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ObjectivesTo examine whether and to what extent hospital strain will increase the risk of death from Covid-19. DesignRetrospective cohort study. SettingEngland. ParticipantsData on all the 147,276 Covid-19 deaths and 601,084 hospitalized Covid-19 patients in England during the period between 9 April 2020 and 11 March 2022 were extracted on a daily basis from the UK Health Security Agency. Main outcome measuresThe number of Covid-19 patients currently in hospitals was used as the measure of hospital strain. Daily case fatality was estimated as the measure of risk of death from Covid-19. The study was divided into 4 periods, which represented largely the wild, Alpha, Delta and Omicron waves. Weighted linear regression models were used to assess the association between hospital strain and Covid-19 fatality with adjustment for potential confounders including vaccination score, hospital admission rate, percentage of deaths outside hospitals, study period and interaction between patients currently in hospitals and study period. ResultsThe daily case fatality from Covid-19 increased linearly as the number of patients currently in hospitals increased in the 4 study periods except the Omicron wave. After adjusting for potential confounders, an increase in 1000 patients currently in hospitals was associated with a relative increase of 6.3% (95% CI: 5.9%~6.8%), 1.4% (95% CI: 1.3% ~ 1.5%) and 12.7% (95% CI: 10.8%~14.7%) in daily case fatality during study periods 1, 2 and 3 respectively. Compared with the lowest number of patients currently in hospitals, the highest number was associated with a relative increase of 188.0% (95% CI: 165.9%~211.6%), 69.9% (95% CI: 59.0%~81.8%) and 58.2% (95% CI: 35.4%~89.0%) in daily case fatality in the first 3 study periods respectively. Sensitivity analyses using the number of patients in ventilation beds as the measure of hospital strain showed similar results. ConclusionsThe risk of death from Covid-19 was linearly associated with the number of patients currently in hospitals, suggesting any (additional) effort to ease hospital strain or maintain care quality be beneficial during large outbreaks of Covid-19 and likely of other similar infectious diseases. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABS- During the Covid-19 pandemic, tremendous efforts have been made in many countries to suppress epidemic peaks and strengthen hospital services so as to avoid hospital strain with an ultimate aim to reduce the risk of death from Covid-19. - These efforts were made according to the widely held belief that hospital strain would increase the risk of Covid-19 death but good empirical evidence was largely lacking to support the hypothesis. - A few small studies showed that shortage in intensive care was associated with an increased Covid-19 fatality but strains may occur in many areas in the healthcare system besides intensive care and they may all increase the risk of death from Covid-19. - The totality of hospital strain can be approximated by the number of patients currently in hospitals but its effects on the risk of Covid-19 death has not been demonstrated. What this study adds- We found the risk of death from Covid-19 was linearly associated with the number of patients currently in hospitals before the Omicron period. - Compared with the lowest number of patients currently in hospitals in an outbreak, the highest number could be associated with a relative increase in the risk of death between 58.2% and 188.0%. - The number of patients currently in hospitals during the Omicron period was not found associated with the risk of death but there remains uncertainty if the number of patients currently in hospitals reached a level much higher than that actually occurred in England or in places other than England. How this study might affect research, practice, or policy- Facing the on-going Covid-19 pandemic and future outbreaks alike, the linear relation between hospital strain and fatality suggests importantly any (additional) effort to reduce hospital strain would be beneficial during a large Covid-19 outbreak.
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BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has been declared a pandemic by the World Health Organization, while several key epidemiological parameters of the disease remain to be clarified. This study aimed to obtain robust estimates of the incubation period, upper limit of latent period (interval between infectors exposure and infectees exposure), serial interval, time point of exposure (the day of infectees exposure to infector relative to the latters symptom onset date) and basic reproduction number (R0) of COVID-19. MethodsBetween late February and early March of 2020, the individual data of laboratory confirmed cases of COVID-19 were retrieved from 10728 publicly available reports released by the health authorities of and outside China and from 1790 publications identified in PubMed and CNKI. To be eligible, a report had to contain the data that allowed for estimation of at least one parameter. As relevant data mainly came from clustering cases, the clusters for which no evidence was available to establish transmission order were all excluded to ensure accuracy of estimates. Additionally, only the cases with an exposure period spanning 3 days or less were included in the estimation of parameters involving exposure date, and a simple method for determining exposure date was adopted to ensure the error of estimates be small (< 0.3 day). Depending on specific parameters, three or four of normal, lognormal, Weibull, and gamma distributions were fitted to the datasets and the results from appropriate models were presented. FindingsIn total, 1155 cases from China, Japan, Singapore, South Korea, Vietnam, Germany and Malaysia were included for the final analysis. The mean and standard deviation were 7.44 days and 4.39 days for incubation period, 2.52 days and 3.95 days for the upper limit of latent period, 6.70 days and 5.20 days for serial interval, and -0.19 day (i.e., 0.19 day before infectors symptom onset) and 3.32 days for time point of exposure. R0 was estimated to be 1.70 and 1.78 based on two different formulas. For 39 (6.64%) cases, the incubation periods were longer than 14 days. In 102 (43.78%) infector-infectee pairs, transmission occurred before infectors symptom onsets. In 27 (3.92%) infector-infectee pairs, infectees symptom onsets occurred before those of infectors. Stratified analysis showed that incubation period and serial interval were consistently longer for those with less severe disease and for those whose primary cases had less severe disease. Asymptomatic transmission was also observed. InterpretationThis study obtained robust estimates of several key epidemiological parameters of COVID-19. The findings support current practice of 14-day quarantine of persons with potential exposure, but also suggest that longer monitoring periods might be needed for selected groups. The estimates of serial interval, time point of exposure and latent period provide consistent evidence on pre-symptomatic transmission. This together with asymptomatic transmission and the generally longer incubation and serial interval of less severe cases suggests a high risk of long-term epidemic in the absence of appropriate control measures. FundingThis work received no funding from any source.
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Modern epidemiology is the art and science of investigating quantitatively regularities or general laws regarding applied healthcare issues.The validity of epidemiological studies is primarily determined by the study design and the precision by the sample size.Large randomized controlled trial (RCT) is thus the most rigorous and most precise epidemiological study design.Due to ethical concerns,RCTs can however be used only to evaluate medical interventions.Rigorousness of study design and sample size required for a study are inversely related to the anticipated size of effect to be evaluated:the smaller the effect,the more rigorous the study design and larger the sample size are required.Thus,large RCTs are necessary and called upon when and only when the effectiveness to be proved is relatively small;large effectiveness can be verified with small or medium-sized RCTs or even observational studies.In the stages of scientific research,large RCTs are confirmatory rather than original investigations on new hypotheses,whereas the value of a study is ultimately determined by the importance and novelty of the research question rather than methodology and the P value.Overemphasis on large RCTs has been causing:1) overemphasis on interventions of small or moderate effect;2) overemphasis on confirmatory studies and on size of study and funding and weakening original creative work;3) increasing the risk of research resources,medical activities,and patients' well-being being hijacked by pharmaceutical companies.
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Modern epidemiology is the art and science of investigating quantitatively regularities or general laws regarding applied healthcare issues.The validity of epidemiological studies is primarily determined by the study design and the precision by the sample size.Large randomized controlled trial (RCT) is thus the most rigorous and most precise epidemiological study design.Due to ethical concerns,RCTs can however be used only to evaluate medical interventions.Rigorousness of study design and sample size required for a study are inversely related to the anticipated size of effect to be evaluated:the smaller the effect,the more rigorous the study design and larger the sample size are required.Thus,large RCTs are necessary and called upon when and only when the effectiveness to be proved is relatively small;large effectiveness can be verified with small or medium-sized RCTs or even observational studies.In the stages of scientific research,large RCTs are confirmatory rather than original investigations on new hypotheses,whereas the value of a study is ultimately determined by the importance and novelty of the research question rather than methodology and the P value.Overemphasis on large RCTs has been causing:1) overemphasis on interventions of small or moderate effect;2) overemphasis on confirmatory studies and on size of study and funding and weakening original creative work;3) increasing the risk of research resources,medical activities,and patients' well-being being hijacked by pharmaceutical companies.
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Objective@#The burden of chronic disease has been continuously increasing in China since the early 1980s. Besides the worsening of risk factors, the change in diagnostic criteria is very likely an important explanation for the increase in the prevalence of hypertension, hyperlipidemia and diabetes mellitus, three commonest, major chronic conditions that can lead to major vascular events and deaths. This study aims to estimate the contribution of changes in diagnostic criteria to the increase in the prevalence of the three conditions in China.@*Methods@#The data from two representative nation-wide surveys in China in 2002 and 2009, with 145 254 and 8 813 adults included respectively, were used to estimate the prevalence rate of the three conditions and the proportion attributable to the change in diagnostic criteria around year 2000. The new and old cutoff values for hypertension, hyperlipidemia, and hyperglycemia were 140/90 and 160/95 mmHg (1 mmHg=0.133 kPa), 5.7 and 6.2 mmol/L, and 7.0 and 7.8 mmol/L, respectively. The prevalence was standardized according to the distribution of age, sex and rural-urban residence of the 2000 national census of the country so as to compare between the old and new diagnostic criteria and project the situation for the entire country.@*Results@#The standardized prevalence of hypertension, hyperlipidemia, and diabetes mellitus for the entire Chinese adult population in 2002 was 8.21%, 1.71% and 1.43% according to the immediate previous diagnostic criteria, and 19.18%, 3.53% and 2.66% according to the new criteria. In 2009, the prevalence was 11.89%, 9.34% and 4.29% according to the old criteria, and 24.78%, 18.36% and 6.55% according to the new criteria. The total cumulative prevalence of the three conditions was increased by 124% in 2002 and 95% in 2009 as a result of change in diagnostic criteria. Put it differently, the change in diagnostic criteria increased the number of the three conditions from 2002 to 2009 by approximately 359 million and could increase the annual drug costs by some 271 billion RMB if all the conditions are treated. The drug costs alone of treating all the three conditions could consume 56% of the total health budget of the Government in 2010.@*Conclusion@#About half of the number of the three conditions is a result of the change in diagnostic criteria. These criteria were adopted from western populations, which are designed to meet the population need and suit healthcare resources available in these countries. It is important for China to consider the resources available and needs and values of the population in addition to the benefits, harms and costs of treatment in determining the cutoff values for defining these conditions for drug interventions.
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Objective To discuss the levels of evidence provided by different study designs.Methods Websites of N Engl J Med, JAMA, Lancet, and BMJ were accessed to identify research articles (systematic review and meta-analysis included) published in 2009. A standardized data collection form was established using Epidata 3. 1 software to extract the "title", "country of lead author", "clinical problem" (such as treatment, diagnosis, etc. ) and "study design" of eligible studies. Descriptive statistics was conducted with SPSS 13.0. Results Over all, 844 studies were included, among which 35.7% were RCT,9. 4% systematic review and Meta-analysis, and 54. 9% other types of studies. Regarding clinical problems,34. 2%, 19. 7%, 13.7%, 6. 0% and 5. 1% of the included researches addressed the issues of treatment,etiology/risk factors, prevention, disease frequency and prognosis, respectively. The study designs that were most frequently adopted to explore these problems were RCT (70.6%), cohort study (44. 6% ), RCT (68. 1% ), cross-sectional study ( 56. 9% ), and cohort study ( 93.0% ), respectively. Conclusions High-level evidence does not come exclusively from RCT and systematic review, as each type of study may have its unique value in health related research. The clinical problem of interest, the previous work that has been done to approach the same issue, as well as other factors should be taken into account when deciding whether the selected study design is appropriate.
