Transferring inpatients between wards drives large nosocomial COVID-19 outbreaks, Wales, 2020-22: a matched case-control study using routine and enhanced surveillance data.

BACKGROUND: The role of the hospital environment in the spread of COVID-19 is unclear. AIM: To measure associations between ward characteristics and outbreak size to inform mitigations. METHODS: Wards with large (case wards) and small (control wards) outbreaks in three acute hospitals were compared. Cases were healthcare-associated COVID-19 inpatients (positive polymerase chain reaction test ≥8 days post admission). Case wards were adult medical/surgical wards with ≥10 cases within rolling 14-day periods, between April 1st, 2020 and April 30th, 2022. Control wards were equivalents with 2-9 cases. Demographic and laboratory data were extracted from routine surveillance systems. Continuous data were aggregated fortnightly and analysed as binary variables according to median values. Each case ward was compared with two control wards matched on outbreak start date (±14 days) to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) using univariable and conditional multivariable logistic regression. FINDINGS: From 170 outbreaks (median: 5 cases; interquartile range: 2-9), 35 case wards were identified. Community admissions were lower in case wards vs control wards (5 vs 10 median admissions; P<0.01, respectively), whereas transfers between wards within the same hospital were higher (58 vs 29 median transfers; P<0.01, respectively). Wards with more transfers in the preceding fortnight were significantly more likely to experience a large outbreak (≥35 vs <35 transfers; adjusted OR: 9.08; 95% CI: 2.5-33). CONCLUSION: We recommend safely minimizing patient movements, such as by asking clinicians to record the rationale for transfer, to reduce the likelihood of disease transmission.

Failure probability under parameter uncertainty.

In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location-scale families (including the log-normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications.

Spectral frequency and the modulation of the acoustic startle reflex by background noise.

The rat's (Long-Evans) acoustic startle reflex to a high-frequency tone burst (10.5 kHz) was depressed by intense high-frequency band-pass noise (8-16 kHz) but enhanced by low frequency noise (1-2 kHz). However, contrary to the hypothesis that the depression of startle in intense background noise is produced by sensory masking, the reflex to a low-frequency tone burst (at 1 kHz) was depressed by both high- and low-frequency band-pass noise. Two additional hypotheses are offered to supplement sensory masking in order to explain the asymmetry in these data. The first is that the intratympanic reflex, which acts as a high pass filter on acoustic input, is elicited in intense backgrounds. The second is that acoustic startle reflexes elicited by intense low-frequency tones are in part elicited by their high-frequency distortion products and that these distortion products are then masked by high-frequency background noise.

Startle reflex inhibition in the rat: its persistence after extended repetition of the inhibitory stimulus.

Startle reflexes to intense sound bursts are inhibited by weak stimuli that briefly precede their elicitation. In three experiments the startle stimulus (a 110-dB SPL tone burst) was presented 100 ms after the final link in a train of stimuli, the length of the train varying from 1 to 1,000, its repetition rate varying from 1 per s to 10 per s, and its constituents being 40 dB or 50 dB white noise bursts of 25 ms duration. Inhibition was invariant across train length and repetition rate. In a final experiment the startle stimulus was presented a variable interval after the final link, from 40 ms to 1280 ms, with 1 or 100 noise bursts (50 dB) in the train. Inhibition developed more rapidly following the last member of the 100-stimulus train, suggestive of a "priming" or sensitization effect of stimulus repetition, but its overall strength and subsequent rate of decay were not different in the two conditions. The general persistence of inhibition following these extended series of stimuli reveals that reflex inhibition must be the outcome of a fixed and obligatory process associated with sensory input.