers of adaptive genetic differences in intelligence; humans evolved within a predictable 'environment of evolutionary adaptedness': and this environment characterizes contemporary sub-Saharan Africa, offering little selection for intelligence. It also demonstrates that the macro-level data on which these analyses were based were collected across a range of different years, using a range of different sources, and were significantly intercorrelated. While none of these analyses adjusted for study year or study type, all were susceptible to multicollinearity and the 'ecological fallacy'. These flaws were compounded by the selective presentation and partial interpretation of the analyses, which focused on the absence of an 'independent' relationship between 'national IQ' and health within sub-Saharan Africa, but ignored the fact that this is also true for every other region of the world. Likewise, the individual-level analyses did not explore the relationship between IQ scores, self-reported income and health by race, which would have demonstrated the impact of the ecological fallacy. Instead, Kanazawa (2006) mistook statistical associations for evidence of causality and falsely concluded that populations in sub-Saharan Africa are less healthy because they are unintelligent and not because they are poor.

A patient survey of out-of-hours care provided by Emergency Care Practitioners.

BACKGROUND: Emergency Care Practitioners (ECPs) have recently been deployed to provide out-of-hours primary care home visits - a practice development that has been supported by policy makers. The aim of the study was to evaluate the care provided to patients receiving out-of-hours home visits from ECPs in London from the patients' perspective and to assess their wellbeing following the visit. METHODS: A bespoke telephone-administered questionnaire was designed to survey all patients who had received out-of-hours care in Bromley Primary Care Trust from ECPs during a ten week period in 2005 (n = 174). RESULTS: Sixty three patients (36.2%) were excluded because: no telephone number was available; they had a diagnosis of dementia; or had not received a study information sheet. The remainder (n = 111) were contacted 3-5 days after the home visit, and 81 of these (73.0%) completed the survey. Of those respondents treated at home who gave unequivocal answers (n = 60), all but one (8.3%) reported that they felt that their treatment had been 'right' and/or had followed any advice given. However, overall only 86.4% reported that they had been clear about their ECP's assessment, and only 58.0% reported that their health was now 'better'. Those who reported that they were not clear about their assessment were less likely to report that their health was 'better' (p = 0.03) and more likely to have subsequently used hospital-based health services (p = 0.03). CONCLUSION: Most patients treated at home by ECPs appeared satisfied and compliant with the care provided, according to the measures used in this study. However, it appears that a sizeable minority of patients were unclear about ECP assessments and it remains to be seen whether these patients had pre-existing health complaints which made them less likely to recover and more likely to seek hospital care, or whether the lack of clarity about their assessment undermined their subsequent recovery and necessitated hospital care. Further research is required to establish if the assessments provided by ECPs are less clear than those provided by other practitioners, and whether it is possible to ensure that all such assessments are clear to all patients. Patients hold a mainly positive view of out-of-hours home visit care provided by ECPs, although a lack of clarity about their assessment was evident, with a possible impact on their continuing health.