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Development and Implementation of the Standards for Evaluating and Reporting Epidemiologic Studies on Chronic Disease Incidence or Prevalence

Tatyana Shamliyan*, Mohammed T. Ansari, Gowri Raman, Nancy Berkman, Mark Grant, Gail Janes, Margaret Maglione, David Moher, Mona Nasser, Karen Robinson, Jodi Segal, Sophia Tsouros

Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis

Abstract: We aimed to develop quality checklists for observational non-therapeutic studies. Based on a systematic review of current practices of quality assessment of observational studies, collaborating co-authors from Evidence-based Practice Centers and the Centers for Disease Control and Prevention developed a new checklist for studies examining incidence and prevalence of chronic conditions, evaluated face and content validity, and discrimination validity to distinguish reporting from methodological quality. This new checklist is available in text format or as a relational database to produce standardized reports with flaws in reporting quality, external (six criteria), and internal (five criteria) validity of the studies. Study and hypotheses (subgroups) level analyses are possible with predetermined in protocol templates criteria of major and minor flaws. Consensus around justified research specific methodological standards and reliability tests should precede quality evaluation of primary studies to assure confidence in quality assessment. To be effective, policy decisions should be made based on comprehensive systematic evidence reviews that include transparent, standardized quality appraisals. Implementation of the developed checklists would increase transparency and quality of research leading to effective informed decisions in health care.

Keywords: risk factors, morbidity, reproducibility of results, validation studies, bias (epidemiology), quality control, review literature as topic

Received January 8, 2013; Revised August 14, 2013; Accepted August 16, 2013

Citation: Tatyana Shamliyan, Mohammed T. Ansari, Gowri Raman, Nancy Berkman, Mark Grant, Gail Janes, Margaret Maglione, David Moher, Mona Nasser, Karen Robinson, Jodi Segal, Sophia Tsouros, Development and Implementation of the Standards for Evaluating and Reporting Epidemiologic Studies on Chronic Disease Incidence or Prevalence. American Journal of Public Health Research, 1 (7). 183-190. January 2013. doi:10.12691/ajphr-1-7-7

1. Introduction

Decision makers in public and health care settings need comprehensive critically appraised synthesis of evidence about incidence and prevalence of chronic diseases [1]. Evidence based decision making process involves thorough systematic appraisal of internal and external validity in individual studies and body of evidence [2,3,4,5]. In the US chronic diseases cost an estimated $1.5 trillion annually [6]. The large number of systematic reviews to summarize incidence or prevalence reflects a growing interest in such estimations [7,8]. Estimates, however, vary across the studies. For example, the estimated prevalence of dementia in the US varies from 6 to 10% in older adults and from 40 to 58% in elderly persons [9]. Estimated age-standardized incidence of dementia varied from 10.5 to 15.6 per 100000 in men and from 15.2 to 19.4 per 100000 in women [10]. Quality of the primary studies may contribute to differences in the estimates and should be carefully appraised with predefined validated tools[1].

Prevalence or incidence of chronic diseases can be evaluated only in observational studies which are prone to increased risk of bias [11]. Thus, assessing the quality of observational studies is essential in conducting systematic reviews and evidence based reports [1,12]. While several tools have been validated for therapeutic studies [13,14,15], an extensive review of available quality appraisals for observational studies concluded a need for reliable quality ratings for non-therapeutic studies of incidence and prevalence of chronic diseases [12,16,17,18].

We conducted a comprehensive review of the published checklists and scales for quality assessment of observational studies [16,17]. We analyzed 145 systematic reviews of observational nontherapeutic studies [17] and 84 publications that described 96 tools to assess quality of observational studies [16]. We defined observational nontherapeutic studies as observations of patient outcomes that did not examine procedures concerned with the remedial treatment or prevention of diseases [19].We examined how systematic reviews [20,21,22] appraised quality of the primary studies [23], which tools they used (checklist [24] or scale [20]), information about content and previous validation and reliability of the tools, domains of quality assessment (external and internal validity, level of evidence), and how systematic reviews incorporated quality assessment into the synthesis of evidence. We use the term tools interchangeably for the checklists and scales for quality assessment. We concluded that available tools require subjective judgments about “appropriateness” of study design and execution or “adequacy” of the reducing bias strategies that vary substantially depending on specific areas of research [16,17]. Available tools did not discriminate various quality criteria; for example, the same score would be given for prospective study design or using valid outcome measurement [21]. The available tools did not discriminate reporting quality with internal or external validity [22,25,26,27].

To address this gap in quality assessment of non- therapeutic observational studies we aimed to develop valid and reliable quality criteria for observational studies that examine incidence and prevalence of chronic diseases. Our objectives included testing the validity and reliability of the checklist to achieve agreement around criteria for the design, reporting standards, and assessment of nontherapeutic observational studies in systematic reviews and evidence-based reports. Developed criteria ought to improve quality of systematic reviews and informed evidence based decision making [1].

2. Methods

Our analytical framework included several steps. First, based on our systematic literature review we developed a checklist that is available in the format of a relational database (Access) and in text format with the manual and instructions. Then we organized a collaborating effort to test the credibility and content validity of the checklist. We conducted a pilot reliability test of these quality appraisals by participating experts. After that we finalized content and interface of the checklist and identified directions for checklist implementation and future research.

The protocol for the development of the checklist to evaluate quality of nontherapeutic studies was based on a conceptual model of the development of indexes, rating scales, or other appraisals to describe and measure symptoms, physical signs, and other clinical phenomena in clinical medicine [28]. We analyzed actual published tools using previously published criteria [29] and evaluated each criterion by applicability to incidence or prevalence studies and by relevance to examine external or internal validity [11,12,30,31]. We created the tool to assess the quality of studies of incidence/prevalence that included all validated quality components of external and internal validity.

We defined external validity as the extent to which the results of the study can be generalized to the target population. [11] Applicability may differ from external validity by the definition of the target population; for instance, well-designed studies from different countries with good external validity can have low applicability to the U.S. population. The definition of the target population is not a quality criterion; however, the extent to which the results can be generalizable to the target population (external validity) is. We defined internal validity as the extent to which results of the studies are correct for the study subjects and the associations detected in the study are truly caused by exposure. [11] We addressed risk of bias in primary studies but avoided labeling the biases in the quality evaluation because of differences in definitions of biases among scholars. For example, selection bias was defined as “the introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study” [32] or “systematic differences in comparison groups” [13,33] as a result of selective nonrandom treatment assignment. Selection of the criteria was designed to avoid duplication in the evaluation process.

We discriminated reporting quality from methodological quality of the studies by having the option of “not reported” for all quality criteria. We discriminated flaws in external and internal validity with two different reports; one with the list of poorly reported or flawed quality criteria of external, and another of internal validity. We used pre-specified major and minor flaws in external and internal validity. The standard reports separated for internal and external validity of the study have been developed to list major and minor flaws without formal scaling of criteria or summarizing them into global arithmetic score or obscured nontransparent quality rank. Incidence or prevalence estimates, therefore, can be compared across the studies with different reporting and methodological quality. The investigators of systematic reviews can incorporate reporting or methodological quality into sensitivity analyses and overall synthesis of evidence.

The co-authors from EPCs and from the CDC judged face content validity [28,34,35] and discriminant validity; and conducted pilot reliability testing.

We conducted a pilot test to examine inter-rater reliability by the participating experts.[36] We used Landis & Koch's measure of inter-rater agreement for multiple raters, with papers (studies) in place of subjects, when different studies were rated by different groups of raters. [37,38] We also calculated generalized kappa [39] and AC1 statistics for each quality component and each article [40,41,42] using Excel [39] and SAS [41] software .Since none of the statistical tests for reliability of nominal multi-rater responses using checklists is ideal [28], we compared percentage agreement, Fleiss and generalized kappa, and AC1 statistics to detect areas of disagreement. We interpreted kappa values of 0.0-0.19 as poor, 0.20-0.39 as fair, and 0.40-0.59 as moderate, 0.60-0.79 as substantial, and 0.80-1.00 as almost perfect agreement.

3. Results

We formulated the requirements for the checklist to assess quality of the studies of incidence or prevalence of chronic diseases.

We aimed to develop a comprehensive tool. The tool should include an exhaustive range of criteria and possible responses plus the option of open questions. Definitions of research specific biases should be pre-specified in the protocols of quality evaluation.

The tool should have mutually exclusive responses to avoid ambiguity in evaluations. The interface should have options to choose the best response, mark all applicable responses, or specify each quality component using access interface.

We aimed to develop a tool with realistic quality evaluation. The tool should define the best (gold standard) methodology that CDC uses to conduct Public Health Surveillance for Chronic Conditions for incidence/ prevalence studies. The reviewers should have the flexibility to define biases that can be specific for research questions.

We aimed to develop a tool that discriminates overall quality estimation. We suggested that the proposed checklist includes predefined major flaws that must be pre-specified depending on the research topic. We decided to seek a balance between rigorous quality assessment and flexible applicability of the tools in different areas of research. Quality assessment would require transparent and justified definitions of the flaws that are planned in the protocol of the systematic reviews. The tool can’t evaluate the exact probability of bias in external or internal validity since “true universal association” is unknown in most cases of observational nontherapeutic research. The report should contain a conclusion of applicability of the results to the general population or specific subpopulations and a conclusion of validity of the estimated incidence/prevalence.

We aimed to develop a tool with hypothesis level analyses of quality. No one published tool gave an opportunity to assess more than one hypothesis examined in the study [16]. However, subgroup analyses are preferable to make individualized decisions but at the same time are most vulnerable to bias. We proposed that the checklist must be able to evaluate validity of incidence or prevalence estimates overall and in subpopulations.

We decided that the grading the level of evidence should require additional information about consistency in results across the studies and should not be part of the standard report for individual studies.

We aimed to develop a tool with coherent quality evaluation. Basic knowledge in epidemiology should be required to complete the tool. Judgment about appropriateness of strategies to reduce bias should be standardized with minimal subjectivity in the evaluation.

We evaluated all components of the published tools for applicability to assess external or internal validity of observational studies. Then we generated the bank of criteria by applicability to observational studies of incidence/prevalence and by assessment of external or internal validity. Finally, we selected components relevant to studies of incidence/prevalence of chronic conditions. The draft checklist included an exhaustive range of criteria and possible responses plus the option of open questions. Definitions of research specific biases were pre-specified prior to development of the draft checklist. In this case, we used the CDC definitions used in conducting Public Health Surveillance for Chronic Conditions for incidence/ prevalence studies.

A detailed description of the development of the checklists, validation and pilot reliability testing is reported elsewhere. [36] We then evaluated the face and content validity of the checklist (content, definitions of the flaws, and internal algorithm for the reports) and agreed upon six criteria for assessing external validity and five criteria for assessing internal validity. Pilot testing demonstrated face and content validities and discrimination of reporting vs. methodological qualities. [36] Inter-rater agreement was poor with a lower than expected kappa.

In order to improve reliability, we analyzed the reasons for poor reliability and proposed explicit operational definitions of the research specific quality standards. We detected areas of disagreement due to multiple response options for each question. Lack of clarity around research specific quality standards was the major area of disagreement. We recommend a priori discussion and consensus around appropriate definitions of the target population, population subgroups, or the reference methods of the measurements. [36] The experts suggested future reliability testing of the checklists in systematic reviews with preplanned protocols, a priori consensus about research-specific quality criteria, and training of the reviewers.

The finalized checklist has descriptive information about the study, six criteria of external and five criteria of internal validity (can be downloaded from https://netfiles.umn.edu/xythoswfs/webui/_xy-17471658_1-t_aRG151Im). The checklist is available in the format of an Access database that produces standardized reports categorizing criteria by reporting quality as well as by major and minor flaws in external and internal validity (can be downloaded from https://netfiles.umn.edu/xythoswfs/webui/_xy-17471658_1-t_aRG151Im ). The reports are available in text format (Access reports) and spreadsheets that can be analyzed by statistical software to incorporate quality criteria to an overall strength of evidence grade.

The instruction manual provides examples and definitions of the quality components and examples from previously published research. These instructions and examples are also available as “help” files in the Access database. A template is also available to help reviewers achieve consensus about target populations and availability of gold standard to measure outcomes.

The checklist assesses external validity by assessing the sampling, inclusion, and exclusion of subjects from the study, and the differences between target and study population. [11] Studies which maintain participants through each stage reduce the risk of a sampling bias and increase the probability that eligible subjects from the target population would be selected to the study (Table 1). Sampling bias is, thus, defined as failure to ensure that all members of the reference population have a known chance of selection in the sample. Each eligible individual in the target population can have the same (random population based samples) or different (nonrandom samples) probability of selection into the study[43,44,45,46]. The checklist suggests sampling strategies based on the practice of the CDC to use random multistage population based sampling.[43,44,45,46] We define random sampling restricted to geographic areas as a minor flaw if the aim of the study was to examine incidence/prevalence in the general population. Such restrictions may lead to false estimations of incidence; for example, age adjusted incidence of prostate cancer per 100,000 male population varied from 360 or more in New Jersey and the District of Columbia to less than 300 in ten other states. [47] We defined a major flaw of a sampling frame as those derived from non-population based environments such as place of health care or employment, or symptom based inclusion criteria because prevalence of chronic diseases among specialty clinic or hospital or a working population, or among those with pre-defined symptoms may differ substantially compared to the general population. For example, the prevalence of fecal incontinence varied from approximately 0.7 -5-8% in a community based studies [48,49] to 12-19% among adults visiting primary care physicians or gastroenterologists [50].

Table 1. Methodological Evaluation of Observational Research (MORE) – observational studies of incidence or prevalence of chronic diseases

The checklist identifies valid measures used to diagnose the chronic diseases as important considerations of internal validity. For example, prevalence of clinical manifestation of genital herpes was less than 10% while seroprevalence is dramatically larger at 20% in the adults in the United States.[51] The definition and prevalence of urinary incontinence varied widely, with over 20 definitions having been used. [52] We defined a minor flaw in internal validity when the duration of symptoms differed from recommended without justification; for example, a study on chronic stable atrial fibrillation should include patients with symptoms for at least 12 months. [53] Valid outcome measurement is an essential quality component. [11] If there is a gold standard or reference standard available, methods used in studies should ideally use these standards, or validate the methods used compared to these established standards.

The checklist was designed to evaluate quality of both the prevalence of disease in the general population as well as sub-populations in the same study. These subgroups should be identified a priori. We defined a minor flaw when different methods are used to measure the outcomes in the total sample versus in subgroups or the method could have different validity or reliability in subpopulations. For example, prevalence estimate differed when history of tuberculosis was self-reported in the total sample of the general population, was obtained from x-rays of legal immigrants, or was obtained from voluntarily performed x-rays in a subpopulation of illegal immigrants. [54,55,56] We defined a flaw in internal validity when crude estimates of incidence or prevalence are provided in race, gender, or other subgroups.

In the absence of a gold standard, a formal test for criterion validity was not feasible. Testers noted that complete quality assessment was time-consuming. Poor quality studies with major flaws required more time to assess quality than well designed studies. However, while time-consuming, comprehensive assessment of the risk of bias of a study is an essential element to evidence-based research. Identification of pre-defined stopping rules improved the efficiency of the quality review process by identifying major flaws for which a study may be triaged due to low quality. We proposed using the developed checklist in systematic reviews of non- therapeutic studies with predefined in the protocols topic-specific methodological standards and essential reliability testing [36].

4. Discussion

As a result of our collaborative effort we develop and validated a checklist for comprehensive quality evaluation of observational non-therapeutic studies. In contrast with previously available scales [20] or checklists [24] our tool discriminates reporting vs. methodological quality and external vs. internal validity. Previously published systematic review of non-therapeutic observational studies used different tools for quality appraisal since the authors found no single tool applicable for their research questions. [20,21,22,25,27] Our tool was already utilized in several published systematic reviews [57-64].

Recent publications of the systematic reviews using the developed checklist demonstrated the importance of predefined research specific quality standards [57-65]. We believe that with predefined research specific quality standards in review protocols our tool is applicable for all topics concerned with incidence or prevalence of chronic conditions.

The researchers continue developing new checklists for observational studies that examined prevalence of specific diseases because quality standards differ for various diseases and chronic conditions.[66,67] We have argued that our proposed generic measures can be adapted to various diseases [36,38] We proposed first to achieve consensus around universal flaws and then a priori defined disease-specific flaws with regard to external and internal validity [36].

Our work has policy implications. Evidence based decisions in public health and clinical settings should be made based on comprehensive literature reviews [1]. The Institute of Medicine developed standards for comprehensive evidence reviews [1]. Through quality appraisal of the primary studies contributed to the reviews is critical part when providing valid evidence for decision makers [1]. We propose using the developed checklist to appraise quality of the studies of incidence or prevalence of chronic diseases in systematic reviews of such studies. Protocols of systematic reviews of nontherapeutic observational studies should include justified definitions of research specific quality components and methodological flaws and preplanned reliability testing of the evaluations. All protocols of systematic reviews should be registered in the international prospective register of systematic review protocols in health and social care [69,70]. Systematic reviews should incorporate quality of the studies into the synthesis of evidence to estimate to what extent quality was associated with the results of the primary studies and conclusions of the review [33].

The evaluation of the level of evidence from several observational nontherapeutic studies was beyond our present goals and should be conducted in the future. Future research should also establish the best practices incorporating quality of the primary studies into the synthesis of evidence and actionable guideline recommendations [71].

Acknowledgements

We would like to thank our reviewers David Atkins, MD, John Hoey, MD, and Christine Laine, MD, for reviewing and commenting on the draft. We also want to thank the librarian, Judith Stanke, for her contributions to the literature search; research assistants Stacy Dickinson, MPH, Emily Zabor candidate for MS in biostatistics, and Akweley Ablorh, candidate for MS in biostatistics, for the data abstraction, quality control, and synthesis of evidence; Zhihua Bian candidate for MS in biostatistics, for her statistical help; Zhiyuan Xu, candidate for MS in applied economics, for his work creating the ACCESS database; Dean McWilliams for his assistance in database development; Qi Wang, research fellow, for her statistical expertise in reliability testing; Susan Duval, PhD, for her help estimating sample size; Jeannine Ouellette for her help in writing the manuscript, Marilyn Eells for editing and formatting the report; and Nancy Russell and Rebecca Schultz for their assistance gathering data from the experts and formatting the tables.

Statement of Interest

The authors have no competing interests.

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