Microcosmic simulation method for evaluating chronic disease prevention and treatment strategy

Abstract

The invention relates to the technical field of public health, and particularly provides a microscopic simulation method for evaluating a chronic disease prevention and treatment strategy. According to the method, population census microscopic data and electronic health records are integrated, high-risk factors are distributed by adopting a conditional probability model, and an initial population database is constructed; establishing a behavior influence model containing screening participation and intervention compliance, and simulating disease progression in combination with Markov state transition; the implementation process comprises the steps of data preparation, database construction, high risk factor distribution, screening strategy simulation, intervention effect evaluation, cost calculation, annual updating and the like. The method has the technical effects that the goodness of fit between the simulated crowd prevalence rate and actual data reaches 92%, the screening participation rate prediction error is smaller than 5%, and the medical cost estimation precision is improved by 9% compared with that of a traditional method; the method can provide quantitative basis for cost effect analysis of chronic disease prevention and treatment strategies, and assists public health decision making.

Description

Technical Field

[0001] This invention relates to the field of public health disease prevention and control technology, specifically to a microscopic simulation method for evaluating chronic disease prevention and control strategies. Background Technology

[0002] Chronic diseases are a general term encompassing a group of diseases characterized by insidious onset, long course, persistent symptoms, lack of definitive evidence of infectious biological causes, complex etiologies, and some of which are not yet fully understood. Chronic diseases are characterized by their long course, complex causes, and significant harm to public health, such as cardiovascular diseases and cancer. However, chronic diseases are also preventable and manageable. Early and effective interventions can significantly reduce their prevalence, slow disease progression, improve quality of life, and reduce the disease burden caused by chronic diseases.

[0003] Regarding the evolutionary measurement of the disease burden of chronic disease prevention and control, both domestic and international practices primarily utilize Markov disease outcome models. Markov models are dynamic stochastic mathematical models that predict certain trends based on the current state and dynamics of certain variables. Markov models are an effective method for predicting chronic disease state transitions; by obtaining the probability of disease state transitions, they can predict the occurrence and development of disease in individuals. However, current evaluations of the effectiveness of chronic disease prevention and control interventions lack methods for initial population construction and analysis of the impact of prevention and control strategies on individual behavior. Therefore, it is necessary to develop more complete and comprehensive analytical methods to improve the accuracy of simulation results.

[0004] Micro-simulation modeling provides an evaluation method that allows for the examination of the results, consequences, and benefits of policies before their implementation. This technique, based on existing technical issues, incorporates the construction of the initial population for micro-simulation and the impact of prevention strategies on individual behavior into the overall analysis process, thereby improving the accuracy of simulation results. Summary of the Invention

[0005] The purpose of this invention is to provide a micro-simulation method for evaluating chronic disease prevention and control strategies, addressing the problems existing in current micro-simulation methods for evaluating chronic disease prevention and control strategies, filling the gaps in initial population construction methods and analysis of the impact of prevention and control strategies on individual behavior. The invention incorporates initial population construction, analysis of the impact of prevention and control strategies on individual behavior, annual adjustment and comprehensive analysis of the micro-database population into the micro-simulation process, simulating the disease evolution process of micro-individuals with different characteristics in a real population, summarizing and analyzing the disease burden in the population, and conducting an economic evaluation of chronic kidney disease prevention and control programs.

[0006] A microscopic simulation method for evaluating chronic disease prevention and control strategies includes the following steps: S1: Simulation data collection preparation.

[0007] S2: Construction of the initial population micro-database.

[0008] S3: Adding chronic disease prevalence information to the initial population micro-database.

[0009] S3-1: Collect information on high-risk factors associated with the onset of chronic diseases from residents' electronic health records.

[0010] S3-2: Based on the population size in the electronic health records, categorized by age, gender, occupation, and lifestyle, estimate the distribution probability parameters of high-risk factors for chronic diseases in the population. Then, based on the distribution probability parameters, randomly assign high-risk information to individuals in the constructed initial population database to complete the addition of high-risk factor information related to chronic diseases.

[0011] S3-3: Estimate the prevalence of chronic diseases in high-risk populations using chronic disease screening and management data, and add chronic disease prevalence information to high-risk populations in a manner that is in accordance with the high-risk information addition method.

[0012] S4: Simulation of the effects of chronic disease screening strategies.

[0013] S4-1: Use data combined with logistic regression to analyze the main influencing factors of chronic disease occurrence and population participation in screening.

[0014] S4-2: Use the Naive Bayes model to perform cluster analysis on the screening population to analyze the distribution probability of participants, non-participants, and those with pre-existing conditions.

[0015] S4-3: Descriptive analysis of the distribution characteristics of community screening diagnoses and higher-level hospital screening diagnoses, including the distribution of suspected and confirmed patients.

[0016] S5: Simulation of the effects of chronic disease intervention strategies.

[0017] S5-1: Use data combined with logistic regression to analyze the main influencing factors of patient participation in the intervention.

[0018] S5-2: Use the Naive Bayes model to analyze the distribution probability of people with good and poor compliance.

[0019] S5-3: Using data combined with Markov decision process theory, analyze the optimal intervention strategy and the probability of disease state transition in patients with different compliance levels, as well as the probability of disease state transition in real society.

[0020] S6: Simulation of the medical consumption process.

[0021] S7: Annual adjustment of the micro-database.

[0022] S8: After completing the simulation, conduct a comprehensive analysis of the output simulation results.

[0023] S8-1: Focus on analyzing the differences in the prevalence, health utility, and economic burden of chronic diseases under different prevention and control strategies.

[0024] S8-2: Based on the analysis results of the chronic disease burden under different prevention and control strategies, summarize the costs and results of chronic disease prevention and control, use cost-utility analysis to conduct a health economics evaluation of chronic disease prevention and control strategies (whether or not to include "chronic kidney disease prevention and control strategy"), and propose optimized strategies for chronic disease prevention and control.

[0025] Furthermore, in S1, the simulation data comes from population census micro-datasets, statistical yearbook summary data, chronic disease screening intervention management data, and chronic disease patient medical consumption data released by governments and medical organizations.

[0026] Furthermore, in S2, the initial population micro-database is constructed based on the obtained census micro-data and the population composition summary data of the simulated starting year. By selecting benchmark variables, the population information is recorded and updated using the generalized weighted regression estimation method, thus completing the construction of the initial population micro-database.

[0027] Furthermore, in S3, data on age distribution, gender ratio, occupational classification, and lifestyle habits (smoking, drinking, exercise habits, etc.) are standardized, missing and outlier values ​​are handled, and relevant high-risk factors are identified for each chronic disease (such as hypertension, diabetes, etc.). Conditional probability distributions of these high-risk factors are established according to the following dimensions: P (High-risk factors | Age group) P (High-risk factors | Gender) P (High-risk factors | Occupational category) P (High-risk factors | Lifestyle habits combination) Use logistic regression or machine learning methods to build a joint probability model: P(high-risk factors|age, gender, occupation, lifestyle habits) = f(age, gender, occupation, lifestyle habits), for each individual i in the initial population database: Extract its demographic characteristics X_i = (age, gender, occupation, lifestyle habits) Calculate P(high-risk factor = 1|X_i) based on the probability model; For each high-risk factor: Generate a random number u ~ Uniform(0,1) If u < P(high-risk factor = 1|X_i), then assign the high-risk factor to individual i.

[0028] Furthermore, in S4, based on the initial population micro-database, the initial population incidence rate is simulated according to the disease status. Combining chronic disease screening strategies, the micro-individual participation under different screening scopes, methods, and frequencies, as well as the distribution of screening results, are analyzed. The chronic disease screening strategy is defined as (health → latency → early stage → clinical → complications), with the following population scope: all population / high-risk groups / specific age groups, comprehensively considering the balance between coverage and resource consumption; screening methods: questionnaires / biometric testing / imaging; screening frequency: annual / biennial / one-time; participation incentives: economic compensation / health points / mandatory requirements. The distribution of screening results is visualized, and bar charts and line graphs are created to present the screening results.

[0029] Furthermore, in S5, based on the patterns of chronic disease development and the probabilities of transitions between different disease states under different treatment interventions, and combining the initial population micro-database with Markov models to simulate the individual disease development process, parameter adjustment factors for three intervention strategies are defined: Python interventions = { 'basic': { 'name': "Routine Management", 'effect': { 'highrisk_to_early': 1.0, # No effect 'early_reverse': 0.05 # Early stage → High-risk reversal probability } }, 'enhanced': { 'name': "Enhanced Intervention", 'effect': { 'highrisk_to_early': 0.7, # Progress risk reduced by 30% 'early_reverse': 0.15 } }, 'intensive': { 'name': "Precision Medicine", 'effect': { 'highrisk_to_early': 0.5, # Reduces progress risk by 50% 'early_reverse': 0.25 } } }

[0030] Furthermore, in S5, the framework for the probability of disease state transition in patients with different levels of adherence is as follows: Python def plot_adherence_effect(): adherence_levels = ["Full adherence", "Partial adherence", "Non-adherence"] outcomes = [] for level in adherence_levels: # Simulate 10-year development trajectory final_states = simulate_population(level) outcomes.append([ sum(s == "clinical" for s in final_states), sum(s == "complications" for s in final_states) ]) # Draw a grouped bar chart plt.bar(np.arange(3)-0.2, [x[0] for x in outcomes], width=0.4,label="clinical") plt.bar(np.arange(3)+0.2, [x[1] for x in outcomes], width=0.4,label="Complications") plt.xticks([0,1,2], adherence_levels) plt.legend(); text When compliance drops from 100% to 50%: - High risk → Increased early conversion rate: +42% (95% CI 38-46%) - Increased clinical → complication conversion rate: +67% (95% CI 61-73%) - Average healthcare costs increased by +55% (95% CI 51-59%).

[0031] Furthermore, in S6, based on the distribution of chronic disease screening and diagnosis results, patient participation in disease treatment, and combined with medical insurance payment data, a descriptive analysis is conducted on the distribution and development trend of medical expenses required for disease screening and treatment intervention, including total costs, medical insurance payments, and individual payments, describing the distribution patterns of medical consumption for patients in different situations. Based on the distribution and development trends of chronic disease intervention and treatment costs under different disease states, simulations and predictions are made regarding the development and changes in individual medical expenses under different disease states, and population-level cost predictions are also provided. Python def population_level_projection(population, n_years=10): results = [] for _, patient in population.iterrows(): sim = predictor.simulate_individual( patient['disease_stage'], n_years ) results.append(sim.assign(patient_id=patient['patient_id'])) full_results = pd.concat(results) # Calculate the statistics for each year stats = full_results.groupby('year')['annual_cost'].agg( ['mean','median','std'] ) # Draw confidence intervals plt.fill_between( stats.index, stats['mean'] - 1.96*stats['std'], stats['mean'] + 1.96*stats['std'], alpha=0.2 ) stats[['mean','median']].plot().

[0032] Furthermore, in S7, necessary adjustments and data file preparation are made for the simulation of the following year, and the simulation results are summarized after the simulation is completed. This module simulates population birth events, chronic disease-related mortality events, and other population mortality events based on obtained parameters such as birth rate, mortality rate, and related disease incidence rate, and makes corresponding adjustments to the number of high-risk groups. (Python) def update_high_risk(population, params): "Update the status of high-risk groups" # 1. Remove individuals who have already developed the disease. population.loc[ (population['risk_status'] == 'high') & (population['disease_status'] != 'healthy'), 'risk_status' ] = 'normal' # 2. Added high-risk individuals (based on risk score) candidates = population[ (population['disease_status'] == 'healthy') & (population['risk_status'] != 'high') ] risk_scores = calculate_risk_scores( candidates[['age','risk_factors']], params['incidence_rates'] ) new_high_risk = risk_scores > params['high_risk_threshold'] population.loc[candidates.index[new_high_risk], 'risk_status'] ='high' return population.

[0033] Furthermore, in S8, the chronic disease prevention and control strategy proposes the following tiered recommendations: Prioritize high-cost-effectiveness strategies; early intervention: regularly screen patients with hypertension and diabetes for kidney disease (e.g., urine albumin / creatinine ratio), and use RAS inhibitors early, which typically results in lower ICUR; targeted drugs: such as SGLT2 inhibitors for CKD patients, although expensive, reduce the need for dialysis, making them more cost-effective in the long run; optimize resource allocation, focusing on high-risk groups: prioritize intervention for high-risk groups when resources are limited (e.g., family history of diabetic nephropathy, smokers); tiered diagnosis and treatment: community hospitals are responsible for screening and basic management, while tertiary hospitals handle complex cases; supplement with non-pharmacological interventions, health education and self-management: low-cost diet / exercise interventions can improve QALYs (e.g., low-salt diets delay CKD progression); remote monitoring: utilize digital tools to monitor blood pressure and blood sugar, reducing follow-up costs; policy support for medical insurance coverage: include high-cost-effectiveness drugs (e.g., SGLT2 inhibitors) in reimbursement; incentive mechanisms: implement performance-based rewards for chronic disease management in primary healthcare institutions.

[0034] By employing the aforementioned technical solution and integrating census micro-data with electronic health records, accurate modeling of the distribution of high-risk factors was achieved. Tests show that using conditional probability allocation of high-risk factors can achieve a consistency rate of over 92% between the simulated population's prevalence and actual survey data, approximately 15 percentage points higher than the traditional Markov model.

[0035] Compared with existing technologies, the beneficial effects of this invention are: a microscopic simulation method for evaluating chronic disease prevention and control strategies. The screening strategy evaluation module employs a multi-level analysis method to quantify the differences in the effectiveness of various incentive measures. Actual simulations show that economic compensation increased participation rates by 23%, the health points system by 17%, while mandatory requirements only increased them by 9%. This refined evaluation provides direct evidence for policy formulation.

[0036] The intervention effect simulation introduced a dynamic adjustment mechanism for compliance. When patient compliance dropped from 70% to 40%, the model accurately captured the non-linear change of a 58% increase in the incidence of complications. This effect is usually simplified to a linear relationship in traditional models.

[0037] The cost prediction module uses an individual-level cumulative calculation method. Simulations on a cohort of 100,000 people show that the error between the total medical expenses predicted by this method over 5 years and the actual medical insurance expenditure data is within ±6%, which is significantly better than the ±15% error level of the population mean estimation method.

[0038] The annual update mechanism enables real-time response to dynamic population changes. In tests that include migration factors, the system can automatically keep the total population fluctuation within a reasonable range of ±0.3%, while maintaining the stability of the age and gender structure. Attached Figure Description

[0039] Figure 1 A schematic diagram illustrating the construction of an initial micro-database of the population; Figure 2 A diagram illustrating the addition of chronic disease prevalence information to the initial population micro-database; Figure 3 A schematic diagram illustrating the overall evaluation of chronic disease prevention and control strategies; Figure 4 This is a schematic diagram of the microscopic simulation process for evaluating chronic disease prevention and control strategies. Detailed Implementation

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Please see Figure 1-4 This invention provides a technical solution: a micro-simulation method for evaluating chronic disease prevention and control strategies. The simulation data collection and preparation stage requires integrating multi-source data: obtaining micro-datasets from the most recent population census from government health departments, including basic information such as age, gender, and occupation at the individual level; simultaneously collecting summary data on chronic disease prevalence and mortality rates from statistical yearbooks; for medical data, extracting electronic health record data from the past three years from regional health information platforms, focusing on lifestyle information such as smoking history, drinking habits, and exercise frequency; and for chronic disease screening data, collaborating with community health service centers to obtain structured data including test results such as blood glucose and blood pressure.

[0042] The initial database was constructed using a stratified sampling method: census data was used as the sampling frame, and proportional sampling was conducted according to the age and gender structure published in the statistical yearbook of the initial simulation year. Missing occupational information was filled in using a generalized weighted regression model, with education level and residential area type selected as the weight variables. After sampling, a unique identifier was generated for each virtual individual, establishing a basic database containing demographic characteristics.

[0043] The allocation of high-risk factors for chronic diseases employs a conditional probability method: High-risk factor criteria are established for target diseases such as hypertension and diabetes. Based on electronic health record data, the distribution probability of high-risk factors is calculated for each age and gender group. Specifically, for male smokers aged 40-49, the probability of hypertension is calculated to be 18.7% based on historical data. Through Monte Carlo simulation, disease status is randomly assigned to individuals in the database who meet this characteristic, with a random number threshold set at 0.187.

[0044] The screening strategy simulation employs discrete event simulation: a screening participation rate of 60% is defined, with approximately 35% of those initially screening positive at community health service centers going to higher-level hospitals for diagnosis. In the code implementation, a logistic regression model is constructed to predict the probability of an individual participating in screening, with independent variables including age, education level, and distance to medical institutions. For individuals participating in screening, the false positive rate and false negative rate are calculated based on their high-risk factor combinations, generating simulated test results.

[0045] The intervention effect was evaluated using the Markov chain Monte Carlo method, which divided disease progression into five states: healthy, high-risk, early, clinical, and complication. The baseline transformation probability was set according to clinical guidelines when defining the state transition matrix. For the intensive intervention group, the probability of transformation from high-risk to early was reduced by 30%, and the probability of early reversal was increased to 15%. Each simulation period was set to one year, and 10 periods were run to observe long-term effects.

[0046] Medical expenses are calculated using the event-based costing method: based on medical insurance settlement data, an annual distribution of medical expenses is established for different disease states. The average annual cost for early-stage diabetes patients is set at 2,500 yuan, rising to 18,000 yuan after the onset of kidney complications. In the micro-simulation, when an individual's disease state changes, the corresponding cost distribution is automatically matched and a specific amount is sampled and generated.

[0047] The annual update module implements dynamic population changes: new individuals are added to the database each year at a birth rate of 1.2%, and newborn characteristics are generated based on the population structure of that year. Mortality event handling is divided into two categories: chronic disease-related deaths are calculated based on the current disease status, and background mortality rates are referenced from the life table data. Simultaneously, the age field of all surviving individuals is updated, and migrant populations are randomly deleted or added at a rate of 0.5%.

[0048] The results analysis phase employed a multi-indicator evaluation system: Quality-adjusted life years (QALYs) were calculated for each simulated scenario, with the health status utility value set at 1 and decreasing to 0.6 for the complication stage. In the cost-effectiveness analysis, the intervention cost was compared with the incremental QALYs gained. For screening strategies in primary healthcare institutions, simulations showed that an investment of 10,000 yuan yielded 3.2 QALYs, with an ICUR value of 3125 yuan / QALY.

[0049] Sensitivity analysis employed a univariate test method: 1000 Monte Carlo sampling operations were conducted within the possible ranges for key parameters such as screening participation rate and intervention adherence. Results showed that the cost-effectiveness ratio began to deteriorate significantly when the screening participation rate fell below 45%. This provides a quantitative basis for establishing a minimum participation rate standard.

[0050] The visualization output includes three types of charts: a radar chart showing the changes in the proportion of patients at each stage over 10 years; a stacked cost composition chart distinguishing between medical insurance payments and out-of-pocket expenses; and a scatter plot showing the cost-effectiveness distribution of different intervention strategies. All charts are implemented using the ggplot2 package to ensure that the results are interactive and explorable.

[0051] The entire simulation was run on a server equipped with a Xeon Silver 4210 processor, and a simulation of a million people took approximately 2 hours. To improve repeatability, a random number seed of 12345 was set, and all key parameters were managed using JSON configuration files. The final output includes annual population health status statistics, a cost summary report, and strategy optimization recommendations.

Claims

1. A microscopic simulation method for evaluating chronic disease prevention and control strategies, characterized in that, The steps include: S1: Simulation data collection and preparation; S2: Construction of the initial micro-database of the population; S3: Adding chronic disease prevalence information to the initial population micro-database; S3-1: Collect information on high-risk factors associated with the onset of chronic diseases from residents' electronic health records; S3-2: Based on the population size of the population in the electronic health records, according to age, gender, occupation and lifestyle, estimate the distribution probability parameters of high-risk factors for chronic diseases in the population, and randomly assign high-risk information to individuals in the initial population database based on the distribution probability parameters, thus completing the addition of high-risk factor information related to chronic diseases. S3-3: Estimate the prevalence of chronic diseases in high-risk populations using chronic disease screening and management data, and add chronic disease prevalence information in high-risk populations in the manner of adding high-risk information; S4: Simulation of the effects of chronic disease screening strategies; S4-1: Analyze the main influencing factors of chronic disease incidence and population participation in screening using data and logistic regression methods; S4-2: Use the Naive Bayes model to perform cluster analysis on the screening population and analyze the distribution probability of participants, non-participants, and the original disease population; S4-3: Descriptive analysis of the distribution characteristics of community screening diagnoses and higher-level hospital screening diagnoses, including the distribution of suspected and confirmed patients; S5: Simulation of the effects of chronic disease intervention strategies; S5-1: Analyze the main influencing factors of patient participation in the intervention using data combined with logistic regression; S5-2: Analyze the distribution probability of people with good and poor compliance using the Naive Bayes model; S5-3: Using data combined with Markov decision process theory, analyze the optimal intervention strategy and the probability of disease state transition in patients with different compliance levels, as well as the probability of disease state transition in real society. S6: Simulation of the medical consumption process; S7: Annual adjustment of the micro-database; S8: After completing the simulation, comprehensively analyze the output simulation results; S8-1: Focus on analyzing the differences in the prevalence, health utility value, and economic burden of chronic diseases under different prevention and control strategies; S8-2: Based on the analysis results of the chronic disease burden under different prevention and control strategies, summarize the costs and results of chronic disease prevention and control, use cost-utility analysis to conduct a health economics evaluation of chronic disease prevention and control strategies (whether or not to include "chronic kidney disease prevention and control strategy"), and propose optimized strategies for chronic disease prevention and control.

2. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S1, the simulation data comes from population census micro-datasets, statistical yearbook summary data, chronic disease screening intervention management data, and chronic disease patient medical consumption data released by governments and medical organizations.

3. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In step S2, the initial population micro-database is constructed based on the obtained population census micro-data and the population composition summary data of the simulated starting year. By selecting benchmark variables, the population information is recorded and updated using the generalized weighted regression estimation method, thus completing the construction of the initial population micro-database.

4. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S3, data on age distribution, gender ratio, occupational classification, and lifestyle habits (smoking, drinking, exercise habits, etc.) are standardized, missing and outlier values ​​are processed, and relevant high-risk factors are identified for each chronic disease (such as hypertension, diabetes, etc.). Conditional probability distributions of these high-risk factors are established according to the following dimensions: P (High-risk factors | Age group) P (High-risk factors | Gender) P (High-risk factors | Occupational category) P (High-risk factors | Lifestyle habits combination) Use logistic regression or machine learning methods to build a joint probability model: P(high-risk factors|age, gender, occupation, lifestyle habits) = f(age, gender, occupation, lifestyle habits), for each individual i in the initial population database: Extract its demographic characteristics X_i = (age, gender, occupation, lifestyle habits) Calculate P(high-risk factor = 1|X_i) based on the probability model; For each high-risk factor: Generate a random number u ~ Uniform(0,1) If u < P(high-risk factor = 1|X_i), then assign the high-risk factor to individual i.

5. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S4, based on the initial population micro-database, the initial population incidence is simulated according to the disease status; combined with the chronic disease screening strategy, the participation of micro-individuals under different screening scopes, screening methods, and screening frequencies, as well as the distribution of population screening results, are analyzed. The chronic disease screening strategy is defined as (health → latent → early stage → clinical → complications), with the population scope being: the entire population / high-risk groups / specific age groups, taking into account the balance between coverage and resource consumption; screening methods are: questionnaires / biological testing / imaging, and screening frequency is: annual / biennial / one-time. Incentives for participation: economic compensation / health points / mandatory requirements; visualization analysis of screening result distribution; bar charts and line charts to present the screening results.

6. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S5, based on the patterns of chronic disease development and the probabilities of transitions between different disease states under different treatment interventions, and using a Markov model to simulate the individual disease development process using an initial population micro-database, parameter adjustment factors for three intervention strategies are defined: Python interventions = { 'basic': { 'name': "Routine Management", 'effect': { 'highrisk_to_early': 1.0, # No effect 'early_reverse': 0.05 # Early stage → High-risk reversal probability } }, 'enhanced': { 'name': "Enhanced Intervention", 'effect': { 'highrisk_to_early': 0.7, # Progress risk reduced by 30% 'early_reverse': 0.15 } }, 'intensive': { 'name': "Precision Medicine", 'effect': { 'highrisk_to_early': 0.5, # Reduces progress risk by 50% 'early_reverse': 0.25 } } }。 7. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S5, the framework for the probability of disease state transition in patients with different adherence levels is as follows: Python def plot_adherence_effect(): adherence_levels = ["Full adherence", "Partial adherence", "Non-adherence"] outcomes = [] for level in adherence_levels: # Simulate 10-year development trajectory final_states = simulate_population(level) outcomes.append([ sum(s == "clinical" for s in final_states), sum(s == "complications" for s in final_states) ]) # Draw a grouped bar chart plt.bar(np.arange(3)-0.2, [x[0] for x in outcomes], width=0.4, label="clinical") plt.bar(np.arange(3)+0.2, [x[1] for x in outcomes], width=0.4, label="complications") plt.xticks([0,1,2], adherence_levels) plt.legend(); text When compliance drops from 100% to 50%: - High risk → Increased early conversion rate: +42% (95% CI 38-46%) - Increased clinical → complication conversion rate: +67% (95% CI 61-73%) - Average healthcare costs increased by +55% (95% CI 51-59%).

8. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In section S6, based on the distribution of chronic disease screening and diagnosis results, patient participation in disease treatment, and combined with medical insurance payment data, a descriptive analysis is performed on the distribution and development trend of medical expenses required for disease screening and treatment intervention, including total costs, medical insurance payments, and personal payments, describing the distribution patterns of medical consumption for patients in different situations. Based on the distribution and development trends of chronic disease intervention and treatment costs in different disease states, the analysis simulates and predicts the development and changes in individual medical expenses under different disease states, providing population-level cost prediction. Python def population_level_projection(population, n_years=10): results = [] for _, patient in population.iterrows(): sim = predictor.simulate_individual( patient['disease_stage'], n_years ) results.append(sim.assign(patient_id=patient['patient_id'])) full_results = pd.concat(results) # Calculate the statistics for each year stats = full_results.groupby('year')['annual_cost'].agg( ['mean','median','std'] ) # Draw confidence intervals plt.fill_between( stats.index, stats['mean'] - 1.96*stats['std'], stats['mean'] + 1.96*stats['std'], alpha=0.2 ) stats[['mean','median']].plot().

9. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S7, necessary adjustments and data file preparation are made for the simulation of the following year, and the simulation results are summarized after the simulation is completed. This module simulates population birth events, chronic disease-related death events, and other population death events based on obtained parameters such as birth rate, death rate, and related disease incidence rate, and adjusts the number of high-risk groups accordingly. (Python) def update_high_risk(population, params): "Update the status of high-risk groups" # 1. Remove individuals who have already developed the disease. population.loc[ (population['risk_status'] == 'high') & (population['disease_status'] != 'healthy'), 'risk_status' ] = 'normal' # 2. Added high-risk individuals (based on risk score) candidates = population[ (population['disease_status'] == 'healthy') & (population['risk_status'] != 'high') ] risk_scores = calculate_risk_scores( candidates[['age','risk_factors']], params['incidence_rates'] ) new_high_risk = risk_scores > params['high_risk_threshold'] population.loc[candidates.index[new_high_risk], 'risk_status'] = 'high' return population.

10. The microscopic simulation method for evaluating chronic disease prevention and control strategies according to claim 1, characterized in that, In S8, the chronic disease prevention and control strategy proposes the following tiered recommendations: Prioritize high-cost-effectiveness strategies; early intervention: regularly screen patients with hypertension and diabetes for kidney disease (e.g., urine albumin / creatinine ratio), use RAS inhibitors early, and the ICURR is usually lower; targeted drugs: such as SGLT2 inhibitors for CKD patients, although expensive, reduce the need for dialysis, resulting in better long-term costs; optimize resource allocation, focusing on high-risk groups: when resources are limited, prioritize intervention for high-risk groups (e.g., family history of diabetic nephropathy, smokers); tiered diagnosis and treatment: community hospitals are responsible for screening and basic management, while tertiary hospitals handle complex cases; supplement with non-pharmacological interventions, health education and self-management: low-cost diet / exercise interventions can improve QALYs (e.g., low-salt diets delay CKD progression); remote monitoring: use digital tools to monitor blood pressure and blood sugar, reducing follow-up costs; policy support for medical insurance coverage: include high-cost-effectiveness drugs (e.g., SGLT2 inhibitors) in reimbursement; incentive mechanisms: implement performance rewards for chronic disease management in primary healthcare institutions.

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