Donaldson M, Yordy K, Lohr K, Vanselow N. Primary care America’s health in a new era; 1996. https://doi.org/10.1002/clc.4960190202.
Book
Google Scholar
Scheffler RM, Weisfeld N, Ruby G, Estes EH. A manpower policy for primary health care. N Engl J Med. 1978;298:1058–62. https://doi.org/10.1056/NEJM197805112981905.
Article
CAS
PubMed
Google Scholar
Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83:457–502. https://doi.org/10.1111/j.1468-0009.2005.00409.x.
Article
PubMed
PubMed Central
Google Scholar
Shi L, Starfield B. Primary care, income inequality, and self-rated health in the United States: a mixed-level analysis. Int J Health Serv. 2000;30:541–55. https://doi.org/10.2190/N4M8-303M-72UA-P1K1.
Article
CAS
PubMed
Google Scholar
Shi L, Macinko J, Starfield B, Politzer R, Wulu J, Xu J. Primary care, social inequalities, and all-cause, heart disease, and cancer mortality in US counties, 1990. Am J Public Health. 2005;95:674–80.
Article
Google Scholar
Transforming the primary care landscape: Engaging the GP community and our stakeholders in the journey | Ministry of Health. https://www.moh.gov.sg/content/moh_web/home/pressRoom/pressRoomItemRelease/2011/transforming_theprimarycarelandscapeengagingthegpcommunityandour.html. Accessed 7 June 2018.
Macinko J, Harris MJ. Brazil’s family health strategy — delivering community-based primary care in a universal health system. N Engl J Med. 2015;372:2177–81. https://doi.org/10.1056/NEJMp1501140.
Article
CAS
PubMed
Google Scholar
Rijckmans M, Garretsen H, Van De Goor I, Bongers I. Demand-oriented and demand-driven health care: the development of a typology. Scand J Caring Sci. 2007;21:406–16.
Article
Google Scholar
Low LL, Yan S, Kwan YH, Tan CS, Thumboo J. Assessing the validity of a data driven segmentation approach: a 4 year longitudinal study of healthcare utilization and mortality. PLoS One. 2018; https://doi.org/10.1371/journal.pone.0195243.
Article
Google Scholar
Lynn J, Straube BM, Bell KM, Jencks SF, Kambic RT. Using population segmentation to provide better health Care for all: the “bridges to health” model. Milbank Q. 2007;85:185–208. https://doi.org/10.1111/j.1468-0009.2007.00483.x.
Article
PubMed
PubMed Central
Google Scholar
Zhou Y. Improving Care for Older Adults: a model to segment the senior population. Perm J. 2014:18–21. https://doi.org/10.7812/TPP/14-005.
Low LL, Kwan YH, Liu N, Jing X, Low ECT, Thumboo J. Evaluation of a practical expert defined approach to patient population segmentation: a case study in Singapore. BMC Health Serv Res. 2017;17:771.
British Columbia Ministry of Health. The Health System Matrix 6.1: Understanding the Health Care Needs of the British Columbia Population through Population Segmentation. 2015. http://www.cihiconferences.ca/usersday/downloads/presentations/Session_1_-_M_Burd_Halifax_Data_User_BC_matrix_2014_final.pdf. Accessed 30 Mar 2018.
Vuik SI, Mayer E, Darzi A. A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population. Popul Health Metrics. 2016;14:44. https://doi.org/10.1186/s12963-016-0115-z.
Article
Google Scholar
Vuik SI, Mayer EK, Darzi A. Patient segmentation analysis offers significant benefits for integrated care and support. Health Aff. 2016;35:769–75.
Article
Google Scholar
Eissens van der Laan MR, van Offenbeek MAG, Broekhuis H, Slaets JPJ. A person-centred segmentation study in elderly care: towards efficient demand-driven care. Soc Sci Med. 2014;113:68–76.
Article
CAS
Google Scholar
Ledere BS, Bégin C, Cadieux É, Goulet L, Allaire JF, Meloche J, et al. A classification and regression tree for predicting recurrent falling among community-dwelling seniors using home-care services. Can J Public Heal. 2009;100:263–7.
Google Scholar
Bird M, Datta GD, van Hulst A, Cloutier MS, Henderson M, Barnett TA. A park typology in the QUALITY cohort: implications for physical activity and truncal fat among youth at risk of obesity. Prev Med (Baltim). 2016;90:133–8.
Article
Google Scholar
Dodd LJ, Al-Nakeeb Y, Nevill A, Forshaw MJ. Lifestyle risk factors of students: a cluster analytical approach. Prev Med (Baltim). 2010;51:73–7.
Article
Google Scholar
Holland ML, Xia Y, Kitzman HJ, Dozier AM, Olds DL. Patterns of visit attendance in the nurse-family partnership program. Am J Public Health. 2014;104:e58–65.
Article
Google Scholar
Siroux V, Basagana X, Boudier A, Pin I, Garcia-Aymerich J, Vesin A, et al. Identifying adult asthma phenotypes using a clustering approach. Eur Respir J. 2011;38:310–7. https://doi.org/10.1183/09031936.00120810.
Article
CAS
PubMed
Google Scholar
Tsai J-S, Wu C-H, Chiu T-Y, Chen C-Y. Significance of symptom clustering in palliative care of advanced cancer patients. J Pain Symptom Manag. 2010;39:655–62. https://doi.org/10.1016/j.jpainsymman.2009.09.005.
Article
Google Scholar
Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–82.
Article
Google Scholar
Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9.
Article
Google Scholar
Agency for Integrated Care Singapore. Chronic Dis Manag Programme - Handbook for Healthcare Professionals. 2018. https://www.primarycarepages.sg/Documents/Practice%20Management/CDMP%20Handbook%20for%20Healthcare%20Professionals%202018.pdf. Accessed 20 Jan 2019.
Sharabiani MTA, Aylin P, Bottle A. Systematic review of comorbidity indices for administrative data. Med Care. 2012;2012:1109–18.
Article
Google Scholar
Dominick KL, Dudley TK, Coffman CJ, Bosworth HB. Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis. Arthritis Care Res. 2005;53(5):666–72.
Article
Google Scholar
Muthén LK, Muthén BO. Mplus User’s Guide. 8th ed; 2017.
Google Scholar
Liu LF, Tian WH, Yao HP. The heterogeneous health latent classes of elderly people and their socio-demographic characteristics in Taiwan. Arch Gerontol Geriatr. 2014;58:205–13.
Article
Google Scholar
Lanza ST, Collins LM, Lemmon DR, Schafer JL. PROC LCA: a SAS procedure for latent class analysis. Struct Equ Model A Multidiscip J. 2007;14:671–94. https://doi.org/10.1080/10705510701575602.
Article
Google Scholar
Muthén L, Muthén B. Mplus Version 7 user’s guide. Los Angeles: CA Muthén Muthén; 2012.
Google Scholar
Collins LM, Lanza ST. Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences; 2010. https://doi.org/10.1002/9780470567333.
Book
Google Scholar
Muthen B, Muthen LK. Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res. 2000;24:882–91. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x.
Article
CAS
PubMed
Google Scholar
Vermunt JK, Magidson J. Factor analysis with categorical indicators: a comparison between: traditional and latent class approaches. In: New developments in categorical data analysis for the social and behavioral sciences; 2004. p. 33–51.
Google Scholar
Brinkley-Rubinstein L, Craven K. A latent class analysis of stigmatizing attitudes and knowledge of HIV risk among youth in South Africa. PLoS One. 2014;9:e89915.
Article
Google Scholar
Raftery AE. Bayesian model selection in social research. Sociol Methodol. 1995;25:111–63.
Article
Google Scholar
Hayden JA, Côté P, Steenstra IA, Bombardier C. Identifying phases of investigation helps planning, appraising, and applying the results of explanatory prognosis studies. J Clin Epidemiol. 2008;61(6):552–60.
Article
CAS
Google Scholar
Kent P, Stochkendahl MJ, Christensen HW, Kongsted A. Could the clinical interpretability of subgroups detected using clustering methods be improved by using a novel two-stage approach? Chiropr Man Ther. 2015;23(1):20.
Chung RJ, Touloumtzis C, Gooding H. Staying young at heart: cardiovascular disease prevention in adolescents and young adults. Curr Treat Options Cardiovasc Med. 2015;17(12):61.
Alderwick H, Ham C, Buck D. Population health systems: going beyond integrated care. 2015. https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/population-health-systems-kingsfund-feb15.pdf. Accessed 1 Oct 2017.
Bartholomew Eldrigde LK, Markham CM, Ruiter RAC, Fernàndez ME, Kok G, Parcel GS. Planning health promotion programs: an intervention mapping approach; 2011.
Google Scholar
Lafortune L, Béland F, Bergman H, Ankri J. Health state profiles and service utilization in community-living elderly. Med Care. 2009;47:286–94.
Article
Google Scholar
Simon GE, Goldberg DP, Von Korff M, Üstün TB. Understanding cross-national differences in depression prevalence. Psychol Med. 2002;32(4):585–94.
Article
CAS
Google Scholar
Littlewood R. From categories to contexts: a decade of the “new cross-cultural psychiatry.”. Br J Psychiatry. 1990;156(3):308–27.
Article
CAS
Google Scholar
Lo Siou G, Yasui Y, Csizmadi I, McGregor SE, Robson PJ. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns. Am J Epidemiol. 2011;173:956–67.
Article
Google Scholar
Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–4. https://doi.org/10.1214/aos/1176344136.
Article
Google Scholar
Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model. 2007;14:535–69. https://doi.org/10.1080/10705510701575396.
Article
Google Scholar
Reboussin BA, Song E-Y, Shrestha A, Lohman KK, Wolfson M. A latent class analysis of underage problem drinking: evidence from a community sample of 16-20 year olds. Drug Alcohol Depend. 2006;83:199–209. https://doi.org/10.1016/j.drugalcdep.2005.11.013.
Article
PubMed
Google Scholar
Bailey RL, Gutschall MD, Mitchell DC, Miller CK, Lawrence FR, Smiciklas-Wright H. Comparative strategies for using cluster analysis to assess dietary patterns. J Am Diet Assoc. 2006;106:1194–200.
Article
Google Scholar
Erlich Z, Gelbard R, Spiegler I. Evaluating a positive attribute clustering model for data mining. J Comput Inf Syst. 2003;43:100–8.
Google Scholar
Jadczaková V. Review of segmentation process in consumer markets. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 2013;61:1215–24.
Article
Google Scholar
Wedel M, Kamakura WA. Market segmentation - conceptual and methodological foundations. 2nd ed: Springer US; 2000. https://doi.org/10.1007/978-1-4615-4651-1.
Book
Google Scholar