Trends in number of visitors to the travel clinic
of the Public Health Service (GGD) Amsterdam
between 1 January 2001 and 31 December 2008
The importance of data entry
Marianne Heling
2013
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Trends in number of visitors to the travel clinic
of the Public Health Service (GGD) Amsterdam
between 1 January 2001 and 31 December 2008
The importance of data entry
Student:
M.M.A. Heling
ID: 0484822
e-mail:
Marianne.heling@student.uva.nl
Mentor:
G.J.B. Sonder, MD, PHD
LCR & Travelers vaccination clinic
gsonder@ggd.amsterdam.nl
SRP address:
GGD Amsterdam
Department Infectieziekten
Nieuwe achtergracht 100
1018 WT Amsterdam
Tutor:
Prof. Dr. A. Hasman
Department of Medical Informatics
Academic Medical Center - University of Amsterdam
a.hasman@amc.uva.nl
Second assessor
Prof. dr. Ameen Abu-Hanna
Department of Medical Informatics
Academic Medical Center - University of Amsterdam
a.abu-hanna@amc.uva.nl
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Content
Abstract ... 8
Introduction ... 10
Immune compromised travelers ... 10
Data collection ... 11
Theoretical framework ... 13
Research questions ... 15
Methods ... 16
Study population ... 16
Data ... 16
Statistical analysis ... 17
Literature search ... 17
Questionnaires... 17
Interviews ... 18
UML ... 18
Results ... 19
Trends in travel clinic visitors ... 19
Diseases and Medication ... 22
Data ... 30
Questionnaire ... 36
Discussion ... 38
Recommendations ... 42
References ... 46
Appendix ... 49
Appendix A. Diseases and Medication ... 49
Appendix B. Registration Form ... 62
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Abstract
The Public Health Service Amsterdam has one of the largest Travelers Health and Vaccination Clinics in the Netherlands. Every year, a large group of travelers visits the travel clinic before traveling. Among these travelers are also people with immune deficiency disorders, who nowadays, travel more frequently and to far, tropical, destinations. These travelers are at increased risk of contracting an infectious disease and the course of the diseases might be different and more severe than in healthy travelers. The Public Health Service Amsterdam does a lot of research on this group of travelers, but the trends in travelers, who visited the travel clinic of the Public Health Service Amsterdam, has never been quantified or described. Since 2000, the Public Health Service uses a computer program to collect the data of the travelers. In 2007, the
program ‘Vaccins’ was reprogrammed and called ‘Odysseus’.
The aim of this study is to describe the trends in numbers and proportion of visitors to the travel clinic of the Public Health Service who suffer from certain diseases currently studied by the Public Health Service. The main reason for describing and analyzing these trends is to investigate whether the hypothesis that people with an immune deficiency disorder who visit the travel clinic of the GGD Amsterdam travel more frequently nowadays is true. A second aim is to analyze the validity of the data and describe how the system can be improved to improve the usability and data quality.
All travelers who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008 were included in this study. Information about the travelers and his/her travel were extracted from the data base. SPSS was used to identify the trends in travelers and their travels. Frequencies and descriptive statistics were used to identify these trends. With the use of EpiInfo’s statcalc, statistical significance was determined using the chi-square for trends. A questionnaire was made and send out to the intakers to analyse the usability of the system.
201345 travelers who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008 were included. 8741 of these travelers were immune compromised and 8635 of them had a mental disorder. Although the total number of travelers visiting the travel clinic has decreased over the years, the number and proportion of immune compromised travelers and travelers with a mental disorder has increased. It is not likely that this is a result of better data entry, but it is more likely that travel has become more common for these groups of travelers. 14 of the 18 intakers filled in the questionnaire about the current system. Some changes need to be made to the system to make it easier to use and improve the data quality.
Keywords
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Introduction
The Public Health Service (GGD) Amsterdam has one of the largest Travelers Health and Vaccination Clinics in the Netherlands. Annually, about 25.000 travelers visit the GGD for vaccination and malaria prophylactic advice before traveling. Since the 1970's, a still increasing number of people travel to far, (sub)tropical, destinations. Developing areas that were rarely visited before are becoming more popular. At these destinations travelers are exposed to certain risks, such as infectious diseases. These risks depend on factors that vary from region visited, duration of travel and activities to characteristics of the traveler, like age and health status (CDC, 2008a). There are different ways to contract an infectious disease. Diseases can be transmitted through contaminated food and water, through insects, through environmental or animal contact and from person-to-person (Blair, 1997). The best way to prevent infectious diseases is to avoid traveling to areas with high risks of infection (Dupouy-Camet et al., 2003). If the traveler does go to an area with high risks, he/she should protect him/herself against infection as much as possible. The best way to prevent malaria, dengue or other infections transmitted through insect bites is to avoid mosquito bites (Blair, 2007; Kotton et al., 2005; Wolfe, 1997; Rizvon
et al., 1999; Thomas, 2000a). Insect repellent (30-35% DEET), bed nets and permethrin-soaked or sprayed
clothing should be used for personal protection. Travelers should be careful with food, water and hygiene (Blair, 2007; Wolfe, 1997; Rizvon et al., 1999; Mileno et al., 1998; Thomas, 2000b). Enteric infections, like diarrhea, can be prevented by not buying food from street vendors, eating only food that has been prepared under hygienic conditions, by avoiding uncooked food, by peeling fruit and vegetables, by avoiding ice cubes, by boiling water, and by washing hands before eating something. Waterborne infections can also be prevented by not swimming in contaminated water. For rabies, a pre-exposure vaccine exists (Wolfe, 1997; Rizvon et al., 1999; Mileno et al., 1998; Thomas, 2000c). This vaccine does not completely protect against Rabies after contact, and when a traveler is exposed to a rabid animal, two booster doses are required as soon as possible. Therefore, travelers should be cautioned against contact with dogs, cats and other infected animals in areas where the incidence of rabies is high. Travelers need to be aware of the risks of infectious diseases and the ways they can prevent contracting them. Therefore, visiting a travel clinic for pre-travel advice and vaccination is the best preparation before traveling abroad.
Immune compromised travelers
Among the large group of travelers are people with certain immune deficiency disorders, such as HIV-infected persons, diabetics, asplenic people, and people who use immune suppressive medication, who also travel more frequently nowadays. An immune compromised person can be defined as: "A human whose immunologic mechanism is deficient because of an immunodeficiency disorder or other disease or as the result of the administration of immunosuppressive drugs or radiation" (CDC, 2008a). These groups of travelers are at increased risk of contracting infectious diseases and when they are infected, the course of the disease might be different and more severe than in healthy persons (McCarthy et al., 2006; Ericsson, 2003; Castelli et al., 2000). This group is more vulnerable to infectious disease then healthy travelers. The response to vaccines in this group of travelers is less than in healthy travelers and some vaccines can even cause vaccine-induced diseases.
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There are different diseases that cause immune suppression. They can be divided into three different groups: Severely immune compromised (non-HIV), HIV-infected, and chronic disease with immune deficits (CDC, 2008b). People with leukemia or lymphoma, malignancy, aplastic anemia, currently receiving radiation therapy, solid organ transplants, bone marrow transplants and people taking immunosuppressive drugs are considered to be severely immune compromised.
Chronic diseases with immune deficits are asplenia, chronic renal disease, chronic hepatic disease and diabetics. HIV-infected persons can be subdivided into groups, based on their CD4+ count, a number to determine the severity of the disease: CD4+ count <200, CD4+ count 200-500, and CD4+ count >500. HIV-infected people with a CD4+ count >500 are not considered immune suppressed (Loutan, 1997). Pregnant women are also included in the group of immune compromised travelers. For these travelers, live vaccines can cause vaccine-induced diseases (MedlinePlus, 2009). For instance, the oral polio vaccine can cause fatal paralytic poliomyelitis (Mileno et al., 1998). Also, the response to vaccines is reduced (Loutan, 1997; Gelinck et al., 2008; CATMAT, 2007). There is a fourth group that can be taken into account. These people are not immune suppressed, but they are more vulnerable than healthy travelers. In this group are the persons with depression or other psychiatric disorders. Some anti-malaria medication is contraindicated in travelers who use anti-depressants or anti-psychotics (Dupouy-Camet et al., 2003). Before vaccinating an immune compromised person, the risk of infection should be weighed against the risk of vaccination (Loutan, 1997). When the risk for infection is very low, vaccinating a traveler can do more harm than not vaccinating him/her. For example, the live vaccine for yellow fever can cause vaccine strain encephalitis (Mileno et al., 1998). If a person is not traveling to a highly endemic area, he or she should not be given the Yellow Fever vaccine. That is why vaccination and travel advice are important for these travelers (Castelli et al., 2000). Early pre-travel consultation is in these cases especially important (McCarty et al., 2006). The trends and the increase in immune compromised travelers, who visited the travel clinic of the Public Health Service Amsterdam, has never been quantified or described.
Data collection
In 2000, the GGD developed a computer program called 'Vaccins', in which details of all travelers were stored, such as general demographics data, destination, date of departure, duration of travel and information on the visitors' health, including use of medication. All visitors entered in the database automatically received a unique number. In 'Vaccins', the data of approximately 140.000 travelers were entered. Although most questions about the travelers health and medication use, like ‘Do you have diabetes?’, ‘Do you use any medication?’ were categorized by disease as much as possible, it was still possible to enter this data at different places in the system. Therefore, 'Vaccins' was completely reprogrammed in 2007. An effort was made to improve the categorization of the questions about travelers' health and medical history. The new program is called 'Odysseus'. The data collected by the GGD since 2001 offers a great opportunity to study changes and trends in the numbers and proportion of immune compromised travelers who visited the GGD travel clinic before traveling. The GGD does a lot of research on immune compromised travelers. Therefore, it’s very
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interesting for them to know how many immune compromised travelers exactly visited the travel clinic over the past few years.
The aim of this research project is to describe the trends in the number and proportion of visitors to the GGD travel clinic who suffer from certain diseases currently studied by the GGD. These visitors are in particular travelers who are more vulnerable to infectious diseases than healthy travelers because their immunity is impaired. The GGD suspected that people with an immune deficiency disorder travel more frequently nowadays. Therefore, during this project, the hypothesis that people with an immune deficiency disorder who visit the travel clinic of the GGD Amsterdam travel more frequently will be tested. A second goal is to analyze the validity of data, examine the usability of the system and propose changes that can be made to 'Odysseus' to improve the quality of the data gathered at the travel clinic. This thesis will start with a theoretical framework. After that, the research methods will be explained and the results of the study will be described. Furthermore, the results will be discussed and the thesis will end with a conclusion and recommendations.
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Theoretical framework
This project and this thesis consists of two parts. Part one describes and discusses the trends in immune compromised travelers who visited the travel clinic of the Public Health Service Amsterdam. The GGD does a lot of research on immune compromised travelers. For example, they started a research project in 2003 about the prevalence of infectious diseases among immune compromised travelers (GGD, 2010). Another research project, that started in August 2009, examines the response to the combined hepatitis A and B vaccine in children with HIV or children who use immune suppressive drugs. Furthermore, they recently published an article about “the trends and characteristics among HIV-infected and diabetic travelers seeking pre-travel advice” (Elfrink, et al., 2013). The findings and conclusions of these research projects are used to improve the travel advice and vaccination policy. To determine on which groups more research can be done, the GGD needs more information about the immune compromised travelers who visit their travel clinic before traveling. It is known that the number of people with a chronic disorder increases. Research showed that people from the USA with an immune deficiency disorder travel more frequently nowadays (CDC, 2010). The GGD Amsterdam suspected that this is also true for people with an immune deficiency disorder who visited their travel clinic, but this increase has never been quantified and described. Therefore, the main reason for analyzing and describing the trends in immune compromised travelers during this project was to investigate whether the hypothesis that people with an immune deficiency disorder travel more frequently nowadays is true for travel clinic visitors of the GGD Amsterdam. This also applies to the other trends described in this thesis. Because these trends have never been quantified and described for visitors of the travel clinic of the GGD Amsterdam, it is not known what is true and what is not; we can assume that certain hypotheses about travelers are true, but as long as they are not quantified and described, we can never know for sure. Furthermore, it will also give the GGD Amsterdam insight in who visited their travel clinic over the years and for what reason, and they will be able to compare characteristics of travelers with different immune deficiency disorders; they will learn more about their visitors. This might lead to more adequately trained travel health professionals and up-to-date guidelines so that travel clinic visitors with a disorder can be informed better about required and recommended vaccinations and other health advice. In this report trends and proportions of immune compromised travelers are described and discussed. Appendix A gives an overview of all the data on the travelers with an immune deficiency disorder that visited the travel clinic over the years.
There is some doubt about the validity of the data. Is the data correctly entered at the right place? This is an important question because a lot of research is done with this data. Improvements to the system might be needed to increase the validity of the data. Therefore, part two of this research project is testing the usability of the program that is used to enter the travelers data into the database. The analysis of the data in part 1 is used as a basis, problems with data and data entry can partly be found by looking at and analyzing the data. The framework used to test the usability of the program was derived from Ham et al. (2006) and Yoshovska (2006) and adapted to the needs of this research. The framework is shown in figure 1. Usability can be defined as “the degree to which specific users can achieve specific goals within a particular environment; effectively, efficiently, comfortably, and in an acceptable manner” (ISO 9241-11). Different indicators were used to test the
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usability. These indicators are effectiveness, efficacy, learnability, and satisfaction. Effectiveness means that the user is able to fulfill a task (Yoshovska, 2006). Effectiveness can be measured as the success-no success ratio, frequency of commands used, customers problems with the system, and the quality of the input and output data. Efficiency means the ability to fulfill a task for a minimum amount of time with the minimum amount of effort. Measurements of efficiency are the time needed to fulfill a task, the number of activities needed to fulfill a task, the time needed to search for information either in documentation or online, and the time needed to correct errors. Learnability is about the ease with which users learn how to use and work with the system. For new users, a system should be easy to learn, easy to understand, and easy to master, so it can be used as effectively as possible, as soon as possible. Measurements of learnability are the time and efforts a new user needs to learn how to use the system. Satisfaction is the degree to which users are satisfied with the system. A system should be pleasant to work with. Furthermore, the system should have a clear layout and should have the necessary capabilities and functions. The more satisfied the users are, the more likely it is that they will encourage others to use the system.
Figure 1 – Conceptual framework usability, including all its aspects. Blue means that this part was studied by looking at the data and analysing the data from the database, which was also part 1 of the project. Green means asked to the employees via a questionnaire. Yellow is a combination of data analysis and questionnaire.
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Research questions
This research project consists of two parts, trends in immune compromised travelers, and data entry and data quality. Therefore, the following research questions were answered during this project.
1. What are the trends in travelers who visited the GGD Travel clinic?
2. What are the trends in immune compromised travelers who visited the GGD Travel clinic? 3. What are the processes and procedures during a travel clinic visit?
4. Which problems with data and data entry exist?
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Methods
Different methods were used to answer the research questions. The methods were data analysis, literature search, interviews with employees, and questionnaires. These methods are discussed below. Furthermore, the study population and the specific data used for the analysis are discussed.
Study population
All travelers who visited the Travelers Vaccination Clinic of the Public Health Service Amsterdam and who were entered in the database between the 1st of January 2001 and 31 December 2008 were included in the data analysis part of this study. Information about the traveler him/herself and his/her travel was extracted from the database. For the interviews and questionnaires, the travel clinic employees who enter data into the system during a clients visit, also called intakers, were approached. These employees were included based on the amount of time they work with ‘Odysseus’.
Data
Characteristics of the traveler are gender, date of birth, country of birth, zip code, and health status. Travel characteristics are date of departure, reason for travel, and destination. Another characteristic taken into account is the date of visit to the travel clinic. Age was calculated with the use of date of birth and date of travel clinic visit. Different reasons for travel are holiday, family visit, work, pilgrimage - further called hajj -, study, emigration, remigration, world travel, other and unknown. A second calculated value is the number of weeks between travel clinic visit and departure. This was calculated using the date of the travel clinic visit and date of departure. Visit in the 1st week before departure means that the travelers’ departure is 0 to 7 days after the visit.
For non-healthy travelers, medication use and medical history are also extracted from the database.
The non-healthy travelers are, in this case, travelers with an immune deficiency disorder or those taking immune suppressive drugs. This group of travelers can be divided into several categories: 1) Insulin dependent diabetes mellitus; 2) Diabetes Mellitus with oral anti-diabetics; 3) HIV-infected; 4) Asplenic, both functional and physical; 5) Crohn's disease; 6) Colitis Ulcerosa; 7) Use of Prednisone or other corticosteroids; 8) Rheumatoid arthritis with immune suppressive medication; 9) Malignancy treated less than 3 months ago; 10) Other disorders with use of immune suppressive drugs; and 11) Pregnancy. Two other interesting groups are: 12) Depression; and 13) Other mental disorders.
The group of travelers using prednisone are only those who use high dosages for a longer period, because travelers using a low dosage of prednisone are not at risk. Other disorders for with immune suppressive drugs are used can be leukemia or lymphoma, nephrotic syndrome, bone marrow transplants, organ transplant with use of immune suppressive drugs. Depressed travelers or travelers with an ‘other’ mental disorder, including fear and other psychiatric disorders, are not immune compromised, but are at risk because of contra-indication between anti-depressants and some anti-malaria medication. A free text search was performed to make sure
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all the travelers with an immune deficiency, depression or other mental disorder were included. Search terms used to search the comment field were the name of the disorder, abbreviations, and synonyms; for example, to search for Diabetes Mellitus, ‘diabetes’, ‘mellitus’, ‘dia’, and ‘suikerziekte’ were used as search terms. Clients that were found searching the comment field were also included. The analyses of the data was done by year.
Statistical analysis
For the statistical analysis SPSS and EpiInfo were used. SPSS was used for the descriptive statistics, like crosstabs and frequencies. Frequencies and cross tabs were used to identify the trends in travel clinic visitors. Furthermore, SPSS was used to merge the data, and to create tables and figures. With the use of MS Excel, the tables and figures were adjusted so that they only show the most important information. EpiInfo’s statcalc was used to determine the p-values of the trends between 2001 and 2008. The p-value was calculated using the chi-square for trends. A p-value less than 0.05 can be taken as a reasonable indication that the trend is statistically significant, meaning that the trend is unlikely to have occurred by chance.
Literature search
A literature search was conducted to gain more insight in general travel advice and vaccination, immune compromised travelers, and the problems with vaccinating travelers with immune disorders. Furthermore, the literature was searched for an explanation of certain results and to make sure the questionnaire on the systems usability contained all the relevant questions which are needed to get all the important information from the intakers. For the literature search, the websites of the Center for Disease control and the GGD, and search engines like Pubmed, Google Scolar, Embase, and Web of Science were used.
Questionnaires
Questionnaires were used to test the usability of the system. To find out how the users of ‘Odysseus’ think and feel about the system and how, according to them, ‘Odysseus’ can be improved, the questionnaire was sent to the most important users, the intakers. The questionnaire was sent to 18 intakers. Only the intakers were included because they use the, for this research project, most important part of the system and spent the most time working with it. The questions on the questionnaire were based on the conceptual framework described before, which is based on literature, and on the results of the analysis of the data in part one of this project. The data analysis performed was used to get an insight in what and where things can go wrong in data collection. Therefore, the questionnaire was made and sent out to the intakers after part one was completed; after the data was analysis and the trends were described. A questionnaire was chosen as a method for the usability analysis because it can reach a large group of people in a short period of time (Evalued a, 2006). Data is collected in a standardized way and the information from all the questionnaires can be analysed more quickly.
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Interviews
Interviews with the travel clinic employees were conducted to gain more insight in how the travel clinic works. The interviews were not structured, employees were asked to explain the work they were doing and follow-up questions emerged from their explanation. The interview topic were mostly what does a clients visit look like, which steps does a client have to take before he or she receives vaccinations, what does the system look like, how do they use the system, and by whom is the system used. In addition, an interview was conducted with the programmers of ‘Odysseus’ to gain more insight in the system and its database. Interviews were chosen as a method for this part of the project because in-depth information can be obtained, detailed questions can be asked and ambiguities or incomplete answers can easily be clarified with follow-up questions (Evalued b, 2006).
UML
The structure of the database was analyzed with the use of UML diagrams. A logical data model was made to describe the structure and organization of the data used for analysis and the relations between the different types of data. An activity diagram was made to see where the data comes from, at what point the data is collected and who is part of the data collection process.
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Results
Trends in travel clinic visitors
Between 2001 and 2008, the number of travelers visiting the travel clinic of the Public Health Service Amsterdam decreased over the years (figure 2). In 2001, 29193 travelers visited the travel clinic before departure. In 2008, this number was 22850. The total number of travelers between 2001 and 2008 was 201345. The p-value of the trend in visitors between
2001 and 2008 is less than 0.001.
During the study period, June and July were the months in which most travelers visited the travel clinic (figure 3). February, March and April had the lowest number of travelers. The month in which the highest number of travelers visited was June 2007, with 4318 travelers. February 2003 was the month with the lowest number (1185).
The mean age of all visitors was around 31 years and quite stable (range: 31 in 2008 to 32 years in 2004). Most travelers (n = 65514, 32.5%) belonged to
the age category 25 to 34 years. The percentage of travelers in this age category decreased over the study period (figure 4).
Of all travelers, 9.6% (n = 19313) were between 0 and 4 years old.
Slightly more women (52%) than men visited the travel clinic before traveling (figure 2) .
Approximately, 76% (n = 153269) of the travelers were born in the Netherlands. The percentage of travelers born in the Netherlands increased slightly over the years. Following the Netherlands, most travelers were born
Figure 3. - Percentage of visitors per month who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 2. - Number of travellers and the number of men and women (per year) who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 4. - Percentages of travelers in different age categories who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
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in Surinam (n = 8178, 4.1%), Ghana (n = 6540, 3.3%) and Morocco (n = 4746, 2.4%). The percentages of travelers born in Surinam and Ghana decreased from res. 5.4% (n = 1567) and 4.5% (n=1318) in 2001 to res. 3.4% (n = 783) and 2.4% (n = 549) in 2008. Travelers born in Morocco increased from 2% (n = 595) in 2001 to 2.8% (n = 670) in 2007.
Of all travelers living in Amsterdam ( n = 178120, 88%), most were living in the districts ‘West’ (n = 37393, 18.6%) or ‘Centrum’ (n = 36293, 18%). The percentage of visiting travelers living in ‘Oost’ and ‘Nieuw-West’ increased from 11.1% (n = 3239) and 9.6% (n = 2801) in 2001 to 13,7% (n = 3136) and 11.3% (n = 2587) in 2008 respectively. The number of travelers coming from ‘Zuidoost’ (2001: n = 2459, 8.4%, 2008: n = 1447, 6.3%) and from outside Amsterdam (9.3% (n = 2724) in 2001 to 7.2% (n = 1653) in 2008) declined over the last years.
The most common reason for travel is holiday (figure 5). Of all travelers, 82% (n = 165186) named holiday as their reason. This percentage decreased from 88.8% in 2001 to 71.1% in 2008. Family visit, hajj and work were increasingly mentioned as the reason for traveling. Family visited increased from 2% (n= 578) in 2001 to 14.8% (n= 3393) in 2008 and work increased from 2% in 2001 (n = 581) to 7.2% in 2008 (n= 1649). Hajj as a reason for travel increased from 0.8% (n = 233) in 2001 to 4.2% (n= 1020) in 2007 and decreased after that to 2.9% (n = 666) in 2008. Thailand was the most visited country among visitors to the travel clinic (n = 21135, 8.1%). The 14 most visited countries and trends in the number of visitors to these countries are shown in figure 6.
The number of visitors who intend to go to Morocco is increasing. In 2001, 880 (2.5%) travelers visited Morocco, in 2008, this number was
1681 (5.3%). Another country that was getting more popular by travelers visiting the travel clinic was China, with 765 travelers in 2001 (2.1%) and 1188 (3.6%) in 2007.
Figure 5. - Reason for traveling for travelers who visited the travelers vaccination clinic of the Public Health Service Amsterdam before traveling between 2001 and 2008 - 1. Work; 2. Holiday; 3. Hajj; 4. Family visit; 5. Study; 6. Emigration 7. Re-migration; 8. World travel; 9. Other; 10. Unknown
Figure 6. - Destinations for travelers who visited the travelers
vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008 - TH = Thailand; GH = Ghana; SR = Surinam; ID = Indonesia; IN = India; EG = Egypt; TR = Turkey; MA = Morocco; SA = Saudi-Arabia; BR = Brazil; CH = China; MX = Mexico; MY = Malaysia; ZA = South-Africa
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The mean number of days between travel clinic visit and departure increased over the years. More people visited the travel clinic longer before traveling. The lowest mean was 25 days (4th week before departure) in 2003 and the highest 29 days (5th week) in 2008.
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Diseases and Medication
The immune compromised travelers can be divided into 11 sub groups. These groups are: 1. Insulin dependent diabetes mellitus
2. Diabetes mellitus with oral antidiabetics 3. HIV infected
4. Without spleen (functional or physical) 5. Crohn’s disease
6. Colitis ulcerosa
7. Use of prednisone or other corticosteroids 8. Rheumatoid arthritis with medication use 9. Malignancies treated less than 3 months ago
10. Other disorders (with the use of immune suppressive drugs) 11. Pregnancy
Trends in these groups will all be discussed individually. There are two other interesting groups. They are not immune compromised, but are at risk because of the contra-indication between depressant and anti-malaria medication. These two groups are:
12. Depression
13. Other mental disorders
The table below, table 1, shows the frequency and percentage of immune compromised travelers and travelers with metal disorders who visited the travel clinic.
Category 2001 2002 2003 2004 2005 2006 2007 2008 TOTAL P-Value Total # visitors 29193 26205 24698 25241 24758 24172 24228 22850 201345 0.000 Total immune compromised 808 (2.77) 1030 (3.93) 1093 (4.43) 1149 (4.55) 1223 (4.94) 1140 (4.72) 1174 (4.85) 1124 (4.92) 8741 (4.34) 0.000 1 214 (0.73) 258 (0.98) 238 (0.96) 236 (0.93) 213 (0.86) 255 (1.05) 255 (1.05) 224 (0.98) 1893 (0.94) 0.002 2 300 (1.03) 369 (1.41) 426 (1.72) 475 (1.88) 488 (1.97) 429 (1.77) 420 (1.73) 363 (1.59) 3270 (1.62) 0.000 3 95 (0.33) 91 (0.35) 114 (0.46) 114 (0.45) 129 (0.52) 119 (0.49) 119 (0.49) 155 (0.68) 936 (0.46) 0.000 4 13 (0.04) 23 (0.09) 22 (0.09) 29 (0.11) 25 (0.10) 14 (0.06) 28 (0.12) 30 (0.13) 184 (0.09) 0.007 5 31 (0.11) 29 (0.11) 23 (0.09) 32 (0.13) 35 (0.14) 36 (0.15) 32 (0.13) 38 (0.17) 256 (0.13) 0.016 6 31 (0.11) 38 (0.15) 38 (0.15) 34 (0.13) 39 (0.16) 46 (0.19) 34 (0.14) 40 (0.18) 300 (0.15) 0.051 7 16 (0.05) 21 (0.08) 15 (0.06) 28 (0.11) 28 (0.11) 17 (0.07) 33 (0.14) 24 (0.11) 182 (0.09) 0.005 8 25 (0.09) 29 (0.11) 32 (0.13) 28 (0.11) 37 (0.15) 34 (0.14) 40 (0.17) 37 (0.16) 262 (0.13) 0.002
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9 11 (0.04) 13 (0.05) 18 (0.07) 25 (0.10) 16 (0.06) 13 (0.05) 20 (0.08) 26 (0.11) 142 (0.07) 0.005 10 7 (0.02) 18 (0.07) 12 (0.05) 19 (0.08) 28 (0.11) 32 (0.13) 27 (0.11) 34 (0.15) 177 (0.09) 0.000 11 65 (0.22) 141 (0.54) 155 (0.63) 129 (0.51) 185 (0.75) 145 (0.60) 165 (0.68) 153 (0.67) 1138 (0.57) 0.000 Total mental disorder 733 (2.51) 1009 (3.85) 1019 (4.13) 1126 (4.46) 1183 (4.78) 1146 (4.74) 1264 (5.22) 1155 (5.05) 8635 (4.29) 0.000 12 569 (1.95) 762 (2.91) 775 (3.14) 837 (3.32) 881 (3.56) 833 (3.45) 780 (3.22) 693 (3.03) 6130 (3.04) 0.000 13 164 (0.56) 247 (0.94) 244 (0.99) 289 (1.14) 302 (1.22) 313 (1.29) 484 (2) 462 (2.02) 2505 (1.24) 0.000 Table 1. - Numbers and percentages (%) of immune compromised travelers and travelers with mental disorders who visited the travelersvaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
The percentage of immune compromised travelers (figure 7) and travelers with mental disorders (figure 8) has increased over the years. The total number of immune compromised visitors ranges from 810 (2.77%) in 2001 to 1225 (4.95%) in 2005. After 2005, the number of compromised travelers decreased slightly. The overall p-value for this trend is less than 0.001. For the travelers with a mental disorder the numbers range from 733 (2.51%) in 2001 to 1264 (5.22%) in 2007. The p-value for this trend between 2001 and 2008 is less than 0.001.
Figure 7. - Percentage and number (#) of immune compromised travelers who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 8. - Percentage and number (#) of travelers with mental disorders who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
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Tables with the main characteristics of visitors to the travel clinic between 2001 and 2008 with the different diseases are shown in appendix A.
1. Insulin dependent diabetes mellitus The number and percentage of travelers who visited the travel clinic and who had insulin dependent diabetes increased over the years (figure 9). In 2001, 0.73% (n = 214) of all the travelers had insulin dependent diabetes. In 2008, it was 0.98% (n = 224). The p-value for this trend is 0.002. In total, 1893 (0.94%) travelers who visited the travel clinic between 2001 and 2008 had Insulin dependent diabetes
mellitus. 1044 (55.15%) were men, and 818 (43.21%) women. The majority, 1349 (71.26%), is 44 years or older. Most of the travelers in this group, 945 (49,92%), were born in the Netherlands. However, Surinam and Morocco were also frequent countries of birth; respectively 336 (4.11% of all travelers born in Surinam) and 357 (7.52%) travelers were born in these countries. 1255 (66.30%) travelers in this group indicated that holiday is the main reason for traveling. In addition, a large group of travelers indicated that hajj was their reason for traveling; 335 went on hajj, which is 6.98% of the total number of travelers going on hajj. Therefore, the most frequent travel destination was Saudi-Arabia (27.21%; 515 travelers).
2. Diabetes mellitus with oral anti-diabetics
Travelers with diabetes mellitus with oral anti-diabetics visiting the travel clinic increased from 1.03% (n = 300) of all the travelers in 2001 to 1.97% (n = 488) in 2005 (figure 10). After 2005 the percentage decreases to 1.59% (n = 363) in 2008. The overall p-value for this trend is less than 0.001. A total number of 3270 travel clinic visitors (1.62%) had Diabetes mellitus and used oral anti-diabetics. 52.45% (1715 travelers) is male and 47.55% (1555) female. The majority, 87.13% (2849) was older than 44 years. 31.16% (1019) of the travel clinic visitors in this group was born in the Netherlands. However, of all travelers born in Surinam or Morocco, respectively 10.45% (855) and 15.38% (730) had Diabetes mellitus and used oral anti-diabetics. 64.86% (2121) indicated holiday was their reason for traveling. In addition, 14.44% (693) of all travelers going on hajj belong to this group of travelers. Therefore, for most of these travelers, Saudi-Arabia was the travel destination (31.90%; 1043).
Figure 10. - Percentage and number (#) of travelers with diabetes mellitus with oral anti-diabetics who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 9. - Percentage and number (#) of travelers with insulin dependent diabetes mellitus who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
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3. HIV infected
The percentage of HIV infected travelers who visited the travel clinic increased over the years (figure 11), from 0.33% (n = 95) of the total number of visitors
in 2001 to 0.68% (n = 155) in 2008 (p < .001). There was an increase of almost 0.2% from 2007 to 2008. 936 (0.46%) travelers were HIV infected. Of these travelers, 794 (84.83%) were male and 142 (15.17%) were female. The majority, 60.47% (566 travelers) were between the ages 25 and 44. Most of the male travelers with HIV, 65.24% (518), were born in the Netherlands; most of the female travelers with HIV, 28.87% (41), were born in Ghana, closely followed by the
Netherlands, 23.24% (33). Holiday was for both male and female travelers the most mentioned reason for traveling; holiday was the reason for 681 (85.77%) males and 82 (57.75%) females. The second most mentioned reason differed per gender; for male travelers it was work, 56 (7.05%), and in case of a female traveler family visit, 48 (33.80%).
4. Without spleen (functional or physical)
More travelers without a spleen, functional or physical, visited the travel clinic every year (figure 12). This percentage decreased in 2006 from 0.1% (n = 25) to 0.06% (n = 14) of the total number of visitors. In 2001, 0.04% (n = 13) of all the travelers visiting the travel clinic were functional or physical spleen-less. In 2008, it was 0.13% (n = 30). The p-value for this trend is 0.007. 0.09% (n = 184) of all travelers who visited the travel clinic between 2001 and 2008 were functionally or physically asplenic. 44.02% (81) of these travelers were men and 55.98% (103) were women. Most of the travelers in this group, 76.09% (140), were born in the Netherlands. However, a small group of 16 travelers (8.70%) were born in Ghana. Although holiday was the most mentioned reason for traveling, 96.20% (177), the most visited country was Ghana, 19.02% (35).
Figure 11.- Percentage and number (#) of HIV infected travelers who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 12. - Percentage and number (#) of travelers without a spleen (functional or physical) who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
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5. Crohn’s disease
The percentage of travelers with crohn’s disease increased over the years (figure 13). There were small dips in 2003 and 2007. The percentage ranges from 0.11% (n = 31) of all travelers in 2001 to 0.17% (n = 38) in 2008 (p = .016). 256 (0.13%) travel clinic visitors were travelers with Crohn’s disease. Of these 256 travelers, 26.95% (69) were male and 73.05% (187) were female. The majority, 59.38% (152) was between 25 and 44 years old. Most of the travelers in this group were born in the Netherland, but approximately 16% (41 travelers) were born in Surinam. Furthermore, 215 travelers (83.98%) indicated holiday was their reason for traveling.
6. Colitis ulcerosa
The percentage of travelers with colitis ulcerosa visiting the travel clinic increased from 0.11% (n = 31) of the total number of travelers in 2001 to 0.19% (n = 46) in 2006 (figure 14). In 2007, it decreased to 0.14% (n = 34). After that it increased again to 0.18% (n = 40) in 2008. The overall p-value for this trend is 0.051. 0.15% (n = 300) of all travelers who visited the travel clinic between 2001 and 2008 had Colitis ulcerosa. 110 travelers (36.67%) in this group were male and 190 travelers (63.33%) were female. The majority of
travelers with Colitis ulcerosa was between the ages of 25 and 44, 64.33% (193), and their main reason for traveling was holiday, 85.33% (256). Although most travelers in this group were born in the Netherlands, 29 travelers (9.67%) were born in Surinam.
7. Use of prednisone or other corticosteroids
Travelers who use prednisone or other corticosteroids and visited the travel clinic were 0.05% (n = 16) of all the travelers visiting the travel clinic in 2001 (figure 15). This percentage increased to 0.16% (n = 24) in 2008 (p = .005). There was a small dip in 2003 (n = 15, 0.06%) and one in 2006 (n = 17, 0.07%). 0.09% (n = 182) of all travelers who visited the travel clinic
Firgure 14. - Percenatge and number (#) of travelers with colitis ulcerosa who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 15. - Percentage and number (#) of prednisone using travelers who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 13. - Percentage and number (#) of travelers with crohn’s disease who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
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between 2001 and 2008 used prednisone or other corticosteroids. Most of these travelers, 85.16% (155) were female; 52.20% (95) was male. 48.35% (88) of the travelers who use prednisone or other corticosteroids are between the ages 25 and 44; 39.01% (71) are older than 44. 31 travelers (17.03%) in this group were born in Surinam, but the majority was born in the Netherlands. In addition, 139 travelers (76.37%) indicated holiday was their reason for traveling.
8. Rheumatoid arthritis with medication use
An increasing line from 0.09% (n = 25) in 2001 to 0.16% (n = 37) in 2008 (p = .002), with little dips in 2004 (n = 28, 0.11%) and 2006 (n = 34, 0.14%), represents the percentage of the travelers visiting the travel clinic who have rheumatoid arthritis (figure 16). Out of all travel clinic visitors, 262 travelers (0.13%) had Rheumatoid arthritis and used medication. 56 (21.37%) of them were male, but most of them, 203 (77.48%), were female travelers. Furthermore, the majority was older than 44 and born in the Netherlands. 194 travelers (74.05%) with Rheumatoid arthritis had holiday as a
reason for traveling. However, 9.92% (26) went on hajj and 12.98% (34) had Saudi-Arabia as their travel destination.
9. Malignancies, treated < 3 months ago
The percentage of travelers with malignancy treated less than 3 months ago increases from 0.04% (n = 11) of all the visitors in 2001 to 0.1% (n = 25) in 2004 (figure 17). Until 2006 it decreased with 0.05% (n = 13) to increase again to 0.11% (n = 26) in 2008. The p-value for the trend between 2001 and 2008 is 0.005. 0.07% (n = 142) of all travelers who visited the travel clinic between 2001 and 2008 were treated for malignancies less than 3 months before their travel clinic visit. The majority of these travelers, 60.56% (86), were women; 39.44% (56), were male travelers. Furthermore, most of them, 66.20% (94),
were older than 44, and born in the Netherlands, 88.73% (126). Although holiday was the most mentioned reason for traveling, 78.87% (112), 10.56% (15) traveled with the intention of visiting family.
Figure 16. - Percentage and number (#) of travelers with rheumatoid arthritis who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 17. - Percentage and number (#) of travelers with
malignancies treated < 3 months ago who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
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Figure 19. - Percentage and number (#) of pregnant travelers who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 200810. Other disorders (with the use of immune suppressive drugs)
0.02% (n = 7) of the travelers who visited the travel clinic in 2001 had an ‘other’ disorders with the use of immune suppressive drugs (figure 18). This percentages increased to 0.15% (n = 34) in 2008. The p-value for this trend is less than 0.001. There were little decreases in 2003 (from 18, 0.07% to 12, 0.05%) and 2007 (from 32, 0.13% to 27, 0.11%). 0.09% (n = 177) of all travel clinic visitors had an Other disorder for which they used immune suppressive drugs. 96 travelers (54.24 %) in this group were male, 81 (45.76%) female, and in most cases, 49.15% (87), between the ages of 25 and 44. The majority was born in the Netherlands, 93.79% (166), and had holiday as the reason for traveling, 83.62% (148).
11. Pregnancy
Pregnant women traveling and visiting the travel clinic increased until 2005 (figure 19). The percentage increased from 0.22% (n = 67) of the total
number of visitors in 2001 to 0.75% (n = 187) in 2005. After 2005, it decreases to 0.68% (n = 156) in 2008. The overall p-value for the trend is less than 0.001. 1138 travel clinic visitors (0.57%) were pregnant. The majority, 90.60% (1031), was between the ages 25 and 44. 66.61% (758) was born in the Netherlands. Another common country of birth for pregnant travelers was Ghana, 6.24% (71). Furthermore, of all travel clinic visitors born in Morocco, 3.50% was pregnant. Family visit was mentioned as a reason
for traveling by 8.35% (95) of the pregnant traveler, but most of them went on holiday, 81.20% (924). Most common destinations were Thailand, 10.72% (122), Indonesia, 7,73% (88), and Ghana, 7,12% (81).
Figure 18. - Percentage and number (#) of travelers with ‘other’ disorders drugs who visited the travelers vaccination clinic of the Public Health Service Amsterdam between 2001 and 2008
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12. Depression
The percentage of travelers with a depression who visited the travel clinic increased gradually form 1.95% (n = 569) in 2001 to 3.56% (n = 881) in 2005 (figure 20). After 2005, this percentage decreased slightly to 3.03% (n = 693) in 2008. The p-value for the trend is less than 0.001. A total of 6130 (3.04%) travelers who visited the travel clinic between 2001 and 2008 were depressed. Most of them, 67.81% (4157), were female travelers;
32.19% (1973) was male. 3630 (59.22%) depressed traveler were between 25 and 44 years old, and 2018 (32.92%) were older than 44. Although most depressed travel clinic visitors were born in the Netherlands, 3.95% of all travel clinic visitors born in Surinam were depressed. Holiday was the most mentioned reason for traveling; 5254 (85.71%) travelers in this group went on holiday. In addition, 3.44% of all travel clinic visitors who traveled for work reasons were depressed.
13. Other mental disorders
More travelers with other mental disorders visit the travel clinic every year (figure 21). The percentage increased from 0.56% (n = 164) of all travelers in 2001 to 2.02% (n = 462) in 2008 (p < .001). This increase is strongest in 2007. From 2006 to 2007 it increased with 0.71% (n = 171). 1.24% (2505) of all travel clinic visitors had an ‘Other mental disorder’. From these 2505 travelers, 39.16% (981) was male, and 60.72% (1521) was female. The majority, was born in the Netherlands and 61.44% (1539) was between 25 and 44 years old. 2061 (82.28%) travelers in this group went on holiday. Therefore, Thailand, 12.38% (310), Indonesia, 10.06% (252), and India, 8.50% (213), were the most common destinations. In addition, of all travelers who visited the travel clinic between 2001 and 2008 and gave work as a reason for traveling, 2.16% had an ‘Other mental disorder’.
Figur20. - Percentage and number (#) of depressed travelers who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
Figure 21. - Percentage and number (#) of travelers with an ‘other’ mental disorder who visited the travel clinic of the Public Health Service Amsterdam between 2001 and 2008
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Data
The data used for analyzing the trend in visitors to the travelers vaccination clinic of the GGD Amsterdam was the data from travelers who visited the clinic between 2001 and 2008. As mentioned before, until 2007, the travelers vaccination clinic used a computer program called ‘Vaccins’. This program was developed by the GGD in 2000 and reprogrammed into ‘Odysseus’ in 2007. ‘Vaccins’ was reprogrammed into ‘Odysseus’ to improve the quality of the data by improving data entry. In ‘Vaccins’ not all diseases under study were pre-programmed. Although questions about the travelers health and medication use were categorized by disease as much as possible, it was still possible to enter data at different places in the system. Figure 22 shows the different possibilities for entering data into ‘Vaccins’ and ‘Odysseus’.
In ‘Vaccins’ there were four different possibilities for entering a disease. For example, diabetes mellitus could be entered as pre-programmed ‘Diabetes Mellitus’, under ‘Other disease: diabetes’, under ‘Medication: Diabetes waarvoor insuline’, or diabetes mellitus could be entered in free text as a note. This led to the need to search the database in different ways in order to retrieve all the diabetics, or other travelers with a disease under study. In ‘Odysseus’ categorization was improved, but there are still three different ways in which a disease can be entered into the system; as a programmed disease under ‘medical history’, as a pre-programmed disease under ‘disease/medication’, and in free text as a note (figure 22). The database from ‘Vaccins’ is similar to the one from ‘Odysseus’. It contained only a few changes; extra information was added. For example, the place of birth of the travelers’ parents was added to the system. The data entered into ‘Vaccins’ was transferred to ‘Odysseus’ by the programmers. Figure 23 shows what the database looks like. The data used for the analysis was extracted from this database.
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The system displays the database output in tables. The top of each box in figure 23 shows the names of the tables. The columns of the table are the items shown in the remaining part of the box; each item is one column. For example, for the table ‘Destination’, clientnr, requestnr, destination, amount, and period are columns. Each row shows the information that was entered into the database for one specific client. When multiple entries are possible for one item, for example, more than one destination, the client gets two rows in the table. Each table has at least one primary
or foreign key. For the table ‘Client’ the client number, clientnr, is the primary key. This number is given to a travel clinic visitor, the client, at his or hers first visit and is unique for every client. The clientnr is directly linked to the clients personal information. Every time a client visits the travel clinic, he or she is searched in the system using his or her name and date of birth. The clientnr is used by the system to identify the client and find the clients data. Clientnr is copied into several other tables, namely ‘Medication’, ‘Questionnaire’, ‘Advice’, ‘Destination’, ‘Note’, and ‘Request’. In these tables clientnr acts as a foreign key. This foreign key connects tables and the data belonging to one client to each other. This means that by entering the clientnr into the system, all the data from that specific client is retrieved and shown. The table ‘Request’ has the foreign key clientnr and a primary key of its own. This primary key is the request number, requestnr. This number is unique for every trip a client makes. Different trips from one client get a different requestnr. Requestnr is also a foreign key in other tables. These tables are ‘Questionnaire’, ‘Advice’, ‘Destination’, ‘Note’. This means that through the requestnr, information specific for one travel request can be retrieved. All the different tables are linked to each other through clientnr and most are also related through requestnr. With the use of these two numbers, data from different clients and different travels can be differentiated.
The data is collected by different employees of the travelers vaccination clinic at various moments in the visit to the travel clinic. Figure 24 shows the different steps between entering and leaving the travel clinic. It also shows at which step during the visit the data is collected and entered into the database. The client enters the travel clinic and takes a number. When it is the clients turn, the visit is registered into the system. At this point, data about the trip and the destinations are collected and entered into the system. If the client visits the travel clinic for the first time, his or her personal information will also be collected. During the intake, a nurse or physician and the client discuss the clients medical history, his or her travel plans, and the travel advice and
Figure 23.- Logical data model. Data extracted from ‘Odysseus’ for analyzing the trends in visitors to the travel clinic between 2001 and 2008. PK = Primary key; FK = Foreign key
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needed vaccinations. When there is no vaccination needed and the client does not have to pay for anything else, the client can go home; when vaccination is needed or the client needs to pay for something other than a vaccine, the client goes to the cash register. After paying, the client either receives vaccination, or the client can go home. The data which is collected during the client’s visit is used by different employees for different purposes. It is mostly used for research about travelers and their trip and as a reference when the traveler visits the travelers vaccination clinic again.
The system used by the travel clinic employees consists of different screens on which the information about the traveler, his or her travel plans, and the travel advice can be entered. There are seven screens that are mostly used; these are called, client, request, destination, medication, questionnaire, advice, and note. An example of what such a screen looks like is shown in figure 25.
Figure 24. - Activity diagram. The clients path from entering the vaccination clinic until he/she leaves, including information about at which point during the visit data is entered into the system.
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Figure 25. – Client screen from ‘Odysseus’. Here, information about the client can be entered and looked at.As can be seen in figure 25, the client screen contains the clients personal information. At the bottom of the screen buttons are shown which, by clicking on them, will bring the user to another screen. By clicking on ‘Anamnese’, a questionnaire about the travelers health is shown; details about the travelers health status can be entered here. These details can also be entered at the disease/medication screen; this screen show the diseases a clients has, which medication he or she is using, and from when until when he or she suffered from the disease. At the request screen, information about the clients travel plans can be entered and shown; this information contains the duration of the travel-duration, date of departure, reason for traveling, and the destination. The advice screen shows the travel advice which is given to the traveler by the intaker; this advice can either be information, like watch out for mosquitoes, or it can be a vaccination. Furthermore, at the note screen, additional information can be entered into the database in free text; information can be entered which intakers find important, but for which there is no other suitable place to enter them.
Various things can go wrong in the collection of the data. Data can be entered incorrectly, at the wrong place or not at all. This will influence and bias research done using the data. Data about the client and his/her travel is collected at the point of registration (figure 24). Several mistakes can be made at this point. The client has to fill out a registration form every time he or she visits the travel clinic. This registration form contains two parts. A part about his or her personal information, part 1, and travel plans, and a part about the travelers health
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status, part 2. The registration form is shown in appendix B. At the registration desk, an assistant enters the data about the client and his or her trip into ‘Odysseus’. Most mistakes made at this point are that data is not entered into the system. In 2001, 10% (2994/29193) of all travelers who visited the clinic that year were missing a date of departure. In 2002, 4% (1141/26205) was missing a date of departure. This number was reduced to 0.63% (144/22850) in 2008. Another value that was missing often between 2001 and 2006 was the country of birth of the travelers parents. The percentage missing ranged from 88% (mother: 25801/29193; father: 25802/29193) in 2001 to 76% (mother: 18311/24172; father: 18316/24172) in 2006. This percentage was reduced to 0.2% in 2008 (mother: 49/22850; father: 44/22850). Furthermore, gender was missing more in 2007 (0.28%; 69/24228) and 2008 (0.13%; 30/22850) then in the years before (0% to 0.01%). Incorrectly entered data is hard to find.
Data about the clients health status is collected at the intake by a nurse or a physician, an intaker. The intakers enter the remaining information on the registration form, part 2, into the database. Questions like ‘Do you take medication?’ or ‘Do you have diabetes?’ are answered. But is the information given by the client correct? Intakers are trained to ask the client for additional information about the answers they gave, but the client might still give an incorrect answer. Analyzing this data can lead to bias. Table 2 shows the answers to the question ‘Do you take medication?’ among the different groups of immune compromised travelers or travelers with a mental disorder. From table 2 can be seen that the information given by the client is not always correct or that the data is not entered correctly. For example, 2 clients who use medication for rheumatoid arthritis, category 8, claim not to use any medication. Is this the result of the client not being completely honest about
his or hers health status or an error is data entry?
Table 2. - Answers to the registration form question ‘Do you take medication’ among the different groups for which the trends were analyzed in 2008. 1. IDDM; 2. DM with oral anti-diabetics; 3. HIV; 4. Without spleen; 5. Crohn’s disease; 6. Colitis ulcerosa; 7. Use of prednisone or other corticosteroids; 8. Rheumatoid arthritis with medication use; 9. Malignancies treated < 3 months ago; 10. Other disorders with the use of immune suppressive drugs; 11. Pregnancy; 12. Depression; 13. Other mental disorders.
Another example is shown in table 3. 115 travelers with insulin dependent diabetes mellitus answered the question ‘Do you have diabetes?’ with ‘No’; 150 travelers with diabetes mellitus with the use of oral anti-diabetics answered the same question with ‘No’.
Do you take medication?
Category Yes No No answer Total
1 62 151 11 224 2 124 206 33 363 3 36 112 5 153 4 11 23 4 38 5 11 28 1 40 6 4 25 1 30 7 5 9 0 14 8 7 15 0 22 9 4 17 5 26 10 8 25 1 34 11 8 133 15 156 12 126 531 36 693 13 85 355 22 462
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Do you have diabetes?
Category Yes No No answer Total
1 98 115 11 224
2 180 150 33 363
Table 3. - Answers to the registration form question ‘Do have diabetes’ among the travelers with IDDM (1) and the travelers with diabetes mellitus with the use of oral anti-diabetics (2) in 2008
Intakers can enter more detailed information about the clients health status by using the ‘Ziekte/medicatie’ part of the system. They can enter the disease a client has and medication he or she is using. When an intaker wants to enter a certain disease into the database, a pop-up screen appears with all the possible options. An example of this screen is shown in figure 26.
This pop-up screen leads to mistakes. Intakers are not always able to find the disorder they are looking for or it takes too long to find it. To save time, intakers are inclined to enter the disorder as a note in the comment field. When a disorder is entered as a note, it cannot be found with a regular search. Therefore, some travelers could only be included in this study after a comment field search was done. Travelers that were missed more frequently with a regular search were travelers using prednisone or travelers with rheumatoid arthritis. The numbers of travelers included after searching the comment field is shown in table 4.
Figure 26. - Pop-up from ‘Odysseus’ that appear when entering more detail about a travelers health status into the database. The intaker starts by clicking on a disease from the left column; after clicking on a disease the middle column appears with more detailed information about the disease the intaker selected; the intaker clicks again on the information he or she wants to enter; then, the right column appears, in which the medication the client uses can be selected; after clicking on an item in the right column, the data is entered into the database. The middle and right column differ per disease.
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Table 4 – Number of travelers using prednisone and travelers with rheumatoid arthritis who were included after searching the comment fields per year.
Questionnaire
A questionnaire about ‘Odysseus’ was send out to 18 intakers. 14 (78%) of them responded. The questionnaire contained 15 closed questions and three open questions. The closed questions were subdivided into 7 categories; orientation and ease of use, error correction, interface/need of improvement, finding data, entry location, data entry and security. The results are shown in table 5. The questionnaire and the results of each question separately are shown in appendix C. One of the main findings of the questionnaire was that intakers did not always know where to enter data about the clients health status. According to half of the respondents, the ‘Ziekte/Medicatie’ part of the system needs improvement.
Bad Fairly bad Neutral Fairly
good Good No answer
Orientation and ease of use 3 (4%) 8 (11%) 3 (4%) 18 (26%) 37
(53%) 1 (1%) Error correction 2 (14%) 5 (36%) 0 (0%) 6 (43%) 1 (7%) 0 (0%) Interface/Need of improvement 0 (0%) 6 (21%) 8 (29%) 10 (36%) 4 (14%) 0 (0%) Finding data 0 (0%) 5 (12%) 3 (7%) 16 (38%) 18 (43%) 0 (0%) Data entry 0 (0%) 6 (11%) 4 (7%) 22 (39%) 24 (43%) 0 (0%) Security 0 (0%) 0 (0%) 7 (50%) 4 (29%) 3 (21%) 0 (0%)
Table 5. - Different categories of the questions on the questionnaire about ‘Odysseus’ and the answers from the intakers.
Firstly, 2 intakers (14%) disagreed and 5 (36%) slightly disagreed with the statement that an error is easy to correct. One intaker stated that he or she misses a cancel-button; therefore, it is not possible to undo a wrong action. Other intakers also stated that correcting an error is not easy. One of them mentioned that when you, for example, enter a wrong date into the system, you have to delete all the entered data, and re-enter it; it is not possible to just correct the date. In addition, one intaker mentioned that when making a telephonic appointment, errors cannot be corrected at all. Secondly, there is confusion among the intakers about the connection between the ‘Anamnese’ screen and the ‘Ziekte/Medicatie’-screen. These two screens seem to do the same thing, they only look different; intakers do not know where to enter the data about the travelers health status. One intaker stated that entering data using the ‘Anamnese’-screen seems useless, because the data will appear at the ‘Ziekte/Medicatie’-screen and it is also possible to enter data there. Thirdly, the list with
Year Prednisone Rheumatoid arthritis
2001 8 19 2002 7 28 2003 4 30 2004 6 22 2005 8 30 2006 5 32 2007 11 12 2008 9 12
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diseases, figure 25, needs to be improved. Intakers stated that the list is cluttered and some relevant diseases, which need to be taken into account for the advice, are missing. One intaker mentioned that the list is too long and usually you do not even find what you were looking for; this leads to the use of the option ‘other diseases’ or the comment field to enter a disease. Another intaker suggested that the top of the list should contain the most common and the most relevant diseases. Finally, some intakers (5, 12%) would like it to be easier to find clients in the database by adding more search options, like day of visit, destination or gender; some intakers would like to get an alert when they prescribe medication with a contra-indication; and most intakers don’t know if the security of the data is properly organized. They assume the security is okay and only people who need to see the data can look at it.
