Who’s Using PDAs? Estimates of PDA Use by Health Care Providers: A Systematic Review of Surveys

Background Personal digital assistants (PDAs) find many uses in health care. Knowing rates of collective PDA use among health care providers is an important guiding step to further understanding those health care contexts that are most suited to PDA use and whether PDAs provide improved health outcomes.
Introduction
A handheld computing device, also commonly known as a personal digital assistant (PDA), is a mobile computer about the size of the palm of the hand. More modern devices can access external networks or the Internet through a wireless connection. Since 1993, when Apple launched the first PDA (Newton MessagePad), use of PDAs has increased worldwide, with global PDA sales projected to surpass 17 million in 2008. This represents a compounded annual growth rate of 17.8% between 2002 and 2008 [1].
Health care has not been immune to this technological advance in handheld computing. In fact, PDAs find many applications in health care. Family physicians and specialists have been using PDAs for general medical reference, such as drug interactions, pharmacopeias, and cardiac risk [2-4]. Other important applications of PDAs are those involving data collection and management, as in patient tracking, electronic Case Report Forms in clinical trials, patient diaries, and infection surveillance [4-9]. However, the suitability of PDAs across all health care contexts and whether they benefit health outcomes remain open questions.
Many of us would agree that it is necessary to evaluate a technology before its adoption to allow health care providers to make informed decisions. However, given that technology is a moving target, a common problem with evaluation is that practice frequently precedes research. By the time researchers have obtained funding, completed a study, and published it, the technology is either in widespread use or has been abandoned [10]. As well, the appropriate type of evaluation is not independent of the stage of adoption of the technology. For example, if 90% of the target users have already adopted a technology, then studies evaluating its general utility will no longer inform the adoption decision. In this case, research should focus on optimization of the technology in use. This is a familiar scenario in information technology research, and it underscores the importance of understanding the rates of adoption in helping direct approaches to research [10].
In a general overview article, Fischer et al (2003) summarized the current literature covering the use of handheld devices in medicine, primarily related to PDA functionality [4]. While implementation issues were discussed, rates of adoption were not addressed. Further, a recent review of PDA use in health care by Baumgart (2005) examined operating systems, basic functionality, security and safety, and limitations of PDA use [11]. It is a thorough overview of studies published since 2000 that addresses applications of handheld computers for health care professionals, but it touches only briefly on the prevalence of handheld use. Therefore, to our knowledge, there has not been any structured review conducted to date that specifically addresses the extent of use of handheld devices and estimated adoption rates. As such, this paper aims to systematically summarize all available survey data on health care providers’ use of PDAs with the view of presenting the best available estimates of current PDA use. This paper also aims to project expected future adoption based on established technology diffusion models. From this information we draw implications for research and practice.
Methods
For the purposes of this systematic review of surveys, the term PDA is used synonymously to refer to any handheld device. Some examples include the following: Blackberry; Palm operating system devices, which include Palm Tungstens, Handspring Visor, and Sony Clie; and Pocket PC devices, which include the Compaq iPAQ and HP Jordana.
Data Sources
Surveys were identified as a subset selected from a broader systematic review examining all studies related to handheld devices in health care settings. Thus, initial search strategies and retrieved articles reflected this more extensive focus. This comprehensive literature search was conducted in consultation with an information specialist. The searched bibliographic databases covered both medical and engineering disciplines, including the following eight databases: Medline, Current Contents, Inspec, BA/RRM, Biotechnology, Biological Abstracts, EI Compendex, and EMBASE. The search was restricted to English-language literature published January 1993 (corresponding to the development of the first palm device) to February 2005. An updated search of Medline (PubMed) and EI Compendex (EI Village 2) was run near the project’s completion (January 30, 2006).
Furthermore, the reference lists from included studies were examined in an effort to identify additional surveys not captured in the reference databases. In addition, surveys identified from Google searches and those known to the authors to have been conducted by private market research firms as well as physician groups were nominated for inclusion in our screening.
Electronic Search Strategy
The intent of searching the biomedical databases was to retrieve all studies related to handheld devices in health care. It is for this reason that the word survey was not included as a specific term in the original search strategy. The search did include the sample search terms detailed in Appendix 1. The search strategy for engineering databases limited retrievals to those articles relating to both handheld computing and health. All bibliographic databases were searched using subject headings tailored to each database and free-text terms in the titles and abstracts.
Eligibility Criteria
Surveys were included for this present review if they met the following initial criteria: related to an application in human health care and involved the use of a PDA device; contained original data; written in English (not including abstract or conference proceedings); published after 1993; and specifically reported handheld usage rates (prevalence of PDA use as a metric) in populations of health care professionals who were surveyed about the extent of their PDA use. Although conference proceedings were excluded, if deemed potentially relevant, a cross-check was conducted to see if there was an ensuing journal publication. A survey was not included if the handheld device being evaluated had undergone extensive custom modifications. A final set of unique references was identified and posted to the proprietary Web-based screening system SRS (Systematic Review Software).
Selection Process
The selection process for this present survey review consisted of two phases. First, it began with a screen of full-text articles that had already been retained because their title, abstract, or keywords suggested they contained relevant information on PDA use in health care settings. Therefore, for assessment of relevance, surveys were included if they appeared to contain pertinent study information and if there was no unequivocal reason for exclusion. Second, upon updating the searches, authors returned to the screening of the title, abstract, and keywords for each citation strictly to identify potentially relevant and most recent PDA usage surveys. Eligibility criteria were applied to the full-text surveys, which were reviewed independently by two reviewers (CG and KE). Disagreements were resolved by consensus. Figure 1 provides a modified QUOROM flow chart outlining the process for selecting identified PDA usage surveys.
Figure 1
Modified QUOROM Flow Chart for Identified PDA Usage Surveys
Data Abstraction
The contents of each included survey were abstracted by one reviewer (CG), with an additional research assistant providing verification (TR).
Analysis
The data from all included surveys were extracted in a predefined, standardized fashion with abstraction verified by a second person and assessed descriptively (Appendix 2). Quality assessment methods for descriptive study designs such as surveys have not been established. Although some assessment frameworks exist for assessing survey research [12,13], none of them have been validated or empirically shown to include criteria that are associated with the reduction of bias in empirical surveys. Therefore, survey quality was not formally assessed.
Table 1
Included surveys
Year of Survey/Publication
Author
Prevalence of PDA Use
Health Care Professional Group
1
1999/2000
Hucko [18]
15% (use in clinical work)
Physicians
2
NS/2001
ACP-ASIM [19]
47% (use in clinical work)
Specialists (Internists)
3
2001/2001
Versel* [20]
60% (use in practice)
Physician Executives (organizational survey)
4
2001/2001
Martin [21]
19.3% (use in clinical practice)
Physicians & Specialists
5
2001/2001
Taylor [22]
26% (use in practice)
Physicians
6
2001-2002/2002
AAP [23]
38% (NS)
Specialists (Pediatricians)
7
2000-2001/2002
Criswell* [24]
67% (use in practice)
Residents (Family Medicine) (organizational survey)
8
2001/2004
Miller [25]
26.2% (office-based use)
Physicians
9
2001/2004
Balen [26]
33% (use at work or home)
Pharmacists
10
2001-2002/2004
Barrett [27]
75% (use in practice)
Medical Residents
11
2002/2002
Martin [2]
27.9% (use in clinical practice)
Physicians & Specialists
12
2002/2002
Versel* [28]
33% (use in physician offices)
Physician Executives (organizational survey)
13
2002/2003
McCleod [29]
46% (use at medical institutions)
Specialists, Medical Residents, & Fellows (Internists)
14
2002/2004
Carroll [30]
35% (use at work)
Specialists (Pediatricians)
15
2002/2004
DeGroote [31]
61% (use on an academic health science campus)
Health Sciences Faculty & Medical Residents
16
2003/2003
Martin [32]
32.9% (use in clinical practice)
Physicians & Specialists
17
NS/2003
Vincent [33]
36% (use alone or in conjunction with log-card procedure in documenting)
Medical Residents (Family Practice)
18
2003/2003
Versel* [34]
75% (carry & use PDAs)
Physician Excutives (organizational survey)
19
2004/2005
AMA/Forrester [14]
57% (use regularly in a work week)
Physicians, Specialists (Surgeons), & Medical Residents
20
2004/2005
Wilden [35]
91% own; 85% use on daily basis; 9% weekly; 215% monthly
Specialists (Anestheologists)
21
2001/2005
Stromski* [36]
64% of programs report “most or all” residents use for clinical purposes
Medical Resident Programs (Emergency Medicine) (organizational survey)
22
NS/2005
Stroud [37]
67% (NS)
Nurse Practitioners & Students
23
NS/2005
Boonn [38]
45.1% (own or use daily)
Specialists (Radiologists)
NS/2004
Joy† [17]
Difficult to interpret the prevalence numbers among the resident respondents
Medical Residents (Obstetrics & Gynecology)
2004/2005
National Physician Survey (Canada)† [15]
Unable to establish overall prevalence due to way data have been presented;48.6% of medical students have a PDA (although unable to infer use)
Physicians, Specialists (various), & Medical Students
Note: An excerpt from the “Taking the Pulse” study published in October 2004 by Manhattan Research [16] reports that 40% of all US physicians currently use a PDA, increasing from 35% in 2003. However, for this present review, the authors were unable to obtain a full copy of the report in spite of having contacted Manhattan Research on two separate occasions (February 2006).
NS = not specified
*Survey conducted at organizational level (vs individual level responses)
†Survey of PDA use but prevalence data could not be established (referred to descriptively only)
Results
From a total of 816 full-text articles that underwent relevance assessment for a systematic review of the literature examining broad-ranging PDA use in health care, a subset of 18 surveys reporting PDA prevalence rates were identified (see Figure 1). Additionally, upon updating the search, an additional 959 records were retrieved and screened, from which 5 additional unique surveys were included. Furthermore, a total of 8 surveys were reviewer nominated, 3 of which were identified upon updating. Unfortunately, the authors were not able to obtain access to one Internet market research report. Prevalence numbers from 2 surveys were found too difficult to interpret, and, therefore, these data could not be utilized further in our results; however, we refer to both studies descriptively.
It is from this pool of literature that a total of 23 unique surveys were identified (Table 1):15 were published articles in scientific journals, and 8 were nonacademic, reviewer-nominated citations that were either reports available for purchase, press releases, or trade magazine articles and thus not subject to formal peer review. Of these 8 surveys, 5 were conducted by Internet market research firms, 2 were conducted by physician groups, and 1 was conducted by a market research firm in conjunction with a physician group (American Medical Association).
Survey Characteristics
The included surveys were published between 2000 and 2005, with survey data collected between 1999 and 2004. One survey had a four-year lag between data collection and publication, three surveys had a lag of three years, and three surveys had a lag of two years. We were unable to determine publication lag in four surveys as no data collection dates were provided. Surveys were from the United States (16), Canada (4), Australia (1), both the United States and Puerto Rico (1), and both the United States and Canada (1). Survey methodology reflected the following: self-administered questionnaires distributed solely by mail (11); telephone interviews (2); Web-based online surveys (4); and combined distribution by electronic or postal mail as determined by the recipient (4). Two studies did not report the methodology used. Response rates ranged from 5.7% to 92.6% across 13 of the included surveys; 10 surveys did not report such rates.
PDA Use
In presenting the results, we group the PDA users by type of health care provider and personal characteristics (eg, age).
In terms of PDA use, physician specialists were surveyed exclusively in five surveys. Three surveys examined practicing physicians, three included physicians and specialists combined, two included medical residents exclusively, while two surveyed an amalgam of physicians, specialists, medical residents, and/or students. Three surveys targeted physician executives and organizational practice leaders. One survey was directed at directors of family practice residency programs, while a further survey targeting individual PDA use in emergency medicine resident programs was completed at the organizational level.
In addition to physicians as users of technology, one survey targeted practicing hospital pharmacists and another targeted a national sample of nurse practitioner students and faculty. One survey included faculty and residents across several health science disciplines, including medicine, dentistry, nursing, public health, pharmacy, and applied health science.
To more accurately reflect handheld use across time, reported surveys were examined, when possible, from the timepoint when survey data were collected versus when published. When not possible, the publication date was the reported timepoint used. Collectively, the included surveys do indicate that PDA use is high, albeit somewhat variable, across studies. The reported prevalence rates of PDA use lend themselves well to an estimation of trend over time (Figure 2), and, as such, since 1999, there is evidence of an increase in PDA usage. Results do not include surveys completed at the organizational level. Surveys are presented according to data collection dates, with the exception of the American College of Physicians study (2001) [19], Stroud (2005) [37], and Boonn (1995) [38], which report publication dates only. The noted drop in 2003 is due to the paucity of surveys conducted in that year. Based on the most recent survey statistics (2004/2005), the current overall adoption rate varies between 45% and 85%, as derived from individual level survey data. In addition, of the five surveys completed at the organization level (eg, physician executives or medical program directors speaking on behalf of their individual members), the PDA use of their group members was estimated to be 60% (2001) [20], 67% (2001) [24], 64% (2001) [36], 33% (2002) [28], and 75% (2003) [34].
Figure 2Range plots of PDA usage by health care providers (n = 17); middle points represent range medians
To elaborate on the percentage of overall adoptions rates, a US survey of 769 practicing physicians conducted in 1999 found that only 15% of physicians use a PDA in practice [18]. In a 2000/2001 survey of directors of family practice residency programs in the United States and Puerto Rico, use of handheld computers by either an individual or group was reported in 67% of the residency programs [24]. In 2001, 47% of 489 US-based internists surveyed were using a PDA [19]. A subsequent 2001 survey of 834 practicing physicians found that the proportion using PDAs had increased to 26% [22]. If we only look at professional use, then the increase is from 10% in 1999 to 18% in 2001 [22]. Among a national sample of practicing physicians surveyed in 2001, 26% reported using PDAs for office-based work [25]. In 2001/2002, 38% of 696 office-based physicians indicated that they used a PDA in their practice [23]. Of practicing hospital pharmacists surveyed in 2001, 33% reported using a PDA at work or home, with 28% using one on a daily basis [26]. These numbers reflect both types of use: personal and professional (ie, as an integral part of everyday practice). In 2001, 75% of residents in a teaching hospital reported using their PDA on a daily basis [27]. In 2002, 35% of US pediatricians were using a PDA at work, and 40% had one for personal use [30], and 46% of internal medicine physicians and residents were reporting PDA use [29].
In Canada, similar PDA use data have been collected since 2001 as part of the annual Physician Resource Questionnaire conducted by the Canadian Medical Association. PDA use among physicians increased from 19% in 2001 [21] to 28% in 2002 [2] and to a third in 2003 [32]. These data conclude that, in 2003, a third of Canadian physicians were using PDAs, which marked a 73% increase from 2001. Further, more than 50% of Canadian medical doctors under 35 years of age reported that they were using a PDA or wireless device in clinical practice [32]. The data did not differentiate type of professional use.
In a PriceWaterhouseCoopers survey in 2001, 60% of the physician executives who responded indicated that their organization had at least one physician with a PDA [20]. Reportedly, this represented an upward trend from 26% in a similar 2000 survey. Further, in 2003, the trend continued, and 75% of respondents reported that their organization’s physicians were using PDAs. This increase in PDA use came after a steep decline to 33% in 2002 [28,34]. A sample of health science faculty and medical residents was surveyed in 2002 about their PDA use. Combined results from the various faculties and residents indicated that 61% used a PDA [31].
In 2004, 57% of a sample of US physicians indicated that they regularly used a handheld computer in a typical work week [14]. Results obtained in 2004 from a survey of members of the Austalian Society of Anaesthetists indicated that 91% of respondents owned a PDA; 85% reported using it on a daily basis, and 66% were reportedly “dependent” upon the handheld device, although the term dependent was not defined [35]. In 2005, when physician members of the Radiologicial Society of North America were surveyed, 45.1% reported owning or using a PDA on a daily basis [38]. However, the survey authors suggested use among this group of specialists appeared to be lower than for other physicians because a radiologist often works in front of a full workstation in clinic and therefore relies less on a mobile device. Further, PDAs are not yet well equipped to handle the tasks radiologists need to perform. In 2005, Stroud et al became the first group of researchers to address the use of PDAs in the field of nursing. Survey results concluded that the majority (67%) of participants used this technology [37].
While PDA use has clearly increased since 1999, it appears as though only a handful of studies have examined the prevalence and usage patterns of such technology outside of physician groups. Furthermore, when comparing the included surveys in depth, distribution of use is not uniform across selected characteristics of surveyed health care professionals. Therefore, further subgroup analyses from the included surveys are provided below. Patterns of handheld use are also briefly examined.
Patterns of PDA Usage
Age
Based on a survey of 250 family physicians, as far back as 1995, younger physicians (less then 40 years of age) were more likely to consider carrying a handheld computer than older physicians (94% vs 84.5%) [39]. More recent data from this present review also suggest an age differential in usage patterns. A 2001 survey of 834 US practicing physicians found that use of handheld devices was higher among doctors under age 45 (33%) than among older doctors (21%) [22]. Another study found that pediatricians graduating from medical school in the last five years were more likely to use a PDA in practice than those who graduated more than five years ago [30]. According to a survey conducted by the American Academy of Pediatricians in 2001, PDA use was highest among those members under 30 years of age, with a reported usage rate of 75% [23]. Another study found that 60% of US internists below 40 years of age used a PDA, while only 34% older than 51 years did [19]. McLeod et al (2003) also found that PDA usage captured in 2002 among a sample of internal medicine physicians and residents under 30 years was much higher (68%) versus those over 40 years of age (37%) [29]. In Canada, 2003 usage was highest among younger physicians, with more than half of those under the age of 35 years (53%) using a PDA, compared with 15% of physicians aged 65 or older [32]. According to the American Medical Association/Forrester Research 2005 Physician and Technology Study, more doctors under the age of 40 years were reportedly using PDAs (55%) than those over 40 years (45%) [14]. In 2005, the mean age of nurse practitioners and students who reported using a PDA was 42 years [37].
Students and Medical Residents
Residents tend to be younger, therefore it follows that they are more likely to use PDAs. This is also substantiated by direct evidence. A survey of directors of family practice in the United States and Puerto Rico conducted in November 2000 (306 responses) found that use of handhelds in residency programs, either by an individual or group, was 67% [24]. A 2001 survey of residents in a teaching hospital reported that more than 75% used their PDA on a daily basis [27]. Stromski et al (2005) surveyed emergency medicine residency programs in 2001 to identify the methods of procedure documentation to examine the number of programs transitioning to more advanced information technology systems (eg, PDA use). Their results indicated that 13% of the residency programs required the use of PDAs, 15% of programs purchased PDAs for their residents, and a similar proportion reported that PDAs were used by “most or all” of their residents to document procedures. Further, 64% of programs reported that “most or all” of their residents utilized PDAs for clinical purposes. DeGroote et al found that, in 2002, 71% of medical residents reported using PDAs versus 56% of faculty members [31]. In a 2002 survey, McLeod et al noted that the percent of frequent PDA users among internal medicine residents and fellows in training exceeded 70%, compared to only 50% of attending physicians [29]. From a survey of the experiences of family resident graduates in obtaining hospital privileges and in documenting procedures and deliveries, Vincent et al (2003) concluded that 36% of the respondents used a PDA alone or in conjunction with a log-card, paper-based system. Unfortunately, this study did not present any other prevalence data on PDA use [33]. However, from survey data captured in 2004, the handheld technology gap between residents and physicians began to close: a US study concluded that 73% of residents regularly used a handheld computer in a typical work week, followed closely by 71% of family/general practitioners [14]. In a survey of PDA use by nurse practitioner students and faculty, Stroud et al found that of the total respondents who reported PDA use, 73% were nursing students [37].
One survey by Joy et al (2004) met our initial criteria but could not be incorporated into the results analysis. Although this study did examine PDA use in obstetrics and gynecology residency programs, it was difficult to interpret the prevalence numbers among the resident respondents. Likewise, the National Physician Survey (2004) did not present overall PDA prevalence rates but did ask Canadian medical students if they had a PDA or wireless device [15]. Of the 2721 respondents, 24% in first year, 40.6% in second year, 70.6% in third year, and 71.6% in fourth year reported having a PDA, representing an overall average of 48.6% among students [15]. Unfortunately, these 2004 figures provide no information on how medical students were using this technology and in what contexts.
Gender
PDA usage among men and women was equal in a 2001 survey of internists [19]. Similarly, McLeod et al (2002) found no significant gender difference in PDA users among a 2002 sample of internal medicine physicians and residents [29]. However, pediatrician PDA users were most likely male, as reported in 2002 [30]. As well, the 2003 Physician Resource Questionnaire analysis concluded that male physicians were somewhat more likely to use a PDA in their practice than were females (35% vs 30%) [32]. More recent data from a 2004 survey of PDA use among US physicians, specialists, and medical residents suggested that male clinicians were slightly more likely than their female counterparts to regularly use handhelds (53% vs 47%) [14]. On the other hand, nurse practitioner data from 2005 show that men (82%) were notably more likely than women (64%) to use a PDA (P < 0.05) [37]. However, the authors cautioned that they were unable to determine the significance of this finding given that the actual survey sample of men (n = 38) as opposed to women (n = 188) was small. The authors suggested that if ease with PDA technology is less common in women, then the nursing profession, dominated by females, may need elevated momentum to adopt PDA technology across nursing practice [37].
Family Physicians versus Specialists
The most recent Physician Resource Questionnaire (2003) analysis concluded that Canadian family physicians were just as likely to use a PDA (33%) when compared to medical (34%) and surgical (32%) specialists [32]. This was the third consecutive year these figures rose consistently across all physician groups in Canada [2,21,32]. However, according to a US survey of physicians published in 2005, the biggest adopters of PDAs in professional practice were family and general practitioners (71%) when compared to surgical specialists (54%) [14]. The above mentioned studies are the only survey data available directly comparing general physician use to that of specialists.
Large and Hospital-Based Practices
A US survey of practicing physicians found that use was higher among those who were wholly or partly hospital-based (33% and 29%, respectively) than among those who were office-based (23%) [22]. Usage was also higher among physicians in large practices (33%) than in solo practice (16%) [22]. Carroll et al (2004) also found that PDA users tended to not be in private practice [30]. Additional survey data from 2004 indicated that of US physicians practicing in primary practice offices with fewer than 10 physicians, 49% reported regular use of a handheld computer [14]. Miller et al (2004), reporting on a national sample of practicing physicians, found that in a group practice consisting of an average of nine physicians, handheld use was approximately 56% [25].
Urban versus Rural Physicians
From a random sample of US pediatricians in 2002, PDA users were most likely from urban communities [30]. Similarly, results from Canada’s Physician Resource Questionnaire in 2001 indicated PDA use to be higher among physicians practicing in urban centers (19.9%) than in rural centres (13.4%) [21]. However, by 2002, rural use (29.6%) surpassed urban use (27.7%) among physicians [2]. In Canada, this trend continued in 2003, with 36.9% of rural respondents indicating PDA use versus 32.5% of urban respondents [32].
Professional Use
Five surveys considered PDA use in both a professional and personal context; 17 studies exclusively captured professional use. One study reported general prevalence rates for PDA use among pediatricians; however, it did not specify if use was in clinical practice or outside of work.
In order to discern professional use more closely, we explored administrative PDA uses versus direct use in clinical patient care. We found that of the surveys that concern PDA use within a health care setting, 17 of 23 studies (74%) reported use pertaining to administrative or organizational tasks, while 14 of 23 studies (61%) addressed PDA use in patient care. Billing and coding were the most frequently performed administrative PDA functions in 50% of the surveys reporting administrative uses. This was followed by 44% reporting calendar scheduling, 31% reporting Web and email access, 25% reporting address book use, and 25% stating use in charting patient details into an electronic health record. Other reported administrative tasks included the following: word processing, calculator, charge capture, procedure documentation, outpatient tracking, resident hours, telephone message tracking, general time management/personal organizer, patient referrals, procurement of supplies, patient census, order entry, dictation, and passwords and pins.
In terms of patient care, access to drug information was reported in 93% of the surveys reporting clinical PDA use, while 50% reported prescribing, 43% stated accessing patient records, 43% described medical calculator use, and 36% indicated use in reference to laboratory values. Other reported clinical PDA uses included access to medical references, patient tracking and patient reminders, clinical decision pathways and managed care applications, telemedicine, and diagnostic imaging or radiology applications.
Only one survey reported PDA use for patient education, and one referred to PDA use for research purposes.
Discussion
This paper summarizes the results from surveys examining adoption of PDA use. These survey data are in reasonably good agreement and suggest a sizable proportion of physicians use handheld devices. However, most of the sources of survey data did not distinguish well between types of applications being used most often and whether the PDAs were being used professionally for administrative purposes or for direct clinical work. It is encouraging to note that our findings are similar to those of an analysis of online registrations and downloads of a PDA drug reference guide, which concluded that approximately one fifth of US physicians (150000) and half of medical students in the United States (33000) were PDA users [40].
Our grouped survey data suggest that there is little information on the PDA usage rates among nonphysician health care providers. However, collectively, these data suggest that use of handheld devices has become a subject that health care professionals need to know about. By systematically gathering this usage information, it is difficult to deny the prevalence of PDAs in health care. With this basic understanding of current handheld usage patterns, we need to consider the impact of this development of mobile handheld technology on both practice and research.
According to a commonly accepted descriptive model of the diffusion of innovations developed by Rogers, when the cumulative rate of users of a new invention is plotted versus time, the result is an S-shaped curve [41]. Interestingly, this appears to be true of most technological innovations, irrespective of the technology. For example, Hall and Khan (2003) reviewed the S-shape adoption patterns of a variety of 20th century consumer products (eg, washing machines, video cassette recorders) [42], while Teng et al (2002) developed historical diffusion curves for information technologies (eg, personal computers, email) [43]. Variations in diffusion slopes do exist given that some technologies will diffuse more rapidly than others.
Health care information technologies have also been examined within this diffusion framework. England et al (2000) studied organizational and technological factors determining the rate at which innovations diffuse in the health industry [44]. In 2005, RAND Health completed a report characterizing the diffusion of electronic health records along an S-shaped adoption curve [45].
Technologies typically go through multiple phases during their adoption life cycle, which may last for many years [41,46]. The characteristics of the adopters change over time and so does the nature of suitable evidence to inform their adoption decisions. For example, innovators (the first 2.5% who adopt a new technology) do not need evidence to make an adoption decision. Early adopters (the next 13.5%) are satisfied with case studies and examples of successful adoption and benefits [41]. Examining the typical technology adoption curve for handheld devices (Figure 3) based on the adoption percentage of PDAs thus far from the most recent available data (2004/2005), it can be concluded that we are now at the steepest stage in the adoption S-curve, with a transition from the early majority to the late majority.
Figure 3
The S-shaped diffusion of technology curve [41]
The increase in PDA adoption means a potential reduction in hardware and training costs when using handheld devices in the provision of care and in research. Because of the high probability that target health care professionals may already have a handheld device and will already know how to use one, the overall hardware purchase costs could be reduced, and the end user will not necessarily have to be trained from scratch.
To date, use of PDAs in health care appears to have preceded extensive evaluative research. PDA adoption rates, already high, continue to be a moving mark with projections for rapid growth in the short term. By comparing handheld device diffusion to other health information innovations, and by placing PDA use within existing diffusion models, we are able to better predict the future of handheld growth in health care and therefore develop more timely and appropriate evaluative research to accompany such growth.
Unfortunately, we were unable to include information from two national physician surveys. The first report entitled “Taking the Pulse” was published in October 2004 by Manhattan Research [16]. Information gleaned from a report excerpt stated that 40% of all US physicians surveyed in 2004 were using a PDA, marking an increase from 35% in 2003. Reported top activities performed on a PDA by all US physicians (in order) were personal scheduling, professional scheduling, accessing a drug reference database, accessing online information, writing/entering clinical notes, and mobile email access [47]. These report findings are similar to our overall findings in this present review.
The second national physician survey not incorporated into our analysis was the Canadian National Physician Survey (NPS) (2004), which provides valuable insight into what information technology, including PDAs, physicians and specialists have in their main patient care settings [15]. However, overall prevalence rates could not be determined from the data provided given the manner in which they were presented. Nonetheless, in reviewing the national data, we can descriptively draw some conclusions. First, it appears as though male physician PDA use is higher than that of females. This appears to be consistent across all tasks involving PDA use although differences do appear to be small. This is consistent with our general findings in which males are only marginally more likely to use a PDA than are females. Interestingly, when examining age-related data from the NPS, it appears as though the age factor may in fact be PDA task-specific. For example, electronic health record usage appears to decrease as the age of physician users decreases. However, PDA use for drug interaction information increases when the age of the physician user decreases. This appears contrary to most other surveys that show younger age is associated with higher general PDA use. Perhaps what this information tells us is that handheld use may be more complex when broken into task-specific strata.
It is worthy to note that, with the exception of one survey focusing on nurse practitioner students, little mention was made in the surveys of PDA use by students across health care disciplines, including medicine. Several universities in Canada and the United States now mandate use of PDAs for medical undergraduate students and residency programs; therefore, it is assumed this could potentially affect prevalence rates. However, because none of the included surveys examined mandated use, we are unable to infer if this is responsible for recent increases. However, this raises an important issue to be considered in future studies related to students and rates of handheld adoption.
To better understand the prevalence rates among the included surveys, it became important to categorize the drivers for PDA use as either professional or personal. We therefore attempted to discern what specific PDA tasks the respective health care professionals were performing. This was done by classifying, whenever possible, the use as administrative versus care. On the surface, it would appear that administrative and organizational tasks on a PDA exceed those related to patient care, perhaps signaling where the growth in adoption is most likely to occur.
In this present review, we can only speak broadly to rates of adoption and patterns of use. Drawing inferences from the survey data was often limited by lack of, or differences in, operational definitions in aspects of handheld use being measured. For example, the term use was often not defined by frequency (eg, specific units of time—day, week, month). Taking these issues into consideration would be a useful exercise for future surveys as well as information technology prevalence studies in health care.
In conclusion, physicians are increasingly accustomed to using a PDA, and, therefore, technology expertise will not likely be a barrier to deploying handheld applications. There is an urgent need to evaluate the effectiveness and efficiency of specific tasks using PDA technology (eg, implementation, searching, reference, data entry, reporting) to inform those persons developing and those using handheld applications. Furthermore, it is not clear why there is a paucity of evidence on the extent of adoption of PDAs by other health care providers: is it that they lag in the use of this technology or is it simply that they have yet to be studied?
Limitations
This review has a number of limitations. Issues around response bias and inability to draw causal inferences weaken survey methodology. It may be the case that those surveyed feel a stronger affinity to the survey sponsor, who has a greater interest in the questions asked, or are in complete disagreement with the topic at hand. This can skew results in difficult-to-measure ways. Quite possibly, the nonrespondents are the least committed (ie, nonusers of PDAs). As a result, the critical objective of drawing a true random sample of the populations that are the focus of the survey is compromised and the findings somewhat impure.
The reported methodologies across these surveys appear to be heterogeneous, which limits their comparability. As noted, the quality of the included surveys could not be determined given the absence of validated quality assessment instruments, and, therefore, there was no adequate way to assess the influence of bias. A related issue is that some of the included surveys did not go through a rigorous peer-review process. These combined issues made judging the strength of the evidence not possible. One would assume surveys identified from scientific journals would be a source of less biased information. However, in defense of the nonacademic surveys, there is a consistency in results between those peer-reviewed versus those that were not. This may suggest that our main conclusions regarding adoption rates are fairly robust and not disconnected even with the inclusion of non–peer-reviewed evidence.
Conclusions
The objective of this study was to determine the adoption rates of PDAs in health care settings, and to project expected adoption in the future based on established technology diffusion models. Our findings from a systematic review indicate the current overall adoption rate for professional use of PDAs among health care providers, namely physicians, is 45% to 85%. Younger physicians, residents, and those working in large and hospital-based practices are more likely to use a PDA. Professional use in health care settings appears to be more focused on administrative tasks when compared to those related to patient care, although this requires further study. The adoption rate is now at its highest rate of increase according to a commonly accepted diffusion of innovations model. Additionally, the impact of PDA use on practice appears to be immediate in terms of costs and training. Familiarity will not likely be a barrier to deploying handheld applications in health care. However, there is a critical need to evaluate the effectiveness and efficiency of specific tasks using handheld technology within the health care system and across health care provider PDA user groups.

health care
systematic review
survey
personal digital assistant
health technology adoption


Sickness absence due to depressive symptoms

Objective There is no information on the duration of absence of depressed Dutch workers. The aim of this study was to determine the duration of sickness absence due to depressive symptoms in the working population.
Introduction
Mental illness is an important cause of disability and its impact on job performance and productivity is substantial. Depression is one of the most common mental disorders. According to the World Health Organization (2002), depression will be the leading cause of morbidity by the year 2010. Depression was found to be related to long absence from work (Harrison and Martocchio 1998; Nieuwenhuijsen et al. 2006) and to be a significant predictor of work disability (Kouzis and Eaton 1994; Skodol et al. 1994; Kessler et al. 1999). Depressed employees had a 28 times higher risk of absence as compared to employees who did not suffer from depression (Kouzis and Eaton 1994). Respondents with depression reported a mean of 35 days (95% CI 27–44) in the past year when they were totally unable to work or carry out their normal activities because of their depression (Kessler et al. 2003). When depressed employees stayed at work, they had a lower than normal productivity (Kessler and Frank 1997).
Depressive episodes were reported to vary widely in their duration, with a median ranging from 2 to 12 months and with rates of chronicity (i.e. a duration of 12 months or more) between 15% and 50% (Keller et al. 1982; Coryell et al. 1994; Mueller et al. 1996; Eaton et al. 1997; Solomon et al. 1997; Spijker et al. 2002; Furukawa et al. 2005). Estimates of the mean duration of depressive episodes varied from 13 to 27 weeks (Blazer et al. 1994; Eaton et al. 1997; Kendler et al. 1997; Kessler et al. 2003).
In international research the duration of sickness absence due to depressive disorder was 142 days in 213 adult psychiatric outpatients (Sorvaniemi et al. 2003). In Norway, the mean sickness absence duration in psychiatric patients amounted to 110 days in men and 103 days in women (median 57 and 52 days, respectively), with depression as the most common diagnosis (Hensing et al. 2000). In a Swedish sick-leave database a mean sickness absence duration due to combined depression and anxiety disorders was 73 days in men and 75 days in women (Hensing et al. 1996). In a reference population consisting of working people, the mean duration of a major depression was 52 days for a single episode (with 4.1% lasting longer than 6 months) and 74 days for a recurrent episode (with 9.2% lasting longer than 6 months) according to the Medical Disability Advisor (2006). There is no information on the duration of absence of depressed workers in The Netherlands (Raddjoe and van der Hoek 2005). The results of international studies are not comparable to the Dutch situation, because of differences in social legislation and sickness absence regulations.
As it is very likely that the results of international studies are not comparable to the Dutch situation, the aims of this study were: (a) to investigate the mean (and median) duration of absenteeism due to depressive symptoms in the Dutch working population by sector and company size, and (b) to investigate gender and age influences on the mean (median) duration of absenteeism due to depressive symptoms.
Studies on sickness absence because of depression revealed gender differences, with men having a higher risk of sickness absence when depressed (Laitinen-Krispijn and Bijl 2000). Women had a higher incidence of sickness absence due to mental illness (North et al. 1993; Hensing et al. 1996, 2000), but the duration of sick-leave spells due to mental illness were longer for men (Hensing et al. 1996, 2000). Based on Dutch national statistics (Central Statistical Office of the Netherlands 2006), in which women have longer absence durations and higher disability figures than men, we hypothesize that depressed women have longer absence durations than men (H1). We also hypothesize that older employees have longer absence durations than younger employees (H2) based on Dutch national statistics and studies of Hensing et al. (1996, 2000). Furthermore, we hypothesize that employees in small companies have longer absence durations as compared to employees in large companies (H3), because of less reintegration opportunities in small companies. Finally, we hypothesize that employees in educational and health care services have longer absence durations as compared to employees in other sectors (H4). Employees in the education and care sectors have a substantially increased risk of being at work when sick, because of difficulties in replacement (Aronsson et al. 2000) and therefore, when they take absence the duration may be longer.
Methods
Data
The prevention, supervision and medical examination of sickness absence is a task of the employer for which he can choose to engage an occupational health department. ArboNed (the second largest occupational health department in the Netherlands) employs an absenteeism registration system for their affiliated companies. We register sickness absence and its causes in about 15% of the total Dutch working population. In our population, commercial services are over-represented (57% vs. 41%) and non-commercial services (e.g. health care, civil servants) are under-represented (17% vs. 34%) as compared to the total working population in the Netherlands.
All absence periods that started between April 2002 and November 2005, and were diagnosed as depression were selected from the registration system. Absence periods were encoded as depression by the occupational physician when symptoms of depressed mood (such as feelings of profound sadness or emptiness) and/or reduced interest in activities that used to be enjoyed lasted for at least two weeks in combination with 3 or more of the following symptoms: decreased energy, decreased motivation, appetite changes, disturbed sleep pattern, agitation or inhibition, guilt feelings, low sense of self-worth, impaired concentration, or self-destructive thoughts. The symptoms must be accompanied by evident suffering and adverse effects on personal as well as social functioning.
The age of the employees was registered at the moment of sick-leave. Absent men had a mean age of 41.6 years (SD = 9.9) and women of 38.2 years (SD = 9.9) (T = 17.2; df = 9,864; P < 0.01). The company size in which the employee worked was divided into four categories: <75 employees, 75–500 employees, 500–5,000 employees and >5,000 employees. In 95% of cases the company size was known. The companies were categorized into the following sectors (mean age ± standard deviation): construction industry (40.2 ± 10.2), health care (39.6 ± 10.1), trade (38.1 ± 10.3), catering industry (37.5 ± 10.0), industry (40.9 ± 9.8), education and public sector (43.9 ± 9.9), transportation and communication (41.0 ± 9.7), commercial services (38.6 ± 9.9), Other/unknown (39.9 ± 9.9). In 72% of cases the sector was known.
Absence duration
Between April 2002 and November 2005 a total of 9,910 newly originating episodes of absence due to depressive symptoms were registered. The period (in calendar days) between the first day of sick-leave and the date of return to work or disability pension was computed. In The Netherlands an employee can benefit a disability pension after one year of work incapacity1. Absence periods of 365 or more calendar days were considered to reach this limit, and therefore the maximal duration of an absence episode was 365 days.
Absence duration was not corrected for part-time return to work, unless mentioned otherwise. When the number of absence days is corrected for part-time return to work, this is done by dividing the number of absence days by 1/reintegration percentage. For example, when the employee returns to work for 50% the number of absence days is divided by 2.
When estimating the duration of absence spells, it is important to censor absences which have not ended by the end of the observation period or at the dismissal date (Blossfeld and Rohwer 2002). Therefore, we censored absences that did not end before the retrieval date (November 1, 2005). Absences which ended, because the employee resigned, were also censored.
Statistical methods
The data were analyzed using SPSS for Windows, version 13. Kaplan–Meier survival curves were computed in order to obtain mean and median absence duration. The Kaplan–Meier method (Kaplan and Meier 1958) calculates the risk set at every point in time where at least one recovery occurred. The risk set also includes episodes that are censored at this point in time. A censored episode contains the information that there was no return to work or reaching the one-year absence limit at the end of the study period or the dismissal date. The survival functions were plotted using life tables. The mean number of absence days corrected for part-time return to work in the different groups was compared using analysis of variance.
If the main effect was significant, the Tukey post hoc test was performed in order to determine which groups differed from each other.
Results
In the period April 2002 to November 2005, 9,540 employees were absent with depressive symptoms. This is about 1% of the population covered by the occupational health department. Of these employees 9,196 (96.4%) had one absence episode, 321 (3.4%) had two absence episodes and 23 (0.2%) had three or more episodes of absence due to depressive symptoms. In Table 1, the course of sick-leaves with depressive symptoms is presented. Men more often returned to work and women more often reached disability pension (χ2 = 13.6; df = 3; P < 0.01).
Table 1Number and course of depressive episodesMenWomenTotaln%n%n%Number of employees4,5225,0189,540Number of absences4,735100.05,175100.09,910100.0Returned to work within a year3,20567.73,35564.86,56066.2One year of incapacity for work71915.290317.41,62216.4Left the employment during the illness3076.53807.36876.9Not returned to work at end of observation period50410.653710.41,04110.5
Men and women of age groups <35 years, 35–44 years, 45–54 years and ≥55 years were distinguished. Figure 1 shows the life table survival function of episodes with depressive symptoms in each of these subgroups.
Fig. 1Survival function of the duration of absence due to depression in men and women by age
Women with depressive symptoms had a lower return to work rate than men. After 26 weeks 53.0% were still absent as compared to 48.8% in men. More women (25.5%) than men (22.3%) reached the disability pension date. In Table 2 the results of the Kaplan–Meier estimation are presented by age, company size and sector. The estimation of the mean duration of absence due to depressive symptoms (with a maximum of one year) was 200 (median 179) days in men and 213 (median 201) days in women.
Table 2Mean (95% CI) and median (95% CI) duration of depressive episodes by age, company size and sector in men and womenNumber of absencesMean (95% CI)Median (95% CI)MenWomenMenWomenMenWomenAge  <35 years1,3012,105184 (177–191)204 (198–209)153 (143–163)182 (171–193)  35–44 years1,6101,729200 (194–206)217 (211–223)181 (170–192)206 (193–219)  45–54 years1,3571,081212 (205–219)223 (215–230)199 (184–214)223 (205–241)  ≥55 years456248205 (193–217)222 (205–238)182 (163–201)207 (163–251)Company size  <75 employees1,3491,214214 (207–221)226 (218–233)198 (182–214)229 (210–248)  75–500 employees1,1741,225200 (192–207)210 (203–217)181 (167–195)201 (187–215)  500–5,000 employees1,4212,032196 (189–203)213 (207–218)174 (163–185)200 (188–212)  >5,000 employees531423188 (177–198)208 (195–220)170 (157–183)179 (145–213)Sector  Construction industry33839206 (192–220)206 (174–239)182 (151–213)189 (133–245)  Health care1961,218212 (194–230)214 (207–221)202 (163–241)200 (184–216)  Trade524406205 (194–216)212 (199–224)183 (163–203)200 (171–229)  Catering industry176345197 (177–216)203 (189–217)168 (136–200)174 (144–204)  Industry1,077341189 (182–197)205 (192–219)163 (149–177)192 (167–217)  Education and public sector165196232 (213–251)242 (224–260)236 (199–273)272 (223–321)  Transportation and communication417332196 (183–208)208 (194–221)182 (159–205)177 (151–203)  Commercial services645682213 (203–223)219 (209–229)196 (176–216)212 (187–237)  Other/unknown1,1971,616194 (186–201)212 (206–218)171 (160–182)201 (188–214)Total4,7355,175200 (196–204)213 (210–217)179 (172–186)201 (193–209)
In all age categories, women with depressive symptoms were absent longer than men. Elderly employees had a longer duration of absence and a higher risk of reaching disability. Employees in companies with less than 75 employees were absent for a longer period than employees working in large sized companies. Employees in the educational and public sector had the longest absence duration, followed by employees in commercial services and health care. Men working in the industrial sector had the shortest duration of absence due to depressive symptoms.
Table 3 shows the mean number of absence days corrected for part-time return to work. Men with depressive symptoms had significantly less absence days than women: 154 and 165 absence days, respectively (T = −5.3; df = 9,844; P < 0.01). Because the mean percentage of part-time return to work was almost equal in men and women, this was due to a shorter duration of absence episodes in men. Elderly employees with depressive symptoms had more absence days than younger employees (F(3, 9,862) = 16.5; P < 0.01), because of a longer duration of the absence episodes and lower part-time return to work percentages. The post hoc Tukey test revealed that the difference was significant in employees aged 35 years and more as compared to younger employees.
Table 3Mean (95% CI) number of absence days corrected for partial work resumption of depressive episodes by age, company size and sector in men and womenMenWomenTotalMean (95% CI)Mean (95% CI)Mean (95% CI)Age   <35 years140 (134–146)156 (151–160)150 (146–153)  35–44 years153 (148–158)169 (164–174)161 (158–165)  45–54 years164 (158–170)176 (169–183)169 (165–174)  ≥55 years163 (152–173)178 (163–192)168 (160–176)Company size  <75 employees166 (160–172)176 (169–182)171 (166–175)  75–500 employees154 (148–161)166 (159–172)160 (156–165)  500–5,000 employees150 (144–156)163 (158–168)158 (154–161)  >5,000 Employees134 (125–142)157 (146–168)144 (137–151)Sector  Construction industry164 (152–176)160 (132–189)164 (152–175)  Health care168 (152–185)167 (161–174)168 (162–173)  Trade158 (149–168)166 (155–177)162 (154–169)  Catering industry153 (137–170)163 (151–176)160 (150–170)  Industry143 (137–150)157 (145–169)147 (141–152)  Education and public sector181 (162–199)196 (179–213)189 (177–201)  Transportation and communication141 (130–151)151 (139–163)145 (138–153)  Commercial services162 (154–171)164 (155–172)163 (157–169)  Other/unknown151 (145–158)166 (161–172)160 (156–164)Total154 (150–157)165 (162–169)160 (158–162)
Among employees working in companies with more than 5,000, we found more part-time return to work than in smaller companies, resulting in a relatively low mean number of corrected absence days (F(3, 9,344) = 14.7; P < 0.01). The post hoc Tukey test revealed that three subsets based on decreasing number of absence days could be distinguished: companies with less that 75 employees, companies with 75–5,000 employees and companies with more than 5,000 employees. The difference in mean number of absence days between the sectors was significant (F(8, 9,880) = 8.2; P < 0.01). According to the post hoc Tukey test, employees in the educational and public sector had the highest number of absence days corrected for part-time return to work. Employees in the transportation and communication sector had a significantly lower number of absence days as compared to the other sectors.
Discussion
Our study showed that depressed women were absent longer from work than men, which confirmed our first hypothesis (H1). The mean duration of absenteeism due to depressive symptoms was 213 (median 201) days in women and 200 (median 179) days in men. This could be due to gender differences in the severity of the depressive symptoms. However, no gender differences were found in the duration of depressive episodes in a population sample (Eaton et al. 1997) and in the severity or symptomatology of depressive disorders in general practice (Hildebrandt et al. 2003).
In studies on absence and depression, reverse gender differences are found. For sickness absence in general, women have more sickness absence days than men (Harrison and Martocchio 1998; Kivimäki et al. 2003; Central Statistical Office of the Netherlands 2006). According to Laitinen-Krispijn and Bijl (2000), depression was a stronger risk factor for absence in men than in women. However, Hensing et al. (1996, 2000) found a higher incidence of absence due to psychiatric disorders in women as compared to men. Hensing et al. (1996, 2000) even found longer absence durations for men with psychiatric disorders as compared to women. They reported about neurosis, and we investigated depressive symptoms, which might explain part of the difference.
Hensing et al. (1996, 2000) reported that the longer duration of absence among men could be explained by gender differences in co-morbidity with alcohol problems and other psychiatric disorders. They also mentioned gender differences in seeking health care, diagnostic procedures and treatment of psychiatric disorders. The social consequences of having a psychiatric disorder might be worse for men than for women, leading to under-reporting among men. Men might postpone the use of health care and get into a poorer mental condition. As a consequence, they need a longer period of recovery and are absent longer from work. Gender differences in the (assessment of) severity of psychiatric problems might also explain the different duration of sick-leave. However, if these mechanisms do play a role in our study, they are more unfavourable to depressed women than to depressed men.
Dewa et al. (2003) reported that depressed women returned to work more often than men, rather than leaving employment. They defined the end of the absence period as returning to work either part-time or fulltime. In our study men returned more often to work than women. We regarded fulltime return to work as the end of an absence period, because we did not have the exact dates of part-time return to work. It is possible that women returned more often to work part-time than men.
Depressed elderly workers are absent longer than young employees with depressive symptoms, confirming our second hypothesis (H2). Sickness absence increases with age, regardless of diagnosis (North et al. 1993; Marmot et al. 1995). Hensing et al. (2000) found higher rates of absence with psychiatric diagnoses in older ages. Like in our study, the increase they found is less linear than in general absence studies. This can be explained by the fact that sickness absence with a psychiatric diagnosis differs from sickness absence due to other diagnoses, since the duration of absence is long in the youngest age groups as well. Age differences in the severity of psychiatric disorder, in help-seeking behavior, recognition and/or treatment may influence the duration of sick-leave due to depression. An alternative factor, which may contribute to the longer duration, is that elderly employees prefer to retire from the labour force. In contrast with Hensing et al. (1996) we found that men in all age groups, especially in the youngest age groups, had fewer days of sick-leave than women in the corresponding age group. Further study is necessary to investigate these gender differences by age.
In accordance with our third hypothesis (H3), the periods of absence due to depressive symptoms were shorter in large-scale companies than in smaller ones. The reintegration percentage is higher in large companies, resulting in a lower number of absence days. Probably, large companies have more opportunities for part-time return to work. Moreover, large companies have structured protocols as to how to deal with long-term absence from work. In the transportation and communications sectors, part-time return to work was most often observed. In our sample, these are mostly large companies in the postal and telecommunications sector, offering more reintegration possibilities. Although blue-collar workers generally have higher sickness absence than white-collar workers, depressed employees in industrial settings had shorter absence durations. A possible explanation might be that employees in the industrial sector are more susceptible to stigmatization of having mental problems, and return to work earlier. Also, in blue-collar workers, depressive symptoms may more often be masked by or diagnosed as somatic diseases.
Employees in the educational and public sectors had longer duration of depressive episodes, resulting in more absence days, confirming our fourth hypothesis (H4). An explanation is that these employees find it harder to report themselves sick because of the difficulty to substitute their work and the personal ties with their clients and pupils (Aronsson et al. 2000). Probably they have more severe depressive symptoms at the moment of sick-leave. Another explanation for their longer absence might be the lesser reintegration opportunities in these sectors. Finally, the mean age of employees in the educational and public sectors was higher than average, and it is known from Dutch national sickness absence surveys that elderly employees are absent longer than younger ones. More research is needed to examine these possible explanations.
Duration of absence due to depression compared to other absenteeism studies
The duration of absence due to depressive symptoms is longer than reported in international research (Hensing et al. 1996, 2000; Sorvaniemi et al. 2003) and the figures according to the Medical Disability Advisor (2006). Differences in definitions and methods may account for this finding. Hensing et al. (1996) reported on episodes of combined depression and anxiety disorder, exceeding 7 days and without maximum duration. In contrast to our study, they did not censor for the end of the observation period. Hensing et al. (2000) investigated sickness absence due to neurosis with a minimum duration of 15 days until the end of the absence. We investigated absence due to depressive symptoms with a minimum duration of 1 day and maximized at 365 calendar days. In our study, 98% of cases had a minimum absence duration of 15 days. The code depressive symptoms was given in the second or third week of absence, when the absent employee consults the occupational physician.
Sorvaniemi et al. (2003) studied sick-leaves with depression according to DSM-III-R in psychiatric outpatients. They used exact beginning and ending dates of sick-leaves, resulting in durations ranging from 2 to 374 days and a mean duration of 142 days. At this point, it should be reminded that depressive symptoms were not strictly assessed according to the DSM criteria, limiting the comparability of results.
Besides, the occupational physician can only give one diagnosis per absence episode. Comorbidity could not be coded. Approximately two-thirds (64%) of respondents with 12-month major depressive disorder meet the criteria for at least one other mental disorder, with anxiety disorders (58%) more common than substance abuse (9%) or impulse control (17%) (Kessler et al. 2003). Comorbidity may substantially prolong the duration of an absence episode. The long duration of absence due to depressive symptoms might be explained by comorbid features. Alternatively, it is possible that depressive symptoms were reactive to somatic disease. In case the symptoms of somatic disease appeared first, the occupational physician encoded the somatic disease and not the reactive depressive symptoms. During sick-leave, the occupational physician evaluates the medical situation and recovery every 6 weeks. Bias can only be expected when the somatic disease was not known in the sick-leave period of the depressed worker. Further investigation of the influence of comorbidity by more detailed research of the medical files is necessary, as Chisholm et al. (2003) reported that the economic consequences of depression (e.g. number of lost work days) were influenced to a greater extent by the presence of medical comorbidity than by symptom severity alone.
Duration of a depressive episode compared to the duration of absence
The median duration of absence due to depression in our study exceeds the estimations of duration of depressive episodes in the general Dutch population (Spijker et al. 2002). In a population survey, Kessler et al. (2003) found a mean duration of a depressive episode of 16 weeks using the composite international diagnostic interview (CIDI). This is about half of our estimated duration of absence. Population studies may underestimate the duration of a depressive disorder, because adults with severe forms of depression are less likely to participate in such studies (Centers for Disease Control and Prevention 2004). Studies on the duration of a depressive episode using the additional life chart interview showed a mean duration of 26 weeks in men and 27 weeks in women (Eaton et al. 1997). This corresponds to the mean absence durations we found: 28.6 weeks in depressed men and 30.4 weeks in depressed women. Blazer et al. (1994) found an overall mean duration of 22.6 weeks by means of a diagnostic interview. For the purpose of their analysis, however, 3% of the participants who had had very long episodes were excluded.
When chronic cases are considered, our estimates were comparable to the study of Spijker et al. (2002) in the general population. They found a chronic course (duration 12 months or more) in 24% of cases. Our survival analysis also indicated a chronic course in 24% of cases. It is important to investigate which employees are at risk for a chronic course.
The duration of a depressive episode in The Netherlands is comparable to other countries, whereas the duration of absenteeism is longer. It could be hypothesized that Dutch employees return to work when they are completely recovered, while in other countries employees start working (part-time) during recovery. We think that differences in social legislation and benefits contribute to these differences. In most other European countries compensation schemes apply to work-related diseases (“risque professional”). In The Netherlands, however, the social insurance system does not take into account the cause of the disease (“risque social”). Moreover, social benefits could have contributed to the relatively long absence duration. Dutch employers are obliged to pay the employee on sick-leave at least 70% of his/her salary. In almost all cases the full salary is paid during the first year of sick-leave, which does not motivate employees to return to their work.
Limitations of the study
Our population is not a random sample from the total Dutch working population, because employers voluntarily engage our occupational health department. Therefore, results are not representative of the whole Dutch working population. However, the considerable sample size and the use of registered, rather than self-rated information on sickness absence, are strong points of this study.
A depressive episode was diagnosed in employees who visited the occupational physician. Persons with lower grade depressive moods might not have reported complaints, and stayed at work. If absent, they might have returned to work before they consulted the occupational physician. Alternatively, they might have called off a visit to the occupational physician, because they intended to resume their work soon. Therefore, more severe cases of depression were included in our sample. The duration of absence due to depressive symptoms is related to the severity of depression (Dewa et al. 2003).
We investigated the interaction between depression and work sectors, but could not examine the influence of work content and working conditions. Rugulies et al. (2006) reported work environment to influence the risk of developing severe depressive symptoms. Work factors also influence the probability of returning to work once absent (North et al. 1996; Väänänen et al. 2003; Nielsen et al. 2004).
Clinical implications
Because of the risk of chronicity, it is important that occupational physicians recognize depressive symptoms in an early stage of the absence episode
It is recommended to develop and apply tools for recognizing employees at risk for chronic depression
Special attention should be given to employees in educational and public services, commercial services and health care as they are at risk for longer absence duration in case of depressive symptoms.
Study limitations
Diagnosis of depressive symptoms in absent employees may be biased towards absences with a longer duration. Mildly depressed employees, who did not report ill or returned to work before consulting the occupational physician, were not included
Depressive symptoms were not strictly assessed according to DSM criteria, limiting the comparability to other studies
Our absence registration system does not account for comorbidity.

depression
sector
company size
duration of sick-leave
mental health
gender and age differences