Temporal Trends and Severity in Injury and Illness Incidence in the National Basketball Association Over 11 Seasons.

BACKGROUND: There is a paucity of current data describing injuries in professional basketball players. Utilizing publicly available injury data will allow for greater transparency for stakeholders to use the data as a shared resource to create future basketball injury prevention programs. PURPOSE: To describe injury and illness incidence, severity, and temporal trends in National Basketball Association (NBA) players. Among those who develop time-loss injury or illness, we estimated severity based on games missed because of injury or illness. STUDY DESIGN: Descriptive epidemiology study. METHODS: Publicly available NBA data were extracted through a reproducible computer-programmed process from the 2008 to 2019 seasons. Data were externally validated by 2 independent reviewers through other publicly available data sources. Injury and illness were calculated per 1000 athlete game-exposures (AGEs). Injury severity was calculated as games missed because of injury or illness. Injury and illness data were stratified by body part, position, severity (slight, minor, moderate, or severe), month, and year. RESULTS: A total of 1369 players played a total of 302,018 player-games, with a total of 5375 injuries and illnesses. The overall injury and illness incidence was 17.80 per 1000 AGEs. The median injury severity was 3 games (interquartile range, 0-6 games) missed per injury. Overall, 33% of injuries were classified as slight; 26%, as minor; 26%, as moderate; and 15%, as severe. The ankle (2.57 injuries/1000 AGEs), knee (2.44 injuries/1000 AGEs), groin/hip/thigh (1.99 injuries/1000 AGEs), and illness (1.85 illnesses/1000 AGEs) had the greatest incidence of injury and illness. Neither injury or illness incidence nor severity was different among basketball playing positions. Injury incidence demonstrated increasing incremental trends with season progression. Injuries were similar throughout the 11-year reporting period, except for a substantial increase in the lockout-shortened 2012 season. CONCLUSION: The ankle and knee had the greatest injury incidence. Injury incidence was similar among basketball positions. Injury incidence increased throughout the season, demonstrating the potential relationship between player load and injury incidence.

Temporal trends in incidence of time-loss injuries in four male professional North American sports over 13 seasons.

Sports-related injuries increase healthcare cost burden, and in some instances have harmful long term physical and psychological implications. There is currently a lack of comprehensive data on temporal injury trends across professional North American sports. The purpose of this study was to compare temporal trends, according to incidence and time-loss injuries, by body part in professional baseball, basketball, football, and ice hockey. Public injury data from Major League Baseball, National Basketball Association, National Football League, and National Hockey League from 2007 to December 2019 were extracted and used. A mean of 62.49 injuries per 100 players per season was recorded for all professional sports. The groin/hip/thigh reported the greatest season proportional injury incidence for baseball, football, and ice hockey, with the groin/hip/thigh as the third highest injury incidence in basketball. When stratifying by more specific body part groupings, the knee demonstrated the greatest injury proportional incidence for basketball, football, and ice hockey, with the knee as the third highest proportional injury incidence for baseball. There was an increased in basketball ankle injuries following 2011-2012 season. Football and ice hockey reported the greatest concussion proportion incidence, with football demonstrating an increase in concussions over time, and a substantial increase in concussions from the 2014 to 2015 season. These publicly extracted data and findings can be used as a shared resource for professional baseball, basketball, football, and ice hockey for future individual and across sport collaborations concerning resource allocation and decision making in order to improve player health.

Health state utility values in major depressive disorder treated with pharmacological interventions: a systematic literature review.

BACKGROUND: Major depressive disorder (MDD) is associated with decreased patient well-being and symptoms that can cause substantial impairments in patient functioning and even lead to suicide. Worldwide, MDD currently causes the second-most years lived with disability and is predicted to become the leading cause of disability by 2030. Utility values, capturing patient quality of life, are required in economic evaluations for new treatments undergoing reimbursement submissions. We aimed to identify health state utility values (HSUVs) and disutilities in MDD for use in future economic evaluations of pharmacological treatments. METHODS: Embase, PubMed, Econlit, and Cochrane databases, plus gray literature, were searched from January 1998 to December 21, 2018, with no language or geographical restrictions, for relevant studies that reported HSUVs and disutilities for patients with MDD receiving pharmacological interventions. RESULTS: 443 studies were identified; 79 met the inclusion criteria. We focused on a subgroup of 28 articles that reported primary utility data from 16 unique studies of MDD treated with pharmacological interventions. HSUVs were elicited using EQ-5D (13/16, 81%; EQ-5D-3L: 11/16, 69%; EQ-5D-3L or EQ-5D-5L not specified: 2/16), EQ-VAS (5/16, 31%), and standard gamble (1/16, 6%). Most studies reported baseline HSUVs defined by study entry criteria. HSUVs for a first or recurrent major depressive episode (MDE) ranged from 0.33 to 0.544 and expanded from 0.2 to 0.61 for patients with and without painful physical symptoms, respectively. HSUVs for an MDE with inadequate treatment response ranged from 0.337 to 0.449. Three studies reported HSUVs defined by MADRS or HAMD-17 clinical thresholds. There was a large amount of heterogeneity in patient characteristics between the studies. One study reported disutility estimates associated with treatment side effects. CONCLUSIONS: Published HSUVs in MDD, elicited using methods accepted by health technology assessment bodies, are available for future economic evaluations. However, the evidence base is limited, and it is important to select appropriate HSUVs for the intervention being evaluated and that align with clinical health state definitions used within an economic model. Future studies are recommended to elicit HSUVs for new treatments and their side effects and add to the existing evidence where data are lacking.