Trends in mortality and life expectancy in Fiji over 20 years

Background Fiji, a Pacific Island nation of 884,887 (2017 census), has experienced a prolonged epidemiological transition. This study examines trends in mortality and life expectancy (LE) in Fiji by sex and ethnicity over 1996–2017, with comparisons to published estimates. Methods Trends in infant mortality rates (IMR), under-5 mortality (U5M), adult mortality (probability of dying), LE (at birth) and directly age-standardised death rates (DASRs) by sex and ethnicity, are calculated (with 95% confidence limits) using unit death records from the Fiji Ministry of Health and Medical Services. The LE gap between populations, or within populations over time, is examined using decomposition by age. Period trends are assessed for statistical significance using linear regression. Results Over 1996–98 to 2014–17: IMR and U5M for i-Taukei and Fijians of Indian descent declined; U5M decline for i-Taukei (24.6 to 20.1/1000 live births) was significant (p = 0.016). Mortality (15–59 years) for i-Taukei males was unchanged at 27% but declined for Indians 33 to 30% (p = 0.101). Mortality for i-Taukei females increased 22 to 24% (p = 0.011) but declined for Indians 20 to 18% (p = 0.240). DASRs 1996–2017 were lower for i-Taukei (9.3 to 8.2/1000 population) than Indian males (10.6 to 9.8/1000). DASRs declined for i-Taukei (both sexes, p < 0.05), and for Indians (both sexes, p > 0.05). Over 22 years, LE at birth increased by 1 year or less (p = 0.030 in male i-Taukei). In 2014–17, LE (years) for males was: i-Taukei 64.9, Indians 63.5; and females: i-Taukei 67.0 and Indians 68.2. Mortality changes in most 5-year age groups increased or decreased the LE gap less than 10 weeks over 22 years. Compared to international agency reports, 2014–17 empirical LE estimates (males 64.7, females 67.8) were lower, as was IMR. Conclusions Based on empirical data, LE in Fiji has minimally improved over 1996–2017, and is lower than some international agencies report. Adult mortality was higher in Indian than i-Taukei men, and higher in i-Taukei than Indian women. Exclusion of stillbirths resulted in IMRs lower than previously reported. Differing mortality trends in subgroups highlight the need to collect census and health data by ethnicity and sex, to monitor health outcomes and inform resource allocation.

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

Estimated incidence of COVID-19 illness and hospitalization — United States, February–September, 2020

Background In the United States, laboratory confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time. Methods To estimate the cumulative incidence SARS-CoV-2 infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of under-detection based on testing practices in inpatient and outpatient settings and assay sensitivity. Results We estimated that through the end of September, 1 of every 2.5 (95% Uncertainty Interval (UI): 2.0–3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8–9.0) non-hospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the U.S. population from February 27–September 30, 2020. Conclusions These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning.

Measurement, modeling and QALYs

Over the past 30 years, a mainstay of health technology assessment has been the creation of modeled incremental cost-per-quality adjusted life year (QALY) claims. These are intended to inform resource allocation decisions. Unfortunately, the reliance on the construction of QALYs from generic utility scales is misplaced. Those advocating QALY-based lifetime modeled claims fail to appreciate the limitations placed on these constructs by the axioms of fundamental measurement. Utility scales, such as those created by the EQ-5D-3L instrument, are nothing more than multidimensional, ordinal scales. Such scales cannot support basic arithmetic operations. Interval scales can support addition and subtraction; ratio scales the further operations of multiplication and division. Those who advocate the construction of QALYs fail to appreciate that such an operation is only possible if the utility scale is unidimensional and has ratio properties with a true zero. The utility measures available do not meet these requirements. As we cannot produce meaningful utility values, the QALY is an invalid construct. Consequently, cost-per-incremental QALY claims are impossible to sustain and the application of cost-per QALY thresholds meaningless. As utility is a latent, unidimensional variable, the best a measure of utility could achieve would be unidimensionality and interval scaling properties. Where such measures are available, they could support claims for response to therapy. Consequently, there would be no need to continue constructing imaginary lifetime value assessment frameworks. Admitting that the QALY is a fatally flawed construct means rejecting 30 years of cost-per-QALY models.

Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth

Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.

Addressing Health System Values in Health Technology Assessment: The Use of Evidence-Informed Deliberative Processes

Health technology assessment (HTA) is increasingly used around the globe to inform resource allocation decisions. Furthermore, the importance of using explicit and transparent criteria for coverage decision making in line with health system values has been acknowledged. However, the values of a health system are often not explicitly taken into account in the HTA process. This situation influences the allocation of scarce resources and could lead to a discord between the HTA outcome and the values of the health system. We describe how evidence-informed deliberative processes (EDPs) can help to improve this situation. EDPs are integrating two theoretical frameworks; multi-criteria decision-analysis and accountability for reasonableness. Through the use of EDPs, HTA agencies can ensure that health system values are more explicitly and consistently taken into account in the HTA process, enhancing the legitimacy of coverage decisions.

Novel Approaches for Estimating Female Sex Worker Population Size in Conflict-Affected South Sudan (Preprint)

BACKGROUND Limited data exist describing the population size of female sex workers (FSW) in South Sudan. A population size estimation exercise among FSW was undertaken in Juba and Nimule during the Eagle Survey. OBJECTIVE The study aimed to estimate the number of FSW in Juba and Nimule to inform resource allocation and service provision for FSW. METHODS We utilized service and unique object multipliers, and 3-source capture-recapture methods in conjunction with a respondent-driven sampling (RDS) survey to estimate the number of FSW in Juba and Nimule. For service multiplier, the number of FSW testing for HIV in 2015 (Juba) and 2016 (Nimule) was obtained from the LINKAGES program targeting FSW. Survey participants were asked whether they had been tested for HIV by LINKAGES during the relevant period. A total of 2 separate unique object distributions were conducted in Juba and Nimule. In Nimule, these were combined to produce a 3-source capture-recapture estimate. The exercise involved distribution of key chains and bangles to FSW, documentation of the number of those who received unique objects, and questions during RDS survey to assess whether participants received unique objects. RESULTS In Juba, the service multiplier method yielded an estimate of 5800 (95% CI 4927-6673) FSW. The unique object estimate (key chain and RDS participation) yielded 5306 (95% CI 4673-5939). Another estimate using RDS participation and receipt of a bangle yielded a much lower estimate of 1863 (95% CI 1776-1951), as did a 2-source estimate of key chain and bangle (2120, 95% CI 2028-2211). A 3-source capture-recapture estimate could not be produced because aggregate rather than individual level data were collected during the third capture. The multiplier estimate using key chain and RDS participation was taken as the final population estimate for FSW in Juba, which constitutes more than 6% of the female population aged 15 to 64 years. In Nimule, the service multiplier method yielded an estimate of 9384 (95% CI 8511-10,257). The 2-source estimates for key chain and RDS yielded 6973 (95% CI 4759-9186); bangles and RDS yielded a higher estimate of 13,104 (95% CI 7101-19,106); key chains and bangles yielded a lower estimate of 1322 (95% CI 1223-1420). The 3-source capture-recapture method using Bayesian nonparametric latent-class model-based estimate yielded a population of 2694 (95% CI 1689-6945), and this was selected as the final estimate for Nimule, which constitutes nearly 40% of female population aged 15 to 64 years. CONCLUSIONS The service and unique object multiplier, and 3-source capture-recapture methods were successfully used to estimate the number of FSW in Nimule, whereas service and unique object multiplier methods were successfully used in Juba. These methods yielded higher than previously estimated FSW population sizes. These estimates will inform resource allocation and advocacy efforts to support services for FSW.

Risk, politics, and science: A new approach to UK marine biodiversity monitoring.

The UK recognises the importance of understanding marine ecosystems and biodiversity to achieve its ambition of ‘clean, healthy, safe and biologically diverse seas’. Yet comprehensive UK marine monitoring presents a considerable challenge in terms of the resources required. The Joint Nature Conservation Committee has led the development of an ambitious framework for marine monitoring that will integrate the different components of biodiversity together, and with other marine monitoring. This has required a range of work from novel science to developing monitoring indicators and survey methods. It also seeks efficiency savings through, practical integration of survey time on vessels, and access to new data e.g., satellites. Our approach uses risk to inform resource allocation by utilising human activities data and the interactions with biodiversity to create risk models and determine survey priorities. This is achieved by engaging scientists and policy makers to develop monitoring options for different biodiversity components (i.e., benthic habitats, cetaceans, seals, seabirds, fish, cephalopods and plankton). This process will allow policy makers to successfully conclude on the level of resourcing required for marine monitoring, that reflects the risk to biodiversity and the public’s concerns for the marine environment, and fulfils our national and international legislative obligations.