Projecting prevalence, costs and evaluating simulated interventions for diabetic end stage renal disease in a Canadian population of aboriginal and non-aboriginal people: an agent based approach

Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping

BACKGROUND

The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings.

METHOD

Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation.

RESULTS

84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %).

CONCLUSION

The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

Agent-Based Modeling of Autosomal Recessive Deafness 1A (DFNB1A) Prevalence with Regard to Intensity of Selection Pressure in Isolated Human Population

An increase in the prevalence of autosomal recessive deafness 1A (DFNB1A) in populations of European descent was shown to be promoted by assortative marriages among deaf people. Assortative marriages became possible with the widespread introduction of sign language, resulting in increased genetic fitness of deaf individuals and, thereby, relaxing selection against deafness. However, the effect of this phenomenon was not previously studied in populations with different genetic structures. We developed an agent-based computer model for the analysis of the spread of DFNB1A. Using this model, we tested the impact of different intensities of selection pressure against deafness in an isolated human population over 400 years. Modeling of the "purifying" selection pressure on deafness ("No deaf mating" scenario) resulted in a decrease in the proportion of deaf individuals and the pathogenic allele frequency. Modeling of the "relaxed" selection ("Assortative mating" scenario) resulted in an increase in the proportion of deaf individuals in the first four generations, which then quickly plateaued with a subsequent decline and a decrease in the pathogenic allele frequency. The results of neutral selection pressure modeling ("Random mating" scenario) showed no significant changes in the proportion of deaf individuals or the pathogenic allele frequency after 400 years.

Cost of Hemodialysis Treatment and Associated Factors Among End-Stage Renal Disease Patients at the Tertiary Hospitals of Addis Ababa City and Amhara Region, Ethiopia

Purpose

Hemodialysis is a renal replacement therapy for end-stage renal disease (ESRD) patients who consume substantial healthcare resources, which increases the economic burden. Plenty of factors affects the cost of hemodialysis treatment, particularly in resource-limited settings. Moreover, the demand for hemodialysis may decrease as the cost increases, but there is limited evidence in Ethiopia. Thus, this study aimed to estimate the cost of hemodialysis treatment among ESRD patients in the tertiary hospitals of Addis Ababa City and Amhara region, Ethiopia.

Patients and Methods

An institutional-based cross-sectional study was conducted among 172 ESRD patients undergoing hemodialysis treatment. A structured questionnaire and patients’ medical chart were used to estimate the costs, and the human capital approach was applied to calculate the indirect costs. A generalized linear model (GLM) was fitted after the modified park test to identify the associated factors. In the final GLM, a p-value of <0.05 and a 95% CI were used to declare the significant variables.

Results

The mean annual cost of hemodialysis treatment was 121,089.27ETB ($4466.59) ± 33,244.99 ($1226.29). The direct and indirect costs covered 77.0% and 23.0% of the total costs, respectively. Age (ex(b): 1.01, p-value <0.001), highest wealth status (ex(b): 1.09, p-value: 0.008), eight (ex(b): 1.27, p-value <0.001) and 12 visits/month (ex(b): 1.34, p-value <0.001), anemia (ex(b): 1.13, p-value <0.001), and comorbidity (ex(b): 1.09, p-value: 0.039) were the factors associated with the costs of hemodialysis treatment.

Conclusion

The annual cost of hemodialysis treatment among ESRD patients was high compared to the national per capita health expenditure, and two-thirds covered by the direct medical costs. Old age, high wealth status, more visits, anemia, and comorbidity were factors associated with the costs of hemodialysis. Therefore, the healthcare system must make a great effort for cost reduction and reduce the patients with kidney disease before they reach end-stages.