An estimation of the health-cost of unfilled medical positions in Malawi: A Thanzi La Onse Mathematical Modelling study.
Perinpakumar, A.; She, B.; Mangal, T.; Mohan, S.; Chalkley, M.; Colbourn, T.; Collins, J. H.; Graham, M. M.; Janouskova, E.; Nkhoma, D.; Twea, P. D.; Phillips, A. N.; Revill, P.; Tamuri, A. U.; Mfutso-Bengo, J.; Hallett, T. B.; Molaro, M.
Show abstract
Background Malawis healthcare system faces strain due to an insufficient number of healthcare workers (HCWs). The number of HCWs currently employed falls below the Malawian governments own facility-based staffing standards, which are known as the establishment target. While vacancy rates from this target have been estimated, the health consequences of this workforce gap on the population have not. Methods This study quantifies the health-cost of unfilled establishment HCW positions using the Thanzi La Onse (TLO) model, an "all diseases - whole healthcare system" individual-based model, which self-consistently accounts for the dynamics between health system constraints and population health. We constructed two staffing scenarios: one (Current) in which the currently employed staff are represented, and another (Target) where all positions planned under the establishment target are filled. Using the TLO model, we then estimate the health impact of filling all establishment positions as the difference in the Disability-Adjusted Life Years (DALYs) incurred between the two scenarios. Results Our results indicate that fulfilling Target positions could reduce the health losses by 13.6% (43.1 million DALYs averted, 95% CI: 40.8-48.6) over the projection period. The largest proportional reductions are for DALYs caused by HIV/AIDS (41%), tuberculosis (26%), and malaria (24%) compared to the Current provision. Conclusions The analysis shows the potential health benefits associated with increasing the fulfilment of establishment positions in Malawi and offers key quantifications for policymakers as they strive to achieve Universal Health Coverage.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Improving the efficiency of scale-up and deployment of community health workers in Mali: a geospatial analysis 95%
- The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting 94%
- Rural prioritization may increase the impact of COVID-19 vaccines in Sub-Saharan Africa due to ongoing internal migration: A modeling study 93%
Similar papers in this journal
- Modelling the effect of infection prevention and control measures on rate of Mycobacterium tuberculosis transmission to clinic attendees in primary health clinics in South Africa 95%
- Impact of the COVID-19 pandemic and response on the utilisation of health services during the first wave in Kinshasa, the Democratic Republic of the Congo 94%
- Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia 93%
Similar papers in this journal
- A Healthcare Service Delivery and Epidemiological Model for Investigating Resource Allocation for Health: The Thanzi La Onse Model 96%
- Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study 94%
- Travel-time, bikes, and HIV elimination in Malawi: a geospatial modeling analysis 92%
Similar papers in this journal
- SARS-CoV-2 infection risk during delivery of childhood vaccination campaigns: a modelling study 93%
- Non-pharmaceutical interventions and vaccinating school children required to contain SARS-CoV-2 Delta variant outbreaks in Australia: a modelling analysis 92%
- Quantifying the dynamics of COVID-19 burden and impact of interventions in Java, Indonesia 91%
Similar papers in this journal
- Modelling trachoma post 2020: Opportunities for mitigating the impact of COVID-19 and accelerating progress towards elimination 94%
- What does the COVID-19 pandemic mean for the next decade of onchocerciasis control and elimination? 93%
- Predicting the impact of disruptions in lymphatic filariasis elimination programmes due to the outbreak of coronavirus disease (COVID-19) and possible mitigation strategies 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.