Global Epidemiology最新文献

Background

The changes in shield strategies, treatments, emergence variants, and healthcare pathways might shift the profile and outcome of patients hospitalized with COVID-19 in successive waves of the outbreak.

Methods

We retrospectively analysed the characteristics and in-hospital outcomes of all patients admitted with COVID-19 in eight university hospitals of Catalonia (North-East Spain) between Feb 28, 2020 and Feb 28, 2021. Using a 7-joinpoint regression analysis, we split admissions into four waves. The main hospital outcomes included 30-day mortality and admission to intensive care unit (ICU).

Findings

The analysis included 17,027 subjects admitted during the first wave (6800; 39.9%), summer wave (1807; 10.6%), second wave (3804; 22.3%), and third wave (4616; 27.1%). The highest 30-day mortality rate was reported during the first wave (17%) and decreased afterwards, remaining stable at 13% in the second and third waves (overall 30% reduction); the lowest mortality was reported during the summer wave (8%, 50% reduction). ICU admission became progressively more frequent during successive waves. In Cox regression analysis, the main factors contributing to differences in 30-day mortality were the epidemic wave, followed by gender, age, diabetes, chronic kidney disease, and neoplasms.

Interpretation

Although in-hospital COVID-19 mortality remains high, it decreased substantially after the first wave and is highly dependent of patient's characteristics and ICU availability. Highest mortality reductions occurred during a wave characterized by younger individuals, an increasingly frequent scenario as vaccination campaigns progress.

Funding

This work did not receive specific funding.

Objective

Sensitivity analysis for random measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been compared for this purpose.

Study design and setting

A simulation study was conducted comparing the performance of regression calibration and simulation-extrapolation for linear and logistic regression. The performance of the two methods was evaluated in terms of bias, mean squared error (MSE) and confidence interval coverage, for various values of reliability of the error-prone measurement (0.05–0.91), sample size (125–4000), number of replicates (2−10), and R-squared (0.03–0.75). It was assumed that no validation data were available about the error-free measures, while correct information about the measurement error variance was available.

Results

Regression calibration was unbiased while simulation-extrapolation was biased: median bias was 0.8% (interquartile range (IQR): −0.6;1.7%), and −19.0% (IQR: −46.4;−12.4%), respectively. A small gain in efficiency was observed for simulation-extrapolation (median MSE: 0.005, IQR: 0.004;0.006) versus regression calibration (median MSE: 0.006, IQR: 0.005;0.009). Confidence interval coverage was at the nominal level of 95% for regression calibration, and smaller than 95% for simulation-extrapolation (median coverage: 85%, IQR: 73;93%). The application of regression calibration and simulation-extrapolation for a sensitivity analysis was illustrated using an example of blood pressure and kidney function.

Conclusion

Our results support the use of regression calibration over simulation-extrapolation for sensitivity analysis for random measurement error.

Background

The coronavirus disease 2019 (COVID-19) has taken millions of lives and disrupted living standards at individual, societal, and worldwide levels, causing serious consequences globally. Understanding its epidemic curve and spatio-temporal dynamics is crucial for the development of effective public health plans and responses and the allocation of resources. Thus, we conducted this study to assess the epidemiological dynamics and spatio-temporal patterns of the COVID-19 pandemic in Rwanda.

Methods

Using the surveillance package in R software version 4.0.2, we implemented endemic-epidemic multivariate time series models for infectious diseases to analyze COVID-19 data reported by Rwanda Biomedical Center under the Ministry of Health from March 15, 2020 to January 15, 2021.

Results

The COVID-19 pandemic occurred in two waves in Rwanda and showed a heterogenous spatial distribution across districts. The Rwandan government responded effectively and efficiently through the implementation of various health measures and intervention policies to drastically reduce the transmission of the disease. Analysis of the three components of the model showed that the most affected districts displayed epidemic components within the area, whereas the effect of epidemic components from spatial neighbors were experienced by the districts that surround the most affected districts. The infection followed the disease endemic trend in other districts.

Conclusion

The epidemiological and spatio-temporal dynamics of COVID-19 in Rwanda show that the implementation of measures and interventions contributed significantly to the decrease in COVID-19 transmission within and between districts. This accentuates the critical call for continued intra- and inter- organization and community engagement nationwide to ensure effective and efficient response to the pandemic.

Access to improved water, sanitation, and hygiene (WASH) services at the household level remains a good strategy to improve the health and well-being of individuals. Informal settlements, such as urban slums, are at risk of the spread of diseases due to the relative lack of access to safe, clean drinking water and basic sanitation, as well as poor hygiene. Global initiatives, such as the Sustainable Development Goals (SDGs) adopted by the United Nations, are aimed at transitioning households and communities from unimproved to sustained improved states of WASH services. To deepen understanding of the time dynamics between states of WASH services in the Nairobi Urban and Demographic Surveillance System (NUHDSS), this study employs the multi-state transition model to assess the influence of potential risk factors on these transitions. Results indicated that study sites, wealth tertile, age of household head, poverty status, the ethnicity of household head, household ownership, and food security were associated with household transitions of WASH services. There was a lower probability for households to transition from unimproved to improved toilet services than the reverse transition, but a higher chance for households to transition from unimproved to improved water and garbage services. The estimated average time that households spent in the unimproved and improved states before transitioning were, respectively, 35 months and 9 months for toilet services, 7 months and 66 months for water services, and 16 months and 19 months for garbage services. Thus, households tend to remain longer in the unimproved state of toilet and garbage services, and when in the improved states, they transition back relatively faster compared to water services. In conclusion, sanitation services in Nairobi informal settings remain largely unsatisfactory as transitions to improved services are not sustained. It is therefore important for governments, policy-makers, and stakeholders to put in place policies and interventions targeting vulnerable households for improved and sustained WASH services.

Background

Childhood nephrotic syndrome, if left untreated, leads to progressive kidney disease or death. We quantified the prevalence of steroid-sensitive nephrotic syndrome, steroid-resistant nephrotic syndrome, and histological types as the epidemiology of nephrotic syndrome in Africa remains unknown, yet impacts outcomes.

Methods

We searched MEDLINE, Embase, African Journals Online, and WHO Global Health Library for articles in any language reporting on childhood nephrotic syndrome in Africa from January 1, 1946 to July 1, 2020. Primary outcomes included steroid response, biopsy defined minimal change disease, and focal segmental glomerulosclerosis (FSGS) by both pooled and individual proportions across regions and overall.

Findings

There were 81 papers from 17 countries included. Majority of 8131 children were steroid-sensitive (64% [95% CI: 63–66%]) and the remaining were steroid-resistant (34% [95% CI: 33–35%]). Of children biopsied, pathological findings were 38% [95% CI: 36–40%] minimal change, 24% [95% CI: 22–25%] FSGS, and 38% [95% CI: 36–40%] secondary causes of nephrotic syndrome.

Interpretation

Few African countries reported on the prevalence of childhood nephrotic syndrome. Steroid-sensitive disease is more common than steroid-resistant disease although prevalence of steroid-resistant nephrotic syndrome is higher than reported globally. Pathology findings suggest minimal change and secondary causes are common. Scarcity of data in Africa prevents appropriate healthcare resource allocation to diagnose and treat this treatable childhood kidney disease to prevent poor health outcomes.

Funding

Funding was provided by the Canadian Institute for Health Research (CIHR) and the National Institute of Health (NIH) for the H3 Africa Kidney Disease Research Network. This research was undertaken, in part, from the Canada Research Chairs program.

Aim

To examine the changes in prevalence, awareness, treatment and control of hypertension between 1998 and 2016 in ≥15-year-old South African men and women and the sociodemographic characteristics associated with those changes.

Methods

In nationally representative surveys in 1998 and 2016, multi-stage sampling was used to select, interview and collect blood pressure and anthropometric measurements in ≥15-year-old adults. Logistic regression analyses evaluated the independent effects of selected sociodemographic characteristics on hypertension management.

Results

Among 13,217 participants in 1998 and 7830 in 2016 (59–60% women in both surveys), hypertension prevalence increased from 27% to 45% in men and 31% to 48% in women. Hypertension increased equally in participants with and without obesity and in daily cigarette smokers vs. their counterparts. Prevalence of awareness among participants with hypertension increased from 7% to 18% (men) and from 17% to 29% (women). Among those aware, hypertension treatment improved markedly from 8% to 85% (men) and from 12% to 82% (women). Hypertension control among those on treatment increased from 17% to 26% (men) and from 21% to 30% (women). Increasing age and survey year were consistently associated with higher prevalence, awareness, treatment and control of hypertension. The richest vs. poorer women, and lower vs. higher educated women were more likely to be treated for hypertension.

Conclusions

The high and rising hypertension burden together with suboptimal awareness and control levels warrant greater attention to curb hypertension-related morbidity and mortality in South Africans. Novel strategies involving community-based or workplace hypertension programmes may overcome some barriers to optimal care.

Purpose

Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples.

Materials and methods

Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented.

Results and conclusions

The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases.

Translation of research into public health policy is featured in common definitions of epidemiology, as an end result of scientific discovery on disease occurrence and causes. This dual nature of epidemiology, which brings together discovery and its use, seems to imply two main dimensions by which to judge epidemiologic research: technical or field-specific quality and societal value. This paper uses our research on the epidemiology of rheumatic and musculoskeletal disorders as a starting point to discuss the interface between these dimensions, exploring a number of conceptual, practical and ethical challenges that epidemiologists increasingly need to address when aiming for research translation. Those include not only the appraisal of the technical quality of research, which is familiar to researchers, but also the judgement on the usefulness and actual use of knowledge, as well as the assessment of the legitimacy of research based on translation potential. Several challenges lie ahead, but interdisciplinary conceptual and technical developments have the potential to guide future epidemiologic research of consequence. Approaches that recognize complexity and formalize the involvement of stakeholders in the research process within transparent frameworks open promising avenues for an effective translation of epidemiologic research projected into the future.