Dataset Information

The hidden costs: Identification of indirect costs associated with acute gastrointestinal illness in an Inuit community.

ABSTRACT: Acute gastrointestinal illness (AGI) incidence and per-capita healthcare expenditures are higher in some Inuit communities as compared to elsewhere in Canada. Consequently, there is a demand for strategies that will reduce the individual-level costs of AGI; this will require a comprehensive understanding of the economic costs of AGI. However, given Inuit communities' unique cultural, economic, and geographic contexts, there is a knowledge gap regarding the context-specific indirect costs of AGI borne by Inuit community members. This study aimed to identify the major indirect costs of AGI, and explore factors associated with these indirect costs, in the Inuit community of Rigolet, Canada, in order to develop a case-based context-specific study framework that can be used to evaluate these costs.A mixed methods study design and community-based methods were used. Qualitative in-depth, group, and case interviews were analyzed using thematic analysis to identify and describe indirect costs of AGI specific to Rigolet. Data from two quantitative cross-sectional retrospective surveys were analyzed using univariable regression models to examine potential associations between predictor variables and the indirect costs.The most notable indirect costs of AGI that should be incorporated into cost-of-illness evaluations were the tangible costs related to missing paid employment and subsistence activities, as well as the intangible costs associated with missing community and cultural events. Seasonal cost variations should also be considered. This study was intended to inform cost-of-illness studies conducted in Rigolet and other similar research settings. These results contribute to a better understanding of the economic impacts of AGI on Rigolet residents, which could be used to help identify priority areas and resource allocation for public health policies and programs.

SUBMITTER: King N

PROVIDER: S-EPMC5955559 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The hidden costs: Identification of indirect costs associated with acute gastrointestinal illness in an Inuit community.

King Nia N Vriezen Rachael R Edge Victoria L VL Ford James J Wood Michele M Harper Sherilee S

PloS one 20180516 5

<h4>Background</h4>Acute gastrointestinal illness (AGI) incidence and per-capita healthcare expenditures are higher in some Inuit communities as compared to elsewhere in Canada. Consequently, there is a demand for strategies that will reduce the individual-level costs of AGI; this will require a comprehensive understanding of the economic costs of AGI. However, given Inuit communities' unique cultural, economic, and geographic contexts, there is a knowledge gap regarding the context-specific ind ...[more]

PMID: 29768456

Similar Datasets

Project description:The burden of illness can be described by addressing both incidence and illness severity attributable to water recreation. Monetized as cost, attributable disease burden estimates can be useful for environmental management decisions.We characterize the disease burden attributable to water recreation using data from two cohort studies using a cost of illness (COI) approach and estimate the largest drivers of the disease burden of water recreation.Data from the NEEAR study, which evaluated swimming and wading in marine and freshwater beaches in six U.S. states, and CHEERS, which evaluated illness after incidental-contact recreation (boating, canoeing, fishing, kayaking, and rowing) on waterways in the Chicago area, were used to estimate the cost per case of gastrointestinal illness and costs attributable to water recreation. Data on health care and medication utilization and missed days of work or leisure were collected and combined with cost data to construct measures of COI.Depending on different assumptions, the cost of gastrointestinal symptoms attributable to water recreation are estimated to be $1,220 for incidental-contact recreation (range $338-$1,681) and $1,676 for swimming/wading (range $425-2,743) per 1,000 recreators. Lost productivity is a major driver of the estimated COI, accounting for up to 90% of total costs.Our estimates suggest gastrointestinal illness attributed to surface water recreation at urban waterways, lakes, and coastal marine beaches is responsible for costs that should be accounted for when considering the monetary impact of efforts to improve water quality. The COI provides more information than the frequency of illness, as it takes into account disease incidence, health care utilization, and lost productivity. Use of monetized disease severity information should be included in future studies of water quality and health. Citation: DeFlorio-Barker S, Wade TJ, Jones RM, Friedman LS, Wing C, Dorevitch S. 2017. Estimated costs of sporadic gastrointestinal illness associated with surface water recreation: a combined analysis of data from NEEAR and CHEERS Studies. Environ Health Perspect 125:215-222;?http://dx.doi.org/10.1289/EHP130.

Project description:Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3-33.1% for the skin model) to 89.4% (66.9-98.7%, for the nose model). Specificity ranged from 42.1% (20.3-66.5%) for the nose model and 94.7% (73.9-99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62-0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35-100.00%) and specificity of 42.11% (20.25-66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

Dataset Information

The hidden costs: Identification of indirect costs associated with acute gastrointestinal illness in an Inuit community.

Publications

The hidden costs: Identification of indirect costs associated with acute gastrointestinal illness in an Inuit community.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets