Fisher’s Linear Discriminant Function Analysis and its Potential Utility as a Tool for the Assessment of Health-and-Wellness Programs in Indigenous Communities

Abstract

Diabetes mellitus is a growing public health problem affecting persons in both developed and developing nations. The prevalence of type 2 diabetes mellitus (T2DM) is reported to be several times higher among Indigenous populations compared to their non-Indigenous counterparts. Discriminant function analysis (DFA) is a potential tool that can be used to quantitatively evaluate the effectiveness of Indigenous health-and-wellness programs (e.g., on-the-land programs, T2DM interventions), by creating a type of pre-and-post-program scoring system. As the communities of the Eeyou Istchee territory, subarctic Quebec, Canada, have varying degrees of isolation, we derived a DFA tool for point-of-contact evaluations to aid in monitoring and assessment of health-and-wellness programs in rural and remote locations. We developed several DFA models to discriminate between those with and without T2DM status using age, fasting blood glucose, body mass index, waist girth, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, and total cholesterol in participants from the Eeyou Istchee. The models showed a ~97% specificity (i.e., true positives for non-T2DM) in classification. This study highlights how varying risk factor models can be used to discriminate those without T2DM with high specificity among James Bay Cree communities in Canada.

Challenges and Opportunities for Physical Activity Interventions in Type 2 Diabetes Mellitus sufferers in Indigenous Areas throughout the world: A Scoping Review

“…By designing interventions that are culturally appropriate and responsive to the needs and preferences of Indigenous populations, there is a greater likelihood of engagement and positive outcomes (Pelletier et al, 2020). (Liberda et al, 2020) (Lewis et al, 2021).…”

“…Culturally appropriate physical activity programs have the potential to engage and motivate T2DM sufferers in Indigenous communities to participate in regular physical activity. These programs can incorporate traditional activities such as walking clubs, weight-loss groups, community gardens, traditional food harvesting activities, canoeing, and dancing (Liberda et al, 2020). By incorporating cultural elements into physical activity interventions, they can resonate with the Indigenous population and promote a sense of community and belonging.…”

“…Therefore, it is imperative to ensure that physical activity interventions in Indigenous areas are accessible, convenient, and tailored to the specific needs and cultural contexts of the community. Furthermore, self-monitoring of blood glucose levels can be an effective method for individuals to assess the effectiveness of a health-and-wellness program (Liberda et al, 2020).…”

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

“…The hydrochemical index of sandstone fracture water from 12 coal system are as shown in Table 6. In order to study the hydraulic connection between individual aquifers, the degree of connection K between them can be calculated quantitatively 21,22,23,24 , and since the Clconcentration is minimally disturbed by other factors and is mainly influenced by the formation itself, the degree of hydraulic connection between two aquifers can be obtained by calculating the difference between their average Clconcentrations .If the K value of the hydraulic connection between the two aquifers is less than 0.2, it means that they have a strong hydraulic connection, if K is greater than 0.4, it means that the hydraulic connection between the two aquifers here is weak, if the final calculated K value is between 0.2 and 0.4, it means that the hydraulic connection is moderately strong. Through equation ( 16), the K values of goaf water and Ordovician carbonate, sandstone fracture water of 13 coal system and sandstone fracture water of 12 coal system are all 0.25, and the degree of hydraulic connection is moderate.…”

Improved Method of Filling Incomplete Information in Network Database Simulation

“…The variables e, b, s, and x i in formula (5) stand for the interval value between the data and the data in the network database, the optimal classification function, the objective function of the classification, and the discriminant function of the i-th data, respectively [15].…”