公共卫生与卫生体系
引入| English | 释义 | 拼音 |
|---|---|---|
| primary prevention | 一级预防 | yī jí yù fáng |
| secondary prevention | 二级预防 | èr jí yù fáng |
| screening | 疾病筛查 | jí bìng shāi chá |
| tertiary prevention | 三级预防 | sān jí yù fáng |
| over-diagnosis | 过度诊断 | guò dù zhěn duàn |
Treating a town, not a patient
- A doctor can treat one smoker's lung disease. A ban on smoking indoors can prevent thousands of cases that no doctor will ever see.
- Public health works on whole populations: preventing disease, finding it early, and organising how care is paid for and reached.
Three levels of prevention
- Primary prevention 一级预防 stops disease from starting (vaccination, stopping smoking). Secondary prevention 二级预防 finds it early, before symptoms, mainly by screening 疾病筛查.
- Tertiary prevention 三级预防 limits damage from disease that is already there (rehabilitation after a stroke, palliative care).
- (Fictional) One smoking-related cancer, three levels. Primary: help to stop smoking. Secondary: targeted screening of high-risk people. Tertiary: rehabilitation and palliative care.
- Each level has its own target group and its own measure of success.
Match each activity to its level of prevention.
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Primary stops disease starting, secondary finds it early, tertiary limits its damage.
When is screening worth it?
- Screening tests people who feel well. Testing someone who already has symptoms is diagnosis, not screening.
- The classic criteria: the condition is important, it has a detectable early stage, early treatment helps, the test is valid and acceptable, and the programme's benefits outweigh its harms.
Testing a patient who comes in with a breast lump is an example of screening.
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The patient has a symptom, so this is diagnosis. Screening tests people who feel well.
Screening arithmetic
- In a well population most people do not have the disease, so even a good test produces many false positives. The positive predictive value (PPV) falls as the disease gets rarer.
- (Synthetic) 10,000 people, 1% with the disease, sensitivity 90%, specificity 95%. True positives: 90. False positives: 5% of 9,900 = 495. PPV = 90 ÷ 585 ≈ 15%.
(Synthetic) 20,000 people are screened; 0.5% have the disease. Specificity is 98%. How many false positives are expected?
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0.5% of 20,000 = 100 have the disease, so 19,900 do not. 2% of 19,900 = 398 false positives.
(Synthetic) In the same programme sensitivity is 90%, giving 90 true positives beside the 398 false positives. What is the PPV, in % to the nearest whole number?
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PPV = 90 ÷ (90 + 398) = 90 ÷ 488 ≈ 0.18, so about 18%.
The hidden cost: over-diagnosis
- Over-diagnosis 过度诊断 is finding a disease that would never have caused symptoms or death. Those people gain nothing and still bear the tests, worry and treatment.
- A programme is judged by the harms it causes as well as the cases it finds.
Over-diagnosis means —
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The disease is real but harmless in that person's lifetime, so finding it brings only the costs of testing and treatment.
How health care is paid for
- Beveridge model: funded by general taxation, a national health service (UK). Bismarck model: social insurance paid from wages (Germany).
- Also private insurance and out-of-pocket payment. Each model decides who is covered, who pays at the point of care, and who controls access to specialists.
A system funded mainly by general taxation, with a national health service, is the —
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Beveridge is tax-funded (as in the UK); Bismarck is social insurance from wages (as in Germany).
Common slips
- Calling any early test screening. Screening is for people without symptoms.
- Judging screening by cases found. Count the false positives and over-diagnosis too.
- Comparing systems by spending alone. Coverage and outcomes differ at the same spending.
Recap
- Primary prevents, secondary detects early (screening), tertiary limits damage.
- Screening tests the well; in rare diseases the PPV is low, and over-diagnosis is a real harm.
- Financing models: Beveridge (tax), Bismarck (social insurance), private, out-of-pocket.