Volume 100, Issue 8 p. 1734-1743
Original Article
Free Access

Community-based multiple screening model§

Design, implementation, and analysis of 42,387 participants Taiwan community-based integrated screening group

Tony Hsiu-Hsi Chen D.D.S., Ph.D.

Corresponding Author

Tony Hsiu-Hsi Chen D.D.S., Ph.D.

Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

Fax: 886-2-23587707

Tony Hsiu-Hsi Chen was responsible for program design, implementation, data collection, and analysis; interpretation of the findings; and wrote the report. Yueh-Hsia Chiu, Dih-Ling Luh, Ming-Fang Yen, Hui-Min Wu, Tao-Hsin Tung, Li-Sheng Chen, Chih-Chung Huang, Ming-Neng Shiu, Yen-Po Yeh, and Chang-Chuan Chan contributed to implementation of the program, data retrieval, management, and analysis; interpretation of findings; and editing of all drafts. Horng-Huei Liou, Chao-Sheng Liao, Hsin-Chih Lai, Chun-Pin Chiang, Hui-Ling Peng, Chuen-Den Tseng, Ming-Shyen Yen, Wei-Chih Hsu, and Chih-Hung Chen contributed to data collection, interpretation of findings, and the revision of article.

Institute of Preventive Medicine, College of Public Health, National Taiwan University, Room 207, 2F, No. 19 Suchow Road, Taipei 100, Taiwan===Search for more papers by this author
Yueh-Hsia Chiu M.Sc.

Yueh-Hsia Chiu M.Sc.

Institute of Health Informatics and Decision Making, National Yang-Ming University, Taipei, Taiwan

Health Bureau of Keelung City, Keelung City, Taiwan

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Dih-Ling Luh Ph.D.

Dih-Ling Luh Ph.D.

Department of Public Health, College of Health Care and Management, Chung Shan Medical University, Taichung City, Taiwan

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Ming-Fang Yen M.Sc.

Ming-Fang Yen M.Sc.

Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

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Hui-Min Wu M.Sc.

Hui-Min Wu M.Sc.

Graduate Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Taiwan

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Li-Sheng Chen M.Sc.

Li-Sheng Chen M.Sc.

Institute of Health Informatics and Decision Making, National Yang-Ming University, Taipei, Taiwan

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Tao-Hsin Tung M.Sc.

Tao-Hsin Tung M.Sc.

Institute of Public Health, National Yang-Ming University, Taipei, Taiwan

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Chih-Chung Huang B.Sc.

Chih-Chung Huang B.Sc.

Department of Social Work, College of Social Science, National Taiwan University, Taipei, Taiwan

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Chang-Chuan Chan Sc.D.

Chang-Chuan Chan Sc.D.

Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan

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Ming-Neng Shiu M.D., Ph.D.

Ming-Neng Shiu M.D., Ph.D.

Health Bureau of Taipei County, Taipei County, Taiwan

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Yen-Po Yeh M.D.

Yen-Po Yeh M.D.

Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

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Horng-Huei Liou M.D., Ph.D.

Horng-Huei Liou M.D., Ph.D.

Department of Pharmacology, College of Medicine, National Taiwan University, Taipei, Taiwan

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Chao-Sheng Liao M.D., M.Sc.

Chao-Sheng Liao M.D., M.Sc.

Department of Gastroenterology, Shin Kong Memorial Hospital, Taipei, Taiwan

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Hsin-Chih Lai Ph.D.

Hsin-Chih Lai Ph.D.

School of Medical Technology, College of Medicine, National Taiwan University, Taipei, Taiwan

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Chun-Pin Chiang D.D.S., D.M.Sc.

Chun-Pin Chiang D.D.S., D.M.Sc.

School of Dentistry, National Taiwan University, Taipei, Taiwan

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Hui-Ling Peng M.D.

Hui-Ling Peng M.D.

Department of Diagnostic Radiology, Shin Kong Memorial Hospital, Taipei, Taiwan

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Chuen-Den Tseng M.D., Ph.D.

Chuen-Den Tseng M.D., Ph.D.

Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan

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Ming-Shyen Yen M.D.

Ming-Shyen Yen M.D.

Department of Obstetrics & Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan

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Wei-Chih Hsu M.D., M.Sc.

Wei-Chih Hsu M.D., M.Sc.

Department of Neurology, Shin Kong Memorial Hospital, Taipei, Taiwan

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Chih-Hung Chen M.D.

Chih-Hung Chen M.D.

Department of Internal Medicine, Chang-Gung Memorial Hospital of Keelung, Keelung City, Taiwan

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First published: 03 March 2004
Citations: 128

See related editorial on pages 000–000, this issue.

Steering committee for design and analysis: Institute of Preventive Medicine (Tony Hsiu-Hsi Chen, D.D.S., Ph.D., Ming-Fang Yen, M.Sc., Chao-Sheng Liao, M.D., Ph.D., and Yen-Po Yeh, M.D.), Graduate Institute of Epidemiology (Hui-Min Wu, M.Sc.), and Institute of Occupational Medicine and Industrial Hygiene (Chang-Chuan Chan, Sc.D.), College of Public Health, National Taiwan University; School of Medical Technology (Hsin-Chih Lai, Ph.D.), School of Dentistry (Chun-Pin Chiang, D.D.S., D.M.Sc.), and Department of Pharmacology (Horng-Huei Liou, M.D., Ph.D.), College of Medicine, National Taiwan University; Department of Social Work (Chih-Chung Huang, B.Sc.), College of Social Science, National Taiwan University; Department of Public Health (Dih-Ling Luh, Ph.D.), College of Health Care and Management, Chung Shan Medical University; Institute of Public Health (Tao-Hsing Tung, M.Sc.) and Institute of Health Informatics and Decision Making (Li-Sheng Chen, M.Sc. and Yueh-Hsia Chiu, M.Sc.), National Yang-Ming University; and Health Bureau of Keelung City (Yueh-Hsia Chiu, M.Sc.).

§

Clinical committee for case referral and care: Department of Internal Medicine (Chuen-Den Tseng, M.D., Ph.D. and M. H. Wang), College of Medicine, National Taiwan University; Department of Gastroenterology (Chao-Sheng Liao, M.D., M.Sc.), Department of Neurology (Wei-Chih Hsu, M.D., M.Sc.), and Department of Diagnostic Radiology (Hui-Ling Peng, M.D.), Shin Kong Memorial Hospital; Department of Obstetrics and Gynecology (Ming-Shyen Yen, M.D.), Taipei Veterans General Hospital; Health Bureau of Keelung City (Yueh-Hsia Chiu, M.Sc. and Ming-Neng Shiu, M.D., Ph.D.); and Department of Internal Medicine, Chang-Gung Memorial Hospital of Keelung (Chih-Hung Chen, M.D.)

Implementation committee: Health Bureau of Keelung City (Yao-Der Chen, Po-En Wang, Chih-Fang Tsou, Ting-Ting Wang, Hsiang-Lan Kuo, Shu-Yuan Hu, Yu-Lan Shih, Hui-Chen Lee, Chun-Liang Wu, Yi-Feng Huang, Hui-Chen Chen, Mo-Sin Chung, and Lin-Lon Tseng) and Shin-Kong Memorial Hospital (Kuan-En Chu and Wei-Hsu Ko).

Fax: 886-2-23587707

† †

Tony Hsiu-Hsi Chen was responsible for program design, implementation, data collection, and analysis; interpretation of the findings; and wrote the report. Yueh-Hsia Chiu, Dih-Ling Luh, Ming-Fang Yen, Hui-Min Wu, Tao-Hsin Tung, Li-Sheng Chen, Chih-Chung Huang, Ming-Neng Shiu, Yen-Po Yeh, and Chang-Chuan Chan contributed to implementation of the program, data retrieval, management, and analysis; interpretation of findings; and editing of all drafts. Horng-Huei Liou, Chao-Sheng Liao, Hsin-Chih Lai, Chun-Pin Chiang, Hui-Ling Peng, Chuen-Den Tseng, Ming-Shyen Yen, Wei-Chih Hsu, and Chih-Hung Chen contributed to data collection, interpretation of findings, and the revision of article.

Abstract

BACKGROUND

Multiple disease screening may have several advantages over single disease screening because of the economics of scale, with the high yield of detecting asymptomatic diseases, the identification of multiple diseases or risk factors simultaneously, the enhancement of the attendance rate, and the efficiency of follow-up.

METHODS

An integrated model of community-based multiple screening was designed and conducted between 1999 and 2001 in Keelung, Taiwan. The authors used a Papanicolaou (Pap) smear screening program as a base to integrate other screening regimens encompassing four other neoplastic diseases and three nonneoplastic chronic diseases. Screening methods, the interscreening interval, and the follow-up for each screening regimen were designed based on evidence-based literature and current national screening policy.

RESULTS

A total of 42,387 subjects participated in the screening activities. A 25% increase in the attendance rate for Pap smear screening was demonstrated after the introduction of multiple disease screening programs. At the first screen, this program yielded a total of 677 asymptomatic neoplasms (16.0 per 1000), including a large proportion of precancerous lesions and small presymptomatic tumors without lymph node involvement. The association between the occurrence of neoplasm and the presence of comorbid nonneoplastic chronic disease was found to be statistically significant (odds ratio, 1.64; 95% confidence interval, 1.38–1.94 [P < 0.05]). The authors also identified 5314 subjects with metabolic syndrome who were at a greater risk for colorectal and oral neoplasias.

CONCLUSIONS

The results of the current study demonstrate that an outreach and community-based multiple screening program not only enhances attendance rates but also has a high yield of early cases of various diseases simultaneously, and provides a natural opportunity to elucidate the correlation between neoplastic disease and nonneoplastic chronic disease. Cancer 2004. © 2004 American Cancer Society.

Screening for cancers and other chronic diseases using available screening tools often is addressed from the aspect of a single disease rather than multiple diseases. Significant mortality reductions due to cancer screening have been demonstrated in earlier studies, including breast cancer screening with mammography,1-3 colorectal cancer screening with fecal occult blood testing (FOBT),4-6 Papanicolaou (Pap) smear screening for cervical neoplasm,7-9 liver cancer screening with sonography in areas with high rates of hepatitis B infection,10 and visual inspection for the early detection of oral premalignancies and oral cancer in areas in which there is highly frequent use of betel quids.11 In addition to cancer, emphasis has been put on the early detection of other chronic conditions, including asymptomatic diabetes mellitus (DM) through the use of fasting blood sugar measurements,12 mild and moderate hypertension,13, 14 and elevated cholesterol,15 combined with health education for metabolic control.16 Although these single disease screening strategies have been demonstrated to significantly reduce morbidity or mortality with varying degrees of evidence, it is worth considering whether these preventive services can be integrated into a multiple screening program. The advantages of a multiple screening program over individualized screening for each disease potentially include the simultaneous ascertainment of two or more asymptomatic diseases, the identification of multiple risk factors associated with metabolic syndrome, the elucidation of the association between neoplastic disease and nonneoplastic chronic disease, the potential enhancement of screening attendance rates, and the reduction of duplicate manpower mobilized to conduct a community-based screening activity.

Inspired by these ideas, a multiple screening program was devised and implemented in Keelung community, the northernmost county of Taiwan. We first assessed whether the attendance rate for Pap smear screening (the only national screening program initiated since 1996 but suffering from low attendance rates) could be enhanced. The yield of asymptomatic cases in the first round of screening also was studied. Comorbidity profiles of nonneoplastic chronic diseases, neoplasia, and metabolic syndrome also were elucidated.

MATERIALS AND METHODS

Population and Study Design

We developed a multiple disease screening in Keelung community, the northernmost county of Taiwan, between January 1, 1999 and December 31, 2001. From the beginning, we expect to apply this model, named the Keelung Community-based Integrated Screening (KCIS), to the target population of 217,884 subjects ages 30–79 years once the current model is successfully implemented.

The selection of diseases in the KCIS program was determined by screening criteria17, 18 and evidence-based literature.19, 20 In addition to these criteria, the KCIS project also built on the current nationwide policy of Pap smear screening for cervical cancer, the most frequently diagnosed cancer in Taiwan, as of 1996. We therefore used the Pap smear screening program as a basis to integrate other disease screening activities into a unified system.

Figure 1 shows the screening regimens for the five neoplastic diseases and three nonneoplastic chronic diseases in the KCIS program, including the screening method, procedure of screening, interscreening interval, referral, surveillance of high-risk groups, and confirmatory diagnosis. The details of the screening regimen for each disease were as follows.

Details are in the caption following the image

A variety of screening designs for five neoplastic diseases and three nonneoplastic chronic diseases. KCIS: Keelung Community-based Integrated Screening; HCC: hepatocellular carcinoma; FOBT: fecal occult blood test; Pap: Papanicolaou; TC: total cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; CT: computed tomography; AFP: α-fetoprotein; CRC: colorectal carcinoma; USPSTF: U.S. Preventive Service Task Force; CIN: cervical intraepithelial neoplasia; OSF: oral submucous fibrosis; IFG: impaired fasting glucose; SBP: systolic blood pressure; DBP: diastolic blood pressure. *: only alanine aminotransferase (ALT), aspartate aminotransferase (AST), and α-fetoprotein were tested repeatedly; **: subjects who were positive for the hepatitis B surface antigen or antihepatitis C virus antigen were monitored annually with sonography.

Neoplastic disease

We divided the cervical cancer screening program into two epochs: 1996–1998 (before KCIS) and 1999–2001 (after KCIS). Before the KCIS program, screening sites included institutions and ambulatory units located near shopping centers. Direct outreaching services to the community were provided after the introduction of the KCIS.

Because the incidence of breast cancer in Taiwan is considerably lower than in Western countries, general population screening was not believed to be cost-effective. Therefore, a two-stage method was adopted. The first stage was to identify the high-risk group with a family history of breast cancer or a risk score higher than the second tertile that was calculated by weighting menstrual and reproductive factors with the estimated regression coefficients from the previous study.21 For example, women who had an early age at menarche and a late age at the time of first full-term pregnancy most likely would be eligible for breast cancer screening with mammography. Although the incidence rate of breast cancer for women age < 50 years is low, a two-stage screening method for these young women still is considered, partly because of the opportunity to identify these women through cervical screening and partly because of the inclusion of family history as high-risk criteria. Information concerning reproductive and menstrual risk factors was obtained using a questionnaire. The high-risk group was referred to receive annual mammography (Fig. 1). For women in the low-risk group, breast self-examination (BSE) was recommended. We realized teaching BSE cannot lead to a significant reduction in mortality.22 In the current study, the recommendation for BSE is not aimed at reducing mortality from breast cancer but rather is intended to enhance awareness of the early detection of breast cancer.

For colorectal cancer, subjects ages 50–79 years were invited to undergo an annual FOBT test. Those who had had a positive FOBT that was followed by a negative colonoscopy result would be screened every 5 years. Surveillance of polyps followed the guidelines established by the U. S. Preventive Service Task Force (USPSTF).23, 24

A two-stage method for screening liver cancer was used to identify a high-risk group having at least one positive result with regard to five markers: hepatitis B surface antigen (HBsAg), hepatitis C antibody (anti-HCV), a high level of aspartate aminotransferase (AST), a high level of alanine aminotransferase (ALT), and a high level of α-fetoprotein (AFP ≥ 20 ng/mL) in the first stage.10 Ultrasound screening for subjects with positive results, surveillance of the high-risk group, and confirmatory diagnosis are summarized in Figure 1. Note that cirrhosis, hemangioma, pseudotumor, and chronic hepatitis also were ascertained.

Subjects with the habit of chewing betel quids, smoking, and drinking were randomized into two arms: oral inspection by a dentist or inspection by a dentist plus toluidine blue test. The details of the randomized study will be described in a future article. The current study only reports the combined results for both arms.

Nonneoplastic chronic diseases

Subjects with levels of fasting blood sugar > 110 mg/dL were referred to confirm the status of Type 2 DM. Those with levels of fasting blood sugar ≥ 126 mg/dL were defined as having Type 2 DM.25 Information regarding medical treatment and metabolic control of preexisting cases of DM was obtained from a self-administrated questionnaire. Those with a blood sugar level between 110–125 mg/dL were defined as having impaired fasting glucose (IFG). Mass screening for hypertension and hyperlipidemia defined by total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) also were conducted (Fig. 1). We also screened for obesity. Subjects with a body mass index (BMI) ≥ 25 were defined as obese according to the Asian Pacific Steering Committee in Obesity.

Information collected in the KCIS project included a constellation of risk factors associated with metabolic syndrome. This enabled us to identify those who had a metabolic syndrome defined by the following risk factors: obesity (BMI ≥ 25), triglyceride level (≥ 150 mg/dL), HDL (< 40 mg/dL in men and <50 mg/dL in women), blood pressure (systolic blood pressure ≥ 130 mm Hg or diastolic blood pressure ≥ 85 mm Hg), and fasting blood sugar (≥ 110 mg/dL).16

Implementation of the KCIS Program

A total of 30,384 women who had no history of having undergone a Pap smear or had undergone a Pap smear ≥ 3 years previously received a telephone call plus a mailed pamphlet inviting them to attend the organized screening program for a Pap smear examination (Table 1). Their husbands and other relatives age ≥ 30 years also were invited to attend the KCIS program voluntarily. These were defined as dependent screenees because of the index subjects attending the Pap smear screen. Approximately 257 screening centers were established in cooperation with local health authorities. In addition to screening items (as shown in Figure 1), a questionnaire also was administered to obtain data regarding demographic characteristics, lifestyle variables, family history of cancer and chronic disease, history of personal disease, and reproductive factors. Prior to testing, participants were fully informed of the benefits and limitations, as well as the potential side effects of screening. Also, after providing detailed informed consent, participants in the screening program were invited to provide a blood sample for storage for further research on disease biomarkers. This project was approved by local health committee, which is run by the health authority in Keelung.

Table 1. The Target Population, Invited and Attendants to Pap Smear, and Dependent Screenees in the KCIS Program
Age group (yrs) Target population Pap smear screening Dependent screenee Overall KCIS
Invited Attendants Attendance rate Male Female Total Total Coverage rate
30–39 70,718 7073 5238 74.1% 2763 1359 4122 9360 13.2%
40–49 65,738 7531 6776 90.0% 3810 769 4579 11,355 17.3%
50–59 35,455 7956 5014 63.0% 2574 285 2859 7873 22.2%
60–69 26,199 4821 4668 96.8% 3090 348 3438 8106 30.9%
70–79 19,774 3003 2773 92.3% 2860 60 2920 5693 28.8%
Total 217,884 30,384 24,469 80.5% 15,097 2821 17,918 42,387 19.5%
  • Pap: Papanicolaou; KCIS: Keelung Community-based Integrated Screening.

Statistical Analyses

Logistic regression modeling was used to estimate the association between diseases after adjustment for age and gender. Odds ratios (ORs) and their 95% confidence intervals (95% CI) also were calculated.

RESULTS

Basic Characteristics

Because the exact number of persons invited to undergo a Pap smear was known but the number of relatives who were invited via the Pap smear screenees was not, we calculated the attendance rate for Pap smear screening but only the coverage rate for the KCIS program. The overall attendance rate for Pap smear screening was 80.5% (Table 1).

For the overall KCIS program, a total of 42,387 subjects participated in integrated screening activities in the various outreach sites (19.5% coverage rate of the population of 217,884 persons age ≥ 30 years), including 24,469 women who received a Pap smear and 17,918 dependent screenees who attended screening through the index subjects. The average number of dependent screenees per index subject attending Pap smear screening was 0.7.

Yield of Screening

Neoplastic Disease

With regard to Pap smear screening, we first assessed whether the attendance rate of Pap smear screening was enhanced via the KCIS program. Prior to the KCIS project (January 1996–Dec 1998), a total of 69,433 women were invited to undergo a Pap smear examination. Of these, 38,565 women attended screening. The overall attendance rate was 55.5%. Since January 1999, 24,469 of the 30,384 women invited to undergo screening have received Pap smear examinations, for an overall attendance rate of 80.5%.

Table2 summarizes screening outcomes. These include the compliance rates with colonoscopy or abnormal ultrasonography, the compliance rate with confirmatory diagnoses, the number of cases, the detection rates, and tumor attributes. The overall yield of detecting asymptomatic neoplasia was 16.0 per 1000 (677 of 42,387) in the KCIS program. A substantial proportion of small invasive tumors (without lymph node involvement in the case of breast cancer) were identified (Table 2).

Table 2. Basic Screening Characteristics, Detection Rate, and Tumor Attributes for Five Neoplastic Diseases from the KCIS Program
Screening for neoplastic disease Screening age range (yrs) Attendants Positive rate of screening/compliance rate with referral No. of cases Detection rate Tumor attributes
CINI: 88 (32.5%)
Cervical neoplasm 30–79 24,469 Abnormal rate of pap smear: 1.7%/ CIN: 250 1.02% CINII: 49 (18.1%)
 100% for clinical diagnosis or Invasive: 21 0.09% CINIII: 113 (41.7%)
 biopsy Invasive: 21 (7.7%)
Breast cancer 30–79 28,429 (high-risk recall ratea: 21%/referral rate for Invasive: 17 0.16%b ≤ 2 cm: 9 (64.3%) NK: 3
 group: 10809)  surgical biopsy: 2.87% DCIS: 1 > 2 cm: 5 (35.7%)
Lymph node positive: 4 (26.7%)
Lymph node negative: 11 (73.3%), NK: 2
Colorectal neoplasm 50–79 21,672 Returned rate of FOBT: 80.6%, positive Colorectal cancer: 33 0.28%c Dukes A: 13 (39.4%)
 rate of FOBT: 6.0%/67.1% Dukes B: 13 (39.4%)
 compliance rate with colonoscopy Dukes C: 6 (18.2%)
Dukes D: 1 (3.0%)
< 5 mm: 99 (57.6%)
Colorectal adenoma: 177 1.51%c 5–10 mm: 37 (21.5%)
> 10 mm: 36 (20.9%), NK: 5
Liver cancer 30–79 23,654 Positive rate: 20.4%d/77.7% Hepatocarcinoma: 62 0.34%e < 3 cm: 33 (63.5%)
 compliance with abdominal 3–5 cm: 11 (21.1%)
 ultrasound > 5 cm: 8 (15.4%)
Liver cirrhosis: 134 0.73%e
Oral neoplasm 30–79 10,547f Abnormal rate: 2.7%/100% for clinical diagnosis or biopsy Precancerous lesions: 114 1.08% Precancerous lesions: OSF: 23 (20.2%)
Leukoplakia: 86 (75.4%)
Erythroplakia: 5 (4.4%)
Oral cancer: 2 0.02% Oral cancer: 2
  • KCIS: Keelung Community-based Integrated Screening; Pap: Papanicolaou; CIN: cervical intraepithelial neoplasia; DCIS: ductal carcinoma in situ; NK: not known; FOBT: fecal occult blood test; OSF: oral submucous fibrosis.
  • a The recall rate for further examination and referral rate for surgical biopsy was calculated on the basis of high-risk group.
  • b The detection rate was calculated on the basis of high-risk group.
  • c Adjusted with returned rate and compliance rate.
  • d One positive result among the five risk factors: alanine aminotransferase, aspartate aminotransferase, α-feto protein, hepatitis B surface antigen, and hepatitis C virus antigen.
  • e Adjusted with compliance rate.
  • f Only targeted to subjects who chewed betel quids, smoked, or drank.

Nonneoplastic chronic disease

Of 42,387 participants, we found 3752 cases of Type 2 DM (8.9%), including 1362 newly diagnosed cases and 2390 previously treated cases. There were 1161 subjects with IFG We also found 13,415 subjects (31.6%) with hypertension, including 7881 newly diagnosed cases and 5534 previous cases. A total of 7055 subjects (16.6%) were classified as having hyperlipidemia, including 4692 newly diagnosed cases and 2363 previous cases with cardiovascular disease, cerebrovascular disease, DM, hypertension, or hyperlipidemia who had received clinical treatment before KCIS. In all, we identified 13,935 asymptomatic findings (32.9%) including hypertension, Type 2 DM ,and hyperlipidemia. Of 42,387 subjects, 1150 had no information regarding obesity status. The overall prevalence rate of obesity (BMI ≥ 25) was 42.6%. A total of 5314 subjects were identified as having metabolic syndrome, for an overall prevalence rate of 12.5%.

Comorbidity Profiles

Table 3 shows a series of combinations of concomitant nonneoplastic chronic diseases by the presence of an asymptomatic neoplasm detected in the KCIS program. Subjects with neoplasms were more likely to have comorbidity with at least one type of nonneoplastic chronic disease compared with those without an asymptomatic neoplasm. The association between the occurrence of a neoplasm and the presence of comorbid nonneoplastic chronic disease was found to be statistically significant (OR, 1.64; 95% CI, 1.38–1.94 [P < 0.05]). When we considered these nonneoplastic chronic diseases as the principal components of metabolic syndrome, we found that of the 5314 subjects identified as having metabolic syndrome, the most common neoplasm was colorectal neoplasm (1.2%), followed by cervical neoplasm (1.1%), oral neoplasm (1.1%), and chronic liver neoplasm (0.7%).

Table 3. Comorbidity of Nonneoplastic Chronic Disease by the Presence of Neoplasm, KCIS Program
Comorbidity status Neoplasm Nonneoplasm
None 188 (28.06%) 16,255 (38.99%)
Only one disease 220 (32.84%) 13,560 (32.5%)
 DM 9 608
 Obesity 111 7138
 Hypertension 64 3694
 Hyperlipidemia 36 2120
Two comorbidities 195 (29.10%) 8620 (20.66%)
 DM and obesity 15 564
 DM and hypertension 14 524
 DM and hyperlipidemia 5 175
 Obesity and hypertension 120 4942
 Obesity and hyperlipidemia 25 1544
 Hypertension and hyperlipidemia 16 871
Three comorbidities 61 (9.10%) 2904 (6.96%)
 DM, obesity, and hypertension 32 1054
 Obesity, hypertension, and hyperlipidemia 19 1492
 DM, hypertension, and hyperlipidemia 6 183
 DM, obesity, and hyperlipidemia 4 175
Four comorbidities 6 (0.90%) 378 (0.91%)
 DM, obesity, hypertension, and hyperlipidemia 6 378
Total 670 41,717
  • KCIS: Keelung Community-based Integrated Screening; DM: diabetes mellitus.

Associations between Neoplasms and Nonneoplastic Chronic Diseases

The associations between nonneoplastic chronic disease and neoplastic disease are presented in Table 4. Subjects with metabolic syndrome were found to be at greater risk for colorectal neoplasia, particularly colorectal adenoma, and oral neoplasm. By single nonneoplastic chronic disease, hyperglycemia was found to be statistically positively associated with chronic liver disease. Hypertension was found to be significantly associated with colorectal neoplasia and oral neoplasia. Obesity also was found to be positively associated with oral neoplasia.

Table 4. Associations (OR and 95% CIs) between Multiple Diseases Adjusted for Gender and Age
Disease Hyperglycemia (IFG and DM) Hypertension Hyperlipidemia Obesity MS
Colorectal neoplasm 1.11 (0.80–1.56) 1.42 (1.06–1.89)a 0.94 (0.67–1.33) 1.12 (0.85–1.48) 1.35 (0.98–1.87)
Colorectal adenoma 1.17 (0.81–1.67) 1.50 (1.09–2.05)a 0.96 (0.66–1.39) 1.23 (0.91–1.66) 1.43 (1.01–2.02)a
Cervical neoplasm 0.94 (0.82–1.07) 1.00 (0.91–1.10) 1.05 (0.94–1.17) 0.99 (0.91–1.08) 0.99 (0.87–1.12)
Liver cancer 1.60 (1.16–2.21)b 0.89 (0.66–1.20) 0.60 (0.41–0.89)a 0.85 (0.64–1.12) 1.07 (0.74–1.54)
Oral neoplasm 1.32 (0.70–2.50) 1.67 (1.07–2.59)a 0.60 (0.31–1.17) 1.89 (1.23–2.90)b 1.68 (1.03–2.75)a
Breast cancer 2.33 (0.42–12.97) 0.50 (0.10–2.61) 2.03 (0.37–11,18) 1.41 (0.39–5.07) 1.86 (0.37–9.39)
  • OR: odds ratio; 95% CI: 95% confidence interval; IFG: impaired fasting glucose; DM: diabetes mellitus; MS: metabolic syndrome.
  • a 0.01 ≤ P < 0.05.
  • b 0.001 ≤ P < 0.01.
  • cP < 0.001.

DISCUSSION

Efficiency of the KCIS Program

We have successfully developed a community-based multiple screening model in Keelung, Taiwan. We found such a community-based multiple screening model to be efficient for several reasons.

First, the results of the current study demonstrated a 25% increase in the attendance rate for Pap smear screening after the introduction of the KCIS program. The increase most likely is due to the introduction of other screening regimens that reduced the psychologic embarrassment of undergoing a Pap smear for Chinese women.

The early detection of a greater number of asymptomatic cases was achieved in the KCIS program because the KCIS program was an outreach and community-based mass screening program rather than a program of opportunistic screening or case finding performed in clinics or hospitals. Compared with the prevalence rates of presymptomatic cases of 0.5% in solitary breast cancer screening, 0.2% of colorectal neoplasms from solitary colorectal cancer screening, and 8.6% of asymptomatic Type 2 DM from single screening for Type 2 DM in earlier studies,1, 6, 26 there was a high yield of detection of asymptomatic neoplasia (16.0 per 1000) and nonneoplastic chronic disease (32.9%) in the KCIS program.

In addition, the KCIS program provides an efficient way to tentatively evaluate disease screening programs with less supporting evidence (such as liver cancer, oral neoplasms, and hyperlipidemia) in conjunction with established screening programs, such as Pap smear screening,7 breast cancer screening,2 and colorectal cancer screening.4

A multiple screening project in which several diseases are integrated together may reduce the duplication of resources, including invitation, baseline information collection, and eligibility decisions. In addition, a multiple screening program also may reduce the repeated follow-up for the same individual with multiple diseases in single disease screening. More important, the surveillance and referral system of the KCIS program also appears to fit in well with the organization of the public health or local health care system (i.e., diabetic care provided by general practitioners or specialist units).

Comorbidity and Associations with Multiple Diseases

In contrast to single disease screening, multiple screening cannot only identify the correlations among primary diseases but also those between multiple risk factors and multiple diseases, as noted in an earlier study, the Multiple Risk Factors Intervention Trial (MRFIT),27 although the design and execution are very complex. However, unlike the MRFIT study, which only addressed non neoplastic chronic diseases, the elucidation of a correlation between neoplastic disease and nonneoplastic chronic disease was attempted from the beginning in our multiple screening program.

In the current study, we found that subjects diagnosed with neoplasms had a higher likelihood of having concomitant nonneoplastic chronic diseases compared with those patients not diagnosed with a neoplasm. The detailed correlations between specific neoplasms and certain nonneoplastic chronic diseases are elucidated further in Table 4. Our finding that hyperglycemia was statistically significantly associated with chronic liver disease was consistent with earlier epidemiologic studies.28 A positive association between hyperglycemia and colorectal neoplasia, albeit lacking in statistical significance, also was consistent with previous findings.29-31 Hypertensive subjects were more prone to develop colorectal and oral neoplasias; this may be due to the fact that these subjects may have a higher consumption of meat, alcohol, and betel quids, or perhaps are undergoing some form of drug treatment.32, 33 One major limitation of these early findings is that one can only infer an association rather than a causal relation between two diseases because of the cross-sectional nature of the first screen. However, the temporal pathways and strong evidence of causal relations can be elucidated by following those subjects who are free of disease or those with only one disease detected at the time of the first screen.

Multiple screening also enables one to identify risk factors associated with metabolic syndrome. The identification of metabolic syndrome has been regarded as a secondary target of risk-reduction therapy, after the primary target: LDL cholesterol. Moreover, the early detection of metabolic syndrome together with appropriate treatment has been reported to reduce the risk of coronary heart disease.15, 34, 35 We also found that subjects with metabolic syndrome were more susceptible to colorectal and oral neoplasias.36

Evaluation of Multiple Screening

Despite the efficiency of multiple screening, determining its efficacy should be based on the evaluation of long-term mortality and the long-term reduction in the incidence of chronic disease. This cannot be observed in the early period. However, significant mortality reductions are expected in our KCIS program. This can be inferred from three findings. First, compared with the incidence rates of cervical cancer (37 per 100,000), breast cancer (41 per 100,000), colorectal cancer (41 per 100,000), liver cancer (41 per 100,000), and oral cancer (19 per 100,000) in Keelung, our KCIS program had high yield of precancerous lesions and presymptomatic cases. Second, a substantial proportion of precancerous lesions and invasive carcinoma with favorable tumor attributes (small tumor without lymph node involvement) were identified. Third, the simultaneous ascertainment of a large proportion of presymptomatic cases may produce a greater reduction in mortality compared with single disease screening. To project the mortality reduction, we assumed the efficacy of mass screening for six diseases following the evidence-based data reported in the literature with mortality reductions of 40% for Type 2 DM,37 50% for hypertension,38 90% for cervical cancer,7 21% for breast cancer,2 24% for hepatocellular carcinoma,10 and 15% for colorectal cancer4 after 10-years of follow-up. To be conservative, we excluded hyperlipidemia and oral neoplasms because to our knowledge the efficacy of early detection in both diseases has not been completely substantiated. We also assumed that the efficacy of screening for two or more concomitant diseases in the same individual is independent. We therefore projected the overall mortality reduction by a product of empiric distribution of each disease from 42,387 attendants, the proportion of the contributory cause to all deaths, and the efficacy of screening for each disease. This gave a rate of 0.55% for the overall mortality reduction from 6 diseases in the underlying population. Assuming an 80% attendance rate for the population ages 30–79 years in Keelung when the full program is implemented (174,307 subjects), we expect that early detection through the KCIS program will avert 959 deaths during the 10-year follow-up period. In addition to mortality reduction, more benefit would be expected, including avoiding the development of DM or chronic cardiovascular conditions, or risk reductions associated with maintaining a normal BMI through our KCIS program.

There are two major concerns with regard to the current study. Because we used Pap smear screening as a base to provide an incentive for including other screening regimens, the screened population in the initial stage of the study was therefore highly selected. However, we believe the entire population will be covered after the wide application of this model to all individuals in the community. Moreover, a detailed stratified analysis by gender (data not shown) or an adjustment for gender with statistical modeling, if necessary, also can reduce this problem. For example, the confounding effects of gender on multiple disease association were controlled using a logistic regression model (Table 4). Second, multiple screening also may result in false-positive findings such as overdiagnosis and unnecessary diagnosis (lead time without benefit). However, we believe the overall benefit from KCIS should remain significant because an organized regimen enhances the compliance rate and the negative effect from psychologic labeling due to false-positive results for certain diseases can be counteracted by the benefit accrued from screening for other diseases. This is one of the major rationales for multiple disease screening. From an economic viewpoint, the question of whether the effectiveness of mortality reduction multiple screening offers after adjustment for lead-time bias can outweigh cumulative costs (including direct costs, indirect costs, and intangible costs) from false-positive results should be assessed further using a cost-effectiveness analysis in an ongoing study.

We successfully developed a novel integrated multiple screening model for the early detection of three nonneoplastic chronic diseases and five common neoplasms. Early findings from the KCIS project suggest that such an outreach and community-based multiple screening program not only enhances the attendance rate but also has a high yield of early cases, which offers a natural opportunity to elucidate correlations between neoplasms and nonneoplastic chronic diseases. Whether the high yield of presymptomatic cases could lead to a substantial reduction in morbidity and mortality is worthy of further investigation and should be assessed by follow-up.