Healthcare Workers' Willingness to Report to Work During an Influenza Pandemic: A Systematic Literature Review.
Author(s)Rossow, Caren Colleen.
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AbstractHEALTHCARE WORKERS' WILLINGNESS TO REPORT TO WORK DURING AN INFLUENZA PANDEMIC: A SYSTEMATIC LITERATURE REVIEW Caren Colleen Rossow A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Health Administration School of Health Sciences Central Michigan University Mount Pleasant, Michigan September 2012ii Accepted by the Faculty of the College of Graduate Studies, Central Michigan University, in partial fulfillment of the requirements for the doctoral degree Dissertation Committee: Svetlana Ivanitskaya, Ph.D. Committee Chair Lawrence Fulton, Ph.D. Faculty Member William Fales, M.D. Faculty Member September 11, 2012 Date of Defense Roger Coles, Ed.D. Dean College of Graduate Studies November 28, 2012 Approved by the College of Graduate Studies iii This manuscript is dedicated to my mother, Evelyn Rosely Schutze who’s lifelong encouragement made this possible; to my husband David, for his love and support, and my children Valerie and Andrew for their patience throughout this lengthy process.iv ABSTRACT HEALTHCARE WORKERS' WILLINGNESS TO REPORT TO WORK DURING AN INFLUENZA PANDEMIC: A SYSTEMATIC LITERATURE REVIEW by Caren Rossow Objective To systematically evaluate the evidence of healthcare workers’ willingness to report to work during a pandemic influenza. Design Systematic review of published articles. Data Survey Cinahl, CSA Illumina, Healthcare Reference Center, Health Sciences, Health Source: Nursing /Academic Edition, Nursing and Allied Health, Medline, ProQuest, PubMed Central, Google Scholar and Citation Lists. Review Method Articles describing healthcare workers’ willingness to report to work during an influenza pandemic and associated outcomes were selected for review. Only scholarly journals were accepted. Group contrasts were analyzed using identical measurements and numerical scales. Results Of the 206 studies reviewed, 28 studies met inclusion criteria. Of these, 18 studies, split into two methodologically homogenous groups, were included in the quantitative analysis. In 10 studies researchers elicited responses using a scale with a midpoint, unsure or an undecided v option, which were coded as "not willing to report to work." In these studies, mean willingness to report to work was 56.12% (SD = 21.36, range = 59.20). In 8 studies researchers elicited responses using a forced choice scale without a midpoint, coding the top half of the scale as "willing to report to work". In these studies, mean willingness to report to work was 75.26%, (SD = 10.81, range = 33.10). Means differed significantly by study group, as indicated by Mann-Whitney nonparametric U test, U =17.00 , p = 0.043. A detailed meta-analysis could not be performed due to vast variation in study design, such as use of scale points, dichotomization methods and coding of HCW groups. Conclusion There is little consistency in findings that summarize percent of healthcare workers willing to report to work. A large proportion of them (up to 40%) may fall into the “unsure” group, which needs to be carefully studied. Scales without a midpoint systematically overestimated willingness to report to work and the magnitude of this overestimation was high (about 20%). To enable generalization, we recommend ways to standardize the design and reporting of future studies. Specifically, we recommend scales with the option of unsure or not applicable, inclusion of tables that show willingness to report to work by HCW type, gender, and scale point.vi ACKNOWLEDGMENTS I wish to thank my committee for their patience and steadfast dedication to the completion of this manuscript; Dr. Svetlana Ivanitskaya (Chairperson), Dr. Lawrence Fulton, and Dr. William Fales.vii TABLE OF CONTENTS LIST OF TABLES ....................................................................................................................... viii LIST OF FIGURES ....................................................................................................................... ix KEY TO ACRONYMS ...................................................................................................................x CHAPTER I. INTRODUCTION ...............................................................................................................1 II. MATERIALS AND METHODS .........................................................................................5 Subjects ....................................................................................................................5 Literature Search Strategy........................................................................................5 Study Screening and Inclusion Criteria ...................................................................6 Study Selection Data Abstract and Processing ........................................................6 III. RESULTS ............................................................................................................................7 Literature Search ......................................................................................................8 Characteristics of the Included Studies ....................................................................9 IV. DISCUSSION ....................................................................................................................38 Summary of Evidence ............................................................................................38 V. LIMITATIONS ..................................................................................................................43 VI. FUTURE RESEARCH ......................................................................................................45 APPENDICES ...............................................................................................................................47 REFERENCES ............................................................................................................................134 viii LIST OF TABLES TABLE PAGE 1. Inclusion/Exclusion Criteria ................................................................................................6 2. Number of Studies and Participants by Healthcare Sector ................................................12 3. Analysis of Scale Points and Dichotomization Methods ...................................................17ix LIST OF FIGURES FIGURE PAGE 1. Process Flow of the Literature Survey Method from Survey to Analysis of Data ..............9 2. Percent of HCWs’ Willing to Report to Work: Ten Studies that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Eight Studies that used the Second Method of Dichotomization (a scale without a midpoint split in the middle) ..............................................................................................35 3. Percent of HCWs’ Willing to Report to Work: Eight Studies (without avian influenza) that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Four Studies (without avian influenza) that Used the Second Method of Dichotomization (a scale without a midpoint split in the middle) ......35 4. Percent of HCWs’ Willing to Report to Work: Eight Studies (without H1N1) that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Seven Studies (without H1N1) that Used the Second Method of Dichotomization (a scale without a midpoint split in the middle) .....................................36 5. Percent of HCWs’ Willing to Report to Work: Seven Studies that Reported Physicians and Twelve Studies that Reported Nurses .......................................................36 x KEY TO ACRONYMS AI – Avian Influenza AIDS – Acquired Immunodeficiency Syndrome CCU – Critical Care Unit CI – Confidence Intervals CS – Convenience Sample CSS – Cross Sectional Survey DNK – Do Not Know DO – Doctor of Osteopathic ED – Emergency Department ER – Emergency Room EMS – Emergency Medical Services EMT – Emergency Medical Technician EPPM- Extended Parallel Process Model FG – Focus Group GP – General Practitioner HCW – Heath Care Worker HSS – Health and Human Services ICU – Intensive Care Unit INT - Interview IT – Information Technology LPN – Licensed Practical Nurse MD – Medical Doctor xi NA – Not Applicable NRS – Non-random Sample OR – Odds Ratio PAC – Physician Assistant Certified PCP- Primary Care Physicians PEDS – Pediatrics PhD – Doctor of Philosophy PPE – Personal Protective Equipment RCP – Respiratory Care Providers RN – Registered Nurse RR - Response Rate RS – Random Sample SMO-Senior Medical Officer SRS – Stratified Random Sample US – United States WHO – World Health Organization 1 CHAPTER I INTRODUCTION Pandemics have long been described since the 16th century in degrees of gravity, impact, and intervals fluctuating between 10 and 50 years.1 In the 20th century three pandemics, particularly the severe Spanish Flu pandemic of 1918 “killed more people in six months than acquired immunodeficiency syndrome (AIDS) has killed in the last 25 years and more than were killed in all of World War I.” 2 Caused by subtype H1N1, the 1918 pandemic was accountable for illness of approximately 20 to 40 percent of the population globally and death in 50 million individuals.3 The US alone experienced an estimated 675,000 deaths in an eight month period of time.3 Less severe pandemics followed including the Asian Flu in 1957 and the Hong Kong Flu in 1968, which killed approximately 70,000 and 34,000 Americans respectively.3 In 2009 the H1N1 Swine Flu pandemic caused illness in an estimated 43 million to 88 million in the US, with an estimated 195,086 to 402,719 hospitalizations and 8,868 to 18,306 deaths.4 The World Health Organization (2010) estimates that 20–40 percent of populations in regions of the globe were infected by the H1N1 virus.5 These differences largely relate to the severity and virulence of the influenza viruses that cause the pandemics. However, 20th century pandemics reveal commonality. In each, about 30 percent of the US population developed the illness, approximately half-seeking medical care.6 Children under the age of 18 tended to have the highest rate of illness.7 The pandemics spread swiftly with all communities experiencing outbreaks in a relatively short period of time.7 Present day pandemic threat is linked to an outbreak of avian influenza by the H5N1 strain of the influenza A virus.2 Transmitted by infected poultry, hundreds of millions of 2 chickens, ducks, turkeys and geese have died or been culled to prevent the spread of the virus. 8 A total of 574 human confirmed cases were reported to the WHO between January 2003 and 2011 from a number of countries in Asia, the Near East, Africa, and Europe.9 The virus currently seems limited to poultry workers and has shown an inability to spread from human to human. The widespread nature of H5N1 in birds and the possibility of mutations over time raise fears and apprehensions that the virus will become communicable between humans with disastrous consequences.3 Federal planning efforts, based on these characteristics assume that the number of patients seeking medical care will be approximately 50 percent.6 Those afflicted with the disease and the worried well could overwhelm the healthcare systems.6 If extremely virulent, the “pandemic influenza may quickly rise to raise the level of a catastrophic incident resulting in mass casualties, placing overwhelming demanding on religious, cultural, and emotional burdens on local communities and families of victims.”6 The need for inpatient acute care and intensive care beds with ventilation services could increase by more than 25 percent.6 Non-urgent and elective medical and surgical procedures will cease.10 Alternate care sites will open utilizing public schools, stadiums, churches, and other settings; converted to provide needed medical services.10 This devastating impact on society may include a 40 to 50 percent absentee rate, an overwhelmed healthcare system, interruption impacting transportation, the supply chain, including medical supplies, water, electricity, financial systems, and the overall economy. 10 The duration and multiple waves of a severe pandemic are predicted to last several months to more than a year.11 The national response and contingency plans suggests healthcare worker (HCW) shortages will occur primarily due to illness and mortality amongst the health care providers.11 3 Preparing for the influx or surge in patients begins with identifying critical staffing and services necessary to provide medical care.6 Medical, nursing, respiratory therapists, pharmacists, and laboratory personnel are vital for health care delivery. The current nationwide shortage of HCWs worsens the staffing dilemma.12 Medical students ran inpatient wards in the 1918 pandemic, performing both medical and nursing duties.13 A recommended strategy in the US Department of Health and Human Services Pandemic Flu Plan is cross training staff for areas such as the emergency department and intensive care.6 Ancillary support personnel are also essential for infrastructure support: food, environmental services, medical supply stores, maintenance and other services.11 Pandemic healthcare workforce planning is essential to mitigate the loss of life. An effective health care response ensures adequate amount of supplies of a variety of skilled HCWs who are willing, able, and available to serve in a pandemic event. Reallocating healthcare providers from non-acute outpatient and community care sites to emergency response assignments in acute care will optimize the workforce potential.6 Recruiting retired HCWs, qualified volunteers, reserve or retired military medical and nursing providers, as well as dentists, dental assistants, pharmacists, and health professional students will be essential to meet the overwhelming workforce demands.6 In a systematic review of 27 articles published between 1991 and 2007 Chaffee identifies certain factors influencing the willingness to work, including the type of disaster. 14 Inclusive of several disaster scenarios: weather related, radiological, nuclear, biological, and chemical, Chaffee cites a biological outbreak as a significant barrier to willingness to work.14 The systematic review discusses the lowest willingness to report to work rate that was observed in response to a scenario of a hypothetical pandemic influenza outbreak in New York City. A mere 4 11 percent of health care aides and 37 percent of registered nurses were willing to take care of patients infected with the influenza.15 Chaffee recommends that future researchers enhance their measurement tools in order to build confidence in the data and utilize information for preparedness planning. This study is a systematic review of studies that specifically examined HCWs’ willingness to report to work during influenza pandemics. How can preparedness planning be informed by generalizing the results of these studies to future influenza pandemics? 5 CHAPTER II MATERIALS AND METHODS Subjects The population of interest includes all HCWs such as employees in any sector of healthcare (pre-hospital, hospital, community, and public health), clinical personnel (doctors, nurses, paramedics EMTs, allied health, students), non-clinical personnel (clerical, environmental services, maintenance, food services, and laundry staff), and administrative staff members (administrators, management, and supervisors) who participated in a survey of willingness to report to work. Literature Search Strategy Information sources were obtained from nine databases: Cinahl (1950 – 2011); CSA Illumina (earliest – 2011); Healthcare Reference Center (all); Health Sciences (1879 – 2011); Health Source (all); Nursing and Allied Health (all); Medline (1992 – 2011); ProQuest (all); PubMed Central (all); and Google Scholar (1950- 2011) in December 2009, with an updated search in December 2010, and May 2011. Date ranges were selected based on advanced search availability on the databases. Terms included key words: HCWs’ willingness: to report to work, to respond, to risk one’s life and care for patients, to provide clinical services, likelihood of reporting, of working or continuing to work in a pandemic influenza, as well as barriers and strategies to enhance willingness. The researcher reviewed all scholarly journals. Reference lists of all eligible articles were manually searched to identify additional relevant studies. 6 Study Screening and Inclusion Criteria The researcher reviewed all titles and abstracts. Duplicates were removed. A full review was completed if inclusion criteria requirements were met. Studies that reported only subjective perspectives or commentary on willingness to report to work were not included. Table 1. Inclusion/Exclusion Criteria Inclusion Criteria Exclusion Criteria Key Respondents - healthcare workers Time Frame - oldest on record to May 2011 Distinguishing Features - healthcare sector: EMS, hospital/hospital based system, homecare, community, and other Distinguishing Features - reported finding on willingness/likelihood to report or to work or respond to an influenza pandemic Research Method - qualitative or quantitative Publication Type - peer reviewed research articles and abstracts The study was not in English The study did not assess willingness to report to work The study was not relevant to the research questions The study did not present original work The study was a meeting abstract or editorial Study Selection Data Abstract and Processing All articles and abstracts meeting the inclusion criteria (n = 206) were thoroughly examined in full text by the researchers (Appendix A-I). Abstracted data from eligible studies included: first author, year of publication, country, influenza or avian pandemic, healthcare sector, population, study design, and statistical analysis of measures (Appendix B-I). Research 7 articles were further examined utilizing methodology modified from Shi (2008).16 The researchers built comparative tables that showed response rates, sampling methods and respondent characteristics (Appendix B-II); operational definitions of dependent variables and author-stated percentage of willingness to report to work (Appendix B-III); as well as any other significant findings and notes (Appendix B-IV). Finally, the quality of the research design was analyzed for each of the included studies, such as sampling procedures, response bias and statistical analysis. 8 CHAPTER III RESULTS Literature Search A systematic literature review of nine electronic data bases completed in May 2010 revealed 263 records with any of the following keywords HCWs’ willingness to report to work, willingness to respond, likelihood of reporting to work, likelihood of working and likelihood of continuing to work during a pandemic. As shown in Figure 1, a total of 111 articles were identified through database search; 152 by reference list and Internet search. After duplicates were removed, 206 were screened to identify abstracts that met inclusion criteria. One hundred-twenty were removed because they failed to meet inclusion criteria. For the remaining 83 abstracts, complete study records were obtained and reviewed in depth. Based on the review, 52 articles were eliminated for failure to meet inclusion criteria. The remaining studies were mostly quantitative (28 out of 31). The number of qualitative studies (N = 3) was too small to conduct a separate review. Consequently, a review was performed on 28 quantitative studies (Figure 1). 9 Figure 1. Process Flow of the Literature Survey Method from Survey to Analysis of Data From: Moher D, Liberati A, Tetziaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analysis 17 Characteristics of the Included Studies Quality Assessment Quality characteristics of the 28 included studies (Appendix B-II) were examined, such as the purpose of the study, study design, and sampling. The explicit purpose of the study was 10 well stated in all studies, and three studies listed hypotheses. Random samples were employed in 9 out of 28 studies. Potential bias and limitations were reported in 26 of the 28 selected studies. Concerns of potential study design bias were stated in eight of the studies, selection bias in 12 studies, response rate in six studies, and non-response bias in seven studies (Appendix C-I). Survey instrument bias due to wording or survey instrument bias or phrasing were noted in four studies. Anonymity concerns or the lack of privacy and confidentially during respondent completion of the survey were voiced in two studies. In 13 studies, the authors stated concerns related to social desirability bias or the inability to predict whether HCWs who reported willingness to work during a pandemic will actually be working during this event. Statistical procedures were performed in all 28 studies (Appendix C-II). Specifically, the authors computed odds ratio to measure effect size (14 studies), conducted regression analyses for predicting outcomes (14 studies), and calculated chi-square (13 studies), Student’s t (6 studies) and Spearman’s correlation (2 studies). Descriptive statistics for dependent variables were reported in all studies (Appendix C-II), however, the level of detail provided varied from an overall count or percent of workers willing to report to work (multiple scale points on the high end of the scale) to frequencies by scale point. Scale points are discussed in greater detail below. Geographic location Of the 28 studies, 15 (54%) were conducted in the US (Appendix C-III). The remaining studies 13 (46%) came from Australia, China, Egypt, Japan, Taiwan and the United Kingdom. Approximately one-half (53%) of all study participants were US HCWs, n= 16,847. 11 Event In most studies (n = 24) researchers asked HCWs if they would be willing to work during an influenza pandemic, the remaining studies examined HCWs’ willingness to work during an avian influenza that is associated with particularly high death rates. One study presented both an influenza and avian influenza. In addition to pandemic influenza, the authors of two studies, 6,23 2036, measured willingness to report to work during other disasters, such as an earthquake, ice storm, hurricane and a chemical or radiation event. Number of study participants A total of 28 studies and 31, 663 respondents were included in the review. The minimum number of respondents per study was 56 and the maximum was 7,378. The mean sample size was 1131.14 (SD = 1490.15) (Appendix C-IV). Healthcare Sector There were nineteen unique categories of the healthcare sectors represented in the study. The largest healthcare sector was represented by hospitals and hospital based systems (18 studies), EMS (two studies), home care (one study) and local health department (three studies). The category “other,” as shown in Table 2, included homecare, medical reserve corps, community nurses, licensed nurses in Maryland, and primary-care physicians. Table 2 also shows the number of study participants by sector. 12 Table 2. Number of Studies and Participants by Healthcare Sector Number of studies Number of study participants Hospitals 18 23,792 EMS 2 1,311 Public Health 3 4,557 Home Care 1 384 Other 4 1,619 Total 28 31, 663 HCW Type Many different types of HCWs were studied by the authors of the 28 studies included in this review. Overall, of 204 different HCW groups covered by the studies, there were 148 HCW groups with unique labels, for example, attending physician, administrator and community health staff. The number of groups included in a study varied from 1 to 22 with the mode of 2 and the mean of 7 (Appendix C-IV). In three of the studies, categories were listed extremely broadly, such as “all categories of healthcare workers,” “hospital staff” and “categories of professional classifications.” Descriptions of professional groups also varied widely. For example, nurse groups (mentioned in 25 of 28 studies) were labeled as follows: nurse (9 studies), clinical nurse consultant, clinical nurse specialist, community nurse, early childhood nurses, ER nurse, licensed practical nurse, any nurse, nurse educators and nurse managers, nurse practitioners, nurse unit manager, nursing, nursing administration (2 studies), nursing staff (2 studies), nursing assistant (2 studies), nursing students with a lesser degree nursing credential enrolled in a bachelor degree program. Similarly, 17 different labels applied to physician or doctor groups: attending 13 physicians, doctors (2 studies), dentist (2 studies), ER physicians, general practitioners, hospital doctors, house staff/resident or fellow, intern, consultant, internal Medicine House Staff, MD, DO/PhD, medical registrar, medical staff, medical staff specialists, practitioners, registrars (2 studies), and residents. The category of “other” was noted in 5 studies (Appendix C-IV). Several studies referred to department-based groups, such as administration, maintenance and engineering, community health workers, laboratory services, occupational therapy, pharmacy and telecom to name a few. Finally, some authors used broad classifications of professionals, such as allied health professionals, clinical staff, non-clinical staff, clerical staff, management and supervisors. The authors of 11 studies collapsed several HCW groups into two to six broader categories of professionals. After duplicates were removed, there were 40 unique categories (Appendix C-IV). Nurses were in a stand-alone category in seven of the eleven studies. Other common categories represented clerical, clinical, non-clinical, allied health, community and public health. Generally, the collapsed categories were broader in nature in their representation of HCWs but there was little consistency in how HCW groups were collapsed. For example, in study 219 HCW groups were labeled clinical staff vs. non-clinical staff and in study 825 HCW group labels were doctors, nurses, allied professionals, ancillary workers, managers, general practitioners, and community health workers. Willingness to report to work was not always reported by HCW type or group. When such reports were available, they could not be easily compared across studies due to differences in group composition. Thirteen out of 28 studies reported willingness to report to work for nurse-related groups. These results are summarized below. 14 Participant’s Gender Gender was reported in 24 out of the 28 studies. As shown in Appendix C-V, most respondents (71.21%) were female however, percent of females ranged from 34.10% to 96.20% across the 24 studies. Only eight studies contained gender-specific reports on willingness to report to work, which limited our ability to examine gender effects (Appendix C-VI). Conceptualization of Willingness to Report to Work The authors of the 28 studies conceptualized willingness to report to work in different ways. As can be seen in Table 3, in addition to willingness to report to work, dependent variables with significant conceptual overlap were likelihood of reporting to work, willingness to care for patients, willingness to volunteer to work, being available to work and willingness to come in to work. Variables with a lower degree of conceptual overlap were willingness to accept the risk of contracting pandemic influenza, willingness and ability to come to work to perform one’s job for additional worked hours beyond your contracted hours at standard overtime pay rates and acceptance of the risk of contracting avian influenza as part of the job. In all studies the items were worded positively, except for the article in which HCWs reported if they would refuse to work when asked to deal with H1N patients. Hypothetical Scenarios Eleven studies utilized hypothetical scenarios, that is, measured willingness to report to work under conditions that were described with a greater degree of specificity than working under a general threat of pandemic influenza. There was also a great variability of scenarios and events. Two studies 623 and 2036 presented lists of disasters in addition to pandemic influenza. Earthquakes, ice storms, snow storms, tornados, floods, hurricanes, biological, chemical, and 15 radiation related events were a few of the conditions offered to choose from. Six of the studies 1531, 1834, 2036, 2238, 2642, and 2844 examined a hypothetical avian influenza pandemic. Most conditions related to the nature of the event, availability of treatment or the need to provide direct patient care, for example, scenarios involving a transmissible agent with only experimental prophylaxis/treatment, multiple patients admitted with a new strain of human influenza during a pandemic influenza, confirmed cases of H5N1 avian influenza, avian pandemic with patient being treated at the hospital, and widespread human-to-human transmission occurring with work duties requiring direct face-to-face contact with people who could be infected. Two studies assessed willingness to report under more than one scenario, such as natural disasters vs. bioterrorism. Finally, three studies utilized the Witte’s Extended Parallel Process Model, citing scenarios. If studies presented multiple pandemic scenarios, the worst-case scenario was chosen for inclusion in this study. For example, study 522 presented a scenario of pandemic peak, high-risk job duties requiring direct face to face contact with patients who could be infected, represented worst-case, whereas early pandemic, low-risk job duties not requiring direct face to face contact was not considered worst-case. Realistic Pandemic Threats The notes for Table 3 identify four studies that were conducted during the global 2009 H1N1 pandemic influenza. First, study 1935 in Egypt was conducted when the country was on the edge of the pandemic. Secondly, the study of Hong Kong community nurses, study 2743 was conducted during the peak of the pandemic, WHO pandemic alert level 6. The remaining two studies were conducted during the second wave of the pandemic. 16 Scaling of Responses Specific differences that could potentially affect the study findings included item working, scale type, scale point labels, use of a midpoint or additional response options (do not know and not applicable), and a method used for dichotomizing responses into willing to report vs. unwilling to report. The scaling differences are discussed below. In 14 of the 28 studies, researchers used a variety of Likert-type scales that produced ratio data: 1-4 (one study), 1-5 (five studies), 1-6 (one study), 1-7 (one study), 1-9 (one study), 1-10 (four studies), and 1-100 (one study). It is important to note not only the varying number of scale points but also variations in the presence of a midpoint, which may indicate neutrality. Midpoint was present in scales with an odd number of response options, such as 1-5, 1-7, and 1-9. In five studies where a 1-5 scale was used, all researchers dichotomized responses (willing vs. unwilling to report) by including participants who selected a midpoint of 3 into the unwilling group. But in a study that used a 1-9 scale, everyone who selected a midpoint of 5 (neutrality) was excluded from subsequent calculations of the percent of respondents who were willing vs. unwilling to report to work. Two studies utilizing a 1-10 scale also had an option of checking do not know. These respondents were assigned the median value on the Likert scale. One study provided an option of not applicable which was excluded from the denominator. 17 Table 3. Analysis of Scale Points and Dichotomization Methods ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 1 18 How likely are you to report to work during an IP related emergency?* |-------|-------| 1 3 5 1 = very likely to report to work, 5 = not likely at all. Dichotomized as 1-2 vs. 3-5 [M1] Midpoint of 3 included in the not likely response 53.8% likely to report to work (1-2) 2 19 I will report to work if required* |----------|---------| 1 5 9 [ ] DNK*** 1 = strongly agree, 5 = neutrality, 9 = strongly disagree. An option of DNK. Dichotomized as 1-4 vs. 6-9 Neutrality and DNK were excluded from the analysis 82.5% likely report to work if required (1-4) 3 20 I will report to work if required.* |--------------------| 1 10 [ ] DNK*** 1 = strongly agree, 10 = strongly disagree. An option of DNK. Dichotomized as 1-5 vs. 6-10 DNK were assigned the median value of the Constructs in the Likert scale responses 93.1% willing if required (1-5) 18 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 4 21 I will report to work if required.* |--------------------| 1 10 [ ] DNK*** 1 = strongly agree, 10 = strongly disagree. An option of DNK. Dichotomized as 1-5 vs. 6-10. DNK were assigned the median value of the Constructs in the Likert scale responses 92% willing to report to work if required (1-5) 5 22 Peak pandemic: If your work duties did require you to have direct face-to-face contact with people who could be infected, how likely would you be to report to work? |-------|-------| 1 3 5 1= very likely, 2 = somewhat likely, 3= neither likely nor unlikely, 4= somewhat unlikely 5= very unlikely. Dichotomized as 1-2 vs. 3-5 Midpoint of 3 excluded 56.2% peak pandemic, high risk duties if required (1-2) Nurses: 68.7% 19 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 6 23 During which of these disasters you would be willing and able to come to work to perform your job for additional worked hours beyond your contracted hours, at standard overtime pay rates? Assume that roads and conditions are safe and passable and that your family is sage and taken care of. Earthquake, ice storm, snowstorm, tornado, flood, hurricane, flu epidemic, biological event, chemical event, radiation event, fire/rescue/collapse. [ ] Flu pandemic (there were 10 other options, ranging from fire rescue to a biological or chemical event) Dichotomized as checked vs. not checked [M2] 72% willing to report to work during flu pandemic 20 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 7 24 I am willing to report to work in a pandemic influenza. * [ ] Yes, [ ] No, [ ] Unsure Dichotomized as yes vs. no and unsure [M1] Unsure included in the no response 60% willingness to work (yes) 34% Unsure 8 25 If there was an outbreak of pandemic influenza how likely is it that you would work? [ ] Likely, [ ] Unlikely, [ ] DNK, [ ] N/A Likely = 5, Unlikely = 3. Likelihood score = Likely/total answered x 100 DNK and N/A were excluded from the denominator 59.3% mean likelihood of working Nurses: 49.3% Physicians: 67% 21 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 9 26 How likely are you to come to work during an influenza pandemic? |-------------| 1 4 [ ] Unsure*** 1=do not agree, 4=agree Dichotomized as 1-2 vs. 3-4 [M1] Unsure was included in the study with the do not agree 79% likely to report to work (3-4) 6% Unsure Nurses: 78% Physicians: 87% 10 27 Are you willing to report to work, report to duty?* |------------------| 0 100 0 = absolutely will not report for duty; 100 = absolutely will report. No dichotomization method reported 75.6% willingness to report to work 22 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 11 15 Are you willing to care for new patients during a pandemic outbreak? [ ] Willing, [ ] Not Willing, [ ] Not Sure Dichotomized as willing vs. not willing or not sure [M1] Not sure included in the not willing 27% willing to care for new patients 23 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 12 28 Are you willing to report to work during future pandemics? |--------------------| 1 10 1 = agree, 10 = disagree Dichotomized as definitely agree 1-3 vs. others 4-10 47% willingness to report to work (1-3) Only nurses were studied 13 29 I am willing to respond if required * |--------------------| 1 10 1 = agree, 10 = disagree. Dichotomized as agreeing 1-3 vs. others 4-10 67% willing to report if required (1-3) Nurses: 65% Physicians 67% 24 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 14 30 I would accept the risk of contracting pandemic influenza at work. |-------------| 1 6 [ ] NA*** 1=strongly agree, 2=agree, 3= probably agree, 4= strongly disagree, 5= disagree, 6= probably disagree. Dichotomized as 1-3 vs. 4-6 [M2] NA excluded from the study 74.5% accept risk of contracting disease at work (1-3) Nurses: 49.9% 15 31 Would you report to work as usual?α [ ] Yes [ ] Maybe [ ] No Dichotomized as yes vs. no vs. maybe [M1] Maybe excluded from dichotomization 50% willing to report to work as usual (yes) 42% Maybe Nurses: 44% Physicians: 73% 25 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 16 32 I am willing to care for H1N1patients.*π |-------|-------| 1 3 5 1=completely agree, 2= agree, 3= neither agree nor disagree, 4=disagree, 5= completely disagree. Dichotomized as 1-2 vs. 3-5 [M1] Midpoint of 3, included in the study with the disagree responses 82.3% willing to care for H1N1 patients (1-2) 17 33 Would you work during the flu pandemic?*π [ ] Yes, [ ] No Dichotomized to yes vs. no [M2] 90.1% reported they would work (yes) Only nurses were studied 26 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 18 34 Multiple patients admitted with a new strain of human influenza during a pandemic. Would you continue to work?α [ ] Yes, [ ] No Dichotomized to yes vs. no [M2] 36% would not attend work (yes) Physicians: 66% 19 35 I would refuse to work if asked to deal with H1N1 patients.π [ ] Would work, [ ] would not work, [ ] DNK Dichotomized as work vs. not work; DNK excluded DNK excluded 41% would not be willing to work Nurses: 59% 27 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 20 36 Six confirmed cases of H5N1 avian influenza in NYC. One suspect case in Nassau. Nassau to distribute antivirals to identified at-risk populations with assistance of MRC. Are you willing to volunteer? Α [ ] Willing, [ ] Not willing, [ ] Not sure Dichotomized as willing vs. not willing/not sure [M1] Not sure included with the not willing 79% willing to volunteer 28 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 21 37 I would report to work in a patient in my ward or department had an influenza like illness.* [ ] Would Respond, [ ] Would not Dichotomized as would respond vs. would not [M2] 83% willing to work if a patient in their ward had an influenza like illness 22 38 I would volunteer to work in an avian influenza, if provided with the necessary input, protection, tools, and education. *α [ ] Would Volunteer, [ ] Would Not Volunteer Dichotomized as would volunteer vs. would not volunteer [M2] 79% would volunteer Nurses: 76.6% 29 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 23 39 I would expect to be available to work in a pandemic. |-------------| 1 5 1=strongly disagree to 5=strongly agree, 3=unsure Dichotomized as agree vs. unsure vs. disagree [M1] Midpoint of 3, included in the study with the disagree responses 67% stated they would be available to work (agree) 23% Unsure Nurses: 70% 24 40 The CDC identifies the agent as transmissible with only experimental treatment. Your workplace has asked you to come in to work, will you come in? [ ] Yes, [ ] Probably Yes, [ ] Undecided, [ ] Probably No, [ ] No Dichotomized as yes, probably yes, vs. no, probably no, and undecided [M1] Undecided included in the probably no, no 40% willing to come in to work (yes, probably yes) Physicians: 73% 30 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 25 41 I would be willing to work in an influenza pandemic. |-------------| 1 5 Dichotomization not specified. 56.3% willing to work to work. 26 42 I am willing to care for patients with bird flu.*α 0 No, 1 Yes Dichotomized as yes vs. no [M2] 57% willingness to care for patients infected with bird flu Only nurses were studied 27 43 I am willing to take care of patients during H1N1 IP.*π [ ] Not Willing, [ ] Not Sure, [ ] Willing Dichotomized as not willing and unsure vs. willing [M1] Not sure included in the not willing 23.1 Willing 43.6 Not Sure Only nurses were studied 31 Table 3. Analysis of Scale Points and Dichotomization Methods (continued) ID Item* Scale end points and, if available, midpoint Scale point labels and dichotomization method for calculating percent willing to report to work Midpoint/ DNK/Unsure/NA Percent willing to report to work (scale points used as an indicator of willingness) 28 44 I accept the risk of contracting avian influenza as part of the job. α |-------------| 1 6 1=Strongly disagree, 2= disagree, 3=not sure but probably disagree, 4=not sure but probably agree, 5= agree, and 6=strongly agree. Dichotomized as 1-3 vs. 4-6*** [M2] 82.5% willing to accept the risk (4-6) Physicians: 82.5% π Survey distributed during pandemic influenza. α An avian influenza scenario was used. [M1] Dichotomization method one [M2] Dichotomization method two *Reconstructed from an operational definition. **DNK = Do not know. *** Not stated specifically by the authors but implied in their descriptions of methods or results. 32 Some authors designed nominal measures using check boxes. Specifically, in one study respondents indicated willingness to respond by checking a box labeled “pandemic influenza." If this field was left unchecked, it was counted as unwillingness to respond. In addition, participants’ reactions were elicited using yes vs. no checkboxes (6 studies), likely vs. unlikely (1 study), willing vs. not willing (3 studies), and would work/respond/volunteer vs. would not work/respond/volunteer (3 studies). A checkbox of do not know was available to respondents in five studies, an option of unsure/undecided/not sure was given in five studies and a not applicable option was available in two studies. To calculate the final percent of willing to report to work, do not know and not applicable responses were either excluded from calculations (3 studies) or included in the percentage of respondents who were unwilling to report to work (two studies). These two strategies use different comparative bases for calculating percent of respondents; therefore, their results cannot be compared. Common Dichotomization Methods The pronounced variation in scale point labels and dichotomization methods utilized by the researchers restricted the ability to perform statistical analyses, limiting this study to descriptive statistics and recommendations for standardizing design and reporting to improve researchers’ ability to generalize findings across studies. Of the 28 studies, two groups of studies could be identified with similar scale points and dichotomization methods. The first method of dichotomizing results was based on coding a midpoint as an unwilling to report response, studies 118, 724, 926, 1115, 1531, 1632, 2036, 2339, 2440, and 2743 (Table 3). Percent of respondents willing to report to work, as reported in these ten studies, is displayed in a stem-and-leaf plot in Figure 2. It shows an asymmetrical shape with a low of 23.10% and a high of 82.30%. Five studies listed 33 percent of respondents who were unsure or checked a do not know option. Their results are listed in Table 3 under percent willing to report to work. The unsure group was always (with one exception, possibly an outlier) larger than the unwilling group, M =29.72, SD = 15.57, median = 34. Two of ten studies were conducted during an actual H1N1 pandemic influenza; their authors found higher than average willingness to report to work of 82.3%, study 1632 and 76.9%, study 2743as compared to the mean of 56.1 for all ten studies. In addition, the group included two studies of hypothetical avian events that produced somewhat different results, namely, 50% and 79% of HCWs willing to report to work. The second common method of dichotomizing was in studies that used a scale that was easy to split in half because there was no neutral midpoint studies 623, 1430, 1733, 1834, 2137, 2238, 2642, and 2844 (Table 3). In these studies, the respondent did not have an option to express neutrality. Participants with neutral attitudes may have chosen not to answer at all or they may have been forced to choose between positive and negative responses. The stem-and-leaf plot in Figure 2 displays willingness to report to work findings from eight studies, one of which was conducted during an actual H1N1 pandemic influenza, study 17 33and four of which were based on hypothetical avian influenza scenarios, studies 1834, 2238, 2642, and 2844 that are associated with particularly high death rates. As compared to method one, method two in Figure 2 is more symmetrical with a low of 57%, and a high of 90.10%, and the mean of 75.26 %. Oddly, the mean willingness to report to work is higher for articles that used dichotomization method two than for articles based on method one. The former included four avian flu studies and the latter included only two. In conclusion, the two dichotomization methods differ in how they handle mid-range attitudes. The first group of studies 34 consistently included these responses into an unwilling group. Yet, in the second set of studies it is unknown how these attitudes were distributed between willing vs. unwilling to report to work because study participants were not given an opportunity to pick a mid-range response. Next, we recreated stem-and-leaf plots after removing avian flu studies due to high morbidity and fear associated with avian influenza. The stem-and-leaf plots for the remaining studies (Figure 3) demonstrate the same differences as the initial stem-and-leaf plot (Figure 2): no-midpoint scales produce higher means and less variation across studies in the percent of respondents willing to report to work than odd-numbered scales with midpoints. The same pattern was observed in stem-and-leaf plot that excluded studies conducted during the 2009 H1N1 influenza pandemic (Figure 4). Mann-Whitney nonparametric U test for independent samples was used to determine if the distribution of willingness to report to work was the same across methods. The mean willingness to report to work for all studies coded as method one differed significantly from the mean willingness reported in method two studies (Figure 2), U =17.00 , p = 0.043. After removing the avian influenza studies (Figure 3), the difference in mean willingness remained significant U = 4.00, p = 0.048. No statistically significant difference was observed when H1N1 studies were removed (Figure 4), U =12.00, p = .072. The means for method two studies are consistently higher than the means for method one studies. In addition, two out of three Mann-Whitney nonparametric U tests indicate statistically significant differences in willingness to report to work. 35 Figure 2. Percent of HCWs’ Willing to Report to Work: Ten Studies that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Eight Studies that used the Second Method of Dichotomization (a scale without a midpoint split in the middle) Figure 3. Percent of HCWs’ Willing to Report to Work: Eight Studies (without avian influenza) that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Four Studies (without avian influenza) that Used the Second Method of Dichotomization (a scale without a midpoint split in the middle) 36 Figure 4. Percent of HCWs’ Willing to Report to Work: Eight Studies (without H1N1) that Used the First Method of Dichotomization (midpoint indicates unwillingness to report) and Seven Studies (without H1N1) that Used the Second Method of Dichotomization (a scale without a midpoint split in the middle) Figure 5. Percent of HCWs’ Willing to Report to Work: Seven Studies that Reported Physicians and Twelve Studies that Reported Nurses 37 Finally, we attempted to understand if results differed significantly by HCW type. Physicians and nurses were the only two group for whom willingness to report to work could be examined, although the number of studies was still small (N = 7, and N= 13) respectively (Figure 5). The method of dichotomization could not be taken into account because there were only three studies for method one and two studies for method two available for physician group and four studies available for each method for the nurse group. Mean willingness to report to work for physicians was 73.64% (median 73.00%, SD = 8.20) and nurses was 59.82% (median 59.00%, SD = 17.60). As compared to nurses, physicians demonstrate a higher willingness to report to work (mean difference is +13.82%). Caution should be exercised when interpreting due to the small number of studies, methodological heterogeneity and great variation in findings. As can be seen in Table 3, there were four studies that included no other HCW group but nurses. The studies produced a wide range of results on willingness to report to work, from 23.1% study 2844 to 90.1% study1733. Interestingly, both studies were completed during H1N1 pandemic influenza. In the remaining nine studies the mean percent of nurses willing to report to work (M = 63.67, SD =13.15) was similar to the mean percent of all HCWs in a study (M = 62.28, SD =12.38). However, as compared to all HCWs’ willingness to report to work, in three studies the nurses were more willing to report to work (mean difference is +11%) and in six studies they were less willing to report to work (mean difference is -8%). 38 CHAPTER IV DISCUSSION Summary of Evidence A comprehensive review of 28 studies on willingness to report to work during a pandemic influenza was completed. Researchers studied different dependent variables, which were all related to willingness to report to work, yet had varying degrees of conceptual overlap. Most authors gave detailed operational definitions of their dependent variables. When not provided (12 studies), we could still reconstruct these definitions using available information. Despite our bests efforts to provide a systematic review with meta-analysis we faced several challenges. Gender data was limited. Even though 24 out of 28 studies offered the number of male and female respondents, only eight studies specified percent of respondents, by gender, who were willing to report to work. The researchers studied many HCW types. Some were specifically interested in a particular group of HCWs: study 2642 studied nursing students, 2743 studied community nurses and 2844 examined primary care physicians. The greatest diversity in HCW groups was found in studies of hospitals and healthcare systems. For example, study 219 initially provided 18 categories of HCWs spanning foodservice/linen to hospital staff, clinical staff, physicians and nurses. Categories were then collapsed to clinical and non-clinical staff. Study 825 initially provided ten categories of HCW groups that were collapsed into seven categories doctors, nurses, allied professional, ancillary, manager, general practitioner and community healthcare workers. In the majority of the studies, willingness to report to work by HCW category was absent. Therefore, it is premature to generalize HCW-specific findings across available 39 studies. The only exception is the comparison of physicians and nurses, which showed higher willingness to report to work among physicians. Social Desirability Bias All studies in this review examined HCWs’ self-reports, that is, what they said they would do during a pandemic influenza. Behavioral intentions and actual behaviors may indeed be different when confronted with a real life situation. The desire to present oneself in a favorable way is manifested in social desirability bias, which was acknowledged as a possible threat in 13 of 28 studies. A serious barrier to researchers’ ability to generalize results across the 28 studies included in this review is the scaling of responses. First, the majority of authors did not list the number (or percent) of study participants who selected each scale response option when they reported on their willingness to work. Second, most authors attempted to dichotomize their findings by sorting study participants into willing vs. unwilling to report to work but this was not done in a consistent manner. In scales with midpoints (method 1), midpoint (neutral) responses were either excluded from calculations or lumped with a not willing response, resulting in mean willingness of 56.12%. For scales without midpoint, responses were evenly split in the middle but there was no choice for those who were unsure. These respondents might refrain from providing their ratings or pick the closest available response on either positive or the negative side of the scale, thus inflating the number of those who were willing to report to work. This resulted in a significantly higher mean of 75.26%, which is nearly 20% higher than for studies included in the method one 40 group. This pattern was replicated when we recalculated means after removing studies on avian influenza and 2009 H1N1, although the latter finding was not statistically significant. Of the ten studies in method one, five reported percent unsure. Two out of five studies had over 40% of respondents in the unsure group. This evidence is consistent with the finding of 20% greater willingness to report to work in studies without an unsure option, as compared to studies with that option. Assuming that the unsure responses can be evenly split between the willing and the unwilling groups, when an unsure or do not know option is not provided, a 20% overestimate is to be multiplied by two, resulting in 40% of unsure respondents. This convergence of evidence provides additional support for our conclusion that the even-numbered scales are prone to systematic bias that may lead researchers to overestimate the percent of respondents who are willing to report to work. Implications for Research The findings of this study have important implications for research. Foremost, conducting studies that truly assess the HCWs’ willingness to report to work is extremely difficult. Even hypothetical scenarios with decisions points still rely on the respondents’ truthfulness in answering the survey. Therefore, social desirability bias must be acknowledged. The evidence from this study suggests that generalization of research findings is problematic when different scales, scale points, and dichotomization methods are utilized. Taken together, these results suggest that opportunity exits to enhance rigor and the validity of future studies. Implication for Clinical Practice and Preparedness Planning An influenza pandemic will place tremendous burden on the healthcare system, therefore, pre-pandemic planning is essential to maintain the continuum of care. An effective response 41 begins with an adequate healthcare workforce ready and willing to respond to the surge of patients. It is imperative for workforce planning to have accurate data demonstrating HCWs’ willingness to report to work. These 28 studies demonstrate great variability in results from a low of 23.1% study 2743 of community health nurses (during the 2009 H1N1pandemic) to a high of 93.1% study 320 of EMS providers. Based on these findings, worst case scenario planners may assume the low of 23.1% willingness to report to work. Taking into account all studies with responses scaled using midpoints (which was coded as unwillingness to report) the mean willingness is 56.12%. With much caution, 56% might be the best available estimate of HCWs’ willingness to report to work. Based on our findings, we recommend not including results from studies that did not have a midpoint or some other unsure option because such studies overestimate willingness by about 20%. HCWs who are unsure or do not know if they would report to work during an influenza pandemic (fence-sitters) represent a group that is not yet well understood. There is evidence that this group can be trained to increase willingness. For example study 1248 reported pretest willingness of 47%. After completion of an on-line training module and one day exercise on pandemic influenza, a posttest survey demonstrated results of 82%, enhanced by 35%. Because many HCWs are likely to fall into the fence-sitter group, the pandemic response can be greatly improved if many of these individuals become willing and able to report to work during a pandemic influenza event. To develop a strategy for engaging the fence-sitters, the size of this group and its members' motivations need to be understood. How do unsure HCWs differ from those who are not willing? This information can be used to develop tailored interventions, which can be evaluated by assessing these individuals' intentions and behaviors during simulated and actual events. 42 Once again, social desirability bias remains a serious threat to the validity of estimates obtained with the use of hypothetical scenarios. Research-practitioner collaborations during a real-life influenza pandemic event are perhaps the best opportunity to estimate willingness while minimizing social desirability bias. Implications for Policy Preparedness training for pandemics and other disasters remains an important national, state, and professional priority. Disaster training exercises are mandated by regulatory agencies, and are aimed at enhancing response and mitigating the loss of life. Yet, minimal data is available on the effectiveness of this training in changing HCWs’ willingness to report to work. Great variability in findings from studies conducted during a H1N1 real-life event, coupled with large dispersion of results by researchers who gave hypothetical scenarios indicates that it would be premature to use any single-point estimate for HCWs’ mean willingness to report to work. A reasonable approach may be to plan separately for the worst-case scenario while standardizing training programs and other interventions that produce the biggest change in the number of HCWs who switch from being unsure to being willing to report to work. This knowledge must be shared with disaster response, academic, and regulatory communities in order to prioritize funding, education, training, and ultimately improve disaster response. 43 CHAPTER V LIMITATIONS A number of important limitations need to be considered. The most important limitation lies in scale bias amongst the 28 studies. Different scales, scales points, and dichotomization methods limited the systematic review and quantitative analysis. The absence of HCW gender, worker group, and healthcare sector information limited the ability to draw inferences on HCWs’ willingness to report to work. In addition, the studies were limited to English. The studies included in the review were published as of May 2011 and after this time newly published articles may include relevant data. This analysis comprised only studies conducted for the purpose of evaluating willingness to report to work in a pandemic influenza. Studies that accessed ability to report to work were not included unless they were in addition to willingness. This systematic review did not explore enticements, incentives, or interventions both positively and negatively associated with willingness to report to work. 44 CHAPTER V CONCLUSIONS Major methodological limitations and insufficient reporting in studies prevent us from generalizing the results across studies on HCWs’ willingness to report to work during the influenza pandemic. Below, we highlight several important methodological shortcomings, suggest ways of remedying them and refer to specific studies that could serve as a model for future research efforts. 45 CHAPTER VI FUTURE RESEARCH To facilitate generalization of results, future researchers should follow the following recommendations. First, they should carefully design their scales paying special attention to the unsure group. A serious thought should be given to the inclusion of such response options as midpoints (which are often interpreted as neutral or unsure), do not know and not applicable. The not applicable option may be needed if some respondents are not in a position to make decisions, for example in the study 825, not applicable option was provided for a question inquiring about decisions on who not to treat or care for. Not applicable may also be used when HCWs are reporting on how family obligations, care of a partner, and children affect their willingness to report to work. Studies 118, 1632, and 2339 provide examples of measures with neutral midpoints and replicable methodologies for dichotomizing results. This technique reveals the extent of indecisiveness and, therefore, offers an opportunity to enhance willingness to report to work. Second, we also encourage authors to provide a chance for respondents to explain their position, especially if they are unsure or do not know. Understanding of this group’s motivations and concerns will help individuals involved in emergency preparedness to plan their strategies for involving these HCWs. Third, we strongly recommend inclusion of tables that show willingness to report to work by HCW type, gender, and scale point. To assist with emergency planning analysis, it is imperative that HCW groups be we explained. For example, are nurse categories inclusive of student nurses, nursing assistants, and patient care technicians? Future meta-analyses will benefit from researchers’ 46 explanations of how they collapsed HCW groups into categories. As a model, study 7 provides a detailed description of occupational categories that would be relevant to many researchers. Forth, researchers should include expanded operational definitions of their dependent variables that explain the context (scenarios or events) and specify how the items were worded, scaled and dichotomized. Finally, the use of scenarios enhances the rigor and validity of the study. Study 522 provides specific scenarios to determine the relationship between willingness to report and pandemic stages. In addition, study 522 provides a definition low-risk job duties, those with no direct face to face contact and high-risk job duties, those with direct face to face contact. Study 2440 also provides an evolving scenario developed from a federal table top exercise. In each case fear escalates and respondents reach decisions points when they must indicate their willingness to report to work. Both provide insight for emergency preparedness planners in the development of specific response plans. Funding None declared.47 APPENDICES48 APPENDIX A-I LITERATURE REVIEW Appendix A-1: Articles Generated Through Databases and Other Sources 1. Abstracts. Eur J Pedatr. 2006 Nov; 165; (Suppl 1):1-389. 2. Abramson DM, Morse SS, Garrett AL, Redlener I. Public health disaster research: Surveying the field, defining its future. Disaster Med Public Health Prep. 2007 Jul; 1(1):57-62 3. Ahmed GY, Balkhy HH, Bafaqeer S, Al-Jasir B, Althaqafi A. Acceptance and adverse effects of H1N1 vaccinations among a cohort of national guard health care workers during the 2009 hajj season. BMC Res Notes. 2011:4(61). 4. Amaratunga CA, O’Sullivan TL, Phillips KP, Lemyre L, O’Connor E, Dow D, et al. Ready, aye ready? Support mechanisms for healthcare workers in emergency planning: a critical gap analysis of three hospital emergency plans. Am J Disaster Med. 2007; 2(4): 195-210. 5. Anantham D, McHugh W, O’Neil S, Forrow L. Clinical review: Influenza pandemic – physicians and their obligations. Crit Care. 2009:12(3). 6. Avery GH, Zabriskie-Timmerman J. The impact of federal bioterrorism funding programs on local health department preparedness activities. Eval Health Prof. 2009 Jun; 32(2):95-127. 7. Balicer RD, Barnett DJ, Thompson CB, Hsu EB, Catlett CL, Watson CM, et al. Characterizing hospital workers’ willingness to report to duty in an influenza pandemic through threat-and efficacy-based assessment. BMC Public Health. 2010 Jul 26; 10(436). 8. Balicer RD, Omer SB, Barnett DJ, Everly GS. Local public health workers; perceptions towards responding to an influenza pandemic. BMC Public Health. 2006; 6(99). 49 9. Balicer RD, Omer SB, Barnett DJ, Everly GS. Survey of local public health workers’ perceptions toward responding to an influenza pandemic. J Healthc Prot Manage. 2006; 22(2):1-14. 10. Bar-Dayan Y, Natan Manor S, Nolder N, Kremer I, Iohan Barak M, Bar-Dayan Y. Relationship between sources of information and the willingness of healthcare workers to risk their lives for a patient during the peak of A/H1N1 pandemic in Israel. Open Epidemiol J. 2010; 3: 53-57. 11. Barnett DJ, Balicer RD, Thompson CB, Storey JD, Omer SB, Semon NL, et al. Assessment of local public health workers’ willingness to respond to pandemic influenza through the application of the extended parallel process model. PloS One. 2009 Jul 24; 4(7). 12. Barnett DJ, Levine R, Thompson CB, Wijetunge GU, Oliver AL, Bentley MA, et al. Gauging U.S., emergency medical services workers’ willingness to respond to pandemic influenza using a threat- and efficacy-based assessment framework. PLoS One. 2010; 5(3). 13. Barr M, Raphael B, Taylor M, Stevens G, Louisa J, Giffin M, et al. Pandemic influenza in Australia: Using telephone surveys to measure perceptions of threat and willingness to comply. BMC Infect Dis. 2008; 8(117). 14. Bartlett J, Perl TM, Quinn TC. Pandemic influenza: A call to action. Johns Hopkins Advanced Studies in Medicine. 2007 Oct; 7(11): 331-350. 15. Basta NE, Edwards SE, Schulte J, Assessing public health department employees’ willingness to report to work during an influenza pandemic. J Public Health Manag Pract. 2009; 15(5): 375-383. 50 16. Beatty ME, Beutels P, Meltzer MI, Shepard DS, Hombach J, Hutubessy R, et al. Health economics of dengue: A systematic review and expert panel’s assessment. Am J Trop Med Hyg. 2011 Mar 4; 84(3): 473-488. 17. Beaumont M, Duggal HV, Mahmood H, Olowokure B. A survey of the preparedness for an influenza pandemic of general practitioners in the West Midlands, UK. Eur J Clin Microbiol Infect Dis. 2007; 26: 819-823. 18. Blake KD, Blendon RJ, Viswanath K. Employment and compliance with pandemic influenza mitigation recommendations. Emerg Infect Dis. 2010 Feb; 16(2): 212-218. 19. Blendon RJ, Koonin LM, Benson JM, Cetron MS, Pollard WE, Mitchell EW, et al. Public response to community mitigation measures for pandemic influenza. Emerg Infect Dis. 2006 May; 14(5): 778-786. 20. Bootsma MCJ, Ferguson NM. The effect of public health measures on the 1918 influenza pandemic in U.S. cities. Proc Natl Acad Sci U S A. 2007 May 1; 104(18): 7588-7593. 21. Braunack-Mayer AJ, Street JM, Rogers WA, Givney R, Moss JR, Hiller JE. Including the public in pandemic planning: a deliberative approach. BMC Public Health. 2010; 10(501). 22. Brower V. Variability is its specialty. EMBO Rep. 2005 Jan; 6(1):13-16. 23. Brown LH, Aitken P, Leggat PA, Speare R. Self-reported anticipated compliance with physician advice to stay home during pandemic H1N1 2009: Results from the 2009 Queensland Social Survey. BMC Public Health. 2010 Mar 16; 10. 24. Bruce RD, Altice FL. Clinical care of the HIV-infected drug user. Infect Dis Clin North Am. 2007 Mar; 21(1): 149-ix. 51 25. Bryce E, Copes R, Gamage B, Lockhart K, Yassi A. Staff perception and institutional reporting: two views of inflectional control compliance in British Columbia and Ontario three years after an outbreak of server acute respiratory syndrome. J Hosp Infect. 2008; 69(2): 169-176. 26. Carlson A, Budd AP, Perl TM. Control of influenza in healthcare settings: early lessons from the 2009 pandemic. Curr Opin Infect Dis. 2010; 23: 293-299. 27. Carlson J. Labor’s lead. Mod Healthc. 2009 Nov 16; 39(46); 17. 28. Carroll J. A crash course. Biotechnol Healthc. 2009 Oct; 6(4): 23-25, 27-28. 29. Chaffee M, Making the decision to report to work in a disaster. Am J Nurs. 2006; 106(9): 54- 57. 30. Chaffee M. Willingness of healthcare personnel to work in a disaster: An integrative review of the literature. Disaster Med Public Health Prep. 2009; 3(1): 42-56. 31. Chasm R, Jerrand D, Van Wie D, Wegner S. Chemical biological attack: Will the providers put themselves at risk? Ann Emerg Med. 2003 Oct; 42(4). 32. Chor JSY, Ngai KLK, Goggins WB, Wong MCS, Wong SYS, Lee N, et al. Willingness of Hong Kong healthcare workers to accept pre-pandemic influenza vaccination at different WHO alert levels: two questionnaire surveys. BMJ. 2009 Sep 29; 339 (618). 33. Coker R. UK preparedness for pandemic influenza. BMJ. 2007 May 12; 334(7601): 965-966. 34. Cole LA. Ethics and terror medicine. In Essentials of Terror Medicine. Newark: Springer; 2009, 425-439, DOI: 10.1007/978-0-387-09412-0_25. 35. Coleman CH. Beyond the call of duty: Compelling healthcare professionals to work during an influenza pandemic. Iowa Law Review. 2008: 1-47. 52 36. Cone DC, Cummings BA. Hospital disaster staffing: If you call, will they come? Am J Disaster Med. 2006 Nov/Dec; 1(1): 28-36. 37. Cowden J, Crane L, Lezotte D, Glover J, Nyquist A. Pre-pandemic planning survey of healthcare workers at a tertiary care children’s hospital: ethical and workforce issues. Influenza Other Respi Viruses. 2010 Jul; 4(4): 213-222. 38. Crane JS. McCluskey JD, Johnson GT, Harbison RD. Assessment of community healthcare providers ability and willingness to respond to emergencies resulting from bioterrorist attacks. J Emerg Trauma Sock. 2010 Jan/Mar; 3(1):13-20. 39. Dalton CB, Durheim DN, Conroy MA. Likely impact of school and childcare closures on public health workforce during an influenza pandemic: a survey. Communicable Disease Intelligence. 2008 Jun; 32(2): 261. 40. Damery S, Draper H, Wilson S, Greefield S. Ives J, Parry J, et al. Healthcare workers; perceptions of the duty to work during an influenza pandemic. J Med Ethics. 2010 Jan; 36(1): 12-8 41.Damery S, Wilson S, and Draper H. Vaccination is key to keeping nurses working in an influenza pandemic. Primary Health Care. 2009 Jul; 19(6):13. 42. Damery S, Wilson S, Draper H, Gratus C, Greenfield S, Ives J, et al. Will the NHS continue to function in an influenza pandemic? A survey of healthcare workers in West Midlands, UK. BMC Public Health. 2009 May 14; 9(142). 43. Daniels N. Duty to treat or right to refuse? Hastings Cent Rep. 1991 Mar/Apr. 21(2): 36-46. 44. Daughtery EL, Perl TM, Rubinson L, Bilderback A, Rand CS. Survey study of the knowledge, attitudes, and expected behaviors of critical care clinicians regarding and influenza pandemic. Infect Control Hosp Epidemiol. 2009 Dec;30(12): 1143-1149. 53 45. Delany JB. The national disaster medical system’s reliance on civilian-based medical response teams in a pandemic is unsound. Homeland Security Affairs. 2007:III ( 2): 1-9. 46. Delany JB. Firefighters’ ability and willingness to participate in a pandemic. [Master thesis].Naval Post Graduate School Monterey CA; 2008. 47. De Maeseneer J, Willems S, De Sutter A, Van de Geuchte ML, Billings M. Department of Family Medicine and Primary Health Care, Ghent University. Global Health through Education, Training and Service, Attleboro, USA. Primary care as a strategy for achieving equitable care: a literature review commissioned by the Health Systems Knowledge Network. 2007 Mar Available from: http://www.who.int/social_determinants/resources/csdh_media/primary_health_care_2007en.pdf 48. del Rio C, Guarner J. The 2009 influenza A (H1N1) pandemic: What have we learned in the past six month. Trans Am Clin Climatol Assoc. 2010;121: 128-140. 49. de Zwart O, Veldhuijzen IK, Elam G, Aro AR, Abraham T, Bishop GD, et al. Avian influenza risk perception, Europe and Asia. Emerg Infect Dis. 2007 Feb; 13(2): 290-293. 50. de Zwart O, Veldhuijzen IK, Elam G, Aro AR, Abraham T, Bishop GD, et al. Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: Results of an international survey. Int J Behav Med. 2009 Mar; 16(1): 30-40. 51. Docksai R. Preparing for a new pandemic. Futurist. 2009 Sep/Oct; 43(5): 11. 52. Doctera SP, Streetb J, Braunack-Mayerc AJ, van der Wilt GJ. Public perceptions of pandemic influenza resource allocation: A deliberative forum using Grid/Group analysis. J Public Health Policy 2011 Jan; doi: 10.1057/jphp.2010.49. 53. Draper H, Sorell T, Ives J, Damery S, Greenfield S, Parry J, et al. Public Health Ethics. 2010; 3(1); 23-34. 54 54. Draper H, Wilson S, Ives J, Gratus C, Greenfield S, Parry J, et al. Healthcare workers’ attitudes towards working during pandemic influenza: A multi method study. BMC Public Health. 2008; 8(192). 55. Dwyer J, Tsai DFC. Developing the duty to treat: HIV, SARS and the next epidemic. J Med Ethics. 2008;34: 7-10. 56. Eastwood K, Durrheim D, Francis JL, Tursan d’Espaignet E, Duncan S, Islam F, et al. Knowledge about pandemic influenza and compliance with containment measures among Australians. Bull World Health Organ. 2009 Aug; 87(8): 588-594. 57. Ehrenstein BP, Hanses F, Salzberger B. Influenza pandemic and professional duty: family or patients first? A survey of hospital employees. BMC Public Health. 2006; 6(311). 58. Esteves-Jaramilllo AZ, Omer SB, Gonzalez-Diaz E, Salmon DA, Hixson B, Navarro F, et al. Acceptance of a vaccine against novel influenza A (H1N1) virus among health care workers in two major cities in Mexico. Arch Med Res. 2009 Nov; 40(8): 705-711. 59. Estrada LC, Fraser MR, Cioffi JP, Sesker D, Walkner L, Brand MW, et al. Partnering for preparedness: The project public health ready experience. Public Health Rep. 2005; 120(Suppl 1): 69–75. 60. Eyck T. Ability of regional hospitals to meet projected avian flu pandemic surge capacity requirements. Prehosp Disaster Med. 2008; 23(2):103–112. 61. Fischer P, Kabir K, Weber O, Wirtz DC, Bail H, Ruchholtz S, et al. Preparedness of German paramedics and emergency physicians for a mass casualty incident: A national survey. Unfallchirurgie. 2008; 5: 443-450. 62. French EP. Enhancing the legitimacy of local government pandemic influenza planning through transparency and public engagement. Pub Adm Rev. 2011 Mar/Apr 71(2): 253-264. 55 63. French EP, Raymond ES. Pandemic influenza planning: an extraordinary ethical dilemma for local government officials. Public Adm Rev. 2009 Sep/Oct; 69(5): 823-830. 64. Gardiner D. Are you coming to work during pandemic flu? Anaesthesia. 2008; 63: 803-805. 65. Garnett AL, Soo Park Y, Redlener I. Mitigating absenteeism in hospital workers during a pandemic. Disaster Med Public Health Prep. 2009; 3(Suppl 2): S141-S147. 66. Gershon RR, Magda LA, Canton AN, Riley HEM, Wiggins F, Young W, et al. Pandemic-related ability and willingness in home healthcare workers. Am J Disaster Med. 2010 Jan/Feb; 5(1): 15-26. 67. Gershon RRM, Qureshi KA, Stone PW, Pogorzelska M, Silver A, Damsky MR, et al. Home health care challenges and avian influenza. Home Health Care Manag Pract. 2007 Dec; 20(1): 58-69. 69. Gershon RRM, Vandelinde N, Magda LA, Pearson JM, Werner A, Prezant D. Evaluation of a pandemic preparedness training intervention for emergency medical services personnel. Prehosp Disaster Med. 2009 Nov/Dec; 24(6): 508-511. 70. Good LS. Addressing hospital nurses’ fears of abandonment in a bioterrorism emergency. AAOHN J. 2007; 55(12): 493-498. 71. Good LS. Development and psychometric evaluation of the provider response to emergency pandemic (PREP) tool. [Doctoral dissertation]. San Diego, CA: University of San Diego; 2009. 72. Good LS. Ethical decision making in disaster triage. J Emerg Nurs. 2008 Apr; 34(2): 112-115. 73. Gostin LO, Berkman BE. Pandemic influenza: ethics, law and the public’s health. Adm Law Rev. 2007 Winter; 59(1): 121-175. 56 74. Goulia P, Mantas C, Dimitrouls D, Mantis D, Hyphantis T. General hospital staff worries, perceived sufficiency of information and associated psychological distress during the A/H1N1 influenza pandemic. BMC Infect Dis. 2010 Nov 9; 10(332). 75. Hamburg MA. Bioterrorism: A challenge to public health and medicine. Public Health Manag Pract. 2000 6(4): 38-44 76. Hanley ME, Bogdan GM. Mechanical ventilation in mass casualty scenarios. Augmenting staff: project XTREME. Respir Care. 2008; 52(2): 176-189. 77. Haynes B, Freeman C, Rubin JL, Koehler GA, Enriquez SM, Smiley DR. Medical response to catastrophic events: California’s planning and the Loma Prieta Earthquake. Ann Emerg Med. 1992 Apr; 21(4): 30-36. 78. Hesse BW, Hansen D, Finhott T, Munson S, Kellogg W, Thomas JC. Social participation in health 2.0. Computer. 2010 Nov 11; 43(11): 45-52. 79. Hogg W, Huston P, Martin C, Soto E. Enhancing public health response to respiratory epidemics. Can Fam Physician. 2006 Oct; 52: 1254-1260. 80. Hope K, Durrheim D, Barnett D, D’Este C, Kewley C, Dalton C, et al. Willingness of frontline health care workers to work during a public health emergency. Australian Journal of Emergency Management. 2010; 25(3): 39-47. 81. Hope K, Massey PD, Osbourn M, Durrheim DN, Kewley CD, Turner C. Senior clinical nurses effectively contribute to the pandemic influenza public health response. Aust J Adv Nurs. 2009; 28(3): 47-52. 82. Ibuka Y, Chapman GB, Meyers LA, Li M, Galvani AP. The dynamics or risk perception and precautionary behavior in repose to 2009 H1N1 pandemic influenza. BMC Infec Dis. 2010; 10(296). 57 83. Imai T, Takahashi K, Todoroki M, Kunishima H, Hoshuyama, T, Ide R, et al. Perception in relation to a potential influenza pandemic among healthcare workers in Japan: Implications for preparedness. J Occup Health. 2008; 50: 13-23. 84. Imai H, Matsuishi K, Ito A, Mouri K, Kitamura N, Akimoto K, et al. Factors associated with motivation and hesitation to work among health professionals during a public crisis: a cross sectional study of hospital workers in Japan during the pandemic (H1N1) 2009. BMC Public Health. 2010; 10(1): 672. 85. Irvin CB, Cindrich L, Patterson W, Southall A. Survey of hospital healthcare personnel response during a potential avian influenza pandemic: will they come to work? Prehosp Disaster Med. 2008 Jul/Aug; 23(4): 328-335. 86. Iserson KV, Heine CE, Larkin G, Moskop JC, Baruch J, Aswegan AL. Flight or fight: The ethics of emergency physician disaster response. Ann Emerg Med. 2008 Apr; 51(4): 345-353. 87. Ives J, Greenfield S, Parry JM, Draper H, Gratus C, Petts JI, et al. Healthcare workers’ attitudes to working during pandemic influenza: a qualitative study. BMC Public Health. 2009 Feb12; 9(56). 88. Johnston Roberts K, Newman PA, Duan N, Rudy ET. HIV vaccine knowledge and beliefs among communities at elevated risk; conspiracies, questions, and confusion. J Natl Med Assoc. 2005 Dec; 97(12): 1662-1671. 89. Jorgensen AM, Mendoza GL. Emergency preparedness and disaster response core competency set for perinatal and neonatal nurses. J Obstet Gynecol Neonatal Nurs. 2010 Jul/Aug; 39(4): 450-467. 58 90. Kass NE, Otto J, O'Brien D, Minson M. Ethics and severe pandemic influenza: Maintaining essential functions through a fair and considered response. Biosecur Bioterror. September 2008, 6(3): 227-236. 91. Kaufman H. Fear of bureaucracy: a raging pandemic. Pub Adm Rev. 1981 Jan/Feb; 41(1): 1-9. 92. Kaye D, Pringle CR. Avian influenza viruses and their implication for human health. Clin Infect Dis. 2005 Jan; 40: 108-112. 93. Kinnair D. Swine flu: coping with the strain. Nursing Management UK. 2009 Sep;16(5): 3. 94. Knebel A, Phillips SJ, eds. Agency for Healthcare Research and Quality. Home Health Care During an Influenza Pandemic: Issues and Resources. AHRQ Publication No. 08- 0018. Rockville, MD: Agency for Healthcare Research and Quality: 2008 95. Kotalik J. Preparing for an influenza pandemic: Ethical issues. Bioethics; 2005; 19(4): 422-431. 96. Lanzilotti SS, Galanis D, Leoni N, Craig B. Hawaii Medical Professional Assessment. Hawaii Med J. 2002 Aug; 61: 165-173. 97. Lee A, Chuh AAT. Facing the threat of influenza pandemic – roles and implications to general practitioners. BMC Public Health. 2010; 10(661). 98. Lee VJ, Chen MI. Effectiveness of neuraminidase inhibitors for preventing staff absenteeism during pandemic influenza. Emerg Infect Dis. 2007 Mar; 13(3): 449-457. 99. Lee VJ, Yin Tok, M, Chow VT, Hong Phua K, Eong Ooi, E, Tambyah PA, et.al. PLoS One. 2009; 4 (9). 59 100. Lesperance AM, Miller JS. Preventing absenteeism and promoting resilience among health care workers in biological emergencies. The U.S. Department of Energy Pacific Northwest National Laboratory; 2009 Aug. Contract No: DE-AC05-76RL01830 101. Levin PJ, Gebbie EN, Qureshi K. Can the health-care system meet the challenge of pandemic flu? Ethical and workforce considerations. Public Health Rep. 2007 Sep/Oct; 122(5): 573-578. 102. Liao Q, Cowling BJ, Wing Tak Lam, W, Fielding R. Factors affecting intention to receive and self-reported receipt of 2009 pandemic H1N1 vaccine in Hong Kong: A longitudinal study. PloS One. 2011; 6(3). 103. Lind M, Lewis J. Are your prepared for a pandemic influenza? Australasian Emergency Nurse Journal. 2007; 10(4):190-191. 104. Ly S, Van Kerkhove MD, Holl D, Froehlich Y, Vong S. Interaction between humans and poultry, rural Cambodia. EmergInfect Dis. 2007 Jan; 13 (1): 130-132. 105. Ma X, He Z, Wang Y, Jiang L, Xu Y, Qian C, et al. Knowledge and attitudes of healthcare workers in Chinese intensive care units regarding 2009 H1N1 influenza pandemic. BMJ Infect Dis. 2011 Jan 22; 11(24). 106. Mackler N, Wilkerson W, Cinti S. Will first-responders show up for work during a pandemic? Lessons from a smallpox vaccination survey of paramedics. Disaster Manag Response. 2007 Apr/June; 5(2): 45-48. 107. Mahmoud M, El-Harbi K. Pandemic influenza perceptions of medical students medical college. Taihah University Medina Saudi Arabi 2009. Bull Alexandria Fac. 2010;46(1): 57-64. 108. Mangtani P, Breeze E, Stirling S, Hanciles S, Kovats S, Fletcher A. Cross-sectional survey of older peoples’ views related to influenza vaccine uptake. BMC Public Health. 2006; 6 (249). 60 109. Martens KA, Hantsch CE, Starke CE. Emergency preparedness survey: Personnel availability and support needs. Ann Emerg Med. 2003 Oct; 42(4): S105. 110. Martin, SD. Nurses; ability and willingness to work during pandemic flu. J Nurs Manag. 2011; 19: 98-108. 111. Martinese F, Keijzers G, Grant S, Lind J. How would Australian hospital staff react to an avian influenza admission, or an influenza pandemic? Emerg Med Australas. 2009 Feb 17; 21(1): 12-24. 112. Maunder RG, Leszcz M, Savage D, Adam MA, Peladeau N, Romano D, et al. Applying the lessons of SARS to pandemic influenza. Can J Public Health. 2008; 99(6): 486-488. 113. May D. A work in progress. Mod Healthc. 2010 Apr 12; 40(15): 22. 114. McHugh MD. Hospital nurse staffing and public health emergency preparedness: implications for policy. Public Health Nurs. 2010 Sep; 27(5): 442-449. 115. McHugh MD. The legal context of nurses volunteering in mass casualty events. PA Nurse. 2007 Jun; 62(2): 14-15. 116. Meslin EM, Alyea JM, Helft PR. Pandemic influenza preparedness: Ethical issues and recommendations to the Indiana State Department of Health Indiana University Center for Bioethics. 2008. https://scholarworks.iupui.edu/handle/1805/1912. 117. Miller WL, Crabtree BF, Nutting PA, Stange KC, Roberto Jaen C. Primary care practice development: A relationship-centered approach. Ann Fam Med. 2010 May; 8(Suppl1): s68-s79. 61 118. Mockiene V, Suominena T, Välimäkib M, Razbadauskasc A, Caplinskasd S, Martinkenas A. Nurses' willingness to take care of people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) — does a teaching intervention make a difference? Nursing Education Today. 2010 Nov 13; 119. Morens DM, Taubenberger JK, Folkers GK, Fauci AS. An historical antecedent of modern guidelines for community pandemic influenza mitigation. 2009 Jan/Feb; 124(1): 22-25. 120. Morimoto T, Ishikawa H. Assessment of intervention strategies against a novel influenza epidemic using an individual-based model. Environ Health Prev Med. 2010 May; 15(3): 151-161. 121. Morrison LG, Yardley L. What inflectional control measures will people carry out to reduce transmission of pandemic influenza? A focus group study. BMC Public Health. 2009; 8(258). 122. Nap RE, Andriessen MP, Meessen NE, Miranda Ddos R, van der Werf TS. Pandemic influenza and excess intensive care workload. Emerg Infect Dis. 2008 Oct; 14(10): 1518-1525. 123. Nap RE, Andriessen MP, Meessen NE, van der Werf . Pandemic influenza and hospital resources. Emerg Infect Dis. 2007 Nov; 13(11): 1714-1719. 124. Newall AT, Wood JG. Oudin N, MacIntyre C. Cost-effectiveness of pharmaceutical-based pandemic influenza mitigation strategies. Emerg Infect Dis. 2010 Feb; 16(2): 224-230. 125. O’Boyle C, Robertson C, Secor-Turner M. Nurses’ beliefs about public health emergencies: Fear of abandonment. Am J Infect Control. 2006 Aug; 34(6): 351-357. 126. Pahlman I, Tohmo H, Gylling H. Pandemic influenza: human rights, ethics and duty to treat. Acta Anaesthesiol Scand. 2010; 54(1): 9-15. 62 127. Pareek M, Clark T, Dillon H, Kumar R, Stephenson I. Willingness of healthcare workers to accept voluntary stockpiled H5N1 vaccine in advance of pandemic activity. Vaccine Feb 18; 27(8): 1242-1247. 129. Burke RJ, Psycho-social impacts of bioterrorism and stress in the wake of 9/11. In: Burke RJ, Cooper CL, editors. International terrorism and threats to security. United Kingdom: Edward Elgar Publishing Limited. 2008. 3-33. 130. Pena ME, Irvin CB, Takla RB. Ethical considerations for emergency care providers during pandemic influenza –ready or not. Prehosp Dis Med. 2009 Mar/Apr; 24(2): 115-119. 131. Pereeira JA, Quach S, Heidebrecht C, Foisy J, Quan S, Finkelstein M, et al. Pan-Canadian assessment of pandemic immunization data collection: study methodology. BMC Med Res Methodol. 2010; 10(51). 132. Philpott D. Pandemic update. Homeland Defense Journal. 2007 Oct; 5(10): 14-16. 133. Poland GA. The 2009-2010 influenza pandemic: effects on pandemic and seasonal vaccine uptake and lessons learned for seasonal vaccination campaigns. Vaccine. 2010 Sep 7; 28(4); D3-D13. 134. Prosser LA, Bridges CB, Uyeki TM, Rego VH, Ray GT, Meltzer MI, et al. Values for preventing influenza-related morbidity and vaccine adverse events in children. Halth Qual Life Outcomes. 2005; 3(18). 135. Quinn SC, Kumar S, Freimuth VS, Kidwell K, Musa D. Public willingness to take a vaccine or during under emergency use authorization during the 2009 H1N1 pandemic. Biosecur Bioterror. 2009 Sep; 7(3): 275-290. 136. Qureshi K, Gershon RM, Conde F. Factors that influence medical reserve corps recruitment. Prehosp Disaster Med. 2008 May/Jun; 23(Supp1): S27-S34. 63 137. Qureshi K, Gershon RRM, Sherman MF, Straub T, Gebbie E, McCollum M, et al. Health care workers’ ability and willingness to report to duty during catastrophic disasters. J Urban Health. 2005; 82(3): 378-388. 138. Qureshi KA, Merrill JA, Gershon RRM, Calero-Breckheimer A. Emergency preparedness training for public health nurses: A pilot study. J Urban Health. 2002 Sep; 79(3): 413-416. 139. Raboud J, Shigayeva A, McGeer A, Bontovics E, Chapman M, Gravel D, et al. Risk factors for SARS transmission from patients requiring intubation: A multicentre investigation in Toronto, Canada. PLoS One. 2010; 5(5). 140. Raude J, Caille-Brillet AL, Setbon M. The 2009 pandemic H1N1 influenza vaccine in France: who accepted to receive the vaccine and why? PLoS Curr. 2010 Oct; 19(2). 141. Reed LD. A message from the editor. Pub Health Rep. 2010 Apr; 3(125): 1-2. 142. Reynolds B, and Quinn SC. Effective communication during an influenza pandemic: The value of using a crisis and emergency risk communication framework. Health Promot Prac. 2008 Oct; 9(4): 13S-17S. 143. Riba S, Reches H. When terror is routine: How Israeli nurses cope with multi-casualty terror. Online J Issues Nurs. 2002 Sep 30; 7(3): 13. 144. Roddick JA. Delirium and fever in the antipodes: Nursing and the 1918 influenza epidemic in Dunedin Hospital, New Zealand. Journal of Research in Nursing. 2006 Jul; 11(4): 357-340. 145. Rosoff PM. The ethics of care: Social workers in an influenza pandemic. Soc Work Health Care. 2008; 47(1): 49-59. 64 146. Rosselli RT, Davis MK, Simeonsson K, Johnson M, Goode B, Casani J, et al. An academic/government partnership to provide technical assistance with pandemic influenza planning to local health departments in North Carolina. Pub Health Rep. 2010 Nov/Dec; 125: 92-99. 147. Rosychuk RJ, Bailey T, Haines C, Lake R, Herman B, Young O, et al. Willingness to volunteer during an influenza pandemic; perspectives from students and staff at a large Canadian university. Influenza Other Respi Viruses. 2008 Mar; 2(2): 71-79. 148. Rothstein MA. Should health care providers get treatment priority in an influenza pandemic? J Law Med Ethics. 2010 Sum; 38(2): 412-419. 149. Rubin GJ, Amiot R, Page L, Wessely S. Public perceptions, anxiety, and behavior change in relation to the swine flu outbreak; cross sectional telephone survey. BMJ. 2009; 339. 150. Ruderman C, Tracey CS, Bensimon CM, Bernstein M, Hawryluck L, Ziotnik Shaul R, et al. On pandemics and the duty to care; whose duty? Who cares? BMC Med Ethics. 2006; 7(5). 151. Saleh DA, Elshaer I. Nurses’ perspectives and concerns towards an infectious disease epidemic in Egypt. The Egyptian Journal of Community Medicine. 2010 Apr; 28(2): 1-17. 152. Sandman PM, Miller PM, Johnson BB, Weinstein ND. Agency communication, community outrage, and perception of risk: Three simulation experiments. Risk Anal. 1993; 13(6): 585-598. 153. Sarikaya O, Erbaydar T. Avian influenza outbreak in Turkey through health personnel’s views: a qualitative study. BMC Public Health. 2007; 7: 330. 154. Sayles JN, Macphail CL, Newman PA, Cunningham WE. Future HIV vaccine acceptability among young adults in South Africa. Health Educ Behav. 2010 Apr; 37(2): 193-210. 65 155. Schechter S. Medical reserve corps volunteers’ ability and willingness to report to work for the Department of Health during catastrophic disasters. [Master thesis]. Monterey, CA: Naval Postgraduate School; 2007. 156. Schluger NW. Suppose they gave an epidemic and nobody came? AJOB 2008 Aug; 8(8): 23-5. 157. Schwartz B, Orenstein WA. Prioritization of pandemic influenza vaccine: rationale and strategy for decision making. Curr Top Med Mycol. 2009; 333(5): 495-507. 158. Seale H, Haywood AE, McLaws ML, Ward KF, Lowbridge CP, Van D, et al. Why do I need it? I am not at risk! Public perceptions towards the pandemic (H1N1) 2009 vaccine. BMC Infec Dis. 2010; 10(99). 159. Seale H, Kaur R, Wang Q, Yang P, Zhang Y, Wang X, et al. Acceptance of a vaccine against pandemic influenza A (H1N1) virus amongst healthcare workers in Beijing, China. Vaccine. 2011 Feb 11; 29(8): 1605-10. 160. Seale H, Leask J, Po K, MacIntyre CR. Will they just pack up and leave? – Attitudes and intended behaviors of hospital health care workers during and influenza pandemic. BMC Health Serv Res. 2009; 9(30). 161. Section I; Oral Sessions. J Urban Health. 2007 Nov; 84(6): 841-925. 162. Shabanowitz RB, Reardon JE. Avian flu pandemic-flight of the healthcare worker? HEC Forum. 2009 Dec; 21(4): 365-385. 163. Shapira Y, Marganitt B, Roziner I, Shochet T, Bar Y, Shemer J. Willingness of staff to report to their hospital duties following an unconventional missile attack: a state-wide survey. Isr J Med Sci. 1991; 27(11-12): 704-711. 66 164. Shaw KA, Chilcott A, Hansen E, Winzenburg T. The GP’s response to pandemic influenza: a qualitative study. Fam Pract. 2006; 10: 267-272. 165. Shimabukuro TT, Wortley PM, Bardenheier B, Bresnitz EA, DeBlois AM, Hahn CG, et al. Survey of state practices during the 2004-2005 influenza vaccine shortage. Pub Health Rep. 2007 Mar/Jun; 122(3): 311-318. 166. Simonds AK. Sokol DK. Lives on the line? Etyhics and practicalities of duty of care in pandemics and disasters. Eur Respir J. 2009; 34(2): 303-309. 167. Singer PA, Benatar SR, Bernstein M, Daar AS, Dickens BM, MacRae SK, et al. Ethics and SARS: lessons from Toronto. BMJ. 2003 Dec6; 327: 1342-1344. 168. Singleton CM. Avian/pandemic flu issues for consideration and pandemic planning: what a local government faces. Adm Law Rev. 2006 Sum; 58(3): 645-650. 169. Slowther A. Planning for and managing pandemic influenza. Clin Ethics. 2009;4: 116-118. 170. Smith E. Emergency healthcare workers’ willingness to work during major emergencies and disasters. The Australian Journal of Emergency Management. 2007; 22 (2): 21- 24. 171. Smith EC, Burkle FM, Holman PF. Dunlap JM, Archer FL. Lessons from the front lines: The Prehsopital experience of the 2009 Novel H1N1 outbreaks in Victoria, Australia. Disaster Med Public Health Prep. 2009; 3(2): S154- S159. 172. Smith KJ, Raymund M, Nowalk MP, Roberts MS, Zimmerman RK. Cost-effectiveness of healthcare worker pneumococcal polysaccharide vaccination during pandemic influenza. Am J Manag Care. 2010 Mar; 16(3): 200-206. 173. Smith M, Morgan A, Qureshi K, Burke F, Archer F. Paramedics’ perceptions of risk and willingness to work during disasters. The Australian Journal of Emergency Management. 2009 Aug; 24(3). 67 174. Sokol DK. Virulent epidemics and scope of healthcare workers’ duty of care. Emerg Infect Dis. 2006 Aug; 12(8): 1238-1241. 175. Steffen C, Masterson L, Christo S, Kordick M. Willingness to respond: A survey of emergency department personnel and their predicted participation in mass causality terrorist events. Ann Emerg Med. 2004 Oct; 44(4): S34. 176. Stevens G, Jones A, Smith G, Nelson J, Agho K, Taylor M, et al. Determinants of paramedic response readiness for CBRNE threats. Biosecur Bioterror. June 2010, 8(2): 193-202. 177. Stohr K. Avian influenza and pandemics-research need and opportunities. N Engl J Med. 2005 Jan 27; 352(4): 405-407. 178. Stuart RL, Gillespie E. Preparing for an influenza pandemic: healthcare workers’ opinions on working during a pandemic. Healthcare Infection. 2008; 13: 95-99. 179. Swaminathan A, Martin R, Gamon S, Aboltins C, Athan E, Braitberg G, et al. Personal protective equipment and antiviral drug use during hospitalization for suspected or pandemic influenza. Emerg Infect Dis. 2007 Oct; 13(10): 1541-1547. 180. Swayne DE. Transcript of the question and answer session from the fifth international symposium on avian influenza. Avian Dis. 2003; 47: 1219-1255. 181. Syrett JL, Benitez JG, Livingston WH, Davis EA. Will emergency health care providers respond to mass causality incidents? PreHosEmeg.Care. 2007;11: 49-54. 182. Tam DKP, Lee S, Lee SS. Impact of SARS on avian influenza preparedness in healthcare workers. Infection. 2007; 35(5): 320-325. 68 183. Taylor BL, Montgomery HE, Rhodes A, Sprung CL. Taylor BL, Montgomery HE, Rhodes A, Sprung CL. Protection of patients and staff during a pandemic. In: Recommendations and standard operating procedures for intensive care unit and hospital preparations for an influenza epidemic or mass disaster. Intensive Care Med. 2010 Apr;36(Suppl 1): S45-54. 184. Ten Eyck RP. Ability of regional hospitals to meet projected avian flu pandemic surge capacity requirements. Prehosp Disaster Med. 2008; 23(2): 103-112. 185. Thompson AK, Faith K, Gibson JL, Upshur REG. Pandemic influenza preparedness: an ethical framework to guide decision-making. BMC Med Ethics. 2006; 7(12). 186. Thoon KC, Chong CY, Survey of healthcare workers’ attitudes, beliefs and willingness to receive the 2009 pandemic influenza A (H1N1) vaccine and the impact of educational campaigns. Ann Acad Med Singapore. 2010 Apr; 39(4): 307-6. 187. Tippett VC, Watt K, Raven SG, Psych (Hons) B, Kelly HA, Coory M, et al. Anticipated behaviors of emergency prehospital medical care providers during an influenza pandemic. Prehosp Disaster Med.2010 Jan/Feb; 25(1): 20-25. 188. Trainor J, Aguirre BE, Barnshaw J. Social scientific insights on preparedness for public health emergencies. In: A report prepared by the Disaster Research Center for the Delaware Department of Health and Social Services, Division of Public Health, Disaster Preparedness. Delaware: University of Delaware; 2008. Section P. 1-93. 189. Tuite AR. Fisman DH, Kwong JC, Greer AL. Optimal pandemic influenza vaccine allocation strategies for Canadian population. PLoS One. 2010; 5(5). 190. Tzeng, HM, Yin CY. Nurses’ fears and professional obligations concerning possible human-to-human avian flu. Nurs Ethics. 2006;13(5): 455-470. 69 191. Van D, McLaws ML, Crimmins J, MacIntryre CR, Seale H. University life and pandemic influenza: Attitudes and intended behaviors of staff and students towards pandemic H1N1 2009. BMC Public Health. 2010; 10(130). 192. Van Rijswoud E. Virology experts in boundary zone between science, policy and public A biographical analysis. Minerva. 2010 June; 48(2): 145-167. 193. Vawter DE, Garnett JE, Prehn AW, Gervais KG. Health care workers’ willingness to work in a pandemic. Am J Bioeth. 2008 Aug; 8(8): 21-23. 194. Vawter DE, Gervais KG, Garrett JE. Allocating pandemic influenza vaccines in Minnesota: Recommendations of the Pandemic Influenza Ethics Work Group. Vaccine. 2007; 25: 6522-6536. 195. Voo TC, Capps B. Influenza pandemic and the duties of healthcare professionals. Singapore Med J. 2010; 51(4) : 275-281. 196. Watkins RJ, Barnett DJ, Links JM. Corporate preparedness for pandemic influenza a survey of pharmaceutical and biotechnology companies in Montgomery County, Maryland. Biosecur Bioterror. 2008 Sep; 6(3): 219-226. 197. Watt K, Tippett VC, Raven SG, Psych (Hons) B, Jamrozik K, Coory M, et al. Attitudes to living and working in pandemic conditions among emergency prehospital medical care personnel. Prehosp Disaster Med. 2010 Jan/Feb; 25(1): 13-19. 198. Weisfuse IB, Berg D, Gasner DB, Layton M, Misener M, Zucker JR. Pandemic influenza planning in Newy York City. J Urban Health; 2006 May; 83(3): 351-354. 199. Wickler S, Rabenaur HF, Gottschalk R. Influenzapandemie: Wurde das krankenhaus-personal zur Arbeit kommen? Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2009; 8: 862-869. 70 200. Wilson AGC. The willingness and ability of staff to report to work following a disaster [Master thesis]. Canada. Royal Roads University; 2009. 201. Wong ELY, Wong SYS, Kung K, Cheung AWL, Gao TT, Griffiths S. Will the community nurses continue to function during H1N1 influenza pandemic: a cross-sectional study of Hong Kong community nurses? BMC Health Serv Res. 2010 Apr 30; 10(107). 202. Wong SYS, Wong ELY, Chor J, Kung K, Chan P, Wong C, et al. Willingness to accept H1N1 pandemic influenza vaccine: a cross-sectional study of Hong Kong community nurses. BMJ Infect Dis 2010 Oct 29; 10(316). 203. Wong TY, Koh GCH, Cheong SK, Lee HY, Fong YT, Sundram M, et al. Concerns, perceived impact and preparedness in an avian influenza pandemic – a comparative study between healthcare workers in primary and tertiary care. Ann Acad Med Singapore; 2008 Feb; 37(2): 96-102. 204. Wong TY, Koh GC, Cheong SK, Sundram M, Koh K, Chia SE, et al. A cross-sectional study of primary-care physicians in Singapore on their concerns and preparedness for an avian influenza outbreak. Ann Acad Med Singapore; 2008 Jun; 37(6): 458-464. 205. Wortley PM, Schwartz B, Levy PS, Quick LM. Healthcare workers who elected not to receive smallpox vaccination. Am J Prev Med. 2006 Mar; 30(3): 258-65 206. 21st ESICM Annual Congress. Oral Presentations. Intensive Care Med. 2008 Sep; 34(Supp1):5-92. 71 Study ID First Author, Year of Publication Country 1 = US 2= Non-US Events 1= IP 2= AI Events other than PI/AI included in research Yes/No Healthcare Sector 1=EMS 2=Hospital/system 3=Public health 4=Homecare 5=Other Populations of Healthcare Workers Study Design 1 = Quantitative 2 = Qualitative Statistical Analysis of Measure 118 Balicer et al., 2006 1 1 No 3 Professional Technical/support staff 1 Odds ratio, logistic regression 219 Balicer et al., 2010 1 1 No 2 Hospital staff Clinical staff: physicians, nurses, physician extenders, med/nursing students Non-clinical staff: foodservice/linen, IT legal, executive officers, nursing administration, parking, pharmacy, safety, social workers, supply chain telecom 1 Odds ratio, logistic regression APPENDIX B-I ADDITIONAL METHODS Appendix B-I: Characteristics of the Research Studies, HCWs’ Willingness to Report to Work During a Pandemic Influenza 72 320 Barnett et al., 2010 1 1 Yes 1 Paramedics and EMTs 1 and 2 Odds ratio, logistic regression 421 Barnett et al., 2009 1 1 Yes 3 Clinical and non-clinical staff 1 Odds ratio, logistic regression 522 Basta et al., 2009 1 1 Yes 3 Nursing and non-nursing staff 1 Odds ratio 623 Cone et al., 2006 1 1 Yes 2 Primarily emergency department: nurses, physicians, mid-level providers, clinical and non-clinical staff 1 Descriptive statistics, students t-testing, x2 testing 724 Cowden et al., 2010 1 1 No 2 Physicians, pediatric residents and fellows, hospital based nurses, allied health, and med/technical 1 Odds ratio, chi square, logistic regression, student t-test, Spearman’s correlation 825 Damery et al., 2009 2 1 Yes 2 Doctors, nurses, allied professional, ancillary manager, general practitioner, community 1 Odds ratio, logistic regression 926 Daugherty et al., 2009 1 1 No 2 Critical care clinicians, faculty, fellows, nurses and RCP 1 Logistic regression 73 1027 Garnett et al., 2009 1 1 Yes 2 Clinical: nursing, physicians, dentists Operational: security, administration, facilities Support: laboratory, blood bank 1 and 2 Student t-test, percentages 1115 Gershon et al., 2010 1 1 No 4 Home health aides Home attendants Personal care workers Registered nurses 1 Odds ratio, logistic regression, basic descriptive statistics, Pearson’s X2 1228 Hope et al., 2009 2 1 Yes 2 Clinical nurse consultants, nurse educators and nurse managers from areas defined as not critical during early phase of an influenza pandemic. 1 Chi-square, basic descriptive statistics 1329 Hope et al., 2009 2 1 Yes 2 Hospital staff Select community health staff: nurses, social workers, early childhood nurses, aboriginal workers. 1 Chi-square, logistic regression 1430 Imai et al., 2008 2 1 No 2 Physician, nurses, and other 1 Chi-square, logistic regression, Spearman’s correlation 74 1531 Irvin et al., 2008 1 1 Yes 2 Doctors, nurses, clerical, and other 1 Descriptive statistics 1632 Ma et al., 2011 2 1 No 2 Healthcare workers: Physicians, nurses, and others 1 Odds ratio, Chi-square , student t-test 1733 Martin, 2011 1 1 No 5 RNs LPNs 1 The Z-test 1834 Martinese et al., 2009 2 2 Yes 2 Medical, nursing, allied health, and support staff 1 Odds ratio, logistic regression, Pearson’s X2, student t-test, regression. 1935 Saleh et al., 2010 2 1 No 2 Nurses, student nurses 1 and 2 Odds ratio, logistic regression 2036 Schechter, 2007 1 1, 2 Yes 5 Physicians, nurses, psychologists, dentists, social workers and veterinarians 1 Descriptive statistics 2137 Seale et al., 2009 2 2 No 2 Medical, nursing, allied health personnel, ancillary 1 Proportions 2238 Shabanowitz et al., 2009 1 2 No 2 MD/DO, PhD, PAC, nurse. research personnel, business personnel, support staff, administrative and other 1 Chi-square 75 2339 Stuart et al., 2008 2 1 No 2 Medical, nursing, clerical support, administration, hotel services 1 Pearson’s X2 test 2440 Syret et al., 2006 1 1 Yes 2 ER physicians, nurses, and support staff EMS/Fire 1 Odds ratio, percentages, Pearson’s X2, 2541 Tippett et al., 2010 2 1 No 1 Emergency pre-hospital medical care providers, administration, educators, and management 1 Odds ratio, logistic regression 2642 Tzeng et al., 2006 2 2 No 2 Nursing students 1 Chi-square, descriptive statistics, Student t-test 2743 Wong E et al., 2010 2 1 No 5 Community nurses 1 Odds ratio, logistic regression 2844 Wong TY et al., 2008 2 2 No 5 Primary care physicians 1 Chi-square, logistic regression 76 Study ID Purpose of the Study Sampling Sample Response Rate (%) Respondents Respondent Characteristics (%) 1 To understand local public health workers’ perceptions towards a pandemic influenza response. Non-randomized Convenience Sample Three health departments ranging in size from 132 to 225 employees. 58 308 Female, 83 2 To understand public health workers’ perceptions toward pandemic influenza. Non-randomized Convenience Sample A 984 bed tertiary-care academic teaching hospital 18.4 3426 Female, 73.7 3 To identify the relative influences of perceived threat and efficacy on EMS workers’ response Simple Randomized Sample Nationally representative sample of EMS personnel 38 586 Female, 34.1 APPENDIX B-II ADDITIONAL METHODS Appendix B-II: Research Design: Purpose and Sampling 77 willingness in the face of a pandemic threat, and to uncover additional relevant barriers and facilitators of pandemic influenza response willingness among this cohort. 4 To examine the relative influences of perceived threat and efficacy on public health workers’ response willingness to pandemic influenza. Non-randomized Four clusters of local public health departments in the midwest and eastern US. 83 1835 Female, 81 5 To determine how informed health department employees are about pandemic response and how willing they are to report to work during a pandemic. Random Sample Stratified Cluster Sample Sixty-seven county health departments in Florida 51 2414 Female, 77.9 . 6 To assess hospital employees’ attitudes and needs regarding work commitments during disasters. Non-randomized Convenience Sample Nine large hospitals, most academic centers distributed amongst five states. 85.3 1711 Female, 67 78 7 To determine the relationship between health care worker reporting willingness to report to work during a pandemic and perception of job importance, belief that one will be asked to work, Non-randomized Convenience Sample Free standing tertiary Children’s Hospital 31 778 Female, 76.9 8 To investigate the factors associated with willingness to work during an influenza pandemic, and to promote the continued presence at work of the health care workers otherwise unwilling or unable to attend. Randomized Sample Trusts included a wide range of health care settings. 34.4 1032 Female, 56.4 9 To assess ICU health care workers’ knowledge, attitudes, and expected behaviors in the event of an influenza pandemic.. Non-randomized Convenience Sample Two hospitals in Baltimore Maryland: 945-bed tertiary care academic medical center and a 310-bed community teaching hospital. 88 256 Female, 60 79 10 To evaluate interventions intended to mitigate absenteeism in hospital workers and provide recommendations to emergency planners. Focus Groups Non-randomized Convenience Sample Five large urban facilities: medical centers, pediatric, community, and behavioral health hospitals. 17 2864 Female, 75.4 11 To assess willingness of home health care workers to care for clients with a serious infectious disease. Convenience Sample Cross-sectional Survey Home attendant and home health care agencies. Not cited. 384 Female, 96 12 To determine the appropriateness of engaging advanced nurses as public health surge staff and to evaluate whether a training package and exercise participation changed individual’s perceptions and confidence of working. Non-randomized Regional area of New South Wales. 87 93 52 Training: n= 47 Pre-exercise: n= 56 Post exercise: n=32 Gender demographics not cited. 80 13 To determine front line staffs’ perceived willingness to work during three public health emergency scenarios: weather event, influenza pandemic, and bioterrorism event. Random Sample Cross-sectional Survey Acute and community facilities in the Hunter New England 66 868 Female,77 14 To assess individual preparedness among health care workers, as determined by their recognition of preventive measures, perceptions of institutional measures, and attitude toward coping with risk; institution preparedness as determined by reported expertise in dealing with infectious diseases, general measures for infection control, and specific measures related to pandemic influenza; and the inter-relationship between individual and institutional preparedness. Non-randomized Convenience Sample Seven tertiary hospitals, two designed to accommodate patients with severe infectious diseases. 68.7 7,378 Female,72.6 81 15 To determine the willingness to hospital personnel to report to work in a hypothetical avian influenza and factors that may influence their decisions. Cross-sectional Survey Trauma Center with 600 - beds. 90 169 Female, 68 16 To assess the knowledge and attitudes of critical care clinicians in Chinese ICU’s during the current influenza pandemic. To identify independent predictors of unwillingness to work in order to formulate an effective strategy to improve the preparedness of health care workers. Random Sample Twenty-one adult Intensive Care Units 89.9 695 Female, 43 17 To determine factors affecting nurses’ ability and willingness to work during a pandemic flu. Simple Random Sample Approximately 22,000 nurses with a Maine home address. 61.3% 735 Female, 95.3 82 18 To describe how an avian or pandemic influenza threat would affect hospital staff in an Australian setting. Effects are described in terms of expected absentee rates, work attitudes, concerns, and incentives, which may enhance work attendance should an patient admission occur with influenza or a pandemic. Convenience Sample A major metropolitan hospital – 570 beds 98 560 Female, 64.8 19 To assess the effect of the 2009 H1N1 pandemic on nurses’ working behavior, to identify the nurses’ willingness to work, concerns and persuading factors towards working during infectious disease epidemics. Cross-sectional study A tertiary care hospital, primary health care facility, university, and secondary technical nursing school Not cited 266 Female, 91 20 To determine the ability and willingness of the Medical Reserve Corp volunteers to work in a public health emergency. Non-randomized Convenience Sample Medical Reserve Corp Volunteers 61.1 198 Female, 65 83 21 To extend previous research by assessing the knowledge and intended behavior during an influenza pandemic amongst clinical and non-clinical hospital staff. Random Sample Two tertiary teaching hospitals, adult, and pediatric 74.5 894 Female, 74.8 22 To garner opinions from health care workers’ themselves on their perceived duty to treat and how they might respond to a severe avian influenza pandemic. Non-randomized Rural tertiary and quaternary care health system 9 1003 Female, 74 23 To solicit the opinions of health care workers as to their attitude to working in a pandemic. Non-randomized Large metropolitan health service 14 1440 Female, 61 24 To determine emergency health care providers’ willingness to report to work during a mass casualty incident. Non-randomized Convenience Sample A hospital designated as a primary receiving for mass casualty events and decontamination hospital 100 180 Gender demographics not cited. 84 25 To investigate the association between knowledge and attitudes regarding avian influenza on likely behavioral responses of emergency pre-hospital medical health care providers’ in a pandemic condition Stratified Random Sample Nine ambulance services providing hospital pre-hospital care 24.7 725 Gender demographics not cited. 26 To illustrate the factors contribute to nurses’ fear about an avian influenza and their willingness to care for infected patients. Convenience Sample Nursing students attending a two year bachelor’s degree nursing program. 95.3 225 Gender demographics not cited. 27 To explore the willingness of community nurses to continue to work during the H1N1 influenza pandemic Cross-sectional Survey Forty-eight centers affiliated with hospitals and organizational groups. 66.6 401 Female, 96.2 28 To examine and compare concerns, perceived impact and preparedness among primary care physicians in the private and public primary-care outpatient clinics for a possible avian influenza pandemic. Random Sample Cross-sectional Survey Eighteen polyclinics and private clinics 72.7 285 Female, 45 85 Study ID Context Dependent Variable (s) Dependent Variable Operational Definition Survey Scale Overall % Willing to Report to Work 1 Pandemic influenza related emergency Likelihood of reporting to work Operational definition not provided. Likert scale 5point: very likely to not likely at all Dichotomized into responses with a score of 2 or less and all others 53.8% indicated they would likely report to work during an emergency 2 Pandemic Influenza Willingness to respond 1. Willingness to respond but not required Operational definition not provided Likert scale 9 point: 1 – strong agreement, 5 indicating neutrality, 9 – strong disagreement, and a DNK Willingness to respond to an influenza pandemic: APPENDIX B-III S ADDITIONAL METHODS Appendix B-III: Context, Dependent Variable, Operational Definition and Percent of Willingness to Report to Work 86 2. Willingness to respond if required. Dichotomized into categories of < 4 positive responses and > 6 negative responses 1. 72% if asked but not required 2. 82.5% if required to respond 3 Pandemic Influenza Willingness to report to work 1. If required: willing to report to work during pandemic flu emergency 2. If asked, but not required: willing to report to work during pandemic flu emergency Operational definition not provided Like scale 10point: strong agreement to strong disagreement Dichotomized into categories < 5 positive and > 5 negative responses, and don’t know option 1. 93% if required willing to report to work 2. 88% if asked, but not required willing to report to work 87 4 Four scenarios: weather related emergency, pandemic influenza, dirty bomb radiological terrorism event, and inhalational anthrax bioterrorism. Willingness to respond to a pandemic flu emergency 1. If required: willing to report to work during pandemic flu emergency 2. If asked, but not required: willing to report to work during pandemic flu emergency Operational definition not provided Likert scale 10point: response of 1 indicating strong agreement, 10 indicating strong disagreement, and a DNK Dichotomized into categories < 5 positive response, agreement and > 6 negative response, disagreement 1. 92% if required: willing to report to work during a pandemic 2. 86% if asked but not required: willing to report during pandemic flu emergency 88 5 Pandemic Influenza 1. Early pandemic, low risk duties 2.Early pandemic, high risk duties 3. Peak pandemic, low risk duties 4. Peak pandemic, high risk duties Likelihood of reporting to work 1. Likely to report to work if your work required direct face to face contact with infected patients. 2. Likely to report to work if work required direct face to face contact with infected patient. “If your work duties did not required you to have direct face-to-face contact with people who could be infected, how likely would you be to report to work” “If your work duties did require you to have direct face-to-face contact with people who could be infected, how likely would you be to report to work” 5-point Likert scale: very likely, somewhat likely, neither likely nor unlikely, somewhat unlikely, or very unlikely Dichotomized into those likely to report compared with those not likely. Neither likely nor unlikely were excluded from the analysis. 1. 92.3% early pandemic, low risk duties 2. 66.4 % early pandemic, risk duties 3. 82.7% peak pandemic, low risk duties 4. 56.2% peak pandemic, high risk duties 89 6 Various disaster scenarios: mass casualty, biological, chemical radiological events and natural disasters, flu pandemic Staff willing and able to come to work, perform job, and work additional hours “From the list below, check off those types of disasters during which you would be willing and able to come to work to perform you job for additional worked hours beyond your contracted hours, at standard overtime pay rates” Respondents provided flu epidemic tick box. 72% willing to work flu pandemic 7 Pandemic Influenza “The primary dependent variable assessed in the study was willingness to report to work in a pandemic influenza.” Operational definition not provided Respondent scale: Yes, No, and Unsure Dichotomized as yes vs. no and unsure 60% of staff willing 90 8 Pandemic Influenza Likelihood of working in various conditions. “If there was an outbreak of pandemic influenza how likely is it that you would work in the following circumstance: if there was a greater than usual risk of become infected at work and falling ill yourself.” Respondent scale: Likely, Don’t know, Unlikely, N/A. Responses were calculated with a likelihood score. DNK/NA excluded from the denominator 59.3% likelihood of working if there was greater than usual risk of becoming infected at work and failing ill. 9 Pandemic Influenza The likelihood of not reporting to work in the event of a pandemic. Operational definition not provided Likert scale 4 point: categorized as agree if the response was a 1 or 2, and not agree if the response was a 3 or 4. Unsure option. Unsure included in the not likely. 20% reported not being likely to report to work during a pandemic 79% reported being likely to report to work during a pandemic 91 10 Moderate hypothetical influenza pandemic. Willingness to work before and after a series of interventions in a pandemic Operational definition not provided. Respondent scale: 0 to 100 scale with 0 absolutely not willing to report to duty and 100 absolutely willing report to duty. 75.6% baseline willingness to report to work 11 Pandemic Influenza Willingness to report to duty during a pandemic 1. Willingness to care for current patients during a pandemic outbreak. 2. Willingness to care for new patients during a pandemic outbreak. Operational definition not provided Respondent scale: willing, not willing, and not sure Dichotomized into two categories: willing versus not willing/not sure 1. 43% willingness to care for current patients during a pandemic outbreak. 2. 27% willingness to care for new patients during a pandemic outbreak. 92 12 Pandemic Influenza Influenza pandemic. Four hour on-line training and four day pandemic exercise 1. Pre intervention 2. Post intervention “Willing to work in the future if required” Respondent scale 10 point: 1 as agree and 10 disagree. Dichotomized into those who definitely agree 1- 3, and others 4 -10. 1. 47% pre interventions 2. 82% post interventions 13 Three scenarios: weather related event, influenza pandemic, and bioterrorism event. Willingness to respond if required to an influenza pandemic. Operational definition not provided. Respondent scale: 10 point with 1- agree to 10-disagree. Dichotomized in to those agreeing 1- 3. 67% willing to report to work during an influenza pandemic. 14 Pandemic Influenza Accept the risk of contracting pandemic influenza at work. “Do you feel that you would accept the risk of contracting pandemic influenza at work in the event of an influenza pandemic?” Respondent scale: 7-point, strongly agrees, agree, probably agree, probably disagree, disagree, and strongly disagree. 74.5% acceptance of risk of contracting disease at work 93 Dichotomized into positive responses, strongly agree, agree, probably agree, and negative responses strongly disagree, disagree, and probably disagree 15 Pandemic Influenza Willingness to report to work with patients being treated at the hospital with a 50% mortality rate with treatment and 10% of the general population was sick. “In the event of an avian influenza pandemic and patients were being treated at St. John’s Hospital and medical Center, would you report to work as usual?” Respondent scale: yes, no, maybe. Maybe excluded from dichotomization. 50% willing to report to work as usually. 94 16 Pandemic Influenza H1N1. Willingness to care for H1N1 patients Operational definition not provided. Respondent scale 5point 1=completely agree to 5=completely disagree. Dichotomized as completely agree/agree vs. disagree, completely disagree. Midpoint of 3 included with disagree response. 82.3% willingness to care for H1N1 patients. 17 Pandemic Influenza Willingness to work during a pandemic flu. Operational definition not provided. Respondent scale: yes and no, researcher noted if left blank 90.1% reported they would work 18 Hypothetical influenza scenarios Willingness to continue to work during a pandemic. “If there was a patient with a confirmed case of avian influenza admitted to this hospital tomorrow, Respondents scale: yes, no 1: 13% would not attend work, 87% would attend. 95 1. “A single patient with avian influenza admitted.” 2.”Multiple patients admitted with a new strain of influenza during a pandemic.” would you continue to work?” “If there were many patients admitted to the hospital with this new strain of human influenza that had merged with the avian influenza virus, would you continue to work?” 2:36% would not attend work, 64% would attend 19 2009 H1N1 Influenza Pandemic Willingness and concerns with working during infectious disease pandemics. Operational definition not provided. Respondents scale – work, not work, and DNK DNK excluded 41.5% would not be willing to report to duty, 96 20 Avian Influenza Scenario: Six confirmed cases of H5N1 (Avian) Influenza in NYC. One suspected case in Nassau. Willingness to volunteer “Would you be willing to volunteer with the Nassau MRC?” Respondent scale - willing to volunteer, not willing to volunteer, not sure. Dichotomized as willing vs. not willing/not sure 79% willing to volunteer in an Avian Influenza 21 Pandemic influenza Would be present to work if a patient in the ward or department had influenza like illness. Operational definition not provided. Respondent scale: world responds vs. would not. 83.3% willing to work if a patient in their ward had an influenza like illness 22 Avian Pandemic Willingness (volunteer) to work in the event of a virulent avian pandemic. “Would volunteer, if all above were provided” Respondent scale: would volunteer, and would not volunteer 79% Would volunteer to wo
Thesis (D.H.A.)--Central Michigan University, 2012. xi, 137 leaves : ill. Includes bibliographic references (leaves 131-137).
TypeGraduate Research; Dissertation