Categories
Original Research Articles

General practitioner awareness of pharmacogenomic testing and drug metabolism activity status amongst the Black-African population in the Greater Western Sydney region

Background:  Individuals  of  black-African  background  have  a high variability in drug metabolising enzyme polymorphisms. Consequently, unless these patients are tested for these polymorphisms, it becomes difficult to predict which patients may have a sub-therapeutic response to medications (such as anti- depressants) or experience an adverse drug reaction. Given the increasing population of black-Africans in Australia, GPs are on the front line of this issue, especially in Greater Western Sydney (GWS) – one of the country’s rapidly increasing populations due to migration. Aim: To ascertain the awareness of GPs regarding drug metabolising enzyme polymorphisms in the black-African population and pharmacogenomic testing in the GWS community. Methods:  A  descriptive,  cross-sectional  study  was  conducted in GWS by analysing GP responses to a questionnaire consisting of closed and open-ended questions. Results: A total of 46 GPs completed the questionnaire. It was found that 79.1% and 79.5% of respondents were unaware of: the high variability in drug metabolism enzyme activity in the black-African population and pharmacogenomic testing (respectively). No respondents had ever utilised pharmacogenomic testing. Only a small proportion of GPs “always” considered a patient’s genetic factors (13.9%) and enzyme metaboliser status (11.1%) in clinical practice. Preferred education media for further information included written material, direct information from other health professionals (such as pharmacists) and verbal teaching sessions. Conclusion: There was a low level of awareness of enzyme metaboliser status and pharmacogenomic testing amongst GPs in GWS. A future recommendation to ameliorate this includes further education provision through a variety of media noted in the study.

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Introduction

Depression accounts for 13% of Australia’s total disease burden, making it an important health issue in the current context. [1] General Practitioners (GPs) are usually the first point of contact for patients seeking help for depression. [2,3] Antidepressant prescription is the most common treatment form for depression in Australia with GPs prescribing an antidepressant to treat up to 40% of all psychological problems. [2] This makes GP awareness of possible treatment resistance or adverse drug reactions (ADRs) to these medications vital.

Binder et al. [4] described pharmacogenomics as “the use of genome- wide approaches to elucidate individual differences in the outcome of drug therapy”. Detecting clinically relevant polymorphisms in genetic expression can potentially be used to identify susceptibility to ADRs. [4] This would foster the application of personalised medicine by  encouraging  an  inter-individual  approach  to  medication  and dose prescriptions based on an individual’s predicted response to medications. [4,5]

Human DNA contains genes that code for 57 cytochrome (CYP) P450 isoenzymes; these are a clinically important family of hepatic and gastrointestinal isoenzymes responsible for the metabolism of over 70% of clinically prescribed drugs. [5-10] The CYP family of enzymes are susceptible to polymorphisms as a result of genetic variations, influenced by factors such as ethnicity. [6,5,10] Research has shown that polymorphisms in certain CYP drug metabolising enzymes can result in phenotypes that class individuals as “ultrarapid metabolisers (UMs), extensive metabolisers (EMs), intermediate metabolisers (IMs) and poor metabolisers (PMs).”[6,10] These categories are clinically important as they determine whether or not a drug stays within the therapeutic range. Individuals with PM status may be susceptible to experiencing ADRs as a result of toxicity, and conversely, those with UM status may not receive a therapeutic effect. [5,6,10,11]

When considering the metabolism of antidepressants, the highly polymorphic CYP enzymes: CYP2C19 and CYP2D6 are known to be involved. [5,10,12] A study by Xie et al. [13] has shown that for the CYP2D6 enzyme alone, allelic variations induce polymorphisms that result in a PM phenotype of “~1%” in Asian populations, “0-5%” among Caucasians and a variation of between “0-19%” in black- African populations. This large disparity of polymorphism phenotypes was reproduced in a recent study, which also showed that the variation is not exclusive to the CYP2D6 enzyme. [6] It has been reported that the incidence of ADRs among PMs treated with drugs such as antidepressants is 44% compared to 21% in other patients. [5,14] Consequently, increased costs have been associated with the management of UM or PM patients. [5]

The black-African population in Australia and specifically Sydney (where GWS is one of the fastest growing regions) continues to rise through migration and humanitarian programs. [15-18] Almost 30% of Africans settling in Australia in the decade leading to the year 2007 did so under humanitarian programs including under refugee status. [15-17] As refugees are at a higher risk of having mental health problems including depression  due  to  their  traumatic  histories  and  post-migratory difficulties, GPs in GWS face increased clinical interactions with  black-Africans  at  risk  of  depression.  [19,20]  Considering  the high  variability of enzyme   polymorphisms   in   this   population, pharmacogenomic testing may play a role in the primary care of these patients. We therefore conducted a study to assess GP awareness of pharmacogenomic testing and the differences in enzyme metaboliser status (drug metabolism phenotypes). We also investigated the GP preferences of media for future education on these topics.

Methodology

Study Design and Setting

This is a descriptive, cross-sectional study. Ethics approval was granted by the Human Research Ethics Committee.

Considering GWS is the fastest growing region in Sydney, we focussed on particular suburbs in GWS (Blacktown, Parramatta and Holroyd Local Government Areas). [17-20] Using geographical cluster sampling, a list of GP practices were identified with the aim of recruiting 50 participants.

Study tool

Data was collected using a questionnaire validated by university supervisors and designed to elicit the level of understanding and awareness among GPs. The main themes of the questionnaire involved: questions regarding basic demographic information; questions aimed at determining the level of GP awareness regarding differences in drug metabolising phenotypes and pharmacogenomic testing; and open- ended questions eliciting the preferred methods of education with respect to pharmacogenomic testing.

Data Collection

We invited 194 GPs between April and May 2014 to participate in the study. The questionnaire and participant information sheet were either given to the practice managers or to the GPs in person. Questionnaires were collected in person within the following two weeks.

Data Analysis

Data was analysed using SPSS (version 22, IBM Australia). Descriptive statistics were used to summarise findings, with p-values calculated using Chi-square analysis (with Yates correction) to compare two sets of data. A p-value of <0.05 indicated statistical significance.

Results

The overall response rate was 23.7% (46/194). Our respondents included: 27 females and 19 males. The mean number of years of experience in general practice was 13.9 and most GPs (93.4%, 43/46) had received some form of training in antidepressant prescription in the last 5 years. The number of patients of black-African background seen in the last 6 months ranged from 0 to greater than 100. Only

26.1% (12/46) of GPs reported no consultations with a patient of black- African background within this timeframe. Of the 73.9% (34/46) of GPs who had seen at least one patient from this cohort, 55.9% (19/34) had treated at least one patient for depression with antidepressants.

GPs experience of ADRs in patients of black-African background treated for depression

From 46 participants, 19 had treated a patient of black-African background with antidepressants, 18/19 reported having identified at least one ADR (Figure 1).

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GP awareness and consideration of drug metabolism activity status and genetic factors

Awareness amongst GPs of the different drug metabolism activity phenotypes in black-Africans was low with 79.1% (34/43) being unaware. Patients’ genetic factors and enzyme metaboliser status were “always” considered by only 13.9% (5/36) and 11.1% (4/36) of GPs, respectively. There was no statistically significant difference regarding awareness between GPs who had treated black-African patients and those who had not (21.1% vs 13.3% respectively, p=0.89).

GP awareness and use of pharmacogenomic testing

The awareness of methods for testing a patient’s key drug metabolising enzymes, also known  as  pharmacogenomic testing, was extremely low with 79.5% (35/44) of GPs being unaware of the testing methods available in Australia. Of the 20.5% of GPs (9/44) who were aware, none had utilised pharmacogenomic testing for their black-African patients. These nine GPs then nominated factors that would influence their utilisation of pharmacogenomic testing on these individuals. Three main categories of influence emerged (Table 1). When specifically asked whether they would be more inclined to utilise pharmacogenomic testing on black-African patients who had previously experienced ADRs, 88.9% (8/9) GPs stated that they would be more inclined.

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Preferred education media

GPs that were aware of pharmacogenomic testing were asked, through an open-ended question, how they obtained information regarding these  methods.  Three  main  categories  were  identified  based  on their responses (Table 2). All GPs were then asked to note down their preferred medium of education for pharmacogenomic testing (Table 3). Multiple responses were allowed.

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Discussion

This study showed that there is a low level of awareness regarding pharmacogenomic testing and the differences in drug metabolism phenotypes among GPs. Additionally, we identified the preferred education media for providing information to GPs (Table 3). Awareness of pharmacogenomic testing and of the differences in drug enzyme metaboliser status (phenotype) could be valuable in the clinical setting. Improved patient outcomes have been noted when doctors are able to personalise management based on information from pharmacogenomic testing,[21] with Hall-Flavin et al. [21] noting significantly improved baseline depression scores amongst patients with depression whose doctors were provided with information on pharmacogenomics.

A previous study reported that a high proportion (97.6%) of physicians agreed that differences in genetic factors play a major role in drug responses.  [22]  Whilst  it  is  arguable  that  knowledge  of  genetic factors holistically playing a role in drug response may be universal, our study specifically focussed on the knowledge of differences in enzyme metaboliser status. It was found that 79.1% of GPs (34/43) were unaware, with only a small number of GPs “always” considering enzyme metaboliser status (11.1%) in their management. Given the aforementioned  importance  of  genetic  factors  and  the  potential to reduce ADRs using personalised medicine, this is an area for improvement.

When considering pharmacogenomic testing, we found 79.5% (35/44) of GPs to be unaware of testing methods. No GP had ever utilised pharmacogenomic testing, this low rate of utilisation is also reported previously in other several studies. [22-24] A lack of utilisation and awareness arguably forms a barrier against the effective incorporation of personalised medicine in the primary care setting. These low figures represent a lack of education regarding pharmacogenomics and its clinical applications. This is an issue that has been recognised since the arrival of these testing methods. [25] McKinnon et al. [25] highlighted that this lack of education across healthcare professionals is significant enough to be considered a “barrier to the widespread uptake of pharmacogenomics”. To ameliorate the situation, the International Society of Pharmacogenomics has issued recommendations in 2005 for  pharmacogenomics  to  be  incorporated  into  medical  curricula. [26]  Another  contributing  factor  to  the  low  utilisation  of  testing could include the lack of subsidised tests available through Medicare. Currently, pathology labs do provide pharmacogenomic testing (such as Douglas Hanley Moir and Healthscope), however this is largely done so through the patient’s expenses as only two methods are subsidised by Medicare. [23,27,28]

Amongst those aware of pharmacogenomic testing, eight out of nine GPs answered that they would be more likely to utilise pharmacogenomic testing in black-African patients who had previously experienced ADRs; this is consistent with findings noted by van Puijenbroek et al. [29]. Among these GPs, factors that were noted to be potential influences in their utilisation of testing included: patient factors such as compliance and the reliability of the test, and, factors affecting the clinical picture (as described in Table 1). This is consistent with findings by studies that have also identified cost and a patient’s individual response to drugs as influential factors in a physician’s decision making. [29,30]

Considering that the majority of information regarding enzyme metabolism and pharmacogenomic testing was published in pharmacological journals,[6,8-14,30-32] much of this knowledge may not have been passed on to GPs. In order to understand the preferred media of information for GPs, we posed open-ended questions and discovered that the majority of GPs who answered the question (32/39), would prefer information in the form of writing (Table 3). This could be either in the form of online sources (such as guidelines, summaries, the National Prescribing Service or the Monthly Index of Medical Specialities) or peer reviewed journal articles. Current literature also reflects this preference for GPs to gain education regarding pharmacogenomics through journal articles. [22] The other preferred medium of education was through verbal teachings, peer discussions and presentations (Table 3), with there being specific interest in information being disseminated by clinical pathology laboratories; this is also reflected in the literature. [22,29]

Strengths and limitations

Small sample size is a limitation of this study with possible contributing factors including: the short amount of time allowed for data collection and the low response rate due to GP time constraints. Strengths of the study include the use of a validated questionnaire catered to our target population and open-ended questions which gave us further insight into GP preferences.

Implications and future research

Currently, anti-coagulants provide an example of the clinical applications of considering enzyme polymorphisms in patient management. [33,34] Warfarin is a particular example where variability in INR has been associated with enzyme polymorphisms, leading to the utilisation of dosage algorithms to optimise clinical outcomes. [34] Similarly, when using antidepressants, pharmacogenomic testing could play a role in clinical decision making with Samer et al. [5] suggesting dose reductions and serum monitoring for those with known PM status. However, as identified in our study, there is an overall lack of awareness regarding the differences in enzyme metaboliser status and the methods available for pharmacogenomic testing.

Future studies should focus on the clinical practicality of utilising these tests. Additionally, future studies should determine the effectiveness of the identified GP preferred modalities of education in raising awareness.

Conclusion

There is a low awareness among GPs regarding both the differences in enzyme metaboliser status in the black-African community, and the methods of pharmacogenomic testing.

To optimise clinical outcomes in black-African patients with depression, it  may  be  useful  to  inform  GPs  of  the  availability  and  application of pharmacogenomic testing. We have highlighted the preferred education modalities through which this may be possible.

Acknowledgements

We would like to acknowledge and thank Dr. Irina Piatkov for her support as a supervisor during this project.

Conflict of interest

None declared.

Correspondence

Y Joshi: 17239266@student.uws.edu.au

References

[1] Australian Institute of Health and Welfare. The burden of disease and injury in Australia 2003  [Internet].  2007  [cited  2014  April  25].  Available  from:  http://www.aihw.gov.au/ publication-detail/?id=6442467990

[2] Charles J, Britt H, Fahridin S, Miller G. Mental health in general practice. Aust Fam Physician. 2007;36(3):200-1.

[3] Pierce D, Gunn J. Depression in general practice: consultation duration and problem solving therapy. Aust Fam Physician. 2011;40(5):334-6.

[4]  Binder  EB,  Holsboer  F.  Pharmacogenomics  and  antidepressant  drugs.  Ann  Med. 2006;38(2):82-94.

[5] Samer CF, Lorenzini KI, Rollason V, Daali Y, Desmeules JA. Applications of CYP450 testing in the clinical setting. Mol Diagn Ther. 2013;17(3):165-84.

[6]  Alessandrini  M,  Asfaha  S,  Dodgen  MT,  Warnich  L,  Pepper  MS. Cytochrome  P450 pharmacogenetics in African populations. Drug Metab Rev. 2013;45(2):253-7.

[7] Yang X, Zhang B, Molony C, Chudin E, Hao K, Zhu J et al. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Res. 2010;20(8):1020-36.

[8] Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities and impact of genetic variation. Pharmacol Therapeut. 2013;138(1):103-41.

[9]  Guengerich  FP.  Cytochrome  P450  and  chemical  toxicology.  Chem  Res  Toxicol. 2008;21(1):70-83.

[10] Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J. 2005;5:6-13.

[11] Zhou S. Polymorphism of human cytochrome P450 2D6 and its clinical significance. Clin Pharmacokinet. 2009;48(11):689-723.

[12] Li-Wan-Po A, Girard T, Farndon P, Cooley C, Lithgow J. Pharmacogenetics of CYP2C19: functional and clinical implications of a new variant CYP2C19*17. Br J Clin Pharmacol. 2010;69(3):222-30.

[13] Xie HG, Kim RB, Wood AJJ, Stein CM. Molecular Basis of ethnic differences in drug disposition and response. Ann Rev Pharmacol Toxicol. 2001;41:815-50.

[14] Chen S, Chou WH, Blouin RA, Mao Z, Humphries LL, Meek QC et al. The cytochrome P450  2D6  (CYP2D6)  enzyme  polymorphism:  screening  costs  and  influence on  clinical outcomes in psychiatry. Clin Pharmacol Ther. 1996;60(5):522–34.

[15]  Hugo  G.  Migration  between  Africa  and  Australia:  a  demographic  perspective  – Background paper for African Australians: A review of human rights and social inclusion issues. Australian Human Rights Commission [Internet]. 2009 Dec [cited 2014 April 26]. Available  from:  https://www.humanrights.gov.au/sites/default/files/content/Africanaus/papers/Africanaus_paper_hugo.pdf

[16]  Joint  Standing  Committee  on  Foreign  Affairs,  Defence  and  Trade.  Inquiry  into Australia’s relationship with the countries of Africa [Internet]. 2011 [cited 2014 April 26]. Available  from:  http://www.aph.gov.au/Parliamentary_Business/Committees/House_of_Representatives_Committees?url=jfadt/africa%2009/report.htm

[17] Census 2006 – People born in Africa [Internet]. Australian Bureau of Statistics; 2008 August 20 [updated 2009 April 14; cited 2014 April 26]. Available from: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/3416.0Main+Features32008

[18]    Greater    Western    Sydney    Economic    Development    Board.    Some    national transport  and  freight  infrastructure  priorities  for  Greater  Western  Sydney  [Internet]. Infrastructure    Australia;    2008    [cited    April    25    2014].    Available    from:    http:// w w w. i n fras tru ctu r eau s tral i a. g o v. au /p u b l i c_su b mi ssi o ns/p u b l i sh ed /fi l es/368_ greaterwesternsydneyeconomicdevelopmentboard_SUB.pdf

[19] Furler J, Kokanovic R, Dowrick C, Newton D, Gunn J, May C. Managing depression among ethnic communities: a qualitative study. Ann Fam Med. 2010;8:231-6.

[20] Robjant K, Hassan R, Katona C. Mental health implications of detaining asylum seekers: systematic review. Br J Psychiatry. 2009;194:306-12.

[21] Hall-Flavin DK, Winner JG, Allen JD, Carhart JM, Proctor B, Snyder KA et al. Utility of integrated pharmacogenomic testing to support the treatment of major depressive disorder in a psychiatric outpatient setting. Pharmacogenet Genomics. 2013;23(10):535- 48.

[22] Stanek EJ, Sanders CL, Taber KA, Khalid M, Patel A, Verbrugge RR et al. Adoption of pharmacogenomics testing by US physicians: results of a nationwide survey. Clin Pharmacol Ther. 2012;91(3):450-8.

[23] Sheffield LJ, Phillimore HE. Clinical use of pharmacogenomics tests in 2009. Clin Biochem Rev. 2009;30(2):55-65.

[24] Corkindale D, Ward H, McKinnon R. Low adoption of pharmacogenetic testing: an exploration and explanation of the reasons in Australia. Pers Med. 2007;4(2):191-9.

[25]  McKinnon  R,  Ward  M,  Sorich  M.  A  critical  analysis  of  barriers  to  the  clinical implementation of pharmacogenomics. Ther Clin Risk Manag. 2007;3(5):751-9.

[26]  Gurwitz  D,  Lunshof  J,  Dedoussis  G,  Flordellis  C,  Fuhr  U,  Kirchheiner  J  et  al. Pharmacogenomics      education:      International      Society      of      Pharmacogenomics recommendations for medical, pharmaceutical, and health schools deans of education. Pharmacogenomics J. 2005;5(4):221-5.

[27]  Pharmacogenomics  [Internet].  Healthscope  Pathology;  2014  [cited  2014  October 22]    Available    from:    http://www.healthscopepathology.com.au/index.php/advanced pathology/pharmacogenomics/

[28]  Overview  of  Pharmacogenomic  testing.  Douglas  Hanley  Moir  Pathology;  2013 [cited  2014  October  22].  Available  from:  http://www.dhm.com.au/media/21900626/pharmacogenomics_brochure_2013_web.pdf

[29] van Puijenbroek E, Conemans J, van Groostheest K. Spontaneous ADR reports as a trigger for pharmacogenetic research: a prospective observational study in the Netherlands. Drug Saf. 2009;32(3):225-64.

[30]  Rogausch  A,  Prause  D,  Schallenberg  A,  Brockmoller  J,  Himmel  W.  Patients’  and physicians’ perspectives on pharmacogenetic testing. Pharmacogenomics. 2006;7(1):49- 59.

[31] Akilillu E, Persson I, Bertilsson L, Johansson I, Rodrigues F, Ingelman-Sundberg M. Frequent distribution of ultrarapid metabolizers of debrisoquine in Ethopian population carrying duplicated and multiduplicated functional CYP2D6 alleles. J Pharmacol Exp Ther. 1996;278(1):441-6.

[32] Bradford LD. CYP2D6 allele frequency in European Caucasians, Asians, Africans and their descendants. Pharmacogenomics. 2002;3:229-43.

[33] Cresci S, Depta JP, Lenzini PA, Li Ay, Lanfear DE, Province MA et al. Cytochrome p450 gene variants, race, and mortality among clopidogrel-treated patients after acute myocardial infarction. Circ Cardiovasc Genet. 2014 7(3):277-86.

[34] Becquemont L. Evidence for a pharmacogenetic adapted dose of oral anticoagulant in routine medical practice. Eur J Clin Pharmacol. 2008 64(10):953-60

Categories
Case Reports

Impact of socioeconomic status on the provision of surgical care

In Australia, there is an association between low socioeconomic status (SES) and poor health outcomes. Surgical conditions account for a large portion of a population’s disease burden. The aim was to determine the difference in provision of surgical care and patient satisfaction between low and high SES communities in Sydney, Australia. A cross sectional analytical study was conducted using questionnaire-based data. Patients were recruited from five general practice centres across low and high SES areas. Participants were eligible for this study if they had surgery performed under general anaesthesia  within  the  last  five years.  Analysis  was performed to determine whether waiting times for surgery and surgical consultations were different between low and high SES groups, and whether private health insurance impacted on waiting times. A total of 107 patient responses were used in the final data analysis. Waiting times for elective surgery were longer in the low SES group (p=0.002).The high SES group were more likely to have private health insurance (p <0.001) and were 28.6 times more likely to have their surgery in a private hospital. Private health insurance reduced waiting times for elective surgical procedures (p = 0.004), however, there was no difference in waiting times for initial surgical consults (p=0.449). Subjective patient satisfaction was similar between the two groups. In conclusion, our study demonstrates that SES does not impact on access to a surgical consultation, but a low SES is associated with longer waiting times for elective surgeries. Despite this, patients in both groups remained generally satisfied with their surgical care.

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Introduction

In Australia, low socioeconomic status (SES) has been linked to poor health outcomes [1] with a 1.3 times greater mortality risk in low SES areas when compared to the highest SES areas. [2-3] Individuals living in more disadvantaged areas are more likely to engage in unhealthy behaviours, and their poorer health is reflected in more frequent utilisation of health care services. [4] Greater Western Sydney represents one of the lowest SES areas in Sydney, Australia [5] and according to the Socio-Economic Indexes of Areas (SEIFA), contains eight of the ten most disadvantaged areas in Sydney. [5-6] For general elective procedures, average waiting times in Greater Western Sydney hospitals varied from 23 to 93 days, compared with 4 to 36 days in other areas of Sydney. [6] Thus, timely and easily accessible provision of surgical services is a growing necessity for the expanding population of Greater Western Sydney.

Methods

The research was approved by the University of Western Sydney Human Research Ethics Committee (H9067).  The SEIFA [7] score was used to determine the areas chosen for data collection. A total of five Sydney General Practices, three located in low SES areas and two in high SES areas, were chosen randomly for patient recruitment.

The data collection tool employed was a survey which included questions relating to SES factors, health fund status, comorbidities, details of the surgical procedures undertaken, waiting times for operations,  follow-up   consultations,  post-operative   complications and patient satisfaction. The survey and written consent were offered to all General Practice waiting room patients over a period of two weeks by the authors. Patients were eligible to participate if they had undergone a surgical procedure in Sydney, performed under general anaesthesia within the last five years. The survey was anonymous with no personally identifying information recorded.

Data were analysed using Microsoft Excel 2010 and SPSS software version 22.0. Logarithmic values were calculated for all data sets and t-tests performed for analysis. Chi-squared analyses were conducted to assess the effect of private health insurance on hospital choice.

Results

A total of 107 surveys were eligible for analysis after excluding dental procedures, colonoscopies, procedures performed outside Sydney, emergency procedures, caesarean sections and respondents under 18 years of age.

Table 1 illustrates the characteristics of the sample studied. Notable differences between responses from high and low SES areas include level of education and private health insurance status. The median ages were 56 for low SES and 66 for high SES (p=0.02). Table 2 displays the types of surgical procedures that were included in the study.

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Waiting times

The average waiting time for consultation with a surgeon was 2.5 weeks in the low SES group and two weeks in the high SES group (p=0.449). Private health insurance status did not influence this waiting time. Waiting times for elective surgery were on average six weeks in the low SES group and 2.5 weeks in the high SES group (p=0.002). Possession of private health insurance was associated with a decreased waiting time (p=0.004).

Private health insurance and choice of hospital

Responders with private health insurance were 28.6 times (p < 0.001) more likely to have surgery performed at a private hospital.

Patient satisfaction

Table 3 demonstrates rates of patient satisfaction between the low and high SES groups. There was an overall trend for patients in the lower SES groups to be dissatisfied with waiting times but be generally satisfied with other aspects of surgery.

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Discussion

The study found that patients from lower SES groups had less private health insurance and longer wait times for surgery. Despite this, a high level of satisfaction was expressed across both SES groups regarding surgical outcomes and overall medical care during hospital admission.

These findings were anticipated and are consistent with previous research which has shown that patients in the public system experienced longer waiting times and were 60-95% less likely to undergo surgery than private patients. Furthermore, privately insured patients were also found to have greater access to surgical care, shorter overall length of stay and lower mortality rates. [8] This relationship creates the premise that increasing access to private care will relieve the burden on the public system and reduce waiting times. However, the converse has been shown to be the case, with an increase in waiting times for surgery when access to private hospitals is increased. [9] The trend for generally high levels of satisfaction is counter-intuitive, however, is consistent with the literature. [10-11]

The implications of longer waiting times in Western Sydney is of concern because the region’s population is expected to grow by 50% over the next 20 years, a growth of 1 million people [12], and the availability of health care services will have to expand to accommodate this increasing population. There are increasing numbers of additions to public hospital elective surgery waiting lists every year. [13] Availability and staffing of beds in public hospitals are lower in the Western Sydney region, and there is a relative lack of private hospitals compared to the wider Sydney metropolitan area [6]. Compounding the issue of access

to healthcare are lower rates of private health insurance membership and the generally poorer health of low SES populations. [6] It becomes apparent  that  there  is  a  relative  lack  of  services  available  in  low SES areas of Sydney. It is estimated that the cost of funding enough public hospital beds to accommodate a populace of this size would be a minimum of $1.29 billion a year. This poses the risk of escalating inequality in access to health services between the low SES areas of Western Sydney and the wider metropolitan area. [6] The NSW government has invested $1.3 billion from the recent health budget to upgrade existing hospitals [14], however, ongoing funding of these hospitals will need to increase to accommodate the growing demand. [6]

Data were collected from a small number of locations across only three SES regions in Sydney, providing a limited sample size for analysis. Recall bias would also have an impact on accuracy of responses, despite the criteria for a five year cut off. Future research would benefit from increasing data collection across a larger number of SES sites to reduce any possible sample bias. Furthermore, expanding data sources to include hospital databases would minimise recall bias, allowing for more objective and accurate data regarding the length of time spent on surgical waiting lists and utilisation of private health cover.

Conclusion

It is well established that a low SES is associated with poorer health. This study has found that patients from low SES areas experienced longer waiting times for elective surgery. A contributing factor to the longer waiting times was possession of private health insurance. Patients from low SES areas felt that they waited too long for their surgery; however, overall satisfaction ratings were generally high across both SES groups. The interplay between SES and the public and private health systems has created a disparity in access to timely elective surgery.

Acknowledgements

None.

Conflict of interest

None declared.

Correspondence

Z El-Hamawi: z.elhamawi@hotmail.com

References

[1] Armstrong BK, Gillespie JA, Leeder SR, Rubin GL, Russell LM. Challenges in health and health care for Australia. Med J Aust. 2007;187(9):485-489.

[2] Korda RJ, Clements MS, Kelman CW. Universal health care no guarantee of equity: Comparison of socioeconomic inequalities in the receipt of coronary procedures in patients with acute myocardial infarction and angina. BMC Public Health. 2009 14;9:460.

[3] Clarke P, Leigh A. Death, dollars and degrees: Socio-economic status and longevity in Australia. Economic Papers: 2011 Sept 3;30(No. 3): 348–355.

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