The design of randomized controlled clinical studies can greatly benefit from iterative assessments of population representativeness of eligibility criteria. We propose a multi-trait metric - GIST 2.0 that can compute the a priori generalizability based on the population representativeness of a clinical study by explicitly modeling the dependencies among all eligibility criteria. We evaluate this metric on twenty clinical studies of two diseases and analyze how a study’s eligibility criteria affect its generalizability (collectively and individually). We statistically analyze the effects of trial setting, trait selection and trait summarizing technique on GIST 2.0. Finally we provide theoretical as well as empirical validations for the expected properties of GIST 2.0.

Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.

Randomized controlled trials can benefit from proactive assessment of how well their participant selection strategies during the design of eligibility criteria can influence the study generalizability. In this paper, we present a quantitative metric called generalizability index for study traits 2.0 (GIST 2.0) to assess the a priori generalizability (based on population representativeness) of a clinical trial by accounting for the dependencies among multiple eligibility criteria. The metric was evaluated on 16 sepsis trials identified from ClinicalTrials.gov, with their adverse event reports extracted from the trial results section. The correlation between GIST scores and adverse events was analyzed. We found that the GIST 2.0 score was significantly correlated with total adverse events and serious adverse events (weighted correlation coefficients of 0.825 and 0.709, respectively, with P < 0.01). This study exemplifies the promising use of Big Data in electronic health records and ClinicalTrials.gov for optimizing eligibility criteria design for clinical studies.

Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the “holy grails” in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.

Local comparisons of effects, responses and mitigations to the Covid-19 pandemic are of vital importance in building a sustainable tourism. This is particularly the case for conservancies in Africa which is largely dependent on international tourism. Qualitative interviews were carried out in the Kenya Maasai Mara Wildlife Conservancies Association (MMWCA)with landowners, lodge managers and staff, tourism operators, community organisations and NGOs between January and May 2021. The MMWCA is an important case study as conservancies pay lease payments to more than 14,528 landowners through tourism revenues. The results show how partner conservancies took different paths in securing payments of leases and salaries by rotating staff, attracting international funding and by targeting domestic tourism. Meanwhile, landowners experimented with alternative economic activities such as cattle herding and diary production. The study shows the strength of MMWCA as a stakeholder partnership to proactively design measures including renegotiation of lease-payments, in soliciting external funding and in re-distributing funding. The positive role of domestic tourism is also stressed. The pandemic brought to the forefront discussions on equity and benefit sharing and on the sustainability of the model itself. Recommendations are given to strengthen possibilities for alternative incomes sources and for a diversification of strategies of the MMWCA partners, including the need to stimulate domestic tourism as a parallel source of income. These recommendations are also relevant to conservation areas across the African continent.