The Effects of the COVID-19 Pandemic on Nutrition, Health and Environment in Indonesia: A Qualitative Investigation of Perspectives from Multi-Disciplinary Experts.

OBJECTIVES: The COVID-19 pandemic impacted food systems, health systems and the environment globally, with potentially greater negative effects in many lower-middle income countries (LMICs) including Indonesia. The purpose of this qualitative study was to investigate the potential impacts of the COVID-19 pandemic on diets, health and the marine environment in Indonesia, based on the perspectives of a multidisciplinary group of informants. METHODS: We conducted remote in-depth interviews with 27 key informants from many regions of Indonesia, who are either healthcare providers, nutrition researchers or environmental researchers. Interview question guides were developed based on a socio-ecological framework. We analyzed the data using a qualitative content analysis approach. RESULTS: Informants suggested that while the COVID-19 brought increased awareness about and adherence to good nutrition and health behaviors, the impact was transitory. Informants indicated that healthy food options became less affordable, due to job losses and reduced income, suggesting a likely increase in food insecurity and obesity. Environmental researchers described higher levels of marine pollution from increase in hygienic wastes as well as from plastic packaging from food orders. CONCLUSIONS: Our findings reveal perceptions by informants that the increased awareness and adherence to health behaviors observed during the pandemic was not sustained. Our results also suggest that the pandemic may have exacerbated the double-burden paradox and marine pollution in Indonesia. This study offers information for generating hypotheses for quantitative studies to corroborate our findings and inform policies and programs to mitigate the long-term impacts of the COVID-19 on diets, health, and the marine environment in Indonesia.

How Drivers of Seasonality in Respiratory Infections May Impact Vaccine Strategy: A Case Study in How Coronavirus Disease 2019 (COVID-19) May Help Us Solve One of Influenza's Biggest Challenges.

Vaccines against seasonal infections like influenza offer a recurring testbed, encompassing challenges in design, implementation, and uptake to combat a both familiar and ever-shifting threat. One of the pervading mysteries of influenza epidemiology is what causes the distinctive seasonal outbreak pattern. Proposed theories each suggest different paths forward in being able to tailor precision vaccines and/or deploy them most effectively. One of the greatest challenges in contrasting and supporting these theories is, of course, that there is no means by which to actually test them. In this communication we revisit theories and explore how the ongoing coronavirus disease 2019 (COVID-19) pandemic might provide a unique opportunity to better understand the global circulation of respiratory infections. We discuss how vaccine strategies may be targeted and improved by both isolating drivers and understanding the immunological consequences of seasonality, and how these insights about influenza vaccines may generalize to vaccines for other seasonal respiratory infections.

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA.

Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University's (JHU's) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU's data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90-2.69 [95%CI: 1.38-4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51-0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.

Providing Food and Nutrition Services during the COVID-19 Surge at the Javits New York Medical Station.

Military field hospitals typically provide essential medical care in combat zones. In recent years, the United States (US) Army has deployed these facilities to assist domestic humanitarian emergency and natural disaster response efforts. As part of the nation's whole-of-government approach to the coronavirus disease (COVID-19) pandemic, directed by the Federal Emergency Management Agency and the Department of Health and Human Services, during New York City's (NYC) initial surge of COVID-19, from 26 March to 1 May 2020, the US Army erected the Javits New York Medical Station (JNYMS) field hospital to support the city's overwhelmed healthcare system. The JNYMS tasked a nutrition operations team (NuOp) to provide patient meals and clinical nutrition evaluations to convalescent COVID-19 patients. However, few guidelines were available for conducting emergency nutrition and dietary response efforts prior to the field hospital's opening. In this case study, we summarize the experiences of the NuOp at the JNYMS field hospital, to disseminate the best practices for future field hospital deployments. We then explain the challenges in service performance, due to information, personnel, supply, and equipment shortages. We conclude by describing the nutrition service protocols that have been implemented to overcome these challenges, including creating a standardized recordkeeping system for patient nutrition information, developing a meal tracking system to forecast meal requirements with food service contractors, and establishing a training and staffing model for military-to-civilian command transition. We highlight the need for a standardized humanitarian emergency nutrition service response framework and propose a Nutrition Response Toolkit for Humanitarian Crises, which offers low-cost, easily adaptable operational protocols for implementation in future field hospital deployments.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.