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.

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.

Obstacles to "race equality" in the English National Health Service: Insights from the healthcare commissioning arena.

Inequitable healthcare access, experiences and outcomes across ethnic groups are of concern across many countries. Progress on this agenda appears limited in England given the apparently strong legal and policy framework. This disjuncture raises questions about how central government policy is translated into local services. Healthcare commissioning organisations are a potentially powerful influence on services, but have rarely been examined from an equity perspective. We undertook a mixed method exploration of English Primary Care Trust (PCT) commissioning in 2010-12, to identify barriers and enablers to commissioning that addresses ethnic healthcare inequities, employing:- in-depth interviews with 19 national Key Informants; documentation of 10 good practice examples; detailed case studies of three PCTs (70+ interviews; extensive observational work and documentary analysis); three national stakeholder workshops. We found limited and patchy attention to ethnic diversity and inequity within English healthcare commissioning. Marginalization of this agenda, along with ambivalence, a lack of clarity and limited confidence, perpetuated a reinforcing inter-play between individual managers, their organisational setting and the wider policy context. Despite the apparent contrary indications, ethnic equity was a peripheral concern within national healthcare policy; poorly aligned with other more dominant agendas. Locally, consideration of ethnicity was often treated as a matter of legal compliance rather than integral to understanding and meeting healthcare needs. Many managers and teams did not consider tackling ethnic healthcare inequities to be part-and-parcel of their job, lacked confidence and skills to do so, and questioned the legitimacy of such work. Our findings indicate the need to enhance the skills, confidence and competence of individual managers and commissioning teams and to improve organizational structures and processes that support attention to ethnic inequity. Greater political will and clearer national direction is also required to produce the system change needed to embed action on ethnic inequity within healthcare commissioning.

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.

Analysis of Continuous Glucose Monitoring in Pregnant Women With Diabetes: Distinct Temporal Patterns of Glucose Associated With Large-for-Gestational-Age Infants.

OBJECTIVE: Continuous glucose monitoring (CGM) is increasingly used to assess glucose control in diabetes. The objective was to examine how analysis of glucose data might improve our understanding of the role temporal glucose variation has on large-for-gestational-age (LGA) infants born to women with diabetes. RESEARCH DESIGN AND METHODS: Functional data analysis (FDA) was applied to 1.68 million glucose measurements from 759 measurement episodes, obtained from two previously published randomized controlled trials of CGM in pregnant women with diabetes. A total of 117 women with type 1 diabetes (n = 89) and type 2 diabetes (n = 28) who used repeated CGM during pregnancy were recruited from secondary care multidisciplinary obstetric clinics for diabetes in the U.K. and Denmark. LGA was defined as birth weight ≥90th percentile adjusted for sex and gestational age. RESULTS: A total of 54 of 117 (46%) women developed LGA. LGA was associated with lower mean glucose (7.0 vs. 7.1 mmol/L; P < 0.01) in trimester 1, with higher mean glucose in trimester 2 (7.0 vs. 6.7 mmol/L; P < 0.001) and trimester 3 (6.5 vs. 6.4 mmol/L; P < 0.01). FDA showed that glucose was significantly lower midmorning (0900-1100 h) and early evening (1900-2130 h) in trimester 1, significantly higher early morning (0330-0630 h) and throughout the afternoon (1130-1700 h) in trimester 2, and significantly higher during the evening (2030-2330 h) in trimester 3 in women whose infants were LGA. CONCLUSIONS: FDA of CGM data identified specific times of day that maternal glucose excursions were associated with LGA. It highlights trimester-specific differences, allowing treatment to be targeted to gestational glucose patterns.

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.

Seasonal variation in blood pressure is modulated by gender and age but not by BMI in a large Taiwanese population, 1996-2006.

Previous research has found that blood pressure tends to be higher in winter and lower in summer. The present study examined seasonal variation in blood pressure by gender, hypertension medication, age group, and body mass index using contemporary Taiwanese data. Over 400,000 health screening records collected biennially between 1996 and 2006 were used to calculate average monthly systolic (SBP) and diastolic blood pressure (DBP) measurements. Generalized estimating equations were used to estimate the difference between the highest and lowest mean monthly blood pressure measurements. Mean monthly blood pressure measurements were higher in winter than in summer for all age groups, regardless of medication for hypertension. The largest difference in mean monthly blood pressure between summer and winter months was 5.3 mm Hg (Standard error = 0.7) for SBP and 3.2 mm Hg (Standard error = 0.7) for DBP. These differences were more pronounced: in SBP than in DBP; in men than in women; and in older than in younger participants. Body mass index was not clearly associated with seasonal variation in blood pressure. Seasonal variation in blood pressure among contemporary Taiwanese populations is modest and may only approach clinical significance for the diagnosis and treatment of hypertension and the prevention of cardiovascular disease amongst older male individuals.

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.

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.

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.

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.

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.