Emerging Themes in Epidemiology最新文献

A key aspect of the article by Lousdal on instrumental variables was a discussion of the monotonicity assumption. However, there was no mention of the history of the development of this assumption. The purpose of this letter is to note that Baker and Lindeman and Imbens and Angrist independently introduced the monotonicity assumption into the analysis of instrumental variables. The letter also places the monotonicity assumption in the context of the method of latent class instrumental variables.

We commend Nickerson and Brown on their insightful exposition of the mathematical algebra behind Simpson's paradox, suppression and Lord's paradox; we also acknowledge there can be differences in how Lord's paradox is approached analytically, compared to Simpson's paradox and suppression, though not in every example of Lord's paradox. Furthermore, Simpson's paradox, suppression and Lord's paradox ask the same contextual questions, seeking to understand if statistical adjustment is valid and meaningful, identifying which analytical option is correct. In our exposition of this, we focus on the perspective of context, which must invoke causal thinking. From a causal thinking perspective, Simpson's paradox, suppression and Lord's paradox present very similar analytical challenges.

Background: Health monitoring in Germany falls short on generating timely, reliable and representative data among migrants, especially transient and marginalized groups such as asylum seekers and refugees (ASR). We aim to advance current health monitoring approaches and obtain reliable estimates on health status and access to essential healthcare services among ASR in Germany's third largest federal state, Baden-Württemberg.

Methods: We conducted a state-wide, cross-sectional, population-based health monitoring survey in nine languages among ASR and their children in collective accommodation centres in 44 districts. Questionnaire items capturing health status, access to care, and sociodemographic variables were taken from established surveys and translated using a team approach. Random sampling on the level of 1938 accommodation centres with 70,634 ASR was employed to draw a balanced sample of 65 centres with a net sample of 1% of the state's ASR population. Multilingual field teams recruited eligible participants using a "door-to-door" approach. Parents completed an additional questionnaire on behalf of their children.

Results: The final sample comprised 58 centres with 1843 ASR. Of the total sample expected eligible (N = 987), 41.7% (n = 412) participated in the survey. Overall, 157 households had children and received a children's questionnaire; 61% (n = 95) of these were returned. Age, sex, and nationality of the included sample were comparable to the total population of asylum applicants in Germany. Adults reported longstanding limitations (16%), bad/very bad general health (19%), pain (25%), chronic illness (40%), depression (46%), and anxiety (45%). 52% utilised primary and 37% specialist care services in the previous 12 months, while reporting unmet needs for primary (31%) and specialist care (32%). Younger and male participants had above-average health status and below-average utilisation compared to older and female ASR.

Conclusions: Our health monitoring survey yielded reliable estimates on health status and health care access among ASR, revealing relevant morbidities and patterns of care. Applying rigorous epidemiological methods in linguistically diverse, transient and marginalized populations is challenging, but feasible. Integration of this approach into state- and nation-wide health monitoring strategies is needed in order to sustain this approach as a health planning tool.

Vulnerability has become a key concept in emergency response research and is being critically discussed across several disciplines. While the concept has been adopted into global health, its conceptualisation and especially its role in the conceptualisation of risk and therefore in risk assessments is still lacking. This paper uses the risk concept pioneered in hazard research that assumes that risk is a function of the interaction between hazard and vulnerability rather than the neo-liberal conceptualisation of vulnerability and vulnerable groups and communities. By seeking to modify the original pressure and release model, the paper unpacks the representation or lack of representation of vulnerability in risk assessments in global health emergency response and discusses what benefits can be gained from making the underlying assumptions about vulnerability, which are present whether vulnerability is sufficiently conceptualised and consciously included or not, explicit. The paper argues that discussions about risk in global health emergencies should be better grounded in a theoretical understanding of the concept of vulnerability and that this theoretical understanding needs to inform risk assessments which implicitly used the concept of vulnerability. By using the hazard research approach to vulnerability, it offers an alternative narrative with new perspectives on the value and limits of vulnerability as a concept and a tool.

The relationship between collapsibility and confounding has been subject to an extensive and ongoing discussion in the methodological literature. We discuss two subtly different definitions of collapsibility, and show that by considering causal effect measures based on counterfactual variables (rather than measures of association based on observed variables) it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure, from the components that are due to structural bias such as confounding. We provide new weights such that the causal risk ratio is collapsible over arbitrary baseline covariates. In the absence of confounding, these weights may be used for standardization of the risk ratio.

Background: Participation in epidemiologic studies has declined, raising concerns about selection bias. While estimates derived from epidemiologic studies have been shown to be robust under a wide range of scenarios, additional empiric study is needed. The Georgia Study to Explore Early Development (GA SEED), a population-based case-control study of risk factors for autism spectrum disorder (ASD), provided an opportunity to explore factors associated with non-participation and potential impacts of non-participation on association studies.

Methods: GA SEED recruited preschool-aged children residing in metropolitan-Atlanta during 2007-2012. Children with ASD were identified from multiple schools and healthcare providers serving children with disabilities; children from the general population (POP) were randomly sampled from birth records. Recruitment was via mailed invitation letter with follow-up phone calls. Eligibility criteria included birth and current residence in study area and an English-speaking caregiver. Many children identified for potential inclusion could not be contacted. We used data from birth certificates to examine demographic and perinatal factors associated with participation in GA SEED and completion of the data collection protocol. We also compared ASD-risk factor associations for the final sample of children who completed the study with the initial sample of all likely ASD and POP children invited to potentially participate in the study, had they been eligible. Finally, we derived post-stratification sampling weights for participants who completed the study and compared weighted and unweighted associations between ASD and two factors collected via post-enrollment maternal interview: infertility and reproductive stoppage.

Results: Maternal age and education were independently associated with participation in the POP group. Maternal education was independently associated with participation in the ASD group. Numerous other demographic and perinatal factors were not associated with participation. Moreover, unadjusted and adjusted odds ratios for associations between ASD and several demographic and perinatal factors were similar between the final sample of study completers and the total invited sample. Odds ratios for associations between ASD and infertility and reproductive stoppage were also similar in unweighted and weighted analyses of the study completion sample.

Conclusions: These findings suggest that effect estimates from SEED risk factor analyses, particularly those of non-demographic factors, are likely robust.

Background: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models.

Results: Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant.

Conclusions: In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.

Background: In biomedical research much effort is thought to be wasted. Recommendations for improvement have largely focused on processes and procedures. Here, we additionally suggest less ambiguity concerning the questions addressed.

Methods: We clarify the distinction between two conflated concepts, prediction and explanation, both encompassed by the term "risk factor", and give methods and presentation appropriate for each.

Results: Risk prediction studies use statistical techniques to generate contextually specific data-driven models requiring a representative sample that identify people at risk of health conditions efficiently (target populations for interventions). Risk prediction studies do not necessarily include causes (targets of intervention), but may include cheap and easy to measure surrogates or biomarkers of causes. Explanatory studies, ideally embedded within an informative model of reality, assess the role of causal factors which if targeted for interventions, are likely to improve outcomes. Predictive models allow identification of people or populations at elevated disease risk enabling targeting of proven interventions acting on causal factors. Explanatory models allow identification of causal factors to target across populations to prevent disease.

Conclusion: Ensuring a clear match of question to methods and interpretation will reduce research waste due to misinterpretation.