F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.18205.1 Research Article Articles Burden of health behaviours and socioeconomic position on health care expenditure in Ontario

[version 1; peer review: 1 approved, 1 approved with reservations]

 Manuel Douglas G. Conceptualization Formal Analysis Methodology Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0003-0912-0845 a 1 2 3 4 5 6 Bennett Carol Conceptualization Formal Analysis Methodology Project Administration Visualization Writing – Original Draft Preparation Writing – Review & Editing 1 2 Perez Richard Conceptualization Formal Analysis Methodology Writing – Review & Editing 1 2 Wilton Andrew S. Conceptualization Formal Analysis Investigation Methodology Writing – Review & Editing 2 Rohit Dass Adrian Formal Analysis Investigation Methodology Writing – Review & Editing 7 Laporte Audrey Conceptualization Investigation Methodology Supervision Writing – Review & Editing 2 7 Henry David A. Conceptualization Investigation Methodology Project Administration Supervision Writing – Review & Editing 2 7 8 Ottawa Hospital Research Institute, Ottawa, Canada ICES, Toronto and Ottawa, Canada Statistics Canada, Ottawa, Canada Department of Family Medicine, University of Ottawa, Ottawa, Canada School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada Bruyère Research Institute, Ottawa, Canada Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada Centre for Research in Evidence-Based Practice, Bond University, Gold Coast, Australia dmanuel@ohri.ca

No competing interests were disclosed.

 18 3 2019 2019 8 303 6 3 2019 Copyright: © 2019 Manuel DG et al. 2019 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Smoking, unhealthy alcohol consumption, poor diet and physical inactivity are leading risk factors for morbidity and mortality, and contribute substantially to overall healthcare costs. The availability of health surveys linked to health care provides population-based estimates of direct healthcare costs. We estimated health behaviour and socioeconomic-attribute healthcare costs, and how these have changed during a period when government policies have aimed to reduce their burden.

Methods: The Ontario samples of the Canadian Community Health Surveys (conducted in 2003, 2005, and 2007-2008) were linked at the individual level to all records of health care use of publicly funded healthcare. Generalized linear models were estimated with a negative binomial distribution to ascertain the relationship of health behaviours and socioeconomic risk factors on health care costs. The multivariable cost model was then applied to unlinked, cross-sectional CCHS samples for each year from 2004 to 2013 to examine the evolution of health behaviour and socioeconomic-attributable direct health care expenditures over a 10-year period.

Results: We included 80,749 respondents, aged 25 years and older, and 312,952 person-years of follow-up. The cost model was applied to 200,324 respondents aged 25 years and older (CCHS 2004 to 2013). During the 10-year period from 2004 to 2013, smoking, unhealthy alcohol consumption, poor diet and physical inactivity attributed to 22% of Ontario’s direct health care costs. Ontarians in the most disadvantaged socioeconomic position contributed to 15% of the province’s direct health care costs. Taken together, health behaviours and socioeconomic position were associated with 34% ($134 billion) of direct health care costs (2004 to 2013). Over this time period, we estimated a 1.9% reduction in health care expenditure ($5.0 billion) attributable to improvements in some health behaviours, most importantly reduced rates of smoking.

Conclusions: Health behaviours and socioeconomic position cause a large direct health care system cost burden.

 Burden smoking alcohol diet physical activity healthcare cost Ontario Ministry of Health and Long-Term Care This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

Smoking, unhealthy alcohol consumption, poor diet and physical inactivity are leading risk factors for morbidity and mortality worldwide 1 . Despite this knowledge, prevalence of these risk factors remains high and reduction efforts may be hindered by failure to understand the full human and cost burdens these risk factors impose on societies 2 . In an era of increasing health care expenditure most political focus has been on payments for services and the growing impacts of diagnostic and therapeutic technologies. There is also a need to consider costs resulting from upstream health behaviours, and how these have changed over time, to help prioritize public health strategies and support public health decision makers. These relationships are likely to be complex because of some conflicting trends in the prevalence of health behaviours 3, 4 .

While health behaviours have a leading role in morbidity and mortality, it is also recognized that there is uneven distribution of health across socioeconomic position (i.e., social and economic factors that influence what position individuals hold within the structure of a society 5 ). Canadian research indicates that individuals with lower socioeconomic position tend to be less healthy than those who enjoy greater educational, income and occupational advantages 6– 9 . As with health behaviour-attributable health care use, evidence of the economic cost of these health disparities helps us understand the health and financial benefits of reducing the gap 6 .

We sought to estimate the economic burden attributable to four health behaviour risk factors (smoking, unhealthy alcohol consumption, poor diet and physical inactivity), how these have changed over time, and how they interact with socioeconomic position. The study had three objectives: 1) to examine the direct healthcare costs associated with smoking, unhealthy alcohol consumption, poor diet and physical inactivity; 2) to examine the change in direct health care costs as a consequence of changes, over time, in health behaviours; and, 3) to examine the direct health care costs associated with socioeconomic position (i.e., education, family income, home ownership, and neighbourhood deprivation).

Past studies typically infer health care costs indirectly—where aggregate health care expenditure data is categorized by disease, for example. We used unique Canadian data that individually link respondents from large repeated population health surveys to comprehensive health care utilisation and cost data covering hospital and primary care sectors in Ontario. These data provide, to our knowledge, the largest and most complete population-based examination of the relationship between health behaviours and direct public healthcare costs. We believe this is the first study to measure directly how changes in health behaviours result in changes in health care use. The linked data also provide the means to assess the degree to which health costs are associated with socioeconomic inequalities.

 Methods Ethical approval

This study was approved by the Ottawa Health Science Network Research Ethics Board. Datasets were linked using unique encoded identifiers and analysed at ICES. ICES is an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyse health care and demographic data, without consent, for health system evaluation and improvement.

 Study cohorts

We used the Ontario sample drawn from a national population health survey—the Canadian Community Health Survey (CCHS), conducted in 2003, 2005, 2007-2008, 2009-2010, 2011-2012, and 2013-2014 to develop linked and unlinked study cohorts. The CCHS is a cross-sectional survey conducted by Statistics Canada that collects data related to health determinants, health status and health care use—details of data collected and used in the analyses are provided below. The survey employs a complex multistage sampling strategy to randomly select households in each health region. Details of the survey methodology have previously been published 10 . A weight, which reflects the number of individuals represented in the target population, is assigned to each respondent; the target population includes individuals aged 12 years and older living in Canada’s ten provinces and three territories. Individuals living on First Nation Reserves, institutionalized residents, full-time members of the Canadian Forces and residents of certain remote areas are excluded from the survey’s sampling frame.

For the linked study cohort, the Ontario sample of the 2003, 2005, and 2007-2008 CCHS cycles provided 128,501 valid interviews. Of these respondents, a subset agreed to share and link their interview information and 101,506 were successfully linked to their provincial health card number using a deterministic and probabilistic algorithm. We included respondents aged 25 years and older if they were eligible for provincially funded health care and not pregnant at the time of survey administration. For individuals with multiple interviews, only the earliest interview was included. We excluded individuals who were lost to follow-up in the first year following their interview (i.e., they were not available at the beginning of the study). This resulted in a final cohort of 80,749 unique Ontario respondents (see Figure 1).

Creation of study cohort for multivariable cost model.

CCHS, Canadian Community Health Survey; OHIP, Ontario Health Insurance Program.

The Ontario sample of all six CCHS waves provided 200,324 valid respondents aged 25 years and older for the unlinked study cohort. This cohort was used to estimate direct health care costs by applying the multivariable model that was derived using the linked cohort CCHS data (see Model Development).

 Behavioural and other risk factors for health care use

The CCHS waves were used to examine the following risk factors for their association with health care use: age, sex, four health behaviours (smoking, alcohol consumption, diet and physical activity; see Table 1), sociodemographic factors (immigrant status, education level, urban dwelling, neighbourhood deprivation, household income, home ownership, marital status), self-perceived stress, preventive health behaviour (flu vaccination), and health status indicators (body mass index, hypertension, diabetes, heart disease, cancer, history of stroke, dementia, and extent of difficulty in performing basic tasks or participating in activities).

Definitions of behavioural health risks.
Behaviour Category * Definition
Smoking Heavy smoker Current daily smoker (≥20 cigarettes/day)
Light smoker Current daily smoker (<20 cigarettes/day) or current occasional smoker with ≥100 lifetime cigarettes
Former heavy smoker Former daily smoker (≥20 cigarettes/day)
Former light smoker Former daily smoker (<20 cigarettes/day) or former occasional smoker with ≥100 lifetime cigarettes
Non-smoker Never smoker or occasional smoker <100 lifetime cigarettes
Alcohol Heavy drinker Bingeing ‡ or >21 (men) or >14 (women) drinks/week
Moderate drinker ≤21 (men) or ≤14 (women) drinks/week with no bingeing
Non-drinker No alcohol consumption in the last 12 months
Diet Poor diet Index score † 0 to <2.5
Fair diet Index score 2.5 to <5
Adequate diet Index score 5 to 10
Physical activity Inactive 0 to <1.5 METs/day
Moderately active 1.5 to <3 METs/day
Active ≥3 METs/day

*Highest risk levels are in bold and lowest risk levels (reference group) are in italics.

‡Bingeing: five or more drinks on any day in the previous week or weekly bingeing behaviour in the previous month.

†Index score: the healthiness of a diet based on consumption of fruit and vegetables. Individuals start with 2 points and achieve up to 8 additional points for each average daily serving of fruits and vegetables (maximum score = 10). Points are deducted for daily fruit juice servings exceeding 1 (-2 points), no carrot consumption (-2 points), or daily potato consumption exceeding 1 serving for males and 0.7 servings for females (-2 points). Scores that result in negative values after deductions are recoded to zero, resulting in a final range of 0 to 10 for the index.

MET, metabolic equivalent of task (a measure of calories burned by type, duration and frequency of physical activity).

 Public health care spending data

We examined person-level health care costs across three sectors: 1) hospital care (inpatient hospitalizations, same day surgeries, emergency department visits, rehabilitation hospitals, and complex continuing care centres); 2) drugs (for Ontarians age 65 and older and Ontarians receiving social assistance, Ontario Drug Benefit costs were captured); and 3) community care (primary care billings, specialist billings, lab billings, capitation services, and home care services).

Costs to operate the health care system (e.g., health ministry administrative costs) and capital costs for large scale projects (e.g., building new hospitals) are not reflected in the person-level costs. To account for these exclusions in the health care cost analysis, we obtained annual total health care expenditures from publicly available Ontario Ministry of Finance records (fiscal years 2003 to 2013, where fiscal year is April 1 to March 31), the Canadian Institute for Heath Information’s National Health Expenditure Trends publication, and the MOHLTC’s Report Card for the Ontario Drug Benefit Program publications 11– 23 . The expenditures from each year were categorized into our three health care sectors and expenditures that did not correspond with any of the three sectors were assigned to an ‘other’ category.

All costs are expressed in 2014 Canadian dollars with past costs inflated using the annual general Consumer Price Index reported by Statistics Canada.

 Model development

To develop multivariable cost models, Ontario respondents to the 2003, 2005 and 2007-2008 cycles of CCHS were linked, at the individual level, to all records of health care use that were paid for by the Ontario Ministry of Health and Long-Term Care (MOHLTC). The cost associated with each record was estimated using costing methods developed for Ontario health administrative data 24 . Briefly, a payer (the MOHLTC) cost perspective was taken, using person-level health care utilization and per-use fee information or budgetary data. Cost information for sectors (i.e., acute hospitalization, same day surgery, emergency department, inpatient rehabilitation, and complex continuing care) that are funded using global budgets (e.g., by institution) are determined using a top-down approach through case-mix methodology. Sectors that have fee payments associated with each use (e.g., prescription drugs, physician services, and home care) have costs estimated directly.

Beginning one year after survey administration, CCHS respondents were followed for a four-year period (between 2004 and 2013) to develop multivariable models estimating the effect of health behaviours on health care costs. We used a generalized linear model with a negative binomial distribution and an offset to account for variation in follow-up times to create separate, sex-specific models for each of the health care sectors: hospital care, drugs, and community care. To assess confounding and mediation, we use a pre-specified, stepwise modelling approach. We started with a health behaviour model followed by a basic sociodemographic model, a primary attribution model that adjusted for additional sociodemographic risk factors, a distal mediator model that included health status indicators, and a proximal mediator model that included a measure of fragility (see Figure 2). This stepwise analysis resulted in 15 models for each sex.

Stepped approach to model building—consideration of risk factors leading to healthcare use and costs.

The model building approach sought to address three issues in assessing the contribution of health behaviours to health care costs. First, we were interested in having appropriate adjustment for other risk factors for increased costs that are correlated with health behaviours (e.g., age and sociodemographic risk factors). Second, we were attentive to risk factors that may mediate the relationship between health behaviours and health care costs (e.g., body mass index or hypertension). Our concern was that their inclusion in the model could inappropriately attenuate the risk from health behaviours. Third, we considered pre-existing illness that may have led to health behaviour change. For example, as people become ill and frail they may become less physically active; in such a situation, physical inactivity may be associated with increased health care cost that is more appropriately identified as illness-associated inactivity.

 Estimating population attributable fractions of health care costs

We calculated the proportion of the health care costs that can be attributed to health behaviours—the population attributable fraction—for fiscal years 2003 to 2013, for each health care sector. Using the unlinked CCHS cohort, we estimated annual population attributable fractions using each CCHS cycle and for years between CCHS cycles, by averaging the population attributable fractions from the preceding and succeeding year.

We used a factor-deleted approach to calculate population attributable fractions that involved three steps. In the first step, we estimated expected annual population health care costs for a specific sector by applying the corresponding sector-specific primary attribution models to the weighted CCHS cycle. In the second step, we repeated the calculation after recoding each respondent’s health behaviour to the counterfactual reference or “no exposure” category. For example, taking the weighted cohort, we estimated hospital costs using all smoking exposures (i.e., current, former, and non-smokers) and re-estimated hospital costs assuming all current and former smokers were non-smokers. The difference between the two calculations was an estimate of the annual contribution of smoking to hospital costs. In the final step, we divided this difference by the original population estimate (from the first step) to produce a population attributable fraction. In our example, this would be the population attributable fraction of hospital costs associated with smoking. Health sector specific population attributable fractions were calculated for each health behaviour, and the combination of health behaviours. The same analysis was performed for different socioeconomic groups defined by education, family income, home ownership, and neighbourhood deprivation.

 Estimating the health care cost burden of health behaviours and socioeconomic position in Ontario

We calculated annual estimates of costs attributable to health behaviours and socioeconomic position (fiscal years 2004 to 2013) by applying the sector-specific population attributable fractions to the annual public health care expenditures and summing the health care sector results together.

 Estimating costs attributable to changes in health behaviours

The change in health care costs attributable to the change in health behaviours was estimated for fiscal years 2004 to 2013. A baseline population attributable fraction for total health care costs associated with all health behaviours was estimated for fiscal year 2003 using the previously described methods for population attributable fractions and attributable costs. The overall health care budget was estimated over the subsequent decade, assuming that health behaviours in 2003 remained constant (e.g., the baseline population attributable fraction did not change over time). The difference between the counterfactual health care budget and the actual health care budget provided an annual estimate of the change in health care costs attributable to changes in health behaviours.

 Sensitivity analysis

We performed three sets of sensitivity analyses. First, we created five models representing a stepwise approach to assess confounding and mediation. The five models assessed health care costs and burden estimates with progressive adjustment for risk factors: age, sex and health behaviours (Model 1), basic socio-demographic characteristics (Model 2), additional sociodemographic characteristics (Model 3), health status mediators (Model 4), and fragility measures (Model 5) ( Figure 2). The estimate derived from Model 3 was assumed to be our most accurate and appropriate estimate of the attributable burden due to health behaviours. The estimates derived from Model 1 (simply age and health behaviours) and Model 5 (the over-adjusted model) were used as upper and lower bounds of uncertainty.

For our second sensitivity analysis, we compared age-standardized cost ratios after excluding the top 5% of health care users to assess the possibility of overly influential respondents. The use of health care varies considerably between people, particularly for hospital care and other speciality services. Only a small proportion of people are hospitalized and, of those hospitalized, a small proportion has multiple admissions and complicated long hospital stays. The skewed distribution of health care services has potential to distort the attributable health care expenditure analysis because a small proportion of CCHS respondents may have a strong influence on overall or total population estimates.

Third, we replicated analyses using an inverse propensity-weighted model to assess robustness of the health care cost ratios attributable to smoking. The inverse propensity-weighted model, a complimentary approach to the generalized linear model, is an alternative approach to adjust cost ratios for multivariable risk factors. Our inverse propensity-weighted analyses included several covariates in addition to all those used in the multivariable analyses (i.e., the primary attribution model): household type, highest level of household education, main source of household income, labour force participation, sense of belonging to the community, regional health authority, and survey cycle.

 Results

The population attributable fractions for the four behavioural risks were calculated using responses from 80,749 Ontarians surveyed between 2003 and 2008. In total, there were 312,952 person-years of follow-up. Characteristics of the study cohort are presented in Table 2.

Baseline description of the study cohorts.
Male cohort Female cohort
Survey sample * % Person- years Represented population ‡ % Survey sample * % Person- years Represented population ‡ %
(N=36,807) (N=3,962,088) (N=43,942) (N=4,131,570)
Age group (years)
      25 to 29 7.4 10,668 9.1 7.3 12,642 8.8
      30 to 34 9.0 13,001 10.0 8.5 14,714 9.1
      35 to 39 10.3 14,957 11.5 9.1 15,888 11.5
      40 to 44 11.1 16,183 14.5 8.8 15,326 12.5
      45 to 49 9.0 13,059 11.5 8.1 14,149 11.6
      50 to 54 9.6 13,847 10.2 9.4 16,256 10.2
      55 to 59 10.0 14,420 9.6 10.3 17,760 9.1
      60 to 64 9.0 12,897 7.2 9.0 15,451 7.2
      65 to 69 7.9 11,152 5.6 8.0 13,568 6.2
      70 to 74 6.8 9,408 4.6 7.5 12,650 5.0
      75 to 79 5.3 7,004 3.4 6.5 10,654 4.2
      80 to 84 3.1 3,907 2.0 4.8 7,575 2.8
      85 to 89 1.2 1,356 0.7 2.1 3,257 1.3
      90+ 0.3 320 0.2 0.7 883 0.4
Health Behaviours
Smoking status
      Heavy smoker 10.7 15,116 9.2 5.8 9,828 4.8
      Light smoker 14.5 20,642 15.4 14.8 25,234 13.8
      Former heavy smoker 20.0 27,998 16.0 9.5 16,010 7.7
      Former light smoker 17.4 24,531 16.8 18.0 30,678 16.3
      Non-smoker 36.5 52,432 41.8 51.0 87,361 56.6
      Missing 1.1 1,460 0.9 1.0 1,661 0.8
Alcohol consumption
      Heavy drinker 12.3 17,695 10.9 3.4 5,762 3.3
      Moderate drinker 70.7 100,787 71.8 72.2 124,162 70.0
      Non-drinker 15.1 20,940 15.3 23.3 38,974 25.6
      Missing 2.0 2,758 2.0 1.1 1,876 1.1
Physical activity
      Inactive 46.5 65,794 47.8 52.8 89,455 54.3
      Moderately active 25.2 36,032 24.3 25.6 44,103 24.5
      Active 26.1 37,473 25.1 20.8 35,913 19.7
      Missing 2.2 2,880 2.8 0.9 1,302 1.5
Diet
      Poor diet 15.0 21,400 14.2 8.6 14,632 8.3
      Fair diet 41.7 59,340 40.3 30.0 50,933 29.2
      Adequate diet 38.6 55,167 40.7 58.2 99,981 59.0
      Missing 4.7 6,272 4.8 3.3 5,227 3.6
Sociodemographic Indicators
Immigrant status
      Immigrant 21.8 30,837 33.5 21.5 36,779 33.9
      Non-immigrant 78.1 111,177 66.2 78.3 133,765 65.8
      Missing 0.1 165 0.3 0.1 228 0.4
Ethnicity
      White 88.9 126,274 78.9 89.7 152,990 79.3
      Non-white 10.7 15,357 20.5 10.0 17,183 20.1
      Missing 0.4 548 0.6 0.4 600 0.5
Education
      Less than high school 18.9 25,984 14.8 20.2 33,702 16.5
      High school graduate 22.7 32,530 22.2 25.0 42,730 24.9
      Post-secondary graduate 57.5 82,404 61.8 54.1 93,212 57.7
      Missing 0.9 1,262 1.2 0.7 1,129 0.9
Marital status
      Married/common-law 67.8 96,810 76.8 57.7 99,508 68.8
      Other 32.2 45,306 23.2 42.3 71,197 31.2
      Missing 0.0 64 0.0 0.0 68 0.0
Residence ownership
      Yes 79.3 113,229 79.9 75.9 130,283 77.8
      No 20.5 28,730 19.7 23.9 40,246 21.9
      Missing 0.2 221 0.3 0.1 244 0.3
Household income ($)
      0 to 29,999 16.6 22,903 11.0 25.9 43,299 16.4
      30,000 to 79,999 45.5 64,687 40.1 41.6 71,561 39.3
      80,000+ 32.2 46,643 40.3 23.5 40,774 31.0
      Missing 5.7 7,946 8.6 9.0 15,140 13.3
Preventive healthcare
Flu shot
      Yes 61.6 87,077 58.0 68.6 116,714 63.4
      No 36.0 51,907 38.9 30.5 52,701 35.0
      Missing 2.5 3,195 3.1 0.9 1,358 1.5
Geography
Urban
      No 22.3 31,709 14.9 21.0 36,089 14.3
      Yes 77.7 110,471 85.1 79.0 134,684 85.7
Neighbourhood deprivation
      High 15.1 21,394 11.8 16.3 27,519 12.6
      Moderate 62.3 88,446 60.8 62.3 106,413 61.2
      Low 20.6 29,532 25.3 19.3 33,198 24.2
      Missing 2.0 2,807 2.1 2.2 3,643 2.0
General health indicators
Self-perceived stress
      Quite a bit or extremely       stressful 20.2 28,881 23.0 21.5 36,892 24.4
      At most, a bit stressful 79.5 112,917 76.7 78.2 133,339 75.4
      Missing 0.3 382 0.3 0.3 542 0.3
Body mass index
      Underweight 0.7 926 0.8 2.7 4,428 3.2
      Normal 43.5 62,221 43.3 29.9 51,187 28.3
      Overweight 15.6 22,224 13.9 12.5 21,474 10.9
      Obese 4.8 6,884 4.0 6.3 10,740 5.3
      Morbidly Obese 34.2 48,428 36.5 45.5 77,566 48.5
      Missing 1.2 1,497 1.5 3.2 5,378 3.9
Indicators of illness
Hypertension
      Yes 22.7 31,629 18.9 25.7 43,146 20.5
      No 77.0 110,133 80.8 74.2 127,408 79.4
      Missing 0.3 417 0.3 0.1 218 0.1
Diabetes
      Yes 8.7 11,757 7.2 7.5 12,319 6.2
      No 91.3 130,305 92.7 92.5 158,364 93.8
      Missing 0.1 118 0.1 0.1 90 0.0
Heart disease
      Yes 9.4 12,559 6.8 7.9 12,819 5.5
      No 90.4 129,348 93.0 91.9 157,654 94.3
      Missing 0.2 272 0.1 0.2 299 0.2
Cancer
      Yes 2.9 3,754 2.1 2.6 4,199 2.0
      No 97.0 138,284 97.9 97.3 166,374 97.9
      Missing 0.1 141 0.1 0.1 200 0.1
Stroke
      Yes 2.0 2,552 1.4 1.8 2,796 1.4
      No 98.0 139,540 98.6 98.1 167,826 98.6
      Missing 0.1 88 0.1 0.1 151 0.0
Dementia
      Yes 0.6 645 0.5 0.4 629 0.5
      No 99.4 141,440 99.5 99.5 170,034 99.4
      Missing 0.1 94 0.1 0.1 109 0.1
Fragility
      Help with basic tasks 6.6 8,570 5.4 13.6 22,096 11.4
      Limitation due to health 21.7 30,749 18.8 20.3 34,852 17.8
      No limitations 71.4 102,527 75.5 65.8 113,449 70.6
      Missing 0.3 334 0.3 0.2 376 0.2

*Data source: Canadian Community Health Survey (CCHS) 2.1, 3.1 and 4.1 (2003, 2005 and 2007/08).

‡Population estimated using the CCHS sampling weights.

 Health behaviour attributable healthcare use

From fiscal years 2004 to 2013, 22% of Ontario’s health care costs could be attributed to the four health behaviour risk factors ( Figure 3). Physical activity had the largest attribution (13%), followed by smoking (10%). However, uncertainty for the burden estimates indicates potential overestimation for physical activity and underestimation for diet. Alcohol-attributable health care costs were also likely underestimated (see limitations section).

Proportion of health care costs attributed to selected health behaviour risk factors for Ontarians aged 25 and older, 2004 to 2013.

Error bars represent high and low boundaries on burden estimates.

 Population health impact of behavioural risks and socioeconomic position

During the 10-year period (2004 to 2013), $89.3 billion in health care costs were attributable to health behaviours. In that same period, the costs attributable to health behaviours improved by 1.9% (23.3% of total healthcare costs in 2004 to 21.4% in 2013). If the proportion of health care expenditure that can be attributed to health behaviours (the population attributable fraction) had remained at 23.3%, use of health care would have been $5.0 billion greater (what we term ‘avoided cost’).

Table 3 presents the burden of health behaviours related to health care costs ($89.3 billion) and the costs avoided by the adoption of healthy behaviours ($5.0 billion). Physical inactivity and smoking contributed the largest proportion of the burden (53% and 41%, respectively). However, a decline in smoking between 2004 and 2013 was responsible for 84% of the avoided costs.

Health care attributable and avoided costs by health behaviour risk factor for Ontarians aged 25 and older, 2004 to 2013.
Risk Attributable costs ($89.3 billion) Avoided costs ($5.0 billion)
Smoking 41% 84%
Alcohol 1% 0%
Diet 5% 5%
Physical activity 53% 11%

The scenarios from our sensitivity analysis demonstrate results that were similar to our main analysis. Not unexpectedly, the attribution of health care costs to health behaviours decreased as we increased the number of risk factors adjusted for in the model. Excluding high-cost health care users demonstrated slightly attenuated age-standardised cost ratios for men and women for almost all health behaviour risks. The inverse propensity-weighted model, which adjusted for additional variables, had similar cost ratios to the main analysis from the multivariable model.

Between 2004 and 2013, $60.7 billion dollars in health care costs (15% of all health care costs for Ontarians aged 25 years and older) were attributable to low socioeconomic position ( Figure 4). When health behaviours and socioeconomic position are considered jointly, the health care cost burden was $134 billion (34% of all health care costs for those aged 25 years and older). The break down by health care sector was similar for health behaviour and socioeconomic position. The largest portion of the burden is due to hospital care costs (46% of health behaviour attribution and 54% of socioeconomic position attribution to health care costs); followed by community care costs (22% and 19%), ‘other’ health care costs (21% and 19%), and drug costs (10% and 9%).

Health behaviour risk factors and socioeconomic position attribution to health care costs for Ontarians aged 25 and older, 2004 to 2013. Discussion

We estimated that smoking, unhealthy alcohol consumption, poor diet and physical inactivity attributed to 22% of Ontario’s direct health care costs during the ten-year period from 2004 to 2013. During this same period, improving health behaviours equated to a nearly 2% reduction in direct health care expenditure. Ontarians in the most disadvantaged socioeconomic group contributed to 15% of the province’s direct health care costs. Taken together, health behaviours and socioeconomic position contributed to a burden of $134 billion in direct health care costs (Ontario, 2004 to 2013).

Estimates of the cost of modifiable health behaviours and socioeconomic risk factors provides evidence to allow policy-makers to prioritize interventions aimed at reducing health care costs. Our estimate of a 1.9% reduction in health care expenditure (i.e., $5 billion) through improved health behaviours, suggests that investments in promotion of healthy living have potential for substantial savings in health care costs in Canada. In our study, reduced smoking was the main contributor to avoided health behaviour attributable health care costs (accounting for 84% of the 1.9% cost reduction). This large cost reduction reflects prominent smoking prevention strategies that were introduced during the study period—including 100% smoke-free public places including restaurants 25 . From 2004 to 2013 there was an 11% reduction in the attributable fraction of smoking, which was solely a consequence of a reduction in the prevalence of current smoking. The large remaining burden from health behaviours and social inequalities suggests that there are significant opportunities to further reduce health care costs through population health strategies.

It is difficult to compare our findings with previous studies for two main reasons. First, various studies have estimated the economic burden in terms of costs for treatment and management of chronic diseases related to smoking, alcohol, diet or physical activity, but very few have evaluated the simultaneous impact of multiple risk factors in a population. These latter studies have used traditional population aggregated-data attributable fraction methods to estimate economic burden 26– 31 . Second, the significant methodological differences between our study and previous literature limits direct comparison of findings. Briefly, traditional population attributable fraction methods identify diseases where health behaviours are risk factors, estimate the health care costs of these diseases, calculate the proportion of the disease that can be attributed to the risk factors (based on relative risk of disease from an external source and prevalence of exposure in the population of interest), and apply these population attributable fractions to the cost data. There are limitations associated with these methods that stem from combining ecological summary measures of exposure, outcome, and hazards across different sources of data 32 . Our use of multivariable algorithms and the direct attribution of health behaviours to health care costs offers advantages over analyses that have been performed to date: controlling for confounders, accounting for complexities in the relationship between multiple exposures and covariates, using consistent definitions of exposure, and using specific measures of risk derived internally from the study population.

Our study has several limitations that we believe underestimate the actual burden of health care costs attributable to the four health behaviours. First, we excluded individuals younger than 25 years of age. In general, health care costs for this age group are small; however, alcohol burden for younger people is a notable omission. Alcohol has an important attribution for injury, suicide and other social burdens that occur disproportionately among young people 33– 35 . Second, our reliance on self-reported health risk exposures will generally result in an underestimation of risk burden, especially for food and alcohol 36– 39 . In what is referred to as “social desirability bias”, survey respondents tend to over-report what they perceive as healthy behaviour and underreport unhealthy behaviour 40 . As an example, self-reported alcohol consumption in surveys accounts about half the volume of alcohol sold 41– 42 . While reporting accuracy affects all risks in this study, burden estimates are mostly affected when people report they are in the healthiest category and they are actually in an unhealthy category. Third, the survey’s brief questions about risks may not capture the full spectrum of behaviour. For example, our measure of physical activity (leisure-time physical activity) did not include active transportation (such as walking and bicycling to work), work activity, or sedentary time and our measure of diet (fruit and vegetable consumption) did not specifically ascertain intake of sodium, trans fats, calories or other aspects of healthy and unhealthy eating. Fourth, we used respondents’ answers to health behaviours and other risks that correspond to their health behaviour at the time of the survey. In general, studies that consider lifetime changes in risks generate higher burden estimates. Our physical activity burden estimates may be an exception, where reverse-causality (i.e., ill health is the cause of reduced physical activity) results in overestimation. Fifth, “ other” health care costs—including health care system operating costs and capital costs—are not included our health care cost data and were estimated indirectly. Sixth, we did not include indirect costs (such as lost productivity, wages and income related to illness associated with unhealthy living, or costs borne by individuals to care for their illness), nor did we include costs for health care beyond those paid by the provincial government (e.g., employee health plans).

Despite these limitations, our study demonstrates the importance of integrating public health and prevention within the health care system. A greater investment in disease prevention and population health could help increase the sustainability of publicly funded health care by reducing spending on illness, particularly with respect to hospital care. Combining this effort with strategies that address social determinants of health could secure further benefits. Indeed, our study suggests that interventions outside the health care system, such as improving levels of income and education, and other risk factors that influence socioeconomic position, will reduce health care costs. The health system is one determinant of population health and attention must be paid to the needs of disadvantaged individuals, populations, and communities in order to avoid increasing health disparities 43 . While health inequities play a significant role in health system costs, estimating the cost of the gap in health care as a result of socioeconomic position can be difficult because of the complexity of the problem. Our study is one of the few that has been able to translate this gap into a dollar figure.

 Conclusions

Our study shows that health behaviours and socioeconomic position contribute to a large health care cost burden. Better health and well-being is the primary goal of improving health behaviours and reducing social inequity. However, it is important to recognize that existing investments in public health also results in a large reduction in expenditure in the acute care sector, particularly hospital care. The health premium of prevention and social equity is an overlooked opportunity for a sustainable health care system.

 Data availability Underlying data

The dataset from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS. The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

 Extended data

Open Science Framework: Burden of health behaviours and socioeconomic position on health care expenditure in Ontario. https://doi.org/10.17605/OSF.IO/KW4C5 44 .

This project contains the sensitivity analysis supplementary file.

Extended data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Acknowledgements

We are grateful for the contributions of Sima Gandhi and Erika Yates as well as our Scientific and Policy Advisory Committees—namely, Peter Austin, Bernard Choi, Prabhat Jha, Isra Levy, Heather Manson, Claudia Sanmartin, Joanne Thanos, and Pegeen Walsh.

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CMAJ. 2016;188(3):182–8. 26755672 10.1503/cmaj.150064 4754179 Manuel DG Bennett C : Burden of health behaviours and socioeconomic position on health care expenditure in Ontario.2019. http://www.doi.org/10.17605/OSF.IO/KW4C5 10.5256/f1000research.19913.r49969 Reviewer response for version 1 Ding Melody 1 Referee https://orcid.org/0000-0001-9850-9224 Prevention Research Collaboration/the Charles Perkins Centre, Sydney School of Public Health, The University of Sydney, Sydney, NSW, Australia

Competing interests: No competing interests were disclosed.

 4 7 2019 Copyright: © 2019 Ding M 2019 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Thank you for the opportunity to review this paper. In this study, the authors presented some novel and interesting analyses to explore health care cost attributable to unhealthy lifestyle patterns and socioeconomic position (SEP; or, perhaps more meaningful, socioeconomic inequalities?). Compared with existing literature on this topic, this study has a few unique contributions: 1) The authors modelled several lifestyle risk factors simultaneously, both in terms of contribution of individual risk factors and combined lifestyle profiles; 2) the authors used data across multiple years and considered the change in attributable and avoided costs over time; 3) the authors considered both lifestyle behaviours and low SEP as risk factors, both individually and combined. However, I have several recommendations for the authors to consider. 

The information regarding the measurement of lifestyle risk factors and SEP is critical and should be presented with details. What measurement instruments were used? What are the reliability and validity of these measures, what is the rationale for the categorisation? Particularly, the small proportion of healthcare cost attributable to poor diet may be a result of the limitations of the measure (I consider the dietary measure to be a fruit and vegetable intake measure only). 

Although it is interesting and informative to examine healthcare cost associated with low SEP, I think this idea may require a little bit more consideration. SEP is a relative concept, so what is the practical interpretation of PAF associated with SEP? Were the authors considering the healthcare cost that could have been averted if everyone was of high socioeconomic status? Particularly, how do we correctly interpret PAF associated with ethnicity? What are the counterfactuals? 

To me, it may be more meaningful to estimate the PAF of socioeconomic inequality, rather than SEP. 

It seems that the authors have a-priori decided to model women and men separately? What is the rationale? However, later on in the paper, when describing the economic cost, all results were presented for both sexes combined. Is there an explanation for this? 

It looks like the authors have conducted thorough and comprehensive statistical analysis. However, most results are not presented. For example, results from the regression analysis (at least for Model 3) could be of interest to readers. 

The change analysis between 2004 and 2013: is this based on modelling using all data points or the two time points only? 

Could you please provide more information and a reference for inverse propensity-weighted analyses? 

Sorry if I have missed this point from the paper. The modelling of SEP is based on adjusted analysis considering lifestyle risk factors? As we know SEP is a strong predictor of lifestyle behaviour, so parts of the association between SEP and healthcare cost would be explained by lifestyle behaviours. It would be useful if the authors more explicitly define the part of PAF attributable SEP independent of lifestyle risk factors. 

Given that the data spans over 10 years, have the authors considered inflation? Particularly healthcare specific inflation? It may be worth discussing that the change in PAF over time could be a result of a change in population health behaviour of that in the average costs of certain health care procedures/items. 

Furthermore, the authors may consider the checklist we developed for reporting cost of illness analysis. 1

Minor comments/questions: 

Abstract: Taken together, health behaviours and socioeconomic position were associated with 34% ($134 billion)… suggest add "these" before health behaviours, because this is only referring to the 4 selected behaviours, not health behaviour in general.

Methods: could the authors provide some justification regarding why these lifestyle behaviours were selected? 

Results: “uncertainty for the burden estimates indicates potential overestimation for physical activity and underestimation for diet.” Could the authors provide some justification for this statement? 

The units for the physical activity measure seem incorrect. Could it be MET hour, instead of MET?

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Lifestyle epidemiology, physical activity and health, cost of illness analysis.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

References : The economic burden of physical inactivity: a systematic review and critical appraisal. Br J Sports Med .2017;51(19) : 10.1136/bjsports-2016-097385 1392-1409 28446455 10.1136/bjsports-2016-097385 10.5256/f1000research.19913.r45912 Reviewer response for version 1 Mehta Suneela 1 Referee https://orcid.org/0000-0003-4386-3300 School of Population Health, Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand

Competing interests: No competing interests were disclosed.

 23 4 2019 Copyright: © 2019 Mehta S 2019 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

This elegant analysis aims to estimate direct healthcare costs in Ontario associated with four adverse health behaviours, the change in direct health care costs associated with these health behaviours over time and the direct healthcare costs associated with low socioeconomic position. This is an extremely valuable endeavour; clarifying the economic cost associated with health behaviour risk factors and lower socioeconomic status assists decision makers in prioritising public health strategies focussed on these domains. The introduction provides a thoughtful background to the study, the definition of the behavioural risk factors (Table 1) and representativeness of the cohort (Table 2) are clearly outlined and the discussion of the study limitations is excellent.

However, some additional details and minor amendments would be useful:

The analysis relates to publicly-funded health care costs in Ontario but as an international reader, it would be helpful to get a brief background of the structure of health system in Canada, and any aspects that are unique to Ontario. In particular, what proportion of healthcare is delivered in the private health sector?

Related to point 1), how generalisable are the findings to Canada as a whole and other countries? 

The descriptions of the public health care spending data considered (on page 4) and the linkage to person-level health care costs (outlined in the Model development section beginning on page 4) are very clear, but the incorporation of the costs to operate the health system into the modelling process needs some further explanation. Are these non-person level costs considered in the MOHLTC costing perspective developed for Ontario health administrative data that is mentioned at the top of page 5? Also, fifteen models have been developed (five per sex for each of the three identified health care sectors) so how have the expenditures associated with the ‘Other” category mentioned on page 4 been considered in the analysis?

In the first paragraph of the section entitled “Sensitivity analysis”, is the explanation of the stepwise model approach describing something different to the description of the model building process detailed on page 5? If not, then I would delete the latter and just leave the last sentence that mentions that models 1 and 5 were used as upper and lower bounds. Furthermore, I would move the information that the primary attribution models were assumed to provide the most accurate estimate of attributable burden of adverse health behaviours to the description on page 5. 

It would be good to briefly outline the rationale for using a four year period of follow-up on page 5 where this is first mentioned.  Also, the titles of tables A-5 to A-8 in the supplementary data state that a five year period between 2004 and 2013 was analysed so these should either be corrected if they are typos or further detail provided in the sensitivity analysis section of the methods.

Results are presented in Figure 4 for health care costs attributable to low socioeconomic position but there is no corresponding definition of low socioeconomic position in the methods or results. 

 Minor amendments:

In the methods section of the abstract, it would be helpful to add that the unlinked, cross-sectional CCHS samples for each year from 2004 to 2013 were Ontario samples only.

The sentence in the results section of the abstract beginning “Taken together, health behaviours…” would be more accurate if stated as “Taken together, these four adverse health behaviours and low socioeconomic position were associated with 34% ….”. Similarly, the conclusion of the abstract and other sentences similarly worded throughout the paper as well as the labels assigned to Figure 4 would benefit from being reworded to something like “adverse health behaviours” and “low socioeconomic position”.

In the results section describing the sensitivity analyses, it would be helpful to the reader to include the corresponding table numbers in the supplementary data. 

The latter part of the sentence in the discussion on page 12 beginning “Fifth, “other” health care costs…” should read ‘are not included in our health care cost data…’

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Epidemiology of chronic diseases, cardiovascular risk prediction modelling, linked administrative health data

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Manuel Doug Ottawa Hospital Research Institute, Canada

Competing interests: No competing interests were disclosed.

 2 5 2019

We thank you for the constructive review and agree with your suggested edits to improve the comprehensiveness of the paper. Our plan is to submit a revised version, addressing the proportion of health care costs covered by Ontario’s public health care system and generalizability of results; clarifying the methods for modelling person-level health care costs, the stepped approach to model building, and follow-up period; and, defining measurement of SEP. However, given the current version is under review by another reviewer, we will wait to submit a revised version.

 Manuel Doug Ottawa Hospital Research Institute, Canada

Competing interests: No competing interests were disclosed.

 4 10 2019

Thank you for your constructive review. We provide a point-by-point summary of response to your comments below.

1. The analysis relates to publicly-funded health care costs in Ontario but as an international reader, it would be helpful to get a brief background of the structure of health system in Canada, and any aspects that are unique to Ontario. In particular, what proportion of healthcare is delivered in the private health sector?

We have added a description of Canada’s health care system to the methods section Public health care spending data.

2. Related to point 1), how generalisable are the findings to Canada as a whole and other countries? 

We have added a new paragraph in the Discussion (paragraph 3) that focuses solely on generalizability.

3. The descriptions of the public health care spending data considered (on page 4) and the linkage to person-level health care costs (outlined in the Model development section beginning on page 4) are very clear, but the incorporation of the costs to operate the health system into the modelling process needs some further explanation. Are these non-person level costs considered in the MOHLTC costing perspective developed for Ontario health administrative data that is mentioned at the top of page 5? Also, fifteen models have been developed (five per sex for each of the three identified health care sectors) so how have the expenditures associated with the ‘Other” category mentioned on page 4 been considered in the analysis?

Failure to indicate our method to incorporate ‘other’ health care costs (i.e., total provincial health care expenditures minus expenditures in hospital care, community care and drugs) was an oversight. We applied the population attributable fraction from community care to the indirectly estimated other health care costs. We have added this detail to the methods section Estimating the health care cost burden of health behaviours and socioeconomic position in Ontario.

4. In the first paragraph of the section entitled “Sensitivity analysis”, is the explanation of the stepwise model approach describing something different to the description of the model building process detailed on page 5? If not, then I would delete the latter and just leave the last sentence that mentions that models 1 and 5 were used as upper and lower bounds. Furthermore, I would move the information that the primary attribution models were assumed to provide the most accurate estimate of attributable burden of adverse health behaviours to the description on page 5.

We have revised the text as suggested to address the confusion regarding the model building approach.

5. It would be good to briefly outline the rationale for using a four year period of follow-up on page 5 where this is first mentioned.  Also, the titles of tables A-5 to A-8 in the supplementary data state that a five year period between 2004 and 2013 was analysed so these should either be corrected if they are typos or further detail provided in the sensitivity analysis section of the methods.

We have added details to the Model development section to clarify the choice of a four-year follow-up time frame.

The supplementary appendices were erroneously labelled. We have revised accordingly.

6. Results are presented in Figure 4 for health care costs attributable to low socioeconomic position but there is no corresponding definition of low socioeconomic position in the methods or results. 

We have added details to the methods section Estimating population attributable fractions of health care costs to define the comparator group the the SEP analysis.

Minor amendments:

1.  In the methods section of the abstract, it would be helpful to add that the unlinked, cross-sectional CCHS samples for each year from 2004 to 2013 were Ontario samples only.

Done

2. The sentence in the results section of the abstract beginning “Taken together, health behaviours…” would be more accurate if stated as “Taken together, these four adverse health behaviours and low socioeconomic position were associated with 34% ….”. Similarly, the conclusion of the abstract and other sentences similarly worded throughout the paper as well as the labels assigned to Figure 4 would benefit from being reworded to something like “adverse health behaviours” and “low socioeconomic position”.

Done

3. In the results section describing the sensitivity analyses, it would be helpful to the reader to include the corresponding table numbers in the supplementary data. 

Done

4. The latter part of the sentence in the discussion on page 12 beginning “Fifth, “other” health care costs…” should read ‘are not included  in our health care cost data…’

Done

 Manuel Doug Ottawa Hospital Research Institute, Canada

Competing interests: No competing interests were disclosed.

 4 10 2019

Thank you for your helpful review. We have added a number of details to respond to your points. See our point-by-point response below.

1. The information regarding the measurement of lifestyle risk factors and SEP is critical and should be presented with details. What measurement instruments were used? What are the reliability and validity of these measures, what is the rationale for the categorisation? Particularly, the small proportion of healthcare cost attributable to poor diet may be a result of the limitations of the measure (I consider the dietary measure to be a fruit and vegetable intake measure only). 

We have added details to the methods section Behavioural and other risk factors for health care use to more fully describe measurement of health behaviour and sociodemographic risk factors. We have also expanded the discussion Limitations section.

2. Although it is interesting and informative to examine healthcare cost associated with low SEP, I think this idea may require a little bit more consideration. SEP is a relative concept, so what is the practical interpretation of PAF associated with SEP? Were the authors considering the healthcare cost that could have been averted if everyone was of high socioeconomic status? Particularly, how do we correctly interpret PAF associated with ethnicity? What are the counterfactuals? 

We agree, SEP is a relative concept. We have framed the analysis as an equity gap and added details to the Estimating population attributable fractions of health care costs to describe the exposures used for the analysis.

3.  To me, it may be more meaningful to estimate the PAF of socioeconomic inequality, rather than SEP. 

See above.

4. It seems that the authors have a-priori decided to model women and men separately? What is the rationale? However, later on in the paper, when describing the economic cost, all results were presented for both sexes combined. Is there an explanation for this? 

Yes, we did a priori model men and women separately. There were several considerations such as differential misclassification bias of exposure (e.g., men and women have different pattern of misclassification error for weight and height). Many/most studies of the underlying leading causes of diseases sex-stratify based, for example, on the observed effect modification for cardiovascular disease and most leading causes of cancer. The results were then combined to clearly present our main focus on health behaviours and SEP.

5. It looks like the authors have conducted thorough and comprehensive statistical analysis. However, most results are not presented. For example, results from the regression analysis (at least for Model 3) could be of interest to readers. 

We agree with your suggestion. As you can appreciate, there was a wide range of components for the study. However, on reflection we agree that these models are of interest, particularly for replication or for use by other jurisdictions who may use the estimated to indirectly model burden. These models will be added to the on-line figure repository. (The analyst who created those models is currently on leave).

6. The change analysis between 2004 and 2013: is this based on modelling using all data points or the two time points only? 

We have revised the methods section Estimating costs attributable to changes in health behaviours to more clearly indicate that this analysis is based on annual estimates.

7. Could you please provide more information and a reference for inverse propensity-weighted analyses? 

We have added additional details and references to the description of the inverse propensity-weighted sensitivity analysis.

8. Sorry if I have missed this point from the paper. The modelling of SEP is based on adjusted analysis considering lifestyle risk factors? As we know SEP is a strong predictor of lifestyle behaviour, so parts of the association between SEP and healthcare cost would be explained by lifestyle behaviours. It would be useful if the authors more explicitly define the part of PAF attributable SEP independent of lifestyle risk factors. 

The findings for both health behaviours and SEP were estimated using Model 3 simultaneously for health behaviours and SEP. We have clarified this in the manuscript.

We agree with the reviewer’s perspective that SEP and health behaviours have strong relationships with complex pathways.

As such, Model 3 likely represents a somewhat conservative estimate of the independent effects of either health behaviours or SEP. The sensitivity analyses were performed with less and more specified models to gauge the potential effect of adding or removing mediators. Model 1 was a simple model of health behaviours without SEP measures. Of note, we didn’t create to complementary model—i.e., a simple model of SEP without health behaviour measures.

Given the complex relationships, we recognize that there are alternative methods approaches that we hope will be further explored in future studies.

9. Given that the data spans over 10 years, have the authors considered inflation? Particularly healthcare specific inflation? It may be worth discussing that the change in PAF over time could be a result of a change in population health behaviour of that in the average costs of certain health care procedures/items. 

For this study, all costs are expressed in 2014 Canadian dollars with past costs inflated using the annual general Consumer Price Index from Statistics Canada. It is unclear what is meant by “…could be a result of a change in population health behaviour of that in the average costs of certain health care procedures/items.”

10. Furthermore, the authors may consider the checklist we developed for reporting cost of illness analysis.

We’ve completed the checklist and added it to the on-line repository. We had been holding off using a reporting guideline until those for population modelling studies ( http://www.mrc-epid.cam.ac.uk/ph-modelling-guidelines/) became available.

We have also performed studies that have examined how modelling/burden studies can be reported and found the CHEERS guidelines are relevant.

Minor comments/questions: 

1. Abstract: Taken together, health behaviours and socioeconomic position were associated with 34% ($134 billion)… suggest add "these" before health behaviours, because this is only referring to the 4 selected behaviours, not health behaviour in general.

We have modified the language in the abstract to more clearly indicate the attributable costs are related to select health behaviours and socioeconomic measures.

2. Methods: could the authors provide some justification regarding why these lifestyle behaviours were selected? 

In the Introduction we highlight our focus on smoking, unhealthy alcohol consumption, poor diet and physical inactivity as the leading risk factors for morbidity and mortality worldwide.

3. Results: “uncertainty for the burden estimates indicates potential overestimation for physical activity and underestimation for diet.” Could the authors provide some justification for this statement?

We have added details to the results section Health behaviour attributable healthcare use to indicate we are referring to the sensitivity analyses represented by the error bars in Figure 3.

4. The units for the physical activity measure seem incorrect. Could it be MET hour, instead of MET?

We have clarified in the table that our measure was MET-hours/day.