Strengths and Limitations. The limitations to our investigations are the test setting for interpretations; there was only one glass slide for each skin biopsy case (although pathologists were asked to assume it was representative); and pathologists were unable to perform immunohistochemical staining or other diagnostic tests. In addition, pathologists were not provided detailed clinical history for the cases, and they were not able to procure a second opinion if desired. Pathologists were also given only four different options for treatment suggestions, which may have limited the ability to fully communicate their suggestions. Furthermore, we are currently refining the MPATH-Dx schema, in light of new research evidence on Class II and III categories, and we are aware there is disagreement on some of the MPATH-Dx classifications and their respective treatment recommendations2–5. Author Manuscript Strengths of our study include the broad spectrum and high number of cases and the large number of participating pathologists from across the U.S. While other studies have found variation in diagnostic interpretations of melanocytic lesions between pathologists6–9,20–31, our study is unique in that it quantifies variation in treatment suggestions. Our study also identified pathologist characteristics associated with providing treatment suggestions that are discordant with national guidelines. Author Manuscript Xxxxx et al. Page 8
Strengths and Limitations. A strength of our population-based cohort study is the large number of included patients (4,502 and 2,287). Moreover, the international context allowed data collection in eight jurisdictions with similar healthcare systems. The data on routes to diagnosis and milestone dates were collected from several different sources, including cancer registries and questionnaires completed by patients and their physicians (GP and SP). The ICBP4 surveys are based on state-of-the-art instruments, which undergo extensive translation and adaptation procedures, cognitive testing and pilot testing to ensure standardised high-quality data on routes to diagnosis and milestone dates [16]. The rich database allowed for multiple agreement analysis across several data sources and variables of interest. A limitation is the variation in some items across data sources used in the agreement analysis. For example, the date of diagnosis was defined by a single general definition in the patient questionnaire and in the registry, whereas this date had to be aggregated in the GP and SP questionnaires using different definitions (for example, date of histological confirmation, date of biopsy undertaken). Additionally, the treatment initiation date was based on a single definition in the questionnaire for SPs, whereas it was defined as the earliest date of either surgery, chemotherapy, radiology or other treatment in the patient questionnaire. Most likely, these issues have led to an underestimated agreement. Subtle differences between jurisdictions were observed in the understanding of ‘screening’ in the route to diagnosis. The patients did not always distinguish between screening and tests for symptom-based diagnosis. For example, GPs in Australia and Canada may use faecal occult blood testing (FOBT) for screening during consultations, whereas this is rare in the UK and Scandinavia. To counter these inconsistencies, we did not include screening data in the analysis if not specified whether it formed part of a national screening programme or was requested by the GP. If two dates for the same milestone differed by more than one year between different sources, this patient was excluded. This approach was taken to as a precaution against severe recall bias and potential misclassifications. Selection bias cannot be ruled out. However, as it accounted for only 3% of the possible cases (Figure 1), this cannot explain the high level of agreement found. A key limitation, as with many questionnaire-...
Strengths and Limitations. As demonstrated with Figure 15 to Figure 20, one of the strengths of the disaggregate approach is that ZIP code areas with increased deaths and DALYs that could be results of underlying socioeconomic and health environment inequity in the neighborhoods can be identified geographically. Such inequities cannot be identified with aggregate approach at the regional level. A geospatially disaggregated ITHIM tool can help visualize the health impacts of different planning scenarios, which can help planners and policy makers reach informed decisions about the region’s future that address the well-beings of every citizen. However, spatial resolution of such analysis is limited by available resources at the neighborhood levels. For example, Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) noted difficulties in obtaining health and leisure time physical activity data at the ZIP code level. Instead, simplified assumptions were made to approximate values for these ZIP codes based on regional statistics. Thus, the study could not fully address the benefits and challenges inherent in modeling disaggregate health outcomes. The research team recommended performing sensitivity analysis to various model formulations to identify the potential range of uncertainties resulting from the data limitation.
Strengths and Limitations. The overall distribution of patients at the four urgency levels in this study is similar to the one found by a larger prospective trial comparing CTA to ADAPT, indicating a representative sample was collected in our study. (5) Although the sample size of 100 seemed sufficient, it is still relatively small compared to the number of annual visits to the ED and the broad spectrum of patients. The number of raters and their experience range (nurses, nursing students, SOSU assistants) reflect the reality of the triage process according to the working procedures in Danish ED’s. This represents a strength regarding the credibility and application of the results to the clinical situation, but the heterogeneity of the group may have resulted in a decreased level of agreement. The large number of raters also introduces an increased statistical uncertainty as reflected in the wide confidence interval. The ED personnel had limited experience with the use of CTA and received only a brief instruction prior to using the triage system. Because the data was collected alternately in the three sections of the ED and sporadically during the study period, the personnel were not given an opportunity of increasing their experience using the system over time. Their lack of experience with the method may have reduced the level of agreement, and it is possible that the interrater agreement would increase over time, if the CTA was implemented as the standard triage method in the ED.
Strengths and Limitations. There are a few areas of weakness in this study that are of importance. On the individual level, the outcome of having a positive test was not guaranteed to indicate only LTBI. While HCWs in the parent study were screened for both LTBI and were referred for TB screening if they had a positive LTBI test, TB disease cannot, with perfect precision, be ruled out. However, it should be of note that only 5-10% of LTBI cases ever convert to TB disease and most convert within 2 years of infection [2]. All HCWs have been followed for 6 months following the baseline LTBI assessment and none have been diagnosed with TB disease. Furthermore, coughing for more than 3 weeks is usually indicative of TB disease. In this study, 11/258 (4.3%) of participants with a cough for more than 3 weeks tested positive for LTBI, which was similar to the proportion with a cough for greater than 3 weeks that were negative for LTBI (X2=1.71, p=.1905). Due to the fact that the prevalence of a persistent cough was not different among those that test positive and test negative for LTBI, and that no HCWs have been identified with TB disease, we believe the results of the baseline LTBI test as utilized in this analysis are indicative of infection status rather than disease. Furthermore, some of the individual variables were not specific enough to determine a true association between exposure and LTBI prevalence. For example, the variable that measured whether or not a HCW had direct patient contact was not specific to tuberculosis and the results, therefore, may not be a proper measurement of level of exposure. If a HCW is consistently with patients, none of which present with TB disease, the variable may not be a logical risk factor to incorporate into the model. It would be more effective to evaluate contact with patients presenting with active TB disease to determine the true association between patient contact and risk for LTBI. This is a similar pattern as seen in the variables that indicate hours worked weekly, total number of years in a given occupation, total number of years in the given facility, in that they are not specific to exposure to TB patients. If a given HCW works more or less in the facility, this does not necessarily indicate that level of exposure to persons with infectious TB disease or infectious materials. In this analysis, few variables had any missing values; however, not all variables were specifically defined in a way that made analysis interpretable. For example,...
Strengths and Limitations. A key strength of this study is its reliance on two methods of qualitative data collection: individual interviews and FGDs. The FGDs took place following the XXXx and corroborated findings from the interviews, contributing to the study’s validity. The inclusion of women from different life stages (UMW, RMW, MW, OW), marital status, and latrine access (toilet vs. open defecation) allowed for variation in the data. This study engaged rural women in Odisha exclusively and the findings are limited to this population, however the diversity captured in the sample provides a broad range of experiences of women at different ages, marital statuses, latrine and water access, and castes. RMW did not participate in FGDs, however they are represented in the FLI sampling frame (12 interviews with RMW), so we do not believe this exclusion impacted our findings. The FGDs had mixed-caste groups, which may have affected the openness to which participants expressed their thoughts. For each life stage, not all caste categories were represented, which may limit the breadth of the results.
Strengths and Limitations. A major strength of this research is in utilizing longitudinal data that was collected as part of a controlled challenge experiment. Prior studies have mostly used cross-sectional data, and as a result, were unable to uniquely analyze the causal effect of the inflammatory response on retinol concentrations. With longitudinal data, we were able to assess the response of retinol and RBP following infection. Other causes of inflammation that could have been occurring simultaneously were not measured, but data was collected prior to inoculation in order to account for any baseline levels of inflammation. Further, as the study population were healthy adults in a country with low prevalence of VAD, other factors that could have altered levels of vitamin A in serum were not a concern. One limitation of this analysis is that other covariates (e.g., BMI, diabetes medications) that could have an impact on the actual mechanism by which inflammation reduces serum RBP concentrations were not accounted for in the analysis as they were not collected in the original study. Another limitation lies in the inability for these results to be extrapolated to the population of most concern: children and pregnant women, particularly in populations where inflammation is common. Further longitudinal analyses should be conducted within these populations to assess the causal relationship between inflammation and altered vitamin A biomarker concentrations.
Strengths and Limitations. A major strength of this study was that the data were readily available due to the established on-going parent project between Emory University and the CDC. Also, the sample of adolescent CHD patients that met the inclusion criteria was robust and allowed for appropriate analyses to be conducted. The study design, a retrospective cohort, easily allowed for assessment of multiple outcomes for CHD adolescents. In terms of limitations, this study relied on exposure variables already present due to the retrospective cohort design. Also, many of the variables were sparsely reported such as race, height, weight, body mass index, and primary language and so, they were excluded from the analysis. The number of years of data also limited results to three years and lapses in care have been reported to be as long as ten years, and so, a wider range of data years could have painted a more explanatory picture of lapses in care. Also, given the flexible recommendations and inconsistent advice regarding exactly when adolescents should transition to an adult cardiac provider, it is possible that there could be misclassification in this study. Patients who were not ready to transition yet or were not recommended to have follow-up appointments within the next year may not have been classified correctly. Also, without knowing family dynamics and other specifics of each patient, it is impossible to know if a patient was ready to transition or not, regardless of their age. While the current study looked at a number of predictor variables, other risk factors mentioned in the literature like parental involvement, patients’ education/knowledge of their CHD, and recommendation from pediatric providers were not available in the data.
Strengths and Limitations. A possible limitation posed in this study includes only eight questions to assess decision- making autonomy, however the data and questionnaire come from a credible and nationally represented survey, with more questions to assess decision-making autonomy compared to other studies. As discussed previously, decision-making is complex and many studies have operationalized it in various forms with some forms showing an association between the outcome and others having no significant association. The data was collected through a survey which could lead to potential recall bias or misinformation by the respondents, influencing the overall results. There is also potential for measurement error in collection of height and weight data of either mother or child. Another limitation of this study is the exclusion of 36% of the eligible sample due to missing data for one or more covariates included. While there is concern that may introduce bias in the results due to selective inclusion of children, our analyses show that the study population resembled the excluded children (and therefore the full eligible sample) with respect to most study variables. The greatest strength of this study lies in the data itself, which comes from a nationally representative and credible survey with objectively-assessed height and weight data.
Strengths and Limitations. A previous study by Xxxxxx & Xxxxxxxx investigated the cost of conducting clinical trials using data from Medidata Solutions, Inc. and found that among biomedical research and development (R&D), grant cost per patient is increasing over time at a rate of 7.5% from 1989 to 2011 (2013). More importantly, they found that the growth rate of clinical trials pertaining to cardiovascular therapeutic areas in the United States increased at an average 14.1% between 2000 and 0000 (Xxxxxx & Xxxxxxxx, 2013). Therefore, our results indicate an increase of 14.56% (described earlier as roughly 15%) per year from 1999-2012 are in agreement with previous literature. Study strengths include strict trial selection criteria, inclusion of impactful variables, and the analysis of transparent, traceable, and publicly available information. Limitations of this partial evaluation study include concerns about sample size, indirect costs, and a few necessary assumptions. The small sample size provides a wide 95% confidence interval, and may have reduced generalizability as a limited, partial evaluation (Xxxxxxxx, 2008; Kumar, Williams, & Xxxxx, 2006). Additionally, there were challenges to collecting data because even among government-only sponsors, individual trial funding data from United States Department of Defense (DOD) and U.S. Department of Veterans Affairs (VA) could not be traced, limiting the trial database to xxxxxxxxxxxxxx.xxx. According to previous literature, trial data from xxxxxxxxxxxxxx.xxx may sometimes show incomplete information, because up to 29% of registered trials remain unpublished (Roumiantseva et al., 2013; Xxxxx et al., 2013). More importantly, this study did not include indirect costs and costs associated with clinical phases that could have provided more information as to the factors impacting cost trends. Additionally, a major limitation is present due to the nature of analyzing study duration and participant enrollment by year first received. More specifically, those studies received more recently by xxxxxxxxxxxxxx.xxx are faced with a temporal bias such that more recent trials are shorter and therefore have smaller durations and potentially less patient enrollment as well as costs. Further studies investigating more complete figures on industry-inclusive funding, private donations, and outcome variables should proceed with acknowledgment of these limitations. Other research, such as a trial focusing on cancer trial costs per patient, looks beyond pub...
