Re-interpreting conventional interval estimates taking into account bias and extra-variation

BACKGROUND: The study design with the smallest bias for causal inference is a perfect randomized clinical trial. Since this design is often not feasible in epidemiologic studies, an important challenge is to model bias properly and take random and systematic variation properly into account. A value for a target parameter might be said to be "incompatible" with the data (under the model used) if the parameter's confidence interval excludes it. However, this "incompatibility" may be due to bias and/or extra-variation. DISCUSSION: We propose the following way of re-interpreting conventional results. Given a specified focal value for a target parameter (typically the null value, but possibly a non-null value like that representing a twofold risk), the difference between the focal value and the nearest boundary of the confidence interval for the parameter is calculated. This represents the maximum correction of the interval boundary, for bias and extra-variation, that would still leave the focal value outside the interval, so that the focal value remained "incompatible" with the data. We describe a short example application concerning a meta analysis of air versus pure oxygen resuscitation treatment in newborn infants. Some general guidelines are provided for how to assess the probability that the appropriate correction for a particular study would be greater than this maximum (e.g. using knowledge of the general effects of bias and extra-variation from published bias-adjusted results). SUMMARY: Although this approach does not yet provide a method, because the latter probability can not be objectively assessed, this paper aims to stimulate the re-interpretation of conventional confidence intervals, and more and better studies of the effects of different biases

Performance of Small Cluster Surveys and the Clustered LQAS Design to estimate Local-level Vaccination Coverage in Mali

<p>Abstract</p> <p>Background</p> <p>Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required.</p> <p>Methods</p> <p>We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A.</p> <p>Results</p> <p>VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans.</p> <p>Conclusions</p> <p>Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.</p

Elevated antiphospholipid antibody titers and adverse pregnancy outcomes: analysis of a population-based hospital dataset

<p>Abstract</p> <p>Background</p> <p>The primary objective of this study was to determine if elevated antiphospholipid antibody titers were correlated with the presence of preeclampsia/eclampsia, systemic lupus erythematosus (SLE), placental insufficiency, and a prolonged length of stay (PLOS), in women who delivered throughout Florida, USA.</p> <p>Methods</p> <p>Cross-sectional analyses were conducted using a statewide hospital database. Prevalence odds ratios (OR) were calculated to quantify the association between elevated antiphospholipid antibody titers and four outcomes in 141,286 women who delivered in Florida in 2001. The possibility that the relationship between elevated antiphospholipid antibody titers and the outcomes of preeclampsia/eclampsia, placental insufficiency, and PLOS, may have been modified by the presence of SLE was evaluated in a multiple logistic regression model by creating a composite interaction term.</p> <p>Results</p> <p>Women with elevated antiphospholipid antibody titers (n = 88) were older, more likely to be of white race and not on Medicaid than women who did not have elevated antiphospholipid antibody titers. Women who had elevated antiphospholipid antibody titers had an increased adjusted odds ratio for preeclampsia and eclampsia, (OR = 2.93 p = 0.0015), SLE (OR = 61.24 p < 0.0001), placental insufficiency (OR = 4.58 p = 0.0003), and PLOS (OR = 3.93 p < 0.0001). Patients who had both an elevated antiphospholipid antibody titer and SLE were significantly more likely than the comparison group (women without an elevated titer who did not have SLE) to have the outcomes of preeclampsia, placental insufficiency and PLOS.</p> <p>Conclusion</p> <p>This exploratory epidemiologic investigation found moderate to very strong associations between elevated antiphospholipid antibody titers and four important outcomes in a large sample of women.</p

Can we apply the Mendelian randomization methodology without considering epigenetic effects?

<p>Abstract</p> <p>Introduction</p> <p>Instrumental variable (IV) methods have been used in econometrics for several decades now, but have only recently been introduced into the epidemiologic research frameworks. Similarly, Mendelian randomization studies, which use the IV methodology for analysis and inference in epidemiology, were introduced into the epidemiologist's toolbox only in the last decade.</p> <p>Analysis</p> <p>Mendelian randomization studies using instrumental variables (IVs) have the potential to avoid some of the limitations of observational epidemiology (confounding, reverse causality, regression dilution bias) for making causal inferences. Certain limitations of randomized controlled trials, such as problems with generalizability, feasibility and ethics for some exposures, and high costs, also make the use of Mendelian randomization in observational studies attractive. Unlike conventional randomized controlled trials (RCTs), Mendelian randomization studies can be conducted in a representative sample without imposing any exclusion criteria or requiring volunteers to be amenable to random treatment allocation.</p> <p>Within the last decade, epigenetics has gained recognition as an independent field of study, and appears to be the new direction for future research into the genetics of complex diseases. Although previous articles have addressed some of the limitations of Mendelian randomization (such as the lack of suitable genetic variants, unreliable associations, population stratification, linkage disequilibrium (LD), pleiotropy, developmental canalization, the need for large sample sizes and some potential problems with binary outcomes), none has directly characterized the impact of epigenetics on Mendelian randomization. The possibility of epigenetic effects (non-Mendelian, heritable changes in gene expression not accompanied by alterations in DNA sequence) could alter the core instrumental variable assumptions of Mendelian randomization.</p> <p>This paper applies conceptual considerations, algebraic derivations and data simulations to question the appropriateness of Mendelian randomization methods when epigenetic modifications are present.</p> <p>Conclusion</p> <p>Given an inheritance of gene expression from parents, Mendelian randomization studies not only need to assume a random distribution of alleles in the offspring, but also a random distribution of epigenetic changes (e.g. gene expression) at conception, in order for the core assumptions of the Mendelian randomization methodology to remain valid. As an increasing number of epidemiologists employ Mendelian randomization methods in their research, caution is therefore needed in drawing conclusions from these studies if these assumptions are not met.</p

Geo-mapping of caries risk in children and adolescents - a novel approach for allocation of preventive care

<p>Abstract</p> <p>Background</p> <p>Dental caries in children is unevenly distributed within populations with a higher burden in low socio-economy groups. Thus, tools are needed to allocate resources and establish evidence-based programs that meet the needs of those at risk. The aim of the study was to apply a novel concept for presenting epidemiological data based on caries risk in the region of Halland in southwest Sweden, using geo-maps.</p> <p>Methods</p> <p>The study population consisted of 46,536 individuals between 3-19 years of age (75% of the eligible population) from whom caries data were reported in 2010. Reported dmfs/DMFS>0 for an individual was considered as the primary caries outcome. Each study individual was geo-coded with respect to his/her residence parish. A parish-specific relative risk (RR) was calculated as the observed-to-expected ratio, where the expected number of individuals with dmfs/DMFS>0 was obtained from the age- and sex-specific caries (dmfs/DMFS>0) rates for the total study population. Smoothed caries risk geo-maps, along with corresponding statistical certainty geo-maps, were produced by using the free software Rapid Inquiry Facility and the ESRI^®ArcGIS system.</p> <p>Results</p> <p>The geo-maps of preschool children (3-6 years), schoolchildren (7-11 years) and adolescents (12-19 years) displayed obvious geographical variations in caries risk, albeit most marked among the preschoolers. Among the preschool children the smoothed relative risk (SmRR) varied from 0.33 to 2.37 in different parishes. With increasing age, the contrasts seemed to diminish although the gross geographical risk pattern persisted also among the adolescents (SmRR range 0.75-1.20).</p> <p>Conclusion</p> <p>Geo-maps based on caries risk may provide a novel option to allocate resources and tailor supportive and preventive measures within regions with sections of the population with relatively high caries rates.</p

On the relationship between individual and population health

The relationship between individual and population health is partially built on the broad dichotomization of medicine into clinical medicine and public health. Potential drawbacks of current views include seeing both individual and population health as absolute and independent concepts. I will argue that the relationship between individual and population health is largely relative and dynamic. Their interrelated dynamism derives from a causally defined life course perspective on health determination starting from an individual’s conception through growth, development and participation in the collective till death, all seen within the context of an adaptive society. Indeed, it will become clear that neither individual nor population health is identifiable or even definable without informative contextualization within the other. For instance, a person’s health cannot be seen in isolation but must be placed in the rich contextual web such as the socioeconomic circumstances and other health determinants of where they were conceived, born, bred, and how they shaped and were shaped by their environment and communities, especially given the prevailing population health exposures over their lifetime. We cannot discuss the “what” and “how much” of individual and population health until we know the cumulative trajectories of both, using appropriate causal language

Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data

BACKGROUND: Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be inferred from the estimated odds ratios. This paper overviews the most commonly used procedures to obtain adjusted prevalence ratios and extends the discussion to the analysis of clustered cross-sectional studies. METHODS: Prevalence ratios(PR) were estimated using logistic models with random effects. Their 95% confidence intervals were obtained using delta method and clustered bootstrap. The performance of these approaches was evaluated through simulation studies. Using data from two studies with health-related outcomes in children, we discuss the interpretation of the measures of association and their implications. RESULTS: The results from data analysis highlighted major differences between estimated OR and PR. Results from simulation studies indicate an improved performance of delta method compared to bootstrap when there are small number of clusters. CONCLUSION: We recommend the use of logistic model with random effects for analysis of clustered data. The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design

Further evidence for association of hepatitis C infection with parenteral schistosomiasis treatment in Egypt

BACKGROUND: Hepatitis C virus (HCV) infection and schistosomiasis are major public health problems in the Nile Delta of Egypt. To control schistosomiasis, mass treatment campaigns using tartar emetic injections were conducted in the 1960s through 1980s. Evidence suggests that inadequately sterilized needles used in these campaigns contributed to the transmission of HCV in the region. To corroborate this evidence, this study evaluates whether HCV infections clustered within houses in which household members had received parenteral treatment for schistosomiasis. METHODS: A serosurvey was conducted in a village in the Nile Delta and residents were questioned about prior treatment for schistosomiasis. Sera were evaluated for the presence of antibodies to HCV. The GEE2 approach was used to test for clustering of HCV infections, where correlation of HCV infections within household members who had been treated for schistosomiasis was the parameter of interest. RESULTS: A history of parenteral treatment for schistosomiasis was observed to cluster within households, OR for clustering: 2.44 (95% CI: 1.47–4.06). Overall, HCV seropositivity was 40% (321/796) and was observed to cluster within households that had members who had received parenteral treatment for schistosomiasis, OR for clustering: 1.76 (95% CI: 1.05–2.95). No such evidence for clustering was found in the remaining households. CONCLUSION: Clustering of HCV infections and receipt of parenteral treatment for schistosomiasis within the same households provides further evidence of an association between the schistosomiasis treatment campaigns and the high HCV seroprevalence rates currently observed in the Nile delta of Egypt

Health differentials in the older population of England: An empirical comparison of the materialist, lifestyle and psychosocial hypotheses

BACKGROUND: In developed countries with old age structures most deaths occur at older ages and older people account for the majority of those in poor health, which suggests a particular need to investigate health inequalities in the older population. METHODS: We empirically compared the materialist, psychosocial and lifestyle/behavioural theoretical mechanisms of explanation for socio-economic variation in health using data from two waves of the English Longitudinal Study of Ageing (ELSA), a nationally representative multi-purpose sample of the population aged 50 and over living in England. Three dimensions of health were examined: somatic health, depression and well-being. RESULTS: The materialist and lifestyle/behavioural paths had the most prominent mediating role in the association between socio-economic position and health in the older population, whereas the psychosocial pathway was less influential and exerted most of its influence on depression and well-being, with part of its effect being due to the availability of material resources. CONCLUSIONS: From a policy perspective there is therefore an indication that population interventions to reduce health differentials and thus improve the overall health of the older population should focus on material circumstances and population based interventions to promote healthy lifestyles