A method of predicting health burden and health-related economic burden resulting from changes in the ozone layer

Abstract

The application discloses a method for predicting health burden and health-related economic burden caused by ozone layer change. The method calculates EESC based on ODSs concentration sequence; combines EESC with the sensitivity of TCO to EESC to reconstruct the ozone layer change and generate gridded TCO; inputs the gridded TCO into the TUV radiation transfer model to calculate the ground surface ultraviolet radiation data; constructs the dose-effect relationship between radiation and health by using the historical ground surface radiation data, the skin cancer morbidity and mortality data of different genders and different age groups in various countries and the DALY data of cataract; calculates the skin cancer morbidity, mortality and the DALY incidence of cataract based on the dose-effect relationship, and obtains the health burden of different genders and different age groups in various countries in combination with population data; and further estimates the health-related economic burden based on the unit treatment cost and VSLY. The application can realize the automatic evaluation of multiple diseases, multiple scenarios and multiple years on the global scale and the national scale, and has the advantages of scalability and high space-time resolution.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric environment and public health economic assessment, specifically involving a method for predicting the health burden and health-related economic burden caused by ozone layer changes, and particularly involving an integrated assessment method for ODSs sequence, EESC calculation, TCO prediction and reconstruction, surface ultraviolet radiation dose simulation, dose-effect relationship construction, and health and health-related economic burden accounting. Background Technology

[0002] The ozone layer plays a crucial role in shielding against solar ultraviolet radiation. Ozone layer depletion leads to increased exposure to ultraviolet radiation at the Earth's surface, thereby increasing the risk of diseases such as skin cancer (including melanoma, basal cell carcinoma, and squamous cell carcinoma) and cataracts, and further causing significant medical expenditures and socioeconomic losses. The implementation of ozone layer protection policies can significantly alter the emissions and atmospheric concentration evolution of ozone-depleting substances (ODSs). Furthermore, quantifying the health burden and related economic burden caused by ozone layer changes can provide scientific early warning and guide the implementation of phase-out technologies and control measures for related ODSs industries. However, quantitatively assessing the long-term effects of "ODS emissions → ozone layer change → ultraviolet radiation change → health burden change → economic burden change" globally still faces the following challenges:

[0003] 1. The chain is fragmented and key links are missing. Changes in the ozone layer caused by ODS emissions, and changes in surface ultraviolet radiation caused by ozone layer changes, are often calculated by different studies, making it difficult to reproduce the entire process. Existing methods for accurately quantifying the health burden caused by changes in surface ultraviolet radiation in various countries around the world are lacking, resulting in a lack of a unified assessment framework.

[0004] 2. Insufficient spatiotemporal resolution. Existing work often only provides rough global aggregate assessments or assessments of some regions, making it difficult to output assessment results at the national or even grid scale, which is insufficient to support the needs of policy evaluation, industrial upgrading and transformation, and regional management.

[0005] 3. Lack of updated and accurate dose-response relationship between radiation and health. The construction of dose-response relationship relies on medical statistics from many years ago, and there is no timely update of the relationship formulas for different genders and age groups in different countries.

[0006] Therefore, there is an urgent need for an automated, comprehensive assessment method that can be implemented at the global and national levels across multiple scenarios, years, and diseases, in order to more accurately and reproducibly quantify the health burden and health-related economic burden caused by ozone layer changes. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by providing a method for predicting the health burden and health-related economic burden caused by ozone layer changes, which enables long-term, high-resolution assessments.

[0008] To achieve the above objectives, the present invention employs the following technical solution:

[0009] A method for predicting the health burden and health-related economic burden caused by changes in the ozone layer, comprising the following steps:

[0010] Step 1: Construct ODSs concentration data, including at least two scenarios (real-world scenario and other scenarios). In the real-world scenario, the ODSs concentration sequence is obtained by measuring atmospheric observation stations; in other scenarios, the ODSs concentration sequence for each year can be calculated based on parameters such as annual ODSs emissions, banks, and atmospheric lifetime.

[0011] Step 2: Based on the ODSs concentration sequence obtained in Step 1, and considering the fractional release factors (FRF), chlorine atom number, bromine atom number, and the weighting coefficient of bromine relative to chlorine for each ODSs, calculate the equivalent effective stratospheric chlorine (EESC) for each year to reflect the impact of ODSs on ozone after entering the stratosphere, taking into account the time lag of ODSs from the Earth's surface to the stratosphere.

[0012] Step 3: Using 1980 as the reference year, establish the sensitivity of TCO to EESC based on historical total column ozone (TCO) data and the changes in EESC and TCO obtained in Step 2; based on the EESC obtained in Step 2 and the sensitivity of TCO to EESC, calculate TCO on any year, month, and latitude / longitude grid to obtain gridded TCO prediction results.

[0013] Step 4: Input the gridded TCO obtained in Step 3 into the Tropospheric Ultraviolet and Visible (TUV) radiation transfer model, calculate the surface ultraviolet radiation data corresponding to health burden (such as skin cancer, cataracts, etc.) in each latitude and longitude grid and each month; further calculate the annual cumulative dose based on monthly radiation and calculate the age-based cumulative dose.

[0014] Step 5: Using the historical ultraviolet radiation dose data obtained in Step 4 and the historical data on the incidence and mortality of skin cancer and disability-adjusted life years (DALYs) of cataracts in different genders and age groups in various countries, calculate the key parameter values ​​in the dose-effect relationship and construct the dose-effect relationship between "radiation-health". Among them, the key parameter value refers to the regional heterogeneity multiplier, which is used to correct for differences in skin color, medical level, registration rate and other aspects among different countries.

[0015] Step 6: Based on the dose-response relationship established in Step 5 and the predicted ultraviolet radiation dose obtained in Step 4, calculate the incidence of skin cancer, mortality rate, and DALY incidence of cataracts. Combined with population data of different genders and age groups in various countries, calculate the number of skin cancer cases and deaths and DALY of cataracts in different genders and age groups in various countries. This is the health burden caused by ozone layer change. At the same time, the health benefits brought by ozone layer protection can also be calculated, that is, the health burden difference between scenarios.

[0016] Step 7: Based on the health burden obtained in Step 6, and combining the unit treatment cost and the value of a statistical life year (VSLY) of various countries, calculate the direct and indirect costs of the disease under different scenarios. The sum of the direct and indirect costs is the health-related economic burden caused by ozone layer change. The difference in health-related economic burden between different scenarios can be used as the health-related economic benefits brought about by ozone layer protection.

[0017] In the above technical solution, step 1 specifically involves: for real-world scenarios (i.e., controlled scenarios), the ODSs concentration sequence provided by observation or authoritative institutions can be directly read; for other scenarios (which can be set as needed), the ODSs concentration can be calculated from ODSs emissions, using the following formula:

[0018]

[0019] in and They represent compounds In the year and year Concentration at time (unit: pmol mol) -1 ), This represents the conversion factor (mol mol) that converts emissions into atmospheric mixture concentration. -1 kg -1 ), This represents the year. Time compound Emissions, Representative compound Atmospheric lifetime (years).

[0020] Further, in step 2, based on the ODSs concentration sequence obtained in step 1, the EESC is calculated using the FRF, the number of chlorine / bromine atoms, and the weighting coefficient of bromine relative to chlorine, as shown in the following formula:

[0021]

[0022] in, Indicates the first EESC of that year Indicates the first Annual compounds The concentration is determined because there is approximately a 3-year time lag between ODS emissions from the Earth's surface and their arrival in the stratosphere. It is a compound The fractional release factor (FRF). and They represent compounds The number of chlorine atoms and bromine atoms in it. It is a constant with a value of 60, which reflects that bromine has a greater ozone-depleting potential than chlorine.

[0023] Furthermore, step 3 specifically includes:

[0024] Using 1980 as the reference year, historical TCO data is resampled, interpolated, and gridded to obtain the TCO distribution for the reference year and the historical baseline period (1979–1991). Combined with the EESC obtained in step 2, the sensitivity of TCO to EESC is calculated using the following formula:

[0025]

[0026] in, express right Sensitivity; It was in 1991 and 1979 at longitude and latitude Different months The difference in TCO between them; This represents the difference in EESC values ​​between 1979 and 1991. The period 1979–1991 was chosen because of this timeframe. and The range of change is relatively large, allowing for relatively accurate calculation. ;

[0027] Based on the EESC obtained in step 2, the TCO distribution for the reference year, and the sensitivity of TCO to EESC, TCO is calculated on any year, month, and latitude / longitude grid. The calculation formula is as follows:

[0028]

[0029] in, and They represent the years. And 1980, longitude and latitude TCO at the location; and Years And the EESC value in 1980.

[0030] Furthermore, in step 4, the effective ultraviolet radiation dose is calculated by combining geographical location and time with surface ultraviolet radiation data corresponding to health burden, and the monthly radiation dose is output by month; the monthly radiation dose is accumulated to obtain the annual cumulative dose; the annual cumulative dose is accumulated by age to obtain the age-based cumulative dose.

[0031] Furthermore, in step 5, the dose-response relationships include: the dose-response relationship between the incidence of skin cancer and radiation, the dose-response relationship between the mortality rate of skin cancer and radiation, and the dose-response relationship between the DALY incidence of cataracts and radiation.

[0032] The dose-response relationship between the incidence of skin cancer and radiation is as follows:

[0033]

[0034]

[0035]

[0036] in, Represents the prevalence of SCC. This represents the prevalence of BCC or MM; Indicates age; and They are based on , Historical values ​​and the right side of the formula, excluding The regional heterogeneity multipliers corresponding to SCC, BCC, or MM, calculated from historical data of other parameters, respectively. The study took into account the effects of race and color, healthcare and health levels, enrollment rates, and other country-specific factors. It is the cumulative radiation dose over age; It is the year The radiation value; , and , These are the coefficients corresponding to SCC, BCC, or MM, obtained from the literature;

[0037] The dose-response relationship between skin cancer mortality and radiation:

[0038]

[0039] in, The mortality rates for MM and SCC; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; These are the corresponding coefficients, obtained from the literature;

[0040] The dose-response relationship between the incidence of DALYs in cataracts and radiation:

[0041]

[0042] in, The incidence of DALYs, representing cataracts; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; These are the corresponding coefficients, obtained from the literature.

[0043] Furthermore, in step 6, after obtaining the dose-response relationship, the incidence, mortality, and DALY (Daily Average Result) of skin cancer and cataracts are calculated under different scenarios using surface ultraviolet radiation data. Combined with population data for different age groups and genders in various countries, the number of skin cancer cases and deaths, and the DALY of cataracts are calculated for each country, representing the health burden caused by ozone layer depletion. Simultaneously, the health benefits of ozone layer protection can also be calculated, specifically defined as: the health burden under the ozone depletion scenario minus the health burden under the controlled scenario.

[0044] Further, in step 7, based on the health burden of ozone layer protection obtained in step 6, the direct and indirect costs of diseases under different scenarios are estimated. Direct costs are calculated by multiplying the number of skin cancer cases or the DALY (Daily Average Cost) for cataracts by the unit treatment cost. Indirect costs are calculated by multiplying the DALY by the VSLY (Various Values ​​Average Cost), where VSLY is a country-specific parameter. The sum of direct and indirect costs represents the health-related economic burden caused by ozone layer change. Simultaneously, the difference in health-related economic burden between different scenarios can be used as the health-related economic benefit of ozone layer protection. The formulas for calculating direct and indirect costs are as follows:

[0045] The formula for calculating direct costs is:

[0046]

[0047]

[0048] in, It is a country Skin cancer The direct total cost; Representing the country Skin cancer The unit treatment cost; Representing the country skin cancer total; It is a country cataract The direct total cost; Representing the country cataract Unit cost of treatment (USD / DALY); Representing the country Total DALY; and Obtained through a meta-analysis of literature;

[0049] The formula for calculating indirect costs is:

[0050]

[0051] in, Represents the country disease VSLY represents each country; Represents the country disease Age and gender DALY in the middle.

[0052] On the other hand, the present invention provides an apparatus for predicting the health burden and health-related economic burden caused by ozone layer changes, the apparatus comprising the following modules:

[0053] Module 1: Used to construct ODS concentration sequences under different scenarios;

[0054] The second module is used to calculate EESC based on the ODS concentration sequence output by the first module.

[0055] The third module is used to generate a gridded TCO based on the sensitivity of the EESC and TCO to the EESC output by the second module.

[0056] The fourth module is used to call the TUV radiative transfer model, calculate the surface ultraviolet radiation data for each latitude and longitude grid and each month based on the TCO output by the third module, and calculate the annual cumulative dose and age-based cumulative dose based on the surface ultraviolet radiation data for each month.

[0057] The fifth module uses the historical annual cumulative dose, age-based cumulative dose, and historical DALY data on skin cancer incidence, mortality, and cataracts in different genders and age groups from the output of the fourth module to calculate key parameter values ​​in the dose-response relationship, constructing a dose-response relationship between "radiation and health"; the key parameter value is the regional heterogeneity multiplier, used to correct for differences in skin color, medical level, and registration rate among different countries.

[0058] Module 6: Based on the dose-response relationship output by Module 5 and the ultraviolet radiation dose output by Module 4, it calculates the incidence of skin cancer, mortality, and DALY of cataracts. Combined with population data of different genders and age groups in various countries, it calculates the number of skin cancer cases and deaths and DALY of cataracts in different genders and age groups in various countries, which is the health burden caused by ozone layer change.

[0059] Module 7: Based on the health burden caused by ozone layer change output from Module 6, and combined with the unit treatment cost and VSLY of various countries, calculates the direct and indirect costs of the disease under different scenarios. The sum of the direct and indirect costs is the health-related economic burden caused by ozone layer change.

[0060] On the other hand, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the method for predicting the health burden and health-related economic burden caused by ozone layer changes.

[0061] On the other hand, the present invention provides a computer-readable storage medium having computer instructions stored thereon, characterized in that the computer instructions are used to cause a computer to perform the steps of the method for predicting the health burden and health-related economic burden caused by ozone layer changes.

[0062] The present invention has the following advantages:

[0063] (1) Establishing the sensitivity of TCO to EESC at the grid scale can significantly improve the accuracy of TCO prediction;

[0064] (2) Based on historical ultraviolet radiation dose data and DALY data on skin cancer incidence, mortality and cataracts in different age groups and genders in various countries, the key parameter values ​​in the dose-response relationship were determined for the first time at a fine population scale, realizing the quantitative calculation of the relationship between ultraviolet radiation and health burden.

[0065] (3) Combine literature meta-analysis to assess both direct and indirect costs, achieve a comprehensive accounting of health-related economic burden, and consider national / regional heterogeneity to improve cross-country assessment consistency and interpretability;

[0066] (4) Provides an integrated end-to-end assessment framework for ozone-ultraviolet-health-economy, which is highly automated and reproducible. It can be applied to a variety of different ODS emission scenarios, serving the industrial and technological upgrading of ODS phase-out, as well as policy implementation;

[0067] (4) It can output long-term evaluation results at the global and national scales and supports gridded computing with a high degree of refinement. Attached Figure Description

[0068] Figure 1 This is a flowchart of an embodiment of the present invention;

[0069] Figure 2 This is a time series of various indicators of health-related economic benefits from ozone layer protection from 1980 to 2100 according to embodiments of the present invention. Detailed Implementation

[0070] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0071] In this embodiment of the invention, see Figure 1 The implementation process includes the following steps:

[0072] (1) Constructing ODS concentration sequences for different scenarios

[0073] In this embodiment, ODSs concentration data (including atmospheric observation data and concentration forecasts) for the period 1955–2100 under the World Meteorological Organization (WMO) A1 scenario (i.e., the controlled scenario governed by the Montreal Protocol) were obtained from the Scientific Assessment of Ozone Depletion: 2022 report. An "uncontrolled" scenario for ozone layer depletion was set up with an average annual growth rate of 2% or 3% for ODSs emissions before the signing of the Montreal Protocol. The ODSs concentrations under the "uncontrolled" scenario for the period 1955–2100 were then calculated using a formula.

[0074] The ODS concentration can be calculated from ODS emissions using the following formula:

[0075]

[0076] in and They represent compounds In the year and year Concentration at time (unit: pmol mol) -1 ), This represents the conversion factor (mol mol) that converts emissions into atmospheric mixture concentration. -1 kg -1 ), This represents the year. Time compound Emissions, Representative compound Atmospheric lifetime (years).

[0077] (2) Calculate the time series of changes in global EESC caused by ODS concentration.

[0078] In this embodiment, based on the obtained ODSs concentration sequence from 1955 to 2100, combined with the FRF, chlorine atom number, bromine atom number and the weighting coefficient of bromine relative to chlorine for each ODSs, and considering the time lag of ODSs from the Earth's surface to the stratosphere, the global EESC sequence from 1958 to 2100 is calculated.

[0079] The formula for calculating EESC is as follows:

[0080]

[0081] in, Indicates the first EESC of that year Indicates the first Annual compounds The concentration is determined because there is approximately a 3-year time lag between ODS emissions from the Earth's surface and their arrival in the stratosphere. It is a compound The fractional release factor (FRF). and They represent compounds The number of chlorine atoms and bromine atoms in it. It is a constant with a value of 60, which reflects that bromine has a greater ozone-depleting potential than chlorine.

[0082] (3) Obtain historical and future gridded TCO data

[0083] In this embodiment, TCO observation data at a monthly scale of 0.5° resolution for the period 1979–1991 were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. This data was then processed to a resolution of 1.25° (longitude) × 1° (latitude) using bilinear interpolation. With 1980 as the reference year, and based on the historical baseline period (1979–1991) TCO data and the obtained EESC, a sensitivity of TCO to EESC was established. Based on this sensitivity, 1.25° × 1° gridded TCO data at a monthly scale for the period 1958–1978 were calculated, and 1.25° × 1° gridded TCO data at a monthly scale for the period 1992–2100 were predicted. The TCO for the period 1880–1957 was fixed at the 1958 value, thus obtaining gridded TCO data at a monthly scale for the period 1880–2100.

[0084] TCO sensitivity to EESC The calculation formula is as follows:

[0085]

[0086] in, It was in 1991 and 1979 at longitude and latitude Different months The difference in TCO between them; This represents the difference in EESC values ​​between 1979 and 1991. The period 1979–1991 was chosen because this timeframe... and The range of change is relatively large, allowing for relatively accurate calculation. .

[0087] The formula for calculating TCO is:

[0088] in, and They represent the years. And 1980, longitude and latitude TCO at the location; and Years And the EESC value in 1980.

[0089] (4) Calculate the surface ultraviolet radiation data and perform cumulative calculation.

[0090] In this embodiment, monthly gridded TCO data from 1880 to 2100 are input into the TUV radiative transfer model, with the time node fixed on the 15th day of each month. The cumulative surface radiation for the day from 4:00 to 20:00 is calculated, including the radiation spectrum of Skin Cancer Utrecht-Philadelphia (SCUP)-human. After obtaining the radiation data corresponding to various health burdens, the monthly dose is obtained by multiplying it by the number of days in the month. The annual cumulative dose and age-based cumulative dose are then obtained through accumulation.

[0091] (5) Constructing the dose-response relationship between radiation and health

[0092] In this embodiment, data on the incidence and mortality of skin cancer and the DALY (Daily Average Result) of cataracts for different genders and age groups in various countries from 1990 to 2021 were obtained from the Global Burden of Disease (GBD) database. Combined with population data and gridded population density data for different genders and age groups in various countries from the World Population Prospects (WPP), the incidence and mortality rates of skin cancer and the DALY incidence rate of cataracts for different genders and age groups in various countries from 1990 to 2021 were calculated. Using historical surface ultraviolet radiation data, key parameter values ​​in the dose-response relationship for different genders and age groups in various countries were calculated to construct the dose-response relationship between "radiation and health".

[0093] Dose-response relationships include: the dose-response relationship between the incidence of skin cancer and radiation, the dose-response relationship between the mortality rate of skin cancer and radiation, and the dose-response relationship between the incidence of DALYs in cataracts and radiation.

[0094] The dose-response relationship between the incidence of skin cancer and radiation is as follows:

[0095]

[0096]

[0097]

[0098] in, Represents the prevalence of SCC. This represents the prevalence of BCC or MM; Indicates age; and They are based on , Historical values ​​and the right side of the formula, excluding The regional heterogeneity multipliers corresponding to SCC, BCC, or MM, calculated from historical data of other parameters, respectively. The study took into account the effects of race and color, healthcare and health levels, enrollment rates, and other country-specific factors. It is the cumulative radiation dose over age; It is the year The radiation value; , and , These are the coefficients corresponding to SCC, BCC, or MM, obtained from the literature;

[0099] The dose-response relationship between skin cancer mortality and radiation:

[0100]

[0101] in, The mortality rates for MM and SCC; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; These are the corresponding coefficients, obtained from the literature;

[0102] The dose-response relationship between the incidence of DALYs in cataracts and radiation:

[0103]

[0104] in, The incidence of DALYs, representing cataracts; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; These are the corresponding coefficients, obtained from the literature.

[0105] (6) Calculate the health burden caused by changes in the ozone layer.

[0106] In this embodiment, based on the constructed dose-response relationship, and combined with calculated surface ultraviolet radiation data and population projection data under two future scenarios, the number of people with skin cancer aged 1–5 years to over 95 years old in different sexes in various countries from 1980 to 2100, using 5-year age groups, was calculated. This represents the health burden caused by ozone layer change. The difference between the two scenarios is the health benefit brought by ozone layer protection from 1980 to 2100.

[0107] (7) Calculate the health-related economic burden caused by ozone layer depletion.

[0108] In this embodiment, a literature meta-analysis was first performed to obtain the unit treatment costs for skin cancer and cataracts in some countries, and the direct economic burden (direct cost) caused by ozone layer depletion was calculated. The indirect economic burden (indirect cost) caused by ozone layer depletion was calculated by multiplying DALY and VSLY. The sum of these two costs yielded the health-related economic burden caused by ozone layer depletion. All costs were assumed to have an average annual inflation rate of 3%, and the health-related economic burden for all years was statistically analyzed using 2020 US dollars as the standard. The difference in health-related economic burden between different scenarios can be considered as the health-related economic benefits brought by ozone layer protection.

[0109] The formula for calculating direct costs is:

[0110]

[0111]

[0112] in, It is a country Skin cancer The direct total cost; Representing the country Skin cancer The unit treatment cost; Representing the country skin cancer total; It is a country cataract The direct total cost; Representing the country cataract Unit cost of treatment (USD / DALY); Representing the country Total DALY; and Obtained through a meta-analysis of literature;

[0113] The formula for calculating indirect costs is:

[0114]

[0115] in, Represents the country disease VSLY represents each country; Represents the country disease Age and gender DALY in the middle.

[0116] This embodiment obtained the health burden of various countries worldwide under an uncontrolled ODS scenario from 1980 to 2100, including the number of skin cancer cases, skin cancer deaths, and DALYs of cataracts in each country. The results overcome the technical limitations of previous studies, which could only provide rough estimates at global or regional average scales and could not distinguish between national, age, and disease type differences. The results show that under an uncontrolled ODS scenario, the global health burden of skin cancer cases, deaths, and DALYs of cataracts from 1980 to 2100 was extremely severe, exhibiting significant regional differences. High-latitude developed countries (such as North America and Europe) have the highest concentration of skin cancer burden due to the combined effects of increased ultraviolet radiation and population aging; while low-latitude developing countries show higher health losses in DALYs of cataracts. This indicates that the mechanisms by which ozone layer depletion affects different disease types, regions, and population structures differ significantly, providing a scientific basis for developing more targeted public health and environmental policies and optimizing resource allocation.

[0117] See Figure 2This embodiment obtained the health-related economic benefits of ozone layer protection to the world from 1980 to 2100, including: time series of direct cost avoidance for skin cancer (a), indirect cost avoidance for skin cancer (b), direct cost avoidance for cataracts (c), and indirect cost avoidance for cataracts (d) in various continental regions. The results constructed a comprehensive monetization framework covering "direct costs + indirect costs (intangible loss of healthy lifespan)," enabling a refined assessment of economic benefits across disease types, regions, and time scales. The results show that from 1980 to 2100, the health-related economic benefits of ozone layer protection exhibited a continuously accelerating growth trend over time, with significant differences between different continental regions. The economic benefits related to skin cancer were far greater than those related to cataracts, and the indirect cost avoidance was significantly higher than the direct cost avoidance, indicating that the long-term impact of ozone layer protection on overall social productivity and healthy lifespan far exceeds medical expenditures themselves. This method provides a standardized and scalable computational path for assessing the economic value of the health benefits of environmental conventions, enabling the long-term economic returns of ozone layer protection to be presented in a clear time series and regional breakdown, which helps to optimize international funding and the allocation of public health resources.

[0118] In practical implementation, the method described in this invention can be implemented by those skilled in the art using computer software, and the calculation process of each module can be completed in an automated manner. Accordingly, any system apparatus used to store and execute a computer program that implements the method of this invention, such as a computer-readable storage medium storing the program, and a computer device or server that includes a processor and is capable of running the program, should be considered to fall within the protection scope of this invention.

[0119] In some possible embodiments, an apparatus for predicting the health burden and health-related economic burden caused by ozone layer change is provided, comprising the following modules:

[0120] Module 1: Used to construct ODS concentration sequences under different scenarios;

[0121] The second module is used to calculate EESC based on the ODS concentration sequence output by the first module.

[0122] The third module is used to generate a gridded TCO based on the sensitivity of the EESC and TCO to the EESC output by the second module.

[0123] The fourth module is used to call the TUV radiative transfer model, calculate the surface ultraviolet radiation data for each latitude and longitude grid and each month based on the TCO output by the third module, and calculate the annual cumulative dose and age-based cumulative dose based on the surface ultraviolet radiation data for each month.

[0124] The fifth module uses the historical annual cumulative dose, age-based cumulative dose, and historical DALY data on skin cancer incidence, mortality, and cataracts in different genders and age groups from the output of the fourth module to calculate key parameter values ​​in the dose-response relationship, constructing a dose-response relationship between "radiation and health"; the key parameter value is the regional heterogeneity multiplier, used to correct for differences in skin color, medical level, and registration rate among different countries.

[0125] Module 6: Based on the dose-response relationship output by Module 5 and the ultraviolet radiation dose output by Module 4, it calculates the incidence of skin cancer, mortality, and DALY of cataracts. Combined with population data of different genders and age groups in various countries, it calculates the number of skin cancer cases and deaths and DALY of cataracts in different genders and age groups in various countries, which is the health burden caused by ozone layer change.

[0126] Module 7: Based on the health burden caused by ozone layer change output from Module 6, and combined with the unit treatment cost and VSLY of various countries, calculates the direct and indirect costs of the disease under different scenarios. The sum of the direct and indirect costs is the health-related economic burden caused by ozone layer change.

[0127] In some possible embodiments, an electronic device is provided, including one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the method for predicting the health burden and health-related economic burden caused by ozone layer changes.

[0128] In some possible embodiments, a computer-readable storage medium is provided having computer instructions stored thereon for causing a computer to perform the steps of the method for predicting the health burden and health-related economic burden caused by ozone layer changes.

[0129] The specific embodiments described in this invention are merely illustrative of the spirit of the invention. Those skilled in the art can make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for predicting the health burden and health-related economic burden caused by changes in the ozone layer, characterized in that, Includes the following steps: Step 1: Construction of ODS concentration sequences In real-world scenarios, ODS concentration sequences are obtained from atmospheric observation stations. In other scenarios, the concentration series of ODSs in each year is calculated based on ODSs emissions, inventory, and atmospheric lifetime; Step 2: EESC Calculation Based on the ODSs concentration sequence obtained in step 1, and combined with the release fraction factor, chlorine atom number, bromine atom number and bromine relative to chlorine weighting coefficient of each ODSs, considering the time lag of ODSs from the surface to the stratosphere, the EESC of each year is calculated. Step 3: TCO Prediction Based on the EESC obtained in step 2 and the sensitivity of TCO to EESC, the TCO for any year in each latitude and longitude grid and each month is calculated, thereby obtaining the gridded TCO prediction results. Step 4: Calculation of ultraviolet radiation dose Input the TCO obtained in step 3 into the TUV radiation transfer model to calculate the surface ultraviolet radiation data for each latitude and longitude grid and each month, and calculate the annual cumulative dose and age-based cumulative dose based on the surface ultraviolet radiation data for each month. Step 5: Dose-response relationship Based on the historical annual cumulative dose, age-based cumulative dose, and DALY data on the incidence, mortality, and cataracts of skin cancer in different genders and age groups in various countries obtained in step 4, the key parameter values ​​in the dose-response relationship are calculated, and the dose-response relationship between "radiation and health" is constructed. The key parameter value is the regional heterogeneity multiplier, which is used to correct for differences in skin color, medical level, and registration rate among different countries. Step 6: Health Burden Assessment Combining the dose-response relationship obtained in step 5 and the ultraviolet radiation dose obtained in step 4, the incidence of skin cancer, mortality, and DALY of cataracts were calculated. In addition, population data of different genders and age groups in various countries were combined to calculate the number of skin cancer cases, deaths, and DALY of cataracts in different genders and age groups in various countries. This is the health burden caused by ozone layer change. Step 7: Health-related economic burden assessment Based on the health burden caused by ozone layer changes obtained in step 6, and combined with the unit treatment cost and VSLY of various countries, the direct and indirect costs of the disease under different scenarios are calculated. The sum of the direct and indirect costs is the health-related economic burden caused by ozone layer changes.

2. The method according to claim 1, characterized in that: In step 5, the dose-effect relationship between "radiation and health" is discussed; specifically, this includes the dose-effect relationship between the incidence of skin cancer and radiation, the dose-effect relationship between the mortality rate of skin cancer and radiation, and the dose-effect relationship between the DALY incidence of cataracts and radiation. The dose-response relationship between the incidence of skin cancer and radiation is as follows: ； ； ； in, Represents the incidence of squamous cell carcinoma. This represents the incidence rate of basal cell carcinoma or melanoma; Indicates age; and They are based on , Historical values ​​and the right side of the formula, excluding The regional heterogeneity multipliers corresponding to squamous cell carcinoma, basal cell carcinoma, or melanoma, calculated from historical data of other parameters, are as follows: The study took into account the effects of race and color, healthcare and health levels, enrollment rates, and other country-specific factors. It is the cumulative radiation dose over age; It is the year The radiation value; , and , These are the coefficients corresponding to squamous cell carcinoma, basal cell carcinoma, or melanoma, respectively. The dose-response relationship between the mortality rate of skin cancer and radiation is as follows: ； in, The mortality rates for MM and SCC; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; These are the corresponding coefficients; The dose-response relationship between the incidence of DALYs in cataracts and radiation is as follows: ； in, The incidence of DALYs, representing cataracts; It is based on Historical values ​​and the right side of the formula, excluding Regional heterogeneity multipliers calculated from historical data of other parameters; It is the corresponding coefficient.

3. The method according to claim 1, characterized in that: In step 7, The formula for calculating direct costs is: ； ； in, It is a country Skin cancer The direct total cost; Representing the country Skin cancer The unit treatment cost; Representing the country skin cancer total; It is a country cataract The direct total cost; Representing the country cataract The unit treatment cost; Representing the country cataracts Total DALY; and Obtained through a meta-analysis of literature; The formula for calculating indirect costs is: ； in, Represents the country disease The sum of indirect costs; VSLY represents each country; Represents the country disease Age and gender DALY in the middle.

4. A device for predicting the health burden and health-related economic burden caused by ozone layer changes, characterized in that, include Module 1: Used to construct ODS concentration sequences under different scenarios; The second module is used to calculate EESC based on the ODS concentration sequence output by the first module. The third module is used to generate a gridded TCO based on the sensitivity of the EESC and TCO to the EESC output by the second module. The fourth module is used to call the TUV radiative transfer model, calculate the surface ultraviolet radiation data for each latitude and longitude grid and each month based on the TCO output by the third module, and calculate the annual cumulative dose and age-based cumulative dose based on the surface ultraviolet radiation data for each month. The fifth module uses the historical annual cumulative dose, age-based cumulative dose, and historical DALY data on skin cancer incidence, mortality, and cataracts in different genders and age groups from the output of the fourth module to calculate key parameter values ​​in the dose-response relationship, constructing a dose-response relationship between "radiation and health"; the key parameter value is the regional heterogeneity multiplier, used to correct for differences in skin color, medical level, and registration rate among different countries. Module 6: Based on the dose-response relationship output by Module 5 and the ultraviolet radiation dose output by Module 4, it calculates the incidence of skin cancer, mortality, and DALY of cataracts. Combined with population data of different genders and age groups in various countries, it calculates the number of skin cancer cases and deaths and DALY of cataracts in different genders and age groups in various countries, which is the health burden caused by ozone layer change. Module 7: Based on the health burden caused by ozone layer change output from Module 6, and combined with the unit treatment cost and VSLY of various countries, calculates the direct and indirect costs of the disease under different scenarios. The sum of the direct and indirect costs is the health-related economic burden caused by ozone layer change.

5. An electronic device, characterized in that: It includes one or more processors, memory, and a program stored in the memory that can be executed by the processor, said program, when executed, implementing the method as described in claims 1-3.

6. A computer-readable storage medium storing computer instructions thereon, characterized in that, The computer instructions are used to cause the computer to perform the method as described in claims 1-3.

Images