Can GPs use smoking status data in electronic health records to recruit patients for lung cancer screening? A descriptive study using the MedicineInsight database

Can GPs use smoking status data in electronic health records to recruit patients for lung cancer screening? 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A descriptive study using the MedicineInsight database Abira Chandrakumar, Fernanda Nobre, Elizabeth Hoon, Nigel Stocks This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8835194/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: In 2025, lung cancer screening commenced in Australia, with general practitioners (GPs) being patients’ first point of contact to access the program. However, there is a gap in understanding how GPs can effectively identify screening-eligible individuals. Therefore, our study examined how smoking status data are recorded in GPs’ electronic health records (EHRs), describing differences in smoking status data by socio-demographic characteristics and co-morbidities, and identifying missing data in EHRs. Methods: A cross-sectional study was conducted. We investigated 1,366,586 regular adult patients attending general practices in the MedicineInsight database, between 1 July 2021 and 30 June 2022. We assessed patient-related information as proportions, including ‘current’ smoking status, socio-demographic characteristics, co-morbidities, and the number of cigarettes smoked per day amongst smokers. Results: In our study, 156,881 (11.5%) regular adult patients were recorded as smokers, 311,503 (22.8%) as ex-smokers, 766,091 (56.1%) as non-smokers, and 132,111 (9.7%) did not have smoking status recorded. Smoking status data was better recorded in older age groups, including the lung cancer screening-eligible population of 50–70-year-olds. Lower socio-economic areas and remote/ very remote areas had higher rates of smokers and ex-smokers. Co-morbidities such as chronic obstructive pulmonary disease, diabetes and cardiovascular disease had higher rates of current and ex-smokers, compared to non-smokers. EHR recording of cigarettes smoked per day were limited to only 776 smokers. Conclusions: Our study indicates that GPs can use their EHRs to help recruit patients for LCS, as ‘current’ smoking status was recorded for over 90% of regular patients. However, GPs need to further assess these patients for screening eligibility, as detailed smoking history data were poorly recorded. Priority populations for GPs include those from lower socio-economic and remote/ very remote areas, and with certain co-morbidities. GPs need to be better supported, especially with adequate funding, given the additional workload and time required. Primary care general practitioners electronic health records lung cancer screening smoking Figures Figure 1 Figure 2 Introduction Lung cancer is a leading cause of cancer-related illness and death in Australia( 1 ). Early detection of lung cancer has been strongly associated with improved outcomes and survival, yet achieving early diagnosis has been challenging as lung cancer presents with non-specific symptoms( 2 ). The Australian National Lung Cancer Screening Program (NLCSP) commenced in July 2025, with the aim of improving early detection of lung cancer through screening of high-risk individuals( 3 ). The current NLCSP eligibility criteria include individuals aged between 50 and 70 years old, who either currently smoke cigarettes or have quit smoking in the past 10 years, and have a 30 or more pack-year smoking history( 3 ). The NLCSP is led by the Commonwealth Government, with support from the states and territories( 3 ). As general practitioners (GPs) are patients’ first point of contact to participate in the program, they are involved in assessing patient eligibility for lung cancer screening (LCS) and referring LCS-eligible individuals for low-dose computed tomography (LDCT)( 3 , 4 ). They are also responsible for reviewing LDCT results and organising necessary follow-ups( 3 ). Although GPs can play a pivotal role in the delivery of LCS, there is a gap in understanding how GPs can effectively identify LCS-eligible individuals within their practices. GPs already experience high workloads and significant time pressure( 5 ), and existing literature has highlighted that the additional workload on GPs could impact LCS implementation( 1 ). Furthermore, the ease of identifying LCS-eligible participants can influence LCS uptake( 1 ). Thus far, the prevalence of LCS-eligible individuals in Australia has been estimated using data from the National Drug Strategy Household Survey and Australian Bureau of Statistics (ABS)( 6 ). Electronic Health Records (EHRs), which are widely used by GPs, also have the capacity to inform LCS eligibility through the availability of comprehensive patient data, including socio-demographic factors and clinical information, such as smoking status( 7 ). EHRs have been used internationally to identify patients for cancer screening, resulting in earlier cancer diagnoses and greater participation in screening programs( 7 ). This is particularly important for targeted screening, such as LCS. However, routinely collected EHR data come with challenges, including data completeness and accuracy( 7 ). Therefore, in this study, we aimed to understand how GPs can use their general practice EHRs to recruit patients for LCS. We used the MedicineInsight database, which contains routinely collected general practice EHR data( 8 ), and explored how smoking status data are recorded in EHRs, including any differences in smoking status data across certain socio-demographic characteristics and co-morbidities. We aimed to help GPs determine priority groups for LCS invitations, as certain socio-demographic characteristics and co-morbidities have been associated with increased rates of lung cancer( 9 ). We also aimed to identify missing EHR data in assessing LCS eligibility and compared our data to national-level data. Methods Study design and data source This descriptive, cross-sectional analysis used data from MedicineInsight, a national general practice database, which contains de-identified EHRs of over 2 million patients from 662 general practices across Australia( 8 ). MedicineInsight was developed and managed by NPS MedicineWise until December 2022 with funding support from the Australian Government Department of Health. It is now managed by the Australian Commission on Safety and Quality in Health Care. The database contains information on patient socio-demographic factors, clinical encounters (excluding progress notes), diagnoses, pathology requests, and prescribed medications( 8 ). For this study, we used the most recent database provided by NPS MedicineWise, in October 2022, to the Discipline of General Practice at The University of Adelaide. Study participants The study population included all adults (18 + years), who attended a MedicineInsight practice between 1 July 2021 and 30 June 2022, with at least three consultations in the preceding two years, in accordance with the Royal Australian College of General Practitioner’s (RACGP) definition of a regular patient( 10 ). This approach accounts for the fact that complete patient information is typically collected over more than one consultation. Patients who are “non-regulars” can introduce bias, due to incomplete and unavailable data in EHRs, leading to under-estimated results( 11 ). The final dataset included 1,366,586 individuals. Please see Fig. 1 for details of the sample selection. To explore the number of cigarettes smoked per day by current smokers, inclusion criteria were broadened to include all adults (18 + years) who attended a MedicineInsight practice between 1 July 2021 and 30 June 2022 (not just regular patients), due to the limited availability of smoking quantity data. Please insert Fig. 1 here *Between 1 July 2021 and 30 June 2022. † Regular patients: ≥3 consultations in 2020–2022 Variables The primary outcome was current smoking status, defined as the most recently recorded status in the patient's EHR. Smoking status was categorised as: smokers, ex-smokers, non-smokers and ‘not recorded’. Information on co-morbidities was extracted from the fields “reason for encounter” and “diagnosis” using a text-based algorithm that searched for diagnostic terms, their synonyms, and acronyms. Conditions used in this study were: hypertension, diabetes, cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), and lung cancer. Socio-demographic characteristics included age, gender, socio-economic status and remoteness. Age was categorised as: 18–34, 35–49, 50–59, 60–70, ≥ 71 years, with the LCS age-eligible population divided into 50–59 years and 60–70 years. Socio-economic status was determined using the ABS Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD), which is an area-level measure of socio-economic status (SES) derived from patients’ postcodes( 12 ). Patients’ remoteness were determined using the ABS Australian Statistical Geography Standard Remoteness Structure(13). We attempted to collect information on smoking pack-year history, but the data were limited to the ‘cigsday’ variable in the database. This was further classified into ‘light smoking (1–9 cigsday)’, ‘moderate smoking (10–19 cigsday)’ and ‘heavy smoking (20 + cigsday)’. Statistical analysis Descriptive statistics summarised the distribution of smoking status, socio-demographic characteristics, and co-morbidities. To assess differences in patient characteristics across smoking status categories, proportions with 95% confidence intervals (CI) were calculated for categorical variables across four categories: current smokers, ex-smokers, non-smokers, and smoking status 'not recorded'. Chi-square tests were used to assess statistical significance of differences in proportions. All analyses were performed in Stata 18.0 (StataCorp, Texas, USA), with practices treated as clusters to account for potential correlation in outcomes among patients attending the same practice. Ethics Ethics approval to use the MedicineInsight database was given by the NPS MedicineWise Data Governance Committee. The Humans Research Ethics Committee of The University of Adelaide has exempted all studies using de-identified MedicineInsight database for ethical review, as it uses secondary data with no possibility of re-identification. Results Participants Out of the 1,366,586 regular patients attending a MedicineInsight general practice between 1 July 2021 and 30 June 2022, 156,881 (11.5%, 95%CI 10.6–12.4) were recorded as smokers, 311,503 (22.8%, 95%CI 22.1–23.5) as ex-smokers, 766,091 (56.1%, 95%CI 54.7–57.4) as non-smokers, and 132,111 (9.7%, 95%CI 8.7–10.8) did not have their smoking status recorded. Similar proportions of males and females were recorded as smokers and ex-smokers, but larger proportions of females were reported as non-smokers (63.4%) or did not have their smoking status recorded (56.7%), as shown in Fig. 2 . 29.1% of patients in the youngest age group (18–34-year-olds) did not have their smoking status recorded, having the highest “not recorded” smoking status across all age groups. Smoking status data were better recorded for 50-59-year-olds and 60-70-year-olds, with only 13.4% and 17.0% respectively having their smoking status data missing. The majority of ex-smokers were in the older age groups, with 25.6% in the 60–70 years-old age group and 31.7% aged 71 years or older. Relevant to LCS, 40.3% of all smokers were in the 50–70-years-old age group. The majority of smokers were in the low and very low quintiles of IRSAD (24.6% and 24.0% respectively), compared to the very high quintile of IRSAD (13.5%). However, ex-smokers were more evenly distributed across all IRSAD quintiles. In ‘remote/ very remote’ areas, more patients were recorded as being smokers (18.9%) or ex-smokers (15.8%), compared to non-smokers (11.4%). Cigarettes per day Out of the 776 individuals attending a MedicineInsight general practice between 1 July 2021 and 30 June 2022, who had data documented on their number of cigarettes smoked per day, 170 (21.9%) were recorded as having light smoking (1–9 cigsday), 259 (33.4%) as having moderate smoking (10–19 cigsday), and 347 (44.7%) as heavy smoking (20 + cigsday). Co-morbidities An estimated 28.7% of smokers and 49.9% of ex-smokers had a recorded diagnosis of hypertension, compared to 32.6% of non-smokers. 17.1% of those with hypertension did not have their smoking status recorded. Both co-morbidities of diabetes and CVD had larger numbers of ex-smokers, with 18.2% and 18.6% respectively, compared to current smokers (12.1% and 9.2% respectively). 11.3% of smokers and 11.1% of ex-smokers were recorded as ever having a diagnosis of COPD, compared to 2.0% of non-smokers and 1.4% of those without their smoking status recorded. Previous lung cancer history was recorded in 0.5% of smokers, 0.8% of ex-smokers, 0.1% of non-smokers and 0.2% of those with no smoking status recorded. Table 1 : Socio-demographic and health characteristics of regular patients by smoking status in 2022 a . Adults aged 18 + years ( N = 1,366,586). Table 1 Socio-demographic and health characteristics of regular patients by smoking status in 2022 a . Adults aged 18 + years ( N = 1,366,586). Smoker Ex-smoker Non-smoker Not stated/not recorded (n = 156,881) (n = 311,503) (n = 766,091) (n = 132,111) Proportion (%) 95% CI Proportion (%) 95% CI Proportion (%) 95% CI Proportion (%) 95% CI Sex Male 50.80 [50.13;51.46] 51.25 [50.63;51.87] 36.58 [36.04;37.11] 43.32 [42.32;44.33] Female 49.20 [48.54;49.87] 48.75 [48.13;49.37] 63.42 [62.89;63.96] 56.68 [55.67;57.68] Age group (in years) 18–34 22.84 [21.71;24.00] 6.74 [6.25; 7.26] 23.83 [22.86;24.82] 29.06 [26.94;31.28] 35–49 28.76 [28.11;29.41] 17.49 [16.77;18.24] 22.20 [21.45;22.97] 20.02 [18.86;21.24] 50–59 22.11 [21.71;22.51] 18.46 [18.04;18.89] 15.51 [15.19;15.83] 13.37 [12.82;13.94] 60–70 18.18 [17.41;18.99] 25.62 [25.20;26.04] 17.46 [16.97;17.96] 16.99 [16.11;17.91] 71+ 8.12 [7.48; 8.80] 31.69 [30.37;33.03] 21.00 [19.90;22.15] 20.56 [18.28;23.04] Ethnicity Neither Aboriginal nor Torres Strait Islander 80.71 [78.32;82.88] 85.12 [82.69;87.26] 83.98 [81.30;86.33] 66.33 [62.60;69.87] Aboriginal and/or Torres Strait Islander 6.66 [5.25; 8.41] 2.28 [1.92; 2.70] 1.58 [1.34; 1.85] 1.54 [1.34; 1.76] Not stated 12.63 [10.83;14.69] 12.60 [10.46;15.10] 14.45 [12.08;17.19] 32.13 [28.54;35.95] State/ Territory in Australia New South Wales (NSW) 30.99 [24.86;37.86] 33.13 [27.85;38.88] 34.57 [29.18;40.39] 29.27 [22.30;37.39] Victoria (VIC) 22.94 [13.88;35.49] 20.18 [14.64;27.14] 20.81 [15.25;27.74] 23.07 [14.08;35.43] Queensland (QLD) 19.12 [14.55;24.72] 19.52 [15.51;24.27] 18.64 [14.80;23.20] 17.32 [12.51;23.49] Western Australia (WA) 12.48 [8.78;17.44] 11.91 [8.66;16.16] 12.89 [9.35;17.51] 17.18 [11.50;24.89] Tasmania (TAS) 9.95 [6.35;15.25] 9.58 [6.21;14.51] 7.23 [4.43;11.59] 9.67 [3.68;23.07] South Australia (SA) 2.14 [1.13; 4.02] 3.01 [1.64; 5.48] 2.91 [1.58; 5.29] 1.81 [0.91; 3.57] Australian Capital Territory (ACT) 1.54 [0.68; 3.47] 2.20 [0.96; 4.99] 2.65 [1.15; 6.02] 1.31 [0.53; 3.21] Northern Territory (NT) 0.84 [0.29; 2.46] 0.47 [0.15; 1.43] 0.30 [0.10; 0.88] 0.36 [0.12; 1.13] Remoteness Urban 51.64 [44.67;58.55] 55.11 [49.37;60.71] 63.77 [58.09;69.08] 60.68 [51.03;69.55] Inner Regional 29.43 [24.16;35.32] 29.13 [24.51;34.22] 24.84 [20.51;29.74] 25.58 [18.68;33.97] Remote/Very Remote 18.93 [15.00;23.60] 15.77 [12.45;19.77] 11.40 [8.87;14.52] 13.74 [10.30;18.11] Quintiles of IRSAD Very High 13.45 [10.91;16.48] 20.07 [16.71;23.91] 25.44 [21.49;29.83] 26.33 [20.90;32.60] High 16.46 [13.98;19.29] 17.42 [15.08;20.04] 20.43 [17.77;23.37] 17.90 [15.15;21.03] Middle 21.47 [18.35;24.96] 20.31 [17.40;23.57] 20.54 [17.68;23.72] 18.90 [15.58;22.75] Low 24.63 [21.38;28.20] 22.84 [19.53;26.54] 19.08 [16.12;22.43] 21.67 [16.43;28.03] Very Low 23.98 [20.06;28.39] 19.35 [16.05;23.16] 14.52 [11.98;17.49] 15.19 [11.69;19.50] Co-morbid conditions Hypertension 28.68 [27.81;29.57] 49.88 [48.92;50.85] 32.63 [31.59;33.68] 17.09 [15.94;18.31] Diabetes 12.14 [11.61;12.69] 18.22 [17.68;18.78] 11.46 [11.03;11.89] 7.05 [6.47; 7.67] Cardiovascular Disease 9.24 [8.66; 9.86] 18.61 [17.95;19.30] 8.89 [8.44; 9.36] 5.10 [4.61; 5.64] COPD (ever recorded) 11.32 [10.75;11.92] 11.09 [10.68;11.52] 2.04 [1.93; 2.16] 1.38 [1.26; 1.51] Lung cancer (ever recorded) 0.49 [0.44; 0.55] 0.80 [0.76; 0.84] 0.14 [0.13; 0.15] 0.16 [0.14; 0.19] a In financial year 2022 (1 July 2021 to 30 June 2022) CI, Confidence Interval IRSAD, Index of Relative Socio-economic Advantage and Disadvantage Please see supplementary additional file for Table 1 Table 2 Pattern and number of cigarettes smoked per day for all patients with recorded smoking status in 2022a. N = 776. Cigarettes smoked per day (cigs/day*) Number (proportion) Light Smoking (1–9 cigs/day*) 170 (21.9%) Moderate Smoking (10–19 cigs/day*) 259 (33.4%) Heavy Smoking (20 + cigs/day*) 347 (44.7%) * Mean of obs_cigsday when multiple observations in the same year a Financial Year 2022 (1 July 2021 to 30 June 2022) Please insert Table 2 here (after Table 1 ) a Financial Year 2022 (1 July 2021 to 30 June 2022) ^By Australian Bureau of Statistics Australian Statistical Geography Standard Remoteness Structure *Australian Bureau of Statistics Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); marker of socio-economic status Discussion The NLCSP is a new cancer screening program in Australia, and to our knowledge, this is the first Australian study to describe how smoking status data, as recorded in EHRs, can be used by GPs for LCS recruitment. Our study demonstrates that GPs can use their EHRs to identify LCS-eligible patients, with smoking status recorded for more than 90% of regular patients in the MedicineInsight database during the study period. This is higher than the Australian Institute of Health and Welfare’s (AIHW) estimates that only 68.6% of regular patients aged 15 years and over had smoking status recorded in their GP record as of July 2025( 14 ). Wade et al. estimated that between 12.8–14.1% of the Australian population aged 50–70 years could meet the NLCSP age and smoking criteria within the first five years of the program( 6 ). Using these estimates, approximately 174,923 to 192,688 patients in our study sample may be eligible for LCS, which is just below our estimates that approximately 63,207 current smokers and 137,310 ex-smokers, aged 50–70 years, may potentially be eligible for LCS and require further assessment. However, approximately 40,108 patients aged 50–70 years did not have their smoking status recorded and may miss out on GPs’ LCS recruitment invitations if they happen to be smokers. We also found that smoking status data was better recorded in the LCS age-eligible population, with 50-70-year-olds having the least missing data on smoking status across all age groups. This could reflect the higher use of GP services in older age groups in Australia( 15 ), suggesting increased opportunities for collection of clinical data. Additionally, many smokers were in the low and very low quintiles of IRSAD, indicating increased smoking prevalence amongst lower SES groups. The strong association between smoking and socio-economic disadvantage has been well-documented in literature( 16 , 17 ) and may also play a role in the increased rates of lung cancer amongst those from lower SES areas( 18 ). Our results also highlight that patients with co-morbidities such as COPD, hypertension, diabetes and CVD also have high rates of current and previous smoking, with almost 50% of those with hypertension being recorded as ex-smokers. This is relevant in LCS, as existing literature highlights that respiratory and cardio-cerebrovascular co-morbidities can be associated with increased risk of lung cancer, likely due to chronic inflammation and oxidative stress( 19 ). This suggests priority patient populations for LCS recruitment. Interestingly, those with diabetes and CVD were more likely to be ex-smokers compared to current smokers, suggesting that some people diagnosed with these conditions may eventually stop smoking, particularly as smoking cessation is an important intervention for CVD secondary prevention( 20 ). A recent Australian study used the ABS 2017–2018 National Health Survey data to describe the population profiles of those who smoke, have formerly smoked or never smoked in Australia( 21 ). In their study, 13.8% of participants smoked daily, compared to 11.5% recorded current smokers in our database. They also reported that 30.0% of their participants were previous smokers, compared to 22.8% ex-smokers in our study. Additionally, 58.8% of those who smoked daily in their study were men and 41.2% women( 21 ). This is different to our estimates of 50.8% of current smokers being male and 49.2% female. These variations may reflect differences in study designs and sampling methodologies, as our study was based on the primary care population, with participants being only those ‘regularly’ attending their GP. Additionally, EHR data may be incomplete, such as the 9.7% of regular patients who did not have their smoking status recorded in our study. Patients may also not be truthful to their GPs about their smoking status( 22 ). Challenges exist in general practice EHR data completeness and quality, which may impact LCS recruitment. For instance, data on smoking pack-year history were lacking in EHRs. We attempted to analyse data on smoking pack-year history in the MedicineInsight database and examined the number of cigarettes smoked per day for all patients who attended a MedicineInsight general practice between 1 July 2021 and 30 June 2022. However, only 776 patients had this recorded in the relevant fields in their EHRs. This indicates that GPs will need to review large numbers of patients to confirm LCS eligibility, often opportunistically during consultations for other issues, which may pose a challenge in LCS recruitment. Additionally, estimation of smoking pack-year history may be difficult for GPs if patients have difficulty remembering when they started and/or quit smoking. As high screening uptake will be needed for a LCS program to be effective( 23 ), EHR data that is not easily extractable creates additional workload on GPs, potentially leading to lower screening uptake and LCS program success. One solution may be for GPs and their practice staff to systematically record smoking pack-year history and quit dates in the relevant fields (compared to progress notes), so that data can be easily extracted to assist with LCS recruitment. Furthermore, to improve EHR data completeness, GPs will need to ascertain and record smoking status for all their patients. GPs could be further supported in using EHRs for LCS recruitment through EHR prompts in practice software. In a recent study from the United States of America, EHR prompts regarding potential patient eligibility for LCS based on smoking information resulted in increased EHR completeness, identification of patients for LCS and requests for LDCTs in primary care( 24 ). By enhancing the use of EHRs and streamlining the process of identifying potential LCS-eligible patients, the LCS-related barriers of inadequate time and staffing in general practice can be reduced( 25 , 26 ) and GPs' capacity to screen patients for cancer improved( 27 ). Furthermore, allocated funding for LCS within general practice is vital. Currently, there are no dedicated MBS item numbers for GPs in assessing LCS eligibility. As GPs’ acceptability of risk-tailored cancer screening can be hindered by health system-related barriers, including the ‘time-based system’ ( 28 ), GPs will struggle to incorporate LCS into their consults without adequate funding, particularly if patients present with other, ‘more complex’ issues( 29 ). This may be particularly pertinent for the larger numbers of patients from lower SES and remote areas who will require LCS assessments due to their increased rates of smoking yet are disproportionately affected by poorer health outcomes( 16 , 30 – 32 ). The NLCSP may exacerbate existing health inequities for these patients, particularly if access to GPs is limited. Ultimately, the introduction of new cancer screening programs requires adequate support and resources for GPs to effectively incorporate the program into their everyday clinical practice. Strengths and limitations We used a large, national general practice database, which is considered broadly representative of the Australian population in terms of socio-demographic factors, as it includes 8.2% of Australian general practices( 8 ). However, as study participants were only those attending a GP, they may have different health characteristics to the general population( 33 ) and co-morbidities may have been over-represented. Further limitations include the MedicineInsight database only containing the most recently recorded smoking status, which may not have been updated for patients whose smoking status may have changed. Furthermore, the database does not contain data from GPs’ progress notes, due to privacy reasons. Therefore, clinical information regarding patients’ smoking status, pack-year history and smoking quit date in progress notes could not be included in our analyses, leading to lower estimates of the true eligible population. However, to improve data quality, our analysis used regular patients as patient information is collected and recorded over multiple consultations. Conclusion Our study indicates that whilst GPs can use EHRs to recruit patients for LCS, with smoking status recorded for over 90% of regular patients in the MedicineInsight database, GPs need to further assess these patients for screening eligibility, as EHR data on smoking pack-year history were poorly recorded. Individuals with certain co-morbidities and socio-demographic factors, such as those living in lower SES areas, were more likely to be smokers and ex-smokers, suggesting priority populations for GPs in LCS recruitment. Ultimately, GPs need to be better supported with funding and improvements in EHR data completeness to efficiently use EHRs for LCS. Abbreviations NLCSP National Lung Cancer Screening Program GP General Practitioners LCS Lung Cancer Screening LDCT Low-Dose Computed Tomography ABS Australian Bureau of Statistics EHR Electronic Health Records IRSAD Index of Relative Socio-Economic Advantage and Disadvantage SES Socio-Economic Status 95%CI 95% Confidence Interval CVD Cardio-Vascular Disease COPD Chronic Obstructive Pulmonary Disease Declarations Ethics approval and consent to participate Ethics approval to use the MedicineInsight database was given by the NPS MedicineWise Data Governance Committee. The Humans Research Ethics Committee of The University of Adelaide has exempted all studies using de-identified MedicineInsight database for ethical review, as it uses secondary data with no possibility of re-identification. Research was conducted in accordance with the National Statement on Ethical Conduct in Human Research (2025). Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding Not applicable Author Contribution Conception and design of the study (AC, NS, EH). Methodology (AC, FN). Statistical analysis (FN). Interpretation of data (AC, FN). Writing of initial manuscript (AC). Review and editing of manuscript (AC, EH, NS). All authors have read and approved the final manuscript. Acknowledgements Not applicable Data Availability The datasets used and/or analysed during the current study are not publicly available, due to restrictions of the MedicineInsight database, but can be considered from the corresponding author upon reasonable request. References Marjanovic S, Page A, Stone E, Currie DJ, Rankin NM, Myers R, et al. Systems mapping: a novel approach to national lung cancer screening implementation in Australia. Transl Lung Cancer Res. 2024;13(10):2466–78. Bradley SH, Kennedy MPT, Neal RD. Recognising Lung Cancer in Primary Care. Adv Ther. 2019;36(1):19–30. Australian Government National Cancer Screening Register. About the National Lung Cancer Screening Program. 2025. [cited 2026 Feb 3].Available from: https://www.ncsr.gov.au/lung-program Ellis SJ, McCusker MW, Melsom S, Pascoe DM, Jones CM, Siemienowicz M. The Australian National Lung Cancer Screening Program: A Radiologist's Perspective. J Med Imaging Radiat Oncol. 2025;69(7):740–4. Khan N. GP workload and patient safety. Br J Gen Pract. 2024;74(746):412. Wade S, Ngo P, He Y, Caruana M, Steinberg J, Luo Q, et al. Estimates of the eligible population for Australia’s targeted National Lung Cancer Screening Program, 2025–2030. Public Health Res Pract. 2024. 10.17061/phrp34342410 . Brown L, Agrawal U, Sullivan F. Using Electronic Medical Records to Identify Potentially Eligible Study Subjects for Lung Cancer Screening with Biomarkers. Cancers (Basel). 2021;13:21. Busingye D, Gianacas C, Pollack A, Chidwick K, Merrifield A, Norman S, et al. Data Resource Profile: MedicineInsight, an Australian national primary health care database. Int J Epidemiol. 2019;48(6):1741–1741. Banham D, Roder D, Stone E, Quayle S, Rushton S, O’Brien T. Demographic, health and socioeconomic characteristics related to lung cancer diagnosis: a population analysis in New South Wales, Australia. Discover Social Sci Health. 2024;4(1):34. The Royal Australian College of General Practitioners. 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Available from: https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure/remoteness-areas#cite-window1 Australian Institute of Health and Welfare. Practice Incentives Program Quality Improvement Measures: annual data update 2024–25: Australian Government. 2025. [cited 2026 Feb 3]. Available from: https://www.aihw.gov.au/reports/primary-health-care/pipqi-measures-2024-25/contents/pipqi-measures/qim-2-smoking-status-recorded-and-result Royal Australian College of General Practitioners. General Practice Health of the Nation 2024. East Melbourne, Vic. 2024. [cited 2026 Feb 3]. Available from: Health-of-the-Nation-2024.pdf. Partos TR, Borland R, Siahpush M. Socio-economic disadvantage at the area level poses few direct barriers to smoking cessation for Australian smokers: findings from the International Tobacco Control Australian cohort survey. Drug Alcohol Rev. 2012;31(5):653–63. Roche A, McEntee A, Kim S, Chapman J. Changing patterns and prevalence of daily tobacco smoking among Australian workers: 2007–2016. Aust N Z J Public Health. 2021;45(3):290–8. Sanghrajka A, Sharp L, Rowlands G. How does socio-economic disadvantage influence the timeliness of lung cancer diagnosis? A systematic review and synthesis of published qualitative studies. Public Health. 2025;249:105975. Sheng Y, Di K, Liu Y, Liu D, Huai B, Wang Y, et al. Associations and mediating mechanisms between lung cancer and chronic comorbidities: a matched case-control study. Heart Lung. 2025;74:238–46. Wu AD, Lindson N, Hartmann-Boyce J, Wahedi A, Hajizadeh A, Theodoulou A, et al. Smoking cessation for secondary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2022;8(8):CD014936. Aw JYH, Heris C, Maddox R, Joshy G, Banks AME. Who smokes in Australia? Cross-sectional analysis of Australian Bureau of Statistics survey data, 2017–19. Med J Aust. 2024;220(3):154–63. O'Connor AM, Frias JC. Lying to your doctor: Exploring age differences and techniques to foster honest patient-doctor communication. J Health Psychol. 2025;30(12):3628–40. Brown M, Myers R, Lam S. Enlarging the Reach of Screening and Early Detection of Lung Cancer. J Thorac Oncol. 2025;20(3):249–51. Steinberg MB, Young WJ, Miller Lo EJ, Bover-Manderski MT, Jordan HM, Hafiz Z, et al. Electronic Health Record Prompt to Improve Lung Cancer Screening in Primary Care. Am J Prev Med. 2023;65(5):892–5. Triplette M, Kross EK, Mann BA, Elmore JG, Slatore CG, Shahrir S, et al. An Assessment of Primary Care and Pulmonary Provider Perspectives on Lung Cancer Screening. Ann Am Thorac Soc. 2018;15(1):69–75. Kota KJ, Ji S, Bover-Manderski MT, Delnevo CD, Steinberg MB. Lung Cancer Screening Knowledge and Perceived Barriers Among Physicians in the United States. JTO Clin Res Rep. 2022;3(7):100331. Ruco A, Khalil A, Ledwos C, Tinmouth J, Kiran T, Lofters A. Exploring barriers and enablers to implementation of cancer screening among primary care professionals seeing marginalized patients. BMC Public Health. 2025;25(1):1578. Dunlop KLA, Smit AK, Keogh LA, Newson AJ, Rankin NM, Cust AE. Acceptability of risk-tailored cancer screening among Australian GPs: a qualitative study. Br J Gen Pract. 2024;74(740):e156–64. Chandrakumar A, Hoon E, Benson J, Stocks N. Barriers and facilitators to cervical cancer screening for women from culturally and linguistically diverse backgrounds; a qualitative study of GPs. BMJ Open. 2022;12(11):e062823. Huang MZ, Liu TY, Zhang ZM, Song F, Chen T. Trends in the distribution of socioeconomic inequalities in smoking and cessation: evidence among adults aged 18 ~ 59 from China Family Panel Studies data. Int J Equity Health. 2023;22(1):86. Spencer M, Wood E, Baker J, Hyett N. The Socio-Ecological Enablers of Smoking and Vaping in Rural Young Adults: A Mixed-Methods Study. Health Promotion J Australia. 2025;36(2):e70044. Carroll SJ, Dale MJ, Bailie R, Daniel M. Climatic and community sociodemographic factors associated with remote Indigenous Australian smoking rates: an ecological study of health audit data. BMJ Open. 2019;9(7):e032173. González-Chica DA, Vanlint S, Hoon E, Stocks N. Epidemiology of arthritis, chronic back pain, gout, osteoporosis, spondyloarthropathies and rheumatoid arthritis among 1.5 million patients in Australian general practice: NPS MedicineWise MedicineInsight dataset. BMC Musculoskelet Disord. 2018;19(1):20. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8835194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595429151,"identity":"2eb077c2-47c2-4ca7-8ee4-beb77ff39622","order_by":0,"name":"Abira Chandrakumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABV0lEQVRIie3RMWvCQBQH8HccpMulrlcU/QonhdJiaz9Il4RAXUw7COLQISDExQ+gKPoVFKFzJGCXw/lAB4vQKUO6CbWlL7oYiqVjh/tDuOPufrz3CICOzn8MJ16yWPgJgCDZ0wAYXIKxOzT+RAwLCf+F7JdDwsSOwBGS6TX9eAPlB9w8xxsZ3mV68j2OGrxweuIEENdDyHSsVJHltNltg1Pjy1mt21ah25m7k7O+5EWfvVmkMw+BqxQBZTexjcD2VPUcWBy6njRHWdPnxOdVQU0/xDcpUkBCtkiGSMgWyVCy9Yf5xW99/hjTTySFNBFIaFJlhIQybGwkmZE1PW5jFaAEiUiTIs5Cc8Kxx+q+RnOy4o6lcXHVn3EHZxHT9rzCinJ1SPKL1ppEjbI9UM6ERLOSO5B0raKn65thy3ldbeqlfP4lPf6+vZ9H+yT/iR271NHR0dE5mm87/4TurKlqQQAAAABJRU5ErkJggg==","orcid":"","institution":"Adelaide University","correspondingAuthor":true,"prefix":"","firstName":"Abira","middleName":"","lastName":"Chandrakumar","suffix":""},{"id":595429152,"identity":"89441826-5ce8-43ea-ad0d-99a60c76eeeb","order_by":1,"name":"Fernanda Nobre","email":"","orcid":"","institution":"Adelaide University","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"","lastName":"Nobre","suffix":""},{"id":595429153,"identity":"58394b4c-4ec1-4b0e-8e5f-8f4dc68dfb3a","order_by":2,"name":"Elizabeth Hoon","email":"","orcid":"","institution":"Adelaide University","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Hoon","suffix":""},{"id":595429154,"identity":"babc2e10-4fbb-4ba2-bb28-4d121a4f4fa5","order_by":3,"name":"Nigel Stocks","email":"","orcid":"","institution":"Adelaide University","correspondingAuthor":false,"prefix":"","firstName":"Nigel","middleName":"","lastName":"Stocks","suffix":""}],"badges":[],"createdAt":"2026-02-10 01:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8835194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8835194/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103349728,"identity":"34fc1769-50c2-418a-a90b-f5f4e3b31964","added_by":"auto","created_at":"2026-02-24 16:46:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61535,"visible":true,"origin":"","legend":"\u003cp\u003eSample selection for regular patients\u003csup\u003e† \u003c/sup\u003eaged 18+ years, who attended a MedicineInsight general practice in Financial Year 2022*\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Between 1 July 2021 and 30 June 2022.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e†\u003c/sup\u003eRegular patients: ≥3 consultations in 2020–2022\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8835194/v1/e94a46351af2b5d7c10f6f91.png"},{"id":103349727,"identity":"e181e2e8-08ba-4599-a3d9-0558e4338a59","added_by":"auto","created_at":"2026-02-24 16:46:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSocio-demographic characteristics by smoking status for regular patients in 2022a\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eFinancial Year 2022 (1 July 2021 to 30 June 2022)\u003cbr\u003e\n^By Australian Bureau of Statistics Australian Statistical Geography Standard Remoteness Structure\u003cbr\u003e\n*Australian Bureau of Statistics Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); marker of socio-economic status \u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8835194/v1/c7e2e934eb0ae452b3e7bdeb.jpeg"},{"id":103507051,"identity":"e915de77-6e1b-4cd1-b954-f9246a050c99","added_by":"auto","created_at":"2026-02-26 13:40:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8835194/v1/4cb7f638-7075-4727-bc71-ebaacfc83a50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can GPs use smoking status data in electronic health records to recruit patients for lung cancer screening? A descriptive study using the MedicineInsight database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is a leading cause of cancer-related illness and death in Australia(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Early detection of lung cancer has been strongly associated with improved outcomes and survival, yet achieving early diagnosis has been challenging as lung cancer presents with non-specific symptoms(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The Australian National Lung Cancer Screening Program (NLCSP) commenced in July 2025, with the aim of improving early detection of lung cancer through screening of high-risk individuals(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The current NLCSP eligibility criteria include individuals aged between 50 and 70 years old, who either currently smoke cigarettes or have quit smoking in the past 10 years, and have a 30 or more pack-year smoking history(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The NLCSP is led by the Commonwealth Government, with support from the states and territories(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As general practitioners (GPs) are patients\u0026rsquo; first point of contact to participate in the program, they are involved in assessing patient eligibility for lung cancer screening (LCS) and referring LCS-eligible individuals for low-dose computed tomography (LDCT)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). They are also responsible for reviewing LDCT results and organising necessary follow-ups(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough GPs can play a pivotal role in the delivery of LCS, there is a gap in understanding how GPs can effectively identify LCS-eligible individuals within their practices. GPs already experience high workloads and significant time pressure(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and existing literature has highlighted that the additional workload on GPs could impact LCS implementation(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Furthermore, the ease of identifying LCS-eligible participants can influence LCS uptake(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus far, the prevalence of LCS-eligible individuals in Australia has been estimated using data from the National Drug Strategy Household Survey and Australian Bureau of Statistics (ABS)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Electronic Health Records (EHRs), which are widely used by GPs, also have the capacity to inform LCS eligibility through the availability of comprehensive patient data, including socio-demographic factors and clinical information, such as smoking status(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). EHRs have been used internationally to identify patients for cancer screening, resulting in earlier cancer diagnoses and greater participation in screening programs(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This is particularly important for targeted screening, such as LCS. However, routinely collected EHR data come with challenges, including data completeness and accuracy(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, in this study, we aimed to understand how GPs can use their general practice EHRs to recruit patients for LCS. We used the MedicineInsight database, which contains routinely collected general practice EHR data(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and explored how smoking status data are recorded in EHRs, including any differences in smoking status data across certain socio-demographic characteristics and co-morbidities. We aimed to help GPs determine priority groups for LCS invitations, as certain socio-demographic characteristics and co-morbidities have been associated with increased rates of lung cancer(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We also aimed to identify missing EHR data in assessing LCS eligibility and compared our data to national-level data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eThis descriptive, cross-sectional analysis used data from MedicineInsight, a national general practice database, which contains de-identified EHRs of over 2\u0026nbsp;million patients from 662 general practices across Australia(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). MedicineInsight was developed and managed by NPS MedicineWise until December 2022 with funding support from the Australian Government Department of Health. It is now managed by the Australian Commission on Safety and Quality in Health Care. The database contains information on patient socio-demographic factors, clinical encounters (excluding progress notes), diagnoses, pathology requests, and prescribed medications(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). For this study, we used the most recent database provided by NPS MedicineWise, in October 2022, to the Discipline of General Practice at The University of Adelaide.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eThe study population included all adults (18 + years), who attended a MedicineInsight practice between 1 July 2021 and 30 June 2022, with at least three consultations in the preceding two years, in accordance with the Royal Australian College of General Practitioner’s (RACGP) definition of a regular patient(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). This approach accounts for the fact that complete patient information is typically collected over more than one consultation. Patients who are “non-regulars” can introduce bias, due to incomplete and unavailable data in EHRs, leading to under-estimated results(\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). The final dataset included 1,366,586 individuals. Please see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for details of the sample selection.\u003c/p\u003e \u003cp\u003eTo explore the number of cigarettes smoked per day by current smokers, inclusion criteria were broadened to include all adults (18 + years) who attended a MedicineInsight practice between 1 July 2021 and 30 June 2022 (not just regular patients), due to the limited availability of smoking quantity data.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlease insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Between 1 July 2021 and 30 June 2022.\u003c/p\u003e \u003cp\u003e \u003csup\u003e†\u003c/sup\u003eRegular patients: ≥3 consultations in 2020–2022\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was current smoking status, defined as the most recently recorded status in the patient's EHR. Smoking status was categorised as: smokers, ex-smokers, non-smokers and ‘not recorded’. Information on co-morbidities was extracted from the fields “reason for encounter” and “diagnosis” using a text-based algorithm that searched for diagnostic terms, their synonyms, and acronyms. Conditions used in this study were: hypertension, diabetes, cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), and lung cancer. Socio-demographic characteristics included age, gender, socio-economic status and remoteness. Age was categorised as: 18–34, 35–49, 50–59, 60–70, ≥ 71 years, with the LCS age-eligible population divided into 50–59 years and 60–70 years. Socio-economic status was determined using the ABS Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD), which is an area-level measure of socio-economic status (SES) derived from patients’ postcodes(\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). Patients’ remoteness were determined using the ABS Australian Statistical Geography Standard Remoteness Structure(13).\u003c/p\u003e \u003cp\u003eWe attempted to collect information on smoking pack-year history, but the data were limited to the ‘cigsday’ variable in the database. This was further classified into ‘light smoking (1–9 cigsday)’, ‘moderate smoking (10–19 cigsday)’ and ‘heavy smoking (20 + cigsday)’.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarised the distribution of smoking status, socio-demographic characteristics, and co-morbidities. To assess differences in patient characteristics across smoking status categories, proportions with 95% confidence intervals (CI) were calculated for categorical variables across four categories: current smokers, ex-smokers, non-smokers, and smoking status 'not recorded'. Chi-square tests were used to assess statistical significance of differences in proportions. All analyses were performed in Stata 18.0 (StataCorp, Texas, USA), with practices treated as clusters to account for potential correlation in outcomes among patients attending the same practice.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eto use the MedicineInsight database was given by the NPS MedicineWise Data Governance Committee. The Humans Research Ethics Committee of The University of Adelaide has exempted all studies using de-identified MedicineInsight database for ethical review, as it uses secondary data with no possibility of re-identification.\u003c/p\u003e "},{"header":"Results","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eOut of the 1,366,586 regular patients attending a MedicineInsight general practice between 1 July 2021 and 30 June 2022, 156,881 (11.5%, 95%CI 10.6–12.4) were recorded as smokers, 311,503 (22.8%, 95%CI 22.1–23.5) as ex-smokers, 766,091 (56.1%, 95%CI 54.7–57.4) as non-smokers, and 132,111 (9.7%, 95%CI 8.7–10.8) did not have their smoking status recorded.\u003c/p\u003e\u003cp\u003eSimilar proportions of males and females were recorded as smokers and ex-smokers, but larger proportions of females were reported as non-smokers (63.4%) or did not have their smoking status recorded (56.7%), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. 29.1% of patients in the youngest age group (18–34-year-olds) did not have their smoking status recorded, having the highest “not recorded” smoking status across all age groups. Smoking status data were better recorded for 50-59-year-olds and 60-70-year-olds, with only 13.4% and 17.0% respectively having their smoking status data missing. The majority of ex-smokers were in the older age groups, with 25.6% in the 60–70 years-old age group and 31.7% aged 71 years or older. Relevant to LCS, 40.3% of all smokers were in the 50–70-years-old age group.\u003c/p\u003e\u003cp\u003eThe majority of smokers were in the low and very low quintiles of IRSAD (24.6% and 24.0% respectively), compared to the very high quintile of IRSAD (13.5%). However, ex-smokers were more evenly distributed across all IRSAD quintiles. In ‘remote/ very remote’ areas, more patients were recorded as being smokers (18.9%) or ex-smokers (15.8%), compared to non-smokers (11.4%).\u003c/p\u003e\u003ch3\u003eCigarettes per day\u003c/h3\u003e\u003cp\u003eOut of the 776 individuals attending a MedicineInsight general practice between 1 July 2021 and 30 June 2022, who had data documented on their number of cigarettes smoked per day, 170 (21.9%) were recorded as having light smoking (1–9 cigsday), 259 (33.4%) as having moderate smoking (10–19 cigsday), and 347 (44.7%) as heavy smoking (20 + cigsday).\u003c/p\u003e\u003ch2\u003eCo-morbidities\u003c/h2\u003e\u003cp\u003eAn estimated 28.7% of smokers and 49.9% of ex-smokers had a recorded diagnosis of hypertension, compared to 32.6% of non-smokers. 17.1% of those with hypertension did not have their smoking status recorded. Both co-morbidities of diabetes and CVD had larger numbers of ex-smokers, with 18.2% and 18.6% respectively, compared to current smokers (12.1% and 9.2% respectively).\u003c/p\u003e\u003cp\u003e11.3% of smokers and 11.1% of ex-smokers were recorded as ever having a diagnosis of COPD, compared to 2.0% of non-smokers and 1.4% of those without their smoking status recorded. Previous lung cancer history was recorded in 0.5% of smokers, 0.8% of ex-smokers, 0.1% of non-smokers and 0.2% of those with no smoking status recorded.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eSocio-demographic and health characteristics of regular patients by smoking status in 2022\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e. \u003cb\u003eAdults aged 18 + years (\u003c/b\u003e\u003cb\u003eN\u003c/b\u003e \u003cb\u003e= 1,366,586).\u003c/b\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and health characteristics of regular patients by smoking status in 2022\u003csup\u003ea\u003c/sup\u003e. Adults aged 18 + years (\u003cem\u003eN\u003c/em\u003e = 1,366,586).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eNot\u0026nbsp;stated/not recorded\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e(n = 156,881)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e(n = 311,503)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e(n = 766,091)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e(n = 132,111)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eProportion\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eProportion\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eProportion\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eProportion\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[50.13;51.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e51.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[50.63;51.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e36.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[36.04;37.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[42.32;44.33]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e49.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[48.54;49.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e48.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[48.13;49.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e63.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[62.89;63.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e56.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[55.67;57.68]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAge group (in years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18–34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[21.71;24.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[6.25; 7.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[22.86;24.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[26.94;31.28]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e35–49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[28.11;29.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.77;18.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[21.45;22.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[18.86;21.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[21.71;22.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[18.04;18.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.19;15.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[12.82;13.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60–70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.41;18.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[25.20;26.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.97;17.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.11;17.91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e71+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[7.48; 8.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e31.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[30.37;33.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[19.90;22.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[18.28;23.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNeither Aboriginal nor Torres Strait Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e80.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[78.32;82.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e85.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[82.69;87.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e83.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[81.30;86.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e66.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[62.60;69.87]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAboriginal and/or Torres Strait Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[5.25; 8.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.92; 2.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.34; 1.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.34; 1.76]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot stated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.83;14.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.46;15.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[12.08;17.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[28.54;35.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eState/ Territory in Australia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNew South Wales (NSW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e30.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[24.86;37.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e33.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[27.85;38.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e34.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[29.18;40.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[22.30;37.39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVictoria (VIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[13.88;35.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[14.64;27.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.25;27.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[14.08;35.43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQueensland (QLD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[14.55;24.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.51;24.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[14.80;23.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[12.51;23.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWestern\u0026nbsp;Australia (WA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[8.78;17.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[8.66;16.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[9.35;17.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[11.50;24.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTasmania (TAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[6.35;15.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[6.21;14.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[4.43;11.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[3.68;23.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSouth Australia (SA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.13; 4.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.64; 5.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.58; 5.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.91; 3.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAustralian Capital Territory (ACT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.68; 3.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.96; 4.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.15; 6.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.53; 3.21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNorthern Territory (NT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.29; 2.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.15; 1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.10; 0.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.12; 1.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eRemoteness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e51.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[44.67;58.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e55.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[49.37;60.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e63.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[58.09;69.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[51.03;69.55]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInner Regional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[24.16;35.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[24.51;34.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[20.51;29.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[18.68;33.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRemote/Very Remote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.00;23.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[12.45;19.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[8.87;14.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.30;18.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eQuintiles of IRSAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.91;16.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.71;23.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[21.49;29.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e26.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[20.90;32.60]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[13.98;19.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.08;20.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.77;23.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.15;21.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[18.35;24.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.40;23.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.68;23.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.58;22.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[21.38;28.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[19.53;26.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.12;22.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.43;28.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[20.06;28.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[16.05;23.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[11.98;17.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[11.69;19.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCo-morbid\u0026nbsp;conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[27.81;29.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e49.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[48.92;50.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[31.59;33.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[15.94;18.31]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[11.61;12.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.68;18.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[11.03;11.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[6.47; 7.67]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[8.66; 9.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[17.95;19.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[8.44; 9.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[4.61; 5.64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOPD (ever recorded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.75;11.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[10.68;11.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.93; 2.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[1.26; 1.51]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLung cancer (ever recorded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.44; 0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.76; 0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.13; 0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0.14; 0.19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003eIn financial year 2022 (1 July 2021 to 30 June 2022)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eCI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eIRSAD, Index of Relative Socio-economic Advantage and Disadvantage\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003ePlease see supplementary additional file for Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePattern and number of cigarettes smoked per day for all patients with recorded smoking status in 2022a. N = 776.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCigarettes smoked per day (cigs/day*)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eNumber (proportion)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLight Smoking (1–9 cigs/day*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e170 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eModerate Smoking (10–19 cigs/day*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e259 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHeavy Smoking (20 + cigs/day*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e347 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e* Mean of obs_cigsday when multiple observations in the same year\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003e Financial Year 2022 (1 July 2021 to 30 June 2022)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003ePlease insert\u003c/em\u003e Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003ehere (after\u003c/em\u003e Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Financial Year 2022 (1 July 2021 to 30 June 2022)\u003c/p\u003e\u003cp\u003e^By Australian Bureau of Statistics Australian Statistical Geography Standard Remoteness Structure\u003c/p\u003e\u003cp\u003e*Australian Bureau of Statistics Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); marker of socio-economic status\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe NLCSP is a new cancer screening program in Australia, and to our knowledge, this is the first Australian study to describe how smoking status data, as recorded in EHRs, can be used by GPs for LCS recruitment.\u003c/p\u003e \u003cp\u003eOur study demonstrates that GPs can use their EHRs to identify LCS-eligible patients, with smoking status recorded for more than 90% of regular patients in the MedicineInsight database during the study period. This is higher than the Australian Institute of Health and Welfare\u0026rsquo;s (AIHW) estimates that only 68.6% of regular patients aged 15 years and over had smoking status recorded in their GP record as of July 2025(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWade et al. estimated that between 12.8\u0026ndash;14.1% of the Australian population aged 50\u0026ndash;70 years could meet the NLCSP age and smoking criteria within the first five years of the program(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Using these estimates, approximately 174,923 to 192,688 patients in our study sample may be eligible for LCS, which is just below our estimates that approximately 63,207 current smokers and 137,310 ex-smokers, aged 50\u0026ndash;70 years, may potentially be eligible for LCS and require further assessment. However, approximately 40,108 patients aged 50\u0026ndash;70 years did not have their smoking status recorded and may miss out on GPs\u0026rsquo; LCS recruitment invitations if they happen to be smokers.\u003c/p\u003e \u003cp\u003eWe also found that smoking status data was better recorded in the LCS age-eligible population, with 50-70-year-olds having the least missing data on smoking status across all age groups. This could reflect the higher use of GP services in older age groups in Australia(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), suggesting increased opportunities for collection of clinical data. Additionally, many smokers were in the low and very low quintiles of IRSAD, indicating increased smoking prevalence amongst lower SES groups. The strong association between smoking and socio-economic disadvantage has been well-documented in literature(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and may also play a role in the increased rates of lung cancer amongst those from lower SES areas(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results also highlight that patients with co-morbidities such as COPD, hypertension, diabetes and CVD also have high rates of current and previous smoking, with almost 50% of those with hypertension being recorded as ex-smokers. This is relevant in LCS, as existing literature highlights that respiratory and cardio-cerebrovascular co-morbidities can be associated with increased risk of lung cancer, likely due to chronic inflammation and oxidative stress(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This suggests priority patient populations for LCS recruitment. Interestingly, those with diabetes and CVD were more likely to be ex-smokers compared to current smokers, suggesting that some people diagnosed with these conditions may eventually stop smoking, particularly as smoking cessation is an important intervention for CVD secondary prevention(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA recent Australian study used the ABS 2017\u0026ndash;2018 National Health Survey data to describe the population profiles of those who smoke, have formerly smoked or never smoked in Australia(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In their study, 13.8% of participants smoked daily, compared to 11.5% recorded current smokers in our database. They also reported that 30.0% of their participants were previous smokers, compared to 22.8% ex-smokers in our study. Additionally, 58.8% of those who smoked daily in their study were men and 41.2% women(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This is different to our estimates of 50.8% of current smokers being male and 49.2% female. These variations may reflect differences in study designs and sampling methodologies, as our study was based on the primary care population, with participants being only those \u0026lsquo;regularly\u0026rsquo; attending their GP. Additionally, EHR data may be incomplete, such as the 9.7% of regular patients who did not have their smoking status recorded in our study. Patients may also not be truthful to their GPs about their smoking status(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChallenges exist in general practice EHR data completeness and quality, which may impact LCS recruitment. For instance, data on smoking pack-year history were lacking in EHRs. We attempted to analyse data on smoking pack-year history in the MedicineInsight database and examined the number of cigarettes smoked per day for all patients who attended a MedicineInsight general practice between 1 July 2021 and 30 June 2022. However, only 776 patients had this recorded in the relevant fields in their EHRs. This indicates that GPs will need to review large numbers of patients to confirm LCS eligibility, often opportunistically during consultations for other issues, which may pose a challenge in LCS recruitment. Additionally, estimation of smoking pack-year history may be difficult for GPs if patients have difficulty remembering when they started and/or quit smoking.\u003c/p\u003e \u003cp\u003eAs high screening uptake will be needed for a LCS program to be effective(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), EHR data that is not easily extractable creates additional workload on GPs, potentially leading to lower screening uptake and LCS program success. One solution may be for GPs and their practice staff to systematically record smoking pack-year history and quit dates in the relevant fields (compared to progress notes), so that data can be easily extracted to assist with LCS recruitment. Furthermore, to improve EHR data completeness, GPs will need to ascertain and record smoking status for all their patients.\u003c/p\u003e \u003cp\u003eGPs could be further supported in using EHRs for LCS recruitment through EHR prompts in practice software. In a recent study from the United States of America, EHR prompts regarding potential patient eligibility for LCS based on smoking information resulted in increased EHR completeness, identification of patients for LCS and requests for LDCTs in primary care(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). By enhancing the use of EHRs and streamlining the process of identifying potential LCS-eligible patients, the LCS-related barriers of inadequate time and staffing in general practice can be reduced(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and GPs' capacity to screen patients for cancer improved(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, allocated funding for LCS within general practice is vital. Currently, there are no dedicated MBS item numbers for GPs in assessing LCS eligibility. As GPs\u0026rsquo; acceptability of risk-tailored cancer screening can be hindered by health system-related barriers, including the \u0026lsquo;time-based system\u0026rsquo; (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), GPs will struggle to incorporate LCS into their consults without adequate funding, particularly if patients present with other, \u0026lsquo;more complex\u0026rsquo; issues(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This may be particularly pertinent for the larger numbers of patients from lower SES and remote areas who will require LCS assessments due to their increased rates of smoking yet are disproportionately affected by poorer health outcomes(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The NLCSP may exacerbate existing health inequities for these patients, particularly if access to GPs is limited. Ultimately, the introduction of new cancer screening programs requires adequate support and resources for GPs to effectively incorporate the program into their everyday clinical practice.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eWe used a large, national general practice database, which is considered broadly representative of the Australian population in terms of socio-demographic factors, as it includes 8.2% of Australian general practices(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, as study participants were only those attending a GP, they may have different health characteristics to the general population(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and co-morbidities may have been over-represented.\u003c/p\u003e \u003cp\u003eFurther limitations include the MedicineInsight database only containing the most recently recorded smoking status, which may not have been updated for patients whose smoking status may have changed. Furthermore, the database does not contain data from GPs\u0026rsquo; progress notes, due to privacy reasons. Therefore, clinical information regarding patients\u0026rsquo; smoking status, pack-year history and smoking quit date in progress notes could not be included in our analyses, leading to lower estimates of the true eligible population. However, to improve data quality, our analysis used regular patients as patient information is collected and recorded over multiple consultations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study indicates that whilst GPs can use EHRs to recruit patients for LCS, with smoking status recorded for over 90% of regular patients in the MedicineInsight database, GPs need to further assess these patients for screening eligibility, as EHR data on smoking pack-year history were poorly recorded. Individuals with certain co-morbidities and socio-demographic factors, such as those living in lower SES areas, were more likely to be smokers and ex-smokers, suggesting priority populations for GPs in LCS recruitment. Ultimately, GPs need to be better supported with funding and improvements in EHR data completeness to efficiently use EHRs for LCS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLCSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Lung Cancer Screening Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Practitioners\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung Cancer Screening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Dose Computed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAustralian Bureau of Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Records\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRSAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndex of Relative Socio-Economic Advantage and Disadvantage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocio-Economic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e95%CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardio-Vascular Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethics approval to use the MedicineInsight database was given by the NPS MedicineWise Data Governance Committee. The Humans Research Ethics Committee of The University of Adelaide has exempted all studies using de-identified MedicineInsight database for ethical review, as it uses secondary data with no possibility of re-identification. Research was conducted in accordance with the National Statement on Ethical Conduct in Human Research (2025).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design of the study (AC, NS, EH). Methodology (AC, FN). Statistical analysis (FN). Interpretation of data (AC, FN). Writing of initial manuscript (AC). Review and editing of manuscript (AC, EH, NS). All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are not publicly available, due to restrictions of the MedicineInsight database, but can be considered from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarjanovic S, Page A, Stone E, Currie DJ, Rankin NM, Myers R, et al. Systems mapping: a novel approach to national lung cancer screening implementation in Australia. Transl Lung Cancer Res. 2024;13(10):2466\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradley SH, Kennedy MPT, Neal RD. Recognising Lung Cancer in Primary Care. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure/remoteness-areas#cite-window1\u003c/span\u003e\u003cspan address=\"https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure/remoteness-areas#cite-window1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare. Practice Incentives Program Quality Improvement Measures: annual data update 2024\u0026ndash;25: Australian Government. 2025. [cited 2026 Feb 3]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aihw.gov.au/reports/primary-health-care/pipqi-measures-2024-25/contents/pipqi-measures/qim-2-smoking-status-recorded-and-result\u003c/span\u003e\u003cspan address=\"https://www.aihw.gov.au/reports/primary-health-care/pipqi-measures-2024-25/contents/pipqi-measures/qim-2-smoking-status-recorded-and-result\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyal Australian College of General Practitioners. General Practice Health of the Nation 2024. East Melbourne, Vic. 2024. [cited 2026 Feb 3]. Available from: Health-of-the-Nation-2024.pdf.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartos TR, Borland R, Siahpush M. 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Associations and mediating mechanisms between lung cancer and chronic comorbidities: a matched case-control study. Heart Lung. 2025;74:238\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu AD, Lindson N, Hartmann-Boyce J, Wahedi A, Hajizadeh A, Theodoulou A, et al. Smoking cessation for secondary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2022;8(8):CD014936.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAw JYH, Heris C, Maddox R, Joshy G, Banks AME. Who smokes in Australia? Cross-sectional analysis of Australian Bureau of Statistics survey data, 2017\u0026ndash;19. Med J Aust. 2024;220(3):154\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Connor AM, Frias JC. Lying to your doctor: Exploring age differences and techniques to foster honest patient-doctor communication. J Health Psychol. 2025;30(12):3628\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown M, Myers R, Lam S. Enlarging the Reach of Screening and Early Detection of Lung Cancer. J Thorac Oncol. 2025;20(3):249\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinberg MB, Young WJ, Miller Lo EJ, Bover-Manderski MT, Jordan HM, Hafiz Z, et al. Electronic Health Record Prompt to Improve Lung Cancer Screening in Primary Care. Am J Prev Med. 2023;65(5):892\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTriplette M, Kross EK, Mann BA, Elmore JG, Slatore CG, Shahrir S, et al. An Assessment of Primary Care and Pulmonary Provider Perspectives on Lung Cancer Screening. Ann Am Thorac Soc. 2018;15(1):69\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKota KJ, Ji S, Bover-Manderski MT, Delnevo CD, Steinberg MB. Lung Cancer Screening Knowledge and Perceived Barriers Among Physicians in the United States. JTO Clin Res Rep. 2022;3(7):100331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuco A, Khalil A, Ledwos C, Tinmouth J, Kiran T, Lofters A. Exploring barriers and enablers to implementation of cancer screening among primary care professionals seeing marginalized patients. BMC Public Health. 2025;25(1):1578.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunlop KLA, Smit AK, Keogh LA, Newson AJ, Rankin NM, Cust AE. Acceptability of risk-tailored cancer screening among Australian GPs: a qualitative study. Br J Gen Pract. 2024;74(740):e156\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrakumar A, Hoon E, Benson J, Stocks N. Barriers and facilitators to cervical cancer screening for women from culturally and linguistically diverse backgrounds; a qualitative study of GPs. BMJ Open. 2022;12(11):e062823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang MZ, Liu TY, Zhang ZM, Song F, Chen T. Trends in the distribution of socioeconomic inequalities in smoking and cessation: evidence among adults aged 18\u0026thinsp;~\u0026thinsp;59 from China Family Panel Studies data. Int J Equity Health. 2023;22(1):86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpencer M, Wood E, Baker J, Hyett N. The Socio-Ecological Enablers of Smoking and Vaping in Rural Young Adults: A Mixed-Methods Study. Health Promotion J Australia. 2025;36(2):e70044.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarroll SJ, Dale MJ, Bailie R, Daniel M. Climatic and community sociodemographic factors associated with remote Indigenous Australian smoking rates: an ecological study of health audit data. BMJ Open. 2019;9(7):e032173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Chica DA, Vanlint S, Hoon E, Stocks N. Epidemiology of arthritis, chronic back pain, gout, osteoporosis, spondyloarthropathies and rheumatoid arthritis among 1.5 million patients in Australian general practice: NPS MedicineWise MedicineInsight dataset. BMC Musculoskelet Disord. 2018;19(1):20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Primary care, general practitioners, electronic health records, lung cancer screening, smoking","lastPublishedDoi":"10.21203/rs.3.rs-8835194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8835194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eIn 2025, lung cancer screening commenced in Australia, with general practitioners (GPs) being patients\u0026rsquo; first point of contact to access the program. However, there is a gap in understanding how GPs can effectively identify screening-eligible individuals. Therefore, our study examined how smoking status data are recorded in GPs\u0026rsquo; electronic health records (EHRs), describing differences in smoking status data by socio-demographic characteristics and co-morbidities, and identifying missing data in EHRs.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted. We investigated 1,366,586 regular adult patients attending general practices in the MedicineInsight database, between 1 July 2021 and 30 June 2022. We assessed patient-related information as proportions, including \u0026lsquo;current\u0026rsquo; smoking status, socio-demographic characteristics, co-morbidities, and the number of cigarettes smoked per day amongst smokers.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn our study, 156,881 (11.5%) regular adult patients were recorded as smokers, 311,503 (22.8%) as ex-smokers, 766,091 (56.1%) as non-smokers, and 132,111 (9.7%) did not have smoking status recorded. Smoking status data was better recorded in older age groups, including the lung cancer screening-eligible population of 50\u0026ndash;70-year-olds. Lower socio-economic areas and remote/ very remote areas had higher rates of smokers and ex-smokers. Co-morbidities such as chronic obstructive pulmonary disease, diabetes and cardiovascular disease had higher rates of current and ex-smokers, compared to non-smokers. EHR recording of cigarettes smoked per day were limited to only 776 smokers.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eOur study indicates that GPs can use their EHRs to help recruit patients for LCS, as \u0026lsquo;current\u0026rsquo; smoking status was recorded for over 90% of regular patients. However, GPs need to further assess these patients for screening eligibility, as detailed smoking history data were poorly recorded. Priority populations for GPs include those from lower socio-economic and remote/ very remote areas, and with certain co-morbidities. GPs need to be better supported, especially with adequate funding, given the additional workload and time required.\u003c/p\u003e","manuscriptTitle":"Can GPs use smoking status data in electronic health records to recruit patients for lung cancer screening? A descriptive study using the MedicineInsight database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:46:33","doi":"10.21203/rs.3.rs-8835194/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T07:21:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T18:14:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T21:59:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3340999913028988295684333905024309341","date":"2026-03-23T22:02:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208531882936591405044624940898012623335","date":"2026-03-15T22:45:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T20:00:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242467023495850747852615820279378292179","date":"2026-03-02T20:03:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T21:38:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T06:12:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T06:11:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2026-02-10T01:30:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd02a137-b017-49fc-b194-8f94edbe4981","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:29:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:46:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8835194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8835194","identity":"rs-8835194","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}