* Encoding: UTF-8.
* Content.
    * Preparation of the data set for further processing.
            **  Renaming of variables.
            **  Labelling of variables.
                    *** Use of health services.
                    *** Perception of medical overuse.
                    *** Reasons for medical overuse.
                    *** Consequences of medical overuse.
                    *** Solutions to reduce medical overuse.
                    *** Demographics.
                    *** Morbidity and health behaviour.
            **  Value definition of variables.
                    *** Use of health services.
                    *** Perception of medical overuse.
                    *** Reasons for medical overuse.
                    *** Consequences of medical overuse.
                    *** Solutions to reduce medical overuse.
                    *** Demographics.
                    *** Morbidity and health behaviour.
    * Checking for completeness of individual participants' records.
            ** Assignment of values for empty variables.
            ** Checking for response to study questions.
            ** Removal of incomplete data sets.
    * Adjustment of the complete data set for the evaluation.
        ** Deletion of unneeded variables.
        ** Creation of grouping variables.
                *** Decision on treatment.
                *** Number of doctors visited.
                *** Number of health problems in general.
                *** Number of chronic diseases.
                *** Number of illnesses for which medicines are taken.
                *** Number of diseases for which doctors are consulted regularly.
        ** Fixing missing values and decimals.
        ** Determination of scale level for Likert scales.
        ** Evaluation of open questions.
    * Descriptive evaluation.
        ** Study population.
        ** Health behaviour.
        ** Morbidity.
        ** Perception of medical overuse.
        ** Reasons for medical overuse.
        ** Consequences of medical overuse.
        ** Solutions to reduce medical overuse.
    * Evaluation of group differences.
        ** Gender.
        ** Knowledge of overuse.
    
* Preparation of the data set for further processing.
GET
    FILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Rohdaten_final_20220406.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.

** Renaming of variables.
RENAME VARIABLES (doctor___0 = doctor_0) (doctor___1 = doctor_1) (doctor___2 = doctor_2) (doctor___3 = doctor_3) (doctor___4 = doctor_4) (doctor___5 = doctor_5) (doctor___6 = doctor_6) (doctor___7 = doctor_7)
    (doctor___8 = doctor_8) ( doctor___9 = doctor_9) (doctor___10 = doctor_10) (doctor___11 = doctor_11) (doctor___12 = doctor_12) (doctor___13 = doctor_13) (doctor___14 = doctor_14).
RENAME VARIABLES (overuse_7___0 = overuse_7_0) (overuse_7___1 = overuse_7_1) (overuse_7___2 = overuse_7_2) (overuse_7___3 = overuse_7_3) (overuse_7___4 = overuse_7_4) (overuse_7___5 = overuse_7_5).
RENAME VARIABLES (solution_11___0 = solution_11_0) (solution_11___1 = solution_11_1) (solution_11___2 = solution_11_2) (solution_11___3 = solution_11_3) (solution_11___4 = solution_11_4) (solution_11___5 = solution_11_5).

RENAME VARIABLES (morbidity___1 = morbidity_1) (morbidity___2 = morbidity_2) (morbidity___3 = morbidity_3) (morbidity___4 = morbidity_4) (morbidity___5 = morbidity_5) (morbidity___6 = morbidity_6) (morbidity___7 = morbidity_7)
    (morbidity___8 = morbidity_8) (morbidity___9 = morbidity_9) (morbidity___10 = morbidity_10) (morbidity___11 = morbidity_11) (morbidity___12 = morbidity_12) (morbidity___13 = morbidity_13) (morbidity___14 = morbidity_14)
    (morbidity___15 = morbidity_15) (morbidity___16 = morbidity_16) (morbidity___17 = morbidity_17) (morbidity___18 = morbidity_18).

RENAME VARIABLES (morbidity_1___0 = morbidity_1_0) (morbidity_1___1 = morbidity_1_1) (morbidity_1___2 = morbidity_1_2) (morbidity_1___3 = morbidity_1_3) (morbidity_2___0 = morbidity_2_0) (morbidity_2___1 = morbidity_2_1)
    (morbidity_2___2 = morbidity_2_2) (morbidity_2___3 = morbidity_2_3) (morbidity_3___0 = morbidity_3_0) (morbidity_3___1 = morbidity_3_1) (morbidity_3___2 = morbidity_3_2) (morbidity_3___3 = morbidity_3_3)
    (morbidity_4___0 = morbidity_4_0) (morbidity_4___1 = morbidity_4_1) (morbidity_4___2 = morbidity_4_2) (morbidity_4___3 = morbidity_4_3)  (morbidity_5___0 = morbidity_5_0) (morbidity_5___1 = morbidity_5_1)
    (morbidity_5___2 = morbidity_5_2) (morbidity_5___3 = morbidity_5_3) (morbidity_6___0 = morbidity_6_0) (morbidity_6___1 = morbidity_6_1) (morbidity_6___2 = morbidity_6_2) (morbidity_6___3 = morbidity_6_3)
    (morbidity_7___0 = morbidity_7_0) (morbidity_7___1 = morbidity_7_1) (morbidity_7___2 = morbidity_7_2) (morbidity_7___3 = morbidity_7_3) (morbidity_8___0 = morbidity_8_0) (morbidity_8___1 = morbidity_8_1)
    (morbidity_8___2 = morbidity_8_2) (morbidity_8___3 = morbidity_8_3) (morbidity_9___0 = morbidity_9_0) (morbidity_9___1 = morbidity_9_1) (morbidity_9___2 = morbidity_9_2) (morbidity_9___3 = morbidity_9_3)
    (morbidity_10___0 = morbidity_10_0) (morbidity_10___1 = morbidity_10_1) (morbidity_10___2 = morbidity_10_2) (morbidity_10___3 = morbidity_10_3) (morbidity_11___0 = morbidity_11_0) (morbidity_11___1 = morbidity_11_1)
    (morbidity_11___2 = morbidity_11_2) (morbidity_11___3 = morbidity_11_3) (morbidity_12___0 = morbidity_12_0) (morbidity_12___1 = morbidity_12_1) (morbidity_12___2 = morbidity_12_2) (morbidity_12___3 = morbidity_12_3)
    (morbidity_13___0 = morbidity_13_0) (morbidity_13___1 = morbidity_13_1) (morbidity_13___2 = morbidity_13_2) (morbidity_13___3 = morbidity_13_3) (morbidity_14___0 = morbidity_14_0) (morbidity_14___1 = morbidity_14_1)
    (morbidity_14___2 = morbidity_14_2) (morbidity_14___3 = morbidity_14_3) (morbidity_15___0 = morbidity_15_0) (morbidity_15___1 = morbidity_15_1) (morbidity_15___2 = morbidity_15_2) (morbidity_15___3 = morbidity_15_3)
    (morbidity_16___0 = morbidity_16_0), (morbidity_16___1 = morbidity_16_1), (morbidity_16___2 = morbidity_16_2), (morbidity_16___3 = morbidity_16_3).

**  Labelling of variables.
*** Use of health services.
VARIABLE LABELS
    insurance 'Insurance status'
    /doctor_0 'No visit to the physician'
    /doctor_1 'Family doctor'
    /doctor_2 'Internist (e.g. cardiologist, gastroenterologist, ...)'
    /doctor_3 'Oncologist'
    /doctor_4 'Orthopedist'
    /doctor_5 'Urologist'
    /doctor_6 'Gynecologist'
    /doctor_7 'Radiologist'
    /doctor_8 'Psychiatrist or psychotherapist'
    /doctor_9 'Ear, nose and throat specialist'
    /doctor_10 'Dermatologist'
    /doctor_11 'Neurologist'
    /doctor_12 'Dentist or oral surgeon'
    /doctor_13 'Eye specialist'
    /doctor_14 'Other speciality'
    /doctor_patient 'Decision about treatment'.

*** Perception of medical overuse.
VARIABLE LABELS
    overuse_1 'Have you ever heard of medical overuse?'
    /overuse_2a 'What do you think overuse means?'
    /overuse_2b 'What do you think could be meant with overuse?'
    /overuse_4a 'Overuse involves treating conditions beyond what is necessary.'
    /overuse_4b 'Overuse means the rapid initiation of medical procedures without waiting for self-healing.'
    /overuse_4c 'Overuse is treatment that would not be strictly necessary for medical reasons.'
    /overuse_4d 'Overuse refers to procedures that are carried out primarily for financial reasons.'
    /overuse_5 'Have you already experienced or perceived overuse according to this definition?'
    /overuse_6 'How would you rate the importance of overuse?'
    /overuse_7_0 'In which medical services do you suspect overuse?: Early detection and screening (e.g. cancer screening)'
    /overuse_7_1 'In which medical services do you suspect overuse?: Prescription of medicine'
    /overuse_7_2 'In which medical services do you suspect overuse?: Surgeries'
    /overuse_7_3 'In which medical services do you suspect overuse?: Individual health services (IGeL)'
    /overuse_7_4 'In which medical services do you suspect overuse?: Imaging procedures (e.g. ultrasound, X-ray, ...)'
    /overuse_7_5 'In which medical services do you suspect overuse?: Blood tests'
    /overuse_8a 'People with private health insurance are more often affected than those with statutory health insurance.'
    /overuse_8b 'People with a higher level of education are more often affected than people with a lower level of education.'
    /overuse_8c 'People with higher income are more often affected than people with lower income.'
    /overuse_8d 'Younger people are more often affected than older people (e.g. pensioners).'.

*** Reasons for medical overuse.
VARIABLE LABELS
    reasons_1 'I perceive a physician as more competent the more tests he performs.'
    /reasons_2 'Willingness to act and actions are more likely to satisfy me than words and waiting.'
    /reasons_3 'In my opinion, it is better to examine more than to miss something.'
    /reasons_4 'Lawsuits lead to overuse, because physicians consequently want to protect themselves diagnostically.'
    /reasons_5 'Clarifying the benefits and harms of tests and treatments to patients fails due to time constraints.'
    /reasons_6 'If medical equipment is available in practices and clinics, it is used.'
    /reasons_7 'When different physicians do not coordinate well in treating a patient, more services are provided.'
    /reasons_8 'Payment for a diagnostic measure/treatment also determines how often it is used.'
    /reasons_9 'Being stressed leads physicians to order tests more quickly.'
    /reasons_10 'Patients also request medical procedures.'
    /reasons_11 'If physicians are inexperienced, they are more likely to order tests.'.

*** Consequences of medical overuse.
VARIABLE LABELS
    cons_1 'I cannot imagine at all that there is really too much medicine.'
    /cons_2 'I do not believe that medicine can cause harm.'
    /cons_3 'The physical well-being of the patients could be endangered.'
    /cons_4 'The mental well-being of patients could be endangered.'
    /cons_5 'Patients might become distrustful towards physicians.'
    /cons_6 'Health care costs could rise, making health insurance premiums more and more expensive.'
    /cons_7 'The more medical treatments and tests are performed, the healthier people stay.'.

*** Solutions to reduce medical overuse.
VARIABLE LABELS
    solution_1 'I think alternative healing methods need to be expanded.'
    /solution_2 'Medical billing must be disclosed and audited more closely.'
    /solution_3 'Patients need neutral information about treatment options.'
    /solution_4 'We need more physicians who each treat a smaller number of patients.'
    /solution_5 'The coexistence of statutory and private health insurance must be abolished.'
    /solution_6 'The exchange between different physicians and treatment providers must be improved.'
    /solution_7 'If patients had to contribute more to the cost of treatment, fewer examinations would be performed.'
    /solution_8 'It should be mandatory for patients to see their family physician first when they have health problems.'
    /solution_11_0 '"Choosing Wisely"'
    /solution_11_1 '"Less is more"'
    /solution_11_2 '"Klug entscheiden"'
    /solution_11_3 '"Smarter Medicine"'
    /solution_11_4 ' "Quartäre Prävention"'
    /solution_11_5 'None of the above'
    /health_system 'Please think about the health care system in 10 years. Do you think it will be better or worse than it is now?'.

*** Demographics.
VARIABLE LABELS
    gender 'Gender'
    /age_group 'Age groups'
    /qualification 'Professional education'
    /employment 'Employment status'
    /residence 'Number of inhabitants of place of residence'.

*** Morbidity and health behaviour.
VARIABLE LABELS
    morbidity_1 'Heart problems'
    /morbidity_2 'Hypertension'
    /morbidity_3 'Lung problems'
    /morbidity_4 'Diabetes'
    /morbidity_5 'Gastrointestinal problems'
    /morbidity_6 'Kidney problems'
    /morbidity_7 'Liver problems'
    /morbidity_8 'Anemia'
    /morbidity_9 'Coagulation disorder (e.g. thrombosis, embolism)'
    /morbidity_10 'Cancer'
    /morbidity_11 'Depression'
    /morbidity_12 'Arthrosis'
    /morbidity_13 'Back pain'
    /morbidity_14 'Rheumatism or autoimmune disorder'
    /morbidity_15 'Allergies'
    /morbidity_16 'Thyroid disease (hyper- or hypothyriodism'
    /morbidity_17 'None of the above'
    /morbidity_18 'No health problems'.

VARIABLE LABELS
    morbidity_1_0 'Heart problems: none of the above'
    /morbidity_1_1 'Heart problems: chronic disease'
    /morbidity_1_2 'Heart problems: regular medication intake'
    /morbidity_1_3 'Heart problems: regular physician visits'
    /morbidity_2_0 'Hypertension: none of the above'
    /morbidity_2_1 'Hypertension: chronic disease'
    /morbidity_2_2 'Hypertension: regular medication intake'
    /morbidity_2_3 'Hypertension: regular physician visits'
    /morbidity_3_0 'Lung problems: none of the above'
    /morbidity_3_1 'Lung problems: chronic disease'
    /morbidity_3_2 'Lung problems: regular medication intake'
    /morbidity_3_3 'Lung problems: regular physician visits'
    /morbidity_4_0 'Diabetes: none of the above'
    /morbidity_4_1 'Diabetes: chronic disease'
    /morbidity_4_2 'Diabetes: regular medication intake'
    /morbidity_4_3 'Diabetes: regular physician visits'
    /morbidity_5_0 'Gastrointestinal problems: none of the above'
    /morbidity_5_1 'Gastrointestinal problems: chronic disease'
    /morbidity_5_2 'Gastrointestinal problems: regular medication intake'
    /morbidity_5_3 'Gastrointestinal problems: regular physician visits'
    /morbidity_6_0 'Kidney problems: none of the above'
    /morbidity_6_1 'Kidney problems: chronic disease'
    /morbidity_6_2 'Kidney problems: regular medication intake'
    /morbidity_6_3 'Kidney problems: regular physician visits'
    /morbidity_7_0 'Liver problems: none of the above'
    /morbidity_7_1 'Liver problems: chronic disease'
    /morbidity_7_2 'Liver problems: regular medication intake'
    /morbidity_7_3 'Liver problems: regular physician visits'
    /morbidity_8_0 'Anemia: none of the above'
    /morbidity_8_1 'Anemia: chronic disease'
    /morbidity_8_2 'Anemia: regular medication intake'
    /morbidity_8_3 'Anemia: regular physician visits'
    /morbidity_9_0 'Coagulation disorder: none of the above'
    /morbidity_9_1 'Coagulation disorder: chronic disease'
    /morbidity_9_2 'Coagulation disorder: regular medication intake'
    /morbidity_9_3 'Coagulation disorder: regular physician visits'
    /morbidity_10_0 'Cancer: none of the above'
    /morbidity_10_1 'Cancer: chronic disease'
    /morbidity_10_2 'Cancer: regular medication intake'
    /morbidity_10_3 'Cancer: regular physician visits'
    /morbidity_11_0 'Depression: none of the above'
    /morbidity_11_1 'Depression: chronic disease'
    /morbidity_11_2 'Depression: regular medication intake'
    /morbidity_11_3 'Depression: regular physician visits'
    /morbidity_12_0 'Arthrosis: none of the above'
    /morbidity_12_1 'Arthrosis: chronic disease'
    /morbidity_12_2 'Arthrosis: regular medication intake'
    /morbidity_12_3 'Arthrosis: regular physician visits'
    /morbidity_13_0 'Back pain: none of the above'
    /morbidity_13_1 'Back pain: chronic disease'
    /morbidity_13_2 'Back pain: regular medication intake'
    /morbidity_13_3 'Back pain: regular physician visits'
    /morbidity_14_0 'Rheumatism or autoimmune disorder: none of the above'
    /morbidity_14_1 'Rheumatism or autoimmune disorder: chronic disease'
    /morbidity_14_2 'Rheumatism or autoimmune disorder: regular medication intake'
    /morbidity_14_3 'Rheumatism or autoimmune disorder: regular physician visits'
    /morbidity_15_0 'Allergies: none of the above'
    /morbidity_15_1 'Allergies: chronic disease'
    /morbidity_15_2 'Allergies: regular medication intake'
    /morbidity_15_3 'Allergies: regular physician visits'
    /morbidity_16_0 'Thyroid disease: none of the above'
    /morbidity_16_1 'Thyroid disease: chronic disease'
    /morbidity_16_2 'Thyroid disease: regular medication intake'
    /morbidity_16_3 'Thyroid disease: regular physician visits'.

**  Value definition of variables.
*** Use of health services.
VALUE LABELS insurance
    0 'Statutory'
    1 'Private'.
VALUE LABELS doctor_0 TO doctor_14
    0 'No'
    1 'Yes'.

*** Perception of medical overuse.
VALUE LABELS overuse_1
    0 'No'
    1 'Yes'.
VALUE LABELS overuse_4a TO overuse_4d
    1 'Totally disagree'
    2 'Rather disagree'
    3 'Rather agree'
    4 'Totally agree'.
VALUE LABELS overuse_5
    0 'No'
    1 'Yes'.
VALUE LABELS overuse_6
    0 'Medical overuse does not exist in our health system.'
    1 'Medical overuse exists but it does not have a negative impact on healthcare provision.'
    2 'There are other issues that need to be addressed.'
    3 'Less medical overuse would improve our health system.'.
VALUE LABELS overuse_7_0 TO overuse_7_5
    0 'No'
    1 'Yes'.
VALUE LABELS overuse_8a TO overuse_8d
    1 'Totally disagree'
    2 'Rather disagree'
    3 'Rather agree'
    4 'Totally agree'.

*** Reasons for medical overuse.
VALUE LABELS reasons_1 TO reasons_11
    1 'Totally disagree'
    2 'Rather disagree'
    3 'Rather agree'
    4 'Totally agree'.

*** Consequences of medical overuse.
VALUE LABELS cons_1 TO cons_7
    1 'Totally disagree'
    2 'Rather disagree'
    3 'Rather agree'
    4 'Totally agree'.

*** Solutions to reduce medical overuse.
VALUE LABELS solution_1 TO solution_8
    1 'Totally disagree'
    2 'Rather disagree'
    3 'Rather agree'
    4 'Totally agree'.
VALUE LABELS solution_11_0 TO solution_11_4
    0 'No'
    1 'Yes'.
VALUE LABELS solution_11_5
    0 'No'
    1 'Yes'.
VALUE LABELS health_system
    0 'System will be significantly worse'
    1 'System will be slightly worse'
    2 'System will be just as good as now'
    3 'System will be slightly better'
    4 'System will be significantly better'.

*** Demographics.
VALUE LABELS gender
    0 'Female'
    1 'Male'
    2 'Diverse'.
VALUE LABELS age_group
    0 '18 to 24 years'
    1 '25 to 44 years'
    2 '45 to 64 years'
    3 '65 or older'.
VALUE LABELS qualification
    0 'No professional training (yet)'
    1 'Professional training'
    2 '(Technical) university degree (Bachelor, Master, Diploma, ...)'.
VALUE LABELS employment
    0 'Not employed'
    1 'In professional training/student'
    2 'Employed (as exployee/worker, civil servant, including mini-job)'
    3 'Self-employed'
    4 'Retired'.
VALUE LABELS residence
    0 'Under 5.000'
    1 '5.000 - 20.000'
    2 '20.000 - 100.000'
    3 'Over 100.000'.

*** Morbidity and health behaviour.
VALUE LABELS morbidity_1 TO morbidity_16
    0 'No'
    1 'Yes'.
VALUE LABELS morbidity_17 morbidity_18
    0 'No'
    1 'Yes'.
VALUE LABELS morbidity_1_1 TO morbidity_16_0
    0 'No'
    1 'Yes'.

* Checking for completeness of individual participants' records.
** Assignment of values for empty variables.
RECODE agreement TO overuse_1 (sysmis=-99).
RECODE overuse_4a TO morbidity_15___0 (sysmis=-99).
RECODE studie_zu_berversorgung_fb_timestamp overuse_2a overuse_2b (' '='fehlend').
EXECUTE.

** Checking for response to study questions.
SELECT IF (agreement EQ 1) OR (end_1 EQ 1) OR (agreement_2 EQ 1).
EXECUTE.

COMPUTE dummy_1 = ANY(-99, insurance, doctor___0, doctor___1, doctor___2, doctor___3, doctor___4, doctor___5, doctor___6, doctor___7, doctor___8, doctor___9, doctor___10, doctor___11, doctor___12, doctor___13, doctor___14, doctor_patient,
    overuse_1, overuse_4a, overuse_4b, overuse_4c, overuse_4d, overuse_5, overuse_6, overuse_7___0, overuse_7___1, overuse_7___2, overuse_7___3, overuse_7___4, overuse_7___5, overuse_8a, overuse_8b, overuse_8c, overuse_8d, reasons_1,
    reasons_2, reasons_3, reasons_4, reasons_5, reasons_6, reasons_7, reasons_8, reasons_9, reasons_10, reasons_11, cons_1, cons_2, cons_3, cons_4, cons_5, cons_6, cons_7, solution_1, solution_2, solution_3, solution_4, solution_5,
    solution_6, solution_7, solution_8, solution_11___0, solution_11___1, solution_11___2, solution_11___3, solution_11___4, solution_11___5, health_system, gender, age_group, qualification, employment, residence, morbidity___1,
    morbidity___2, morbidity___3, morbidity___4, morbidity___5, morbidity___6, morbidity___7, morbidity___8, morbidity___9, morbidity___10, morbidity___11, morbidity___12, morbidity___13, morbidity___14, morbidity___15, morbidity___16,
    morbidity___17, morbidity___18, morbidity_1___0, morbidity_1___1, morbidity_1___2, morbidity_1___3, morbidity_2___0, morbidity_2___1, morbidity_2___2, morbidity_2___3, morbidity_3___0, morbidity_3___1, morbidity_3___2, morbidity_3___3,
    morbidity_4___0, morbidity_4___1, morbidity_4___2, morbidity_4___3, morbidity_5___0, morbidity_5___1, morbidity_5___2, morbidity_5___3, morbidity_6___0, morbidity_6___1, morbidity_6___2, morbidity_6___3, morbidity_7___0,
    morbidity_7___1,  morbidity_7___2, morbidity_7___3, morbidity_8___0, morbidity_8___1, morbidity_8___2, morbidity_8___3, morbidity_9___0, morbidity_9___1, morbidity_9___2, morbidity_9___3, morbidity_10___0, morbidity_10___1,
    morbidity_10___2, morbidity_10___3, morbidity_11___0, morbidity_11___1, morbidity_11___2, morbidity_11___3, morbidity_12___0, morbidity_12___1, morbidity_12___2, morbidity_12___3, morbidity_13___0, morbidity_13___1, morbidity_13___2,
    morbidity_13___3, morbidity_14___0, morbidity_14___1, morbidity_14___2, morbidity_14___3, morbidity_15___0, morbidity_15___1, morbidity_15___2, morbidity_15___3, morbidity_16___0, morbidity_16___1, morbidity_16___2, morbidity_16___3).
EXECUTE.
VALUE LABELS dummy_1
    0 'kein Missing' 1 'Missing vorhanden'.
** Removal of incomplete data sets.
SELECT IF dummy_1 NE 1 .
EXECUTE.

* Adjustment of the complete data set for the evaluation.
** Deletion of unneeded variables.
DELETE VARIABLES redcap_survey_identifier TO agreement_2.
DELETE VARIABLES overuse_2a overuse_2b.
DELETE VARIABLES end_2a TO dummy_1.
EXECUTE.

** Creation of grouping variables.
*** Decision on treatment.
IF (doctor_patient LE 33) doctor_pat_gr = 0.
IF ((doctor_patient GT 33) AND (doctor_patient LE 66)) doctor_pat_gr = 1.
IF (doctor_patient GT 66) doctor_pat_gr = 2.
EXECUTE.

VARIABLE LABELS doctor_pat_gr
    'Entscheidung über Behandlung'.

VALUE LABELS doctor_pat_gr
    0 'Physician always decides.'
    1 'Physician and patient decide together.'
    2 'Patient always decides'.

*** Number of doctors visited.
COMPUTE doctor_sum = SUM (doctor_1 TO doctor_14).
EXECUTE.

VARIABLE LABELS doctor_sum
    'Number of physicians visited per patient (last 3 months)'.

IF (doctor_sum EQ 0) doctor_sum_gr = 0.
IF (doctor_sum GE 1) AND (doctor_sum LE 2) doctor_sum_gr = 1.
IF (doctor_sum GE 3) doctor_sum_gr = 2.
EXECUTE.

VARIABLE LABELS doctor_sum_gr
    'Number of physicians visited - grouped'.

VALUE LABELS doctor_sum_gr
    0 'No physicians visits'
    1 '1-2 physicians visits'
    2 '3 or more physicians visits'.

*** Number of health problems in general.
COMPUTE morb_sum = SUM (morbidity_1 TO morbidity_17).
EXECUTE.

VARIABLE LABELS morb_sum
    'Number of health problems'.

IF (morb_sum EQ 0) morb_sum_gr = 0.
IF (morb_sum GE 1) AND (morb_sum LE 2) morb_sum_gr = 1.
IF (morb_sum GE 3) morb_sum_gr = 2.
EXECUTE.

VARIABLE LABELS morb_sum_gr
    'Number of health problems - grouped'.

VALUE LABELS morb_sum_gr
    0 'No health problems'
    1 '1-2 health problems'
    2 '3 or more health problems'.

*** Number of chronic diseases.
IF (morb_sum EQ 0) chron_sum = -99.
IF (morb_sum GT 0) chron_sum = SUM (morbidity_1_1, morbidity_2_1, morbidity_3_1, morbidity_4_1, morbidity_5_1, morbidity_6_1, morbidity_7_1, morbidity_8_1, morbidity_9_1, morbidity_10_1,
    morbidity_11_1, morbidity_12_1, morbidity_13_1, morbidity_14_1, morbidity_15_1, morbidity_16_1).
EXECUTE.

VARIABLE LABELS chron_sum
    'Number of chronic diseases'.

IF (chron_sum EQ -99) chron_sum_gr = -99.
IF (chron_sum EQ 0) chron_sum_gr = 0.
IF (chron_sum EQ 1) chron_sum_gr= 1.
IF (chron_sum GE 2) chron_sum_gr = 2.
EXECUTE.

VARIABLE LABELS chron_sum_gr
    'Number of chronic diseases - grouped'.

VALUE LABELS chron_sum_gr
    0 'No chronic health problems'
    1 '1 chronic health problems'
    2 '2 or more chronic health problems'.

*** Number of illnesses for which medicines are taken.
IF (morb_sum EQ 0) medis_sum = -99.
IF (morb_sum GT 0) medis_sum = SUM (morbidity_1_2, morbidity_2_2, morbidity_3_2, morbidity_4_2, morbidity_5_2, morbidity_6_2, morbidity_7_2, morbidity_8_2, morbidity_9_2, morbidity_10_2,
    morbidity_11_2, morbidity_12_2, morbidity_13_2, morbidity_14_2, morbidity_15_2, morbidity_16_2).
EXECUTE.

VARIABLE LABELS medis_sum
    'Number of health problems with need of medication intake'.

IF (medis_sum EQ -99) medis_sum_gr = -99.
IF (medis_sum EQ 0) medis_sum_gr = 0.
IF (medis_sum EQ 1) medis_sum_gr= 1.
IF (medis_sum GE 2) medis_sum_gr = 2.
EXECUTE.

VARIABLE LABELS medis_sum_gr
    'Number of health problems with need of medication intake - grouped'.

VALUE LABELS medis_sum_gr
    0 'No health problem with need of medication intake'
    1 '1 health problem with need of medication intake'
    2 '2 or more health problems with need of medication intake'.

*** Number of diseases for which doctors are consulted regularly.
IF (morb_sum EQ 0) visit_sum = -99.
IF (morb_sum GT 0) visit_sum = SUM (morbidity_1_3, morbidity_2_3, morbidity_3_3, morbidity_4_3, morbidity_5_3, morbidity_6_3, morbidity_7_3, morbidity_8_3, morbidity_9_3, morbidity_10_3,
    morbidity_11_3, morbidity_12_3, morbidity_13_3, morbidity_14_3, morbidity_15_3, morbidity_16_3).
EXECUTE.

VARIABLE LABELS visit_sum
    'Number of health problems with need of regular physician visits'.

IF (visit_sum EQ -99) visit_sum_gr = -99.
IF (visit_sum EQ 0) visit_sum_gr = 0.
IF (visit_sum EQ 1) visit_sum_gr = 1.
IF (visit_sum GE 2) visit_sum_gr = 4.
EXECUTE.

VARIABLE LABELS visit_sum_gr
    'Number of health problems with need of regular physician visits - grouped'.

VALUE LABELS visit_sum_gr
    0 'No health problem with need of regular physician visits'
    1 '1 health problem with need of regular physician visits'
    2 '2 or more health problems with need of regular physician visits'.

** Fixing missing values and decimals.
MISSING VALUES ALL (-99).
ALTER TYPE ALL (F6.0).

** Determination of scale level for Likert scales.
VARIABLE LEVEL doctor_sum doctor_patient overuse_4a TO overuse_4d overuse_8a TO solution_8 doctor_sum morb_sum chron_sum medis_sum visit_sum overuse_4_sum TO solution_sum (SCALE).

SAVE OUTFILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Datensatz_20220407.sav'
    /COMPRESSED.

 ** Evaluation of open questions.
GET
    FILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Datensatz_20220407.sav'
    /KEEP record_id overuse_2a overuse_2b.
DATASET NAME DataSet1 WINDOW=FRONT.

SAVE OUTFILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Offene_Fragen_20220414.sav'
    /COMPRESSED.

COMPUTE overuse_2_gr = -99.
EXECUTE.

VARIABLE LABELS overuse_2_gr
    'Definition of medical overuse - grouped'.

VALUE LABELS overuse_2_gr
    0 'No definition'
    1 'Wrong definition'
    2 '"More physicians than needed"'
    3 '"Too much medicine, incl. overtesting and overtreatment'.

/* manual assignment to the groups.

SAVE OUTFILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Offene_Fragen_20220414.sav'
    /COMPRESSED.

GET FILE='V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Offene_Fragen_20220414.sav'.
DATASET NAME DataSet2.
DATASET ACTIVATE DataSet1.
SORT CASES BY record_id.
DATASET ACTIVATE DataSet2.
SORT CASES BY record_id.
DATASET ACTIVATE DataSet1.
MATCH FILES /FILE=*
    /TABLE='DataSet2'
    /BY record_id
    /DROP overuse_2a overuse_2b.
EXECUTE.

SAVE OUTFILE = 'V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Datensatz_20220407.sav'
    /COMPRESSED.

* Descriptive evaluation.
GET
    FILE='V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Datensatz_20220407.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.

** Study population.
FREQUENCIES Variables = insurance gender age_group_2 qualification_2 employment residence
    /ORDER = Analysis.

** Health behaviour.
FREQUENCIES Variables = doctor_0 TO doctor_14 doctor_sum_gr_2 doctor_pat_gr
    /ORDER = Analysis.

FREQUENCIES Variables = doctor_sum morb_sum
    /NTILES 4
    /ORDER = Analysis.

FREQUENCIES Variables = doctor_patient
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

** Morbidity.
FREQUENCIES Variables = morbidity_1 morbidity_2 morbidity_3 morbidity_4 morbidity_5 morbidity_6 morbidity_7 morbidity_8 morbidity_9 morbidity_10 morbidity_11 morbidity_12 morbidity_13 morbidity_14 morbidity_15
    morbidity_16 morbidity_17 morbidity_18
    /ORDER = Analysis.

FREQUENCIES Variables = morbidity_1_1 morbidity_2_1 morbidity_3_1 morbidity_4_1 morbidity_5_1 morbidity_6_1 morbidity_7_1 morbidity_8_1 morbidity_9_1 morbidity_10_1 morbidity_11_1 morbidity_12_1 morbidity_13_1
    morbidity_14_1 morbidity_15_1 morbidity_16_1
    /ORDER = Analysis.

FREQUENCIES Variables = morbidity_1_2 morbidity_2_2 morbidity_3_2 morbidity_4_2 morbidity_5_2 morbidity_6_2 morbidity_7_2 morbidity_8_2 morbidity_9_2 morbidity_10_2 morbidity_11_2 morbidity_12_2 morbidity_13_2
    morbidity_14_2 morbidity_15_2 morbidity_16_2
    /ORDER = Analysis.

FREQUENCIES Variables = morbidity_1_3 morbidity_2_3 morbidity_3_3 morbidity_4_3 morbidity_5_3 morbidity_6_3 morbidity_7_3 morbidity_8_3 morbidity_9_3 morbidity_10_3 morbidity_11_3 morbidity_12_3 morbidity_13_3
    morbidity_14_3 morbidity_15_3 morbidity_16_3
    /ORDER = Analysis.

FREQUENCIES Variables = morb_sum chron_sum medis_sum visit_sum
    /STATISTICS = MEAN STDDEV
    /NTILES 4
    /ORDER = Analysis.

FREQUENCIES Variables = morb_sum_gr
    /ORDER = Analysis.

** Perception of medical overuse.
FREQUENCIES Variables = overuse_1 overuse_4a overuse_4b overuse_4c overuse_4d overuse_5
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

FREQUENCIES Variables = overuse_2_gr overuse_6 overuse_7_0 overuse_7_1 overuse_7_2 overuse_7_3 overuse_7_4 overuse_7_5
    /ORDER = Analysis.

FREQUENCIES Variables = overuse_8a overuse_8b overuse_8c overuse_8d
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

** Reasons for medical overuse.
FREQUENCIES Variables = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11 reasons_sum
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

** Consequences of medical overuse.
FREQUENCIES Variables = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7 cons_sum
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

** Solutions to reduce medical overuse.

FREQUENCIES Variables = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8
    /STATISTICS = MEAN STDDEV MINIMUM MAXIMUM
    /NTILES 4
    /ORDER = Analysis.

FREQUENCIES Variables = solution_11_0 solution_11_1 solution_11_2 solution_11_3 solution_11_4 solution_11_5 health_system
    /ORDER = Analysis.

* Evaluation of group differences.
 GET
            FILE='V:\Forschung\02 Qualifizierungsarbeiten\37 Nürnberger SU\06 Datenerhebung\04_Rohdaten\02_Hauptstudie\Datensatz_20220407.sav'.
        DATASET NAME DataSet1 WINDOW=FRONT.

** Gender.
NPAR TESTS
    /M-W = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11 BY gender (0 1)
    /K-S = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11 BY gender (0 1)
    /MISSING ANALYSIS.

NPAR TESTS
    /M-W = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7 BY gender (0 1)
    /K-S = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7 BY gender (0 1)
    /MISSING ANALYSIS.

NPAR TESTS
    /M-W = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8 BY gender (0 1)
    /K-S = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8 BY gender (0 1)
    /MISSING ANALYSIS.

SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
FREQUENCIES Variables = overuse_4a overuse_4b overuse_4c overuse_4d
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

SPLIT FILE OFF.

** Knowledge of overuse.
NPAR TESTS
    /M-W = overuse_4a overuse_4b overuse_4c overuse_4d BY overuse_1 (0 1)
    /K-S = overuse_4a overuse_4b overuse_4c overuse_4d BY overuse_1 (0 1)
    /MISSING ANALYSIS.

NPAR TESTS
    /M-W = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11 BY overuse_1 (0 1)
    /K-S = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11 BY overuse_1 (0 1)
    /MISSING ANALYSIS.

NPAR TESTS
    /M-W = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7 BY overuse_1 (0 1)
    /K-S = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7 BY overuse_1 (0 1)
    /MISSING ANALYSIS.

NPAR TESTS
    /M-W = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8 BY overuse_1 (0 1)
    /K-S = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8 BY overuse_1 (0 1)
    /MISSING ANALYSIS.


SORT CASES BY overuse_1.
SPLIT FILE LAYERED BY overuse_1.

FREQUENCIES Variables = overuse_4a overuse_4b overuse_4c overuse_4d
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = reasons_1 reasons_2 reasons_3 reasons_4 reasons_5 reasons_6 reasons_7 reasons_8 reasons_9 reasons_10 reasons_11
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = cons_1R cons_2R cons_3 cons_4 cons_5 cons_6 cons_7
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

FREQUENCIES Variables = solution_1 solution_2 solution_3 solution_4 solution_5 solution_6 solution_7 solution_8
    /STATISTICS = MEDIAN
    /FORMAT = NOTABLE
    /ORDER = Analysis.

SPLIT FILE OFF.