{
  "id": "competency:mph-health-economics--policy",
  "code": "CDU-HEA-MPH-900",
  "level": "MSc",
  "name": "MPH Health Economics & Policy",
  "faculty": "Faculty of Health Sciences",
  "lineage": [
    {
      "institution": "Conquest Designer University",
      "year": 2026,
      "note": "authored from the brief, the CEPH criteria and the NICE reference case; no prior institution is claimed"
    }
  ],
  "researchProject": "Value for a health system: cost per QALY, discounted benefit, association and the worth of a test",
  "description": "The quantitative tools of health policy: the incremental cost-effectiveness ratio and net monetary benefit against a willingness-to-pay threshold, QALYs discounted at the reference-case rate, risk ratio, odds ratio and number needed to treat from a 2×2 table, and the predictive values and likelihood ratios of a diagnostic test.",
  "syllabus": [
    "Economic evaluation: incremental cost, incremental QALYs, ICER, dominance, net monetary benefit (NICE PMG36)",
    "Outcomes: QALYs from utility weights, discounting at 3.5%",
    "Epidemiology: risks, risk ratio with its confidence interval, risk difference, odds ratio, NNT",
    "Screening and diagnosis: sensitivity, specificity, prevalence, predictive values, likelihood ratios"
  ],
  "reference": {
    "ICER": "(C_new − C_comparator) ÷ (E_new − E_comparator)",
    "Net monetary benefit": "λ·ΔE − ΔC; λ = £20,000–£30,000 per QALY (NICE PMG36)",
    "Discounting (NICE)": "3.5% a year for costs and health effects; year 1 undiscounted here",
    "Risk ratio 95% CI": "exp(ln RR ± 1.96·√(1/a − 1/(a+b) + 1/c − 1/(c+d)))",
    "PPV": "sens·prev ÷ (sens·prev + (1 − spec)·(1 − prev))"
  },
  "role": "You are a public-health economist advising a health department. Say what the evidence shows and how certain it is, show the calculation, and separate the technical answer from the value judgement the decision-maker owns.",
  "feeUsd": 1,
  "version": "1.0.0",
  "qualification": {
    "award": "MPH",
    "nqfLevel": 9,
    "qualificationType": "Master's Degree (HEQSF qualification type 9a)",
    "totalCredits": 180,
    "minCreditsAtExitLevel": 120,
    "iscedFields": [
      "091",
      "041"
    ],
    "standards": [
      "Council on Education for Public Health (CEPH) Accreditation Criteria, Schools of Public Health & Public Health Programs (2021) — MPH foundational competencies D2",
      "NICE (2022) NICE health technology evaluations: the manual (PMG36) — reference case, 3.5% discounting, £20,000–£30,000 per QALY",
      "Rothman, K. J., Greenland, S. & Lash, T. L. Modern Epidemiology (3rd ed.) — measures of association"
    ],
    "registration": "not registered"
  },
  "modules": [
    {
      "code": "CDU-HEA-MPH-900-901",
      "title": "Coursework — the executable units",
      "nqfLevel": 9,
      "credits": 60,
      "prerequisites": [],
      "coRequisites": [],
      "outcomes": [
        "answer every unit of the qualification against its published reference, with the working shown"
      ],
      "assessment": {
        "formative": [
          "worked examples re-run against the published reference",
          "seeded practice scenarios, deterministic per (pan, unit, attempt)"
        ],
        "summative": "every unit passed once through the seeded assessment (the seeded assessment room)",
        "passMark": 50,
        "subminimum": 40,
        "moderation": "internally assessed by the unit itself — its reference implementation is the marker; the working is published so anyone can re-run the mark (D-13.1 — no model marks anything)"
      },
      "unitIds": [
        "icer_nmb",
        "discounted_qalys",
        "two_by_two",
        "diagnostic_accuracy"
      ],
      "notionalHours": 600
    },
    {
      "code": "CDU-HEA-MPH-900-902",
      "title": "Research report",
      "nqfLevel": 9,
      "credits": 120,
      "prerequisites": [
        "CDU-HEA-MPH-900-901"
      ],
      "coRequisites": [],
      "outcomes": [
        "Value for a health system: cost per QALY, discounted benefit, association and the worth of a test"
      ],
      "assessment": {
        "formative": [
          "worked examples re-run against the published reference",
          "seeded practice scenarios, deterministic per (pan, unit, attempt)"
        ],
        "summative": "the research report, assessed against the qualification’s published reference block",
        "passMark": 50,
        "subminimum": 40,
        "moderation": "internally assessed by the unit itself — its reference implementation is the marker; the working is published so anyone can re-run the mark (D-13.1 — no model marks anything)"
      },
      "unitIds": [],
      "notionalHours": 1200
    }
  ],
  "units": [
    {
      "id": "icer_nmb",
      "name": "ICER and net monetary benefit",
      "answers": "What does each extra QALY from the new intervention cost, and is it worth it at the threshold?",
      "example": {
        "costNew": 24000,
        "costComparator": 15000,
        "qalyNew": 6.2,
        "qalyComparator": 5.8,
        "threshold": 20000
      },
      "reference": "NICE (2022) health technology evaluations manual PMG36: ICER against £20,000–£30,000 per QALY; net monetary benefit λΔE − ΔC",
      "signature": {
        "input": {
          "costNew": {
            "type": "number",
            "describe": "Total cost per patient, new intervention",
            "min": 0
          },
          "costComparator": {
            "type": "number",
            "describe": "Total cost per patient, comparator",
            "min": 0
          },
          "qalyNew": {
            "type": "number",
            "describe": "QALYs per patient, new intervention",
            "min": 0
          },
          "qalyComparator": {
            "type": "number",
            "describe": "QALYs per patient, comparator",
            "min": 0
          },
          "threshold": {
            "type": "number",
            "describe": "Willingness to pay per QALY (λ)",
            "min": 0,
            "default": 20000
          }
        },
        "output": {
          "incrementalCost": {
            "type": "number",
            "describe": "ΔC"
          },
          "incrementalQaly": {
            "type": "number",
            "describe": "ΔE"
          },
          "icer": {
            "type": "number",
            "describe": "ΔC ÷ ΔE (null when ΔE = 0)"
          },
          "netMonetaryBenefit": {
            "type": "number",
            "describe": "λ·ΔE − ΔC"
          },
          "verdict": {
            "type": "string",
            "describe": "dominant, dominated, cost-effective or not cost-effective"
          }
        }
      }
    },
    {
      "id": "discounted_qalys",
      "name": "Discounted QALYs",
      "answers": "How many QALYs does this stream of health-state utilities yield, undiscounted and discounted at the reference-case rate?",
      "example": {
        "utilities": [
          0.85,
          0.82,
          0.8,
          0.78,
          0.75
        ],
        "discountRate": 0.035
      },
      "reference": "NICE PMG36 reference case: health effects discounted at 3.5% a year; QALY = Σ utility × time",
      "signature": {
        "input": {
          "utilities": {
            "type": "number[]",
            "describe": "Mean utility in each successive year (0–1; below 0 for states worse than dead)"
          },
          "discountRate": {
            "type": "number",
            "describe": "Annual discount rate, 0–0.1",
            "min": 0,
            "max": 0.1,
            "default": 0.035
          }
        },
        "output": {
          "lifeYears": {
            "type": "number",
            "describe": "Years counted"
          },
          "undiscountedQalys": {
            "type": "number",
            "describe": "Σ utilities"
          },
          "discountedQalys": {
            "type": "number",
            "describe": "Σ u_t ÷ (1 + r)^t, t = 0 for year 1"
          }
        }
      }
    },
    {
      "id": "two_by_two",
      "name": "Measures of association from a 2×2 table",
      "answers": "In this cohort, how much more (or less) likely is the outcome among the exposed, and how many must be exposed for one extra (or one fewer) case?",
      "example": {
        "exposedCases": 30,
        "exposedNonCases": 70,
        "unexposedCases": 15,
        "unexposedNonCases": 85
      },
      "reference": "Rothman, Greenland & Lash, Modern Epidemiology: risk ratio (log-scale 95% CI), risk difference, odds ratio; NNT = 1/|RD|",
      "signature": {
        "input": {
          "exposedCases": {
            "type": "number",
            "describe": "a: exposed with the outcome",
            "min": 0
          },
          "exposedNonCases": {
            "type": "number",
            "describe": "b: exposed without it",
            "min": 0
          },
          "unexposedCases": {
            "type": "number",
            "describe": "c: unexposed with the outcome",
            "min": 0
          },
          "unexposedNonCases": {
            "type": "number",
            "describe": "d: unexposed without it",
            "min": 0
          }
        },
        "output": {
          "riskExposed": {
            "type": "number",
            "describe": "a ÷ (a + b)"
          },
          "riskUnexposed": {
            "type": "number",
            "describe": "c ÷ (c + d)"
          },
          "riskRatio": {
            "type": "number",
            "describe": "RR"
          },
          "riskRatioLower95": {
            "type": "number",
            "describe": "Lower 95% bound of RR"
          },
          "riskRatioUpper95": {
            "type": "number",
            "describe": "Upper 95% bound of RR"
          },
          "riskDifference": {
            "type": "number",
            "describe": "Risk exposed − risk unexposed"
          },
          "oddsRatio": {
            "type": "number",
            "describe": "(a·d) ÷ (b·c)"
          },
          "numberNeeded": {
            "type": "number",
            "describe": "1 ÷ |RD| (to harm if RD > 0, to treat if RD < 0)"
          }
        }
      }
    },
    {
      "id": "diagnostic_accuracy",
      "name": "Predictive values and likelihood ratios",
      "answers": "In a population with this prevalence, how likely is a positive (or negative) result to be right?",
      "example": {
        "sensitivity": 0.9,
        "specificity": 0.95,
        "prevalence": 0.02
      },
      "reference": "Bayes’ theorem applied to a diagnostic test; likelihood ratios LR+ = sens/(1 − spec), LR− = (1 − sens)/spec",
      "signature": {
        "input": {
          "sensitivity": {
            "type": "number",
            "describe": "P(test + | disease), 0–0.9999",
            "min": 0.0001,
            "max": 0.9999
          },
          "specificity": {
            "type": "number",
            "describe": "P(test − | no disease), 0–0.9999",
            "min": 0.0001,
            "max": 0.9999
          },
          "prevalence": {
            "type": "number",
            "describe": "Pre-test probability, 0–1",
            "min": 0.000001,
            "max": 0.999999
          }
        },
        "output": {
          "ppv": {
            "type": "number",
            "describe": "P(disease | +)"
          },
          "npv": {
            "type": "number",
            "describe": "P(no disease | −)"
          },
          "positiveLikelihoodRatio": {
            "type": "number",
            "describe": "LR+"
          },
          "negativeLikelihoodRatio": {
            "type": "number",
            "describe": "LR−"
          }
        }
      }
    }
  ]
}