Table 1

Study Characteristics
Author Year Country Sample characteristics Analysis Variables/Measures Outcome Quality appraisal
Andersen and Urban [36] 1998 USA Breast cancer survivors n = 485 50–80 years old 3-20+ years post-diagnosis Multiple logistic regression Receipt of mammogram, usual source of care,1 recommendation by physician for mammogram and insurance coverage Receipt of mammogram Average
Andrykowski and Burris [45] 2010 USA SEER database Breast cancer survivors n = 42 Colorectal cancer survivors n = 33 Hematological cancer survivors n = 38 1–5 years post-diagnosis Aged 25–75 years old Multiple regression Socio-demographics, cancer characteristics, mental health resource questionnaire Use of formal and informal mental health services Very good
Boehmer et al. [34] 2010 USA Colorectal cancer survivors Aged 22–92 years old n = 253 Cox proportional hazard models Colonoscopies, sigmoidoscopy, cancer type, stage, co-morbidities, outpatient visits, socio-demographics Receipt of colorectal surveillance procedures Very good
Cooper et al. [29] 2000 USA SEER-MEDICARE database Colorectal cancer survivors Localised disease Surgically treated >65 years old n = 5, 716 Chi-square test Socio-demographics, inpatient claims, outpatient claims, use of endoscopic procedures (colonoscopy, polypectomy or biopsy) Receipt of colorectal surveillance procedures Very good
Cooper and Payes [28] 2006 USA SEER-MEDICARE database Colorectal cancer survivors >65 years old n = 62, 882 survived 1 year follow-up n = 35, 784 survived 3 year follow-up Logistic regression Medicare claims2 for colonoscopy, sigmoidoscopy or barium enema, co-morbidities Use of surveillance procedures for colorectal cancer within 3 years of diagnosis Very good
Cooper, Kou and Reynolds [31] 2008 USA SEER database Colorectal cancer survivors >65 years old n = 9, 426 Multivariate regression Number of physician visits, receipt of carcino-embryonic antigen blood test (CEA),3 colonoscopy, CT and PET scans Adherence to guidelines for cancer follow-up Good
Doubeni et al. [27] 2006 USA Breast cancer survivors n = 797 at baseline (end of treatment) n = 262 after 5 yrs >55 years old 4 geographically diverse Health Maintenance Organisations (HMOs).4 Generalised estimated equations (GEE) Receipt of mammograms. age, date and stage at/of diagnosis, treatment. co-morbidities. visits to primary care provider (primary care physician) and outpatient visits Receipt of yearly mammogram and visits to physicians Very good
Earle et al. [23] 2003 USA SEER database Breast cancer survivors > 65 years old, n = 5,965 Controls n = 6,062 Multivariate regression Frequency of visits to primary care physician, oncologists, other and teaching hospitals, receipt of flu vaccine, lipid test, cervical exam, colon exam, bone densitometry and diabetes test Visits to physicians and receipt of preventive medicine Very good
Earle and Neville [19] 2004 USA SEER database Colorectal cancer survivors > 65 years old n = 14,884 Logistic regression Co-morbidities, socio-demographics, receipt of flu vaccine, lipid testing, bone densitometry and cervical screening Visits to physicians and receipt of preventive medicine Very good
Earle, Neville and Fletcher [43] 2007 USA Breast, lymphoma, colorectal, melanoma and other cancer survivors Mean age 60 years n = 1,111 Controls n = 4,444 Logistic regression ` Mental health diagnoses, co-morbidities, socio-demographics, use of primary care physician, oncologist, psychiatrists, psychologists, social workers and inpatient hospitalisations (both general and mental). Use of mental health provider services Good
Ellison et al. [33] 2003 USA SEER database Colorectal cancer survivors >65 years old n = 52, 105 Kaplan-Meier survival analysis Unconditional regression analysis Cox regression Socio-demographic, hospital and clinical characteristics, receipt of colonoscopy, sigmoidoscopy, endoscopy and barium enema Differential receipt of colonoscopy, sigmoidoscopy, endoscopy and barium enema by race Good
Gray et al. [41] 2000 Canada Breast cancer survivors n = 731 Histologically confirmed and invasive Stepwise logistic regression Use of specialised supportive care services, wish to use services that were not accessed, social and demographic characteristics. Use of professional supportive care services provided by the Ontario health care system Very good
Gray et al. [42] 2002 Canada Breast cancer survivors 63 % <60 years old 23–36 months post-diagnosis n = 731 Logistic regression Supportive care from physicians and nurses, socio-demographics, illness and treatment information Use of professional supportive care Good
Grunfeld et al. [16] 1999 UK Breast cancer survivors n = 148 Two district general hospitals Two-tailed t-test and chi-square Record of visits, average cost of visits, out-of patient expenses, waiting times, lost earnings and lost earnings of accompanying person GP follow-up vs. Hospital follow-up. Cost-effectiveness and cost to patient, Average
Grunfeld et al. [17] 2011 Canada Breast cancer survivors n = 408 Nine tertiary cancer centres Two-tailed t-test Use of survivorship care plans (vs. no survivorship care plans) in primary care physician led follow-up. Frequency of visits to oncologists. Primary care physician led follow-up Very good
Keating et al. [25] 2006 USA SEER-MEDICARE database Breast cancer survivors Stage 1 or 2 Underwent surgery >65 years old Repeated-measures logistic regression Mammogram receipt, visits to primary care physician medical oncologist, general surgeon, radiation oncologist and other specialists, socio-demographics Factors related to mammography use Very good
Keating et al. [11] 2007 USA SEER database Breast cancer survivors >65 years old n = 37,967 in year 1 n = 30,406 in year 2 n = 23,016 in year 3 Repeated-measures logistic regression Receipt of bone scans, tumour antigen tests (TAT), Chest x-rays and other abdominal/chest imaging, frequency of visits to physicians and socio-demographics Receipt of a number of surveillance procedures and visits to physicians over time Very good
Khan et al. [38] 2010 UK GPRD database Breast cancer survivors N = 18, 612 Colorectal cancer survivors N = 5, 764 Prostate cancer survivors N = 4, 868 >30 years old 5 years post-diagnosis Controls N = 116,418 Multivariate regression Socio-demographics, use of primary care, frequency of visits Primary care consultations Very good
Khan, Watson and Rose [20] 2011 UK GPRD database Prostate cancer survivors N = 4,868 Breast cancer survivors N = 18,612 Colorectal cancer survivors N = 5,764 Controls N = 145,662 Logistic regression Co-morbidities, screening (PSA, cervical, mammogram), receipt of preventative procedures and socio-demographics Receipt of screening and preventative care Very good
Knopf et al. [37] 2001 USA SEER database Colorectal cancer survivors >65 years old n = 52, 283 Kaplan-Meier survival analysis Receipt of colonoscopy, sigmoidoscopy, endoscopy and barium enema, age, tumour stage at diagnosis and year of diagnosis Receipt of bowel surveillance procedures Very Good
Lafata et al. [30] 2001 USA Colorectal cancer survivors n = 251 Kaplan-Meier survival analysis Cox proportional hazards Socio-demographics, receipt of colonoscopy, CEA, barium enema, chest x-ray, MRI’s, ultrasounds and liver analysis Receipt of colon screening procedures and other procedures Very good
Mahboubi et al. [15] 2007 France Colorectal cancer survivors <65 years old N = 389 Logistic regression Co-morbidities, chest radiograph, abdominal ultrasound, colonoscopy, CT, TAT, blood tests and reason for testing (routine or symptomatic) Characteristics associated with visits to GPs Very good
Mandelblatt et al. [13] 2006 USA Breast cancer survivors n = 418 Stage 1 and 2 Multivariate linear regression Calendar diary of health service use, socio-demographics, cancer treatment information, co-morbidities and psychological status survey Patterns and determinants of health service use Very good
Mayer et al. [35] 2007 USA

NCI 2003 HINTS5

n = 619 Breast cancer survivors n = 119 Prostate cancer survivors n = 62 Colorectal cancer survivors n = 49 Others n = 389

Logistic regression Based on the health belief model (HBM),6 cancer communication, cancer history, general cancer knowledge, cancer risk and screening, health status and demographics. Screening practices and beliefs Very good
McBean, Yu and Virnig [39] 2008 USA SEER database: Uterine cancer survivors >65 years old n = 14,575 Controls n = 58,420 Multivariate logistic regression Generalised equation modelling Receipt of flu vaccine, bone densitometry, colorectal screening and mammogram no. of physician services and socio-demographics Use of preventive services and frequency of physician visits Very good
Mols, Helfenrath and van de Poll-Fanse [14] 2007a Netherlands Endometrial cancer Prostate cancer Non-Hodgkin’s lymphoma survivors n = 1,112 Linear regression Multivariate linear regression SF-36, self-reported health service use, frequency of visits, co-morbidities and socio-demographics Patterns of physician use Very good
Mols, Coebergh and van de Poll-Fanse [22] 2007b Netherlands Endometrial cancer Prostate cancer, Hodgkin’s and non-Hodgkin’s lymphoma survivors n = 1,231 Chi-square and multivariate logistic regression Co-morbidity, socio-demographics, use of medical specialist, general practitioner, additional services (physiotherapist. and psychologist) Frequency of physician use Very good
Oleske et al. [47] 2004 USA Breast cancer survivors Aged between 21–65 years n = 123 Multivariate logistic regression Use and frequency of physician and admissions, services in past 12 months. reasons for hospitalisations, SRS (social responsiveness scale) and CES-D (depression scale) Determination of factors associated with hospitalisation Very good
Peuckmann et al. [12] 2009 Denmark Breast cancer survivors n = 1,316 Controls n = 4,865 Risk ratios and multiple logistic regression analysis Frequency of physical visits, socio-demographics, physical activity and BMI. HR-QOL (SF-36) and chronic pain Frequency and determinants of health service use Very good
Schapira, McAuliffe and Nattinger [32] 2000 USA SEER database Breast cancer survivors >65 years old n = 3,885 Logistic model Receipt of mammogram, co-morbidity, socio-economic status (SES) and preventive treatment received Receipt of Mammogram over two year period Good
Schootman et al. [44] 2008 USA SEER database Breast cancer survivors >65 years old n = 47, 643 Restricted iterative generalised least squares and first-order marginal quasi-likelihood estimation analysis Frequency of Ambulatory-Care-Sensitive Hospitalizations (ACSH)7 SES, co-morbidity, demographics, availability of medical care, visits to primary care physician and oncologists Frequency of Ambulatory-Care-Sensitive Hospitalizations Very good
Simpson, Carlson and Trew [18] 2001 USA Breast cancer survivors Time point 1 n = 46 Time point 4 n = 30 Controls Time point 1 n = 43 Time point 4 n = 25 ANOVA Average cost of care, no. of cancer centre visits and a number of psychological distress indicators including BDI, POMS and Mental adjustment to cancer scale Billing of Health care as a proxy to use. Visits to cancer centre Correlation of billing to distress. Good
Snyder et al. [9] 2008a USA SEER database Colorectal cancer survivors >65 years old n = 1,541 Poisson regression and logistic regression Clinical and socio-demographic characteristics, visits to primary care physician, oncologist or other physicians. Receipt of influenza vaccine, cholesterol screening, mammogram, cervical screening and bone densitometry Frequency of physician visits and receipt of preventive care Very good
Snyder et al. [10] 2008b USA SEER database Colorectal cancer survivors >65 years old n = 20,068 Poisson regression and logistic regression analysis Co-morbidities, socio-demographics, visits to primary care physician, oncologist and other physicians, receipt of influenza vaccine, cholesterol screening, mammogram, and bone densitometry Visits to physicians and receipt of preventive care Good
Snyder et al. [24] 2009a USA SEER database Breast cancer survivors >65 years old n = 23, 73 Controls n = 23, 731 Poisson regression and logistic regression analysis Use of physician and oncology services, receipt of 5 preventive care services and socio-demographics. Visits to physicians and oncologists and preventive medicine Good
Snyder et al. [26] 2009b USA SEER database Breast cancer survivors >65 years old Stages 1–3 n = 1,961 Controls n = 1,961 Poisson regression and logistic regression analysis Co-morbidities, clinical and demographic characteristics, visits to primary care physician, oncologists and other physicians Frequency of visits to physicians Good
Van de Poll-Fanse et al. [21] 2006 Netherlands Breast cancer survivors Invasive n = 183 Logistic regression Co-morbidities, spontaneously reported problems, use of GP, medical specialist and physiotherapist, health status and psychological well-being Use of physician services Good
Yu, McBean and Virnig [40] 2007 USA SEER database Colorectal cancer survivors >65 years old n = 112, 737. Logistic regression and poisson regression Socio-demographic characteristics, co-morbidities, receipt of mammogram, visits to primary care physician, Gynaecologists only, oncologists and other Receipt of mammogram and visits to physicians Good

1Usual source of care refers to whether an individual receives care from the same physician or different physicians; 2Medicare is a government-funded medical care plan in USA, whereby individuals aged 65 and over that covers medical expenses such as doctor's visits, hospital stays, drugs and other treatment; 3CEA testing is used as a tumour marker for particular cancers, such as colorectal; 4HMOs provide their members with medical services for a fixed fee; 5NCI HINTS is the Health Information National Trends Survey, which collects nationally represented information on how the American public find and use information on cancer; 6Developed by Hochbaum (1958) is an explanatory and predictive model of health behaviours and includes attitudes and beliefs of an individual; 7ACSH are hospitalizations which could have been prevented if primary care services had been initially accessed by the individual.

Treanor and Donnelly

Treanor and Donnelly BMC Health Services Research 2012 12:316   doi:10.1186/1472-6963-12-316

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