DGH Lecture Series: Kenneth Sherr

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“AIDS treatment and the health workforce crisis in Africa: task shifting and quality of care in Mozambique”

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DGH Lecture Series: Kenneth Sherr

  1. 1. AIDS treatment and the health workforce crisis in Africa Task shifting and quality of care in Mozambique Kenneth Sherr, MPH, PhDc Technical Advisor, Health Alliance International ksherr@u.washington.edu April 29, 2009
  2. 2. Presentation overview Introduction to „know-do‟ gap and implementation science Example of research to impact workforce policy and planning – Task shifting and quality of HIV care Eye towards the future: How can research strengthen Primary Health Care?
  3. 3. „Know-do‟ gap (1) Advancements in medical science have far outstripped their application >10 million annual deaths from diseases with proven, low cost prevention or treatment strategies 1 million malaria deaths 6 million preventable child deaths ½ million maternal deaths 3 million HIV-related deaths
  4. 4. „Know-do‟ gap (2) Consider HIV – Unprecedented financial and political commitment > $8 billion spent on HIV programs per year – 10-fold increase in number on ART from 2001-2007 (to 3 million) Still only 30% of need Mortality 28% higher at 6 & 12 months in low- income vs. high-income countries – Promising tools (male circumcision, microbicides), can they be implemented effectively?
  5. 5. Translating science into improved health: where are bottlenecks? Improved Delivery Discovery Development Health Outcomes How to move beyond the “Black Box” of delivery?
  6. 6. Implementation science (1) Research that addresses the know-do gap Defining element is agenda, not methodology – Basic Science: What is the pathophysiology? – Clinical Science: What is the appropriate diagnosis and intervention? – Evaluation Science: Does the intervention and delivery model work in a specific setting? – Implementation Science: How to best deliver and scale-up interventions? How to strengthen health systems?
  7. 7. Implementation science (2): Framework Economics Health Systems Anthropology Research Health Sociology Medicine Systems Delivery Operations Management Research Science Systems Quality Design Improvement Adapted from: Kim, J, “Bridging the Implementation Gap in Global Health”, 2009
  8. 8. Implementation science (3) Engagement in health systems by academic institutions is essential Health Academic Systems Institutions
  9. 9. Workforce and the know-do gap Chronic shortage of trained health workers – Deficit of 2.4 million physicians, nurses and midwives – Workforce expansion of nearly 2.5 times required to meet MDG goals – ART expansion highlights weaknesses Area of research to understand local dynamics and evaluate solutions
  10. 10. Workforce in selected countries Country Doctors Nurses (per 100,000) (per 100,000) Malawi 2 59 Mozambique 3 21 Uganda 8 61 Kenya 14 114 WHO Standard 20 100 South Africa 77 408 Brazil 115 384 USA 256 937 Cuba 591 744 Source: World Health Report, 2006
  11. 11. Workforce solutions Long term: Treat, Train, Retain (TTR) – Treat: HIV prevention, care and ART for health workers – Train: Pre-service and in-service courses – Retain: Monetary incentives and improved working conditions Interim: Task shifting
  12. 12. Task shifting: Background Long history in Africa – At least 25 countries in SSA have a cadre of non-physician clinicians (NPC) – Expanded broadly between 1975-85 with PHC Recent concerns about task shifting for clinical HIV care
  13. 13. Task shifting: Advantages (1) Rapid, pragmatic solution Lower cost Broad health system advantages
  14. 14. Task shifting: Advantages (2) Number of health facilities with ART in Mozambique: 2004-2007 250 Majority of 211 NPC trainings 193 completed 200 155 150 100 47 38 50 29 24 13 13 0 Jan 2004 Jun 2004 Jan 2005 Jun 2005 Jan 2006 Jun 2006 Jan 2007 Jun 2007 Dez 2007 MOH, 2007
  15. 15. Task Shifting: Uncertainties Quality Cost-effectiveness Overload an already overburdened staff “Research on the cost-effectiveness and care outcomes of task shifting is needed to allow decision makers to support such deployments.” – Source: Samb, Celletti, Holloway, Van Damme, De Cock, Dybul. Rapid Expansion of the Health Workforce in Response to the HIV Epidemic. NEJM 2007: 357; 24.
  16. 16. Study: “Task shifting to mid-level clinical health providers: an evaluation of quality of ART provided by non- physician clinicians and physicians in Mozambique” Gimbel-Sherr K1,2, Augusto O4, Micek M1,2, Gimbel-Sherr S1,2, Tomo MI3, Pfeiffer J1,2, Gloyd S1,2 1 University of Washington, Seattle 2 Health Alliance International 3 Ministry of Health, Mozambique 4 Eduardo Mondlane University, Mozambique Supported by the Doris Duke Charitable Foundation’s Operations Research for AIDS Care and Treatment in Africa (ORACTA) Initiative
  17. 17. Study Aims 1. Evaluate the quality of HIV care provided by non-physician clinicians (NPCs) compared with MDs 2. Identify provider-level factors that are associated with quality of HIV care
  18. 18. Study methods (1) Retrospective cohort study of patients initiating ART during the first 3.5 years of the national ART program (7/04 – 11/07) Study sites: 2 specialized (vertical) HIV clinics in Central Mozambique managed by MOH – Vertical approach designed to address high patient volume and ensure supervision – Standardized approach to HIV care – HIV prevalence > 25%
  19. 19. Study methods (2) Data Sources: – Routine clinic database Includes clinical, laboratory, pharmacy, and social worker visit data Evaluated consistency against paper charts; K>0.80 for key variables – Interviews with clinic providers to gather information on provider characteristics, experience and knowledge of MOH protocols – Direct observation to determine provider time in clinic over 4-week period
  20. 20. Study methods (3) „Primary provider‟ defined as first clinical provider at the clinic Exclusion criteria – related to primary provider: – Children (<15 years) – Women initiating ART during pregnancy – Patients in MTCT-Plus – Patients starting ART before July 2004
  21. 21. Study methods (4) Outcomes: – Process indicators reflecting country protocols: CD4 testing at 90-210 days post ART initiation CD4 testing at 330-390 days post ART initiation Frequency of clinical visit (at least 3 of 4 quarters post ART initiation) – Also assessed: Adherence during first 6-months post ART initiation (≥90% as optimal, based on pharmacy records) Lost to follow-up & mortality (combined)
  22. 22. Study methods (5) Data analysis: – Multivariate generalized linear models extended to the binomial family for dichotomous outcomes – Cox Proportional Hazards models for time to event data – All models account for provider-level correlation and adjust for clinic – Forward stepwise approach to identify patient- level covariates for inclusion in final models
  23. 23. Study results (1) Table 1: HIV clinic characteristics Beira Chimoio Mean monthly new ART initiation (>15 years age) 94 80 Mean patients enrolled in study per month 81 66 Mean number of clinical consults per month 1,110 551 Mean number of clinical consults per month with MD 505 (46%) 234 (43%) Mean number of clinical consults per month with NPC 606 (54%) 317 (57%) Observed staffing patterns Observed MD FTE 1.3 0.5 Observed NPC FTE 1.4 2.5 Total 2.7 3.0
  24. 24. Results (2): Table 2. Characteristics of study providers MD NPC N (%) N (%) p Training detail NPC NA 15 (100) General MD 20 (56) NA Specialized MD 16 (44) NA Provider sex Male 27 (75) 12 (80) Female 9 (25) 3 (20) 0.70 HIV knowledge score 16.4 (82) 16.3 (82) 0.83 N (SD) N (SD) p Provider age 40.2 (5.6) 38.8 (13.8) 0.71 Days of HIV-related training 38.8 (39.3) 21.5 (11.2) <0.01 Years of experience 12.7 (6.1) 11.6 (11.4) 0.72 Mean number of HIV consults per provider 745.6 (776.1) 3,233.9 (3,325.8) <0.01
  25. 25. Results (3) Table 3. Patient characteristics by provider type MD NPC N (%) N (%) p Study participants 1,799 (30.5) 4,093 (69.5) Study Clinic Chimoio 981 (54.5) 1,671 (40.8) Beira 818 (45.5) 2,422 (59.2) <0.01 Sex Male 808 (44.9) 1,800 (44.0) Female 991 (55.1) 2,293 (56.0) 0.51 Distance of Residence from Clinic <5 km 1,175 (65.3) 2,495 (61.0) -ref- 5-10 km 391 (21.7) 1,175 (28.7) <0.01 >10 km 233 (13.0) 423 (10.3) 0.089 Mean (SD) Mean (SD) CD4 at enrollment 156.5 (115.6) 151.9 (113.9) 0.16 Age 36.1 (9.9) 35.9 (9.8) 0.44 Years of education 6.7 (3.5) 6.3 (3.4) <0.01
  26. 26. Results (4) Table 4: Primary outcomes by provider type MD NPC N (%) N (%) ARR* (95%CI) CD4 90-210 days post ART-initiation 496 (37.5) 1,210 (44.0) 1.13 (1.01, 1.27) CD 330-390 days post ART-initiation 198 (18.7) 438 (21.2) 1.12 (0.95, 1.33) Clinician visit 3 of 4 quarters post ART 926 (87.6) 1,836 (88.7) 1.02 (0.99, 1.05) Optimal 6-month adherence 986 (74.5) 2,123 (77.3) 1.06 (1.01, 1.10) (≥90% ARV pickup) Death/loss to follow-up 504 (28.0) 1,005 (24.6) 0.89 (0.78, 1.02) *Adjusted for clinic, years of patient education, provider-level correlation
  27. 27. Results (5) Table 5: Study outcomes and provider characteristics Optimal 6-month CD4 90-210 days CD4 330-390 days Frequency of clinician Death/loss to adherence¥ post ART initiation* post ART initiation* visits** follow-up RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) Provider sex (ref=male) 0.62 (0.47, 0.81) 0.67 (0.44, 1.02) 1.03 (0.96, 1.11) 1.12 (1.06, 1.18) 1.11 (0.87, 1.42) HIV knowledge score 1.00 (0.99, 1.01) 0.99 (0.97, 1.01) 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 0.98 (0.97, 0.99) Days of HIV training 0.99 (0.99, 1.00) 1.00 (0.99, 1.01) 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 0.99 (0.99, 1.00) Years of Service 1.00 (0.99, 1.01) 1.01 (1.00, 1.02) 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 1.00 (0.99, 1.01) Cadre NPC (ref) -ref- -ref- -ref- -ref- -ref- General MD 0.93 (0.63, 1.37) 0.65 (0.38, 1.12) 1.06 (0.95, 1.18) 0.90 (0.81, 1.00) 1.68 (1.09, 2.59) Specialized MD 0.84 (0.75, 0.95) 0.70 (0.61, 0.80) 1.01 (0.97, 1.05) 0.96 (0.92, 0.99) 1.31 (1.09, 1.58) Total no. HIV consults 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 1.00 (0.99, 1.00) 0.99 (0.99, 1.00) 1.00 (0.99, 1.00) *Adjusted for clinic, years of patient education, baseline CD4, provider-level correlation **Adjusted for clinic, years of patient education, baseline CD4, provider-level correlation ¥ Adjusted for clinic, years of patient education, baseline CD4, SES
  28. 28. Discussion (1) NPCs are important drivers for ART expansion in the study clinics Measures of service quality for NPCs were equivalent to or better to MDs Inconsistent associations between provider-level characteristics and service quality – Provider cadre, sex, and HIV knowledge score associated with quality of care measures – No association for days of in-service HIV training, years of service and experience with HIV patients
  29. 29. Discussion (2) Study limitations – Switching providers may lead to misclassification of provider type (23% of patients with multiple providers) – Unable to account for all patient and clinic-level characteristics – Additional indicators of quality not measured – Generalizability
  30. 30. Discussion (3) Nevertheless…First study to compare quality of HIV care between NPCs and MDs in Mozambique Implications for implementation Task shifting can expand access with existing resources Augurs for training more NPC cadres Gaps in outcomes identify areas for improvement System-level interventions Better training Development of on-the-job support mechanisms
  31. 31. Acknowledgements Patients at the Beira Central Hospital and Chimoio Provincial Hospital Study providers, MOH managers and co- investigators Doris Duke Charitable Foundation Operations Research for AIDS Care and Treatment in Africa (ORACTA)
  32. 32. Future directions Ongoing research to strengthen integrated Primary Health Care MOH/HAI/UW Operations Research Center in Sofala, Mozambique 7-year project funded by the Doris Duke Charitable Foundation‟s African Health Initiative – Collaboration between: Ministry of Health University of Washington DGH, Industrial Engineering, School of Business Eduardo Mondlane University School of Medicine Health Alliance International
  33. 33. Duke project: Background MOH decentralization to district level management faces multiple hurdles – Fragmentation – Underdeveloped management capacity – Weak data systems – Lack of resources
  34. 34. Duke project: Objectives Improve health outcomes through stronger and integrated Primary Health Care sub- systems Objectives: – Improve management capacity – Strengthen data system quality and use – Budget support for bottlenecks – Focused research & program evaluation
  35. 35. Duke project: Relationship with health system Programmatic Operational Directorate of Health Promo. & Dis. Control Directorate of Medical Care Clinical Care National Planning & Admin & Finance Human Resources Comm Disease Reprod. Laboratory Pharmacy & Mgmnt (HIV) Coop. Mobilization Control Health (Malaria, TB) Prov. Medical Officer Province Planning & Admin & Finance Human Resources Dept. of Community Health Planning & Clinical Nursing Coop. Statistics Care District Management Team Director Medical Pharmacist Statistician Administrator Chief Facilities Ambulatory Inpatient Surgery Antenatal WCC Health Ed/ Care Care Care Outreach Program Outputs Health Outcomes
  36. 36. Conclusion Approach to overcoming know-do gap – Focus on data quality and use – Multiple research approaches – Institutional collaborations – Engagement with health service management Stay tuned!

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