Understanding Stakeholder Dissemination Preferences for an Agriculture, Forestry, and Fishing Injury Surveillance System

References

• U.S. Department of Labor. Number and rate of fatal work injuries, by private industry sector, 2021. Graphics for Economic News Release. U.S. Department of Labor, Bureau of Labor Statistics (BLS); 2022. https://www.bls.gov/charts/census-of-fatal-occupational-injuries/number-and-rate-of-fatal-work-injuries-by-industry.htm. Accessed July 14, 2023.
   Google Scholar
• Wuellner SE, Bonauto DK. Injury classification agreement in linked Bureau of Labor Statistics and Workers’ compensation data [article]. Am J Industrial Med. 2014;57(10):1100–1109. doi:10.1002/ajim.22289.
   PubMed Web of Science ®Google Scholar
• Boden LI. Capture–recapture estimates of the undercount of workplace injuries and illnesses: sensitivity analysis. Am J Ind Med. 2014;57(10):1090–1099. doi:10.1002/ajim.22247.
   PubMed Web of Science ®Google Scholar
• Leigh JP, Du J, McCurdy SA. An estimate of the U.S. government’s undercount of nonfatal occupational injuries and illnesses in agriculture [Article]. Ann Epidemiol. 2014;24(4):254–259. doi:10.1016/j.annepidem.2014.01.006.
   PubMed Web of Science ®Google Scholar
• Statistics BoL. Survey Of Occupational Injuries And Illnesses Data Washington, DC. 2018. https://www.bls.gov/iif/soii-data.htm. Accessed May 14, 2020.
   Google Scholar
• U.S. Department of Agriculture. National Agricultural Statistics Service. Farms and Land in Farms 2022 Summary. 2023. https://downloads.usda.library.cornell.edu/usda-esmis/files/5712m6524/bk129p580/2z10z2698/fnlo0223.pdf. Accessed July 14, 2023.
   Google Scholar
• U.S. Code: Merchant Marine Act, 1920, 46 U.S.C. §§ 861-889 (1958). 1920.
   Google Scholar
• Rautiainen R. Surveillance of Agriculture, Forestry, and Fishing Injury, Illness, and Economic Impacts. J Agromedicine. January 2, 2021;26(1):59–61. doi:10.1080/1059924X.2021.1849508.
   PubMed Web of Science ®Google Scholar
• Kica J, Rosenman KD. Multisource surveillance for non-fatal work-related agricultural injuries. J Agromedicine. 2020;25(1):86–95. doi:10.1080/1059924X.2019.1606746.
   PubMed Web of Science ®Google Scholar
• Landsteiner AM, McGovern PM, Alexander BH, et al. Incidence rates and trend of serious farm-related injury in Minnesota, 2000-2011. J Agromedicine. 2015;20(4):419–26. doi:10.1080/1059924X.2015.1075449.
   PubMed Web of Science ®Google Scholar
• Allen DL, Kearney GD, Higgins S. A descriptive study of farm-related injuries presenting to emergency departments in North Carolina: 2008-2012. J Agromedicine. 2015;20(4):398–408. doi:10.1080/1059924X.2015.1074972.
   PubMed Web of Science ®Google Scholar
• Scott E, Hirabayashi L, Graham J, Krupa N, Jenkins P. Using hospitalization data for injury surveillance in agriculture, forestry and fishing: a crosswalk between ICD10CM external cause of injury coding and the occupational injury and illness classification system. Inj Epidemiol. February 15, 2021;8(1):6. doi:10.1186/s40621-021-00300-6.
   PubMedGoogle Scholar
• Scott E, Hirabayashi L, Levenstein A, Krupa N, Jenkins P. The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports. Health Inf Sci Syst. July 29, 2021;9(1):31. doi:10.1007/s13755-021-00161-9.
   PubMed Web of Science ®Google Scholar
• Yang L, Branscum A, Bovbjerg V, Cude C, Weston C, Kincl L. Assessing disabling and non-disabling injuries and illnesses using accepted workers compensation claims data to prioritize industries of high risk for Oregon young workers. J Safety Res. June 1, 2021;77:241–254. doi:10.1016/j.jsr.2021.03.007.
   PubMed Web of Science ®Google Scholar
• Weichelt B, Heimonen T, Gorucu S, et al. Redesigning a sentinel surveillance system for collecting and disseminating near real-time agricultural injury reports: system usability study. JMIR Form Res. Aug 2, 2019;3(3):e13621. doi:10.2196/13621.
   PubMedGoogle Scholar
• Weichelt B, Gorucu S, Murphy D, Pena AA, Salzwedel M, Lee BC. Agricultural youth injuries: a review of 2015-2017 cases from U.S. J Agromedicine. July 3, 2019;24(3):298–308. doi:10.1080/1059924X.2019.1605955.
   PubMed Web of Science ®Google Scholar
• Weichelt B, Salzwedel M, Heiberger S, Lee BC. Establishing a publicly available national database of US news articles reporting agriculture-related injuries and fatalities. Am J Industrial Med. May 22, 2018;61(8):667–674. doi:10.1002/ajim.22860.
   Web of Science ®Google Scholar
• Kim J, Trueblood AB, Kum H-C, et al. Crash narrative classification: identifying agricultural crashes using machine learning with curated keywords. Traffic Inj Prev. 2020;22(1):1–5. doi:10.1080/15389588.2020.1836365.
   PubMed Web of Science ®Google Scholar
• Doza S, Bovbjerg VE, Vaughan A, et al. Health-related exposures and conditions among US fishermen. J Agromedicine. Jul, 2022;27(3):284–291. doi:10.1080/1059924X.2021.1944416.
   PubMed Web of Science ®Google Scholar
• Johnson A, Baccaglini L, Haynatzki GR, Achutan C, Loomis D, Rautiainen RH. Agricultural injuries among Farmers and ranchers in the Central United States during 2011-2015. J Agromedicine. January 2, 2021;26(1):62–72. doi:10.1080/1059924X.2020.1845268.
   PubMed Web of Science ®Google Scholar
• Beseler CL, Rautiainen RH. Assessing nonresponse bias in farm injury surveillance data. J Agric Saf Health. Oct 21, 2021;27(4):215–227. doi:10.13031/jash.14554.
   PubMed Web of Science ®Google Scholar
• Scott E, Hirabayashi L, Graham J, et al. Health and safety in the Maine woods: assemblage and baseline characteristics of a longitudinal cohort of logging workers. Am J Industrial Med. 2020;63(10):907–916. doi:10.1002/ajim.23165.
   PubMed Web of Science ®Google Scholar
• Thacker SB, Berkelman RL. Public health surveillance in the United States. Epidemiol Rev. 1988;10(1):164–90. doi:10.1093/oxfordjournals.epirev.a036021.
   PubMedGoogle Scholar
• Hartley DM. Using social media and internet data for public health surveillance: the importance of talking. Milbank Q. Mar, 2014 ;92(1):34–9. doi:10.1111/1468-0009.12039.
   PubMed Web of Science ®Google Scholar
• Velasco E, Agheneza T, Denecke K, et al. Social media and internet-based data in global systems for public health surveillance: a systematic review. Milbank Q. 2014, Mar;92(1):7–33. doi:10.1111/1468-0009.12038.
   PubMed Web of Science ®Google Scholar
• Richards CL, Iademarco MF, Atkinson D, et al. Advances in public health surveillance and information dissemination at the Centers for Disease Control and Prevention. Public Health Rep. Jul/Aug, 2017;132(4):403–410. doi:10.1177/0033354917709542.
   PubMed Web of Science ®Google Scholar
• Dodson EA, Geary NA, Brownson RC. State legislators’ sources and use of information: bridging the gap between research and policy. Health Educ Res. Dec 2015;30(6):840–8. doi:10.1093/her/cyv044.
   PubMed Web of Science ®Google Scholar
• Ellen ME, Léon G, Bouchard G, Ouimet M, Grimshaw JM, Lavis JN. Barriers, facilitators and views about next steps to implementing supports for evidence-informed decision-making in health systems: a qualitative study. Implementat Sci. December 5, 2014;9(1):179. doi:10.1186/s13012-014-0179-8.
   PubMedGoogle Scholar
• Turbelin C, Boëlle PY. Open data in public health surveillance systems: a case study using the French Sentinelles network. Int J Med Inform. Oct, 2013;82(10):1012–21. doi:10.1016/j.ijmedinf.2013.06.009.
   PubMed Web of Science ®Google Scholar
• Vocht F, de Vocht F. Defining ‘evidence’ in public health: a survey of policymakers’ uses and preferences. Eur J Public Health. May 1, 2017;27(suppl_2):112–117. doi:10.1093/eurpub/ckv082.
   PubMedGoogle Scholar
• Lavis JN, Robertson D, Woodside JM, et al. How can research organizations more effectively transfer research knowledge to decision makers? Milbank Q. 2003;81(2):221-48, 171–2. doi:10.1111/1468-0009.t01-1-00052.
   Web of Science ®Google Scholar
• Brownson RC, Eyler AA, Harris JK, et al. Getting the word out: new approaches for disseminating public health science. J Public Health Manag Pract. Mar/Apr, 2018;24(2):102–111. doi:10.1097/PHH.0000000000000673.
   PubMed Web of Science ®Google Scholar
• Yang L, Branscum A, Kincl L. Understanding occupational safety and health surveillance: expert consensus on components, attributes and example measures for an evaluation framework. BMC Public Health. 2022;22(1):498. doi:10.1186/s12889-022-12895-6.
   PubMed Web of Science ®Google Scholar
• Shwed A, Hoekstra F, Bhati D, et al. IKT guiding principles: demonstration of diffusion and dissemination in partnership. Res Involv Engagem. July 12, 2023;9(1):53. doi:10.1186/s40900-023-00462-1.
   PubMedGoogle Scholar
• Myers N. Information sharing and community resilience: toward a whole community approach to surveillance and combatting the “infodemic”. World Med & Health Policy. Sep, 2021;13(3):581–592. doi:10.1002/wmh3.428.
   PubMed Web of Science ®Google Scholar
• Gatewood J, Monks SL, Singletary CR, et al. Social media in public health: strategies to distill, package, and disseminate public health research. J Public Health Manag Pract. Sep/Oct, 2020;26(5):489–492. doi:10.1097/PHH.0000000000001096.
   PubMed Web of Science ®Google Scholar
• Hanneke R, Link JM. The complex nature of research dissemination practices among public health faculty researchers. J Med Libr Assoc. 2019, Jul;107(3):341–351. doi:10.5195/jmla.2019.524.
   PubMed Web of Science ®Google Scholar
• Oliver KA, de Vocht F, Mony A, Everett M. Identifying public health policymakers’ sources of information: comparing survey and network analyses. Eur J Public Health. May 1, 2017;27(suppl_2):118–123. doi:10.1093/eurpub/ckv083.
   PubMedGoogle Scholar
• Harris PA, Taylor R, Minor BL, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. July 1, 2019;95:103208. doi:10.1016/j.jbi.2019.103208.
   PubMed Web of Science ®Google Scholar
• Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. April 1, 2009;42(2):377–381. doi:10.1016/j.jbi.2008.08.010.
   PubMed Web of Science ®Google Scholar
• NIOSH. NIOSH Industry and Occupation Computerized Coding System (NIOCCS): U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention. National Institute for Occupational Safety and Health, Division of Field Studies & Engineering, Health Informatics Branch; 2022. https://csams.cdc.gov/nioccs/Default.aspx. Accessed 2023 28 Jun.
   Google Scholar
• Thomas N. NIOCCS - SOC and NAICS Coding Post-Processing: Rpubs – University of New Hampshire Institute on Disability. 2023. https://rpubs.com/UNHIoD/1012492. Accessed June 28, 2023.
   Google Scholar
• Roberts B, Shkembi A, Smith LM, et al. Beware the Grizzlyman: a comparison of job- and industry-based noise exposure estimates using manual coding and the NIOSH NIOCCS machine learning algorithm. J Occup Environ Hyg. Jul, 2022;19(7):437–447. doi:10.1080/15459624.2022.2076860.
   PubMed Web of Science ®Google Scholar
• NIOCCS Team. Discrepancy on industry coding. Recipient: Cristina Hansen-Ruiz. 2023. Accessed June 9, 2023.
   Google Scholar
• McFall BH, Sonnega A Feasibility and reliability of automated coding of occupation in the health and retirement study. Michigan Retirement Research Center Research Paper No 2018-392. 2018.
   Google Scholar
• Scott E, Weichelt B, Lincoln J. The future of U.S. Agricultural injury surveillance needs collaboration. J Agromedicine. 2022;28(1): doi:10.1080/1059924X.2022.2148032.
   Web of Science ®Google Scholar
• Sturgis P, Brunton-Smith I, Jackson J. Trust in science, social consensus and vaccine confidence. Nat Hum Behav. November 1, 2021;5(11):1528–1534. doi:10.1038/s41562-021-01115-7.
   PubMed Web of Science ®Google Scholar
• Agley J. Assessing changes in US public trust in science amid the COVID-19 pandemic. Public Health. June 1, 2020;183:122–125. doi:10.1016/j.puhe.2020.05.004.
   PubMed Web of Science ®Google Scholar
• Eichengreen B, Aksoy CG, Saka O. Revenge of the experts: will COVID-19 renew or diminish public trust in science? J Public Econ. January 1, 2021;193:104343. doi:10.1016/j.jpubeco.2020.104343.
   PubMed Web of Science ®Google Scholar
• Murakami M, Tsubokura M. Deepening community-aligned science in response to wavering trust in science. Lancet. Mar 13, 2021;397(10278):969–970. doi:10.1016/S0140-6736(21)00358-5.
   PubMed Web of Science ®Google Scholar
• Benson-Greenwald TM, Trujillo A, White AD, et al. Science for others or the self? Presumed motives for science shape public trust in science. Pers Soc Psychol Bull. Mar, 2023;49(3):344–360. doi:10.1177/01461672211064456.
   PubMed Web of Science ®Google Scholar