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051: LOCALIZING BURDEN OF DISEASE DATA FOR THE DEVELOPMENT OF A PRIMARY CARE BENEFIT IN THE PHILIPPINES
  1. John Quan Wong1,
  2. Leizel P Lagrada2,
  3. Katherine Ann Villegas Reyes3,
  4. Rizza Majella Lee Herrera3,
  5. Beverly Lorraine Chua Ho3,
  6. Pura Angela Wee Co3,
  7. Diana Beatriz Samson Bayani3
  1. 1Health Sciences Department, Ateneo de Manila University, Quezon City, Philippines
  2. 2Quality Assurance Group, Philippine Health Insurance Corporation, Pasig City, Philippines
  3. 3Alliance for Improving Health Outcomes, (AIHO) Inc., Quezon City, Philippines

Abstract

Background The Philippines is broadening its coverage for primary healthcare by increasing the number of services included in the PhilHealth benefit package, expanding to more members and accrediting more healthcare providers.

Objectives The main objective is to describe the process of benefit scoping for a primary care benefit package, requiring the identification of disease priorities and their corresponding interventions.

Methods The criteria used to identify the target diseases were epidemiologic fit, cost-efficiency, feasibility of delivery, and complete cycle of care optimization. Data sources were: 2010 Global Burden of Disease, (BOD) local epidemiologic data and cost-effectiveness data from global literature. BOD data was integrated with cost-effectiveness data while costing, actuarial analysis, and stakeholder analysis served as counterbalancing weights to the epidemiologic evidence and reduced the scope of the package. Results were tailored fit according to context, particularly PhilHealth's fiscal limitations and stakeholder inputs. This is the first time that PhilHealth is adopting an exhaustive criteria and using data from global literature in the development of benefits.

Result Global and local epidemiologic and clinical economic evidence is available for making coverage decisions for UHC. However, this evidence needs to be balanced by evidence on costs, actuarial analysis, contextual factors like local provider capacity, and politics. Where the data is insufficient, BOD data is partially based on mathematical modeling.

Conclusion Reconciliation of local and global evidence is difficult since both datasets suffer from selection and information bias. The success of the application of this procedure may be measured in the future, by the change in hospitalization rates for preventable diseases. The following recommendations are expected to improve the process: broaden the stakeholder base involved in setting criteria, conduct national BOD studies, and generate research to determine impact of prioritization to health outcomes.

  • SURGERY

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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