"""Modelo presupuestario incremental del Plan Sanidad 2036 de CONVOCA. El modelo no sustituye una memoria económica de la Administración. Construye un escenario reproducible, en euros constantes de 2026, y separa: * escenario base (financiación y capacidad ya existentes); * gasto incremental atribuible a CONVOCA; * gasto recurrente y temporal; * stock al cierre de año y gasto/FTE medio cuando corresponde; * escenarios bajo, central y alto. Las hipótesis de política son explícitas y editables. Las cantidades oficiales y sus fuentes se documentan en el libro de cálculo y en la memoria metodológica. """ from __future__ import annotations from dataclasses import dataclass from typing import Dict, Iterable, Mapping YEARS = list(range(2027, 2037)) POP_M = { 2027: 50.1, 2028: 50.6, 2029: 51.0, 2030: 51.4, 2031: 51.8, 2032: 52.1, 2033: 52.4, 2034: 52.7, 2035: 52.9, 2036: 53.1, } @dataclass(frozen=True) class Scenario: name: str # M1 mf_panel: int ap_support_modules_per_100k: float ap_diagnostics_eur_per_resident: float ap_overlap_deduction_m: float # M2 m2_rest_unit_cost: float m2_reserve_share: float m2_coordination_m: float m2_travel_m: float # M3: incremental annual cohorts above the official/base offer fse_add_mf: int fse_add_psychiatry: int fse_add_child_psychiatry: int fse_add_pir: int fse_add_eir_family: int fse_add_eir_mental: int fse_add_eir_geriatric: int hard_posts: int hard_post_bonus: float homologation_m: float workforce_planning_m: float # M4: incremental community staffing per 100k sm_psychologists: float sm_psychiatrists: float sm_nurses: float sm_social_workers: float sm_special_team_multiplier: float sm_programmes_m: float sm_infra_share: float # M5 home_modules_per_100k: float telemonitor_share: float telemonitor_eur: float care_plan_eur_per_ap_site: float carer_episodes: int carer_episode_eur: float palliative_upgrades: int # M6 public_health_modules_per_100k: float prevention_eur_per_resident: float vaccine_outreach_eur_per_resident: float climate_school_eur_per_ap_site: float screening_evaluation_m: float public_health_overlap_m: float # M7 dental_target_children: float dental_target_65plus: float dental_target_pregnancy: float dental_target_priority: float dental_unit_cost: float mobile_units: int transport_episodes: int territorial_network_m: float cohesion_fund_m: float optical_evaluation_m: float # M8 digital_capex_ap_site: float digital_capex_hospital: float digital_platform_m: float digital_opex_share: float nondigital_admin_fte: int digital_governance_m: float SCENARIOS: Dict[str, Scenario] = { "Bajo": Scenario( "Bajo", 1300, 0.75, 3.0, 60.0, 3500.0, 0.10, 36.0, 5.0, 100, 20, 10, 50, 100, 50, 25, 5000, 8000.0, 12.0, 5.0, 1.0, 0.5, 1.0, 0.5, 0.5, 50.0, 0.10, 0.75, 0.005, 200.0, 3000.0, 50000, 400.0, 80, 0.75, 3.0, 1.0, 3000.0, 25.0, 15.0, 0.50, 0.20, 0.50, 0.40, 55.0, 60, 50000, 54.0, 100.0, 3.0, 10000.0, 250000.0, 60.0, 0.15, 180, 15.0, ), "Central": Scenario( "Central", 1250, 1.0, 5.0, 75.0, 6699.0, 0.25, 54.0, 10.0, 250, 50, 25, 100, 250, 100, 50, 10000, 10000.0, 18.0, 10.0, 2.0, 0.75, 1.5, 0.75, 1.0, 75.0, 0.15, 1.0, 0.010, 300.0, 5000.0, 100000, 500.0, 132, 1.0, 5.0, 2.0, 5000.0, 50.0, 25.0, 0.70, 0.35, 0.70, 0.60, 60.0, 100, 100000, 90.0, 150.0, 5.0, 20000.0, 500000.0, 100.0, 0.20, 360, 25.0, ), "Alto": Scenario( "Alto", 1200, 1.25, 8.0, 90.0, 8758.7, 0.40, 72.0, 20.0, 400, 80, 40, 150, 400, 150, 75, 15000, 12000.0, 30.0, 15.0, 3.0, 1.0, 2.0, 1.0, 1.5, 120.0, 0.20, 1.5, 0.020, 450.0, 8000.0, 150000, 750.0, 200, 1.5, 8.0, 3.5, 8000.0, 100.0, 35.0, 0.90, 0.50, 0.90, 0.80, 70.0, 150, 180000, 144.0, 250.0, 10.0, 35000.0, 800000.0, 180.0, 0.25, 600, 40.0, ), } # Retribuciones anuales brutas 2026 de la tabla del SAS, multiplicadas por una # carga empresarial del 31 %. No incluyen guardias salvo que se indique. EMPLOYER_FACTOR = 1.31 PAY = { "family_doctor": 53_750.98 * EMPLOYER_FACTOR, "psychiatrist": 63_888.48 * EMPLOYER_FACTOR, "psychologist": 53_888.33 * EMPLOYER_FACTOR, "nurse_ap": 35_861.27 * EMPLOYER_FACTOR, "nurse_mental": 40_928.37 * EMPLOYER_FACTOR, "physiotherapist": 35_861.27 * EMPLOYER_FACTOR, "social_worker": 35_262.89 * EMPLOYER_FACTOR, "administrator": 27_443.80 * EMPLOYER_FACTOR, "public_health": 52_514.12 * EMPLOYER_FACTOR, "home_doctor": 63_997.99 * EMPLOYER_FACTOR, } RAMP = { "M1": {2027: .20, 2028: .45, 2029: .75, 2030: 1.0}, "M2": {2027: .10, 2028: .30, 2029: .60, 2030: 1.0}, "M4": {2027: .10, 2028: .25, 2029: .45, 2030: .65, 2031: .85, 2032: 1.0}, "M5": {2027: .10, 2028: .25, 2029: .45, 2030: .65, 2031: .85, 2032: 1.0}, "M6": {2027: .20, 2028: .40, 2029: .60, 2030: .80, 2031: 1.0}, "M7": {2027: .15, 2028: .30, 2029: .50, 2030: .70, 2031: .85, 2032: 1.0}, } def ramp(measure: str, year: int) -> float: points = RAMP[measure] eligible = [y for y in points if y <= year] return points[max(eligible)] if eligible else 0.0 def m1(s: Scenario, year: int) -> Dict[str, float]: adult_assigned = 31_241 * 1_349 new_mf_full = max(0.0, adult_assigned / s.mf_panel - 31_241) new_nurse_full = max(0.0, 31_241 + 6_719 + new_mf_full - 36_424) modules_full = POP_M[year] * s.ap_support_modules_per_100k * 10 admin_full = new_mf_full * .5 maturity = ramp("M1", year) pop_factor = POP_M[year] / POP_M[2032] workforce = maturity * pop_factor * ( new_mf_full * PAY["family_doctor"] + new_nurse_full * PAY["nurse_ap"] + admin_full * PAY["administrator"] + modules_full * ( PAY["physiotherapist"] + .5 * PAY["public_health"] + .5 * PAY["social_worker"] ) ) / 1e6 diagnostics = maturity * POP_M[year] * s.ap_diagnostics_eur_per_resident full_incremental_fte = new_mf_full + new_nurse_full + admin_full + 2 * modules_full capex_total = full_incremental_fte * 30_000 / 1e6 capex = capex_total / 4 if year <= 2030 else 0.0 maintenance = capex_total * .10 if year >= 2031 else 0.0 overlap = min(s.ap_overlap_deduction_m * maturity, workforce + diagnostics + capex + maintenance) return { "personal": workforce, "diagnóstico y actividad": diagnostics, "espacios y equipos": capex, "mantenimiento": maintenance, "descuento programas existentes": -overlap, } def m2_backlog_cost(s: Scenario) -> float: # Pacientes aproximados: porcentajes nacionales redondeados aplicados a # denominadores oficiales del SISLE, diciembre de 2025. unit_costs = { "Bajo": { "cataract": 1_027.98, "hip": 5_639.54, "knee": 7_044.53, "valvular": 27_527.354515, "bypass": 23_704.399830, }, "Central": { "cataract": 1_027.98, "hip": 8_720.029406, "knee": 7_988.955789, "valvular": 29_563.9, "bypass": 25_431.6, }, "Alto": { "cataract": 1_027.98, "hip": 8_720.029406, "knee": 7_988.955789, "valvular": 42_493.996721, "bypass": 29_013.544531, }, }[s.name] cataract = 124_195 * .026 * unit_costs["cataract"] hip = 15_256 * .137 * unit_costs["hip"] knee = 29_312 * .159 * unit_costs["knee"] valvular_unit = unit_costs["valvular"] bypass_unit = unit_costs["bypass"] valvular = 1_676 * .069 * valvular_unit bypass = 253 * .016 * bypass_unit guaranteed = cataract + hip + knee + valvular + bypass # El resto es una cantidad derivada y aproximada por el redondeo de los # porcentajes de los cinco procesos garantizados. guaranteed_patients = ( 124_195 * .026 + 15_256 * .137 + 29_312 * .159 + 1_676 * .069 + 253 * .016 ) rest_patients = 184_715 - guaranteed_patients return (guaranteed + rest_patients * s.m2_rest_unit_cost) / 1e6 def m2(s: Scenario, year: int) -> Dict[str, float]: clearance = {2027: .25, 2028: .30, 2029: .30, 2030: .15}.get(year, 0.0) backlog = m2_backlog_cost(s) structural = backlog * s.m2_reserve_share * ramp("M2", year) return { "absorción backlog 2025": backlog * clearance, "reserva estructural": structural, "coordinación y transparencia": s.m2_coordination_m * ramp("M2", year), "alternativa pública y desplazamientos": s.m2_travel_m * ramp("M2", year), } FSE_DURATION = { "mf": 4, "psychiatry": 5, "child_psychiatry": 5, "pir": 4, "eir_family": 2, "eir_mental": 2, "eir_geriatric": 2, } def cohort_additions(s: Scenario) -> Dict[str, int]: return { "mf": s.fse_add_mf, "psychiatry": s.fse_add_psychiatry, "child_psychiatry": s.fse_add_child_psychiatry, "pir": s.fse_add_pir, "eir_family": s.fse_add_eir_family, "eir_mental": s.fse_add_eir_mental, "eir_geriatric": s.fse_add_eir_geriatric, } def cohort_size(s: Scenario, specialty: str, start_year: int) -> float: if start_year < 2028: return 0.0 factor = .5 if start_year == 2028 else 1.0 return cohort_additions(s)[specialty] * factor def active_extra_residents(s: Scenario, specialty: str, year: int) -> float: duration = FSE_DURATION[specialty] return sum( cohort_size(s, specialty, start) for start in YEARS if start <= year < start + duration ) def cumulative_extra_graduates(s: Scenario, specialty: str, year: int) -> float: duration = FSE_DURATION[specialty] return sum( cohort_size(s, specialty, start) for start in YEARS if start + duration <= year ) def m3(s: Scenario, year: int) -> Dict[str, float]: medical = sum(active_extra_residents(s, x, year) for x in ("mf", "psychiatry", "child_psychiatry", "pir")) nursing = sum(active_extra_residents(s, x, year) for x in ("eir_family", "eir_mental", "eir_geriatric")) training = (medical * 56_000 + nursing * 41_000) / 1e6 maturity = min(1.0, max(0.0, (year - 2026) / 4)) return { "plazas FSE adicionales activas": training, "incentivos difícil cobertura": s.hard_posts * s.hard_post_bonus / 1e6 * maturity, "homologación y evaluación": s.homologation_m * maturity, "planificación, REPS y transparencia": s.workforce_planning_m * maturity, } def m4(s: Scenario, year: int) -> Dict[str, float]: maturity = ramp("M4", year) units_100k = POP_M[year] * 10 core = units_100k * ( s.sm_psychologists * PAY["psychologist"] + s.sm_psychiatrists * PAY["psychiatrist"] + s.sm_nurses * PAY["nurse_mental"] + s.sm_social_workers * PAY["social_worker"] ) / 1e6 special_teams = POP_M[year] * 4 * s.sm_special_team_multiplier special_cost = special_teams * ( PAY["psychologist"] + .5 * PAY["psychiatrist"] + PAY["nurse_mental"] + .5 * PAY["social_worker"] ) / 1e6 staff = maturity * (core + special_cost) gross = staff + staff * s.sm_infra_share + s.sm_programmes_m * maturity overlap = min(39.0 * maturity, gross) return { "red comunitaria multidisciplinar": staff, "espacios, grupos y equipamiento": staff * s.sm_infra_share, "crisis, infancia, adicciones y continuidad": s.sm_programmes_m * maturity, "descuento Plan Salud Mental vigente": -overlap, } def m5(s: Scenario, year: int) -> Dict[str, float]: maturity = ramp("M5", year) modules = POP_M[year] * 10 * s.home_modules_per_100k home_staff = modules * ( .5 * PAY["home_doctor"] + 1.5 * PAY["nurse_ap"] + .5 * PAY["physiotherapist"] + .5 * PAY["social_worker"] + .5 * PAY["administrator"] ) / 1e6 telemonitor = POP_M[year] * 1e6 * s.telemonitor_share * s.telemonitor_eur / 1e6 care_plan = 13_026 * s.care_plan_eur_per_ap_site / 1e6 respite = s.carer_episodes * s.carer_episode_eur / 1e6 palliative = s.palliative_upgrades * .5 * (PAY["psychologist"] + PAY["social_worker"]) / 1e6 return { "equipos de atención en casa": home_staff * maturity, "telemonitorización con alternativa": telemonitor * maturity, "plan único y coordinación": care_plan * maturity, "apoyo y respiro a cuidadores": respite * maturity, "completar equipos paliativos": palliative * maturity, } def m6(s: Scenario, year: int) -> Dict[str, float]: maturity = ramp("M6", year) modules = POP_M[year] * 10 * s.public_health_modules_per_100k staff = modules * (PAY["public_health"] + PAY["nurse_ap"] + PAY["administrator"]) / 1e6 prevention = POP_M[year] * s.prevention_eur_per_resident vaccines = POP_M[year] * s.vaccine_outreach_eur_per_resident climate_school = 13_026 * s.climate_school_eur_per_ap_site / 1e6 gross = maturity * (staff + prevention + vaccines + climate_school + s.screening_evaluation_m) return { "equipos territoriales de salud pública": staff * maturity, "programas comunitarios de prevención": prevention * maturity, "vacunación y captación activa": vaccines * maturity, "escuela, comunidad y clima": climate_school * maturity, "evaluación y cribados basados en evidencia": s.screening_evaluation_m * maturity, "descuento programas solapados": -min(s.public_health_overlap_m * maturity, gross), } def m7(s: Scenario, year: int) -> Dict[str, float]: maturity = ramp("M7", year) pop = POP_M[year] * 1e6 children = pop * .135 older = pop * .22 pregnancies = 350_000 priority = 1_000_000 current = {"children": .304, "older": .03, "pregnancy": .25, "priority": .20} beneficiaries = ( children * max(0, s.dental_target_children - current["children"]) + older * max(0, s.dental_target_65plus - current["older"]) + pregnancies * max(0, s.dental_target_pregnancy - current["pregnancy"]) + priority * max(0, s.dental_target_priority - current["priority"]) ) dental = beneficiaries * s.dental_unit_cost / 1e6 mobile_opex = s.mobile_units * 300_000 / 1e6 mobile_capex = s.mobile_units * 250_000 / 1e6 / 4 if year <= 2030 else 0.0 transport = s.transport_episodes * 250 / 1e6 return { "cobertura bucodental adicional": dental * maturity, "unidades móviles - operación": mobile_opex * maturity, "unidades móviles - inversión": mobile_capex, "transporte sanitario y ayudas": transport * maturity, "redes territoriales compartidas": s.territorial_network_m * maturity, "fondo de cohesión ajustado": s.cohesion_fund_m * maturity, "evaluación de ampliación óptica": s.optical_evaluation_m * maturity, } def m8(s: Scenario, year: int) -> Dict[str, float]: capex_total = ( 13_026 * s.digital_capex_ap_site + 468 * s.digital_capex_hospital + s.digital_platform_m * 1e6 ) / 1e6 capex = capex_total / 4 if year <= 2030 else 0.0 opex = capex_total * s.digital_opex_share if year >= 2030 else 0.0 admin = s.nondigital_admin_fte * PAY["administrator"] / 1e6 maturity = min(1.0, max(0.0, (year - 2026) / 4)) return { "interoperabilidad y acceso - inversión": capex, "operación, seguridad y mantenimiento": opex, "canal no digital": admin * maturity, "registro IA, auditorías y cuadro público": s.digital_governance_m * maturity, } MEASURE_FUNCS = {"M1": m1, "M2": m2, "M3": m3, "M4": m4, "M5": m5, "M6": m6, "M7": m7, "M8": m8} def model(s: Scenario) -> Dict[int, Dict[str, float]]: output: Dict[int, Dict[str, float]] = {} for year in YEARS: output[year] = { measure: sum(func(s, year).values()) for measure, func in MEASURE_FUNCS.items() } return output def controls() -> Iterable[str]: for scenario in SCENARIOS.values(): result = model(scenario) for year, measures in result.items(): for measure, value in measures.items(): if value < -1e-9: yield f"{scenario.name} {year} {measure}: coste negativo {value}" for specialty in FSE_DURATION: for year in YEARS: if cumulative_extra_graduates(scenario, specialty, year) < 0: yield f"{scenario.name} {specialty} {year}: graduados negativos" def summary_table() -> str: lines = ["Escenario,2032_MEUR,Acumulado_2027_2036_MEUR"] for name, scenario in SCENARIOS.items(): result = model(scenario) annual_2032 = sum(result[2032].values()) cumulative = sum(sum(v.values()) for v in result.values()) lines.append(f"{name},{annual_2032:.1f},{cumulative:.1f}") return "\n".join(lines) if __name__ == "__main__": errors = list(controls()) if errors: raise SystemExit("\n".join(errors)) print(summary_table()) central = model(SCENARIOS["Central"]) print("\nCentral 2032 por medida (M€):") for measure, value in central[2032].items(): print(f"{measure}: {value:.1f}")