This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site. Copyright 2007, The Johns Hopkins University and William Reinke. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed. Session 6 Design and Analysis for Operations Research William A. Reinke, Ph.D. Professor Department of International Health Johns Hopkins University School of Hygiene and Public Health Unique Features of Operations Research • Multiple Factors Affect Results • Many Factors Not Subject to Control • Places Emphasis on Analysis of Variation More Than its Control Through Study Design Healthy Status - Mortality - Morbidity - Disability Health Needs - Population - Problems - Demand Service Functions & Programs Health Resources - Human - Physical - Financial Service Capacity - Provider Competence - Service Support Forms of Health Systems Research • Prescriptive – Explicit Mathematical Model – Associated Quantitative Data • Descriptive – Indicators Regarding Factors Subject to Manipulation – Indicators Regarding Population Groups for Targeted Services Potential Outcomes Thousands of Cases Diarrhea Treated (D) 160 140 D + 2.5V = A 120 150 Deaths Averted (A) 100 100 80 60 50 40 20 0 0 10 20 30 Thousands Vaccinated (V) 40 Linear Programming Model 160 120 D + 2.5V = A 0.5D+0.6V ≤ 50 0.3D+1.5V ≤ 45 100 150 Deaths Averted (A) Thousands of Cases Diarrhea Treated (D) 140 50 workers 80 60 40 150 $45,000 Budget Deaths Averted 20 117 0 10 20 30 40 Thousands Vaccinated (V) Unit of Analysis Compared to Unit of Observation • Unit of Analysis – Cases of Diarrhea in Past Two Weeks – Prevalence Approximately 20% in Children Under 5 – Children Approximately 16% in Population • Unit of Observation – Household – Approximately 5 Persons per Household Sample Calculation Based on Measure of Interest Use of Oral Rehydration Expected to be About 50% Precision Required + 5% n = = = 4 p( 1 − p ) D 2 (4 )(.5 )(.5 ) (.0 5 ) 2 400 400 = 1 2 ,5 0 0 P e r s o n s (.2 )(.1 6 ) 1 2 ,5 0 0 = 2 ,5 0 0 H o u s e h o ld s 5 Needs Assessment WHO Health Needs - Population - Problems - Demand Entire Population in Defined Area Selected Target Groups Specific High Risk Traits Service Users WHAT Biological Need Consumer Wants Willingness to Pay Effective Demand HOW EXPRESSED Attitudes and Beliefs Behavior Resources and Services Health Resources Service Coverage Human Physical Financial Technical Quality Provider Knowledge and Skills Availability of Service Support Appropriate Application of Competence Client Satisfaction Perceived Benefit Perceived Cost and Inconvenience Service Functions & Programs Service Capacity Provider Competence Service Support Outcomes Effectiveness - Benefits Level -Cost: Affordability Health Status -Mortality -Morbidity -Disability Cost-effectiveness -Tangible -Intangible Equity -Distribution of Benefit Service Functions & Programs Healthy Quality of Life Determination H Impaired A B C H preferred to A Indifferent between H&B C Preferred to H 0 3 6 9 12 Conclusion: A Year of Impaired Life is Equivalent to 0.8 Year of Healthy Life Framework for Standard Gamble Impaired ? P=? Relieved 100 Death 0 Relationships Among Specified Variables Independent Variables Experimental Interventions Dependent Variables Result of Interest Intervening Variables Additional Factors Present that Could Affect Outcome Effects of Confounding No Confounding Persons Prevalence(%) Cases % with M F M F M F Conditions Smokers 30 30 15 21 60 40% Non-Smokers 70 70 7 21 Diff. 50 70 20 10 Confounding Smokers 40 20 30 20 14 57 36% Overcoming Confounding Non-Smokers 60 80 6 Smokers 50 25 50 24 Diff. 21 35 60 40% Non-Smokers 50 50 5 15 Diff. 20 Smoking Condition Sex Effects of Interaction Proportional Smokers Sample Non-Smokers Persons M F 40 20 60 80 Prevalence (%) M F Cases M F 16 16 12 % with Conditions 53 33% diff. 16 20 20 40 60 50-10=40 70+10=80 10+10=20 30-10=20 Equal Sample Smokers 50 50 40% Diff. Non-Smokers 50 50 10 Smoking Condition Sex 10 20 Common Techniques of Operations Research • Linear Programming • Inventory Models • Queuing Models Economic Order Quantity Qo = 2U C Ch r 1000 800 Annual Cost Total Cost 600 Holding cost 400 200 U = 800 Cr = 50 Ch = 2 Replenishment Cost 0 50 150 250 350 450 550 Order Quantity (Q) 650 750 Traditional Techniques of Statistical Analysis • Univariate Analyses – – – – Frequency Distributions Average Standard Deviations Proportions, Rates and Ratios • Bivariate Analyses – t-tests – X2 Analyses • Multivariate Analyses – – – – Analysis of Variance (ANOVA) Multiple Linear Regression Logistic Regression Discriminant Analysis