Thinking about growth : a cognitive mapping approach to understanding small business development

School of Managemen

Governing Boards and Profound Organizational Change in Hospitals

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69047/2/10.1177_107755878904600204.pd

Exploiting genomic resources for efficient conservation and utilization of chickpea, groundnut, and pigeonpea collections for crop improvement

Both chickpea and pigeonpea are important dietary source of protein, while groundnut is one of the major oil crops. Globally, ~1.1 million grain legume accessions are conserved in genebanks, of which, ICRISAT genebank holds ~50,000 accessions of cultivated species and wild relatives of chickpea, pigeonpea, and groundnut from 133 countries. These genetic resources are reservoirs of many useful genes for the present and future crop improvement programs. Representative subsets in the form of core and mini core collections have been used to identify trait-specific genetically diverse germplasm for use in breeding and genomic studies in these crops. Chickpea, groundnut and pigeonpea have moved from ‘orphan’ to ‘genomic resources rich crops’. The chickpea and pigeonpea genomes have been decoded, and the sequences of groundnut genome will soon be available. With the availability of these genomic resources, the germplasm curators, breeders and molecular biologists will have abundant opportunities to enhance the efficiency of genebank operations, mine allelic variations in germplasm collection, identify genetically diverse germplasm with beneficial traits, broaden the cultigen’s genepool, and accelerate the cultivar development to address new challenges to production, particularly with respect to climate change and variability. Marker-assisted breeding approaches have already been initiated for some traits in chickpea and groundnut, which should lead to enhanced efficiency and efficacy of crop improvement. Resistance to some pests and diseases has been successfully transferred from wild relatives to cultivated species

Untangling the effects of overexploration and overexploitation on organizational performance: The moderating role of environmental dynamism

Because a firm's optimal knowledge search behavior is determined by unique firm and industry conditions, organizational performance should be contingent oil the degree to which a firm's actual level of knowledge search deviates from the optimal level. It is thus hypothesized that deviation from the optimal search, in the form of either overexploitation or overexploration, is detrimental to organizational performance. Furthermore, the negative effect of search deviation oil organizational performance varies with environmental dynamism: that is, overexploitation is expected to become more harmful. whereas overexploration becomes less so with all increase in environmental dynamism. The empirical analyses yield results consistent with these arguments. Implications for research and practice are correspondingly discussed

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

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

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

Within-person variation in serum lipids: implications for clinical trials

Background Little is known about the degree to which behavioural, biological, and genetic traits contribute to within-person variation in serum cholesterol. Materials and Methods The authors studied within-person variation in serum total and high density lipoprotein (HDL) cholesterol in 458 participants of 27 dietary intervention studies in Wageningen, The Netherlands, from 1976 to 1995. Results For a median of 4 days between blood draws, the geometric mean of the within-person standard deviation was 0.13 mmol/l (similar to5 mg/dl, coefficient of variation = 3.0%) for total cholesterol and 0.04 mmol/l (similar to1.5 mg/dl, coefficient of variation = 3.0%) for HDL cholesterol. In mixed-model linear regressions using within-person variance as the dependent variable and including lipid concentration and covariates listed below, within-person variance of both total cholesterol and HDL cholesterol was higher for greater number of days between blood draws and for self-selected diet rather than investigator-controlled diet. Within-person variance of total cholesterol only was higher for non-standardized versus standardized phlebotomy protocol and for female sex. The authors found evidence that the APOA4 -347 (12/22 genotype) and MTP -493 (11 genotype) polymorphisms may increase the within-person variation in total cholesterol. Conclusion Under certain study design (self-selected diet, use of non-standardized phlebotomy protocol) or participant characteristics (female, certain polymorphisms) within-person lipid variance is increased and required sample size will be greater. These findings may have important implications for the time and cost of such interventions