15 March 2004

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Managing the Dynamics of Drug Supply, Demand, and Health Services Utilization

A New Multi-Purpose Tool for Forecasting Drug

Requirements and Resource Management

Hanif Nazerali, Khalid Mohamed, Martin Oteba, and Joseph Mwoga

Ministry of Health, Uganda

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Challenges in the Uganda setting

Uganda is changing from a drug kit system (push) to an order-based system (pull)Drug funding is decentralized and increasing rapidly through a SWAp* but management capacity is still limitedHealth services are utilized only when drugs are available, and this depends on good forecasting, a responsive supply system, and efficient resource managementForecasting in this dynamic environment requires innovative approaches

*Sector-wide approach

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New multi-purpose tool

1. Reports drug use indicators and other performance indicators

2. Identifies most common outpatient conditions and the most prescribed drugs

3. Forecasts PHC drug requirements and costs for 1000 outpatient cases using dual methods (prescription demand and stock consumed)

4. Quantifies gaps between current demand and actual supply per 1000 cases (or oversupply)

5. Validates records and accounts for resources

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Study design and methods

Retrospective review of outpatient registers, stock records, and service statistics for a specified period in a cross-section of health facilities

Data entered into a customized database, with defined lists of diagnoses, drugs, and current prices (MS Access and linked Excel spreadsheets)

Performance indicators analyzed by individual facility, level, district

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Study sample

A systematic sample of 200-400 encounters per health facility depending on level, at intervals over a 12-month period

Total 6,400 outpatient encounters at 24 health facilities in 4 districts

Comprehensive stock record review for all drugs issued during the 12-month period

Consumption data was obtained from 21/24 health facilities, and for 9 of the top 12 drugs, on average, from the 21 facilities.

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Customized DatabaseInputs Outputs

1. Demographic data, diagnoses, & prescriptions for each encounter

2. Consumption data for a specified period from stock records

3. Health facility service statistics (e.g. OP encounters in the study period)

1. Drug use indicators including average cost per case

2. Ranked diagnoses and drugs by frequency per 1000 encounters

3. Comparison of standardized drug requirement per 1000 cases based on prescriptions (demand) and consumption (supply)

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RESULTS (1)Prescribing indicators

Category Num of enctrs

Avg Drugs per enctr

% with INJ

% with AM

% with AB

Avg cost* per enctr

Age < 5 1575 3.2 48% 77% 59%

Age > 5 or not specified

4821 2.9 29% 51% 56%

HU level II 1597 3.1 40% 65% 56% 1039

HU level III 1599 2.9 33% 62% 54% 772

HU level IV 2399 2.8 32% 53% 57% 992

Hospital 801 3.4 26% 48% 61% 1088

District KAM 1599 2.8 30% 57% 63% 1079

District LUW 1600 2.7 39% 52% 54% 864

District MAS 1597 3.2 44% 64% 48% 1150

District RAK 1600 3.2 22% 57% 62% 675

ALL 6396 3.0 34% 57% 57% 942

Cost in Uganda Shillings (approx 2000 USh = 1 Euro = 1 US$)

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RESULTS (2)Prescribing in malaria/ARI

DIAGNOSIS frequency % encounters prescribed:

N % Cum %

AM AB AM+AB

Malaria only 1706 28% 28% 98% 17% 16%

Malaria + ARI(No pneumonia)

582 9% 36% 97% 90% 87%

Malaria + ARI (Pneumonia)

472 7% 43% 97% 97% 93%

ARI only (No pneumonia)

317 5% 48% 10% 90% 9%

ARI only (Pneumonia)

177 3% 51% 14% 97% 14%

Other diagnoses

3142 49% 100% 29% 60% 11%

Total 6396 100% 57% 57% 25%

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RESULTS (3)Most prescribed drugs

Rank Drug Times Rx per 1000 enctrs

% total Rx Cum % Rx

1 Paracetamol oral 532 17.8% 17.8%

2 Chloroquine oral 436 14.6% 32.4%

3 Co-trimoxazole oral 225 7.5% 39.9%

4 Acetylsalicylic acid oral 200 6.7% 46.6%

5 Mebendazole oral 180 6.0% 52.6%

6 Chloroquine inj 144 4.8% 57.4%

7 Procaine Penicillin inj 136 4.5% 61.9%

8 Sulphadox/Pyrim (SP) oral 118 3.9% 65.8%

9 Metronidazole oral 98 3.3% 69.1%

10 Chlorpheniramine oral 71 2.4% 71.5%

11 Amoxy(ampi)cillin oral 68 2.3% 73.8%

12 Doxy(tetra)cycline oral 61 2.0% 75.8%

13 Ferrous sulph. oral 60 2.0%

14 Penicillin V oral 59 2.0%

15 Ibuprofen oral 43 2.0%

16 Mag. Trisil. oral 38 1.4%

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RESULTS (4)Demand and supply

Rank Drug and dose unit Demand per 1000

Mean (±95%CI)

ED Kit Supply per 1000

Total Supply per 1000

Mean (±95%CI)

% Stock-out Time

1 Paracetamol 500mg oral 6165(±782) 2000 5309(±1411) 17%

2 Chloroquine 150mg base oral 3009(±428) 3000 4912(±1050) 6%

3 Co-trimoxazole 480mg oral 2991(±481) 250 3214(±854) 29%

4 Acetylsalicylic acid 300mg oral 3398(±540) 2000 3080(±726) 25%

5 Mebendazole 100mg oral 1511(±279) 1000 2096(±506) 23%

6 Chloroquine 200mg/5ml inj (a) 260(±133) 7 157(±57) 22%

7 Procaine Penicillin 4MU inj (a) 259(±93) 20 78(±27) 16%

8 Sulphadox/Pyrim (SP) 525mg oral

272(±70) 0 622(±230) 37%

9 Metronidazole 200mg oral 1783(±524) 300 1747(±575) 32%

10 Chlorpheniramine 4mg oral 604(±187) 200 943(±337) 26%

11 Amoxycillin 250mg oral (b) 1079(±379) 0 1211(±413) 30-44%

12 Doxycycline 100mg oral (c) 575(±210) 75 425(±113) 39%

Notes: (a) Demand for CQ inj and Procaine inj is overestimated due to known errors in interpretation of dose units. (b) Includes for ampicillin 250mg at factor 3/8(c) Includes for tetracycline 250mg at factor 1/4

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Discussion –Key Findings

Over-prescribing of injections and antibiotics overall, but some facilities are within the expected norms for injections Cost per case is associated with high use of injections, AB, no. of drugsMalaria with ARI is common, and ARI is treated with antibiotics regardless of classification (pneumonia or not)10-16 Core drugs account for 75% of prescriptions and overall cost Demand per 1000 provides a precise forecast for core requirements while supply is unstable and consumption data are missing or unreliable

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Conclusions and recommendations

This is a useful and robust tool that will focus available resources towards meeting demand for core drug requirements The same tool highlights potential cost savings ensuing from better specificity of diagnosis and improved prescriber behaviour e.g. reduced injection useA more expensive anti-malarial treatment regimen will have a dramatic impact on resource useRepeat assessments will measure the effect of interventions, changes in resources, systems or management

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Abstract

Contact: Hanif Nazerali Email: hanif@hsps-ug.org, or hnazerali@cs.com Authors: Nazerali H, Mohamed K, Oteba M, Mwoga J

Institution: Uganda Ministry of Health and Danida Health Sector Programme Support

Title: Managing the Dynamics of Drug Supply, Demand, and Health Service Utilization: A New Multi-Purpose Tool for Forecasting Drug Requirements and Resource Management.

Problem Statement: In 2003 Uganda’s Ministry of Health (MOH) discontinued the essential drug kits for the primary health care (PHC) level (“push” system) in favor of a nationwide order-based supply (“pull” system) and decentralized control of PHC drug budgets. The pull system aims for an optimal match of drug supply and demand while ensuring that resources are focused on the burden of disease (need). Objectives: To field test a multi-purpose rapid assessment tool that forecasts PHC drug requirements and costs based on actual prescribing patterns, reports drug use indicators, and measures the gap between expected demand and actual supply. To guide financing and supply system changes in a resource limited setting . Design: Cross-sectional design, retrospective review of outpatient (OP) registers and stock cards to provide baseline and guide systems development. Planned repeat assessments. Setting and Population: Total 6400 OP encounters in 24 health units (HU) in 4 districts, selected systematically during 2001 and drug consumption at 205/275 HU in 12 districts. Outcome Measures: Monthly OP cases and disease burden; standard drug use indicators; demand-based drug requirement per 1000 outpatients; percentage actually supplied, and resource gap. Results: Reported diagnoses of malaria and/or acute respiratory infections (ARI), in combination, accounted for >80% of diagnoses and >50% of encounters. At least one antibiotic and one antimalarial drug were prescribed in half of all OP encounters. An injection was prescribed in one third of all encounters, and even more frequently in under-fives (48%) and at the first level of care, and 3.0 drugs were prescribed per encounter. Only 10-16 drugs accounted for 75% of prescriptions, and 75% of the projected cost. Kit supply and decentralized drug budgets together met two thirds of the demand/1000 cases due to unfounded assumptions of model prescribing behavior and adherence to STGs, leaving a 33% supply gap for these core drugs. There were notable shortfalls for oral antibiotics e.g. cotrimoxazole, analgesic/antipyretics and all injectables. An optimized standard drug requirement per 1000 cases was derived for core items. Conclusions: This is a robust tool that can guide forecasting, budgeting, and order replenishment based on cost, actual prescribing behaviors, and health benefit in a setting of chronic undersupply, and dynamic service utilization. Increases in public sector drug budgets can be justified against higher OP attendance rates, demonstrated resource gap and the potential impact on household spending by the poor. The study will be repeated for a reference period in 2003 to assess the effect of these tools, changes in the supply system and increased drug funding.

Study Funding: Danish International Development Agency (Danida) through the Health Sector Support Programme, and USAID through the Delivery of Improved Services for Health (DISH) II Project CA-617-A-00-00-00001-00