Have geopolitics influenced decisions on American health foreign assistance efforts during the Obama presidency?

Background: This study sought to characterize the possible relationship between US geopolitical priorities and annual decisions on health foreign assistance among recipient nations between 2009 and 2016. Methods: Data on total planned United States (US) foreign aid and health aid were collected for the 194 member nations of the World Health Organization (WHO) from publicly available databases. Trends in per-capita spending were examined between 2009 and 2016 across the six regions of the WHO (Africa, Americas, Eastern Mediterranean, Europe, Southeast Asia, and the Western Pacific). Data on US national security threats were obtained from the Council on Foreign Relations’ annual Preventive Priorities Survey. Multivariable regression models were fitted specifying planned health aid as the dependent variable and threat level of a recipient aid nation as the primary independent variable. Results: Across the aggregate 80 planned recipient countries of US health aid over the duration of the study period, cumulative planned per-capita spending was stable (US$ 0.65 in both 2009 and 2016). The number of annual planned recipients of this aid declined from 74 in 2009 to 56 in 2016 (24.3% decline), with planned allocations decreasing in the Americas, Eastern Mediterranean, and Europe; corresponding increases were observed in Africa, Southeast Asia, and the Western Pacific. Regression analyses demonstrated a dose-response, whereby higher levels of threat were associated with larger declines in planned spending (critical threat nations: b = -3.81; 95% confidence interval (CI) -5.84 to -1.78, P ≤ 0.001) and one-year lagged (critical threat nations: b = -3.91; 95% CI, -5.94 to -1.88, P ≤ 0.001) analyses. Conclusions: Higher threat levels are associated with less health aid. This is a novel finding, as prior studies have demonstrated a strong association between national security considerations and decisions on development aid

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Have geopolitics influenced decisions on American health foreign assistance efforts during the Obama presidency?

BackgroundThis study sought to characterize the possible relationship between US geopolitical priorities and annual decisions on health foreign assistance among recipient nations between 2009 and 2016.MethodsData on total planned United States (US) foreign aid and health aid were collected for the 194 member nations of the World Health Organization (WHO) from publicly available databases. Trends in per-capita spending were examined between 2009 and 2016 across the six regions of the WHO (Africa, Americas, Eastern Mediterranean, Europe, Southeast Asia, and the Western Pacific). Data on US national security threats were obtained from the Council on Foreign Relations' annual Preventive Priorities Survey. Multivariable regression models were fitted specifying planned health aid as the dependent variable and threat level of a recipient aid nation as the primary independent variable.ResultsAcross the aggregate 80 planned recipient countries of US health aid over the duration of the study period, cumulative planned per-capita spending was stable (US$ 0.65 in both 2009 and 2016). The number of annual planned recipients of this aid declined from 74 in 2009 to 56 in 2016 (24.3% decline), with planned allocations decreasing in the Americas, Eastern Mediterranean, and Europe; corresponding increases were observed in Africa, Southeast Asia, and the Western Pacific. Regression analyses demonstrated a dose-response, whereby higher levels of threat were associated with larger declines in planned spending (critical threat nations: b = -3.81; 95% confidence interval (CI) -5.84 to -1.78, P ≤ 0.001) and one-year lagged (critical threat nations: b = -3.91; 95% CI, -5.94 to -1.88, P ≤ 0.001) analyses.ConclusionsHigher threat levels are associated with less health aid. This is a novel finding, as prior studies have demonstrated a strong association between national security considerations and decisions on development aid

Has development assistance for health facilitated the rise of more peaceful societies in sub-Saharan Africa?

Growing evidence suggests that health aid can serve humanitarian and diplomatic ends. This study utilised the Fragile States Index (FSI) for the 47 nations of the World Health Organizations’ Africa region for the years 2005–2014 and data on health and non-health development aid spending from the United States (US) for those same years. Absolute amounts of health and non-health aid flows from the US were used as predictors of state fragility. We used time-lagged, fixed-effects multivariable regression modelling with change in FSI as the outcome of interest. The highest quartile of US health aid per capita spending (≥$4.00 per capita) was associated with a large and immediate decline in level of state fragility (b = −7.57; 95% CI, −14.6 to −0.51, P = 0.04). A dose–response effect was observed in the primary analysis, with increasing levels of spending associated with greater declines in fragility. Health per-capita expenditures were correlated with improved fragility scores across all lagged intervals and spending quartiles. The association of US health aid with immediate improvements in metrics of state stability across sub-Saharan Africa is a novel finding. This effect is possibly explained by our observations that relative to non-health aid, US health expenditures were larger and more targeted

Probability of positivity for the rapid antigen tests as a function of antigen concentration on their cognate specimen.

Points plot test results (1 = positive, 0 = negative) versus concentration of N antigen in sample. Lines represent fits to data points to model probability of positivity. Panel (A): Probability for positive test result for Lumira (blue) and the STANDARD Q test (magenta) conducted on the ANS specimen as a function of the antigen concentration in the ANS specimen, and the STANDARD Q test (green) conducted on the saliva specimen as a function of the antigen concentration in the saliva specimen. Panel (B): Probability of positive test for the same three tests as a function of viral load in NPS specimens. The antigen concentration or viral load at which there is greater than 90% probability of a positive test result is indicated. The shaded areas show the 95% credible intervals for the probability functions.</p

Case definitions for the study.

Descriptions are of participant symptoms, regardless of test positivity. (DOCX)</p

Relationship between N-antigen signal and rapid antigen diagnostic signal.

Antigen concentration was measured from an anterior nasal swab specimen stored in the STANDARD Q lysis buffer and from saliva. The relationships are shown for Panel (A) the ANS STANDARD Q test signal and the ANS N-antigen concentration, Panel (B) the saliva STANDARD Q assigned test signal and the saliva N-antigen concentration, and Panel (C) the ANS Lumira signal and ANS N-antigen concentration.</p

Relationship between viral load and antigen concentration.

Viral load was measured from NPS stored in VTM. The Saliva Direct assay provides Ct values in the saliva specimen. Antigen concentration was measured from the same NPS-VTM specimen, from ANS specimen stored in the STANDARD Q lysis buffer and from saliva. Correlations are shown between (A) NPS viral load and NPS antigen concentration, (B) saliva viral load (in Ct values) and saliva antigen concentration, (C) NPS viral load and ANS antigen concentration, and (D) NPS viral load and saliva concentration.</p