| 1. |
- Byass, Peter
(författare)
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The democratic fallacy in matters of clinical opinion : implications for analysing cause-of-death data
- 2011
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Ingår i: Emerging Themes in Epidemiology. - : BioMed Central (BMC). - 1742-7622. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Arriving at a consensus between multiple clinical opinions concerning a particular case is a complex issue - and may give rise to manifestations of the democratic fallacy, whereby a majority opinion is misconstrued to represent some kind of "truth" and minority opinions are somehow "wrong". Procedures for handling multiple clinical opinions in epidemiological research are not well established, and care is needed to avoid logical errors. How to handle physicians' opinions on cause of death is one important domain of concern in this respect. Whether multiple opinions are a legal requirement, for example ahead of cremating a body, or used for supposedly greater rigour, for example in verbal autopsy interpretation, it is important to have a clear understanding of what unanimity or disagreement in findings might imply, and of how to aggregate case data accordingly.In many settings where multiple physicians have interpreted verbal autopsy material, an over-riding goal of arriving at a single cause of death per case has been applied. In many instances this desire to constrain findings to a single cause per case has led to methodologically awkward devices such as "TB/AIDS" as a single cause. This has also usually meant that no sense of disagreements or uncertainties at the case level is taken forward into aggregated data analyses, and in many cases an "indeterminate" cause may be recorded which actually reflects a lack of agreement rather than a lack of data on possible cause(s).In preparing verbal autopsy material for epidemiological analyses and public health interpretations, the possibility of multiple causes of death per case, and some sense of any disagreement or uncertainty encountered in interpretation at the case level, need to be captured and incorporated into overall findings, if evidence is not to be lost along the way. Similar considerations may apply in other epidemiological domains.
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| 2. |
- Fottrell, Edward, et al.
(författare)
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A probabilistic method to estimate the burden of maternal morbidity in resource-poor settings : preliminary development and evaluation
- 2014
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Ingår i: Emerging Themes in Epidemiology. - : BioMed Central (BMC). - 1742-7622. ; 11:1, s. 3-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.
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| 3. |
- Nickerson, Carol A., et al.
(författare)
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Simpson's Paradox is suppression, but Lord's Paradox is neither : clarification of and correction to Tu, Gunnell, and Gilthorpe (2008)
- 2019
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Ingår i: Emerging Themes in Epidemiology. - : Springer Nature. - 1742-7622. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Tu et al. (Emerg Themes Epidemiol 5:2, 2008. https://doi.org/10.1186/1742-7622-5-2) asserted that suppression, Simpson’s Paradox, and Lord’s Paradox are all the same phenomenon—the reversal paradox. In the reversal paradox, the association between an outcome variable and an explanatory (predictor) variable is reversed when another explanatory variable is added to the analysis. More specifically, Tu et al. (2008) purported to demonstrate that these three paradoxes are different manifestations of the same phenomenon, differently named depending on the scaling of the outcome variable, the explanatory variable, and the third variable. According to Tu et al. (2008), when all three variables are continuous, the phenomenon is called suppression; when all three variables are categorical, the phenomenon is called Simpson’s Paradox; and when the outcome variable and the third variable are continuous but the explanatory variable is categorical, the phenomenon is called Lord’s Paradox. We show that (a) the strong form of Simpson’s Paradox is equivalent to negative suppression for a 2×2×2 contingency table, (b) the weak form of Simpson’s Paradox is equivalent to classical suppression for a 2×2×2 contingency table, and (c) Lord’s Paradox is not the same phenomenon as suppression or Simpson’s Paradox.
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| 4. |
- Hussain-Alkhateeb, Laith, 1977, et al.
(författare)
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Effects of recall time on cause-of-death findings using verbal autopsy: empirical evidence from rural South Africa.
- 2016
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Ingår i: Emerging Themes in Epidemiology. - : Springer Science and Business Media LLC. - 1742-7622. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Verbal autopsy (VA) is a widely used technique for assigning causes to non-medically certified deaths using information gathered from a close caregiver. Both operational and cultural factors may cause delays in follow-up of deaths. The resulting time lag-from death to VA interview-can influence ways in which terminal events are remembered, and thus affect cause-of-death assignment. This study investigates the impact of recall period on causes of death determined by VA.A total of 10,882 deaths from the Agincourt Health and Demographic Surveillance System (HDSS) with complete VAs, including recall period, were incorporated in this study. To measure seasonal effect, cause specific mortality fractions (CSMFs) were calculated and compared by every cause for VAs undertaken within six months of death and those undertaken from six to 12months of death. All causes were classified into eight broad categories and entered in a multiple logistic regression to explore outcome by recall period in relation to covariates.The majority of deaths (83%) had VAs completed within 12months. There was a tendency towards longer recall periods for deaths of those under one year or over 65years of age. Only the acute respiratory, diarrhoeal and other unspecified non-communicable disease groups showed a CSMF ratio significantly different from unity at the 99% confidence level between the two recall periods. Only neonatal deaths showed significantly different OR for recall exceeding 12months (OR 1.69; p value=0.004) and this increased when adjusting for background factors (OR 2.58; p value=0.000).A recall period of up to one year between death and VA interview did not have any consequential effects on the cause-of-death patterns derived, with the exception of neonatal causes. This is an important operational consideration given the planned widespread use of the VA approach in civil registration, HDSS sites and occasional surveys.
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| 5. |
- Lan, Yihong, et al.
(författare)
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Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes
- 2022
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Ingår i: Emerging Themes in Epidemiology. - : BMC. - 1742-7622. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The pandemic progression is a dynamic process, in which measures yield outcomes, and outcomes in turn influence subsequent measures and outcomes. Due to the dynamics of pandemic progression, it is challenging to analyse the long-term influence of an individual measure in the sequence on pandemic outcomes. To demonstrate the problem and find solutions, in this article, we study the first wave of the pandemic—probably the most dynamic period—in the Nordic countries and analyse the influences of the Swedish measures relative to the measures adopted by its neighbouring countries on COVID-19 mortality, general mortality, COVID-19 incidence, and unemployment. The design is a longitudinal observational study. The linear regressions based on the Poisson distribution or the binomial distribution are employed for the analysis. To show that analysis can be timely conducted, we use table data available during the first wave. We found that the early Swedish measure had a long-term and significant causal effect on public health outcomes and a certain degree of long-term mitigating causal effect on unemployment during the first wave, where the effect was measured by an increase of these outcomes under the Swedish measures relative to the measures adopted by the other Nordic countries. This information from the first wave has not been provided by available analyses but could have played an important role in combating the second wave. In conclusion, analysis based on table data may provide timely information about the dynamic progression of a pandemic and the long-term influence of an individual measure in the sequence on pandemic outcomes.
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