title: The impact of non-severe hypoglycemic events on work productivity and diabetes management.
abstract: OBJECTIVE
  Hypoglycemia is a common complication of treatment with certain diabetes drugs. Non-severe hypoglycemic events (NSHEs) occur more frequently than severe events and account for the majority of total events. The objective of this multi-country study was to identify how NSHEs in a working population affect productivity, costs, and self-management behaviors.
  
  
  METHODS
  A 20-minute survey assessing the impact of NSHEs was administered via the Internet to individuals (≥ 18 years of age) with self-reported diabetes in the United States, United Kingdom, Germany, and France. The analysis sample consisted of all respondents who reported an NSHE in the past month. Topics included: reasons for, duration of, and impact of NSHE(s) on productivity and diabetes self-management.
  
  
  RESULTS
  A total of 1404 respondents were included in this analysis. Lost productivity was estimated to range from $15.26 to $93.47 (USD) per NSHE, representing 8.3 to 15.9 hours of lost work time per month. Among individuals reporting an NSHE at work (n = 972), 18.3% missed work for an average of 9.9 hours (SD 8.4). Among respondents experiencing an NSHE outside working hours (including nocturnal), 22.7% arrived late for work or missed a full day. Productivity loss was highest for NSHEs occurring during sleep, with an average of 14.7 (SD 11.6) working hours lost. In the week following the NSHE, respondents required an average of 5.6 extra blood glucose test strips. Among respondents using insulin, 25% decreased their insulin dose following the NSHE.
  
  
  CONCLUSIONS
  NSHEs are associated with substantial economic consequences for employers and patients. Greater attention to treatments that reduce NSHEs could have a major, positive impact on lost work productivity and overall diabetes management.
authors.affiliation: The Brod Group, Mill Valley, CA 94941, USA. mbrod@thebrodgroup.net
authors.forename: Meryl
authors.initials: M
authors.lastname: Brod
authors.forename: Torsten
authors.initials: T
authors.lastname: Christensen
authors.forename: Trine L
authors.initials: TL
authors.lastname: Thomsen
authors.forename: Donald M
authors.initials: DM
authors.lastname: Bushnell
mesh_terms: D000041:Absenteeism; D000328:Adult; D000704:Analysis of Variance; D001786:Blood Glucose / Q000187:drug effects / Q000378:metabolism; D016009:Chi-Square Distribution; D017281:Cost of Illness; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy / Q000191:economics*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy / Q000191:economics*; D004526:Efficiency; D004651:Employment / Q000191:economics*; D005060:Europe; D005260:Female; D059066:Gross Domestic Product; D019538:Health Care Surveys; D006302:Health Services Research; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000139:chemically induced / Q000175:diagnosis / Q000188:drug therapy / Q000191:economics*; D007004:Hypoglycemic Agents / Q000009:adverse effects* / Q000191:economics; D020407:Internet; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D011934:Reagent Strips / Q000191:economics; D012456:Salaries and Fringe Benefits; D012648:Self Care / Q000191:economics; D012720:Severity of Illness Index; D011795:Surveys and Questionnaires; D013997:Time Factors; D014481:United States
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D011934:Reagent Strips