How dramatic were the effects of handwashing on maternal mortality observed by Ignaz Semmelweis?
Notice bibliographique
Résumé
Questions remain about how much evidence is needed to accept or at least to consider an observation as providing sufficiently strong evidence to justify a change in clinical practice,1,2 even when the key outcome measure is objective, definitive and clinically very important. Maternal death is one such outcome, and Ignaz Semmelweis’ observation of the reduction in maternal deaths associated with the introduction of handwashing in a maternity hospital in Vienna provides an illustrative example.3,4 A plot of the rate of maternal deaths every month from January 1841 until May 1847 shows substantial variability and some evidence of a pattern in the changes, possibly related to the seasons.5 After handwashing was introduced in June 1847, deaths were less frequent and showed less monthly variability in the period up to February 1849. How much analysis is needed to convince people that this is an effect of handwashing and not a chance association, or a reflection of bias?2 Eyeballing the plot shown in Figure 1, many observers might consider the reduction in deaths following the introduction of handwashing to be real and significant. When considering the high risk of bias in studies using historical controls, Glasziou et al.1 suggested considering an effect as likely to be real if a risk ratio of between 5 and 10 has been observed. In this case, a comparison of the average monthly mortality rate before the introduction of handwashing (10.65%, blue points) and after it (1.98%, red points) yields a risk ratio of 5.38. We can explore these data in more depth by analysing this interrupted time series with a Seasonal AutoRegressive Integrated Moving Average model. The monthly mortality rate can be formulated as follows: MRt=B×H+AR×MRt-1+MA×Et-12+Et where MR = mortality rate, t = month, B = handwashing coefficient, H = 0 (before the introduction of handwashing), H = 1 (after the introduction of handwashing), AR = first-order autoregressive coefficient, MA = monthly moving average coefficient and E = random error. Figure 1. Monthly mortality rate from puerperal fever. The estimations of the three coefficients of the model are presented in the table below. This yields an estimated absolute reduction in maternal mortality of 8.87%, which is highly statistically significant (P = 0.0042). The reduction is also of great human significance: for every 11 women examined after handwashing, one fewer died. The analysis could be extended to take into account the data from the two clinics concerned, and to cover an earlier period, during which postmortem examinations were not done routinely.6 This discovery happened before the germ theory take off with the work of Louis Pasteur in the early 1860s. The miasma theory was prevalent and no strong explanation was available to support this impressive observation. These different levels of analysis strongly support the existence of a beneficial impact of handwashing on the risk of maternal mortality. This is not to suggest that data of this level of confidence are sufficient to introduce changes in clinical practice, which is influenced by medical tradition and cultural and other factors.4,6 In Semmelweis’ case, the evidence of an important impact of handwashing on maternal mortality should at least have led traditional medical practices to have been questioned.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».