Fin whale movements in the Gulf of California, Mexico, from satellite telemetry

Fin whales (Balaenoptera physalus) have a global distribution, but the population inhabiting the Gulf of California (GoC) is thought to be geographically and genetically isolated. However, their distribution and movements are poorly known. The goal of this study was to describe fin whale movements for the first time from 11 Argos satellite tags deployed in the southwest GoC in March 2001. A Bayesian Switching State-Space Model was applied to obtain improved locations and to characterize movement behavior as either "area-restricted searching" (indicative of patch residence, ARS) or "transiting" (indicative of moving between patches). Model performance was assessed with convergence diagnostics and by examining the distribution of the deviance and the behavioral parameters from Markov Chain Monte Carlo models. ARS was the predominant mode behavior 83% of the time during both the cool (December-May) and warm seasons (June-November), with slower travel speeds (mean = 0.84 km/h) than during transiting mode (mean = 3.38 km/h). We suggest ARS mode indicates either foraging activities (year around) or reproductive activities during the winter (cool season). We tagged during the cool season, when the whales were located in the Loreto-La Paz Corridor in the southwestern GoC, close to the shoreline. As the season progressed, individuals moved northward to the Midriff Islands and the upper gulf for the warm season, much farther from shore. One tag lasted long enough to document a whale's return to Loreto the following cool season. One whale that was originally of undetermined sex, was tagged in the Bay of La Paz and was photographed 10 years later with a calf in the nearby San Jose Channel, suggesting seasonal site fidelity. The tagged whales moved along the western GoC to the upper gulf seasonally and did not transit to the eastern GoC south of the Midriff Islands. No tagged whales left the GoC, providing supporting evidence that these fin whales are a resident population

Striking the right balance in right whale conservation

Author Posting. © The Authors, 2009. This article is posted here by permission of NRC Research Press for personal use, not for redistribution. The definitive version was published in Canadian Journal of Fisheries and Aquatic Sciences 66 (2009): 1399-1403, doi:10.1139/F09-115.Despite many years of study and protection, the North Atlantic right whale (Eubalaena glacialis) remains on the brink of extinction. There is a crucial gap in our understanding of their habitat use in the migratory corridor along the eastern seaboard of the United States. Here, we characterize habitat suitability in migrating right whales in relation to depth, distance to shore, and the recently enacted ship speed regulations near major ports. We find that the range of suitable habitat exceeds previous estimates and that, as compared with the enacted 20 nautical mile buffer, the originally proposed 30 nautical mile buffer would protect more habitat for this critically endangered species.This work was supported in part by SERDP/DoD grant W912HQ-04-C-0011 to A.J. Read and P.N. Halpin as well as a James B. Duke Fellowship and a Harvey L. Smith Dissertation Year Fellowship to R.S. Schick

Spatial and Temporal Occurrence of Blue Whales off the U.S. West Coast, with Implications for Management

Mortality and injuries caused by ship strikes in U.S. waters are a cause of concern for the endangered population of blue whales (Balaenoptera musculus) occupying the eastern North Pacific. We sought to determine which areas along the U.S. West Coast are most important to blue whales and whether those areas change inter-annually. Argos-monitored satellite tags were attached to 171 blue whales off California during summer/early fall from 1993 to 2008. We analyzed portions of the tracks that occurred within U.S. Exclusive Economic Zone waters and defined the ‘home range’ (HR) and ‘core areas’ (CAU) as the 90% and 50% fixed kernel density distributions, respectively, for each whale. We used the number of overlapping individual HRs and CAUs to identify areas of highest use. Individual HR and CAU sizes varied dramatically, but without significant inter-annual variation despite covering years with El Niño and La Niña conditions. Observed within-year differences in HR size may represent different foraging strategies for individuals. The main areas of HR and CAU overlap among whales were near highly productive, strong upwelling centers that were crossed by commercial shipping lanes. Tagged whales generally departed U.S. Exclusive Economic Zone waters from mid-October to mid-November, with high variability among individuals. One 504-d track allowed HR and CAU comparisons for the same individual across two years, showing similar seasonal timing, and strong site fidelity. Our analysis showed how satellite-tagged blue whales seasonally used waters off the U.S. West Coast, including high-risk areas. We suggest possible modifications to existing shipping lanes to reduce the likelihood of collisions with vessels

Tracking North Pacific Humpback Whales To Unravel Their Basin-Wide Movements

In 2014, Oregon State University (OSU) initiated a multi-year project to study humpback whale (Megaptera novaeangliae) migrations in the North Pacific Ocean using satellite tracking technology in combination with genetic and photo-identification (photo-ID) analyses. The study is highly relevant to management, given the need for new information arising from the recent separation of humpback whales into Distinct Population Segments (DPS) for listing under the US Endangered Species Act, including four DPSs in the North Pacific (“Western North Pacific”, “Hawaii”, “Mexico”, and “Central America”) with different conservation statuses. The project’s objective was to conduct a comprehensive characterization of humpback whale movements during breeding, migration, and feeding periods by tagging animals in both a feeding area (southeastern Alaska) and a breeding area (Hawaii). In order to obtain representative results, the sampling plan called for two field efforts at each site, with Pacific Life Foundation funding the southeastern Alaska portion of the project (2014 and 2015 seasons), and the Hawaii portion being cost-shared through a combination of sources including the Makana Aloha Foundation (2015 season) and the US Department of the Navy (2018 season). This final report provides the combined results and accomplishments from these efforts. Argos-based, fully implantable tags were deployed on 37 humpback whales in Seymour Canal and Frederick Sound, southeastern Alaska, in 2014 and 2015. Tracking periods ranged from 3.3 to 78.3 d (mean = 28.2 d, sd = 16.2 d), with distances traveled ranging from 73 to 6,503 km (mean = 2,010 km, sd = 1,649 km). The tracked locations for these animals ranged over 40 degrees of latitude, from Lynn Canal and Icy Strait (59°N) in southeastern Alaska to the southern tip of Hawaii Island (19°N) in the Hawaiian Archipelago. Genetic and photo-ID analyses revealed that two of the whales tagged in 2014 were re-tagged in 2015, providing a unique opportunity to compare movements between years for the same individuals. For one of these animals the movements and their timing were similar between years, as it moved from Seymour Canal into Frederick Sound with a difference of 4 d between years. However, early failure of the tag in 2014 (after 6.2 d) prevented a longer comparison. In contrast, the movements of the second animal within southeastern Alaska were similar but the timing was very different between the two years, despite a similar tracking period (21.9 d in 2014 versus 19.1 d in 2015). In 2014 this animal spent a substantial amount of time in Seymour Canal (17 d) before moving into Stephens Passage for the remainder of its tracking period, while in 2015 the animal moved into Stephens Passage soon after tagging and only for a brief period before it moved into Frederick Sound, from where it initiated the migration toward Hawaii. Differences in timing notwithstanding, the similarities in the tracks between years for both animals provided some evidence of route fidelity, as has been recently shown for several species of migratory marine animals. Twenty of the whales tagged in southeastern Alaska began their winter migration to a low-latitude breeding area, with start dates ranging from 19 November to 6 January. Three of these whales were tracked to breeding areas, two to Hawaii and one to the Mexican mainland. Another 16 whales were headed in the direction of Hawaii and one in the direction of Mexico when their tags quit. The duration and distance spent on migration for the three animals that reached a breeding area ranged from 29 to 46 d and from 4,200 to 4,700 km, respectively. The two animals that arrived in Hawaii entered the archipelago at Hawaii Island. Forty-five tags were deployed on humpback whales off Maui, Hawaii, in 2015 and 2018. Two of these tags provided no locations. Tracking periods for the remaining 43 whales ranged from 0.1 to 147.2 d (mean = 20.8 d, sd = 29.0 d), with distances ranging from 13 to 11,302 km (mean = 1,217 km, sd = 2,348 km). The tracked locations for these animals ranged over 43 degrees of latitude, from the south coast of Maui (21°N) to the Bering Sea (64°N). While in Hawaiian waters, the majority of locations were in the Maui Nui region (the waters between the islands of Maui, Lanai, Molokai and Kahoolawe), during both in 2015 and 2018. Penguin Bank was another area heavily frequented by the tagged whales. Most tagged whales moved in a predominant northwesterly direction after tagging, with animals leaving Maui headed for Lanai, Molokai, and/or Penguin Bank. Several whales were also tracked to Oahu, and one whale was further tracked to both Kauai and Niihau. Only one whale was tracked southeast to Hawaii Island in 2015, but other tagging studies have documented eastward movements to Oahu, Penguin Bank, and Maui Nui, so it is apparent that whales may move extensively between islands, both in westerly and easterly directions. Nine of the whales tagged in Hawaii began their migration to a high-latitude feeding area, with departure dates ranging from 29 January to 11 April. Four of these whales were tracked to feeding areas, three to northern British Columbia and one to the eastern Aleutian Islands. Another four whales were headed on a northeasterly trajectory toward northern British Columbia and three more on a northerly or northwesterly trajectory toward destinations in the Aleutian Island chain when their tags quit. The three whales that were tracked to northern British Columbia arrived in the Haida Gwaii Archipelago after having spent 30-44 d and 4,300-5,000 km on migration. The animal that migrated north to the eastern Aleutians arrived at an area approximately 200 km south of Unimak Pass, 28 d and 3,775 km after departing Hawaii. These results, together with those obtained from animals tagged in southeastern Alaska that migrated to a breeding area (Hawaii or Mexico), provide evidence that the travel time and distance covered by humpback whales while on migration across the North Pacific Basin can vary widely, with overall ranges of 28-46 d and 3,775-5,000 km, respectively. A 50-km buffer zone around southeastern Alaska and Hawaii was used for purposes of characterizing whale movement speeds and residence times in the feeding and breeding areas (inside the buffer zones), as well as during migration (outside the buffer zones). Residence time was computed as the time period from tag deployment to when a whale crossed the buffer zone boundary as it departed on migration. Residence time in southeastern Alaska in late fall was estimated for 20 whales, ranging from 4.4 to 49.1 d (mean = 17.3 d), although additional information from earlier tagging studies indicated that individual humpback whales may use this feeding area for periods of up to four to five months. In contrast, residence time in Hawaii was estimated for nine whales, ranging from 3.3 to 23.2 d (mean = 14.8 d), consistent with earlier photo-ID and telemetry studies and lending support to the notion that that there is a rapid turnover of individuals in this breeding area during the winter season. In any case, overall true residence time in these areas is likely longer than the minimum values we report based on satellite telemetry, as we cannot know the time a whale had spent in an area prior to tagging. Movement speeds during the different phases of the migration (feeding, breeding, migrating) were calculated based on the portions of the tracks that occurred inside or outside the 50-km buffer zones. Whales tagged in southeastern Alaska moved at a mean speed of 1.01 km/h (median = 0.47 km/h, sd = 1.28 km/h) while in the southeastern Alaska feeding area; 5.51 km/h (median = 5.63 km/h, sd = 1.98 km/h) while migrating; and 1.49 km/h (median = 1.01 km/h, sd = 1.36 km/h) once they arrived in the Hawaii breeding area. Whales tagged in Hawaii moved at a mean speed of 1.36 km/h (median = 1.00 km/h, sd = 1.21 km/h) while in the Hawaii breeding area; 4.44 km/h (median = 4.32 km/h, sd = 2.18 km/h) while migrating; and 2.00 km/h (median = 1.53 km/h, sd = 1.53 km/h) once they arrived in the southeastern Alaska feeding area. These results showed that whales moved much slower while in the feeding and breeding areas than while migrating, and that travel speed from the feeding to the breeding areas was somewhat faster than from the breeding to the feeding areas. Biopsy samples were collected from 27 of the whales tagged in southeastern Alaska in 2014 and 2015, and from 39 of the whales tagged in Hawaii in 2015 and 2018. These 66 samples were identified by a unique multi-locus genotype of at least 14 microsatellite loci, which indicated they represented 64 unique individuals (after accounting for the two animals that were re-tagged). The 25 individuals tagged in southeastern Alaska represented 14 females and 11 males. The 39 individuals tagged in Hawaii represented four females and 35 males. The DNA profiles of the 64 individuals were compared to a reference database of 1,805 individuals sampled from 2004 to 2006 in the North Pacific by the program SPLASH, which revealed nine matches (i.e., genotype recaptures). Of these, six matches were recaptures within an area (four within southeastern Alaska and two within Hawaii) and three were recaptures between whales tagged in Hawaii and sampled previously on feeding areas in either northern British Columbia (n = 2) or southeastern Alaska (n = 1). Mitochondrial deoxyribonucleic acid (mtDNA) sequences of the 64 individuals resolved seven haplotypes for the consensus region of 500 base-pairs. All seven haplotypes had been previously described for North Pacific humpback whales by SPLASH, but only two occurred in the southeastern Alaska samples while all seven occurred in the Hawaii samples, supporting earlier results indicating a greater haplotypic diversity in the Hawaii breeding area than in the southeastern Alaska feeding area. Further, pairwise tests of differentiation between the tagging areas and the 18 SPLASH regional strata were consistent with those reported in that study, supporting our current understanding of humpback whale population structure, migratory destinations, and site fidelity in the North Pacific. Photo-IDs (fluke photographs) were obtained from 30 whales tagged in southeastern Alaska and from 24 whales tagged in Hawaii. Comparisons with the online Happywhale photo-ID database as well as with OSU’s own ID catalog revealed matches for 25 of the tagged whales (18 from southeastern Alaska and seven from Hawaii). Thirty-five percent of the tagged whales with an ID were found in Happywhale and 13 percent in OSU’s catalog. Most matches (19 of 25) were made within the same area in which the whale was tagged, with time spans between sightings of up to 14 years. Two whales tagged in southeastern Alaska in 2014 each had only one photo-ID match in a different area than the one in which they were tagged. Both had been previously photographed in Hawaii, one in 1997 (17 years apart) and in 2004 (10 years apart). The remaining four resighted tagged whales had both within- and between-area matches. Three of these latter whales were tagged in southeastern Alaska, with two of them matching sightings in Hawaii (1987 and 2019, respectively), and the third one being resighted in central California on two consecutive years (2017 and 2018). The fourth whale was tagged in Hawaii and matched sightings over six consecutive years (2013-2018) in southern British Columbia/northern Washington. An additional 26 matches were found in Happywhale from among 149 fluke photographs of untagged whales collected by OSU in Hawaii. Of these, 13 matches were made within Hawaii (with a maximum time span between sightings of 21 years); nine matches were made between Hawaii and different parts of Alaska, including southeastern Alaska, Kodiak Island, Cook Inlet, and the Shumagin Islands; four matches were made between Hawaii and Washington State and Vancouver Island, British Columbia; and one match was made between Hawaii and the Chukchi Sea, near Kolyuchin Island, northeastern Russia. Through the combined use of satellite tagging, genetics, and photo-ID, we characterized the patterns of humpback whale occupation in both a breeding and a feeding area in the North Pacific Ocean, as well as the long-distance migratory movements that these animals undertake seasonally between these areas. The results of this study revealed the complex migratory linkages between Hawaii and the high-latitude feeding areas with unprecedented detail. Genotype and photo-ID recaptures of multiple individuals between migratory destinations supported previously known strong connections between breeding and feeding areas (e.g., Hawaii and southeastern Alaska/northern British Columbia, and Hawaii and Washington/southern British Columbia). Satellite tracking also revealed the movements and migratory connections between Hawaii and feeding areas in the Aleutian Islands and the Bering Sea, while photo-ID recaptures demonstrated additional connections between Hawaii and feeding areas in the northern Gulf of Alaska (Shumagin Islands, Kodiak Island, Cook Inlet) and the Chukchi Sea. Additional years of sampling during different parts of the reproductive season and in other parts of the main Hawaiian islands (e.g., Kauai and Hawaii), as well as in the northwestern Hawaiian Islands, would provide valuable information to address outstanding questions about the humpback whale DPS using this extensive breeding area, as well as its broader connections to remote feeding areas throughout the North Pacific Basin, most of which are poorly known. Also, while the majority of whales tracked from southeastern Alaska showed a strong connection to the Hawaii breeding area, a small proportion of these animals demonstrated a connection to the Mexican mainland breeding area, indicating some mixing of the Hawaii and Mexico DPSs in the southeastern Alaska feeding area. These animals are of particular interest, as in their transit along the western coast of North America they overlap with animals from the Central America DPS, which forages off California and Oregon. Further tagging work to better understand the patterns of habitat use and the extent of the overlap between the Mexico and Central America DPSs in this region would greatly help current needs to improve how animals are assigned to DPS for management purposes in the context of relative exposure to anthropogenic activities, given their different conservation statuses

Mismatches in Scale Between Highly Mobile Marine Megafauna and Marine Protected Areas

Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only \u3c 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed \u3c 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques

Mismatches in Scale Between Highly Mobile Marine Megafauna and Marine Protected Areas

Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only \u3c 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed \u3c 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques

The political biogeography of migratory marine predators

During their migrations, marine predators experience varying levels of protection and face many threats as they travel through multiple countries' jurisdictions and across ocean basins. Some populations are declining rapidly. Contributing to such declines is a failure of some international agreements to ensure effective cooperation by the stakeholders responsible for managing species throughout their ranges, including in the high seas, a global commons. Here we use biologging data from marine predators to provide quantitative measures with great potential to inform local, national and international management efforts in the Pacific Ocean. We synthesized a large tracking data set to show how the movements and migratory phenology of 1,648 individuals representing 14 species-from leatherback turtles to white sharks-relate to the geopolitical boundaries of the Pacific Ocean throughout species' annual cycles. Cumulatively, these species visited 86% of Pacific Ocean countries and some spent three-quarters of their annual cycles in the high seas. With our results, we offer answers to questions posed when designing international strategies for managing migratory species

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global Burden of Disease Study 2016

Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0–100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8–98·1) in Iceland, followed by 96·6 (94·9–97·9) in Norway and 96·1 (94·5–97·3) in the Netherlands, to values as low as 18·6 (13·1–24·4) in the Central African Republic, 19·0 (14·3–23·7) in Somalia, and 23·4 (20·2–26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1–93·6) in Beijing to 48·0 (43·4–53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6–68·8) in Goa to 34·0 (30·3–38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle- SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view—and subsequent provision—of quality health care for all populations.info:eu-repo/semantics/publishedVersio