Emerging Themes in Epidemiology最新文献

Objectives: Neglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed.

Methods: Published literature in three categories are reviewed: (i) identifying non-traditional but critical mechanisms for VL transmission in resource limited regions, (ii) mathematical models used for dynamics of Leishmaniasis and other related vector borne infectious diseases and (iii) examples of modeling that have the potential to capture identified mechanisms of VL to study its dynamics.

Results: This review suggests that VL elimination have not been achieved yet because existing transmission dynamics models for VL fails to capture relevant local socio-economic risk factors. This study identifies critical risk factors of VL and distribute them in six categories (atmosphere, access, availability, awareness, adherence, and accedence). The study also suggests novel quantitative models, parts of it are borrowed from other non-neglected diseases, for incorporating these factors and using them to understand VL dynamics and evaluating control programs for achieving VL elimination in a resource-limited environment.

Conclusions: Controlling VL is expensive for local communities in endemic countries where individuals remain in the vicious cycle of disease and poverty. Smarter public investment in control programs would not only decrease the VL disease burden but will also help to alleviate poverty. However, dynamical models are necessary to evaluate intervention strategies to formulate a cost-effective optimal policy for eradication of VL.

Background: The preferred method to evaluate public health interventions delivered at the level of whole communities is the cluster randomised trial (CRT). The practical limitations of CRTs and the need for alternative methods continue to be debated. There is no consensus on how to classify study designs to evaluate interventions, and how different design features are related to the strength of evidence.

Analysis: This article proposes that most study designs for the evaluation of cluster-level interventions fall into four broad categories: the CRT, the non-randomised cluster trial (NCT), the controlled before-and-after study (CBA), and the before-and-after study without control (BA). A CRT needs to fulfil two basic criteria: (1) the intervention is allocated at random; (2) there are sufficient clusters to allow a statistical between-arm comparison. In a NCT, statistical comparison is made across trial arms as in a CRT, but treatment allocation is not random. The defining feature of a CBA is that intervention and control arms are not compared directly, usually because there are insufficient clusters in each arm to allow a statistical comparison. Rather, baseline and follow-up measures of the outcome of interest are compared in the intervention arm, and separately in the control arm. A BA is a CBA without a control group.

Conclusion: Each design may provide useful or misleading evidence. A precise baseline measurement of the outcome of interest is critical for causal inference in all studies except CRTs. Apart from statistical considerations the exploration of pre/post trends in the outcome allows a more transparent discussion of study weaknesses than is possible in non-randomised studies without a baseline measure.

Background: Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models.

Analysis: In this paper, we provide an overview of currently available methods for checking imputation models. These include graphical checks and numerical summaries, as well as simulation-based methods such as posterior predictive checking. These model checking techniques are illustrated using an analysis affected by missing data from the Longitudinal Study of Australian Children.

Conclusions: As multiple imputation becomes further established as a standard approach for handling missing data, it will become increasingly important that researchers employ appropriate model checking approaches to ensure that reliable results are obtained when using this method.

In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing-notably, the "sufficient-component-cause framework" and the "mark transmission" approach; (b) new acquisitions about disease pathogenesis, e.g. the "branched model" in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of "signals" and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of "cancer causes". We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called "evidential pluralism". According to this view, causal reasoning is based on both "evidence of difference-making" (e.g. associations) and on "evidence of underlying biological mechanisms". We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social-are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

Background: In matched-pair cohort studies with censored events, the hazard ratio (HR) may be of main interest. However, it is lesser known in epidemiologic literature that the partial maximum likelihood estimator of a common HR conditional on matched pairs is written in a simple form, namely, the ratio of the numbers of two pair-types. Moreover, because HR is a noncollapsible measure and its constancy across matched pairs is a restrictive assumption, marginal HR as "average" HR may be targeted more than conditional HR in analysis.

Methods: Based on its simple expression, we provided an alternative interpretation of the common HR estimator as the odds of the matched-pair analog of C-statistic for censored time-to-event data. Through simulations assuming proportional hazards within matched pairs, the influence of various censoring patterns on the marginal and common HR estimators of unstratified and stratified proportional hazards models, respectively, was evaluated. The methods were applied to a real propensity-score matched dataset from the Rotterdam tumor bank of primary breast cancer.

Results: We showed that stratified models unbiasedly estimated a common HR under the proportional hazards within matched pairs. However, the marginal HR estimator with robust variance estimator lacks interpretation as an "average" marginal HR even if censoring is unconditionally independent to event, unless no censoring occurs or no exposure effect is present. Furthermore, the exposure-dependent censoring biased the marginal HR estimator away from both conditional HR and an "average" marginal HR irrespective of whether exposure effect is present. From the matched Rotterdam dataset, we estimated HR for relapse-free survival of absence versus presence of chemotherapy; estimates (95% confidence interval) were 1.47 (1.18-1.83) for common HR and 1.33 (1.13-1.57) for marginal HR.

Conclusion: The simple expression of the common HR estimator would be a useful summary of exposure effect, which is less sensitive to censoring patterns than the marginal HR estimator. The common and the marginal HR estimators, both relying on distinct assumptions and interpretations, are complementary alternatives for each other.

Background: Advances in healthcare have improved the survival of children with neurological disabilities (ND). Studies in the US have shown that children with ND use a substantial proportion of resources in children's hospitals, however, little research has been conducted in the UK. We aimed to test the hypothesis that children with neurological disabilities use more inpatient resources than children without neurological disabilities, and to quantify any significant differences in resource use.

Methods: A retrospective observational study was conducted, looking at the number of hospital admissions, total inpatient days and the reason for admissions for paediatric inpatients from January 1st to March 31st 2015. Inpatients were assigned into one of three groups: children without ND, children with one ND, and children with more than one ND.

Results: The sample population included 942 inpatients (mean age 6y 6mo). Children with at least one ND accounted for 15.3% of the inpatients, 17.7% of total hospital inpatient admission episodes, and 27.8% of the total inpatients days. Neurological disability had a statistically significant effect on total hospital admissions (p < 0.001). Neurological disability also had a statistically significant effect on total inpatient days (p < 0.001). Neurological disability increased the length of inpatient stay across medicine, specialties, and surgery.

Conclusions: Children with ND had more frequent hospital admission episode and longer inpatient stays. We identified a smaller group within this population, with arguably more complex neurological disabilities, children with more than one ND. This group had the highest number of admissions and longest inpatient stays. More frequent hospital admissions and longer inpatient stays may place children with ND at greater risk of the adverse effects of hospitalisations. We recommend further investigations looking at each the effects of the different categories of ND on inpatient resource use, and repeat of this study at a national level and over a longer period of time.

Background: Increasing availability of the Internet allows using only online data collection for more epidemiological studies. We compare response patterns in a population-based health survey using two survey designs: mixed-mode (choice between paper-and-pencil and online questionnaires) and online-only design (without choice).

Methods: We used data from a longitudinal panel, the Hygiene and Behaviour Infectious Diseases Study (HaBIDS), conducted in 2014/2015 in four regions in Lower Saxony, Germany. Individuals were recruited using address-based probability sampling. In two regions, individuals could choose between paper-and-pencil and online questionnaires. In the other two regions, individuals were offered online-only participation. We compared sociodemographic characteristics of respondents who filled in all panel questionnaires between the mixed-mode group (n = 1110) and the online-only group (n = 482). Using 134 items, we performed multinomial logistic regression to compare responses between survey designs in terms of type (missing, "do not know" or valid response) and ordinal regression to compare responses in terms of content. We applied the false discovery rates (FDR) to control for multiple testing and investigated effects of adjusting for sociodemographic characteristic. For validation of the differential response patterns between mixed-mode and online-only, we compared the response patterns between paper and online mode among the respondents in the mixed-mode group in one region (n = 786).

Results: Respondents in the online-only group were older than those in the mixed-mode group, but both groups did not differ regarding sex or education. Type of response did not differ between the online-only and the mixed-mode group. Survey design was associated with different content of response in 18 of the 134 investigated items; which decreased to 11 after adjusting for sociodemographic variables. In the validation within the mixed-mode, only two of those were among the 11 significantly different items. The probability of observing by chance the same two or more significant differences in this setting was 22%.

Conclusions: We found similar response patterns in both survey designs with only few items being answered differently, likely attributable to chance. Our study supports the equivalence of the compared survey designs and suggests that, in the studied setting, using online-only design does not cause strong distortion of the results.

Background: Obtaining population-level estimates of the incidence and prevalence of dementia is challenging due to under-diagnosis and under-reporting. We investigated the feasibility of using multiple linked datasets and capture-recapture techniques to estimate rates of dementia among women in Australia.

Methods: This work is based on the Australian Longitudinal Study on Women's Health. A random sample of 12,432 women born in 1921-1926 was recruited in 1996. Over 16 years of follow-up records of dementia were obtained from five sources: three-yearly self-reported surveys; clinical assessments for aged care assistance; death certificates; pharmaceutical prescriptions filled; and, in three Australian States only, hospital in-patient records.

Results: A total of 2534 women had a record of dementia in at least one of the data sources. The aged care assessments included dementia records for 79.3% of these women, while pharmaceutical data included 34.6%, death certificates 31.0% and survey data 18.5%. In the States where hospital data were available this source included dementia records for 55.8% of the women. Using capture-recapture methods we estimated an additional 728 women with dementia had not been identified, increasing the 16 year prevalence for the cohort from 20.4 to 26.0% (95% confidence interval [CI] 25.2, 26.8%).

Conclusions: This study demonstrates that using routinely collected health data with record linkage and capture-recapture can produce plausible estimates for dementia prevalence and incidence at a population level.