Searching for Cancer Information on the Internet: Analyzing Natural Language Search Queries

Background Searching for health information is one of the most-common tasks performed by Internet users. Many users begin searching on popular search engines rather than on prominent health information sites. We know that many visitors to our (National Cancer Institute) Web site, cancer.gov, arrive via links in search engine result.
Introduction
For members of the general public who use the Internet, many seek medical information [1- 6]. According to a recent systematic review of 24 peer-reviewed publications describing the proportions of Internet users among various populations of cancer patients in the developed world, about 39% of cancer patients are using the Internet directly, and in addition, 15% to 20% of persons with cancer use the Internet "indirectly" through family and friends [7]. Studies have evaluated information-seeking behavior on the Internet by cancer patients generally [8- 10], their companions [11,12], and patients with the following common specific cancer diagnoses: breast [13- 16], prostate [17,18], lung [19], and gastrointestinal cancers [20]. Studies have also evaluated information gathering by cancer patients undergoing radiotherapy [21] and chemotherapy [22], and those from centers outside of North America [23,24]. Individuals from certain disadvantaged groups have been shown to seek medical information online less frequently and with more difficulty [7,25,26].
Eysenbach and Kohler found that general consumers search for medical content using search engines rather than medical portals or sites of medical societies or libraries [27]. Newly-diagnosed cancer patients and their families often start their searches as users less sophisticated in Web and medical terminology. They too commonly begin searching on popular search engines rather than on prominent cancer-information sites. We know that many visitors to our own Web site [28] arrive via search engine result links.
To better understand users' needs this research aimed to establish what lay users really want to know when they search online for cancer information. To do this we evaluated data from Ask.com [29], a popular natural-language-processing (NLP) search engine. Natural-language-processing search engines allow users to create queries using whole phrases and sentences of any length, rather than just key words.
Earlier reports of this project have been published in abstract form only. The abstracts reported a brief project summary [30], and data specific for breast cancer [31] and gastrointestinal cancer [20]. This is the first comprehensive report of the entire project.
Methods
The National Cancer Institute (NCI) partnered with AskJeeves, Inc to develop a methodology to capture, sample, and analyze 3 months of cancer-related queries on the Ask.com Web site, a prominent US natural-language-processing consumer search engine. At the time of the project, Ask.com was receiving over 35 million queries per month.
Search Terms
An NCI oncologist (JLB) developed a benchmark set of 500 terms and word roots that were matched against actual AskJeeves user queries. Most terms and word roots were from the NCI dictionary on the NCI Web site [32]. NCI also suggested additional terms not included in the dictionary. These terms related to anatomy, organ systems, treatments, pharmaceuticals, treatment and diagnostic procedures, genetics, epidemiology, and pathology.
Table 1
Top 37 search terms and roots with ≥ 5 queries per week during test week
Term
Actual Queries During Test Week
% of Total Queries
*cancer*
9765
56.75
*tumor*
1396
8.11
*carcino*
656
3.81
*leukemia*
635
3.69
lymphom*
419
2.43
chemotherapy
378
2.20
biopsy/biopsies
375
2.18
*melano*
348
2.02
*sarcoma*
294
1.71
*dysplasia*
255
1.48
hodgkin*
245
1.42
MRI
214
1.24
clinical trial
187
1.09
mammogr
175
1.02
maligna*
170
0.99
*metasta*
155
0.90
"*" is a placeholder for the part of the search term before or after the root.
The test sample of these 500 words and roots was used to filter cancer queries from the Ask.com Web site for 1 week in August 2001. From these 500 terms, only 37 appeared ≥ 5 times per day over the trial week. The list of 37 terms (plus common misspellings) yielded 17208 queries for the test week. The frequency of each term is shown in Table 1. Queries with common misspellings, (eg, prostate and prostrate, biopsy and biopsey, leukemia and lukemia, chemotherapy and chemothereapy) were captured and analyzed. It was felt that the cut off of 5 times per day (≥ 35 times per week) would capture the key queries and include any common query topic, since even with a frequency of 35 queries a week, the majority of these terms accounted for less than 1% of the total population. Of the original 500 terms supplied by NCI, only 7% (35/500) appeared in the logs at a high frequency, but this 7% accounts for over 37% of user queries identified as cancer related on Ask.com during the study period.
Collecting Queries and Sampling
The process used for collecting and sampling queries is outlined in Figure 1. Using the 37 terms to search the Ask.com query logs, 204165 instances of cancer-related queries were found for June, July, and August 2001. Of these queries, 7500 individual user questions were randomly selected by AskJeeves for detailed analysis (see Appendix 1).
Figure 1
Processing of cancer queries on Ask.com
Very often there were multiples of the same questions. Thus, these 7500 queries actually represented 76077 queries that were entered into Ask.com, about 37% (76077/204164) of all queries identified as cancer-related from the 3 month period of log analysis. For example, a user question might be "Where can I find information about breast cancer?" This individual example represents 1 user question, but might have been queried by more than 100 people on any given day. Each query was counted only once.
Sampling Issues
The random sample of 7500 individual queries provides a confidence interval of 1.11% at a confidence level of 95%. This means that even if more samples were taken from 204165 queries, 95% of those samples should not be off by more than 1.1%. While this means that the samples themselves would not vary more than 1.1% over 95% of the samples taken, as the data are categorized and classified, in effect smaller and smaller samples are taken. Therefore, to offset this problem additional queries were examined, even though a smaller sample would still provide a high degree of confidence in the results.
In other words, although broad generalizations—such as "breast cancer accounts for 25% of all cancer queries"—can be easily presented, a large sample size is required to break down data far enough to conclude that when users ask about breast cancer, they are most often asking about specific types of treatments.
Highest-Level Categories for Queries
User queries were assigned to a set of 6 highest-level categories (as shown in Table 2):
Cancer (ie, specifically mentioning a cancer type)
General Research
Treatment
Diagnosis and Testing
Cause/Risk/Link
Coping
Table 2
Highest-level categories for queries
Highest-Level Category
Number of Queries
Percent of All Sampled Queries
*
Cancer †
59619
78.37
General Research
7808
10.26
Treatment
3832
5.04
Diagnosis and Testing
3315
4.36
Cause/Risk/Link
1249
1.64
Coping
254
0.33
Total
76077
* Percentages do not sum to 100% due to rounding.
† ie, specifically mentioning a cancer type.
Highest-level categories were created in a collaborative effort between the AskJeeves data-analysis team and NCI staff before the study period began, but the final category titles were revised as the actual queries were analyzed. The initial categories were based on user queries entered into Ask.com and a variety of online sources, such as NCI's online dictionary [32] and NCI's Physician Data Query (PDQ) [33].
The highest-level categories were populated using proprietary AskJeeves filters and automated-analysis tools that sorted queries according to specific types of cancers, or—in the absence of mentioning a specific cancer type—whether the query asked about other areas such as Treatment or Coping. (AskJeeves did not share the filters and automated analysis tools with the authors.) Queries that could not be sorted by the filters and automated-analysis tools were placed in a temporarily-uncategorized category; they were categorized during the next step (reading and analysis).
Reading and analyzing each individual query not only verified the automated process, but also helped to refine existing categories and create new categories and subcategories, as appropriate. For example, without this type of analysis, the query "Where can I find a Web site with information on using high protein food to fight Breast cancer?" might have been left under Breast Cancer > Media and Organizations > Web sites (where ">" indicates a change in category level). This would not be correct, as the true user intent was to inquire about Alternative Treatments. As a result, under the category Breast Cancer > Treatment, "Alternative" was added to the Breast Cancer > Treatment category analysis as a subtopic. (Treatment—without a specific cancer site designated—is both a highest-level category and a subcategory under Breast Cancer and under most cancer types.)
Approximately 78% of all categorized queries from the sample referenced a particular type of Cancer, and were placed in the highest-level category Cancer. An example of this kind of query would be "Where can I find information about Breast Cancer?" (This query would be classified as Cancer > Breast Cancer > General Information.) Any query that did not mention a specific kind of Cancer, even though the question was about cancer, was placed on 1 of the 5 other highest-level categories. An example of this type of query would be "Where can I find information on cancer treatment with radiation?" This query was assigned to the Radiation subcategory in the highest-level category Treatment (ie, it was classified as Treatment > Radiation).
Queries that did not relate to a specific Cancer type were placed in 1 of the 5 other highest-level categories: General Research, Treatment, Diagnosis and Testing, Cause/Risk/Link, or Coping. For example the query "How does smoking cause cancer?" would be placed in the Cause/Risk/Link category, as it did not refer to any specific type of cancer.
"Cancer" Queries (Related to Specific Cancer Types)
As shown in Table 3, there were 14 cancer types (N = 59619 queries) selected as subcategories of the Cancer highest-level category. For cancer types with the most-frequent queries, like Digestive/Gastrointestinal/Bowel (D/G/B), Breast, and Genitourinary, there were enough queries to populate standard subcategories like General Information, Treatment, Symptoms, Diagnosis and Testing, and Cause/Risk/Link. These common cancer types often warranted the creation of customized subcategories, like Breast > Media and Organizations > Web sites. For the less-common cancer type queries, like Bile (duct) in D/G/B, few queries were received and only those in General Information are shown.
Table 3
Cancer types
Type within Top-Level Cancer Category
Number of Queries
% Queries in Cancer Category
*
†
% Queries in This Report
*
‡
Digestive/Gastrointestinal/Bowel (D/G/B)
8959
15.0
11.8
Breast
6953
11.7
9.1
Skin
6709
11.3
8.8
Genitourinary
6250
10.5
8.2
Hematologic/Blood
5448
9.2
7.2
Gynecological
5344
9.0
7.0
Lung
4630
7.8
6.1
Soft Tissue/Muscle
3954
6.6
5.2
Lymphoma
3333
5.6
4.4
Head and Neck
2522
4.2
3.3
Brain/Neurological
1852
3.1
2.4
Miscellaneous Cancer
1633
2.7
2.1
Bone
1429
2.4
1.9
Pediatric
603
1.0
0.8
* Percentages do not sum to 100% due to rounding.
† Denominator (N = 59619) was the total number of queries about specific types in the Cancer category.
‡ Denominator (N = 76077) was the total number of queries analyzed in this report.
Privacy Issues
Although NCI helped create the search terms and the categories into which the analyzed data was placed, NCI did not have access to: the raw query logs at AskJeeves, any information about what AskJeeves users did with the searches generated on the AskJeeves Web site (ie, what links they picked), or the identities of any users of the Ask.com Web site. NCI did not require permission from the Institutional Review Board.
Results
Frequency of Top-Level Categories
As shown in Table 2, The 6 highest-level categories in order of decreasing frequency of queries were:
Cancer (N = 59619, 78.37%)
General Research (N = 7808, 10.26%)
Treatment (N = 3832, 5.04%)
Diagnosis and Testing (N = 3315, 4.36%)
Cause/Risk/Link (N = 1249, 1.64%)
Coping (N = 254, 0.33%)
The data in Table 2 indicate that the great majority of users asked for information about specific types of cancers, but rarely asked about a Treatment option or Diagnosis and Testing procedure without specifying the particular cancer about which they were concerned. Similarly, users asked few queries about general Symptoms of cancer unrelated to a specific type of cancer (see Diagnosis and Testing > Symptoms, N = 473, 14.27%). An example would be "what are some symptoms of cancer?"
Subdividing Cancer Queries
Table 3 breaks down the highest-level category Cancer queries (N= 59619) into more specific cancer types. In order of decreasing frequency within the Cancer category, the 14 subcategories were:
Digestive/Gastrointestinal/Bowel (D/G/B) (N = 8959, 15.0%)
Breast (N = 6953, 11.7%)
Skin (N = 6709, 11.3%)
Genitourinary (N = 6250, 10.5%)
Hematologic/Blood (N = 5448, 9.2%)
Gynecological (N = 5344, 9.0%)
Lung (N = 4630, 7.8%)
Soft Tissue/Muscle (N = 3954, 6.6%)
Lymphoma (N = 3333, 5.6%)
Head and Neck (N = 2522, 4.2%)
Brain and Neurological (N = 1852, 3.1%)
Miscellaneous (N = 1633, 2.7%)
Bone (N = 1429, 2.4%)
Pediatric (N = 603, 1.0%)
Any query specifically mentioning a cancer type by name, was assigned to that subcategory. For example, questions about Breast-Cancer-specific Treatment, Diagnosis and Testing, Causes, and Coping are found in the Cancer > Breast Cancer category, within 1 of the 10 subcategories displaying Breast Cancer information. All questions about Leukemia or Myeloma would be found in Hematologic/Blood, Hodgkin's Disease queries in Lymphoma, and Esophageal cancer questions in D/G/B.
The number of subcategories assigned to each of the 14 different cancer types varied somewhat and was driven by the nature and number of the specific queries in those cancer types.
Detailed Analysis of Queries
The detailed categorizations and verbatim display of examples of sampled queries are shown in Appendix 1. There is a breakdown of all the 14 cancer types within the highest-level category Cancer as well as a breakdown of queries within the 5 other highest-level categories not referencing any particular cancer type. These 19 are arranged alphabetically in the Appendix.
Major observations about the 19 categories and subcategories are noted below, in the order they appear in the Appendix. Our comments emphasize issues related to requested cancer content more than technology issues related to the natural language processing.
1.0 Bone Cancer
As shown in Appendix 1, there were 1429 queries about Bone Cancer. The vast majority of Bone Cancer queries asked for General Information (N = 1107, 78%). An example of this category would be: "Where is information on bone cancer?" Users asked questions about Bone Cancers linked to various sites of Anatomy as well as certain Histologies. There were some questions related to Bone Cancers in teenagers that were assigned to this category, rather than the Pediatric category. There were more questions about Diagnosis and Testing (N = 64, 4.48%) and Symptoms (N = 135, 9.45%) than Treatment (N = 26, 1.82%).
2.0 Brain and Neurological Cancer
Of the 1852 Brain and Neurological Cancers queries, General Information accounted for the vast majority (N = 1323, 71.44%). There were 427 (23.1%) questions about specific cancer types in this category. Some cancer types queries asked about Medulloblastoma, which is typically but not always a Pediatric tumor. As with Bone Cancer above, some questions could have been meaningfully assigned to more than 1 top-level Cancer site category. In this category there were more queries about Symptoms (N = 259, 13.98%) than Treatment (N = 112, 6.05%).
3.0 Breast Cancer
As shown in Appendix 1, Breast Cancer was one of the simpler cancer types, from a data-display standpoint. There was only 1 anatomic-cancer type and all of the individual queries for that cancer type were assigned into 1 of 10 subcategories.
The 10 top-level Breast Cancer subcategories were:
General Information (N = 3423, 49.23%)
Symptoms (N = 889, 12.79%)
Treatment (N = 570, 8.20%)
Media/Organization (N = 428, 6.16%)
Cause/Risk/Link (N = 393, 5.65%)
Diagnosis and Testing (N = 376, 5.41%)
Statistics (N = 274, 3.94%)
Pictures (N = 225, 3.24%)
Type (N = 217, 3.12%)
Definition (N = 158, 2.27%)
Nine of the 10 Breast Cancer subcategories were analyzed in detail in Appendix 1. The tenth, Pictures, did not require further analysis. Most queries asked for General Information.
There were more frequent queries about Breast Cancer (N = 6953) than any other cancer type. This may not be apparent from Table 3, which appears to show more D/G/B cancers (N = 8959). However, D/G/B overall is actually composed of 10 cancer types. The most frequently queried cancer type in D/G/B was Colorectal (N = 4,801) which had fewer queries than Breast.
Even though other cancer types may have been assigned more subcategories than the 10 for Breast, the detail and the medical specificity and technical vocabulary of Breast queries appear to be the most complex than other Cancer sites, probably reflecting the sophistication of basic research and clinical data on this topic and the relative sophistication of the breast cancer information seekers.
4.0 Cause and Risk
There were 1249 queries in this highest-level category. Without mentioning a specific cancer by name, there were N = 1115 (89.27%) queries about Causes and Links but only N = 134 (10.73%) about Prevention. Among the 1115 queries in the Causes and Links subcategory, the following topics were noted:
Drugs (N = 287, 25.74%)
Unspecified (N = 247, 22.15%) (eg, "What is cause a cancer?" [sic])
Radiation (N = 247, 22.15%)
Personal (N = 116, 10.40) (eg, "Can anti-persperant [sic] deodorant cause cancer?")
Chemical/Plastics (N = 74, 6.64%)
Environmental (N = 70, 6.28%)
Food Supplement (N = 64, 5.74%)
Genetic Mutation/Virus (N = 10, 0.90%)
Smoking was not in this list, probably because most queries about smoking were included under a query about a specific type of cancer, like Lung or Head and Neck.
5.0 Coping
There were only 254 queries about Coping. The queries referenced Support Groups (N = 127, 50%), Pain (N= 98, 38.58%), and Depression (N = 29, 11.42%). Even though there were few questions in this highest-level category, the issue was of specific interest to NCI, which asked for this category to be created and analyzed separately.
6.0 Diagnosis and Testing
There were 3315 queries in this highest-level category, which did not mention a specific cancer by name. Most were queries about specific Testing (N = 2842, 85.73%). The others (N = 473, 14.27%) were queries about Symptoms. Among Testing queries, CAT/CT scan (Computerized Axial Tomography/Computed Tomography scan) (N = 1509, 53.10%) and MRI (N = 587, 20.65%) were the most-common Testing topics, followed by Biopsy (N = 502, 17.66%).
7.0 Digestive/Gastrointestinal/Bowel (D/G/B)
The presentation of data queries for D/G/B in Appendix 1 is complex because, there were 7 top-level subcategories, including General Information and 10 cancer types identified in the General Information subcategory
As shown in Appendix 1, 8959 queries for D/G/B sites were broken down into 7 subcategories:
General Information (N = 5568, 62.15%)
Symptoms (N = 1506, 16.81%)
Diagnosis and Testing (N = 1125, 12.56%)
Treatment (N = 294, 3.28%)
Statistics (N = 184, 2.05%)
Definition (N = 163, 1.82%)
Cause/Risk/Link (N = 119, 1.33%)
Most queries asked for General Information. Examples of General Information queries would be "Where can I learn about the cancer esophageal cancer?"? and "Where can I find information on Stomach cancer"?
A breakdown of all D/G/B queries by cancer type is shown in the list below. The absolute numbers and percentages (of all D/G/B queries) in the list below differ from the pie diagram in Appendix 1 because the list below includes organ-type queries from General Information plus the 6 other subcategories in D/G/B.
Colorectal (N = 4801, 53.59)
Liver (N = 1413, 15.77%)
Gastrointestinal (stomach) (N = 1094, 12.21%)
Pancreas (N = 965, 10.77%)
Bowel (N = 273, 3.05%)
Esophagus (N = 260, 2.90%)
Other (N = 153, 1.7%)
The organ subsites in Other include Gall Bladder, Bile Duct, Anal, and Abdominal.
As noted in Appendix 1, for D/G/B there were far more questions about Symptoms (N = 1506, 16.81%) than Treatment (N = 294, 3.28%) possibly reflecting the fact that (1) users of Ask.com were just beginning their D/G/B information seeking and (2) there is less complexity in the published Treatment data for D/G/B compared to some other cancer types, like Breast Cancer.
The terms Bowel, Gastrointestinal, Stomach, and Abdominal may have been used interchangeably by users. They appear not to recognize that queries for sigmoid, rectum, cecum, appendix, transverse colon, small bowel, and stomach (gastric) cancer would provide much more useful information.
For D/G/B, some queries about Liver Metastases were included with queries about primary Liver Cancers.
8.0 General Research
There were 7808 queries assigned to the highest-level category General Research, a topic not linked to a specific cancer type. In this category the 5 most-common subcategories were:
Research (N = 2819, 36.10%)
Organization (N = 1656, 21.21%)
Clinical Trials (N = 1272, 16.29%)
Concerns (N = 1201, 15.38%)
Pictures (N = 559, 7.16%)
Among the queries about Organization, there were 1065 queries about the American Cancer Society (ACS) and 223 about the National Cancer Institute (NCI).
Among the 1272 queries about Clinical Trials, the most-common 3 questions/topics were:
What are ... (N = 634, 49.84%) eg, "What are clinical trials?"
Latest ... (N = 260, 20.44%) eg, "latest cancer clinical trial research"
Types of ... (N = 111, 8.73%) eg, "types of cancer trials"
9.0 Genitourinary Cancers
In decreasing order, the frequency of Genitourinary organ-type queries (N = 6250) in all 12 Genitourinary subcategories including General Information was:
Prostate (N = 3141, 50.26%)
Testicular (N = 1772, 28.35%)
Bladder (N = 708, 11.33%)
Kidney (N = 496, 7.94%)
Other (N = 133, 2.12%)
Although it has been estimated that there were 198100 new cases of Prostate Cancer diagnosed in the US in 2001 and only 7200 cases of Testis Cancer [34], the relative frequency of Testis Cancer queries was quite high. One possible reason might be that males diagnosed with Testis Cancer are generally much younger than those diagnosed with Prostate Cancer, and those younger individuals might be more-frequent information seekers on the Internet. It may also reflect the fact that the 2001 Tour de France bicycle race won by Lance Armstrong, a Testis Cancer survivor, was held during July, coinciding with the study period for this project.
As with most sites, the most-common Prostate Cancer questions were General Information (N = 1715, 54.6%). For Prostate Cancer, there were more questions about Treatment (N = 460, 14.65%) than Symptoms (N = 364, 11.59%). This may reflect major medical controversies about treatment options and the typically asymptomatic presentation of the disease.
For the Genitourinary category as a whole, there were more questions about Symptoms (N = 854, 13.66%) than Treatment (N = 604, 9.66%).
Expected misspellings of prostate (prostrate) were noted.
10.0 Gynecological Cancers
There were 5344 queries overall. The breakdown of subcategories in decreasing frequency was:
General Information (N = 3409, 63.79%)
Symptoms (N = 939, 17.57%)
Diagnosis and Testing (N = 452, 8.46%)
Treatment (N = 247, 4.62%)
Definition (N = 158, 2.96%)
Cause/Risk (N = 83, 1.55%)
Statistics (N = 42, 0.79%)
Prevention (N = 14, 0.26%)
In decreasing order of frequency, the cancer types queried in all 8 Gynecological subcategories included the following:
Ovarian (N = 2031, 38.00%)
Cervical (N = 1924, 36.00%)
Uterine (N = 606, 11.34%)
Endometrial (N = 225, 4.21%)
Vulvar (N = 166, 3.11%)
Vaginal (N = 219, 4.09%)
Other or not specified (N = 173, 3.24%)
There were nearly as many questions about Cervical Cancer as Ovarian Cancer despite the fact that in the United States in 2001 the estimated incidence of new Ovarian Cancers was about twice that of invasive Cervical Cancer [34].
There were questions about Endometrial cancer as well as Uterine cancer. These data suggest that Web site information needs to be provided using both labels.
11.0 Head and Neck
There were 2522 queries overall. Most queries asked for General Information (N = 1485, 58.88%). The vocabulary used to ask about specific cancer types within General Information was:
Throat
Mouth
Oral
Tongue
Head
Neck
The vocabulary confirms the need to offer health information with words that are not technical like larynx, glottis, pharynx, or nasopharynx. There were 59 questions asking about Definitions of Head and Neck cancer. Specifics about cancer anatomy of this cancer type may be less familiar to the general public than other sites.
There were 422 queries asking for Pictures of Head and Neck Cancer. There were only 47 questions (1.86%) asking about Cause/Risk/Link issues, despite the fact that there is a great deal known about the Causes and Prevention of Head and Neck Cancer. There were 418 questions (16.57%) about Symptoms and but only 52 (2.06%) about Treatment.
12.0 Hematologic and Blood Cancers
Among 5448 queries in this category, the 5 most common of the 12 subcategories were: General Information (N = 3781, 69.40%), Definition (N = 701, 12.96%), Symptoms (N = 539, 9.89%), Treatments (N = 175, 3.21%), and Organizations (N = 102, 187%). Within General Information users asked about Leukemia (N = 2895, 76.57%), Myeloma (N = 592, 15.66%), Bone Marrow (N = 148, 3.91%), and Blood Cancers (N = 146, 3.86%). Various misspellings of Leukemia were noted and nontechnical terms such as Blood Cancer and Bone Marrow Cancer were frequent.
13.0 Lung Cancer
Lung Cancer (N = 4630) accounted for 8% of organ-type specific queries within the highest-level Cancer category. This is a disproportionately-low percentage given the relative incidence of Lung Cancer in the United States in 2001 [32]. There were more queries about Gynecological and Hematologic/Blood cancers, even though the US incidence for these is far lower.
Among Lung Cancer queries, the queries were classified as follows:
General Information (N = 3223, 69.61%)
Symptoms (N = 530, 11.45%)
Cause/Risk/Link (N = 305, 6.59%)
Treatment (N = 219, 4.73%)
Definition (N = 150, 3.24%)
Statistics (N = 113, 2.44%)
Diagnosis and Testing (N = 90, 1.94%)
In the Cause/Risk/Link category of Lung Cancer, there were only N = 180 queries (59.02%) that asked generally about Causes of Lung Cancer and N = 102 queries (33.44%) that asked specifically about Smoking. There were N = 23 queries (7.54%) asking if Marijuana caused Lung Cancer.
Only N = 255 (7.91%) queries within General Information asked about Lung Cancer by (histologic cell) Type, despite the fact that this is a major determinant of triage for treatment.
For Lung Cancer > Treatment, there were 219 queries (4.73%). Most Treatment queries were Unspecified (N = 118, 53.88%), eg, "What are treatments for lung cancer?" There were 26 Treatment questions about Cure (11.87%). There were few specific questions about Medications (chemotherapy) (N = 21, 9.59%), Radiation (N = 19, 8.68%), or Surgery (N = 10, 4.57%). Although all numbers were small, there were more questions about Alternative Treatment (N = 13, 5.94%) than Surgery (N = 10, 4.57%). There were only 4 Treatment questions (1.83%) about palliative care, despite the grave prognosis for most Lung Cancers. Clearly the questions about Lung Cancer, the most-common lethal cancer, were far less sophisticated than the questions about either Breast Cancer or Prostate Cancer.
14.0 Lymphomas
Among the 3333 queries about Lymphoma (including both Hodgkin's Disease and Non-Hodgkin's Lymphoma), General Information (N = 2391, 71.74%) questions were the most common. Unlike many cancer types, there was frequent mention of histologic types, as is appropriate, given the wide variety of clinically-different prognoses and treatments in this subcategory. There were many different spellings of Hodgkin's Disease.
15.0 Miscellaneous Cancers
There were 1633 queries assigned to this Cancer subcategory. The Miscellaneous Cancers were:
Endocrine (N = 901, 55.17%)
Neoplasm (N = 272, 16.66%)
Kaposis (N = 262, 16.04%)
Ocular (N = 179, 10.96%)
Germ Cell (N = 19, 1.16%)
Several of the Ocular queries, eg, Ocular Melanoma and Retinoblastoma, could have been considered for other subcategories, such as Skin and Pediatric respectively. Germ cell tumors could also have been placed in either Genitourinary or Gynecological subcategories. These ambiguities illustrate the difficulty in categorizing precise user information needs despite the use of natural language processing.
16.0 Pediatric
There were only 603 Pediatric queries, and most asked about a specific cancer type (N= 403, 66.83%). There were relatively few General Information queries (N = 81, 13.43%) eg, "where can I find information on children's cancers?" Since patients with Pediatric cancers in the US are usually managed generally by pediatric oncology specialists at major regional medical centers, those seeking Pediatric cancer information are probably directed to specialized Web sites rather than general sites like Ask.com.
Of 403 queries for cancer types, the most common were Hematologic/Blood (N = 137, 34%), Neuroblastoma (N = 133, 33%), and Rhabdomyosarcoma (N = 68, 16.87%). There were only 4 questions referring to pediatric Brain and Neurological cancers. Since this is such a common Pediatric tumor type, it is possible that some Pediatric neurological tumor questions were assigned to the Brain and Neurologic Cancer category even though the questions were really meant to target a Pediatric issue.
17.0 Skin Cancers
Among 6709 queries in this Cancer subcategory, 3596 (53.60%) asked for General Information. Like Lymphoma, there was frequent mention of specific Skin Cancer types (N = 2157, 32.15%), probably because of the significantly-different clinical prognoses and treatments.
Only 169 queries (2.52%) asked about Cause/Risk/Link, and 60 queries (0.89%) asked about Prevention despite the fact that so much is known about these topics and Skin Cancer.
Among Skin Cancers queried by histologic cancer type (N = 2157, 32.15%), Melanoma was the most common (N = 1707, 79.14%), even though it is far-less common than Basal Cell Skin Cancers (N = 322, 14.93%) [10]. Frequent mention of Melanoma probably reflects its more-serious prognosis and more-complicated clinical triage.
18.0 Soft Tissue Cancers
There were 3954 queries in this Cancer subcategory. Although most appropriately refer to sarcomas of various types, there was a minority of misplaced queries. Some queries appear to reference conditions that are probably benign (Ganglion, Fibroid, Dysplasia, and Lipoma) and others should have been placed in different Cancer subcategories eg, Brain and Neurological (Oligodendroglioma and Glioma) These will be corrected on later analyses.
19.0 Treatment
In the 3832 highest-level category queries about Treatment, most questions were about a specific Treatment Type (N = 3223. 84.11%), even though no specific cancer was mentioned. Within Treatment > Treatment Type there were many general queries about Chemotherapy (N = 2275, 70.59%). There were questions about general Radiation Therapy (N = 534, 16.57%), and few about specialized Radiation Therapy treatments like Gamma Knife, Laser, and Protons. There were more general questions about Alternative Therapies (N = 239, 7.42%) than Surgery (N = 127, 3.94%) Many Alternative Therapy questions also appear in specific organ-type subcategories, particularly Breast.
Query Frequency Relative to US Incidence of Cancer Types
Table 4 compares the incidence of selected cancers in the United States (US) in the year 2001 with the frequency of selected site-specific cancer queries in this report. It has been estimated that there were 1268000 new cancer cases in the US in 2001 [34]. The sites in Table 4 were selected specifically because they were easiest to compare directly.
The relative percentage of specific organ-type queries exceeds the percentage of annual incidence only for rarer cancers. The difficulty of finding useful information on prominent cancer portals or with standard search engines may be one explanation, although there are others. The comparison is not meant to be definitive as there are clearly issues with validity of this comparison:
Cancer prevalencemight be a better benchmark than incidence
US incidence data exclude cases of in situ breast and cervix cancers as well as the very-common basal cell and squamous cell skin cancers
Queries could have come from anywhere in the world, not just the United States
Query total may include those who accessed the site more than once
Queries could have come from individuals who are not newly-diagnosed patients
Table 4
Comparing relative annual US incidence of selected cancers and query frequency
Cancer Site
Estimated Number of New US Cancers Diagnosed in 2001
*
% of Estimated New US Cancers in 2001
*
†
Number of Cancer Site-Specific Queries in This Report
% Queries in Cancer Category
†
‡
Digestive (D/G/B)
235700
18.6
8959
15.0
Prostate
198000
15.6
3141
5.3
Breast
193700
15.3
6953
11.7
Lung
169000
13.3
4630
7.8
Lymphoma
63600
5.0
3333
5.6
Bladder
54300
4.3
708
1.2
Uterus/Endometrial
38300
3.0
931
1.6
Head and Neck
30100
2.3
2522
4.2
Ovary
23400
1.9
2031
3.4
Brain and Neurological
17200
1.4
1852
3.1
Cervix
12900
1.0
1924
3.2
Soft Tissue
8 700
0.69
3954
6.6
Testis
7200
0.57
1772
3.0
* Data from 2001 Estimated Annual US Cancer Incidence Figures (N = 1268000) [10].
† Percentages in columns 3 and 5 do not add up to 100% because only selected cancers were included in this chart.
‡ Only selected Cancers were included in this chart. Denominator (N = 59619) was the total number of queries about specific subsites in the Cancer category.
Other Observations
The query analysis reveals that online users generally seek information about Symptoms and Treatment for specific cancers, rather than about cancers generally. In addition, Symptom queries showed a frequency between 2 and 5 times that of Treatment queries, for most cancers.
For this study we did not specifically target queries about Adult Immune Deficiency Syndrome (AIDS), even though AIDS can often be associated with Cancer. There were 262 questions about Kaposi's Sarcoma in the Miscellaneous Cancers category.
Discussion
General Information was the largest category for almost all cancers, probably reflecting the nature of the Ask.com consumer search engine. It is a consumer-oriented Web-wide search engine where users tend to seek general information that can help them learn either how or where they should further pursue their inquiries. It is likely the users are just starting their Web searches on Ask.com and they are not yet interested in or they do not yet know enough information to ask more-sophisticated questions. This behavior may not reflect that of users who go directly to a known cancer-information portal with a predetermined need for detailed information.
We attempted to capture and analyze all cancer-related queries, including those with correct and incorrect spellings. Misspellings were noted relatively frequently, but we have no data on the number of misspellings, as we did not target this in advance as an endpoint, and we did not have direct access to the raw data logs. Appendix 1 shows verbatim queries with examples of the misspellings. Automating help for users who enter misspelled words is a major issue for search engines in order to optimize query results. Other researchers have noted the search difficulties related to spelling of cancer search terms correctly [35].
Ask.com users entered both keyword searches and sentence-style queries, despite the fact that this is a natural-language-processing search engine. We recognize that even if users typed in a long query it was still sometimes difficult to discern absolutely what specific information the user needed, particularly since we did not have access to the links users picked.
The vocabulary employed by users of Ask.com ranged from unsophisticated to very sophisticated. This suggests that allowing users to employ less-technical language on cancer Web sites would significantly help them find the information they seek.
The queries captured for this study undoubtedly reflect the news and research studies in the public arena during the time period from June to August 2001. A different time period would certainly reflect a different distribution. Examples of the kinds of events that could affect the results include the diagnosis or death of a celebrity with cancer, the publication of a major trial about bone marrow transplantation for breast cancer, or the Food and Drug Administration approval of an important new drug.
The presence of a search engine with natural language processing on a Web site, while potentially valuable to users, does not obviate the need for good user-centered Web site design and information architecture [36]. It has been shown that searching via search engine can be minimized and user satisfaction maximized if information architecture and link titles follow appropriate guidelines [37]. Nevertheless, for less-sophisticated users, a natural-language-processing search engine can be helpful in finding the information users seek and provide enhanced success in searching.
An October 30, 2003 search of the PubMed Web site [38] of the National Library of Medicine [39] yielded 458 search results from a query for "Natural Language Processing." Most citations were from publications within the last 3 years, attesting to the currency of natural language processing as an important research topic cutting across a wide variety of research disciplines. Potential data-mining applications of this tool in medicine extend far beyond the use described in this paper.
Eysenbach and Kohler have recently developed a novel methodology, similar to the method used in this study, to estimate the actual volume and prevalence of health-related searches on the Web in relation to the total number of searches conducted daily on the Internet [40]. They collected queries from 2 search engines, Metacrawler (a search engine of search engines) [41] and Ask.com [29] (the same natural-language-processing search engine used for this report). These 2 search engines were selected because they allowed "peeking" at actual user search-query topics. They concluded that 4.5% of all searches on the Web might be health related. The queries were collected from Metacrawler between February 2001 and April 2002, and from Ask.com between February 2001 and April 2001. The first date range overlapped our study dates and the second occurred just before data collection for our study.
In summary, natural-language-processing tools such as the one used for this study are able to filter and subset raw query data into useful analysis categories. Retrieval and analysis of these data can be used to better understand the actual content users want and the level of understanding and sophistication they have when they come to the Web site. Using the information on a continuing basis can form the basis for updating content on Web sites based on the most-current user needs. If a natural-language search engine were offered on a health-information portal, for example, it could improve customer access to desired information, particularly for those users with less sophistication about content or language. Additional analyses of query results are planned for the future. Consideration has been given to piloting the use of natural language processing on subsites of our Web portal.

cancer
internet
search engines
natural language processing


Pediatric delirium in critical illness: phenomenology, clinical correlates and treatment response in 40 cases in the pediatric intensive care unit

Objective To study the phenomenology, clinical correlates, and response to treatment of delirium in critically ill children in the pediatric intensive care unit (PICU).
Introduction
Delirium is a neuropsychiatric disorder secondary to a general medical condition, and must be considered a serious complication of the underlying disease or its treatment. In the revised fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR®), delirium is defined by four concurrent diagnostic criteria: (1) acute onset and fluctuation of (2) a disturbance of consciousness with reduced ability to focus, shift or maintain attention and (3) a change of cognition with memory deficit, disorientation, language disturbance, perceptual disturbances or hallucinations, (4) caused by the direct physiological consequences of a general medical condition [1]. It is frequently seen in critically ill adult and geriatric patients [2–5] and is associated with a poor prognosis, reflected by longer hospital stay, worse functional and cognitive outcome, and a higher mortality rate after discharge from hospital [3]. In mechanically ventilated critically ill adults, delirium is an independent predictor of elevated 6-month mortality and a longer hospital stay [6]. If appropriate diagnostic tools validated for bedside use by non-psychiatrists [e.g. Delirium Rating Scale (DRS), Confusion Assessment Instrument for the Intensive Care Unit (CAM-ICU)] are used, delirium is diagnosed in over 80% of critically ill adult patients [7]. Thus, systematic monitoring for delirium and appropriate treatment with haloperidol in critically ill adult patients were included in the recently published clinical practice guidelines for sedatives and analgesia of the Society of Critical Care Medicine [8]. However, the optimal management of patients with delirium and the effects of the pharmacological treatment on the outcome are still key concerns for today [9]. Given lack of age-appropriate diagnostic criteria and assessment tools in children, even less is known about the incidence, clinical presentation, response to treatment and consequences of childhood delirium in general, and in critically ill children in particular [10–12]. The few available published data on childhood delirium suggest that morbidity and mortality are higher in children with than in children without delirium [13]. Therefore, delirium in children should be considered a serious complication and be treated accordingly. Unfortunately, while there are comprehensive guidelines on the diagnosis and treatment of delirium in adults, clinical guidelines for delirium in children are nonexistent.
The aim of this study was to investigate the incidence, patient and population characteristics, clinical presentation and response to treatment of delirium in a cohort of critically ill children admitted to a tertiary pediatric intensive care unit (PICU). Given the necessarily multidisciplinary approach to assessment and treatment of these children, the input of four disciplines – child psychiatry, pediatric intensive care medicine, child neurology and adult neuropsychiatry – was used.
Methods
Design, setting and patients
A descriptive study was carried out over a 4-year period (January 2002 to December 2005) in an eight-bed tertiary PICU. This PICU is a tertiary referral center for both general and surgically critically ill children in the southeastern region of the Netherlands (population 1.4 million, 350 annual admissions). Critically ill children, acutely, non-electively and consecutively admitted, were prospectively sampled. Both mechanically ventilated and non-ventilated patients were included.
Diagnostic approach
All children with (1) confusion, agitation, anxiety, moaning, discomfort, or behavioral disturbances with no acceptable medical explanation or (2) failure of standard analgosedative treatment were systematically assessed for the presence of delirium in a two-step diagnostic approach. The standard analgosedative treatment can be summarized as follows: children who required analgosedation because of obvious or expected pain or because of stress related to their underlying disease or treatment received adequate doses of opioids and/or benzodiazepines according to internationally published guidelines for analgesia and sedation in critically ill children [14]. Drug doses were individually tailored to achieve optimal patient comfort and were slowly reduced in order to avoid a withdrawal syndrome. In the event that a withdrawal syndrome was suspected, based on clinical observation or the revised Finnegan score, specific treatment with long-acting benzodiazepines (e.g. lorazepam) or opioids (e.g. methadone) was started according to internationally published guidelines [15, 16].
At the time the assessment for delirium was initiated, none of the patients had signs of imminent life-threatening respiratory, circulatory or neurological failure, while ongoing asphyxia, respiratory acidosis, metabolic disturbances, fighting the ventilator due to inappropriate ventilator settings and withdrawal syndrome all had been excluded systematically as an explanation for the observed behavior.
The first step of the diagnostic approach was a systematic assessment by a child neuropsychiatrist (J.S.) using DSM-IV criteria for delirium. Criteria were evaluated on the basis of (1) hetero-anamnestic information from parents, nurses, intensivists, and child neurologists about behavior and behavioral changes and (2) child psychiatric examination. Based on the findings, patients were categorized as having a (probable) delirium or not. In a second step, the provisional diagnosis of delirium was further tested in a daily multidisciplinary consensus meeting. The team consisted of the child neuropsychiatrist, the attending pediatric intensivist, and occasionally a geriatric neuropsychiatrist specialized in delirium in geriatric patients and/or a child neurologist. If this team agreed that alternative explanations for a child's behavior were unlikely, the consensus diagnosis was delirium.
Based on the dominant clinical presentation, cases of delirium were classified as “hyperactive” when psychomotor agitation was present, and as “hypoactive” when retardation and/or inhibition was present. A number of children presented with cognitive and/or attentional disturbances in the context of severe anxiety states, often accompanied with moaning and restlessness, but without clear agitation or retardation. This latter group was classified as “emerging” or “veiled” delirium [17], in reference to the way delirious syndromes have been described as “partial delirium” in adult ICU patients or as “subsyndromal delirium” in elderly medical patients [7, 18, 19]. The different presentation forms were not always clear-cut, and some fluctuated dramatically over time. The severity of illness was scored according to the Pediatric Index of Mortality (PIM) and Pediatric Risk of Mortality (PRISM) [20].
Therapeutic approach
Whenever delirium was identified or suspected, a two-track treatment approach consisting of both psychosocial and pharmacological interventions was implemented. Psychosocial interventions – the parents' presence and comforting throughout the day (and night), familiar music, favorite toys, pictures of home and pets, friends, school, lighting schedules, sometimes even fragrances – are standard in the PICU. The parents also received an information leaflet on childhood delirium [21]. All patients were also treated with antipsychotic medication after the referring pediatric intensivist had agreed and the parents, because of the off-label use, had given informed consent, which was never refused. In children with psychomotor agitation that was acutely threatening to their health status, haloperidol at a loading dosage of 0.15–0.25 mg i.v. was used, given slowly over a period of 30–45 min, followed by a maintenance dose of 0.05–0.5 mg/kg/24 h i.v. [11, 22, 23]. In less acute situations, and when oral medication was possible, risperidone at a loading dose of 0.1–0.2 mg p.o. was used, followed by a maintenance dose of 0.2–2.0 mg/24 h p.o. as the treatment of choice. Clinical response and side effects were recorded by the child neuropsychiatrist and the pediatric intensivists. In order to tailor the treatment for delirium, daily discussions were held with the multidisciplinary team. Adjustment of treatment was based on the clinical observations and judgements of the parents, nurses, intensivists and child psychiatrist.
Children were followed up for 6 weeks after discharge from the hospital either at the outpatient clinic or by contacting the parents by telephone.
As the study solely involved the structured recording of routine clinical practice, under Dutch law no institutional review board approval was required.
Results
From January 2002 to December 2005, there were 877 acute, non-elective admissions to the PICU. Distribution of age and gender are shown in Table 1. In 61 cases (7%), a systematic assessment by a child neuropsychiatrist was requested, usually for agitation, anxiety, moaning, discomfort, behavioral disturbance or problematic analgosedation.
Table 1Number and incidence of delirium in the total sample* by age and genderAgeTotal sample*Patients with deliriumIncidence (%)0–2.99 years51314 2.7Male310 9 2.9Female203 5 2.53–5.99 years106 4 3.8Male 56 3 5.4Female 50 1 2.06–8.99 years 80 6 7.5Male 46 1 2.2Female 34 514.79–11.99 years 77 3 3.9Male 61 3 4.9Female 16 0 012–14.99 years 70 710Male 35 514.3Female 35 2 5.715–18 years 31 619.4Male 13 430.8Female 18 211.1Total87740 4.6Male52125 4.8Female35615 4.2*Critically ill children, acutely, non-electively and consecutively admitted
during a 4-year period
Of these 61 patients, 40 (61%) were diagnosed with delirium, yielding a cumulative incidence of 5% (boys 5%; girls 4%). Age-specific incidences increased from 3% in the 0–3 years age group (boys 3%; girls 3%), to 19% in the 16–18 years age group (boys 31%; girls 11%) (Table 1, Fig. 1).
Fig. 1Incidence of delirium in the sub-groups by age and gender
The child psychiatric disorders diagnosed in the 61 referrals are summarized in Table 2.
Table 2Child psychiatric diagnosis at the first consultation (n = 61)nDiagnosis40delirium 5adjustment disorders with anxiety and depressed mood, post operative 4psychological–psychiatric factors affecting the medical condition 3anxiety disorder 3emotional and behavioral problems during chronic ventilation 2adjustment disorders with depressed mood 1mood disorder 1adjustment disorder with anxiety 1sleeping problem 1feeding problem
Table 3 summarizes the population characteristics of the sample diagnosed with delirium, while Table 4 lists the underlying somatic disease or causative pharmacological treatment.
Table 3Population characteristics of the 40 PICU cases with delirium, 2002–2005CharacteristicsFrequency (total n = 40)Age (mean ± SD) 7.6 ± 5.9Malen25 age (mean ± SD) 7.6 ± 6.1Femalen15 age (mean ± SD) 7.6 ± 5.8Ethnicity White36 (90%) African 3 (7.5%) Asian 1 (2.5%)Mechanical ventilation34 (85%)PIM score (mean ± SD)9.96 ± 16.20PRISM score (mean ± SD)23.54 ± 24.94Major somatic pharmacological features Recent increase or decrease of analgosedative medication22 (55%) Neurological disorders21 (52%) Infectious disorders20 (50%) Respiratory disorders12 (30%)PIM, Pediatric Index of Mortality; PRISM, Pediatric Risk of MortalityTable 4Patient characteristics of the 40 PICU cases with delirium 2002–2005No.SexAgePrimary diagnosis on admission PICUMechanical ventilationDelirium typeTreatment 1M 3 monthsMultiple congenital malformations+EmergingHaloperidol 2F 4 monthsMeningococcal septic shock+EmergingRisperidone 3M 4.5 monthsSevere CLD+EmergingRisperidone 4F10 monthsNear drowning+EmergingHaloperidol 5F 1 yearPneumonia+EmergingHaloperidol 6M 1 yearSepsis due to perforated appendicitis+EmergingHaloperidol 7M 1 yearSubarachnoid bleeding+EmergingHaloperidol 8M 1 yearPericardial effusion with pretamponade+HyperactiveHaloperidol 9M 1 yearMultiple dysmorphia, upper airway obstruction–EmergingHaloperidol10F 2 yearsMeningococcal septic shock+HyperactiveHaloperidol11M 2 yearsADEM+HyperactiveRisperidone12M 2 yearsCervical mass, upper airway obstruction+EmergingHaloperidol13M 2 yearsAspiration and pneumothorax+Hypoactive–14F 2 yearsMeningococcal meningitis with sepsis and DIC+HyperactiveHaloperidol15F 3 yearsCystic fibrosis and pneumonia+HypoactiveHaloperidol16M 4 yearsIntracerebral hemorrhage, Marfan syndrome+HypoactiveHaloperidol17M 5 yearsMedulloblastoma post surgery+EmergingRisperidone18M 5 yearsUpper respiratory tract infection+HyperactiveHaloperidol19M 6 yearsMultiple trauma+EmergingRisperidone20F 8 yearsMeningo-encephalitis+HyperactiveRisperidone21F 8 yearsViral encephalitis+Hyperactive–22F 9 yearsStatus asthmaticus+HyperactiveRisperidone23F 9 yearsTBI, gunshot wound+HypoactiveRisperidone24M 9 yearsStatus asthmaticus+HyperactiveHaloperidol25M 9 yearsNeural tube defect and drain dysfunction–Emergingfirst Haloperidol, then Risperidone26M11 yearsHypovolemic shock, typhus abdominalis–HyperactiveHaloperidol27F12 yearsTBI+HypoactiveHaloperidol28M12 yearsTBI and fracture of lower leg+EmergingHaloperidol29M13 yearsSepsis, paronychia–HyperactiveHaloperidol30M13 yearsStatus epilepticus+EmergingHaloperidol31F14 yearsTBI+HyperactiveHaloperidol32F15 yearsPost TBI+HypoactiveHaloperidol33M15 yearsPostoperative state+HypoactiveRisperidone34M15 yearsAcute lymphoblastic leukemia–HypoactiveHaloperidol35M15 yearsTBI+EmergingHaloperidol36F15 yearsStatus asthmaticus+HyperactiveHaloperidol37M16 yearsMultiple trauma–HypoactiveHaloperidol38F16 yearsBacterial meningitis+HyperactiveRisperidone39M16 yearsRespiratory failure, Duchenne disease+EmergingHaloperidol40M17 yearsSeptic shock+EmergingHaloperidolCLD, chronic lung disease; ADEM, acute disseminated encephalomyelitis; DIC, diffuse intravascular coagulation; TBI, traumatic brain injury
The underlying features were: recent increase or decrease in analgosedative medication (n = 22), neurological disorders (n = 21), infections (n = 20) and respiratory disorders (n = 12). Usually, a combination of these existed.
All but two patients were treated with an antipsychotic drug. Twenty-seven children were given haloperidol, 10 risperidone, and 1 child received both drugs in succession. In most cases, the beneficial results were observed rapidly, especially in the hyperactive forms, sometimes even after a single dose [11]. Sometimes it took a few hours or days. Two patients experienced acute dystonia as a likely side effect of the haloperidol, responding well to biperidene. Two children received no medication: one because of lack of consensus in our expert team, and one because of an endotracheal intubation at the time that medication was indicated. In most cases, the medication was stopped or tapered off successfully during hospitalization or afterwards in an outpatient setting. Five children (12.5%) with delirium died of their underlying disease; the mean PIM was 10% and the mean PRISM 24% (Table 3).
Discussion
This is the first systematic multidisciplinary study of the phenomenology and treatment of delirium in 40 critically ill children in a PICU context. The low cumulative incidence of 5% is mainly the result of the low incidence in the younger age groups (< 9 years old), this segment constituting the majority of the total sample (80%). A clearly higher incidence is seen in the older age groups. However, in critically ill adult patients substantially higher incidences have been reported, ranging from 10–30% in general hospital settings to 50% in postoperative patients and up to 80% in the terminally ill [2].
There are several possible explanations for this difference. First, the incidence of delirium in young critically ill children may be truly low: differences in age-related resilience and underlying conditions may contribute to true differences in the incidence between the very young and the very old. However this explanation seems unlikely, given the tendency in the very young to develop delirium under even much less severe circumstances [1, 11, 24]. A second explanation may relate to the fact that extensive psychosocial interventions are provided as a routine in Dutch PICU settings, with a possibly preventive effect on delirium in much the same way as observed in geriatric patients [25]. A third factor may be an anti-delirium effect of the routinely used analgosedative medication, although especially benzodiazepines may have excitatory side effects in children. A fourth factor may relate to reluctance on the part of the intensivists and/or child neurologists to request psychiatric evaluation in the case of behavioral changes for fear of adding stigmatization to an already burdened system. Perhaps the fifth, most likely and important explanation is that parents, nurses, pediatric intensivists and child neurologists do not easily recognize delirium, because the medical condition of these critically ill PICU children is so complex and changeable. It is possible that a psychiatric consultation was readily requested only in cases of anxiety and/or hyperactive delirium, not in the hypoactive and or veiled ones.
The differential diagnosis of pediatric delirium consists of acute stress reactions, acute anxiety states, adjustment disorders with mixed emotions, dissociative and/or regressive states and childhood-onset psychosis (see also Table 2). However, differentiating delirium from extreme stress and agitation due to acute and life-threatening conditions is not only impossible, it is also unwanted, because it is usually irrelevant at that point of time in the process of medical care. Causative treatment, if possible, is always the first step to be taken. Furthermore, the diagnosis of delirium in children is complicated by the fact that the criteria for adult delirium are not always easily applicable to children because of important differences in age and developmental levels. The DSM-IV criteria for delirium are not always useful in the case of pediatric delirium, especially in a PICU context. This is a source of concern. Moreover, delirium is not mentioned in the DSM-IV section on child psychiatry. DSM-IV describes as an essential feature of delirium the “disturbance of consciousness” leading to “impairment of the ability to focus, shift and sustain attention”. This, however, is of little relevance in the critically ill in a PICU context, where disturbance of attention is routinely present due to the disorder(s) itself. In fact, attention is often the first “to go” [17, 26]. In addition, patients almost always require treatment with opioids and benzodiazepines, which also have a strong impact on attention. Furthermore, it has been hypothesized that a disturbance of consciousness is not a discriminating characteristic of delirium in an ICU setting [27].
Our case series suggests that in addition to the hyper- and hypoactive forms of delirium, a third form may be characterized by anxiety, moaning, and/or restlessness. This was referred to as an “emerging” or veiled delirium, as described previously [17]. In the PICU population this state did not develop into a frank hyper- or hypoactive form, but appeared to exist in its own right, accompanied by disturbances of consciousness and cognition.
Although atypical presentations of disorders are often not captured in classification systems, the high prevalence of “emerging” delirium in our sample (17/40) stresses the importance of further phenomenological study. Adhering too strongly to DSM-IV criteria for adult delirium, for clinical use in a PICU context, may result in persistent under-diagnosis. On the other hand, the incidence of delirium may be overestimated by using the CAM-ICU, so most important is the issue of what constitutes delirium in critical illness [28].
There are accumulating indications that pediatric delirium can be subtle and accompanied or even dominated by other neuropsychiatric signs such as: reduction of awareness of the caregiver and/or the surrounding environment, purposeless actions, restlessness, inconsolability, signs of autonomic dysregulation and other subtle higher cortical dysfunctions [29–32]. Parents and nurses have a way of discriminating patterns in their children that may be diagnostically important. Thus, parents sometimes state: “This is not my child anymore.” Pediatric delirium therefore may have various subtle presentations and can be considered a spectrum disorder, which makes it sometimes difficult to diagnose [33]. Neither haloperidol nor risperidone is registered for the treatment of childhood delirium, although both are mentioned as the treatment of choice for adults [1]. Moreover, haloperidol is not registered for i.v. administration, even though it is used in this way in many places. We prefer risperidone in non-acute situations because of the theoretically lower risk of extrapyramidal side effects. Haloperidol and risperidone have been used for other indications in young children as well, such as childhood psychosis [34] or aggression in autism [35]. There are two limitations regarding our observations of treatment response. First, no severity scale for pediatric delirium was used, because none exists for this PICU population. Second, because no studies on the natural course of childhood delirium exist that have established the rate of spontaneous remission, spontaneous remissions may have been misclassified as response to treatment. In our opinion, however, the time frame of response points towards a medication effect.
Most medications employed in pediatrics and child psychiatry are used off-label [36], which means that special attention should be paid to information and informed consent procedures. Given the relatively high incidence of extrapyramidal symptoms with haloperidol, the “off-label” use needs further study [37].
This study has several limitations. First, it was a study of routine clinical practice. Observations were based on referrals emerging from care as usual. Although the focus on delirium may have altered referral paths and rates, we did not actively advocate any change, nor did we screen all admissions for delirium. In the absence of a hard clinical indication, no routine blood level measurements were performed to rule out persistent high levels of sedatives as a possible explanation for any neuropsychiatric symptom. Next, the lack of DSM-IV criteria for pediatric delirium and the disputed relevance of its main criterion in a (P)ICU setting make a standardized diagnosis difficult. Finally, treatment was provided in an open setting and based on consensus, especially among child psychiatrist, pediatric intensivist and child neurologist.
In conclusion, critically ill children in a PICU can develop delirium, with a hyperactive, hypoactive or veiled presentation, despite adequate analgosedation and intensive psychosocial interventions. The approach using an algorithmic structuring and an intensifying of daily clinical care, including the use of multidisciplinary daily consensus meetings, appeared effective in assessing, diagnosing and treating childhood delirium at the PICU. The findings suggest that the incidence is much lower than in adults, but a likely explanation is that delirious states requiring child psychiatric referral are still frequently under-diagnosed. There is also still a great need for developing delirium criteria in critically ill patients, children and adults alike, in a (P)ICU setting. Treatment with haloperidol or risperidone was successful in all patients. Future research is necessary to identify the risk factors for pediatric delirium in a multivariate prospective approach, to develop “easy to use” bedside tools for non-psychiatric trained team members for the early detection of delirium in all pediatric PICU patients, and to study the effects of interventions in a double-blind and ideally placebo-controlled fashion.
Disclosure
Jim van Os is a speaker or member of the advisory board for Lilly, BMS, Janssen-Cilag and AstraZeneca. He received grant or research support from Lilly, GSK, BMS and AstraZeneca. Albert Leentjens participated in research for Boehringer Ingelheim and is on the advisory board of a study related to Parkinson's disease by the same company. The other authors have no financial relationships to disclose.

delirium
critically ill children
pediatric intensive care unit (picu)
haloperidol
consensus meetings
risperidone


Impact of the population at risk of diabetes on projections of diabetes burden in the United States: an epidemic on the way

Aims/hypothesis The aim of this study was to make projections of the future diabetes burden for the adult US population based in part on the prevalence of individuals at high risk of developing diabetes.
Introduction
Considerable evidence has been presented on the rise in diabetes prevalence in the United States and the United Kingdom [1, 2]. The prevalence of diabetes has become so large that it has been termed an epidemic [1, 3]. This rise is particularly important for healthcare needs in the US because almost 30% of individuals with diabetes are currently undiagnosed and diabetes is disproportionately represented in minority populations [4].
Projection of future disease prevalence helps to plan for healthcare needs. An understanding of the population at risk of developing the disease is critical when projecting future disease burden. Several studies have projected future diagnosed diabetes prevalence for the US and other countries [2, 5–9]. These studies, however, did not consider that not all individuals are equally at risk of developing diabetes, thereby possibly distorting estimates of downstream prevalence. For example, some risk factors for diabetes, e.g. obesity, have increased substantially in the population [10–12]. Moreover, many of these projections are based on estimates of diagnosed diabetes and exclude estimates of total diabetes (diagnosed and undiagnosed), which may lead to serious underestimation of the diabetes burden in the population.
Major risk factors for diabetes have been identified and are currently used by the American Diabetes Association to guide screening strategies. Although there are various measures for assessing the risk of having undiagnosed diabetes [13–17], few measures are available for assessing the risk of developing diabetes [18, 19]. Moreover, accounting for changes in the proportion of high-risk individuals, particularly as assessed through clinical indicators, has not been incorporated into previous projections of future diabetes burden.
The purpose of this study was to project the prevalence of diabetes for the adult US population up to 2031, using models based on data contained in the nationally representative National Health and Nutrition Examination Survey (NHANES) II mortality survey (1976–1992), NHANES III (1988–1994) and NHANES 1999–2002.
Materials and methods
Diabetes prevalence model
The model for diabetes prevalence used in this study was created using data from the NHANES III (1988–1994), and then fitted to data from the NHANES 1999–2002 as a validity check of the accuracy of the model’s projections. The resulting model was then used to project the number of individuals with diabetes in the US in 10-year increments into the future. We evaluated 10-year age classes at each 10-year interval. Our model has the following components:
Number of individuals with diabetesTime 2 = ∑ (number of individuals with diabetesTime 1i + incident casesi − mortalityi), where i equals each 10-year age group, and incident cases consist of: (1) persons converting from a disease-free state to having diabetes; (2) diabetic patients immigrating to the United States; and (3) persons with diabetes moving into the 20 to 29-year-old age class.The percentage of persons with diabetes, which is calculated thus:percentage diabetesTime 2 = (number of individuals with diabetesTime 2 / total populationTime 2) × 100.The estimate of future diabetes is therefore based on this equation, including the number of individuals with diabetes in the previous time period, conversion to diabetes, migration, and mortality, rather than being a linear extrapolation of the change in diabetes prevalence from the known values of 1991 and 2001.
Data sets
The NHANES is a programme of surveys conducted by the National Center for Health Statistics and designed to assess the health and nutritional status of adults and children in the United States. The survey is unique on a national level in that it combines interviews and physical examinations. The NHANES uses a complex multistage sampling design, making it representative of the non-institutionalised US population and allowing weighted estimates to be computed.
For this study we used several of the NHANES data sets. Specifically, we used the NHANES III (1988–1994) (unweighted n = 4,950) and the NHANES 1999–2002 (unweighted n = 3,804) to estimate among individuals of 20 years of age and older the prevalence of diagnosed diabetes, the total diabetes burden (diagnosed and undiagnosed diabetes), and the proportion of the population at risk of developing diabetes. Since mortality within the population affects future prevalence [20], we also used the cohort from the NHANES II mortality survey (1976–1992) (unweighted n = 3,916) to provide estimates of diabetes mortality. Computation of all analyses using the NHANES data sets to provide nationally representative estimates for the models was designed to account for the complex survey design and the appropriate sample weights. All analyses were conducted using SUDAAN software (Research Triangle Institute, Research Triangle Park, NC, USA).
Variables used in models
Prevalence of diabetes
Diabetes burden was assessed as diagnosed diabetes plus undiagnosed diabetes. Because of the substantial proportion of people with undetected diabetes, we focused on this formula for total diabetes, rather than using diagnosed diabetes to indicate diabetes burden in the population. Moreover, by focusing on diabetes as diagnosed and undiagnosed disease, we minimised the possible impact on future diabetes prevalence of changes in screening practices for diagnosing diabetes during an ensuing time period.
Diagnosed diabetes was assessed as individuals who answered yes to a question of whether a doctor had told them they had diabetes. Undiagnosed diabetes was estimated on the basis of individuals who said they had not had a previous diagnosis of diabetes, but who had fasting plasma glucose (FPG)  > 7.0 mmol/l. Although, the diagnostic criteria for diabetes during the time between the NHANES II and the NHANES III changed from FPG  > 7.8 mmol/l to FPG  > 7.0 mmol/l, we used the newer criteria to gain an awareness of the total diabetes burden at each point in time using the same criteria [21].
Persons converting from a disease-free state to having diabetes
Although a variety of diabetes risk scores exist, most have been created from cross-sectional studies and have as their aim the identification of individuals with undiagnosed diabetes. Their ability, therefore, to make predictions on development of diabetes is unknown [13, 14, 16]. The risk score used in this study is based on one developed for the Atherosclerosis Risk in Communities (ARIC) cohort study [18]. Among individuals without diagnosed diabetes or FPG  > 7.0 mmol/l, we used a scoring strategy which includes: high waist circumference (>102 cm in men, >88 cm for women), raised blood pressure (>130/85 mmHg or antihypertensive medications), low HDL-cholesterol (<1.03 mmol/l for men, <1.29 mmol/l for women), high triacylglycerol (>1.7 mmol/l), BMI > 30 kg/m2, and hyperglycaemia. Each of the characteristics is worth 1 point except for hyperglycaemia, which can be worth 2 points if FPG is > 5.6 mmol/l or 5 points when FPG is >6.1 mmol/l. A score of >4 puts an individual at high risk of developing diabetes, whether diagnosed or undiagnosed. A score of <4 indicates that a person has a low risk of developing diabetes.
This particular risk score was chosen for several reasons. First, it has moderate sensitivity (68%) and specificity (75%). Second, it is computed in a reasonably straightforward manner without having to use coefficients from the ARIC cohort that may be specific to that cohort. Third, data and results provided in the study by Schmidt et al. [18] allowed for computation of the rate of development of diabetes in both the high-risk group and the low-risk group. The ratio of development of diabetes in the high-risk group versus the low-risk group was 4.5:1. Variables needed to compute this diabetes risk score are available only in the NHANES III and the NHANES 1999–2002.
Although the ARIC diabetes risk score did not specifically consider race or age in the computation [18], we computed conversion rates for 10-year age classes for three race/ethnic groups (non-Hispanic Whites, non-Hispanic Blacks and Hispanic individuals) by fitting age categories for the data from 1991 to 2001 and then fitting race/ethnicity on to the same time change. We did not compute specific sex-specific conversion rates because sex was already differentiated in several of the variables in the ARIC diabetes risk score [18].
Migration of persons with diabetes
Migration of individuals with or without diabetes into the population can also affect future diabetes prevalence. Recent projections have included migration within their models [5]. Because we are looking at changes in diabetes prevalence among adults, migration of adults, particularly from ethnic minorities, could substantially affect the 10-year projections. We used data from the NHANES III to estimate migration of persons with diabetes in the 20 years and older age groups. The NHANES III measured how many years foreign-born immigrants had been in the US. Thus, we estimated the number of foreign-born individuals who had been in the country for 9 years or less for the total population as well as for different racial/ethnic groups. The NHANES III data allowed us to make estimates for non-Hispanic Whites, non-Hispanic Blacks and Hispanic individuals.
Persons with diabetes moving into the 20 to 29-year-old age class
For 2011, 2021 and 2031 the total number of persons with diabetes in the 20 to 29-year-old age class was estimated using a linear projection of the NHANES III and NHANES 1999–2002 data. The proportion of 20 to 29-year-olds with diabetes in each race/ethnic group was held constant at the proportions found in the NHANES 1999–2002 data at the later time intervals.
Mortality among individuals with diabetes
Diabetes mortality for the total population was based on data from the NHANES II mortality survey (1976–1992). This population-based cohort study was used to provide estimates of diabetes mortality, since mortality within the population affects future prevalence [20]. Diabetes mortality was estimated as all-cause mortality among individuals with diabetes (either diagnosed or undiagnosed) at baseline, rather than as mortality with diabetes listed as the cause of death. This definition is more consistent with the potential impact of diabetes on future prevalence. Mortality estimates were computed separately for the total population by age classes.
The NHANES II mortality cohort is based on a sample of individuals aged 30 to 75, whereas we made diabetes estimates on individuals aged 20 years and older. Consequently, we assumed no deaths due to diabetes in the 20 to 29-year-old age group over the 10-year period.
Population estimates
Total population of 10-year age classes was estimated using data from NHANES III for 1991, NHANES 1999–2002 for 2001, and US Census Bureau, Middle Series projections for 2011, 2021 and 2031 [22]. Total population of race/ethnic groups was also determined by 10-year age classes using the same sources of information.
Analysis
In an effort to provide an estimate of future trends in diabetes and the population at high risk of developing diabetes, we employed the following procedure. We used the NHANES III data to fit a model to predict total diabetes in the NHANES 1999–2002. We used this strategy prior to making future projections, because it allowed us to develop and fit the model to an existing national estimate of diabetes prevalence. Because both the NHANES III and the NHANES 1999–2002 are based on multi-year data collection, we estimated a mid-point of 1991 and 2001 for the two surveys.
The number of persons with diabetes 10 years post-baseline was calculated for 10-year age classes by first adding baseline prevalence and incidence (the number of low-risk and number of high-risk persons who developed diabetes over the 10-year interval), then adding persons with diabetes who immigrated to the United States, and persons with diabetes who moved into the 20 to 29-year-old age class, and finally subtracting the number of diabetic subjects who died. Percentage of persons with diabetes was estimated for each time period by taking the total number of persons with diabetes and dividing by the expected total population, then multiplying by 100.
Varying model assumptions
Our initial predictions of future diabetes burden were based on the assumption of a constant proportion of individuals at high risk of diabetes at the levels present in the NHANES 1999–2002. To account for potential changes in the proportion of persons at high risk of diabetes, we also evaluated increases in the proportion of persons at high risk by 10, 20 and 30%, as well as estimates based on decreases in the proportion of persons at high risk by 10, 20 and 30%. Theoretically, it is unlikely that the proportion of persons at high risk will remain stable, because from NHANES III to NHANES 1999–2002 the proportion at high risk was seen to increase. Also, a major risk factor for diabetes, obesity, has increased substantially over a 40-year time period [10, 12]. We evaluated the effect of decreasing proportions at high risk, to account for the possibility that interventions to improve lifestyle of adults in the US may be effective.
In addition, to address the potential impact on mortality of healthcare interventions in management of diabetes, we examined potential reductions of 10, 20 and 30% in mortality among individuals with diabetes. Finally, we computed a model examining a combination of effects, assuming that lifestyle interventions would yield a 10% decrease of persons at high risk and healthcare interventions would yield a 10% decrease in mortality of persons with diabetes.
Results
Table 1 shows estimates of the total diabetes burden from the NHANES III and the NHANES 1999–2002 and the future 10-year projections for 2011 through to 2031. The number of individuals at high risk of diabetes based on the multivariable diabetes risk score was 38.4 million in 1991 and 49.9 million in 2001. Using our model to predict the known diabetes prevalence in 2001 from the 1991 data, results were satisfactory and within 0.2% of the actual population prevalence of total diabetes. If the proportion of individuals at high risk within the adult population remains stable at 2001 levels, we could expect 55.8 million in 2011, 60.9 million in 2021, and 66.1 million in 2031. As can be seen, the prevalence of diabetes is projected to increase. The diabetes prevalence of 6.3% in 1991 and 8.8% in 2001 is projected to increase to 14.5% in 2031 with 37.7 million adults having diagnosed or undiagnosed diabetes. Assuming stability in the population proportion of individuals at high risk of developing diabetes, the rate of increase in the number of individuals with diabetes and the proportion with diabetes tends to slow over time. Among individuals aged 30 to 39 years who are not currently targeted for screening according to age, the prevalence of diabetes is expected to rise from 3.7% in 2001 to 5.2% in 2031.
Table 1Number of people (in millions of persons) with and prevalence of diabetes by year and age categoryAge (in years)NHANES IIINHANES 1999–200220112021203120–29Number0.20.40.50.70.9Percentage0.51.01.31.72.030–39Number0.61.61.82.12.3Percentage1.43.74.75.05.240–49Number1.53.54.34.34.9Percentage4.58.110.311.111.250–59Number2.53.96.67.26.9Percentage11.412.215.617.718.260–69Number3.14.16.59.810.2Percentage15.319.822.025.126.6>70Number3.24.15.78.412.4Percentage16.217.420.522.623.9TotalNumber11.117.525.432.637.7Percentage6.38.811.513.514.5NHANES National Health and Nutrition Examination Survey
The results shown in Electronic supplementary material (ESM) Table 1 show the projected prevalence of diabetes according to different racial/ethnic groups. Non-Hispanic White adults are projected to continue to have a lower prevalence of diabetes than both non-Hispanic Black and Hispanic individuals. By 2031, the Hispanic community will have an overwhelming diabetes burden, with more than 20 percent of the adult population having diabetes.
The projections in ESM Table 2 are based on different assumptions regarding changes in the number of individuals who are at high risk of developing diabetes and changes in mortality among individuals with diabetes. As might be expected, as mortality decreases the prevalence of diabetes increases in the subsequent 10 years. The estimate for 2031 indicates that potential decreases in mortality and a potential decrease in individuals at high risk of developing diabetes yields a prevalence similar to that achieved if the proportion at high risk is kept stable from 2001. All of these estimates indicate a larger diabetes burden among Hispanics.
Discussion
This national projection of diabetes prevalence for the US is the first to model the projection on the number of individuals at high risk of developing diabetes using a multivariable risk assessment. Projections suggest a rising and substantial diabetes burden for the population. Hispanic adults will be most affected, with estimates suggesting that by 2031 more than 20% of the adult Hispanic community will have diabetes. These results are particularly worrisome for this community in light of recent evidence that the gap in healthcare quality between Hispanic and non-Hispanic White individuals has continued to widen [23].
Many previous diabetes projections have been limited to estimates of diagnosed diabetes and thus have lower estimates of projected diabetes burden, and have not incorporated an evaluation of the population at high risk of diabetes, with clinical indicators, into their models [5, 9]. Our estimates will be less likely to be affected by changes in screening strategies. Additionally, they incorporate potential changes in the level of risk for diabetes in the US population, a change which is likely given national trends in obesity [3]. Moreover, recent data have suggested that individuals with undiagnosed diabetes are similar to those with diagnosed diabetes with regard to the development of complications; thus our estimates are more robust in describing the burden of disease in the population [24].
Comparing our projections with those from other studies, we note that an estimate, published in 2006, for diagnosed diabetes in the US among individuals aged 20 to 64 years in 2030 is 16.8 million [25]. Our estimates are based both on diagnosed and undiagnosed diabetes, and our projection of total diabetes among that age group for 2031 is higher, namely 19 million. It is possible that estimates based solely on diagnosed diabetes could become more consistent with our estimates, if greater vigilance were shown for screening for undiagnosed diabetes. However, not accounting for the at-risk population in the estimates is likely to lead to inaccurate estimates. A comparison of our estimates of total diabetes with those of another study [7], which projected total diabetes but did not account for the population at high risk of developing diabetes, reveals that the latter’s projections are most probably underestimates. Using data from 1993, the investigators projected a population prevalence estimate of total diabetes in the US among individuals aged 20 years and older for the year 2000 to be 7.6%, while the NHANES 1999–2002 yielded a prevalence of 8.8%. For 2025 the same team [7] projected a prevalence of 8.9% versus 13.5% for 2021 in our study.
The results have several implications for the delivery of healthcare and healthcare financing.
First, we estimated our models under several assumptions for the number of individuals at high risk of diabetes in the population. Regardless of these assumptions, the US will have a substantial number of individuals at high risk of diabetes in 2011, 2021 and 2031. Interventions to modify lifestyle are critical to decrease the number of individuals at high risk, and consequently to lower the expected increase in diabetes in the future. Although some of the diabetes estimates suggest seemingly small decreases in future prevalence, based on decreases in the population at risk, the actual numbers are substantial. For example, a one-percentage point drop in the US population estimate of diabetes among individuals aged 20 and older in 2031 is quite substantial and would account for a decrease in prevalence of diabetes equivalent to 2,600,000 people.
Second, the projection that a substantial proportion of the population will have diabetes indicates greater spending will be necessary to manage the disease. This will include spending on drugs, ongoing monitoring, and treating of complications including nephropathy, retinopathy, and cardiovascular disease.
Third, the disproportionate impact of diabetes on minorities, particularly Hispanics, demands new intervention strategies to decrease the number of individuals at high risk and to deliver care to individuals who have historically had poor access to care. Additionally, with the projected increase in diabetes prevalence among 30 to 39-year-olds, a population not currently targeted for screening, a re-examination of current public health policy and screening strategies may be warranted [26].
There are several strengths to the design of this study. One is that the study utilised multiple NHANES data sets, which have the advantage of allowing for nationally representative population estimates. Thus, the initial data used to fit the model as well as to make mortality estimates of diabetes, both diagnosed and undiagnosed, are nationally representative. Another strength is that this study is the first to make a nationally representative assessment of the at-risk population for development of diabetes and then use that assessment to model the future prevalence of diabetes. The assessment of risk used, moreover, is based on the ARIC diabetes risk score [18], a multivariable risk score that used clinical indicators.
When interpreting our results, however, several limitations need to be considered. Thus, although this is the first study to use a validated diabetes risk score to assess the high-risk population for the development of diabetes for the entire US population, potential limitations exist with regard to the diabetes risk score. The ARIC diabetes risk score [18] was based on a cohort of individuals aged 45 to 64 years at baseline and may therefore be limited when estimating diabetes development among individuals aged 20 years and older. However, we estimated diabetes prevalence in 10-year age increments. Moreover, the risk score’s moderate sensitivity and specificity may cause the model to under- or potentially overestimate future prevalence projections. Another possible limitation is that estimates of future disease burden are based on assumptions about the number at risk of disease and about mortality within the population. We have attempted to address this limitation by presenting the results of a sensitivity analysis, which includes variations in the proportion of the population at risk and in mortality. The third limitation is the diagnosis of diabetes in the NHANES data on the basis of a single FPG value. This strategy, although common in epidemiological studies, could potentially underestimate the prevalence of diabetes associated with isolated post-challenge hyperglycaemia, which occurs more commonly in women, the elderly, and in lean populations. It could also overestimate diabetes prevalence, because a clinical diagnosis of diabetes in asymptomatic patients requires two abnormal fasting glucose levels.
In summary, a continued focus on effective interventions for lifestyle modifications to decrease diabetes risk, as well as vigilant ascertainment of diabetes, appears crucial if the future prevalence and burden of diabetes in the US population are to be adequately addressed. This is especially important for minority populations, particularly the Hispanic community, which is projected to have an overwhelming future diabetes burden. Considering that minorities have historically had limited access to healthcare, these findings emphasise the importance of interventions targeting these populations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Table 1
Projections of non-Hispanic White, non-Hispanic Black and Hispanic persons with diabetesa and prevalence in 2011, 2021, and 2031 (43 kb)
Table 2
Projections, using different assumptions, of diabetes prevalence among individuals aged >20 years in 2031 (32 kb)