SD351.June.22.Amal – 2040 – Test01
920885368a65411892320102e105da11
isee systems, inc.
Stella Architect
2020
2070
20
person
persons
1
dmnl
unitless
fraction
yr
year
This stock is an aggregated stock which is arrayed by the age groups which are 10 groups.
Each group of population with cancer initialized its value to the number of people who have been cancer diagnose in the period (2016 2020)
The reason for choosing this time of period is that the researcher assumes that population with cancer stock will include people who have cancer not only the first year of the model running , but also those who already have been diagnosed 5 years earlier and they still get treatment at the year 2020.
The reason of dividing age groups in to 10 groups is that we can get the behavior of each group , hence w, we can realize which age groups that are growing more than others, so we can also realize which age group that has more cancer incidents , so the policy makers can initiate policies that be targeted to each age group.
Data has been extracted from the Norwegian statistics bank https://sb.kreftregisteret.no/insidens/?lang=en#
This stock increases by 2 inflows (new cases of cancer rate , and recancer ) , and it decreases by 2 outflows which are treatment process rate and death rate of cancer
At the end of 2020 there were:
3856 men and women at the age group (049)
14108 men and women at the age group(5069)
18263 men and women at the age group(70+)
in west Norway who has cancer diagnose in the period 20162020 cancer and they still have cancer .
The researcher could not find specific data for each age of the 10 groups that she used in her research , therefore she used these numbers and divided it between each age group regarding to her conceptualization of the problem and from her understanding to the distribution of cancer incidents between age groups . The researcher found also in the Norwegian Institute of Public Health website https://norgeshelsa.no/norgeshelsa/
the same data.
256
400
700
800
1700
4108
10000
12000
5000
1263
"ReCancer"
New_cases_of_cancer_Rate
Delayed_and_Back_to_System
Treatment_Process_rate
Death_Rate_Of_Cancer
People
This stock represents the number of people who were diagnosed with cancer , but they get treated by starting going through the cancer patient pathway system in Norway. Those treated patients have been moved from the stock population with cancer through the the outflow treatment process rate, which is an inflow to the stock treated and need FollowingUp.
The researcher could not find specific data for this stock , because the Norwegian statistics bank do not differentiate between the two steps(Treatment process and followingup process) .
The researcher initialized this stock arrays values by numbers that are less than the initial data for population with cancer , these are the nearest assumed values.
This stock decreases by the outflow following Up process rate .
120
180
250
300
350
450
600
1500
300
150
Treatment_Process_rate
"FollowingUp_Process_Rate"
Delayed_and_Back_to_System
People
This flow represents the treatment process rate. This is an outflow that decreases the level of stock population with cancer , and also its an inflow that increases the level of people who are treated and needed followingup.
That means when this flow rate is high , there will be less people who are diagnosed with cancer and there will be more people who are treated and need following up, and vice versa.
(Population_with_cancer*Treatment_Fraction)
People/years
Population_with_cancer*"Expected_FractionDeaths_Of_Caner"
People/years
This converter represents the delay time that is needed to treat population with cancer . Regarding helsenorge.no the cancer patient pathway system that is used in Norway (this type of treatment is part of the patient pathway system that the Norwegian ministry of health introduced in its cancer care system in January 2015) there are 3 phases to treat people.
Helsenorge.no. (2020). Cancer patient pathways. Retrieved from https://www.helsenorge.no/en/sykdom/kreft/cancerpatientpathways/#pathwaytimesinthecancerpatientpathway
Møller, Bjørn. (2021). Cancer patient pathways in Norway.
The researcher used in this converter the average delay time that indicates only the second phase of treatment , while the first phase is considered as diagnosing of the case and that is considered to be before treatment , and the third delay time is considered to be during the followingup process.
Average delay time during second phase is 3 years.
This delay time means that in average , each patient needs 3 years to be treated , that includes many appointments scheduled and all needed surgeries.By other words, it takes 3 years to transfer a patient from the stock patients with cancer, to the stock treated and needed followingUp. This converter could be named also treatment time/ treatment needed time. But the researcher used the name delay time as this name is compatible with system dynamics models.
Using the equation if her, means that if there is another delay that caused by external conditions like pandemics as in Covid19 , the shock means that there will be more delay in treatment .
SMTH1(Average_Treatment_Fraction, Time_to_adopt_to_Treatment_Policy)
dmnl/years
("Treated_and_Need_FollowingUp"*"FollowingUp_Fraction")
People/years
This converter represents the delay fraction that is needed to control and followup treated people who are assumed to be passed the dangerous stage of the disease . Regarding helsenorge.no the cancer patient pathway system that is used in Norway (this type of followingup is part of the patient pathway system that the Norwegian ministry of health introduced in its cancer care system in January 2015) there are 3 phases to treat people.
Helsenorge.no. (2020). Cancer patient pathways. Retrieved from https://www.helsenorge.no/en/sykdom/kreft/cancerpatientpathways/#pathwaytimesinthecancerpatientpathway
Møller, Bjørn. (2021). Cancer patient pathways in Norway.
The researcher used in this delay converter SMTH1 equation that indicates only the third phase of treatment which she called (followingup process , to differentiate between the 3 phases of treatment ) , while the first phase is considered as diagnosing of the case and that is considered to be before treatment , and the second phase is condidered during the treatment process.
The purpose of using SMTH1 equation is that there is adelay in controlling patients, that delay is affected by the fraction of people that needs control and the delay time to control them.
Average delay time during third phase is 4 years.
This delay time means that in average , each patient needs 4 years to be controlled after treatment , that includes many appointments scheduled and all needed surgeries or checksup after the main treatment phase .By other words, it takes 4 years to transfer a patient from the stock treated and need followingUp, to the stock recovered population. This converter could be named also followingUp time or Control needed time. But the researcher used the name delay time as this name is compatible with system dynamics models
Using the equation if her, means that if there is another delay that caused by external conditions like pandemics as in Covid19 , the shock means that there will be more delay in followingUp process .
SMTH1("Average_FollowingUp_Fraction", "Time_to_adopt_to_FollowingUp_Policy")
dmnl/years
This stock represents the recovered people who have been totally finished their treatment and controll, and now they considered as recovered population.
Those recovered population have been moved from the stock treated and need followingUp through the the outflow followingUp Process rate, which is an inflow to the stock Recovered population.
The researcher initialized the value for the arrayed age groups by finding the percent of people who are recovered by the end of year 2020 , then extracted the total number of recovered people, then she initialized the values for each group from her conceptualization and understanding to the different behavior for each group from historical data .
170
250
300
800
1200
2000
3000
3500
2500
200
"FollowingUp_Process_Rate"
"ReCancer"
People
50
100
150
200
500
600
700
1500
1700
1000
Death_Rate_Of_Cancer
People
This is an outflow from the stock recovered population, so it causes an increase to this stock.And it is also an inflow to the stock population with cancer , so this stock increases by the inflow recancer.
This flow is a product of the the stock recovered population and and the recancer fraction
"Recancer_Fraction"*Recovered_population
People/Years
This converter represents the fraction of people who has been recovered , but they get cancer again , so they come back to the stock population with cancer.
https://www.kreftregisteret.no/en/Temasider/keyfiguresoncancer/
.05
{RANDOM(.007, .009)
dmnl/years
The researcher divided Population in to 10 years cohort.So there are to 10 groups of population .
Each group of population initialized its value to the number of people in the year 2020 .
The reason of dividing age groups in to 10 groups is that we can get the behavior of each group , hence w, we can realize which age groups that are growing more than others, so we can also realize which age group that has more cancer incidents , so the policy makers can initiate policies that be targeted to each age group.
Data has been extracted from the Norwegian statistics website :
https://www.ssb.no/en
Each value equals the summing of the number of population in Rogaland and Vestland, because these two counties forms the West of Norway health region from the year 2020. As Norwegian authorities applied decentralization in health services and merged these two counties in to one health region .
61908+74089
62397+78414
62589+87788
68651+86042
66138+83158
60609+80107
47850+67925
32497+49969
13933+22857
3240 +6005
Birth_Rate
Death_Rate
Being_Older
People
This inflow increases the stock population groups.This inflow shows how many people per year are growing.
This inflow is an aggregated inflow which is arrayed by the age groups.
the equation for birth rate will be the same for all age groups except the age group (09) years old .
The equation for other age groups is coming from the outflow being older.while the equation for the age group (09) is the total population*birth rate fraction).
Birth rate for the youngest group gives us more realistic results.
expected_Birth_Rate_Fraction*Total_Population_West_Norway_Region
Being_Older[Group_0_to_9]
Being_Older[Group_10_to_19]
Being_Older[Group_20_to_29]
Being_Older[Group_30_to_39]
Being_Older[Group_40_to_49]
Being_Older[Group_50_to_59]
Being_Older[Group_60_to_69]
Being_Older[Group_70_to_79]
Being_Older[Group_90_to_100]
People/Years
This outflow decreases the stock population groups.This outflow shows how many people die per year.
This outflow is an aggregated outflow which is arrayed by the age groups.
Using arrayed outflow gives a detailed result about death rate for each group , so we can check the death rate for each age group and found out which age groups that have more life expectancy, hence , that helps decision makers to know which age groups that are threaten more than other age groups.
The equation for death rate is the same for all aggregated age groups .
The equation for death rate outflow is the result of :
population groups *death rate fraction.
Population_Groups/Expected_Life_Expectancy
People/Years
This stock increases by the inflow to it (death rate)
This stock an aggregated stock which is arrayed by the 10 age groups .
the researcher initialized this stock value by the number of death people in the year 2020 .
Data has been extracted from the Norwegian statistics website :
https://www.ssb.no/en
Deaths. 2020.Both sexes.Rogaland
09 years 5
1019 years 13
2029 years 25
3039 years 41
4049 years 75
5059 years 170
6069 years 342
7079 years 707
8089 years 1 003
90 years or older 736
Deaths. 2020 .Both Sexes. Vestland
09 years 22
1019 years 15
2029 years 31
3039 years 43
4049 years 96
5059 years 198
6069 years 461
7079 years 996
8089 years 1 566
90 years or older 1 338
Each value equals the summing of the number of death population in Rogaland and Vestland, because these two counties forms the West of Norway health region from the year 2020. As Norwegian authorities applied decentralization in health services and merged these two counties in to one health region .
27
28
56
84
171
368
803
1703
2569
2074
Death_Rate
People
This inflow causes an increase in the stock (new cases of cancer).
This inflow shows how many new incidents of cancer are transferred to the stock population with cancer .
the equation of this inflow is the product of population groups by the fraction of new cases of cancer.
This inflow is an integrated flow that is arrayed by the age groups of population.
Using arrayed inflow gives a detailed result about the new cases of cancer rate for each group , so we can check that rate for each age group and found out which age groups that are exposed to get cancer , hence , that helps decision makers to know which age groups that are threaten more than other age groups.
The equation this inflow is the same for all aggregated age groups., but the fraction differs.
The equation for this inflow is the result of :
population groups*fraction of new cases of cancer
Population_Groups*"Expected_Fraction_New_Cancer_Cases"
People/Years
This variable represents the total death population, which is the summing of all death population in the 10 age population groups.
The purpose of this variable is to give the reader a summarized view of the death population behavior.While to get more detailed view , the reader can check the stock death population , that shows the deaths for each age group.
SUM(Death_Population)
People
This variable represents the total death population of cancer , which is the summing of all death population of cancer in the 10 age population groups.
The purpose of this variable is to give the reader a summarized view of the death population of cancer behavior.While to get more detailed view , the reader can check the stock deaths of cancer , that shows the deaths of cancer for each age group.
SUM(Deaths_of_Cancer)
People
This variable represents the total treated people who need following up and control and they are moving to the next stock in the model (Recovered population) through the outflow followingUp process rate.
This variable is the summing of all treated and needed following up population in the 10 age population groups.
The purpose of this variable is to give the reader a summarized view of the stock treated and need followingUp behavior.To get more detailed view , the reader can check that stock which shows the behavior for each age group.
SUM("Treated_and_Need_FollowingUp")
People
This variable represents the total recovered people who have been totally finished their treatment and controll, and now they considered as recovered population.
This variable is the summing of all recovered population population in the 10 age population groups that are arrayed in the stock recovered population .
The purpose of this variable is to give the reader a summarized view of the stock recovered population behavior.To get more detailed view , the reader can check that stock which shows the behavior for each age group.
SUM(Recovered_population)
People
SUM(Population_with_cancer)
People
This outflow represents the rate of people per year that will become older .This is an integrated outflow , that is arrayed by the age groups of population which are 10 groups
The researcher divided population groups in to 10 cohort, therefore to calculate the rate of transfer to the next age group , we need to have 10 outflows.these outflows are aggregated in to this aggregated outflow.
All groups have the same equation except the last age group (90100) , because this age group assumed not to be older.
The equation used in this outflow includes: population groups(For each population) / Time staying in one age group (that is 10 Years)
This equation shows an outflow equation with the stock that is divided by the delay time of 10 years.
Population_Groups[Group_0_to_9]/Time_Staying_in_One_Age_Group
Population_Groups[Group_10_to_19]/Time_Staying_in_One_Age_Group
Population_Groups[Group_20_to_29]/Time_Staying_in_One_Age_Group
Population_Groups[Group_30_to_39]/Time_Staying_in_One_Age_Group
Population_Groups[Group_40_to_49]/Time_Staying_in_One_Age_Group
Population_Groups[Group_50_to_59]/Time_Staying_in_One_Age_Group
Population_Groups[Group_60_to_69]/Time_Staying_in_One_Age_Group
Population_Groups[Group_70_to_79]/Time_Staying_in_One_Age_Group
Population_Groups[Group_80_to_89]/Time_Staying_in_One_Age_Group
0
People/Years
This variable represents the delay time that each age group after the year of 9 takes to go to another age group . We divided age groups in to 10 years cohort, therefore delay time to step to the other page group is 10 years.
10
years
This summing converter includes the total population , which equals all population groups in addition to Migrations . Using this variable is useful when we need to show the behavior of total population regardless dividing people in to age groups.
SUM(Population_Groups[*])
People
This stock represents the number of doctors and nurses who are working in cancer treatment department in health Bergen region.
These resources are treating cancer patients who get treatment and live in west Norway.
https://helsebergen.no/seksjonengelsk/seksjonavdeling/Sider/CancerTreatmentandMedicalPhysics.aspx
This stock increases by the inflow resources allocated , which recruit doctors and nurses. This stock decreases by the outflow resources lost which cause a decrease in the stock when doctors and nurses get retired.
1200
40
Resources_allocated
Resources_Lost
People
This outflow represents the rate of nurses and doctors who get retired after a period of working time.
This rate is affected by the resources loss fraction.
when there is a high loss fraction , that means there will be less resources in the stock total resources allocated and vice versa.
Total_Resources_allocated*Average_Resource_Lose_Fraction
People/Years
This converter
SMTH1(Average_Fraction_of_Resources_Lose, Time_To_adopt_Reallocation_Policy)
dmnl/years
IF Policy_2= 0 THEN (Total_Resources_allocated*Resources_allocation_Fraction) ELSE ((Total_Resources_allocated+(Total_Resources_allocated*.25))*Resources_allocation_Fraction)
People/Years
SMTH1(Average_Fraction_of_Resources_allocation, Time_To_adopt_Reallocation_Policy)
dmnl/years
This converter represents the total number of resources(nurses and doctors ). This value have the same value as the stock (total resources allocated) , but the researcher used this converter to be able to se the behavior of the total resources as the stock of resources is an integrated stock for doctors and nurses.
This converter shows the reader the general behavior for the resources, and to get a detailed curves about nurses and doctors, the reader can check the integrated stock behavior .
SUM(Total_Resources_allocated)
People
This converter represents how many patients are considered as the capacity that all nurses can treat . The researcher assumed that each nurse can hep 4 patients .
So the equation for this converter= total nurses*4
We need this converter to be able to calculate the gap between actual treated patients by nurses and the current capacity for nurses
Treatment_Resources*3
People
0
dmnl
0
dmnl
0
dmnl
0
dmnl
This converter depicts the conditions that will be in the system during first policy.
Using If equation gives the user of the model the flexibility to choose either to use the policy 1 during the shock or without the shock.
When there is a shock condition like for example covid19 or any pandemics that have a negative effect on the system , we can apply this policy . we can test the effect of policy on variables behaviors either during the shock or without the shock .This is the main purpose of using if equation and also the sign <> which means not equal . This equations says:
If the shock Function is not active, so the policy 2 key will be active. And when the shock function will not equal zero that means this converter will equal to policy 2 key.If the shock key equals zero that means this converter will be zero.
Policy 2 converter has been designed to help in determining which value will be used in the followingUp delay time converter and the average followingUp fraction converter.
IF Shock_Key=0 THEN Policy_2_Key ELSE
(IF Shock_Function<>0 THEN Policy_2_Key ELSE 0)
dmnl
This converter includes the birth rate fraction of total population (Population groups ). Population who grow by this birth rate fraction are the group of (09 ) years old. This age group is the group of people who are assumed to grow by the birth rate fraction, while other population groups grow by transferring from age group to the other , by using the outflow (being older) and the delay time of 10 years, except the age group of (90100) which is assumed to to die after that and be transferred to death stock through the death rate outflow.
The researcher used graphical function to reveal the expected birth rate fraction by using the minimum and maximum birth rate fractions that has been found in the Statistics Norway https://www.ssb.no/en
Statistics Norway publishes three alternatives of projections for expected birth rate Fraction which are: High alternative (HHH)and main/medium alternative (MMM)and low alternative(LLL).The researcher chooses to use MMM (Medium alternative, which assumes the medium level for each component, because this is what is assumed to be most plausible.
The same data has been also found in :
https://www.macrotrends.net/countries/NOR/norway/birthrate
TIME
0.021,0.0209772659470195,0.0209492046794412,0.0209145678841997,0.0208718147341054,0.0208190433437319,0.0207539061635284,0.0206735055484446,0.0205742648554078,0.0204517693353903,0.0203005697421064,0.0201139399208205,0.0198835775935368,0.0195992350299212,0.01924826317425,0.0188150489487505,0.0182803207016202,0.0176202909023923,0.0168055979472199,0.0158
dmnl/years
The researcher used graphical function to reveal the expected Life expectancy by using the minimum and maximum values for Life Expectancy that has been found in the Statistics Norway https://www.ssb.no/en
Statistics Norway publishes three alternatives of projections for expected Life Expectancy which are: High alternative (HHH)and main/medium alternative (MMM)and low alternative(LLL).The researcher chooses to use MMM (Medium alternative, which assumes the medium level for each component, because this is what is assumed to be most plausible.
The same data has been also found in :
https://www.macrotrends.net/countries/NOR/norway/lifeexpectancy
TIME
82,82.9187154737335,83.7190625154338,84.4162918595259,85.0236893170467,85.5528289387921,86.0137935606128,86.4153669333823,86.765201098704,87.0699621997259,87.3354575055108,87.5667460694301,87.7682351301942,87.9437640924551,88.0966776872456,88.2298897063367,88.3459385249826,88.4470354710465,88.5351069621888,88.6118312140482,88.6786702188956,88.7368976041191,88.7876229013868,88.8318126889416,88.8703090098962,88.9038454174928,88.933060953071,88.9585123230983,88.9806845072956,89
years
This converter represents how many patients are considered as the capacity that all specialized doctors can treat . The researcher assumed that each doctor can treat 4 patients .
So the equation for this converter= total specialized doctors*4
We need this converter to be able to calculate the gap between actual need for specialized doctors and the current capacity
"FollowingUp_Resources"*5
People
(Total_Resources_allocated[Nurses]+Total_Resources_allocated[Specialized_doctors])*Fraction_Of_Treatment_Resources
People
This key is used to switch policy 1 of and on. when we want to use the first policy , we have to use in this converter the value one . when we do not want to use the first Policy , we have to use in this converter the value Zero.
0
dmnl
(Total_Resources_allocated[Specialized_doctors]+Total_Resources_allocated[Nurses])*"Fraction_Of_followingUp_Resources"
People
1"Fraction_Of_followingUp_Resources"
dmnl
IF Policy_1 =1 THEN 0.1 ELSE 0.5
dmnl
IF Shock_Function=1 THEN .06 ELSE .03
{.06
dmnl/years
IF Shock_Function =1 THEN .02 ELSE .04
{.04
dmnl/years
IF Shock_Function= 0 THEN 1 ELSE 5
years
This converter represents the case in which there is an emergency situation( Like a pandemic as in Covid19) that happens and that causes delay in treatment process. When this key have the value 0 , that means there is no emergency situation, and this key is not active. When this key has the value 1, that means there is an emergency situation and this key is active.
IF Shock_Key=1 THEN STEP(1, Start_of_Shock)+STEP(1, End_of_Shock) ELSE 0
dmnl
This converter depicts to which extend the treatment process or treatment resource is exposed to pressure.We can measure if the treatment resource is able to treat people efficiently or not.
This equation finds out the percentage of cancer patients that the current resources is able to treat them .
If the result of this equation = 1 that means the number of people who needs treatment = the capacity of resources available to treat them
If the result of this equation> 1 that means the number of people who need treatment is higher than the capacity of resources available to treat them.
In real world , it is not easy to achieve the result of this equation to be 1 ,because of the delays in systems.these delays that are in the form of waiting and hiring health personnel, etc.
But the result of this equation could be used as an indicator to evaluate the pressure on treatment resources. The higher result of this equation means that there is a higher pressure on treatment resources , because the treatment process is the main stage in cancer medication stages.The purpose of this equation is to measure and find out how the result of this equation will change when we change policies.The policy that gives less value (Pressure) is better than the policy that gives higher value.
Total_Population_With_Cancer/Patients_Treated_by_current_Resources
dmnl
This converter depicts to which extend the followingUp process or the followingUp resource is exposed to pressure.We can measure if the followingUp resource is able to monitor people efficiently or not.
But from the conceptualization of the followingUp process , the researcher assumes that this pressure will not affect the system negatively because of the flexibility and ability of followingup patients by different ways other than admitting the hospital.
The less pressure on followingUp resources is a good indicator , but when it is compared by the pressure on treatment resources, we have to prioritize to get less pressure on treatment resources than the pressure on followingup resources.
This equation finds out the percentage of cancer patients that the current resources is able to monitor and control them, so they are not in emergence situation as those who need treatment to do tumor excision for example.
The purpose of this equation is mainly to calculate the pressure on the whole health system.
If the result of this equation = 1 that means the number of people who needs to be followedup= the capacity of resources available to follow them up.
If the result of this equation> 1 that means the number of people who need followingUp is higher than the capacity of resources available.
In real world , it is not easy to achieve the result of this equation to be 1 ,because of the delays in systems.these delays that are in the form of waiting and hiring health personnel, etc.
But the result of this equation could be used as an indicator to evaluate the pressure on followingUp resources. The higher result of this equation means that there is a higher pressure on followingUp resources.The purpose of this equation is to measure and find out how the result of this equation will change when we change policies.By assuming that followingUp process needs less resources because of its flexibility and non emergency in patients cases, we can assume that the policy that gives higher value (Pressure) is better than the policy that gives lower value.That assumption is only valid when the other key performance indicators shows positive results in comparison to the same result for other policies .
"Total_treated_and_Need_FollowingUp"/"Patients_FollowedUp_by_Current_Resources"
dmnl
Average Treatment delay time during the second phase is 6 years.
This delay time means that in average , each patient needs 6 to be treated , that includes many appointments scheduled and all needed surgeries or checksup after being diagnosed as a cancer patient in the first phase .By other words, it takes in average 6 years to transfer a patient from the stock population with cancer to the stock treated and need followingUp.
Using the equation if her, means that if we will not apply any of the policies , the average delay time will be 6 years . But when we apply any of the policies, that means that there will be less delay in treatment process, because policy two will add more health personnel, and that is assumed will decrease the average delay time in followingup patients , and policy one will allocate more health personnel in the treatment process. so if any of these policies will be adapted , the average delay time in treatment is considered to be reduced to 3 Years in average .
IF Policy_1 OR Policy_2=1 THEN 1 ELSE 5
years
Average Treatment fraction during the second phase is 0.3 dml/ years.
This average treatment fraction means that in average , there will be a fraction of 0.5 of patients will be treated .By other words, in average there will be a fraction of 0.5 patients per year that will be transferred from the stock population with cancer to the stock treated and need followingUp.
Using the equation if her, means that if we will not apply any of the policies , the average treatment fraction will be .05 dml/ years . But when we apply any of the policies, that means that there will be more people who will be treated , because policy two will add more health personnel, and that is assumed will decrease the average delay time in followingup patients , and the first policy will allocate more health personnel in the treatment process. So if any of these policies will be adapted , the average treatment fraction is considered to increase to 0.7 in average .
IF TIME >=2040 THEN (IF Policy_1 =1 THEN 0.8 ELSE 0.5 AND IF Policy_2 =1 THEN 0.6 ELSE 0.5) ELSE 0.5
dmnl/years
followingUp Average delay time during third phase is 4 years.
This delay time means that in average , each patient needs 4 years to be controlled after treatment , that includes many appointments scheduled and all needed surgeries or checksup after the main treatment phase .By other words, it takes in average 4 years to transfer a patient from the stock treated and need followingUp, to the stock recovered population.
Using the equation if her, means that if we will not apply policy two , the average delay time will be 7 years . But when we apply policy 2 that means that there will be less delay in followingUp process, because policy two will add more health personnel, and that is assumed will decrease the average delay time in followingup patients , which is considered to be reduced to 4 Years in average .
IF Policy_1 OR Policy_2 =1 THEN 1 ELSE 5
years
IF TIME >= 2040 THEN (IF Policy_2=1 THEN 0.5 ELSE 0.4 AND IF Policy_1 = 1 THEN 0.2 ELSE 0.4) ELSE 0.4
dmnl/years
This converter depicts to which extend the health sector is exposed to pressure.We can measure if the health system able to treat people efficiently or not.
The higher result of this equation means that there is a higher pressure on health system , because the treatment process is the main stage in cancer medication stages.The followingUp stage is more flexible and patients can be followedup or controlled by different ways, therefore the researcher created this converter with this equation.The purpose of this equation is to measure and find out how the result of this equation will change when we change policies.The policy that gives less value (Pressure) is better than the policy that gives higher value.
Pressure_On_Treatment_Resources/"Pressure_On_FollowingUp_Resources"
dmnl
This converter represents the time at which the shock condition will start in the system .
Choosing the year 2040 as the start time of the shock has been chosen randomly, and we can change it to test the system during different time to start. But the reason that the researcher preferred to use this time is that becomes in the middle of the simulation period , that give us the chance to see how the system will perform before and after the shock.
2040
years
This converter represents the time at which the shock condition will end in the system .
Choosing the year 2045 as the end time of the shock has been chosen randomly, and we can change it to test the system during different time to start. But the reason that the researcher preferred to use this time is that becomes in the middle of the simulation period , that give us the chance to see how the system will perform before and after the shock.
2045
years
This key is used to switch the shock condition off and on. when we want to test the system during a shock , we have to use in this converter the value one . when we there is no shock condition , we have to use in this converter the value Zero.
0
dmnl
This converter depicts the conditions that will be in the system during the first policy.
Using If equation gives the user of the model the flexibility to choose either to use the policy 1 during the shock or without the shock.
When there is a shock condition like for example covid19 or any pandemics that have a negative effect on the system , we can apply this policy . we can test the effect of policy on variables behaviors either during the shock or without the shock .This is the main purpose of using if equation and also the sighn <> which means not equal . This equations says:
If the shock key is not active, so the policy 2 key will be active. And when the shock function will not equal zero that means this converter will equal to policy 2 key.If the shock Function equals zero that means this converter will be zero.
Policy 2 converter has been designed to help in determining which value will be used in the followingUp delay time converter and the average followingUp fraction converter.
IF Shock_Key=0 THEN Policy_1_Key ELSE
(IF Shock_Function<>0 THEN Policy_1_Key ELSE 0)
dmnl
This key is used to switch the second policy of and on. when we want to use the second policy , we have to use in this converter the value one . when we do not want to use the second Policy , we have to use in this converter the value Zero.
0
dmnl
https://www.kreftregisteret.no/en/Temasider/keyfiguresoncancer/
At the end of 2020 there were:
3856 men and women at the age group (049)
14108 men and women at the age group(5069)
18263 men and women at the age group(70+)
in west Norway who has cancer diagnose in the period 20162020 cancer and they still have cancer .
So the fraction of new cases of cancer at the end of 2020 can be calculated :
Total Population of West Norway/ Total New cases of cancer =
1047000/36227= 0.035
The researcher could not find specific data for each age of the 10 groups that she used in her research , therefore she used this fraction and created this graphical Function , to expect the future fraction of new cases of cancer.Based on historical datat, the fraction of new cases is increasing over time , so , the maximum value that found in data is .04.
The researcher found also in the Norwegian Institute of Public Health website https://norgeshelsa.no/norgeshelsa/
the same data.
TIME
0.035,0.0352958924981239,0.0356018143060595,0.0359181053684013,0.0362451171522046,0.0365832130375402,0.0369327687212876,0.037294172634615,0.0376678263746089,0.0380541451505347,0.0384535582452229,0.0388665094920943,0.039293457768354,0.039734877504902,0.0401912592135274,0.0406631100319722,0.0411509542874696,0.0416553340793841,0.0421768098815995,0.0427159611653255,0.0432733870430144,0.043849706934103,0.0444455612533201,0.0450616121223247,0.0456985441054644,0.0463570649704729,0.047037906474952,0.0477418251795102,0.0484696032884637,0.0492220495190328,0.05
dmnl/years
More than 11.000 Norwegians died of cancer in 2019.
(
https://www.kreftregisteret.no/en/Temasider/keyfiguresoncancer/)
The researcher could not find specific data for each deaths in each health region , therefore she used this fraction and divided it between the four health regions regarding to her conceptualization of the problem and from her understanding to the distribution of cancer incidents between health regions and age groups . The researcher found also in the Norwegian Institute of Public Health website https://norgeshelsa.no/norgeshelsa/
the same data.
The researcher divided the total number of death people by 4 to get this number for each region 2750.Then Death of cancer rate fraction can be calculated by dividing the number of died people of cancer in Health region west by the Total people in West Norway ::
died people per year/Total population West Norway=
2750/1047000= 0.0026
From Historical data, the researcher conceptualized that deaths of cancer increases over time, so she created this graphical function with ,inimum value calculated and the maximum value expected by data which is 0.003.
TIME
0.0026,0.00260789046661664,0.00261604838149492,0.00262448280982404,0.00263320312405879,0.0026422190143344,0.00265154049923434,0.00266117793692307,0.00267114203665624,0.00268144387068092,0.00269209488653928,0.00270310691978918,0.00271449220715611,0.00272626340013072,0.0027384335790274,0.00275101626751926,0.00276402544766586,0.00277747557545024,0.00279138159684265,0.00280575896440868,0.00282062365448038,0.00283599218490941,0.00285188163342187,0.00286830965659533,0.00288529450947905,0.00290285506587928,0.00292101083933205,0.00293978200478694,0.0029591894210257,0.00297925465384088,0.003
dmnl/years
"Treated_and_Need_FollowingUp"*Back_To_System_Fraction
People/years
IF Shock_Function=1 THEN .3 ELSE .1
{.3
dmnl/years
TIME
2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050
1116423,1120066,1124056,1128603,1133224,1138012,1143039,1148009,1152937,1157822,1162645,1167407,1172073,1176649,1181158,1185612,1190004,1194269,1198336,1202202,1205875,1209359,1212644,1215728,1218621,1221323,1223825,1226128,1228240,1230145,1231870
years
SUM(New_cases_of_cancer_Rate)
People/years
Population_with_cancer
Death_Rate_Of_Cancer
"Recancer_Fraction"
"ReCancer"
Recovered_population
"ReCancer"
Population_Groups
Death_Rate
Death_Population
Total_Death_Population
Deaths_of_Cancer
Total_Deaths_Of_Cancer
Recovered_population
Total_Recovered
Population_with_cancer
Total_Population_With_Cancer
Time_Staying_in_One_Age_Group
Being_Older
Population_Groups
Being_Older
Being_Older
Birth_Rate
Total_Population_West_Norway_Region
Birth_Rate
Average_Resource_Lose_Fraction
Resources_Lost
Total_Resources_allocated
Resources_Lost
Resources_allocation_Fraction
Resources_allocated
Total_Resources_allocated
Resources_allocated
Treatment_Fraction
Treatment_Process_rate
Total_Resources_allocated
Total_Resources







"FollowingUp_Fraction"
"FollowingUp_Process_Rate"
Policy 1:
To Increase the fraction of resources allocated on treatment process by (decreasing the fraction of resources allocated on following up process) ,then the behavior of treated and need following up shows an increase, that considered as positive indicator because , we aim to increase this level of population because they are in the most important phase of treatment , and treating more people means that their chance to get mitigated cancer is higher.
Even we decrease the followingUp resources allocated, but to compensate that , when we apply the policy we can use home treatment by using remote treatment, communication between the patient and hospital, that will decrease the admission of patients to hospital and decrease the waiting time for all patients.
That will increase the efficiency of treatment process and lead to positive effect on treatment , hence there will be more people treated and need only followingUp, something that could be done remotely as explained .
Policy 2:
keep the same fraction of resources allocated as in the base Run , but to increase the allocated resources . Using that means that health ministry will recruit more nurses and doctors.( the increased percent is assumed by the researcher to be 25% of the original recruited capacity.
The researcher will test different scenarios, and find out which combination of policies will give the most optimal results , using each policy separately or both of them together.
it seems interesting when we activate the policy keys and check the gap in nurses and doctors . We see that even the number of doctors and nurses did not changed , but the gap changed. The reason for that is the resources allocation policies. Even we use the same resourses, but the efficiency changed and that affect the results in treated and recovered persons
Expected_Life_Expectancy
Death_Rate
Total_Resources_allocated
Treatment_Resources
Total_Resources_allocated
"FollowingUp_Resources"
"FollowingUp_Resources"
"Patients_FollowedUp_by_Current_Resources"
Fraction_Of_Treatment_Resources
Treatment_Resources
Treatment_Resources
Patients_Treated_by_current_Resources
"Fraction_Of_followingUp_Resources"
"FollowingUp_Resources"
Fraction_Of_Treatment_Resources
Population_with_cancer
Treatment_Process_rate
"Treated_and_Need_FollowingUp"
"FollowingUp_Process_Rate"
Average_Fraction_of_Resources_Lose
Average_Resource_Lose_Fraction
Time_To_adopt_Reallocation_Policy
Resources_allocation_Fraction
Average_Fraction_of_Resources_allocation
Resources_allocation_Fraction
Shock_Function
Time_To_adopt_Reallocation_Policy
"Treated_and_Need_FollowingUp"
"Total_treated_and_Need_FollowingUp"
Population_Groups
New_cases_of_cancer_Rate
Patients_Treated_by_current_Resources
Pressure_On_Treatment_Resources
"Patients_FollowedUp_by_Current_Resources"
"Pressure_On_FollowingUp_Resources"
Time_to_adopt_to_Treatment_Policy
Treatment_Fraction
Average_Treatment_Fraction
Treatment_Fraction
"Time_to_adopt_to_FollowingUp_Policy"
"FollowingUp_Fraction"
"Average_FollowingUp_Fraction"
"FollowingUp_Fraction"
Policy_2
"Time_to_adopt_to_FollowingUp_Policy"
Policy_2
"Average_FollowingUp_Fraction"
It seems interesting when we apply both policies;because we get nearly the same behavior for key performance Indicators that we see when we apply only the first policy.
That means, first policy is more effective and efficient, because we can get the same result without needing to recruit more people, indeed we can reallocate rationally resources that we already have.
"Pressure_On_FollowingUp_Resources"
Pressure_On_Cancer_Healthcare_system
Pressure_On_Treatment_Resources
Pressure_On_Cancer_Healthcare_system
Shock_Function
Average_Fraction_of_Resources_allocation
Shock_Function
Average_Fraction_of_Resources_Lose
(1)The researcher created thisSFD from her conceptualization of the cancer patient pathway system that is used in Norway (this type of following is part of the patient pathway system that the Norwegian ministry of health introduced in its cancer care system in January 2015).
(2)The researcher assumes that increasing efficiency in treatment process will have a positive effect on cancer treatment resilience.
This Efficiency depends mainly on resources management allocation.
(3) {Resilience within a health system, a definition: “A health system’s ability to absorb, adapt to, learn and recover from crisis born of short term shocks and accumulated stresses, in order to minimize their negative impact on population health and disruption caused to health services.”
(4) The more demand on treatment will cause more needed allocated resources and that will cause pressure on health sector.
Following process is more flexible and we can find alternatives that can decrease the pressure on capacity utilization, at the same time , we could use these resources to mitigate the pressure on treatment process resources needed.
For more details , please check this article
https://tidsskriftet.no/en/2020/04/debatt/needchangecancerfollow
Start_of_Shock
Shock_Function
End_of_Shock
Shock_Function
Shock_Key
Shock_Function
Policy_1_Key
Policy_1
Policy_1
Policy_1
"Fraction_Of_followingUp_Resources"
Policy_1
Policy_2_Key
Policy_2
Shock_Key
Policy_2
Time_to_adopt_to_Treatment_Policy
Average_Treatment_Fraction
"Expected_Fraction_New_Cancer_Cases"
New_cases_of_cancer_Rate
"Expected_FractionDeaths_Of_Caner"
Death_Rate_Of_Cancer



"Treated_and_Need_FollowingUp"
Delayed_and_Back_to_System
Back_To_System_Fraction
Delayed_and_Back_to_System
Back_To_System_Fraction
"Total_treated_and_Need_FollowingUp"
"Pressure_On_FollowingUp_Resources"
Total_Population_With_Cancer
Pressure_On_Treatment_Resources



expected_Birth_Rate_Fraction
Birth_Rate
New_cases_of_cancer_Rate
Total_New_Cases_Of_Cancer_Rate
Time_To_adopt_Reallocation_Policy
Average_Resource_Lose_Fraction
"Average_FollowingUp_Fraction"
Average_Treatment_Fraction
"Time_to_adopt_to_FollowingUp_Policy"
Time_to_adopt_to_Treatment_Policy
Shock_Function
Policy_2
Policy_2
Resources_allocated
"Fraction_Of_followingUp_Resources"
Shock_Function
Shock_Key
Policy_1
Policy_1_Key
Policy_2_Key
Shock_Key
Shock_Function
Policy_1
Policy_2