536400
research-article2014
JPCXXX10.1177/2150131914536400Journal of Primary Care & Community HealthLi et al
Original Research
Using Systems Science for Population Health Management in Primary Care
Journal of Primary Care & Community Health 2014, Vol. 5(4) 242–246 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/2150131914536400 jpc.sagepub.com
Yan Li1,2, Nan Kong2, Mark A. Lawley2, and José A. Pagán1
Abstract Objectives: Population health management is becoming increasingly important to organizations managing and providing primary care services given ongoing changes in health care delivery and payment systems. The objective of this study is to show how systems science methodologies could be incorporated into population health management to compare different interventions and improve health outcomes. Methods: The New York Academy of Medicine Cardiovascular Health Simulation model (an agent-based model) and data from the Behavioral Risk Factor Surveillance System were used to evaluate a lifestyle program that could be implemented in primary care practice settings. The program targeted Medicare-age adults and focused on improving diet and exercise and reducing weight. Results: The simulation results suggest that there would be significant reductions projected in the proportion of the Medicare-age population with diabetes after the implementation of the proposed lifestyle program for a relatively long term (3 and 5 years). Similar results were found for the subpopulations with high cholesterol, but the proposed intervention would not have a significant effect in the proportion of the population with hypertension over a time period of 150 min/wk of moderate physical activity), had a healthy diet (ate ≥5 fruits or vegetables per day), did not have
diabetes, hypertension, or high cholesterol, and had no history of myocardial infarction (MI) or stroke. Using the 2007 BRFSS data, we estimated the mean and standard deviation of age for insured adults aged 65 to 94 years (inclusive) and the proportion of each category for all the other variables. After excluding respondents with missing data, the sample sizes were 104,670 for the insured population aged ≥65 years and 19,321, 60,678, and 52,912 for the subpopulations with diabetes, hypertension, and high cholesterol, respectively. The NYAM-CHS model is a joint ABM effort from a multidisciplinary team of experts in health services research, health economics, and systems science. The model allows the simulation of different behaviors and health conditions over time.17 ABM has been shown to have unique advantages over other systems science methodologies (eg, discrete-event simulation, system dynamics models) in terms of the capabilities of modeling detailed individual-level behaviors and health outcomes and capturing demographic heterogeneity.18,19 Other strengths of ABM include the ability to represent stochastic variability in input variables and parameters and the possibility of including history dependence in state transitions as well as agent interactions.20 ABM has been used widely in social sciences, but health applications have been limited mostly to modeling infectious disease and addictive behaviors.13,21-23 In the NYAM-CHS model, each agent (person) is defined according to 7 behavior and health factors (ie, smoking, physical activity, healthy diet, healthy weight, cholesterol, blood pressure, and blood glucose) as well as by age, gender, and having a history of MI or stroke. These factors were selected on the basis of the concept of ideal cardiovascular health developed by the American Heart Association, which is defined as not having cardiovascular disease while also having optimal levels of the 7 factors described previously.24 Each agent’s behavior and health factors evolve simultaneously and interactively as time progresses in the model. The predictive validity of the model has been assessed by comparing simulated and actual health outcomes using nationally representative data from the BRFSS.25 The NYAM-CHS model was used to assess health outcomes over time for a primary care practice serving a population of insured adults aged ≥65 years. The health outcomes of the model resulting from normal health progression were compared with the health outcomes obtained from implementing a lifestyle program designed to reduce by half the proportion of the population eating
research-article2014
JPCXXX10.1177/2150131914536400Journal of Primary Care & Community HealthLi et al
Original Research
Using Systems Science for Population Health Management in Primary Care
Journal of Primary Care & Community Health 2014, Vol. 5(4) 242–246 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/2150131914536400 jpc.sagepub.com
Yan Li1,2, Nan Kong2, Mark A. Lawley2, and José A. Pagán1
Abstract Objectives: Population health management is becoming increasingly important to organizations managing and providing primary care services given ongoing changes in health care delivery and payment systems. The objective of this study is to show how systems science methodologies could be incorporated into population health management to compare different interventions and improve health outcomes. Methods: The New York Academy of Medicine Cardiovascular Health Simulation model (an agent-based model) and data from the Behavioral Risk Factor Surveillance System were used to evaluate a lifestyle program that could be implemented in primary care practice settings. The program targeted Medicare-age adults and focused on improving diet and exercise and reducing weight. Results: The simulation results suggest that there would be significant reductions projected in the proportion of the Medicare-age population with diabetes after the implementation of the proposed lifestyle program for a relatively long term (3 and 5 years). Similar results were found for the subpopulations with high cholesterol, but the proposed intervention would not have a significant effect in the proportion of the population with hypertension over a time period of 150 min/wk of moderate physical activity), had a healthy diet (ate ≥5 fruits or vegetables per day), did not have
diabetes, hypertension, or high cholesterol, and had no history of myocardial infarction (MI) or stroke. Using the 2007 BRFSS data, we estimated the mean and standard deviation of age for insured adults aged 65 to 94 years (inclusive) and the proportion of each category for all the other variables. After excluding respondents with missing data, the sample sizes were 104,670 for the insured population aged ≥65 years and 19,321, 60,678, and 52,912 for the subpopulations with diabetes, hypertension, and high cholesterol, respectively. The NYAM-CHS model is a joint ABM effort from a multidisciplinary team of experts in health services research, health economics, and systems science. The model allows the simulation of different behaviors and health conditions over time.17 ABM has been shown to have unique advantages over other systems science methodologies (eg, discrete-event simulation, system dynamics models) in terms of the capabilities of modeling detailed individual-level behaviors and health outcomes and capturing demographic heterogeneity.18,19 Other strengths of ABM include the ability to represent stochastic variability in input variables and parameters and the possibility of including history dependence in state transitions as well as agent interactions.20 ABM has been used widely in social sciences, but health applications have been limited mostly to modeling infectious disease and addictive behaviors.13,21-23 In the NYAM-CHS model, each agent (person) is defined according to 7 behavior and health factors (ie, smoking, physical activity, healthy diet, healthy weight, cholesterol, blood pressure, and blood glucose) as well as by age, gender, and having a history of MI or stroke. These factors were selected on the basis of the concept of ideal cardiovascular health developed by the American Heart Association, which is defined as not having cardiovascular disease while also having optimal levels of the 7 factors described previously.24 Each agent’s behavior and health factors evolve simultaneously and interactively as time progresses in the model. The predictive validity of the model has been assessed by comparing simulated and actual health outcomes using nationally representative data from the BRFSS.25 The NYAM-CHS model was used to assess health outcomes over time for a primary care practice serving a population of insured adults aged ≥65 years. The health outcomes of the model resulting from normal health progression were compared with the health outcomes obtained from implementing a lifestyle program designed to reduce by half the proportion of the population eating
