Assessing the pre-implementation context for financial navigation in rural and non-rural oncology clinics

BackgroundFinancial navigation (FN) is an evidence-based intervention designed to address financial toxicity for cancer patients. FN's success depends on organizations' readiness to implement and other factors that may hinder or support implementation. Tailored implementation strategies can support practice change but must be matched to the implementation context. We assessed perceptions of readiness and perceived barriers and facilitators to successful implementation among staff at nine cancer care organizations (5 rural, 4 non-rural) recruited to participate in the scale-up of a FN intervention. To understand differences in the pre-implementation context and inform modifications to implementation strategies, we compared findings between rural and non-rural organizations.MethodsWe conducted surveys (n = 78) and in-depth interviews (n = 73) with staff at each organization. We assessed perceptions of readiness using the Organizational Readiness for Implementing Change (ORIC) scale. In-depth interviews elicited perceived barriers and facilitators to implementing FN in each context. We used descriptive statistics to analyze ORIC results and deductive thematic analysis, employing a codebook guided by the Consolidated Framework for Implementation Research (CFIR), to synthesize themes in barriers and facilitators across sites, and by rurality.ResultsResults from the ORIC scale indicated strong perceptions of organizational readiness across all sites. Staff from rural areas reported greater confidence in their ability to manage the politics of change (87% rural, 76% non-rural) and in their organization's ability to support staff adjusting to the change (96% rural, 75% non-rural). Staff at both rural and non-rural sites highlighted factors reflective of the Intervention Characteristics (relative advantage) and Implementation Climate (compatibility and tension for change) domains as facilitators. Although few barriers to implementation were reported, differences arose between rural and non-rural sites in these perceived barriers, with non-rural staff more often raising concerns about resistance to change and compatibility with existing work processes and rural staff more often raising concerns about competing time demands and limited resources.ConclusionsStaff across both rural and non-rural settings identified few, but different, barriers to implementing a novel FN intervention that they perceived as important and responsive to patients' needs. These findings can inform how strategies are tailored to support FN in diverse oncology practices

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Additional file 1 of Lessening the Impact of Financial Toxicity (LIFT): a protocol for a multi-site, single-arm trial examining the effect of financial navigation on financial toxicity in adult patients with cancer in rural and non-rural settings

Additional file 1. Post-Implementation Interview Guide – includes a draft of the semi-structured interview guide to be used following intervention implementatio

Additional file 3 of Lessening the Impact of Financial Toxicity (LIFT): a protocol for a multi-site, single-arm trial examining the effect of financial navigation on financial toxicity in adult patients with cancer in rural and non-rural settings

Additional file 3. Baseline Survey – includes the patient experience survey given to patients prior to the interventio

Additional file 4 of Lessening the Impact of Financial Toxicity (LIFT): a protocol for a multi-site, single-arm trial examining the effect of financial navigation on financial toxicity in adult patients with cancer in rural and non-rural settings

Additional file 4. Intake Form – includes the form used by navigators to assess patient financial needs and potential resource

Subtle structures with not-so-subtle functions: A dataset of arthropod constructs and their host plants

The construction of shelters on plants by arthropods might influence other organisms via changes in colonization, community richness, species composition and functionality. Arthropods, including beetles, caterpillars, sawflies, spiders, and wasps often interact with host plants via the construction of shelters, building a variety of structures such as leaf ties, tents, rolls, and bags; leaf and stem galls, and hollowed out stems. Such constructs might have both an adaptive value in terms of protection (i.e., serve as shelters) but may also exert a strong influence on terrestrial community diversity in the engineered and neighboring hosts via colonization by secondary occupants. While different traits of the host plant (e.g., physical, chemical and architectural features) may affect the potential for ecosystem engineering by insects, such effects have been, to a certain degree, overlooked. Further analyses of how plant traits affect the occurrence of shelters may thus enrich our understanding of the organizing principles of plant-based communities. This dataset includes more than a thousand unique records of ecosystem engineering by arthropods, in the form of structures built on plants. All records have been published in the literature, and span both natural structures (90.6% of the records) and structures artificially created byresearchers (9% of the records). The data were gathered between 1932 and 2021, across more than 50 countries and several ecosystems, ranging from polar to tropical zones. Besides data on host plants and engineers, we aggregated data on the type of constructs and the identity of inquilines using these structures. This dataset highlights the importance of these subtle structures for the organization of terrestrial arthropod communities, enabling hypotheses testing in ecological studiesaddressing ecosystem engineering and facilitation mediated by constructs.Fil: Pereira, Cássio Cardoso. Universidade Federal de Minas Gerais; BrasilFil: Novais, Samuel. Instituto de Ecología; MéxicoFil: Barbosa, Milton. Universidade Federal de Minas Gerais; BrasilFil: Negreiros, Daniel. Universidade Federal de Minas Gerais; BrasilFil: Gonçalves Souza, Thiago. Universidade Federal de Pernambuco; BrasilFil: Roslin, Tomas. Swedish University Of Agricultural Sciences; SueciaFil: Marquis, Robert. University of Missouri; Estados UnidosFil: Marino, Nicholas. Universidade Federal do Rio de Janeiro; BrasilFil: Novotny, Vojtech. Biology Centre of the Academy of Sciences of the Czech Republic; República ChecaFil: Orivel, Jerome. Universite de Guyane; GuyanaFil: Sui, Shen. New Guinea Binatang Research Center; GuineaFil: Aires, Gustavo. Universidade Federal de Pernambuco; BrasilFil: Antoniazzi, Reuber. University of Texas at Austin; Estados UnidosFil: Dáttilo, Wesley. Instituto de Ecología; MéxicoFil: Breviglieri, Crasso. Universidade Estadual de Campinas; BrasilFil: Busse, Annika. Bavarian Forest National Park; AlemaniaFil: Gibb, Heloise. La Trobe University. Department Of Ecology, Environment And Evolution; AustraliaFil: Izzo, Thiago. Universidade Federal do Mato Grosso do Sul; BrasilFil: Kadlec, Tomas. Czech University Of Life Sciences Prague; República ChecaFil: Kemp, Victoria. Queen Mary University of London; Reino UnidoFil: Kersch Becker, Monica. University of Alabama at Birmingahm; Estados UnidosFil: Knapp, Michal. Czech University Of Life Sciences Prague; República ChecaFil: Kratina, Pavel. Queen Mary University of London; Reino UnidoFil: Luke, Rebecca. Royal Holloway University of London; Reino UnidoFil: Majnari, Stefan. University Of Zagreb, Faculty Of Science; CroaciaFil: Maritz, Robin. University of the Western Cape; SudáfricaFil: Martins, Paulo Mateus. Universidade Federal de Pernambuco; BrasilFil: Mendesil, Esayas. Jimma University; EtiopíaFil: Michalko, Jaroslav. Slovak Academy of Sciences; EslovaquiaFil: Mrazova, Anna. Biology Centre of the Academy of Sciences of the Czech Republic; República ChecaFil: Peri, Mirela Serti. University Of Zagreb. Faculty Of Science; CroaciaFil: Petermann, Jana. University Of Salzburg. Department Of Biosciences; AustriaFil: Ribeiro, Sérvio. Universidade Federal de Ouro Preto; BrasilFil: Sam, Katerina. University of Missouri; Estados UnidosFil: Trzcinski, M. Kurtis. University of British Columbia; CanadáFil: Vieira, Camila. Universidade Federal de Uberlândia; BrasilFil: Westwood, Natalie. University of British Columbia; CanadáFil: Bernaschini, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Carvajal, Valentina. Universidad de Caldas; ColombiaFil: González, Ezequiel. Czech University of Life Sciences Prague; República ChecaFil: Jausoro, Mariana. Universidad Nacional de Chilecito. Departamento de Ciencias Basicas y Tecnologicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Kaensin, Stanis. New Guinea Binatang Research Center; GuineaFil: Ospina, Fabiola. Universidad de Caldas; ColombiaFil: Pérez, Jacob Cristóbal. Universidad Autónoma del Estado de México; MéxicoFil: Quesada, Mauricio. Universidad Autónoma del Estado de México; MéxicoFil: Rogy, Pierre. University of British Columbia; CanadáFil: Srivastava, Diane S.. University of British Columbia; CanadáFil: Szpryngiel, Scarlett. The Swedish Museum of Natural History; SueciaFil: Tack, Ayco J. M.. Stockholms Universitet; SueciaFil: Teder, Tiit. University of Tartu; EstoniaFil: Videla, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Viljur, Mari Liis. University of Tartu; EstoniaFil: Koricheva, Julia. Royal Holloway University of London; Reino UnidoFil: Fernandes, Geraldo Wilson Afonso. Universidade Federal de Minas Gerais; BrasilFil: Romero, Gustavo Q.. Universidade Estadual de Campinas; BrasilFil: Cornelissen, Tatiana. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas; Brasi

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundRegular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.MethodsThe Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.FindingsThe leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.InterpretationLong-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere