Correlational studies aim to find out if there are differences in the characteristics of a population depending on whether or not its subjects have been exposed to an event of interest in the naturalistic setting. In eHealth, correlational studies are often used to determine whether the use of an eHealth system is associated with a particular set of user characteristics and/or quality of care patterns (Friedman & Wyatt, 2006). An example is a computerized provider order entry (cpoe) study to differentiate the background, usage and performance between clinical users and non-users of the cpoe system after its implementation in a hospital. Correlational studies are different from comparative studies in that the evaluator does not control the allocation of subjects into comparison groups or assignment of the intervention to specific groups. Instead, the evaluator defines a set of variables including an outcome of interest then tests for hypothesized relations among these variables. The outcome is known as the dependent variable and the variables being tested for association are the independent variables. Correlational studies are similar to comparative studies in that they take on an objectivist view where the variables can be defined, measured and analyzed for the presence of hypothesized relations. As such, correlational studies face the same challenges as comparative studies in terms of their internal and external validity. Of particular importance are the issues of design choices, selection bias, confounders, and reporting consistency. In this chapter we describe the basic types of correlational studies seen in the eHealth literature and their methodological considerations. Also included are three case examples to show how these studies are done. Correlational studies, better known as observational studies in epidemiology, are used to examine event exposure, disease prevalence and risk factors in a population (Elwood, 2007). In eHealth, the exposure typically refers to the use of an eHealth system by a population of subjects in a given setting. These subjects may be patients, providers or organizations identified through a set of variables that are thought to differ in their measured values depending on whether or not the subjects were “exposed” to the eHealth system. There are three basic types of correlational studies that are used in eHealth evaluation: cohort, cross-sectional, and case-control studies (Vandenbroucke et al., 2014). These are described below.
A cross-sectional survey is a type of cross-sectional study where the data source is drawn from postal questionnaires and interviews. This topic will be covered in the chapter on methods for survey studies. While correlational studies are considered less rigorous than rcts, they are the preferred designs when it is neither feasible nor ethical to conduct experimental trials. Key methodological issues arise in terms of: (a) design options, (b) biases and confounders, (c) controlling for confounding effects, (d) adherence to good practices, and (e) reporting consistency. These issues are discussed below. There are growing populations with multiple chronic conditions and healthcare interventions. They have made it difficult to design rcts with sufficient sample size and long-term follow-up to account for all the variability this phenomenon entails. Also rcts are intended to test the efficacy of an intervention in a restricted sample of subjects under ideal settings. They have limited generalizability to the population at large in routine settings (Fleurence, Naci, & Jansen, 2010). As such, correlational studies, especially those involving the use of routinely collected ehr data from the general population, have become viable alternatives to rcts. There are advantages and disadvantages to each of the three design options presented above. They are listed below.
Shamliyan, Kane, and Dickinson (2010) conducted a systematic review on tools used to assess the quality of observational studies. Despite the large number of quality scales and checklists found in the literature, they concluded that the universal concerns are in the areas of selection bias, confounding, and misclassification. These concerns, also mentioned by Vandenbroucke and colleagues (2014) in their reporting guidelines for observational studies, are summarized below.
It is important to note that bias and confounding are not synonymous. Bias is caused by finding the wrong association from flawed information or subject selection. Confounding is factually correct with respect to the relationship found, but is incorrect in its interpretation due to an extraneous factor that is associated with both the exposure and outcome. There are three common methods to control for confounding effects. These are by matching, stratification, and modelling. They are described below (Higgins & Green, 2011).
The ispor Good Research Practices Task Force published a set of recommendations in designing, conducting and reporting prospective observational studies for comparative effectiveness research (Berger et al., 2012) that are relevant to eHealth evaluation. Their key recommendations are listed below.
Vandenbroucke et al. (2014) published an expanded version of the Strengthening the Reporting of Observational Studies in Epidemiology (strobe) statement to improve the reporting of observational studies that can be applied in eHealth evaluation. It is made up of 22 items, of which 18 are common to cohort, case-control and cross-sectional studies, with four being specific to each of the three designs. The 22 reporting items are listed below (for details refer to the cited reference).
The four items specific to study design relate to the reporting of participants, statistical methods, descriptive results and outcome data. They are briefly described below for the three types of designs.
Park and colleagues (2010) conducted a retrospective cohort study to examine the association between the use of a cds (clinical decision support) system in post-liver transplant immunosuppressive care and the rates of rejection episode and drug toxicity. The study is summarized below.
Linder, Schnipper, and Middleton (2012) conducted a cross-sectional study to examine the association between the type of ehr documentation used by physicians and the quality of care provided. The study is summarized below.
Nielsen, Halamka, and Kinkel (2012) conducted a case-control study to evaluate whether there was an association between active Internet patient portal use by Multiple Sclerosis (ms) patients and medical resource utilization. Patient predictors and barriers to portal use were also identified. The study is summarized below.
A general limitation of a correlational study is that it can determine association between exposure and outcomes but cannot predict causation. The more specific limitations of the three case examples cited by the authors are listed below.
In this chapter we described cohort, case-control and cross-sectional studies as three types of correlational studies used in eHealth evaluation. The methodological issues addressed include bias and confounding, controlling for confounders, adherence to good practices and consistency in reporting. Three case examples were included to show how eHealth correlational studies are done. 1 ISPOR – International Society for Pharmacoeconomics and Outcomes Research References
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