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Odds Ratio or Relative Risk for CrossSectional Data?
International Journal of Epidemiology (Impact Factor: 9.18). 03/1994; 23(1):2013. DOI: 10.1093/ije/23.1.201
Source: PubMed
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Article: Odds Ratio or Relative Risk for CrossSectional Data?
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 "Odds ratio (OR) is widely used to measure the risk of a disease exposure to a determinant [3840]. The odds ratio is the ratio of the odds that a case has been exposed to a risk factor is compared to the odds for a case that has not been exposed, using equation (4). "
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ABSTRACT: There have been largescale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socioeconomic determinants of HFMD risk at a space scale. HFMD records in Mainland China in May 2008 were collected. Both climate and socioeconomic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results showed that both climate and socioeconomic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially heterogeneous over the Mainland China, which is related to both the climate and socioeconomic variables. The combination of socioeconomic and climate exposures can explain the HFMD occurrences more comprehensively and objectively than those with only climate exposures. The modeled probability of HFMD occurrence at the county level reveals not only the spatial trends, but also the local details of epidemic risk, even in the regions where there were no HFMD case records. 
 "Robust standard errors were estimated to provide appropriate coverage for the confidence intervals [44]. The IRR, which reflects the ratio of incidence for the exposed group divided by the unexposed group, is the most general measure of association [45], provides accurate point and interval estimates [46] and is preferred to the odds ratio obtained from logistic regression [47,48]. The magnitude of the IRR point estimate was generally smaller than the corresponding odds ratio but the direction and significance was almost identical to coefficients obtained from logistic regression (data not shown). "
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ABSTRACT: The quality and quantity of social relationships are associated with depression but there is less evidence regarding which aspects of social relationship are most predictive. We evaluated the relative magnitude and independence of the association of four social relationship domains with major depressive disorder and depressive symptoms. We analyzed a crosssectional telephone interview and postal survey of a probability sample of adults living in Switzerland (N = 12,286). Twelvemonth major depressive disorder was assessed via structured interview over the telephone using the Composite International Diagnostic Interview (CIDI). The postal survey assessed depressive symptoms as well as variables representing emotional support, tangible support, social integration, and loneliness. Each individual social relationship domain was associated with both outcome measures, but in multivariate models being lonely and perceiving unmet emotional support had the largest and most consistent associations across depression outcomes (incidence rate ratios ranging from 1.559.97 for loneliness and from 1.231.40 for unmet support, p's < 0.05). All social relationship domains except marital status were independently associated with depressive symptoms whereas only loneliness and unmet support were associated with depressive disorder. Perceived quality and frequency of social relationships are associated with clinical depression and depressive symptoms across a wide adult age spectrum. This study extends prior work linking loneliness to depression by showing that a broad range of social relationship domains are associated with psychological wellbeing. 
 "Wacholder was one of the first to articulate the estimation challenges inherent in estimating logbinomial models and was one of the first to propose a work around [8]. His suggestion was to evaluate the current fitted values at a given stage in the likelihood maximizing process [after each iteration in the search] and if any fitted values were outside the boundary space to set the fitted values to values known to be inside the space. A few years later, Lee and Chia [9] advocated that Cox regression could be adapted to approximate the solution if one built a dataset where every person had a preset and fixed followup time. Schouten [10] proposed the duplication of each case with the outcome of interest and suggested that modelling the log of the odds for the modified data might be the same as the logbinomial model for the unmodified data. "
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ABSTRACT: Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using logbinomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software may report failed convergence when attempting to fit logbinomial models in certain settings. The methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue. This research looks directly at the loglikelihood function for the simplest logbinomial model where failed convergence has been observed, a model with a single linear predictor with three levels. The possible causes of failed convergence are explored and potential solutions are presented for some cases. Among the principal causes is a failure of the fitting algorithm to converge despite the loglikelihood function having a single finite maximum. Despite these limitations, logbinomial models are a viable option for epidemiologists wishing to describe the relationship between a set of predictors and a binary outcome where relative risk is the desired summary measure. Epidemiologists are encouraged to continue to use logbinomial models and advocate for improvements to the fitting algorithms to promote the widespread use of logbinomial models.