148 Add EQ5D as a person attribute

src/main/java/simpaths/data/ManagerRegressions.java

@@ -64,6 +64,9 @@ public static LinearRegression getLinearRegression(RegressionName regression) {
             case LifeSatisfaction2Females -> {
                 return Parameters.getRegLifeSatisfaction2Females();
             }
+            case HealthEQ5D -> {
+                return Parameters.getRegEQ5D();
+            }
             case SocialCareS1b -> {
                 return Parameters.getRegCareHoursS1b();
             }

src/main/java/simpaths/data/Parameters.java

@@ -334,6 +334,8 @@ else if(numberOfChildren <= 5) {
     public static boolean saveImperfectTaxDBMatches = false;
     public static final int IMPERFECT_THRESHOLD = 5999;
 
+    public static String eq5dConversionParameters = "lawrence";
+
 
     /////////////////////////////////////////////////////////////////// INITIALISATION OF DATA STRUCTURES //////////////////////////////////
     public static Map<Integer, String> EUROMODpolicySchedule = new TreeMap<Integer, String>();
@@ -472,6 +474,7 @@ else if(numberOfChildren <= 5) {
     private static MultiKeyCoefficientMap coeffCovarianceDLS2Males;
     private static MultiKeyCoefficientMap coeffCovarianceDLS2Females;
 
+    private static MultiKeyCoefficientMap coeffCovarianceEQ5D;
 
     //Education
     private static MultiKeyCoefficientMap coeffCovarianceEducationE1a;
@@ -707,6 +710,8 @@ else if(numberOfChildren <= 5) {
     private static LinearRegression regLifeSatisfaction2Males;
     private static LinearRegression regLifeSatisfaction2Females;
 
+    private static LinearRegression regHealthEQ5D;
+
     //Education
     private static BinomialRegression regEducationE1a;
     private static BinomialRegression regEducationE1b;
@@ -972,6 +977,7 @@ public static void loadParameters(Country country, int maxAgeModel, boolean enab
         int columnsLifeSatisfaction1 = -1;
         int columnsLifeSatisfaction2Males = -1;
         int columnsLifeSatisfaction2Females = -1;
+        int columnsHealthEQ5D = -1;
         int columnsSocialCareS1a = -1;
         int columnsSocialCareS1b = -1;
         int columnsSocialCareS2a = -1;
@@ -1132,6 +1138,7 @@ else if(country.equals(Country.UK)) {
             columnsLifeSatisfaction1 = 30;
             columnsLifeSatisfaction2Males = 9;
             columnsLifeSatisfaction2Females = 9;
+            columnsHealthEQ5D = 8;
             columnsSocialCareS1a = 17;
             columnsSocialCareS1b = 18;
             columnsSocialCareS2a = 32;
@@ -1351,6 +1358,8 @@ else if(country.equals(Country.UK)) {
         coeffCovarianceDLS2Males = ExcelAssistant.loadCoefficientMap("input/reg_health_wellbeing.xlsx", countryString + "_DLS2_Males", 1, columnsLifeSatisfaction2Males);
         coeffCovarianceDLS2Females = ExcelAssistant.loadCoefficientMap("input/reg_health_wellbeing.xlsx", countryString + "_DLS2_Females", 1, columnsLifeSatisfaction2Females);
 
+        loadEQ5DParameters(countryString, columnsHealthEQ5D);
+
         //Life satisfaction
 
 //        coeffCovarianceDLS1 = ExcelAssistant.loadCoefficientMap("input/reg_lifesatisfaction.xlsx", countryString + "_DLS1", 1, columnsLifeSatisfaction1);
@@ -1570,7 +1579,6 @@ else if (country.equals(Country.IT)) {
         regLifeSatisfaction2Males = new LinearRegression(coeffCovarianceDLS2Males);
         regLifeSatisfaction2Females = new LinearRegression(coeffCovarianceDLS2Females);
 
-        //Education
         regEducationE1a = new BinomialRegression(RegressionType.Probit, Indicator.class, coeffCovarianceEducationE1a);
         regEducationE1b = new BinomialRegression(RegressionType.Probit, Indicator.class, coeffCovarianceEducationE1b);
         regEducationE2a = new OrderedRegression<>(RegressionType.GenOrderedLogit, Education.class, coeffCovarianceEducationE2a);
@@ -2075,6 +2083,8 @@ public static void setEmploymentsFurloughedFlex(MultiKeyCoefficientMap employmen
     public static BinomialRegression getRegEducationE1b() {return regEducationE1b;}
     public static OrderedRegression getRegEducationE2a() {return regEducationE2a;}
 
+    public static LinearRegression getRegEQ5D() { return regHealthEQ5D; };
+
     public static BinomialRegression getRegPartnershipU1a() {return regPartnershipU1a;}
     public static BinomialRegression getRegPartnershipU1b() {return regPartnershipU1b;}
     public static BinomialRegression getRegPartnershipU2b() {return regPartnershipU2b;}
@@ -3331,4 +3341,12 @@ private static void safeDelete(String filePath) {
             throw e;
         }
     }
+
+    public static void loadEQ5DParameters(String countryString, int columnsHealthEQ5D) {
+
+        coeffCovarianceEQ5D = ExcelAssistant.loadCoefficientMap("input/reg_eq5d.xlsx", countryString + "_EQ5D_" + eq5dConversionParameters, 1, columnsHealthEQ5D);
+
+        regHealthEQ5D = new LinearRegression(coeffCovarianceEQ5D);
+
+    }
 }

src/main/java/simpaths/data/RegressionName.java

@@ -41,6 +41,8 @@ public enum RegressionName {
     LifeSatisfaction2Males(RegressionType.Linear),
     LifeSatisfaction2Females(RegressionType.Linear),
 
+    HealthEQ5D(RegressionType.Linear),
+
     RMSE(RegressionType.Linear),
 
     SocialCareS1a(RegressionType.Probit),

src/main/java/simpaths/model/Person.java

@@ -135,6 +135,9 @@ public class Person implements EventListener, IDoubleSource, IIntSource, Weight,
     private Integer dls;      //life satisfaction - score 1-7
     @Transient private Double dls_temp;
     @Transient private Integer dls_lag1;      //life satisfaction - score 1-7 lag 1
+    @Column(name="he_eq5d")
+    private Double he_eq5d;
+
     @Column(name="dhh_owned") private Boolean dhhOwned; // Person is a homeowner, true / false
     @Transient private Boolean receivesBenefitsFlag_L1; // Lag(1) of whether person receives benefits
     @Transient private Boolean receivesBenefitsFlag; // Does person receive benefits
@@ -667,6 +670,7 @@ public enum Processes {
         Fertility,
         GiveBirth,
         Health,
+        HealthEQ5D,
         HealthMentalHM1, 				//Predict level of mental health on the GHQ-12 Likert scale (Step 1)
         HealthMentalHM2,				//Modify the prediction from Step 1 by applying increments / decrements for exposure
         HealthMentalHM1HM2Cases,		//Case-based prediction for psychological distress, Steps 1 and 2 together
@@ -756,6 +760,9 @@ public void onEvent(Enum<?> type) {
             case HealthMentalHM1HM2Cases -> {
                 healthMentalHM1HM2Cases();
             }
+            case HealthEQ5D -> {
+                healthEQ5D();
+            }
             case InSchool -> {
     //			log.debug("In Education for person " + this.getKey().getId());
                 inSchool();
@@ -1030,6 +1037,22 @@ protected void lifeSatisfaction2() {
         }
     }
 
+    private void healthEQ5D() {
+
+        double eq5dPrediction;
+        eq5dPrediction = Parameters.getRegEQ5D().getScore(this, Person.DoublesVariables.class);
+        if (eq5dPrediction > 1) {
+            he_eq5d = 1.0;
+        }
+        else if (eq5dPrediction < -0.594) {
+            he_eq5d = -0.594;
+        }
+        else {
+            he_eq5d = eq5dPrediction;
+        }
+
+    }
+
     /*
     Case-based measure of psychological distress, Steps 1 and 2 modelled together
     */
@@ -2253,6 +2276,7 @@ public enum DoublesVariables {
         Dehmf_c3_Low,
         Dehsp_c3_Low_L1,				//Partner's education == Low at lag(1)
         Dehsp_c3_Medium_L1,				//Partner's education == Medium at lag(1)
+        He_eq5d,                          //EQ5D quality of life score
         Dgn,							//Gender: returns 1 if male
         Dgn_baseline,
         Dgn_Dag,
@@ -2294,8 +2318,17 @@ public enum DoublesVariables {
         Dls_L1,                            //Life satisfaction status lag(1)
         Dhe_mcs,                        //Mental well-being status
         Dhe_mcs_L1,                        //Mental well-being status lag(1)
+        Dhe_mcs_sq,                     //MCS score squared
+        Dhe_mcs_times_pcs,              //MCS times PCS
+        Dhe_mcs_c_times_pcs_c,          //Centralised MCS times PCS
+        Dhe_mcs_c,                      //MCS centralised by subtracting population mean
+        Dhe_mcs_c_sq,                   //Square of centralised MCS
         Dhe_pcs,                        //Physical well-being status
         Dhe_pcs_L1,                        //Physical well-being status lag(1)
+        Dhe_pcs_sq,                     //PCS score squared
+        Dhe_pcs_cb,                     //PCS score cubed
+        Dhe_pcs_c,                      //MCS centralised by subtracting population mean
+        Dhe_pcs_c_sq,                   //Square of centralised MCS
         Dhmghq_L1,
         Dlltsd,							//Long-term sick or disabled
         Dlltsd_L1,						//Long-term sick or disabled lag(1)
@@ -2860,6 +2893,26 @@ public double getDoubleValue(Enum<?> variableID) {
                     return dhe_mcs_lag1;
                 } else return 0.;
             }
+            case Dhe_mcs_sq -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_mcs * dhe_mcs;
+            }
+            case Dhe_mcs_c -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_mcs - 51.5;
+            }
+            case Dhe_mcs_c_sq -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return Math.pow(dhe_mcs - 51.5, 2);
+            }
+            case Dhe_mcs_times_pcs -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_mcs * dhe_pcs;
+            }
+            case Dhe_mcs_c_times_pcs_c -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return (dhe_mcs - 51.5) * (dhe_pcs - 49.9);
+            }
             case Dhe_pcs -> {
                 return dhe_pcs;
             }
@@ -2868,6 +2921,22 @@ public double getDoubleValue(Enum<?> variableID) {
                     return dhe_pcs_lag1;
                 } else return 0.;
             }
+            case Dhe_pcs_sq -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_pcs * dhe_pcs;
+            }
+            case Dhe_pcs_cb -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_pcs * dhe_pcs * dhe_pcs;
+            }
+            case Dhe_pcs_c -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return dhe_pcs - 49.9;
+            }
+            case Dhe_pcs_c_sq -> {
+                // Used to calculate he_eq5d in regHealthEQ5D
+                return Math.pow(dhe_pcs - 49.9, 2);
+            }
             case Dls -> {
                 return dls;
             }
@@ -2933,6 +3002,9 @@ public double getDoubleValue(Enum<?> variableID) {
             case Dehsp_c3_Low_L1 -> {
                 return (Education.Low.equals(dehsp_c3_lag1)) ? 1. : 0.;
             }
+            case He_eq5d -> {
+                return getHe_eq5d();
+            }
             case Dhhtp_c4_CoupleChildren_L1 -> {
                 return (Dhhtp_c4.CoupleChildren.equals(getDhhtp_c4_lag1())) ? 1.0 : 0.0;
             }
@@ -4110,6 +4182,15 @@ public double getDhe_pcs() {
         return val;
     }
 
+
+    public void setDhe_mcs(Double dhe_mcs) {
+        this.dhe_mcs = dhe_mcs;
+    }
+
+    public void setDhe_pcs(Double dhe_pcs) {
+        this.dhe_pcs = dhe_pcs;
+    }
+
     public void populateSocialCareReceipt(SocialCareReceiptState state) {
         if (SocialCareReceiptState.NoFormal.equals(state)) {
             needSocialCare = Indicator.True;
@@ -5122,4 +5203,12 @@ public Long getIdFather() {
     public boolean getToBePartnered() {return toBePartnered;}
 
     public static void setPersonIdCounter(long id) {personIdCounter=id;}
+
+    public Double getHe_eq5d() {
+        return he_eq5d;
+    }
+
+    public void setHe_eq5d(Double he_eq5d) {
+        this.he_eq5d = he_eq5d;
+    }
 }

src/main/java/simpaths/model/SimPathsModel.java

@@ -588,6 +588,8 @@ public void buildSchedule() {
         yearlySchedule.addCollectionEvent(persons, Person.Processes.HealthPCS2);
         yearlySchedule.addCollectionEvent(persons, Person.Processes.LifeSatisfaction2);
 
+        addCollectionEventToAllYears(persons, Person.Processes.HealthEQ5D);
+
         // mortality (migration) and population alignment at year's end
         addCollectionEventToAllYears(persons, Person.Processes.ConsiderMortality);
         addEventToAllYears(Processes.PopulationAlignment);

src/test/java/simpaths/model/PersonTest.java

@@ -0,0 +1,110 @@
+package simpaths.model;
+
+import org.junit.jupiter.api.*;
+import simpaths.data.Parameters;
+import simpaths.model.enums.Country;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+@DisplayName("Person class")
+public class PersonTest {
+
+    static Person testPerson;
+
+    @BeforeAll
+    static void setup() {
+        testPerson = new Person(true);
+    }
+
+    @Nested
+    @DisplayName("EQ5D process")
+    class Eq5dTests {
+
+        @Nested
+        @DisplayName("With eq5dConversionParameters set to 'lawrence'")
+        class WithLawrenceParameters {
+
+            @BeforeEach
+            public void setupLawrenceCoefficients() {
+
+                Parameters.eq5dConversionParameters = "lawrence";
+                Parameters.loadEQ5DParameters("UK", 8);
+
+            }
+
+            @Test
+            @DisplayName("Calculates low score correctly using Lawrence and Fleishman coefficients")
+            public void calculatesLowScoreCorrectly() {
+
+
+                testPerson.setDhe_mcs(1.);
+                testPerson.setDhe_pcs(1.);
+
+                testPerson.onEvent(Person.Processes.HealthEQ5D);
+
+                assertEquals(-0.594, testPerson.getHe_eq5d());
+
+            }
+
+            @Test
+            @DisplayName("Calculates high score correctly using Lawrence and Fleishman coefficients")
+            public void calculatesHighScoreCorrectly()  {
+
+
+                testPerson.setDhe_mcs(100.);
+                testPerson.setDhe_pcs(100.);
+
+                testPerson.onEvent(Person.Processes.HealthEQ5D);
+
+                assertEquals(1, testPerson.getHe_eq5d());
+
+            }
+
+        }
+
+
+        @Nested
+        @DisplayName("With eq5dConversionParameters set to 'franks'")
+        class WithFranksParameters {
+
+            @BeforeEach
+            public void setupFranksCoefficients() {
+
+                Parameters.eq5dConversionParameters = "franks";
+                Parameters.loadEQ5DParameters("UK", 8);
+
+            }
+
+
+            @Test
+            @DisplayName("Calculates low score correctly using Franks coefficients")
+            public void calculatesLowScoreCorrectly() {
+
+                testPerson.setDhe_mcs(1.);
+                testPerson.setDhe_pcs(1.);
+
+                testPerson.onEvent(Person.Processes.HealthEQ5D);
+
+                assertEquals(-0.594, testPerson.getHe_eq5d());
+
+            }
+
+            @Test
+            @DisplayName("Calculates high score correctly using Franks coefficients")
+            public void calculatesHighScoreCorrectly(){
+
+                testPerson.setDhe_mcs(100.);
+                testPerson.setDhe_pcs(100.);
+
+                testPerson.onEvent(Person.Processes.HealthEQ5D);
+
+                // The maximum possible value given by the Franks coefficients
+                assertEquals(0.9035601, testPerson.getHe_eq5d());
+
+            }
+
+        }
+    }
+
+
+}