Gates Open Research最新文献

Background: Coronavirus disease 2019 (COVID-19) impact varies substantially due to various factors, so it is critical to characterize its main differences to inform decision-makers about where to focus their interventions and differentiate mitigation strategies. Up to this date, little is known about the patterns and regional clustering of COVID-19 waves worldwide.

Methods: We assessed the patterns and regional clustering of COVID-19 waves in Peru by using the weekly mortality rates for each of the 25 regions as an outcome of interest. We obtained the death counts from the National Informatics System of Deaths and population estimates from the National Registry of Identification and Civil Status. In addition, we characterized each wave according to its duration, peak, and mortality rates by age group and gender. Additionally, we used polynomial regression models to compare them graphically and performed a cluster analysis to identify regional patterns.

Results: We estimated the average mortality rate at the first, second, and third waves at 13.01, 14.12, and 9.82 per 100,000 inhabitants, respectively, with higher mortality rates among elders and men. The patterns of each wave varied substantially in terms of duration, peak, impact, and wave shapes. Based on our clustering analysis, during the first wave caused by the index virus, the 25 regions of Peru presented six different wave patterns. However, the regions were clustered in two different wave patterns during the second and third, caused by alpha/lambda/delta and omicron.

Conclusions: The propagation of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) variants behaved in Peru with varying wave patterns and regional clustering. During the COVID-19 pandemic, the weekly mortality rates followed different spatiotemporal patterns with solid clustering, which might help project the impact of future waves of COVID-19.

Background: This paper aims to promote the use of simple interrupted time series (ITS) analyses of routine data as a responsive feedback tool to improve public health programs. Although advanced ITS techniques exist, their use is often not feasible due to limitations in funding or research capacity. We propose an Excel-based analysis that requires minimal resources or statistical expertise, and illustrate it by measuring the effect of a radio campaign to promote a family planning call center in Nigeria on the demand for family planning information.

Methods: We used a single group interrupted time series design (ITS) as a responsive feedback mechanism to determine whether the radio campaign influenced use of the Honey&Banana call center. ITS is ideal when there is no control group. ITS uses the pre-intervention trend to predict what would have happened if the intervention were absent.

Results: After conducting ITS analyses, the results show that the number of calls requesting family planning information increased throughout the campaign period, with a gain of about 500 additional calls per month, and then decreased after the campaign ended. However, the number of calls gained from the campaign was substantially lower than anticipated.

Conclusions: While end-of-project impact evaluations are necessary, there should be regular feedback system to provide program implementers with information about the status of the project, such as failures, successes, and areas of improvements. This would allow implementers to make necessary adjustments as needed throughout the intervention period. The finding that the radio campaign was not living up to expectations helped Honey&Banana program implementers to end the campaign prematurely and re-allocate resources to a more promising activity. Our research shows that basic Excel-based ITS analysis of routine data can be a useful tool for receiving regular feedback to guide programming improvements for organizations that have limited resources and/or research capacity.