Jack Homer Homer Consulting Voorhees, New Jersey Bobby Milstein Centers for Disease Control and Prevention (CDC) Atlanta, Georgia Optimal Decision Making.

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Jack Homer Homer Consulting Voorhees, New Jersey Bobby Milstein Centers for Disease Control and Prevention (CDC) Atlanta, Georgia Optimal Decision Making in a Dynamic Model of Community Health HICSS-37, Waikoloa, Hawaii January 2004

Major strides in disease control and prevention the past half century Rate if trend continued Peak Rate Actual Rate Age-adjusted Death Rate per 100,000 Population Year Actual and Expected Death Rates for Coronary Heart Disease, 1950–1998 Marks JS. The burden of chronic disease and the future of public health. CDC Information Sharing Meeting. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion; 2003.

Source: Centers for Disease Control and Prevention. Health-related quality of life: prevalence data. National Center for Chronic Disease Prevention and Health Promotion, Accessed March 21 at. But overall state of health is getting worse, not better 14% increase

What accounts for poor community health? A history of causal theory God’s will Humors, miasma, ether Poor living conditions, immorality (sanitation) Single disease, single cause (germ theory) Single disease, multiple causes (heart disease) Single cause, multiple diseases (tobacco) Multiple causes, multiple diseases (but no feedback dynamics) (social epidemiology) Dynamic feedback among afflictions, living conditions, and community capacity (syndemic)

Affliction prevalence & burden Community strength Effort to alleviate and prevent affliction B1a Effort to improve living conditions B1b Effort to build community strength B1c Adverse living conditions R1 At-risk fraction Affliction cross-impacts R2c Social disparity R2b R2a R3a Public work fraction United efforts Divided efforts R3b Outside assistance to alleviate and prevent affliction Outside assistance to improve living conditions Outside assistance to build community strength Magnitude of ameliorative efforts R4b B3b R4a B3a Overview of the community health model Key Rectangle: Stock/state variable Blue arrow: same-direction link Green arrow: opposite-direction link Circled “B”: balancing causal loop Circled “R”: reinforcing causal loop

Model parameters The model contains about two dozen parameters that may vary from case to case, constants describing –the community’s baseline strength and living conditions, baseline rates of affliction incidence and recovery, and the strength of linkages among these variables –effectiveness of programs (benefit per unit program effort) –cost-effectiveness of assistance (program effort per unit cost) For each such constant C, three values were defined –C basic, C better, C worse The ‘Basic’ setting is created by using C basic for all constants

Growth of affliction burden: Four scenarios (all with ‘Basic’ parameter setting) Basic scenario: Poor living conditions, weak community, intertwined afflictions Weaker cross-impacts among afflictions Better living conditions Greater community strength Unhealthy days per person per month

Optimization procedure Define six optimization parameters: Fraction of assistance to Affliction program: T0 (time 0-4), T4 (time 4-8), T8 (time 8-12) Fraction of non-affliction assistance to Living Conditions programs: T0, T4, T8 (Remaining assistance is to Community Strength programs) Run the model starting from Time -20, so that it approximates steady state by Time 0 Optimize using Vensim’s standard: modified Powell grid search MINIMIZE: Affliction burden averaged over Time 4 to Time 20 SUBJECT TO: 0 < {Six optimization parameters} < 1

Comparing results under four different assistance schemes (all with Basic parameter setting) Affliction assistance only Conditions assistance only Strength assistance only Optimal assistance scheme: All Strength assistance first 4 years, then all Affliction assistance Avg affliction burden T4-T20: Unhealthy days per person per month

Dialogue

About the Feedback Loops Syndemic: Each affliction increases vulnerability to other afflictions, thereby amplifying the effect of increases or decreases in the prevalence of individual afflictions. Community Response: The community makes efforts to fight affliction and adverse living conditions in response to their prevalence, and to build greater community strength when it is perceived as low. Outside assistance may bolster such efforts. Social Disparity and Community Strength: Response efforts, especially those to improve adverse living conditions, are greater in magnitude when the community is strong and unified. But community strength is hindered by social disparity, which, in turn, is made worse by the very afflictions and adverse living conditions the efforts are trying to fight. Community Strength and Public Work: Community strength is also affected by the character of the response efforts themselves. When problems spread in a strong community, the response tends to be more multi-faceted and elicit greater contributions from ordinary citizens in the form of "public work", a united process that reinforces the community's strength. Conversely, when problems spread in a weak community, problem-fighting efforts tend to be taken over by small groups of professionals who specialize in those problems, a divided process that ends up reinforcing the community's weakness. Present Strategy and Future Strength: Strategies for fighting afflictions or improving living conditions today may also affect the community's ability to mount similar efforts in the future. Outside assistance given to a weak community for problem fighting may amplify the divided response and undermine the community’s internal response capability. Outside assistance to build community strength may prepare the community to make a more united response. R1 R3 R2 R4B3 B1

Understanding the optimized results Optimal under Basic: Start with 100% Strength assistance until T4, then switch to 100% Affliction assistance T4-T12 Initial building of Strength magnifies effectiveness of later Affliction assistance Why no Living Conditions assistance? It detracts from Strength if done early (loop B3b), and its benefits are too slow if done later Rebound in Affliction after T12: Switch from Strength assistance to Affliction assistance allows Strength to erode, so that community is again weak after assistance ends at T12 and can’t maintain programs The optimal decision scheme reflects evaluation period end at T20. A longer evaluation period would give more weight to the Affliction rebound, and could give Strength assistance greater prominence. –If eval end time is {T21, T26}, stay with Strength assist until T8 –If eval end time is T27 or later, stay with Strength assist throughout

Results under Basic Setting with Optimal Assistance Scheme (CS1AF11) Affliction burden (avg unhealthy days per person per month) Adverse conditions prevalence (0-1 scale) Community strength (0-1 scale) Switch from 100% strength assistance to 100% affliction assistance at T4 All assistance terminated at T12 Optimal scheme “CS1AF11”: [100% [100%

Optimized results under alternative parameter settings Did a variety of sensitivity tests using ‘Better’ and ‘Worse’ values of the model’s parameters For most of the sensitivity tests performed, the optimal assistance scheme is the same as for the Basic setting, namely CS1AF11 Even for those settings where CS1AF11 is not the optimal scheme, CS1AF11 often does nearly as well as the optimal scheme in terms of the average affliction burden A handful of parameters can affect which scheme is optimal, namely: –Parameters modulating the effectiveness of community programs –Parameters modulating the cost-effectiveness of assistance Living Conditions (LC) assistance part of the optimal scheme only if the effectiveness of LC programs and assistance are both boosted

Alternative parameter settings

Settings for which optimal scheme is not CS1AF11