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Optimizing Geographic Access to Health Care

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IMPROn järjestämässä Paikkatieto sote-uudistuksen tukena seminaarissa 8.10.2019 Pohjois-Carolinan yliopiston Eric Delmelle esitteli Yhdysvalloissa tekemäänsä tutkimusta terveyspalvelujen optimoinnista paikkatietoa hyödyntäen.

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Optimizing Geographic Access to Health Care

  1. 1. Optimizing Geographic Access to Health Care Eric Delmelle University of North Carolina, Charlotte, U.S.A. October 8 2019 Eric Delmelle Optimizing Access to Care October 8 2019 1 / 31
  2. 2. Introduction Disparities Disparities in Health Care • Maldistribution of the health care workforce leads to the shortages amid surplus paradox • Disparities between races and between the haves and have-nots lead to excessive deaths in the US (100K+ in the US) • Enactment of the Patient Protection and Affordable Care Act; implications for supply, distribution of health care providers. Eric Delmelle Optimizing Access to Care October 8 2019 2 / 31
  3. 3. Introduction Disparities Disparities in Health Care... • US: DHSS implemented programs including the designation of Medically Underserved Areas/Populations (MUA/P) and Health Professional Shortage Areas (HSPAs) for improving access for the underserved • How effective are such programs? Can be estimated through accessibility metrics • Resources can be allocated to the needliest areas. • Research has greatly benefited from GIS and spatial analysis Eric Delmelle Optimizing Access to Care October 8 2019 3 / 31
  4. 4. Introduction Optimization Optimization approaches to measure access • Maximize service coverage • Minimize travel needs of patients • Limit the number of facilities • Maximize health (how to measure that?!) • Combine some of these goals (multi-objective) Eric Delmelle Optimizing Access to Care October 8 2019 4 / 31
  5. 5. Introduction Inequalities Equity • Equity (equal access to health care, for those in equal need) is appropriate to use. Minimizing inequality in health care accessibility helps identifying the adjustment needed to chose these gaps Eric Delmelle Optimizing Access to Care October 8 2019 5 / 31
  6. 6. Introduction Inequalities Inequality • Inequality comes at a personal and societal cost, evidenced by disparities in various health outcomes (can you afford this?) • Different rates of infant mortality and birth weight • Vaccination rates • Complications from preventive and common diseases • Late-stage cancer diagnosis (access?) • Quality patient care and survival Eric Delmelle Optimizing Access to Care October 8 2019 6 / 31
  7. 7. Accessibility measures Accessibility is an important metric • ..refers to the relative ease by which services, here health care, can be reached from a given location Eric Delmelle Optimizing Access to Care October 8 2019 7 / 31
  8. 8. Accessibility measures Spatial Access Spatial Accessibility ... • Emphasizes the importance of spatial separation between supply (health care providers) and demand (population) and how they are connection in space. • Solid, reliable metrics are particularly important for the optimization of health care Eric Delmelle Optimizing Access to Care October 8 2019 8 / 31
  9. 9. Accessibility measures Spatial Access Spatial Accessibility... • Spatial access is determined by where you are. One simple approach is the supply-demand ratio, e.g. population-to-physician ratio. • Population-to-physician ration does not reveal the detailed spatial variation within an areal unit not account for the interaction between population and physicians • Gravity-based model? (Pk: population at k and Sj: capacity of health care provider at j, n number of physicians, m: population locations) Ai = n j=1 Sjd−β ij m k=1 Pkd−β kj (1) Eric Delmelle Optimizing Access to Care October 8 2019 9 / 31
  10. 10. Accessibility measures Spatial Access Spatial Accessibility... • Distance friction β can be location-specific, like defined in Huff. Eric Delmelle Optimizing Access to Care October 8 2019 10 / 31
  11. 11. Accessibility measures 2SFCA Two-steps floating catchment 1 Define the catchment of physical location j as an area composed of all population locations (k) within a threshold travel time (d0) from j and compute the physician-to-population ration (Rj) within the catchment areas Rj = Sj k∈{dkj≤d0} Pk 2 For each population location i search all physician locations j within the threshold travel time (d0) from i and sum up the ratios Rj at these locations. Ai = j∈{dij≤d0} Rj = j∈{dij≤d0} Sj k∈{dkj≤d0} Pk (2) Eric Delmelle Optimizing Access to Care October 8 2019 11 / 31
  12. 12. Accessibility measures 2SFCA Two-steps floating catchment... • The model is essentially a ratio between supply (S) and demand (P), which interacts with each other only within a catchment area • 30-min driving time • easy to implement in GIS Eric Delmelle Optimizing Access to Care October 8 2019 12 / 31
  13. 13. Accessibility measures 2SFCA generalizing... • Generalize distance decay effect as a term f(d), then we have: Ai = n j=1 Sj f(dij) m k=1 Pk f(dij) (3) • f(d) can be discrete or continuous. • What is an acceptable size for catchment area Rj? (different in urban and rural areas) Eric Delmelle Optimizing Access to Care October 8 2019 13 / 31
  14. 14. Accessibility measures 2SFCA Modeling equity • Equity = equal access to health care, equal utilization of health care services, or equal (equitable) health outcomes • Maximize health? - what does it mean in practice? • Given an accessibility measure, minimize the variance of accessibility index Ai across all population locations by redistributing the total amount of supply S among health care facilities min = m i=1 Pi(Ai − a)2 (4) S1 + S2 + ... + Sn = S (5) Eric Delmelle Optimizing Access to Care October 8 2019 14 / 31
  15. 15. Modeling So what popular models exist out there in health care to optimize geographic access (and minimize costs)? Eric Delmelle Optimizing Access to Care October 8 2019 15 / 31
  16. 16. Optimization p-median Location-allocation • Seeks to locate a given number of health care center among a set of candidate sites so that the total travel distance (or time) is between demands and supply facilities is minimized. MINIMIZE F = i j aidijZij (6) Eric Delmelle Optimizing Access to Care October 8 2019 16 / 31
  17. 17. Optimization coverage Location Set Covering Problem • Minimizes the number of facilities needed to cover all demand within a critical distance or time MINIMIZE F = j Xj (7) Eric Delmelle Optimizing Access to Care October 8 2019 17 / 31
  18. 18. Optimization coverage Maximum Covering Location Problem • Maximizes the demand covered within a desired distance or time threshold by locating p facilities. MAXIMIZE F = i aiYi (8) Eric Delmelle Optimizing Access to Care October 8 2019 18 / 31
  19. 19. Optimization center p-center • p-center identifies a location arrangement for p facilities that minimizes the maximum distance to cover all clients • minimax Eric Delmelle Optimizing Access to Care October 8 2019 19 / 31
  20. 20. Optimization Modeling issues Some commonalities and issues • Emphasizes various objectives such as travel time (distance) minimization, resources minimization, maximal coverage and a combination of them • Cost? Capacity? Maximum distance? Closest hospital?, match ratio? Eric Delmelle Optimizing Access to Care October 8 2019 20 / 31
  21. 21. Optimization Modeling issues Modeling equity.... • Ensure that an area is no further than some maximum travel or distance threshold γ • health service area must be served by a facility no further away from γ dij ≤ γ (9) φi = {j|dij ≤ γ} (10) j∈φi Zij = 1 (11) Eric Delmelle Optimizing Access to Care October 8 2019 21 / 31
  22. 22. Optimization Modeling issues Modeling equity.... Eric Delmelle Optimizing Access to Care October 8 2019 22 / 31
  23. 23. Utilization Under or over-utilized • We want to make sure that a set of health facilities is not under or over-utilized • Performance remains feasible and justified under these conditions • lj ≤ aiZij ≤ uj Eric Delmelle Optimizing Access to Care October 8 2019 23 / 31
  24. 24. Hierarchy all services are on the same level Eric Delmelle Optimizing Access to Care October 8 2019 24 / 31
  25. 25. Hierarchy What if you are have different levels of service K • Wellness -diagnostic - surgery. k can be assigned to higher level, not vice-versa. Eric Delmelle Optimizing Access to Care October 8 2019 25 / 31
  26. 26. Discussion Models Modification of the models • Model can be modified for a particular or unique set of circumstances for a given region (by boat?) • Not all objectives and constraints are relevant • Multiple objectives not straightforward to solve • Single weighted approach (how to assign the weights?) • Move objective to a constraint Eric Delmelle Optimizing Access to Care October 8 2019 26 / 31
  27. 27. Discussion Utilization Capacity constrains • Minimum and maximum service requirements can lead to strange assignments • Fractional demand - could lead to spatial inequity • Allocate demand to more than one health care provider Eric Delmelle Optimizing Access to Care October 8 2019 27 / 31
  28. 28. Discussion Distance Effect of distance • Is closer better? Not always, but generally it is agreed that better proximity increases access • Not all services will follow the same distance decay function Eric Delmelle Optimizing Access to Care October 8 2019 28 / 31
  29. 29. Discussion Dynamic Demand changes • Increasing demand suggests more (new) facilities • Good time to rethink the network • Not all facilities may remain open j∈φ Xj = T Eric Delmelle Optimizing Access to Care October 8 2019 29 / 31
  30. 30. Discussion Conclusions Take-home message • Location planning and analysis of health care facilities is a dynamic and complex process • It is important to balance the costs (objectives) and the constrains • Rigorous exercise • Clear objectives are easily defendable Eric Delmelle Optimizing Access to Care October 8 2019 30 / 31
  31. 31. Discussion Conclusions Take-home message... • Taken individually, equity and utilization, cost, access are easy to solve, but not together (much more complex) • Some criteria could be in conflict with one another • Hierarchical model is important: A system must be able to offer the right mix of services across a complex network Eric Delmelle Optimizing Access to Care October 8 2019 31 / 31

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