Ambulatory care sensitive conditions (ACSCs) are used as a measure of access to primary health care. The purpose of this study was to identify factors associated with variation in ACSC admissions at a small area level in Victoria, Australia. The study was ecologic, using Victorian Primary Care Partnerships (PCPs) as the unit of analysis. Data sources were the Victorian Admitted Episodes Dataset, census data from the Australian Bureau of Statistics, and the Victorian Population Health Survey. Age- and sex-adjusted total ACSC admission rates were calculated, and weighted least squares multiple linear regression was used to examine the associations of total ACSC admission rates by various predictor variables. Key variables were categorized into 1 of 4 framework components for analyzing access and use of health care services: predisposing, enabling, need, or structural. Enabling characteristics explained 61.70% of the variation in ACSC admission rates across PCPs. Socioeconomic characteristics (income, education) and percentage with poor self-rated health were important factors in explaining variations in ACSC admissions at a small area-level [R2=0.77]. Community-level variables differentially affect access to primary health care, with significant variation by socioeconomic status. This analytical approach will assist researchers to identify community-level predicators of access across populations at locations, including factors that may be affected by policy change. (Population Health Management 2013;16:190–200)
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References
1.
AnsariMZ, HendersonT. The Victorian Ambulatory Care Sensitive Conditions Study: Opportunities for Targeting Public Health and Health Services Interventions. Melbourne, Victoria: Department of Human Services, 2002.
2.
BillingsJ, ZeitelL, LukomnikJ, CareyTS, BlankAE, NewmanL. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood), 1993; 12:162–173.
3.
MillmanML. Access to Health Care in America. Washington, DC: National Academies Press, 1993.
4.
WeissmanJS, GastonisC, EpsteinAM. Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland. JAMA, 1992; 268:2388–2394.
5.
AnsariZ, LaditkaJN, LaditkaSB. Access to health care and hospitalization for ambulatory care sensitive conditions. Med Care Res Rev, 2006; 63:719–741.
6.
BindmanAB, GrumbachK, OsmondDet al.Preventable hospitalizations and access to health care. JAMA, 1995; 274:305–311.
7.
LaditkaJN, LaditkaSB, MastandunoMP. Hospital utilization for ambulatory care sensitive conditions: Health outcome disparities associated with race and ethnicity. Soc Sci Med, 2003; 57:1429–1441.
8.
AcklandMJ, ChoiBCK, AnsariZ. Indicators and public health policy. Aust J Prim Health, 2005; 11:1–3.
9.
AnsariZ, CarsonN, SerraglioA, BarbettiT, CicuttiniF. The Victorian ambulatory care sensitive conditions study: Reducing demand on hospital services in Victoria. Aust Health Rev, 2002; 25:71–77.
10.
CockleyDE. The Effects of Subsidized Primary Care Centers on Avoidable Hospitalization in West Virginia Communities During 1988–1992. Chapel Hill, NC: University of North Carolina at Chapel Hill, 1996.
11.
Division of Health Care Finance and Policy. Improving Primary Care: Using Preventable Hospitalization as an Approach. Boston, MA: Division of Health Care Finance and Policy (DHCFP), State of Massachusetts, 1995.
12.
FolandJ. Avoidable hospitalizations: An indicator of inadequate primary careIssue Brief #2000-1http://cslib.cdmhost.com/cdm/compoundobject/collection/p128501coll2/id/25245/rec/5. 2012 July 30.
13.
Jones-VesseyK. Potentially Avoidable Hospitalizations in North Carolina, 1997SCHS Studies no. 118Raleigh, NC: North Carolina Public Health, State Center for Health Statistics, 1999.
14.
New Hampshire Department of Health and Human Services. New Hampshire Regional Health Profiles: Healthy New Hampshire 2010. Concord, NH: New Hampshire Department of Health and Human Services (NHDHHS), 2001.
15.
RickettsTC, RandolphR, HowardHA, PathmanD, CareyT. Hospitalization rates as indicators of access to primary care. Health Place, 2001; 7:27–38.
16.
SchreiberS, ZielinskiT. The meaning of ambulatory care sensitive admissions: Urban and rural perspectives. J Rural Health, 1997; 13:276–284.
17.
ShuklaRK, PestianJP. Small Area Analysis of Primary Care Sentinel Events in Virginia. Richmond, VA: Virginia Commonwealth, Williamson Institute for Health Studies, 1996.
18.
SilverMP, BabitzME, MagillMK. Ambulatory care sensitive hospitalization rates in the aged Medicare population in Utah, 1990 to 1994: A rural–urban comparison. J Rural Health, 1997; 13:285–294.
19.
AnsariZ, BarbettiT, CarsonNJ, AucklandMJ, CicuttiniF. The Victorian ambulatory care sensitive conditions study: Rural and urban perspectives. Soz- Praventivmed, 2003; 48:33–43.
20.
RutsteinDD, BerenbergW, ChalmersTC, ChildCG3rd, FishmanAP, PerinEB. Measuring the quality of medical care. A clinical method. N Engl J Med, 1976; 294:582–588.
21.
WennbergJ, Gittelsohn. Small area variations in health care delivery. Science, 1973; 182:1102–1108.
22.
WennbergJ, GittelsohnA. Variations in medical care among small areas. Sci Am, 1982; 246:120–134.
23.
BillingsJ, HasselbladV. Use of Small Area Analysis to Assess the Performance of the Outpatient Delivery System in New York City. New York: Health Systems Agency of New York, 1989.
24.
AndersenRM. Revisiting the behavioral model and access to medical care: Does it matter?J Health Soc Behav, 1995; 36:1–10.
25.
LaditkaJN. Hazards of hospitalization for ambulatory care sensitive conditions among older women: Evidence of greater risks for African Americans and Hispanics. Med Care Res Rev, 2003; 60:468–495, discussion 496–508.
26.
LaditkaJN. Physician supply, physician diversity, and outcomes of primary health care for older persons in the United States. Health Place, 2004; 10:231–244.
27.
WilkinsonD, SymonB. Inequitable Distribution of General Practitioners in Australia: Estimating Need Through the Robin Hood Index. Adelaide, Australia: South Australian Centre for Rural and Remote Health, 2000.
28.
Department of Human Services. The Victorian Admitted Episodes Dataset: An Overview. Melbourne, Victoria: Department of Human Services, Acute Health Division, 2001.
29.
Department of Human Services. The Victorian Population Health Survey (VPHS)—Selected Findings. Melbourne, Victoria: Rural and Regional Health and Aged Care Services, Department of Human Services, 2003.
30.
Australian Bureau of Statistics. 2001 Census of Population and Housing. Number of persons (not including overseas visitors) by sex and five-year age groups, by birthplace of individual, by area of usual residence (Victoria and Australia)Canberra, Australia: Australian Bureau of Statistics, 2005.
31.
AnsariZ, BarbettiT, CarsonNJ. The Victorian Ambulatory Care Sensitive Conditions Study: Opportunities for Targeted Interventions in Cardiovascular Diseases and Chronic Obstructive Pulmonary Disease. Melbourne, Victoria: Department of Human Services, Public Health Division, 2001.
32.
HugoGJ, BamfordE, DunneL. Accessibility Remoteness Index of Australia (ARIA)Department of Health and Aged Care Occasional Papers Series No. 6Canberra, Australia: Commonwealth Department of Health and Aged Care, 1999.
33.
DeyoRA, CherkinDC, CiolMA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol, 1992; 45:613–619.
34.
KleinbaumDG, KupperLL, MullerKE. Applied Regression Analysis and Other Multivariable Methods, 2nd. Boston, MA: PWS-KENT Publishing Company, 1988.
35.
BillingsJ, AndersonGM, NewmanLS. Recent findings on preventable hospitalizations. Health Aff (Millwood), 1996; 15:239–249.
36.
BlusteinJ, HansonK, SheaS. Preventable hospitalizations and socioeconomic status. Health Aff (Millwood), 1998; 17:177–189.
37.
DjojonegoroBM, AdayLA, WilliamsAF, FordCE. Area income as a predictor of preventable hospitalizations in the Harris County hospital district, Houston. Tex Med, 2000; 96:58–62.
38.
LaditkaSB, LaditkaJN. Geographic variation in preventable hospitalization of older women and men: Implications for access to primary health care. J Women Aging, 1999; 11:43–56.
39.
MackenbachJP, StronksK. The development of a strategy for tackling health inequalities in the Netherlands. Int J Equity Health, 2004; 3:11.
40.
ShawR, SmithP. Allocating health care resources to reduce health inequalities. Health Care UK 2001 Spring, 7–14.
41.
IdlerEL, BenyaminiY. Self-rated health and mortality: A review of twenty-seven community studies. J Health Soc Behav, 1997; 38:21–37.
42.
MiilunpaloS, VuoriI, OjaP, PasanenM, UrponenH. Self-rated health status as a health measure: The predictive value of self-reported health status on the use of physician services and on mortality in the working-age population. J Clin Epidemiol, 1997; 50:517–528.
43.
DeckerSL, RemlerDK. How much might universal health insurance reduce socioeconomic disparities in health? A comparison of US and Canada. Appl Health Econ Health Policy, 2004; 3:205–216.
44.
DeatonA. Policy implications of the gradient of health and wealth. Health Affairs (Millwood), 2002; 21:13–30.
45.
ShahBR, GunrajN, HuxJE. Markers of access to and quality of primary care for aboriginal people in Ontario, Canada. Am J Public Health, 2003; 93:798–802.
46.
GargA, ProbstJC, SeaseT, SamuelsME. Potentially preventable care: Ambulatory care-sensitive pediatric hospitalizations in South Carolina in 1998. South Med J, 2003; 96:850–858.
47.
Australian Health Ministers' Advisory Council. Aboriginal and Torres Strait Islander Health Performance Framework Report. Canberra, Australia: Australian Health Ministers' Advisory Council, 2006.
48.
Steering Committee for the Review of Government Service Provision. Report on Government Services 2005 (Indigenous Compendium)Canberra, Australia: Steering Committee for the Review of Government Service Provision, Productivity Commision, 2005.
49.
AnsariMZ, SimmonsD, HartWGet al.Preventable hospitalisations for diabetic complications in Rural and Urban Victoria. Aust J Prim Health, 2000; 6:261–272.
50.
Department of Human Services. Aboriginal Service Plan Key Indicators 2004–05: Koori Human Services Unit, Portfolio Services and Strategic Projects Division. Melbourne, Australia: Department of Human Services, 2006.
51.
Department of Premier and Cabinet. A Fairer Victoria. Creating Opportunities and Addressing Disadvantage. Melbourne, Victoria: Department of Premier and Cabinet, 2005.
52.
Australian Bureaux of Statistics. National Aboriginal and Torres Strait Islander Social Survey, Victoria, 2002. Canberra, Australia: Australian Bureaux of Statistics, 2004.
53.
BasuJ, FriedmanB. Preventable illness and out-of-area travel of children in New York counties. Health Econ, 2001; 10:67–78.
54.
BasuJ, FriedmanB, BurstinH. Primary care, HMO enrollment, and hospitalization for ambulatory care sensitive conditions: A new approach. Med Care, 2002; 40:1260–1269.
55.
LaditkaJN, LaditkaSB, ProbstJC. More may be better: Evidence of a negative relationship between physician supply and hospitalization for ambulatory care sensitive conditions. Health Serv Res, 2005; 40:1148–1166.
56.
ParchmanML, CullerS. Primary care physicians and avoidable hospitalizations. J Fam Pract, 1994; 39:123–128.
57.
ZhanC, MillerMR, WongH, MeyerGS. The effects of HMO penetration on preventable hospitalizations. Health Serv Res, 2004; 39:345–361.
58.
ParchmanML. Small area variation analysis: A tool for primary care research. Fam Med, 1995; 27:272–276.
59.
DiehrP, CainK, ConnellF, VolinnE. What is too much variation? The null hypothesis in small-area analysis. Health Serv Res, 1990; 24:741–771.
60.
SoperDS. Post-hoc statistical power calculator. (Free statistics calculators, online software)http://www.danielsoper.com/statcalc. 2011 March 16.
61.
PhillipsKA, MorrisonKR, AndersenR, AdayLA. Understanding the context of healthcare utilization: Assessing environmental and provider-related variables in the behavioural model of utilization. Health Serv Res, 1998; 33:571–596.
62.
MacIntyreCR, AcklandMJ, ChandrarajEJ. Accuracy of injury coding in Victorian hospital morbidity data. Aust NZ J Public Health, 1997; 21:779–782.
63.
MacIntyreCR, AcklandMJ, ChandrarajEJ, PillaJE. Accuracy of ICD-9-CM codes in hospital morbidity data, Victoria. Aust NZ J Public Health, 1997; 21:477–482.
64.
HarrellFE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression and Survival Analysis. New York: Springer-Verlag, 2001.
65.
SerraglioA, CarsonN, AnsariZ. Comparison of health estimates between Victorian population health surveys and national health surveys. Aust N Z J Public Health, 2003; 27:645–648.
66.
Diex-RouxAV. A glossary for multilevel analysis. J Epidemiol Community Health, 2002; 56:588–594.
67.
Diez-RouxAV. Bringing context back into epidemiology: Variables and fallacies in multilevel analysis. Am J Public Health, 1998; 88:216–222.
68.
SchwartzS. The fallacy of the ecological fallacy: The potential misuse of a concept and the consequences. Am J Public Health, 1994; 84:819–824.
69.
SusserM. The logic in ecological: 1. The logic of analysis. Am J Public Health, 1994; 84:819–824.
70.
AnsariZ, CarsonNJ, SerraglioA, BarbettiT. The Victorian Ambulatory Care Sensitive Conditions Study: Opportunities for Targeted Interventions. Melbourne, Victoria: Department of Human Services, Public Health Division, 2001.
71.
RemlerDK, TeresiJA, WeinstockRSet al.Health care utilization and self-care behaviors of Medicare beneficiaries with diabetes: Comparison of national and ethnically diverse underserved populations. Popul Health Manag, 2001; 14:11–20.
