General practitioner assessment of lifestyle risk factors for chronic disease: a cross-sectional study in urban, rural and remote South Australia

Introduction

A significant burden of chronic disease in Australia is attributable to lifestyle risk factors of smoking, nutrition, alcohol and physical activity (SNAP), which are linked to chronic diseases, such as hypertension, hyperlipidaemia, type 2 diabetes, obesity and cardiovascular disease (CVD; AIHW 2021a, 2021b). In 2011–2012, two-thirds of the adult population (66%) had three or more lifestyle or biomedical risk factors at the same time (AIHW 2015). Managing SNAP risk factors is fundamental to primary prevention of CVD and other chronic diseases, and the Royal Australian College of General Practitioners (RACGP) guidelines recommend routine and/or opportunistic screening for SNAP risks, with regular assessments depending on age and risk of disease (RACGP 2015, 2016).

The burden of chronic disease and the prevalence of SNAP risk factors are higher in rural and remote populations in Australia (AIHW 2021a, 2022a). An estimated 8.6% of the total burden of chronic disease is attributed to smoking, and remote/very remote areas have a higher proportion of daily smokers (19.6% aged ≥14 years) compared with major cities (9.7%; AIHW 2021b). Adults in outer regional and remote areas are also 1.7 times more likely to exceed alcohol consumption guidelines (i.e. more than two standard drinks per day) than those in major cities (24.4% and 14.7%, respectively; AIHW 2022a). Adherence to dietary and physical activity guidelines is poor across Australia, with less than one in 10 adults meeting the recommendations for daily vegetable consumption (AIHW 2022b), and 55% not meeting physical activity guidelines in 2017–2018 (AIHW 2020). Fewer adults in outer regional and remote areas (47%) meet the daily fruit recommendation (i.e. two serves per day) compared with major cities (52%; AIHW 2022b). Rural and remote populations can face barriers to accessing affordable healthy food; furthermore, they have higher levels of socioeconomic disadvantage and reduced access to healthcare services (National Rural Health Alliance 2017).

The higher distribution of at-risk patients and reduced access to health services in rural and remote areas increases the importance of SNAP assessments in rural general practice. Overall, the level of assessment performed by GPs is variable, with some studies reporting <20% of patients being assessed for specific lifestyle risk factors (Amoroso et al. 2005; Denney-Wilson et al. 2010; Passey et al. 2010; Gonzalez-Chica et al. 2019). Differences in SNAP assessment and counselling between urban and rural GPs have also been reported. Using the ‘Bettering the Evaluation and Care of Health’ data, Beattie et al. (2017) reported that rural GPs in Western Victoria (n = 101 GPs and 10 100 patients) were less likely to provide advice and counselling for diet and exercise than their state and national counterparts, despite the higher prevalence of these risk factors in rural populations. Another study including 63 GPs and practice nurses in NSW reported rural clinicians were less likely to provide lifestyle advice about smoking compared with urban clinicians (83% vs 100%), and were generally less positive about the benefits of lifestyle counselling for their patients (Passey et al. 2010). These studies suggest there is scope to improve SNAP assessment and counselling in general practice, especially in rural settings. (Passey et al. 2010; Allenby et al. 2016; Beattie et al. 2017; Gonzalez-Chica et al. 2019). Still, additional evidence is required to assess the magnitude of this urban–rural gap to guide public health policies.

The main aims of this study were: (1) to investigate the prevalence of SNAP assessments performed by the GP in the past 12 months using a large population-based survey from South Australia, (2) to compare whether SNAP assessments differed depending on the remoteness of residence, and (3) to identify if patients at a higher risk or with existing chronic conditions were more likely to have their SNAP assessed in urban or rural settings.

Methods

A cross-sectional population-based survey (Health Omnibus Survey) was conducted in South Australia between September and December 2017 by Harrison’s Research (Taylor et al. 2006). Details of the sampling process have been reported previously (Taylor et al. 2006; Gonzalez-Chica et al. 2019) Briefly, a multistage non-replacement sampling process was used to select a random sample of all individuals aged ≥15 years in the state. From the total 4188 Statistical Areas Level 1 (SA1s), 398 major city and 132 rural or remote SA1s were selected, based on the Accessibility/Remoteness Index of Australia classification (ABS 2016). Due to logistical constraints, very remote areas were not sampled. Ten households were randomly selected from each SA1, and a face-to-face interview was conducted with one randomly selected resident from each household. Interviews included questions about self-reported health conditions, SNAP lifestyle risks, health service use in the past 12 months and sociodemographics. Individuals with a terminal illness or a mental incapacity (n = 60), or unable to speak English (n = 77) were ineligible for the study. Of the 4320 eligible participants, 1343 refused to answer the survey, providing a final sample of 2977 individuals (68.9%). Only those aged ≥18 years who visited a GP in the past 12 months were included in our analyses (n = 2775).

Results

The proportion of individuals (aged ≥18 years) who reported visiting a GP in the past 12 months was 92.2% (95% CI 90.8–93.4), and was higher in urban (93.5%; 95% CI 92.0–94.8) compared with rural (90.2; 95% CI 85.8–93.3) and remote (84.7%; 95% CI 78.8–89.2) locations (P < 0.001). These 2775 individuals (aged ≥18 years who visited their GP in the past 12 months) comprise the sample population for analysis (weighted sample 2675). As shown in Table 1, 52.7% were women, the mean age was 49.1 ± 18.7 years, 35.7% had up to secondary level of education and 77.5% lived in an urban area. Table 1 also shows the proportions with self-reported SNAP risk factors. The most frequent were inadequate fruit/vegetable consumption (67.9%) and physical inactivity (67.2%), followed by excessive alcohol intake (30.4%) and current smoking (15.3%). Furthermore, 62.1% of participants reported having two or more SNAP risk factors. Approximately 25% of participants self-reported having either high blood pressure, high cholesterol or obesity, 12.6% had diabetes and 7.9% had CVD.

Assessment of SNAP factors according to remoteness

Table 2 summarises the distribution of the sociodemographic and SNAP risk factors, and biomedical conditions, by the remoteness of residence. Individuals living in urban areas were more likely to be younger, have a higher education level, be working full-/part-time or with a higher IRSAD than those in rural or remote areas. Smoking was the only SNAP risk factor that differed according to the remoteness area, with a prevalence 1.6–1.8 times higher in rural and remote areas than in urban areas. A higher prevalence of diabetes, high blood pressure and CVD was also observed in rural and remote areas than in urban areas, but the frequency of high cholesterol or obesity was similar in all areas.

Table 2.Distribution of sociodemographic characteristics, SNAP risk factors and biomedical conditions by remoteness of residence in South Australia, Health Omnibus Survey 2017.

Variables	Proportion (%)A			P-valueB
	Urban	Rural	Remote
Sociodemographic
Sex (male)	47.1	46.2	50.2	0.612
Age: mean (s.d.)	48.1 (18.0)	50.4 (19.7)	55.4 (21.1)	<0.001C
Marital status:
Married/living with a partner	64.0	65.3	62.6
Single/divorced/widowed	36.0	34.7	37.4	0.843
Education
Up to secondary	32.3	42.5	54.2
Certificate/diploma/trade	38.4	42.4	33.4
Bachelor or higher	29.3	15.1	12.4	<0.001
Work status
Full/part-time	55.9	48.7	45.8
Not in the workforce	22.0	24.9	14.5
Retired	22.2	26.4	39.8	<0.001
SEIFA-IRSAD
Low	26.9	52.3	49.5
Intermediate	41.1	38.0	43.1
High	32.0	9.8	7.4	<0.001
SNAP risk factors (% yes)
Current smoker	13.1	23.7	21.5	<0.001
Nutrition <5 (fruit or veg)/day	68.6	65.1	66.0	0.393
Alcohol >2 standard doses/day	30.4	31.7	27.9	0.707
Physical activity <150 min/week	68.1	65.2	62.6	0.283
Biomedical conditions (% yes)
Diabetes	11.4	15.6	18.3	0.001
High blood pressure	28.1	32.1	38.9	0.003
High cholesterol	24.2	24.2	27.8	0.453
Obesity	25.6	27.2	24.9	0.750
Existing CVD	6.8	10.8	13.7	<0.001

Urban (MMM1-2 – major cities and regional centres), Rural (MMM3-4 – large and medium rural towns), Remote (MMM5-6 – small rural towns and remote communities). Weighted populations: urban n = 2073; rural n = 357; remote n = 246. SEIFA-IRSAD = Index of relative socioeconomic advantage and disadvantage; SNAP risk factors = current smoker, <5 portions of fruit and/or vegetables/day, >2 standard doses of alcohol/day; <150 min/week of moderate/vigorous physical activity.

SNAP, smoking, nutrition, alcohol intake, physical activity; HOS, Health Omnibus Survey; Obese, body mass index ≥30 kg/m² (self-reported weight and height); CVD, cardiovascular disease including myocardial infarction, angina, heart failure, stroke.

^AFor participants who reported they attended a GP in the last 12 months.

^BChi-squared test for heterogeneity.

^CAdjusted Wald test for equivalence of means.

Fig. 1a shows the number of self-reported SNAP risk factors was not different according to the remoteness of residence (P = 0.541), with <10% meeting all SNAP lifestyle recommendations in any area. Fig. 1b shows the prevalence of SNAP assessments according to remoteness. Overall, 9.8% (95% CI 8.4–11.3) of all participants reported the four SNAP factors were assessed by a GP in the past 12 months, with a higher frequency in rural (15.2%) and remote areas (13.3%) than in urban areas (8.4%; Fig. 1b, P-value < 0.001). Over half of all participants (51.2%; 95% CI 49.3–54.2) reported none of the SNAP factors were assessed in the past 12 months.

Fig. 1.

(a) Number of SNAP risk factors and (b) number of SNAP assessments performed by the GP in the past 12 months according to the remoteness of residence. Urban (MMM1-2 – major cities and regional centres), Rural (MMM3-4 – large and medium rural towns), Remote (MMM5-6 – small rural towns and remote communities). Weighted populations: urban n = 2073; rural n = 357; remote n = 246. SNAP = smoking, nutrition, alcohol intake, physical activity. SNAP risk factors = current smoker, <5 portions of fruit and/or vegetables/day, >2 standard doses of alcohol/day; <150 min/week of moderate/vigorous physical activity. a – Test of heterogeneity comparing the frequency of these variables according to the remoteness of residence.

Fig. 2 shows that, after adjustment, the assessment of ≥2 SNAP factors by a GP in the past 12 months was more prevalent in rural areas (43.8%; 95% CI 36.4–51.2) than in urban (29.7%; 95% CI 27.2–32.2) or in remote areas (34.7%; 95% CI 28.4–41.0).

Fig. 2.

Adjusted prevalence of ≥2 SNAP assessments performed by the GP in the past 12 months according to the remoteness of residence. Urban (MMM1-2 – major cities and regional centres), Rural (MMM3-4 – large and medium rural towns), Remote (MMM5-6 – small rural towns and remote communities). Weighted populations: urban n = 2073; rural n = 357; remote n = 246. SNAP = smoking, nutrition, alcohol intake, physical activity. a – Adjusted prevalence (i.e. marginal adjusted probabilities) obtained using logistic regression model adjusted for sociodemographic differences across geographical areas (sex, age, marital status, educational level, working status and IRSAD). b – Test of heterogeneity comparing the prevalence of SNAP assessments according to the remoteness of residence. Vertical lines represent the 95% CI.

Fig. 3a shows that, irrespective of the remoteness of residence, the adjusted prevalence of ≥2 SNAP assessed by a GP in the past 12 months increased with the number of existing SNAP risk factors. However, the magnitude of the association was two to three times higher in remote areas than in urban or rural areas (P-value for interaction = 0.034). The highest prevalence of ≥2 SNAP assessments was for those living in rural (57.1%; 95% CI 45.3–68.9%) or remote areas (55.5%; 95% CI 41.1–69.8), who self-reported having three or four SNAP risk factors. Fig. 3b shows those with CVD were more likely to be assessed (~50–55%) than those at risk of CVD or with none of the biomedical conditions. In this case, the magnitude of the associations was similar in all geographical areas (P-value for interaction = 0.21).

Fig. 3.

Adjusted prevalence of ≥2 SNAP assessments performed by the GP in the past 12 months according to the remoteness of residence and (a) number of SNAP risk factors or (b) presence of biomedical conditions. Urban (MMM1-2 – major cities and regional centres), Rural (MMM3-4 – large and medium rural towns), Remote (MMM5-6 – small rural towns and remote communities). Weighted populations: urban n = 2073; rural n = 357; remote n = 246. SNAP = smoking, nutrition, alcohol intake, physical activity; OR_adj = adjusted odds ratio. SNAP risk factors = current smoker, <5 portions of fruit and/or vegetables/day, >2 standard doses of alcohol/day; <150 min/week of moderate/vigorous physical activity. At risk of CVD = obese, with diabetes, hypertension and/or dyslipidaemia but no CVD. CVD = myocardial infarction/angina, heart failure and/or stroke. a – Adjusted prevalence (i.e. marginal adjusted probabilities) obtained using logistic regression models adjusted for differences across geographical areas. b – Test for trend in each geographical area. Vertical lines represent the 95% CI. P-value for interaction: SNAP risk factors*remoteness = 0.034; presence of biomedical conditions*remoteness = 0.21.

For the individual SNAP risk factors, the crude proportion of participants who reported being assessed was 32.9% for physical activity, 28.6% for nutrition, 22.7% for alcohol intake and 21.9% for smoking. Fig. 4 shows the adjusted prevalence of assessments for each of the SNAP factors, across remoteness areas. Assessments were higher in rural areas for smoking (32.4%; 95% CI 26.1–38.7; P < 0.001) and alcohol intake (34.4%; 95% CI 27.6–41.2; P < 0.001). No differences were observed for assessments for nutrition and physical inactivity across remoteness areas.

Fig. 4.

Adjusted prevalence of assessments for individual SNAP factors performed by the GP in the past 12 months according to the remoteness of residence. Urban (MMM1-2 – major cities and regional centres), Rural (MMM3-4 – large and medium rural towns), Remote (MMM5-6 – small rural towns and remote communities). Weighted populations: urban n = 2073; rural n = 357; remote n = 246. SNAP = smoking, nutrition, alcohol intake, physical activity. Adjusted prevalence (i.e. marginal adjusted probabilities) obtained using logistic regression model adjusted for sociodemographic differences across remoteness areas (sex, age, marital status, educational level, working status and IRSAD).

Discussion

Our results show that only one-third of survey participants reported being assessed for two or more SNAP factors by their GP in the past 12 months. The prevalence of assessments was higher in rural areas and among those with multiple SNAP risk factors, biomedical conditions or existing CVD. These findings suggest a greater focus on SNAP assessments for secondary rather than primary prevention in all locations.

The overall prevalence of ≥2 SNAP assessments (32%) is low when compared with the levels of SNAP risk factors in the study population, with ~60% of participants reporting the presence of two or more SNAP risks in all remoteness areas. Inadequate nutrition and physical inactivity were most common, reported by two-thirds of all participants. Additionally, approximately one-quarter of all participants reported having high blood pressure, high cholesterol or obesity. Men, smokers and those with biomedical conditions (diabetes, high cholesterol, obesity) were more likely to be assessed for ≥2 SNAP, suggesting GPs are more vigilant in assessing higher-risk patients. Men may be prioritised because of reportedly higher levels of risk factors and risk of CVD (Anand et al. 2008; Ng et al. 2020). Participants in older age groups (≥65 years) were less likely to be assessed for ≥2 SNAP, despite the risk of chronic disease increasing with age. Notably, the RACGP Red Book guidelines for preventive activities do not recommend asking about all SNAP risk factors as part of preventive activities in older age (>65 years), which may account for this finding (RACGP 2016).

The nature of GP encounters differs between urban and rural practice for a variety of reasons (Britt et al. 2001). Having fewer rural practitioners and higher levels of chronic disease would be expected to limit time for preventive care in rural consultations (AIHW 2022a). In contrast, a greater continuity of care in rural practices could increase the prevalence of SNAP assessments (Rygh and Hjortdahl 2007). We found the adjusted prevalence of ≥2 SNAP assessments was 1.5 times higher in rural areas (43.8%) compared with urban areas (29.7%). Assessments for smoking and alcohol intake were 1.6–1.7 times higher in rural areas than in urban areas, but assessments for nutrition and physical activity were similar. Assessments tended to parallel risks, as the prevalence of smoking, diabetes, high blood pressure and existing CVD were all higher in rural and remote areas compared with urban areas. Depending on the rurality, ≥2 SNAP assessments were two to seven times more likely among participants with three or four SNAP risk factors, and 2.5–3.4 times more likely among participants with existing CVD, suggesting a greater focus on assessments for secondary prevention. Alternatively, there may be more opportunities for assessments if patients are visiting the GP more frequently.

Although our findings suggest that rural GPs are assessing SNAP risks at a higher level than urban GPs, other studies have shown mixed results. In contrast to our findings, Beattie et al. (2017) found that rural GPs in Victoria provided advice on nutrition and physical activity less frequently than urban GPs, possibly because of the time required to assess and counsel. Another study in New South Wales found that rural GPs were less likely than urban GPs to provide advice about smoking (Passey et al. 2010). Denney-Wilson et al. (2010) reported that GPs generally assess smoking and alcohol more often than nutrition and physical activity, whereas our results showed higher assessments for physical activity. The lack of consensus may be explained by the different study participants (GPs or patients), study designs and settings, making it difficult to directly compare these findings. One point of consensus across these studies is that advice about lifestyle risk factors is not provided at a rate that matches their prevalence (Denney-Wilson et al. 2010; Passey et al. 2010; Beattie et al. 2017). A range of potential barriers to GP assessments have been reported, including GP attitudes, knowledge and time constraints (Beattie et al. 2017).

A key strength of this study is the use of population-based, representative survey data comprising quality-controlled interview-based assessments. Furthermore, the survey provided information for potential sociodemographic and clinical confounders in relation to GP visits and SNAP assessments. However, we cannot exclude the possibility of recall bias and misclassification, because all measures were self-reported. It is also possible that we underestimated the prevalence of SNAP assessments conducted because of the following: (1) the survey did not capture assessments conducted by practice nurses (Morris et al. 2022), (2) each SNAP factor was assessed using single binary questions, and (3) the length of the recall time for these items (12 months). Another limitation is the time elapsed since the survey was undertaken when there have been substantial changes in health-related behaviours and GP visits due to the COVID-19 pandemic (Chan et al. 2022). However, the data used in our study were from the most recent survey available in South Australia that investigated SNAP assessments by GPs (Wellbeing SA 2022). It would be timely to repeat the survey and expand it to include the growing population of non-English speaking groups, and to broaden the questions to include assessments conducted by practice nurses. Finally, we have only reported the prevalence of assessments for participants who visited their GP in the past 12 months. A greater proportion of rural/remote participants (10–15%) did not visit a GP compared with 6% of those in metropolitan areas, consistent with limited access in rural and remote areas (AIHW 2022a).

In conclusion, our results indicate that greater attention to GP SNAP assessments is warranted, especially for primary prevention, to match the high prevalence of SNAP risks across the state. The higher reporting of SNAP assessments in rural areas is commensurate with higher levels of smoking, CVD and some biomedical risks. Although GP SNAP assessments are fundamental to disease prevention, access to other rural health services and amenities may also be required to promote healthy lifestyles, and reduce the burden of chronic disease in rural and remote populations.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflicts of interest

The authors declare they have no conflicts of interest.

Declaration of funding

This research did not receive any specific funding. Australian Rural Clinical Schools are funded through the Australian Government Rural Health Multidisciplinary Training grant scheme.