The Effect of Ibuprofen on Thermoregulatory Responses and Gastrointestinal Distress to Exercise in Hot Environments

Ibuprofen, a commonly used nonsteroidal anti-inflammatory drug (NSAID) among athletes to alleviate pain and inflammation during exercise, is hypothesized to mitigate exercise-induced increases core temperature (Tc) and improve heat tolerance during exercise in hot environments. However, its prophylactic use specially during exercise associated with heat stress may lead to harmful effects specifically inducing gastrointestinal complications such upper-GI bleeding. PURPOSE: To investigate the effect of ibuprofen on thermoregulatory responses and the occurrence of gastrointestinal symptoms to exercise in a hot environment. METHODS: In a double-blind, randomized, counterbalanced cross-over design, 6 endurance trained males and females (n=12) completed 60-minutes of treadmill running at moderate intensity [65% of maximum oxygen consumption (VO2max)] on two separate occasions. Ibuprofen (1200 mg total) or PL were administered by two capsule ingestions at 12 hours and 60 minutes prior to each trial. Throughout the exercise trials, core temperature using a rectal thermistor, and heart rate (HR) via chest strap was recorded every 5 minutes. Ratings of perceived exertion (RPE) (Borg, 1982), thermal sensation, thermal comfort, and GI symptoms were asked and recorded every 10 minutes of exercise. Urine specific gravity (USG) was measured using a refractometer pre and post trials to determine euhydration RESULTS: Tc and HR increased over time during both experimental trials but no significant differences were observed for peak TC and HR [FA1] (39.01±0.50 P=[FA2] ) between the two groups. There were no significant differences observed between groups in maximum RPE (15.67±1.99 P=0.391), maximum Ts (7.38±0.49 P=0.293), and maximum thermal comfort (3.38±1.06 P=0.397). No differences between groups were found for GI symptoms. CONCLUSION: In summary, our study demonstrates that ibuprofen, when administered with maximum dosage for over counter usage, does not have detrimental effect on GI distress or thermoregulatory responses during exercise in hot conditions. INTRODUCTION Ibuprofen, a widely used nonsteroidal anti-inflammatory drug (NSAID), is commonly employed to alleviate pain and inflammation. It functions by inhibiting two cyclooxygenase isoforms (COX-1 and COX-2) which regulate gastric mucosa, platelet aggregation, renal blood flow, and the production of inflammatory mediators that contribute to pain, inflammation, and fever (Emerson et al., 2021). Athletes may prophylactically take ibuprofen prior to training sessions or competition to prevent pain and inflammation from occurring and potentially improve performance (Warden, 2010). A recent systematic review by Emerson et al. (2021) presented the controversies as to whether the use of ibuprofen may also mitigate increases in core temperature (Tc) during exercise. Although exercise-induced hyperthermia is not driven by pyretic factors, it is hypothesized that inflammation can impact Tc responses and therefore, ibuprofen may lower Tc by eliciting an anti-inflammatory response through prostaglandin inhibition during exercise (Emerson et al., 2021). This in turn could increase heat tolerance during exercise by mitigating hyperthermia and the overall inflammatory response (Emerson et al., 2021). Nonetheless, Emerson et al. (2021) identified two studies reporting no significant effects on Tc when ibuprofen was used during exercise in hot environment. However, these studies were not conducted in runners and thermoregulatory responses were not measured. Additionally, in the study by Farquhar et al. (1999), the authors manipulated salt ingestion and dehydration which may have interfered with thermoregulatory responses and consequently, Tc. While ibuprofen may have these beneficial effects, caution is warranted with prophylactic use as ibuprofen may lead to harmful effects specifically inducing gastrointestinal complications such as benign dyspepsia and esophagitis, which may lead to upper-GI bleeding (Emerson et al., 2021; Warden, 2010). Additionally, evidence suggests that prophylactic ibuprofen use increases GI permeability during exercise; although this is not fully understood (Warden, 2010). Speculations have been made that ibuprofen may reduce local nitric oxide production affecting vasodilation and blood flow in the GI tract. Strenuous and prolonged exercise in hot conditions induce increases in Tc associated with increasing blood flow to the skin, potentially reducing blood flow to the GI system which may cause GI ischemia. Additionally, elevated Tc may also affect cell function and affect cell-wall permeability, leading to increased gut permeability and the passage of endotoxin and pathogens into the systemic circulation, resulting in endotoxemia. In fact, a study reported that 81% of marathon runners with Tc of 42°C exhibited elevated endotoxin levels, and of these individuals, 80.6% reported GI illnesses such as nausea, vomiting and/or diarrhea compared with 17.7% with low endotoxin values (Brock-Utne, 1988). While taking ibuprofen prior to an endurance event in hot conditions may theoretically reduce Tc, its effects on GI distress symptoms remains unclear. Additionally, ibuprofen may exacerbate the severity of GI damage during exercise in hot environments (Van Wijck et al., 2012). Therefore, further investigation into the effects of ibuprofen use during exercise in hot environments is essential to better educate and prepare athletes for performance and health in the heat. PURPOSE The purpose of this study was to investigate the effect of ibuprofen on thermoregulatory responses and the occurrence of gastrointestinal symptoms to exercise in a hot environment. DESIGN/METHODS In a double-blind, randomized, counterbalanced, cross-over design, twelve healthy, endurance-trained males [n=6; age = 30.3±7.2 years, height = 182.4±2.8 cm, body weight (BW) = 79.7±10.6 kg, body fat (BF) = 14.4±6.1 %, maximal oxygen consumption (VO2max) = 51.8±6.2 ml/kg/min] and females (n=6; age = 32±3.7 years, height = 166.7±7.4 cm, BW = 60.4±5.2 kg, BF = 15.9±9.5 %, VO2max = 49.8±8.0 ml/kg/min), were recruited locally via word of mouth for participation in this study. Written consent was obtained from all participants, and the study was approved by the University of New Mexico\u27s Institutional Review Board (IRB). Baseline assessment: The initial visit was comprised signing the informed consent followed by baseline measurements. These measurements included height, BW, and body composition assessment by bioelectrical impedance (InBody520). Subsequently, a maximal graded treadmill exercise test to determine maximum oxygen consumption (VO2max) was completed. Upon completion, the treadmill speed that corresponded to 65% of VO2max was identified and used as the exercise intensity during the subsequent experimental exercise trials in the heat chamber. A metabolic cart was used to perform identify the VO2max and the exercise intensity at 65% of VO2max. Experimental Trials: Two additional visits (visits two and three) involved participants performing 60-minutes of running on a treadmill at their pre-determined workload in a heat chamber set at 35°C and 20-40% humidity. Each participant completed the two exercise trials, separated by 7 days: one with ibuprofen, and one with ingestion of 600 mg of ibuprofen and the other with a placebo (Pl). Ibuprofen or PL were administered by two capsule ingestions at 12 hours and 60 minutes prior to each trial. Participants were provided a standardized breakfast to consume 2 hours prior to each trial. Upon arrival, Urine specific gravity (USG) was measured using a refractometer to determine euhydration (USGACSM’s guidelines for exercise testing and prescription, 2018). Urine flow rate was then measured for 1 hour. Pre-exercise nude body weight was recorded, and venous blood samples were collected via venipuncture. Blood samples were processed and frozen at -81°C freezer for subsequent analysis. Then, the subjects entered in the environment chamber and start performing the 60-minutes of running in the environmental chamber. Throughout the exercise trials, core temperature was monitored every 5 minutes using a rectal thermistor, and heart rate (HR) was continuously recorded via chest strap, also at 5 minutes intervals. Ratings of perceived exertion (RPE) (Borg, 1982), thermal sensation, and thermal comfort were asked and recorded every 10 minutes. A modified analogue scale to measure Upper GI, lower GI, overall gut discomfort, nausea, dizziness, and abdominal stitch GI symptoms was administered at minute 30 during exercise, post-, and 1-hour post-exercise (Gaskell et al., 2019). Upon exercise cessation, participants voided their bladder, obtained nude body weight then rested for 10 minutes, at room temperature, followed by a post-exercise blood sample collection. Water was then provided, and urine flow rate was measured for 1-hour post-exercise. A final 1-hour post-exercise blood and urine sample were collected. Statistical Analysis: Statistical analyses were performed using R Studio. Core temperature at baseline and every 10-minutes during exercise, as well pre- and post-exercise BW, and pre-, post-, and 1-hour post-exercise USG were analyzed using a two-way ANOVA (condition [placebo vs. ibuprofen] x time). Upper GI, lower GI, overall gut discomfort, nausea, dizziness, and abdominal stitch GI symptoms at minute 30, post-, and 1-hour post-exercise were analyzed using a two-way ANOVA. Additionally, a one-tailed paired t-test was used to assess between-group differences in peak Tc, maximum RPE, thermal sensation (Ts), thermal comfort, and HR, and pre- post-exercise urine volumes. An a level of ≤0.05 was used to determine statistical significance and data is presented as mean±standard deviation (SD). RESULTS[FA3] During both experimental trials, there was an increase in Tc recorded at 10-minute intervals over time (P= On a scale of 0-10 (0-3=very mild symptoms; 4-6=severe symptoms; and 7-10=very severe symptoms) upper GI, lower GI, overall gut discomfort, nausea, dizziness, and abdominal stitch GI symptoms were analyzed at minute 30, post-, and 1-hour post-exercise. No significant differences in upper (P=0.127), lower (P=0.536), and overall GI symptoms (P=0.303) when comparing groups and time points. No significant differences in nausea (P=0.254), dizziness (P=0.516) and abdominal stitch (P=0.926) were found when comparing groups and time points. Additionally, no significant differences were found between groups in %change BW (P=0.364) (placebo: -2.64±0.94; ibuprofen: -2.68±0.68). No significant differences were found between groups in pre-exercise urine volumes (P=0.338) (placebo: 454±185; ibuprofen: 428±235). There were also no significant differences found between groups in post-exercise urine volumes (P=0.493) (placebo: 72±65; ibuprofen: 72±67). Significant differences were found within the placebo group from pre- to 1-hour post-exercise USG (P=0.0019) (pre: 1.010±0.01; 1-hour post: 1.020±0.01[FA4] , respectively). There was also a significant difference found in USG pre- to 1-hour post-exercise (P=0.00043) (pre: 1.010±0; 1-hour post: 1.020±0.01) and pre- to post-exercise (P=0.04196) (post: 1.010±0.01)[FA5] within the ibuprofen group. DISCUSSION The purpose of this study was to investigate whether ibuprofen would have an effect on thermoregulatory responses and/or GI symptoms during exercise in a hot environment. Our findings indicate that the administration of 1600 mg of ibuprofen, taken 12-hours and 1-hour prior to a moderate-intensity exercise bout in a hot environment, did not induce any significant differences compared to placebo in core temperature, dehydration, perceived exertion, thermal comfort, and heart rate. Furthermore, ibuprofen did not increase GI symptoms compared to placebo. Ibuprofen is thought to have an effect on Tc responses during exercise by attenuating the inflammatory response typically associated with exercise (Emerson et al., 2021). However, this theory was not supported in the present study as our results indicates no differences in Tc between the ibuprofen and placebo trials. These results are similar to Farquhar et al. (1999) who also did not find an effect in Tc. Therefore, consuming 600mg of ibuprofen 12 hours and 1 hour before exercise does not mitigate increases in Tc during exercise in hot environments. It is possible that the ibuprofen dosage and timing of consumption was not sufficient to induce a significant effect. In this study, we used the maximum dosage of Ibuprofen for over-the-counter usage, but prescription up to 3,200 mg per day can be used with physician’s prescription. According to Emerson et al. (2020), higher doses of ibuprofen (1200 mg) are recommended to elicit an anti-inflammatory and analgesic effect as there is less COX-2 selectivity and shorter half-lives. Also, an increased consumption time (i.e., more than 24 hours) may be needed to observe significant effects. This was demonstrated in a study by Gilbert (1996) where chronic aspirin use affected negatively RPE, lactate, hematocrit, and fatigue during exercise. Although the mechanisms for GI distress during exercise, particularly in hot conditions, is not fully understood, it is suggested to be related to GI ischemia-reperfusion and inflammatory responses (Emerson et al., 2020; Garden & Granger, 2000; Jeukendrup et al., 2000). Furthermore, ibuprofen is known to induce GI side effects and is suggested that ibuprofen use during exercise in hot environments specifically, could potentially exacerbate GI distress (Van Wijck et al., 2012). In the present study, we did not find that ibuprofen significantly increased GI distress symptoms compared to placebo. This finding is similar to the findings by Emerson et al. (2020) who also did not find any significant increases in GI symptoms with the use of Naproxen during a 90-minute cycling exercise trial in a hot environment. However, these authors used a much lower NSAID dose of 250mg and participants consumed this 24 hour prior to their exercise trial. CONCLUSIONS In summary, our study demonstrates that ibuprofen, when administered with maximum dosage for over counter usage, does not have detrimental effect on GI distress or thermoregulatory responses during exercise in hot conditions. REFERENCES ACSM’s guidelines for exercise testing and prescription. (2018). (Tenth edition ed.). Wolters Kluwer. Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine & science in sports & exercise. Brock-Utne, J. G., Gaffin, S. L., Wells, M. T., Gathiram, P., Sohar, E., James, M. F., Morrell, D. F., Norman, R. J. (1988). Endotoxaemia in exhausted runners after a long-distance race. South African Medical Journal, 73(9), 533-536. https://doi.org/doi:10.10520/AJA20785135_9151 Emerson, D. M., Chen, S. C. L., Kelly, M. R., Parnell, B., & Torres-McGehee, T. M. (2021, 2021/04/01/). Non-steroidal anti-inflammatory drugs on core body temperature during exercise: A systematic review. Journal of Exercise Science & Fitness, 19(2), 127-133. https://doi.org/https://doi.org/10.1016/j.jesf.2020.12.003 Emerson, D. M., Davis, J. M., Chen, S. C. L., Torres-McGehee, T. M., Pfeifer, C. E., Emerson, C. C., Bivona, J. D., & Stone, J. V. (2020, 2020/03/01/). A 24 hour naproxen dose on gastrointestinal distress and performance during cycling in the heat. Sports Medicine and Health Science, 2(1), 19-24. https://doi.org/https://doi.org/10.1016/j.smhs.2020.02.003 Farquhar, W. B., Morgan, A. L., Zambraski, E. J., & Kenney, W. L. (1999). Effects of acetaminophen and ibuprofen on renal function in the stressed kidney. Journal of Applied Physiology, 86(2), 598-604. https://doi.org/10.1152/jappl.1999.86.2.598 Garden, D., & Granger, D. (2000). Pathophysiology of ischemia-reperfusion injury. J Pathol, 190(3), 255-266. Gaskell, S. K., Snipe, R. M., & Costa, R. J. (2019). Test–retest reliability of a modified visual analog scale assessment tool for determining incidence and severity of gastrointestinal symptoms in response to exercise stress. International Journal of Sport Nutrition and Exercise Metabolism, 29(4), 411-419. Gilbert, J. A. (1996). Acute and chronic effect of aspirin on selected endurance variables. Research in Sports Medicine: An International Journal, 6(4), 299-307. Jeukendrup, A., Vet-Joop, K., Sturk, A., Stegen, J., Senden, J., Saris, W., & Wagenmakers, A. (2000). Relationship between gastro-intestinal complaints and endotoxaemia, cytokine release and the acute-phase reaction during and after a long-distance triathlon in highly trained men. Clinical science, 98(1), 47-55. Van Wijck, K., Lenaerts, K., Van Bijnen, A. A., Boonen, B., Van Loon, L. J., Dejong, C. H., & Buurman, W. A. (2012). Aggravation of exercise-induced intestinal injury by Ibuprofen in athletes. Medicine & Science in Sports & Exercise, 44(12), 2257-2262. Warden, S. J. (2010, 2010/04/01). Prophylactic Use of NSAIDs by Athletes: A Risk/Benefit Assessment. The Physician and Sportsmedicine, 38(1), 132-138. https://doi.org/10.3810/psm.2010.04.1770 [FA1]Enter the values here for both conditions and p values [FA2]Need p for main effect time here for HR and Tc [FA3]You need to enter the values for the variables below. It is an expanded abstract and they might want to see more information. [FA4]It is missing the third decimal place… 1.000 [FA5]The same comments as abov

Mental Health First Aid Training in Rural Maryland during the COVID-19 Pandemic: Program Implementation through Virtual Delivery

The growing mental health concerns during COVID-19, particularly among rural residents, is a public health emergency. Rural residents are at an elevated risk, as rurality has been associated with various disparities, including lower accessibility to mental health services. Maryland Rural Opioid Technical Assistance (ROTA; Maryland Extension) aimed to address this issue by delivering evidence-based programs on opioid misuse and mental health to rural community members and practitioners throughout Maryland when the COVID-19 pandemic hit the U.S. and all research activities had to transition to the virtual setting. The current study provides an overview of the implementation process of the Mental Health First Aid (MHFA) program and reports the findings from the evaluation efforts. Participants (N = 398) completed a one-time online survey and answered open-ended questions, reporting high satisfaction rates and positive experiences with the virtual delivery of the program. Results suggested that the virtual format was still effective in program content delivery and that virtual delivery of evidence-based programs may be an opportune strategy to reach more rural residents. Recommendations for future research and practice efforts include building sustainable partnerships with local community organizations and considering rurality and prolonged-pandemic factors for effective program implementation

Herbert Hoover Elementary / Charleston Complete Corridor Plan

Completed as a part of CRP 425 bicycle and pedestrian planning, led by Dr. William Riggs, this planning project assessed the Herbert Hoover Elementary site and Charleston corridor in the City of Palo Alto with an aim to bring a complete streets strategy to the area. The plans recommend improved accessibility for bicycles and pedestrians along Charleston Road including modal separation between bicyclists and pedestians, improved vehicular flows to mitigate vehicular traffic congestion during peak hours and increased innovation in safety features to prevent pedestrian-vehicle conflicts

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019 : A systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings: Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation: The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding: Bill & Melinda Gates Foundation