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. Author manuscript; available in PMC: 2021 Nov 7.
Published in final edited form as: Health Rep. 2020 Jul 29;31(7):12–23. doi: 10.25318/82-003-x202000700002-eng

International population-based health surveys linked to outcome data: A new resource for public health and epidemiology

Stacey Fisher 1, Carol Bennett 2, Deirdre Hennessy 3, Tony Robertson 4, Alastair Leyland 5, Monica Taljaard 6, Claudia Sanmartin 7, Prabhat Jha 8, John Frank 9, Jack V Tu 10, Laura C Rosella 11, JianLi Wang 12, Christopher Tait 13, Douglas G Manuel 14
PMCID: PMC7611955  EMSID: EMS137684  PMID: 32761580

Abstract

Background

National health surveys linked to vital statistics and health care information provide a growing source of individual-level population health data. Pooling linked surveys across jurisdictions would create comprehensive datasets that are larger than most existing cohort studies, and that have a unique international and population perspective. This paper’s objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies.

Methods

The design, methodologies and content of the Canadian Community Health Survey (2003 to 2008), the United States National Health Interview Survey (2000, 2005) and the Scottish Health Survey (SHeS) (2003, 2008 to 2010) were examined for comparability and consistency. The feasibility of creating common variables for measuring smoking, alcohol consumption, physical activity and diet was assessed. Sample size and estimated mortality events were collected.

Results

The surveys have comparable purposes, designs, sampling and administration methodologies, target populations, exclusions, and content. Similar health behaviour questions allow for comparable variables to be created across the surveys. However, the SHeS uses a more detailed risk factor evaluation for alcohol consumption and diet data. Therefore, comparisons of alcohol consumption and diet data between the SHeS and the other two surveys should be performed with caution. Pooling these linked surveys would create a dataset with over 350,000 participants, 28,424 deaths and over 2.4 million person-years of follow-up.

Conclusions

Pooling linked national population health surveys could improve population health research and surveillance. Innovative methodologies must be used to account for survey dissimilarities, and further discussion is needed on how to best access and analyze data across jurisdictions.

Keywords: population health, health surveillance, national health surveys

In Canada and elsewhere, national population-based health surveys are increasingly being linked to vital statistics and health care data, bringing together large amounts of high-quality, nationally representative information about health risk factors with individual-level health outcomes.1–5 Health surveys in Canada and the United States alone have collected detailed sociodemographic and health behaviour information from over 1 million respondents since 1997, and have been linked to over 6 million person-years of mortality follow-up.1,6 Because national health surveys often have similar surveillance objectives and designs, pooling data from linked population health surveys could create a new resource for health surveillance and research, with an unparalleled international and population perspective.

National population health surveys vs. traditional epidemiology studies

National population health surveys collect a broad range of information about health status, health behaviours and sociodemographic characteristics from a representative sample of a country’s community-dwelling population. These surveys are a cornerstone of population health surveillance (Table 1) and have a population perspective—they use a sampling approach that is designed to produce a population-representative sample (in terms of sociodemographic characteristics). This sample is used to estimate the prevalence of health conditions and risk factors within the population. It is also used to monitor population trends; inform policy development, implementation and evaluation; inform decisions about health resource allocation; and assess progress toward national health goals.

Table 1. Comparison of the characteristics of traditional epidemiology studies and linked population health surveys.

Primary purpose Population health surveys Traditional epidemiology studies
Population health surveillance Typically etiological questions
Study base Community-dwelling population Specific population subgroups
Sampling method Population-based sampling Often convenience sampling
Size Can be very large Can be very large
Time frame Ongoing; often repeated annually Varies; days to decades
Content General and broad Typically purpose-specific
Information ascertained Sociodemographic characteristics, health behaviours, health status, health care use Sociodemographic characteristics, health behaviours, health events, mortality and disease
Physical measures Generally only collected in small surveys or from a subsample Often include collection of anthropomorphic measurements and biological specimens
Study type Cross-sectional (usually) Longitudinal (usually)
Exposure ascertainment Typically only at baseline; self-response At baseline, often with follow-up; electronic data-capture or chart review
Outcome ascertainment Data linkage increasingly performed to add mortality and disease outcomes for longitudinal analyses Typically active ascertainment of study-specific outcomes
International scope 100+ countries with health surveys; 5+ linked to outcome data International collaboration occurs but is difficult
Data access Public use datasets are increasingly available Not usually accessible
Documentation Easily accessible, and detailed documentation available General methodology available in peer review publications and reports
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Source: Authors compilation.

National population health surveys are typically conducted at regular intervals (often yearly) to provide up-to-date snapshots of the population’s health. In contrast, traditional epidemiology studies—studies that use conventions taught in most introductory epidemiology courses to investigate a specific exposure–outcome relationship—typically have an etiological focus, and often use convenience sampling.

Population health surveys do not usually collect longitudinal information about survey respondents, unlike most traditional epidemiology studies, which typically involve actively determining outcomes, often with repeat exposure assessment during follow-up. Typically, population health surveys determine only baseline exposures, through self-response, and most surveys do not ascertain temporal outcomes because of their cross-sectional nature. However, linking health surveys to outcome data, such as vital statistics and health care data, introduces a longitudinal perspective that greatly increases the surveys’ utility.

In addition to population health surveillance, national health surveys are used for population health research since they collect information that is not available in administrative health files (e.g., health behaviours). These data are used by researchers to study the relationships between social determinants and health outcomes, to evaluate disease and risk factor burden, and to study the role of risk factor modification in prevention. These data are also used to assess the performance of the health care system across sociodemographic and economic groups, and across groups with varying levels of illness. Data are also used to inform the development of health policy. National health surveys are key tools for understanding, monitoring and improving population health.

Individual-level pooling of national population health surveys

Meta-analyses have long been used to summarize collections of traditional epidemiology studies, offering increased statistical power and more precise effect estimates. Individual-level pooling of linked population health surveys may confer similar benefits to population health questions, and could produce a valuable new resource for modern population health planning, including the use of population-level multivariable risk algorithms7–11 and microsimulation12,13 to project disease burden and evaluate risk reduction strategies.

Meta-analysis of traditional epidemiology studies

Meta-analysis is a statistical procedure used to summarize the results from multiple independent clinical trials or observational studies investigating a specific exposure–outcome association.

The key value of meta-analysis is that it involves aggregating data from all relevant studies, which produces a quantitative summary of a body of research with higher statistical power and more precise effect estimates than the individual studies alone. Meta-analysis can be used to reconcile inconsistent results from previous studies, and to investigate rare diseases and uncommon or weak risk factors that individual studies were unable to investigate.14–16

Meta-analyses also offer the opportunity to produce new insights through the exploration of statistical heterogeneity. Statistical heterogeneity is present in a meta-analysis when the effect estimate of interest differs across the studies by more than can be accounted for by sampling variation. This can be caused by differences in study design, statistical methodology or study quality—leading to methodological heterogeneity—or by differences in exposure or outcome definitions, or population characteristics—leading to clinical heterogeneity.17 In all meta-analyses, it is important to identify the presence or absence of heterogeneity because aggregating studies with inconsistent results can lead to inaccurate or misleading conclusions.17–21 However, heterogeneity can also be “our greatest ally”20 since investigating its causes can lead to significant scientific and clinical results.17,21

Individual patient data (IPD) meta-analyses involve pooling and reanalyzing raw data from eligible studies.22 These meta-analyses are considered the gold standard of systematic reviews.23 Pooling and reanalysis allow for the standardization of participant inclusion and exclusion criteria, variable definitions, confounder adjustment, and modelling. This leads to more accurate summary effect estimates,24 and makes it easier to investigate the influence of participant-level characteristics on the effect estimate, and to identify subgroups where risk factor associations may vary. Despite the substantial advantages over meta-analyses without individual-level data, IPD meta-analyses are not frequently performed since they require substantial cooperation and organization, data sharing, and advanced statistical expertise.24,25

Application to national population health surveys

The aggregation of linked international health surveys creates a valuable resource for modern population health care planning and research. Pooling and analyzing individual-level data from national population health surveys using methods similar to IPD meta-analysis could produce more accurate effect estimates with less statistical uncertainty. Additionally, investigating survey-level heterogeneity and subgroups could produce new insights. Survey aggregation could produce improved comparisons of disease risk, burden and trends internationally; facilitate equity analyses; and support health policy and priority setting.

This paper’s objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies. Detailed comparisons of the design, methodologies and content of national health surveys from Canada, the United States and Scotland were performed. Common variables were constructed, and sample size and estimate outcome counts are provided.

Methods

Survey designs for the Canadian Community Health Survey (CCHS) (cycles 2.1 [2003], 3.1 [2005] and 4.1 [2007], and CCHS 2008),26 the United States National Health Interview Survey (NHIS) (2000 and 2005)27,28 and the Scottish Health Survey (SHeS) (2003, 2008 to 2010)29–32 were examined for comparability. This involved evaluating survey content, target populations and exclusions, sampling and administration methods, sample size and response rates, and linkage. Survey year, inclusion of health behaviour topics of interest (e.g., the NHIS collects detailed diet information every five years) and availability of mortality linkage were considered to select the relevant survey cycles.

Questions on smoking, alcohol, physical activity and diet were identified, and question construction, response categorization and structure were compared. Health behaviours were the focus since they are important health risk factors that are collected in virtually all health surveys, and because they are conceptually complex and are observed using different approaches. Health behaviour concepts were assessed for comparability, and existing variables were used to create new common variables. Common variables were constructed to achieve the highest level of detail possible in all surveys, which were assessed and discussed by three reviewers.

Public use files were used to obtain sex-specific sample size estimates of survey respondents aged 20 and older from the three countries. CCHS estimates were obtained through collaboration with Statistics Canada. Public use NHIS data were downloaded from the Centers for Disease Control and Prevention (CDC) website (www.cdc.gov). Public use SHeS data were obtained from the United Kingdom Data Service website (www.ukdataservice.ac.uk).

CCHS mortality estimates were obtained through collaboration with Statistics Canada. NHIS mortality estimates were obtained from public use NHIS files linked to the National Death Index, which is also available for download from the CDC’s website. Mortality estimates for the SHeS were obtained through collaboration with Scotland’s Information Services Division. Mortality follow-up data for the CCHS and the NHIS went to December 31, 2011, while SHeS follow-up data went to December 31, 2014. Research ethics approval was obtained from the Ottawa Health Science Network Research Ethics Board.

Results

Survey comparability

The CCHS, NHIS and SHeS are government-funded, cross-sectional household surveys designed to support national health surveillance efforts in Canada, the United States and Scotland, respectively.2,6,26 The CCHS was administered biennially from 2001 to 2007, and has been administered annually since 2008. The NHIS has been administered annually since 1957. The SHeS was administered in 1995, 1998 and 2003, and annually since 2008. Results of the comparability analysis are summarized in Table 2.

Table 2. Comparison of the characteristics of national health surveys.

Country and population size1 Canadian Community Health Survey National Health Interview Survey Scottish Health Survey
Canada, 37.6 million residents United States of America, 329 million Scotland, 5.3 million
Primary purpose Support national, provincial and intraprovincial health surveillance Support national health surveillance and track progress toward achieving national health objectives Support national health surveillance, mainly for cardiovascular disease and associated risk factors
Survey design Cross-sectional household interview survey Cross-sectional household interview survey Cross-sectional household interview survey
Administration history Biennially from 2001 and annually from 2008 Annually from 1957 1995, 1998 and 2003, and then annually from 2008
Content Health status, heath care utilization and health determinants Health status, heath care utilization and health determinants Health status, heath care utilization, health determinants and biological measurements
Target population Non-institutionalized Canadians 12 years of age and older Non-institutionalized population of the United States Persons living in private households in Scotland
Exclusions Persons living on reserves, full-time members of the Canadian Forces, the institutionalized population and residents of certain remote regions Persons in long-term care facilities, on active duty with the Armed Forces and in prison, and U.S. nationals living in foreign countries Persons not living in private households and residents of certain remote islands
Sampling methods Multistage area-based probability sampling Multistage area-based probability sampling Multistage stratified clustered probability sampling
Administration methods Computer-assisted personal interviews (CAPI) and computer-assisted telephone interviews Personal household interviews using CAPI Personal household interviews using CAPI
Survey length Approximately 45 minutes Approximately 60 minutes Approximately 60 minutes
Modules Core content + province-specific optional modules + rapid response content Core content + co-sponsored supplementary questions Core content + rotating biennial modules since 2008 + biological module from a subsample
Sample size Approximately 130,000 respondents per cycle from 2001 to 2006; approximately 65,000 respondents annually since 2007 Approximately 30,000 adult respondents annually since 1997 Approximately 7,000 adult respondents per survey until 2011; approximately 4,500 adults annually since 2011
Small-area geographies Powered to produce estimates for 110 health regions every two years Selected estimates may be reliable at the state level with data combinations from several survey years Powered to produce estimates for 14 health boards every four years since 2008
Response rates 81% in 2003; 76% in 2008 72% in 2000; 69% in 2005 55% to 60% from 2003 to 2010
Available linkages Mortality and health administrative data including hospitalizations Mortality data Mortality and health administrative data including hospitalizations
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1

Estimated population size in 2019.

Source: Documentation for the Canadian Community Health Survey, United States National Health Interview Survey and the Scottish Health Survey.

Content

Core questionnaires collect information about sociodemographic characteristics, health status, health care services and health determinants. Information about additional health topics of interest are collected in rapid response modules (CCHS), survey supplements (NHIS) and a rotating biennial module (SHeS). The SHeS also collects anthropometric measurements and blood, saliva and urine samples from a subsample of survey respondents.

Target population and exclusions

The CCHS, NHIS and SHeS have comparable target populations that include the non-institutionalized national population and exclude active members of the military, those in prison and long-term care facilities, and those living in some remote areas (CCHS and SHeS) or outside the country (NHIS). The CCHS also excludes those living on reserves. The CCHS collects information only for those aged 12 and older, while both the NHIS and the SHeS collect information on all individuals, regardless of age.

Sampling methods

Although the countries’ populations vary (Canada has 37.6 million residents, the United States has 329 million residents, and Scotland has 5.3 million residents), similar multistage area sampling methods, designed to produce annual national-level data, are used in the CCHS, NHIS and SHeS. The CCHS also produces annual estimates at the levels of the provinces, territories and 110 health regions. The SHeS produces health-board-level data every four years. The sample size of the NHIS is not large enough to provide state-level data with acceptable precision, but data can be evaluated over multiple survey years to obtain estimates.

For the CCHS, sampling was done by allocating the annual sample size among the provinces and territories according to their population size and number of health regions, and then further allocating the sample among the health regions. The NHIS sampling frame for the 2000 and 2005 surveys used 358 primary sampling units—within which two further sampling units were used—and involved oversampling of both Blacks and Hispanics. For the SHeS, each year’s sample was clustered, and the four-year sample was unclustered. In 2008, 25 strata of area deprivation were used in the SHeS to produce estimates at the health-board level, allowing for the oversampling of deprived areas. All the surveys used sample weights to account for selection probabilities and non-response bias.

Administration methods

All the surveys used computer-assisted personal interviews administered by trained interviewers. Approximately half of the CCHS interviews were administered using computer-assisted telephone interviews.

Sample size and response rates

The CCHS was administered to 130,000 respondents every two years when it began in 2001. Since 2007, it has been administered to 65,000 respondents annually. The total adult response rate was 81% in 2003 and 76% in 2008. The NHIS has been administered to approximately 30,000 adult respondents annually since 1997. Response rates from a non-conditional sample of adults were 72% in 2000 and 69% in 2005.27,28 The SHeS has a much smaller sample size than both the CCHS and the NHIS. Until 2011, the SHeS surveyed approximately 7,000 adults per cycle. Since 2011, it has surveyed approximately 4,500 adult respondents annually. From 2003 to 2010, response rates for eligible adults were between 55% and 60%.29,32

Available linkages

The CCHS has been linked to vital statistics data up to December 31, 2011, and to hospital discharge abstracts, with plans for further data linkages.1 Access to these data is restricted to Statistics Canada and the Statistics Canada research data centres. The NHIS has been linked to the National Death Index, with follow-up to December 31, 2011. Information on accessing public use data files, feasibility data files and restricted-access data is available from the CDC (www.cdc.gov). The SHeS has been linked to mortality and health administrative databases, including hospitalizations,2 with mortality follow-up to December 31, 2014. Access to these data can be requested from the Public Benefit and Privacy Panel for Health and Social Care (www.informationgovernance.scot.nhs.uk).

Question construction, response categorization and structure

Common variables were created to measure smoking, alcohol consumption, physical activity and diet in the three surveys (Table 3). The common variables for smoking and physical activity are comparable between the CCHS, NHIS and the SHeS. The common variables for alcohol and fruit and vegetable consumption are comparable between the CCHS and the NHIS. However, the SHeS collects and reports alcohol consumption and diet information using more detailed and standardized measures. Therefore, comparisons of alcohol consumption and diet data between the SHeS and the other two surveys should be performed with caution. In the SHeS, alcohol consumption is reported using units of alcohol. This is not directly comparable with the CCHS and NHIS, which use the more subjective “number of drinks.” Similarly, the SHeS collects detailed fruit and vegetable consumption information, including amount consumed, while the CCHS and NHIS collect only frequency information. In-depth descriptions of how survey similarities and differences influenced common variable creation, and how these differences may affect their interpretation, can be found online at https://osf.io/4rczm/.

Table 3. Creation of comparable smoking, alcohol, physical activity and diet variables from the Canadian Community Health Survey, National Health Interview Survey and Scottish Health Survey.

CCHS† (Canada) NHIS (United States) SHeS (Scotland) Comparability
CCHS NHIS CCHS SHeS NHIS SHeS
Smoking status
Never smoker Has never smoked a whole cigarette (SMKDSTY = 6); Former always occasional smoker with <100 cigarette history (SMKDSTY = 5 and SMK_01A = 2) Nonsmoker with <100 cigarette history (SMKEV = 2 and SMKNOW = 3) Has never smoked or used to smoke cigarettes occasionally (CIGST1 = 1 or 2) High High High
Light smoker Daily smoker with <20 cigarettes/day (SMKDSTY = 1 and SMK_204 <20);
Current occasional smoker (SMKDSTY = 2 or 3);		 Daily smoker with <20 cigarettes/day (SMKNOW = 1 and CIGSDA1 <20); Occasional smoker (SMKNOW = 2) Current smoker (daily or occasional) with <20 cigarettes/day (CIGST1 = 4 and CIGDYAL < 20) High High High
Heavy smoker Daily smoker with >20 cigarettes/day (SMKDSTY = 1 and SMK_204 >20) Daily smoker with >20 cigarettes/day (SMKNOW=1 and CIGSDA1 >20) Current smoker with >20 cigarettes/ day (CIGST1 = 4 and CIGDYAL >20) High High High
Former smoker Former daily smoker (SMKDSTY = 4); Former always occasional smoker with >100 cigarette history (SMKDSTY = 5 and SMK_01A = 1) Former smoker with >100 cigarette history (SMKNOW = 3 and SMKEV = 1) Former smoker (CIGST1 = 3) High High High
Drinking status
Light drinker or non-drinker Has never drunk (ALCDTYP = 4) Has never drunk (ALCSTAT = 1) n/a High Moderate Moderate
Males: Current drinker who consumes 3 or fewer drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY <3) Males: Current drinker who consumes 3 or fewer drinks/week on average (ALCSTAT = (5 6 7 8) and (ALC12MWK x ALCAMT) <3) Males: Consumes 3 or fewer units of alcohol/week on average (DRATING <3) High Moderate Moderate
Females: Current drinker who consumes 2 or fewer drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY <2) Females: Current drinker who consumes 2 or fewer drinks/week on average (ALCSTAT= (5 6 7 8) and (ALC12MWK x ALCAMT) <2) Females: Consumes 2 or fewer units of alcohol/week on average (DRATING <2) High Moderate Moderate
Moderate drinker Males: Current drinker who consumes more than 3 and up to 21 drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY >3 and ALCDWKY <21) Males: Current drinker who consumes more than 3 and up to 21 drinks/week on average (ALCSTAT = (5 6 7 8) and (ALC12MWK x ALCAMT) >3 and ALC12MWK x ALCAMT) <21) Males: Consumes more than 3 and up to 21 units of alcohol/week on average (DRATING >3 and DRATING <21) High Moderate Moderate
Females: Current drinker who consumes more than 2 and up to 14 drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY >2 and ALCDWKY <14) Females: Current drinker who consumes more than 2 and up to 14 drinks/week on average (ALCSTAT = (5 6 7 8) and (ALC12MWK x ALCAMT) >2 and ALC12MWK x ALCAMT) <14) Females: Consumes more than 2 and up to 14 units of alcohol/week on average (DRATING >2 and DRATING <14) High Moderate Moderate
Heavy drinker Males: Current drinker who consumes more than 21 drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY >21) Males: Current drinker who consumes more than 21 drinks/week on average ((ALCSTAT = (5 6 7 8) and (ALC12MWK x ALCAMT) >21)) Males: Consumes more than 21 units of alcohol/week on average (DRATING >21) High Moderate Moderate
Females: Current drinker who consumes more than 14 drinks/week on average (ALCDTYP = (1 or 2) and ALCDWKY >14) Females: Current drinker who consumes more than 14 drinks/week on average (ALCSTAT = (5 6 7 8) and (ALC12MWK x ALCAMT) >14) Females: Consumes more than 14 units of alcohol/week on average (DRATING >14) High Moderate Moderate
Former drinker Former drinker (ALCEDTYP = 3) Former drinker (ALCSTAT = (2 3 4)) Non-drinker who used to drink but stopped (DNNOW = 2 and DNEVR = 2) High Moderate Moderate
Binge drinking
Binge drinker Consumed 5 or more drinks at least once a week in the last year (ALC_3 = [5 or 6]) Consumed 5 or more drinks on 52 or more days in the last year (ALC5UPYR >52) Consumed 5 or more units of alcohol on the heaviest drinking day in the last week (D7UT08 >5) High Moderate Moderate
Daily physical activity (METs) Sum(Number of times activity performed in 12 months x Average duration of activity, in hours x MET value of activity)/365 (PACDEE) Daily METs from vigorous physical activity (6 MET/hour) and moderate/ light physical activity (3 MET/hour) ([6(VIGFREQW x (VIGMIN/60)) + 3(MODFREQW x (MODMIN/60))]/7) Sum([Number of times activity performed in last 4 weeks x Average duration of activity, in hours x MET value of activity]/28) (See MET values in Appendix 1) High High High
Fruit and vegetable consumption (excluding juice and potatoes) Number of times a day consumes fruit and vegetables − Number of times a day consumes juice − Number of times a day consumes potatoes (FVCDTOT - FVCDJUI - FVCDPOT) Number of times a day consumes fruit (not including juice) + Number of times a day consumes salad + Number of times a day consumes other vegetables (not including potatoes) (FRUIT + SALAD + OVEG) Portions of all-sized fruit yesterday + Portions of vegetables yesterday (not including potatoes) + Portion of salad eaten yesterday + Portion of vegetables in composites (PORFRT + PORVEG + PORSAL + PORVDISH) High Moderate Moderate
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†

Variable names correspond to CCHS 3.1

Notes: CCHS = Canadian Community Health Survey, NHIS = National Health Interview Survey, SHeS = Scottish Health Survey, MET = metabolic equivalent of task. Source: Documentation for the Canadian Community Health Survey, United States National Health Interview Survey and the Scottish Health Survey.

Mortality linkage sample size estimates

Approximately 87%, 94% and 83% of CCHS, NHIS and SHeS respondents, respectively, who agreed to data sharing and linkage were successfully linked to national mortality data (Table 4). Among those successfully linked, 19,227 deaths occurred among CCHS respondents during 1.8 million person-years of mortality follow-up. Among NHIS respondents, 6,341 deaths occurred during almost half a million person-years of follow-up. Among SHeS respondents, 2,856 deaths occurred during 160,000 person-years of follow-up.

Table 4. Linkage to mortality data.
Males Females
CCHS NHIS SHeS CCHS NHIS SHeS
N (% † ) N (% † ) N (%†) N (% † ) N (% † ) N (%†)
Total 152,888 … 27,022 … 12,305 … 188,083 … 35,204 … 15,900 …
Linked † 134,524 88 25,342 94 10,273 83 161,883 86 32,890 93 13,379 84
Deaths among linked # 9,675 7 2,973 12 1,429 14 9,552 6 3,368 10 1,427 11
Person-years of follow-up # 819,453 … 214,819 … 71,246 … 994,431 … 282,501 … 93,150 …
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…

not applicable

†

Consented to linkage and were successfully linked.

#

From survey administration to follow-up: CCHS and NHIS follow-up to December 31, 2011; SHeS follow-up to December 31, 2014.

Notes: CCHS = Canadian Community Health Survey, NHIS = National Health Interview Survey, SHeS = Scottish Health Survey.

Sources: Canadian Community Health Survey (2003 to 2008) linked to the Canadian Mortality Database (2011); United States National Health Interview Survey (2000, 2005) linked to the National Death Index (2011); Scottish Health Survey (2003, 2008 to 2010) linked mortality (2014).

Discussion

National population health surveys are the largest population-based cohorts with information on health status, health behaviours, sociodemographic characteristics, health care use and health-related quality-of-life measures. Given the surveys’ broad objectives, these data are well suited for many purposes, especially when linked to health outcome data. Pooling linked health survey data could produce a new population health research resource.

There are two main benefits of pooling linked population health surveys at the individual level across jurisdictions. First, combined data have a larger sample with greater statistical power, which produces more precise effect estimates. This enables additional subgroup analyses and more detailed examinations of mediation and interaction effects. Increased sample size also allows for improved examination of uncommon or weak risk factors, and uncommon outcomes, such as cancer and many chronic diseases. Second, these data could improve the generalizability of study findings. Relationships between survey exposures and linked outcomes that are consistent across jurisdictions are potentially more robust, compared with inconsistent relationships. The investigation of inconsistent relationships can also lead to new insights, similar to the investigation of heterogeneity in meta-analyses. For example, the effect of health behaviours on mortality risk may be associated with country-level differences in socioeconomic inequality or access to health care services.33

Larger sample size and improved generalizability lead to many research and surveillance opportunities. For example, most international analyses rely on aggregated results from different sources, so many studies have difficulties considering sociodemographic variables and addressing mediation, interaction and exposure–outcome lag time.8,34 Because health surveys typically include socio-demographic questions regarding education, work history, income, ethnicity and immigrant status, these data are well suited for investigating health risks from an equity perspective.

Combined health survey data also enhance the ability to monitor the relationship between survey exposures and outcomes. For example, there are concerns that the relationship between smoking and health outcomes has changed over time, given changes in smoking patterns and the composition of smoking products. An international, longitudinal investigation of this relationship is possible with pooled, linked international population health surveys.

Furthermore, pooled, linked international population health surveys could be used to produce improved international comparisons of disease burden estimates. Disease burden reporting requires information about risk factor prevalence, outcome counts and relative risk estimates associated with the exposure of interest. Most current disease burden estimates, including those from the Global Burden of Disease study,35 use the aggregated data approach first described by Levin (1978).36 With this method, aggregate measures of risk factor prevalence and outcome counts are obtained from independent sources. Risk factor prevalence is obtained from population health surveys, outcome counts are obtained from vital statistics data sources, and relative risk estimates are obtained from independent epidemiology studies to describe the association between the risk factor and outcome. However, national health surveys that have been linked to outcome data can be used as single data sources for these studies,8 and pooling these data from multiple countries would allow for a standardized analysis methodology.

Limitations and challenges

One of the greatest challenges of pooling linked population health surveys from different countries is the heterogeneity caused by survey question dissimilarities. It was difficult and labour-intensive to create common health behaviour variables using the surveys from Canada, the United States and Scotland for this study, and the variables created were less detailed and were not entirely comparable across all surveys.

Over time, the ascertainment of behavioural risk factors has become more consistent across countries, and an increasing number of validation studies exist that indicate acceptable ascertainment bias.40 However, there is a need for more consistency. For example, despite international recommendations for smoking ascertainment that are used in over 100 countries,41 the lack of smoking history information in the NHIS prevented the calculation of pack years—a more detailed measure of smoking behaviour than the categorical measure of “smoking status” that was created in this study. Changes to the CCHS also prevented differentiation between former drinkers and non-drinkers in cycle 4.1. In this study, even if a concept was present, the time frame over which the exposure was ascertained often varied. Furthermore, some questions were collected optionally by geographic region, and there were differences in variable definitions and classification.

The comparison of exposures in multiple health surveys is challenging, and health surveys are constantly changing. To help with this, “cchsflow,” an open-source library to support the harmonization of CCHS variables across survey cycles, was developed.37 This approach to variable harmonization can be extended to other international population health surveys, and can be used to harmonize variables both across cycles within a single survey and between surveys from different countries. Survey metadata also support harmonization by improving survey cataloguing. Survey metadata are available in Data Documentation Initiative format, an international metadata standard developed for this purpose.38,39

Another challenge is decreasing response rates. If participants systematically differ from those who do not participate, the survey sample will be non-representative of the target population, and valid inference will be impeded. Non-respondents are repeatedly found to have unfavourable health behaviours and excess mortality compared with respondents.42–45 Data linkage can be used to assess and, potentially, adjust for non-response bias. The extent of non-response bias in the SHeS was evaluated by Gorman et al. (2014)46 by comparing rates of all-cause mortality and alcohol-related harm among survey respondents and the general population. Incidence rates were found to be lower among survey respondents, with survey-to-population rate ratios of 0.69 for alcohol-related harm and 0.89 for all-cause mortality. They concluded that heavy drinkers were less likely to respond to the SHeS than moderate or light drinkers. This type of comparison of respondents and non-respondents can inform weighting and imputation procedures to adjust for non-response bias.

Approaches to combining cycles of population health surveys from a single country have been developed,47 but approaches to pooling surveys from different countries are more complex because of differences in each country’s survey design. Modified meta-analytic methods and techniques used by internationally pooled epidemiology cohort studies, such as the European Prospective Investigation into Cancer and Nutrition,48 may be used. However, new methodologies will need to be established. Additionally, differences in the underlying survey populations may also prevent the estimation of a pooled effect estimate for an exposure–outcome effect of interest. However, this will not be the case for all effects, and investigations into the sources of this heterogeneity could also produce important new insights.

Lastly, the largest practical limitation to pooling international linked population health surveys is data access. The accessibility of outcome-linked health survey data varies across countries. For example, access to the mortality-linked NHIS data is publicly available on the CDC website. In contrast, access to the equivalent information in Canada, Scotland and many other countries is restricted. That said, unlinked health survey data are often publicly available—including data from the CCHS, which has a Statistics Canada Open Licence. The NHIS has demonstrated that it is possible to assess how to include linked outcomes to existing public use surveys, while ensuring there is no increase in re-identification risk and ensuring adherence to existing data sharing principles.

The Strategy for Patient-Oriented Research (www.cdp.hdrn.ca) was developed to address data access and harmonization efforts across Canada. A similar model could be used to facilitate analogous tasks for multi-country studies, including the pooling of linked population health surveys. Within networks such as the International Population Data Linkage Network (www.ipdln.org), there has also been more discussion and interest in conducting studies using data from multiple countries. Improvements to cross-jurisdictional data sharing and privacy issues are necessary for the benefits of pooled health survey analyses to be fully realized. This is beyond the scope of this paper.

Conclusion

The use of pooled national population health surveys linked to health outcomes has enormous potential for international health risk evaluation and comparison, equity analysis, disease burden estimation, and ongoing surveillance. Innovative methodologies will be required to mitigate challenges introduced by survey dissimilarities, and these methodologies can be improved with the introduction of international standards for collecting core health-related measures. Jurisdictional data restrictions and privacy issues require discussion and resolution.

List of abbreviations

CAPI

computer-assisted personal interview

CCHS

Canadian Community Health Survey

CDC

Centers for Disease Control and Prevention

IPD

Individual patient data

NHIS

National Health Interview Survey

SHeS

Scottish Health Survey

Supplementary Material

Appendix

What is already known on this subject?

  • National population health surveys are key tools for understanding, monitoring and improving population health.

  • Around the world, national population health surveys with similar objectives and designs are increasingly being linked at the individual level to vital statistics data and health care data, providing a valuable longitudinal perspective.

  • Creating common variables may enable individual-level pooling of these linked surveys and could produce a new resource for population health research with an untapped international and population perspective.

What does this study add?

  • Health surveys in Canada, the United States and Scotland are largely comparable and common health behaviour variables can be constructed. However, Scotland uses a more detailed and standardized risk factor evaluation for alcohol consumption and diet data. Therefore, comparisons of alcohol consumption and diet data between Scotland and the other two countries should be performed with caution.

  • Pooling national linked population health surveys is feasible and has the potential to be used for international health risk comparison, equity analysis, disease burden estimation and ongoing surveillance.

  • Challenges introduced by survey dissimilarities will require innovative methodologies, and can be improved with the introduction of international standards for collecting core health-related measures. Jurisdictional data restrictions and privacy issues will also require discussion and resolution.

Footnotes

Declarations

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Stacey Fisher, The Ottawa Hospital Research Institute, ICES, and the School of Epidemiology and Public Health at the University of Ottawa, Ottawa, Ontario.

Carol Bennett, The Ottawa Hospital Research Institute, and ICES, Ottawa, Ontario.

Deirdre Hennessy, Statistics Canada, Ottawa, Ontario.

Tony Robertson, Centre for Public Health and Population Health Research, Faculty of Health Sciences and Sport, University of Stirling, Stirling, Scotland.

Alastair Leyland, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Monica Taljaard, the Ottawa Hospital Research Institute, and the School of Epidemiology and Public Health at the University of Ottawa, Ottawa, Ontario.

Claudia Sanmartin, Statistics Canada, Ottawa, Ontario.

Prabhat Jha, The Dalla Lana School of Public Health, and St Michael’s Hospital, University of Toronto, Toronto, Ontario.

John Frank, The Scottish Collaboration of Public Health Research and Policy, and the Usher Institute of Population Health Sciences and Informatics at the University of Edinburgh, Edinburgh, UK.

Jack V. Tu, ICES, the Sunnybrook Schulich Heart Centre, and the Institute of Health Policy, Management and Evaluation at the University of Toronto

Laura C. Rosella, ICES and St Michael’s Hospital, University of Toronto, Toronto, Ontario

JianLi Wang, The School of Epidemiology and Public Health, the Royal’s Institute of Mental Health Research, and the Department of Psychiatry at the University of Ottawa, and the Mathison Centre for Mental Health Research and Education at the University of Calgary.

Christopher Tait, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario.

Douglas G. Manuel, The Ottawa Hospital Research Institute, ICES, the School of Epidemiology and Public Health at the University of Ottawa, Statistics Canada, the Department of Family Medicine at the University of Ottawa and the Bruyère Research Institute, Ottawa, Ontario

Funding

This work was supported by the Canadian Institutes of Health Research, operating grant MOP-142177. The study sponsor had no role in study design; collection, analysis or interpretation of data; manuscript writing; or the decision to submit for publication. Alastair Leyland is funded by the Medical Research Council (MC_UU_12017/13) and the Scottish Government Chief Scientist Office (SPHSU13).

Availability of data and material

Unlinked CCHS public use files are available from Statistics Canada and through Ontario Data Documentation, Extraction Service and Infrastructure (www.odesi.ca). Linked CCHS data are available for use by approved researchers at Statistics Canada and at research data centres (www.statcan.gc.ca/eng/rdc/index). Both linked and unlinked NHIS public use data files are available for download from the CDC website (www.cdc.gov/nchs/data-linkage/mortality-public.htm). Public use SHeS data can be obtained from the United Kingdom Data Service website (www.ukdataservice.ac.uk). Access to linked SHeS data can be requested from the Public Benefit and Privacy Panel for Health and Social Care (www.informationgovernance.scot.nhs.uk).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix
EMS137684-supplement-Appendix.pdf (98.6KB, pdf)

Data Availability Statement

Unlinked CCHS public use files are available from Statistics Canada and through Ontario Data Documentation, Extraction Service and Infrastructure (www.odesi.ca). Linked CCHS data are available for use by approved researchers at Statistics Canada and at research data centres (www.statcan.gc.ca/eng/rdc/index). Both linked and unlinked NHIS public use data files are available for download from the CDC website (www.cdc.gov/nchs/data-linkage/mortality-public.htm). Public use SHeS data can be obtained from the United Kingdom Data Service website (www.ukdataservice.ac.uk). Access to linked SHeS data can be requested from the Public Benefit and Privacy Panel for Health and Social Care (www.informationgovernance.scot.nhs.uk).

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