Role of Area-Level Access to Primary Care on the Geographic Variation of Cardiometabolic Risk Factor Distribution: A Multilevel Analysis of the Adult Residents in the Illawarra—Shoalhaven Region of NSW, Australia

Abstract

Background: Access to primary care is important for the identification, control and management of cardiometabolic risk factors (CMRFs). This study investigated whether differences in geographic access to primary care explained area-level variation in CMRFs. Methods: Multilevel logistic regression models were used to derive the association between area-level access to primary care and seven discrete CMRFs after adjusting for individual and area-level co-variates. Two-step floating catchment area method was used to calculate the geographic access to primary care for the small areas within the study region. Results: Geographic access to primary care was inversely associated with low high density lipoprotein (OR 0.94, CI 0.91–0.96) and obesity (OR 0.91, CI 0.88–0.93), after adjusting for age, sex and area-level disadvantage. The intra-cluster correlation coefficient (ICCs) of all the fully adjusted models ranged between 0.4–1.8%, indicating low general contextual effects of the areas on CMRF distribution. The area-level variation in CMRFs explained by primary care access was ≤10.5%. Conclusion: The findings of the study support proportionate universal interventions for the prevention and control of CMRFs, rather than any area specific interventions based on their primary care access, as the contextual influence of areas on all the analysed CMRFs were found to be minimal. The findings also call for future research that includes other aspects of primary care access, such as road-network access, financial affordability and individual-level acceptance of the services in order to gain an overall picture of the area-level contributing role of primary care on CMRFs in the study region.

Geographic variation in cardiometabolic risk factor prevalence explained by area-level disadvantage in the Illawarra-Shoalhaven region of the NSW, Australia

“…The patterns were consistent across the six CMRFs analysed in this study; and comparable with similar studies reported nationally and globally. Based on our findings, we recommend further area-level research to discern the role of other contextual factors not analysed in this study especially the area-level access to health care services to determine its existing role and adequacy 78 , and evidence based universal interventions for the prevention and control of CMRFs but proportionate to the priority level of the populations based on area-level disadvantage. www.nature.com/scientificreports/ www.nature.com/scientificreports/ www.nature.com/scientificreports/ www.nature.com/scientificreports/ www.nature.com/scientificreports/…”

The application of spatial measures to analyse health service accessibility in Australia: a systematic review and recommendations for future practice

“…Sixteen articles, published between 2003 to 2021, focused on accessibility to general practice (GP) services [ 16 , 33 , 43 , 56 , 73 , 75 – 79 , 81 , 84 , 91 , 93 , 100 , 102 ]. Of those articles, three were from the same study [ 75 , 76 , 79 ].…”

“…Articles focused on specific target populations, including antenatal [ 33 ], > 65 years [ 100 ], patients with chronic disease [ 73 ], and rural, regional, or remote [ 75 , 76 , 78 , 79 , 84 , 93 , 100 ]. Ten articles accounted for SES status using the ABS SEIFA [ 56 , 75 , 76 , 79 ], with six articles further specifying the index used, including the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) [ 91 ] and IRSD [ 16 , 33 , 43 , 73 , 102 ]. Seven articles used standardised geographic classification systems to define remoteness areas, including ARIA [ 81 ], ABS ASGC-RA [ 91 ], and ABS ASGS-RA [ 16 , 43 , 73 , 77 , 100 ].…”

“…Physical access to GP services was analysed using both area- and distance-based measures. Area-based methods that were used include the two-step floating catchment area (2SFCA) method [ 16 , 75 – 79 , 91 , 102 ] and PPR [ 73 , 81 , 93 ]. Distance-based measures include travel distance calculated using road networks [ 33 , 43 , 56 , 84 , 100 ] and travel time using road networks [ 16 , 75 – 79 ].…”