Inclusion of mobile phone numbers into an ongoing population health survey in New South Wales, Australia: design, methods, call outcomes, costs and sample representativeness

Sixteen-year trends in multiple lifestyle risk behaviours by socioeconomic status from 2004 to 2019 in New South Wales, Australia

Few studies have examined trends in inequalities related to lifestyle risk behaviours. This study examined 1) 16-year (2004-2019) trends of individual lifestyle risk factors and a combined lifestyle risk index and 2) trends in socioeconomic inequalities in these risk factors, in New South Wales (NSW; Australia) adults. Data was sourced from the NSW Adult Population Health Survey, an annual telephone survey of NSW residents aged ≥16 years, totalling 191,905 completed surveys. Excessive alcohol consumption, current smoking, insufficient physical activity, insufficient fruit and/or vegetable consumption, sugar-sweetened beverage [SSB] consumption, and a combined lifestyle risk index (overall high-risk lifestyle defined as total number of lifestyle risk behaviours ≥2) were examined. Socioeconomic status was assessed using education attainment, postal area-level disadvantage measured by Index of Relative Socioeconomic Disadvantage (IRSD), and remoteness based on Accessibility-Remoteness Index of Australia Plus (ARIA+). Socioeconomic inequalities were examined as prevalence difference for absolute inequalities and prevalence ratio for relative inequalities. The prevalence of lifestyle behaviours by levels of each socioeconomic status variable were estimated using predicted probabilities from logistic regression models. After adjusting for covariates, there was a decrease in prevalence over time for most lifestyle risk behaviours. Between 2004 and 2019, the prevalence decreased for current smoking from 21.8% to 17.1%, insufficient physical activity from 39.1% to 30.9%, excessive alcohol consumption from 15.4% to 13.7%, daily SSB consumption from 29.9% to 21.2%, and overall high-risk lifestyle from 50.4% to 43.7%. Socioeconomic inequalities, based on one or more of the socioeconomic variables, increased over time for current smoking, insufficient physical activity, daily SSB consumption, and an overall high-risk lifestyle. Overall, the health behaviours of the NSW population improved between 2004 and 2019. However, some socioeconomic inequalities increased during this time, highlighting the need for effective public health strategies that seek to improve health behaviours among the most socioeconomically disadvantaged.

Moving towards a single-frame cell phone design in random digit dialing surveys: considerations from a French general population health survey

Background

Over the last two decades, telephone surveys based on random digit dialing have developed considerably. At the same time, however, the proportion of the population with a cell phone has increased, whereas landline frame coverage has declined, thus raising the possibility of discontinuing landline phone surveys. This paper aims to assess the impact of using a single-frame (SF) cell phone design instead of a dual-frame (DF) design with landlines and cell phones in the context of repeated health surveillance surveys in the general population. We analyze data from a random digit dialing health survey of the French population and assess differences between the DF and the counterfactual SF design that excludes the landline phone sample from the DF design. We evaluate the quality of the two survey designs in terms of survey productivity, response rates, representativeness, balancing of external covariates, and prevalence estimates of key health behavior indicators.

Results

Our results show that a SF cell phone survey has several advantages over a combined DF landline and cell phone survey. Cell numbers require fewer call attempts to complete an interview, leading to a substantial reduction in the mean data collection duration and weight dispersion. The global representativeness of the SF design was slightly better than its DF counterpart, although the elderly were underrepresented. After calibration, differences in health behavior estimates were small for the seven health indicators analyzed.

Conclusions

Switching from a DF random telephone survey to a SF cell phone design has a number of practical advantages and would have a minimal impact on general population health surveys for monitoring health behavior at the population level. However, the different aspects of the survey quality had to be studied to make a decision. Further studies are needed to explore the scope of possibilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01573-1.

Trends in risk factors and management strategies used by people with type 2 diabetes in New South Wales, Australia

Adopting healthy lifestyle behaviours is an important component of Type 2 diabetes (T2D) self-management, which can lower risks of further health complications. Monitoring lifestyle risk factors including overweight or obesity, healthy diet and physical activity behaviours, alcohol consumption, smoking and psychological distress remain important. This study examined prevalence trends in these factors and adoption of three diabetes lifestyle self-management strategies in adults (aged 40+) with T2D in New South Wales (NSW), Australia. Analyses were conducted on NSW Adult Population Health Survey data, 2004-2019 (n = 142,168), using predicted probabilities from generalised linear models, weighted to population estimates. Throughout the study period overweight or obesity prevalence remained higher amongst those with T2D (83.1% to 81.7%) compared to those without diabetes (61.0 to 61.2%); only 8.9% of those with T2D were trying to lose weight. During the study period, there were declines in the proportions of those with T2D reporting sufficient fruit consumption [63.9% to 50.1%], moderate vegetable consumption (3 serves) [49.0% to 37.7%], and achieving sufficient physical activity [40.0% to 34.0%]. There were also declines in those reporting they manage their diabetes through following a special diet [73.6% to 55.9%] and exercising most days [33.5% to 22.2%]. This study highlights potential gaps in T2D secondary prevention and suggests more targeted diabetes education services are needed to address lifestyle risks. Increased understanding of why fewer people with T2D adopt these lifestyle management strategies is needed to inform policy and practice.

Trends in Walking, Moderate, and Vigorous Physical Activity Participation Across the Socioeconomic Gradient in New South Wales, Australia From 2002 to 2015

BACKGROUND

A combination of walking, other moderate physical activity, and vigorous physical activity is recommended for achieving good health. Vigorous activity has unique health benefits but may be less accessible to disadvantaged people. To reduce health inequity, we need to understand the differences in physical activity participation among socioeconomic subgroups and whether this is changing over time.

METHODS

Data from the 2002 to 2015 Adult New South Wales Population Health Surveys (164,652 responses) were analyzed to investigate trends in walking, moderate and vigorous physical activity participation by socioeconomic status as measured by educational attainment. Analysis used age- and sex-adjusted multivariable linear models that accounted for complex survey design.

RESULTS

In 2002, the highest socioeconomic group spent 18.5 (95% confidence interval, 8.2-28.8) minutes per week more than the lowest socioeconomic group being vigorously active. By 2015, this gap had steadily increased to 41.4 (95% confidence interval, 27.6-55.1) minutes per week. Inequity between groups was also found for duration of moderate activity but not for time spent walking.

CONCLUSIONS

Low participation in vigorous activity in the lowest socioeconomic group is likely driving increasing inequities in physical activity and widening participation gaps over time. Barriers preventing the most disadvantaged people in New South Wales from engaging in vigorous activity should be addressed urgently.

Consent for mobile phone surveys of non-communicable disease risk factors in low-resource settings: an exploratory qualitative study in Uganda

BACKGROUND

Lack of data for timely decision-making around the prevention and control of non-communicable diseases (NCDs) presents special challenges for policy makers, especially in resource-limited settings. New data collection methods, including pre-recorded Interactive Voice Response (IVR) phone surveys, are being developed to support rapid compilation of population-level disease risk factor information in such settings. We aimed to identify information that could be used to optimize consent approaches for future mobile phone surveys (MPS) employed in Uganda and, possibly, similar contexts.

METHODS

We conducted an in-depth qualitative study with key stakeholders in Uganda about consent approaches, and potential challenges, for pre-recorded IVR NCD risk factor surveys. Semi-structured interviews were conducted with 14 key informants. A contextualized thematic approach was used to interpret the results supported by representative quotes.

RESULTS

Several potential challenges in designing consent approaches for MPS were identified, including low literacy and the lack of appropriate ways of assessing comprehension and documenting consent. Communication with potential respondents prior to the MPS and providing options for callbacks were suggested as possible strategies for improving comprehension within the consent process. "Opt-in" forms of authorization were preferred over "opt-out". There was particular concern about data security and confidentiality and how matters relating to this would be communicated to MPS respondents.

CONCLUSIONS

These local insights provide important information to support optimization of consent for MPS, whose use is increasing globally to advance public health surveillance and research in constructive ways.

Mobile phone use and incidence of brain tumour histological types, grading or anatomical location: a population-based ecological study

OBJECTIVE

Some studies have reported increasing trends in certain brain tumours and a possible link with mobile phone use has been suggested. We examined the incidence time trends of brain tumour in Australia for three distinct time periods to ascertain the influence of improved diagnostic technologies and increase in mobile phone use on the incidence of brain tumours.

DESIGN

In a population-based ecological study, we examined trends of brain tumour over the periods 1982-1992, 1993-2002 and 2003-2013. We further compared the observed incidence during the period of substantial mobile phone use (2003-2013) with predicted (modelled) incidence for the same period by applying various relative risks, latency periods and mobile phone use scenarios.

SETTING

National Australian incidence registration data on primary cancers of the brain diagnosed between 1982 and 2013.

POPULATION

16 825 eligible brain cancer cases aged 20-59 from all of Australia (10 083 males and 6742 females).

MAIN OUTCOME MEASURES

Annual percentage change (APC) in brain tumour incidence based on Poisson regression analysis.

RESULTS

The overall brain tumour rates remained stable during all three periods. There was an increase in glioblastoma during 1993-2002 (APC 2.3, 95% CI 0.8 to 3.7) which was likely due to advances in the use of MRI during that period. There were no increases in any brain tumour types, including glioma (-0.6, -1.4 to 0.2) and glioblastoma (0.8, -0.4 to 2.0), during the period of substantial mobile phone use from 2003 to 2013. During that period, there was also no increase in glioma of the temporal lobe (0.5, -1.3 to 2.3), which is the location most exposed when using a mobile phone. Predicted incidence rates were higher than the observed rates for latency periods up to 15 years.

CONCLUSIONS

In Australia, there has been no increase in any brain tumour histological type or glioma location that can be attributed to mobile phones.

Reliability of Self-Reported Mobile Phone Ownership in Rural North-Central Nigeria: Cross-Sectional Study

Background

mHealth practitioners seek to leverage the ubiquity of the mobile phone to increase the impact and robustness of their interventions, particularly in resource-limited settings. However, data on the reliability of self-reported mobile phone access is minimal.

Objective

We sought to ascertain the reliability of self-reported ownership of and access to mobile phones among a population of rural dwellers in north-central Nigeria.

Methods

We contacted participants in a community-based HIV testing program by phone to determine actual as opposed to self-reported mobile phone access. A phone script was designed to conduct these calls and descriptive analyses conducted on the findings.

Results

We dialed 349 numbers: 110 (31.5%) were answered by participants who self-reported ownership of the mobile phone; 123 (35.2%) of the phone numbers did not ring at all; 28 (8.0%) rang but were not answered; and 88 (25.2%) were answered by someone other than the participant. We reached a higher proportion of male participants (68/133, 51.1%) than female participants (42/216, 19.4%; P<.001).

Conclusions

Self-reported access to mobile phones in rural and low-income areas in north-central Nigeria is higher than actual access. This has implications for mHealth programming, particularly for women’s health. mHealth program implementers and researchers need to be cognizant of the low reliability of self-reported mobile phone access. These observations should therefore affect sample-size calculations and, where possible, alternative means of reaching research participants and program beneficiaries should be established.

Survey research with a random digit dial national mobile phone sample in Ghana: Methods and sample quality

INTRODUCTION

Generating a nationally representative sample in low and middle income countries typically requires resource-intensive household level sampling with door-to-door data collection. High mobile phone penetration rates in developing countries provide new opportunities for alternative sampling and data collection methods, but there is limited information about response rates and sample biases in coverage and nonresponse using these methods. We utilized data from an interactive voice response, random-digit dial, national mobile phone survey in Ghana to calculate standardized response rates and assess representativeness of the obtained sample.

MATERIALS AND METHODS

The survey methodology was piloted in two rounds of data collection. The final survey included 18 demographic, media exposure, and health behavior questions. Call outcomes and response rates were calculated according to the American Association of Public Opinion Research guidelines. Sample characteristics, productivity, and costs per interview were calculated. Representativeness was assessed by comparing data to the Ghana Demographic and Health Survey and the National Population and Housing Census.

RESULTS

The survey was fielded during a 27-day period in February-March 2017. There were 9,469 completed interviews and 3,547 partial interviews. Response, cooperation, refusal, and contact rates were 31%, 81%, 7%, and 39% respectively. Twenty-three calls were dialed to produce an eligible contact: nonresponse was substantial due to the automated calling system and dialing of many unassigned or non-working numbers. Younger, urban, better educated, and male respondents were overrepresented in the sample.

CONCLUSIONS

The innovative mobile phone data collection methodology yielded a large sample in a relatively short period. Response rates were comparable to other surveys, although substantial coverage bias resulted from fewer women, rural, and older residents completing the mobile phone survey in comparison to household surveys. Random digit dialing of mobile phones offers promise for future data collection in Ghana and may be suitable for other developing countries.

Differences Between Landline and Mobile Phone Users in Sexual Behavior Research

This study investigated differences between the demographic characteristics, participation rates (i.e., agreeing to respond to questions about sexual behavior), and sexual behaviors of landline and mobile phone samples in Australia. A nationally representative sample of Australians aged 18 years and over was recruited via random digit dialing in December 2011 to collect data via computer-assisted telephone interviews. A total of 1012 people (370 men, 642 women) completed a landline interview and 1002 (524 men, 478 women) completed a mobile phone interview. Results revealed that telephone user status was significantly related to all demographic variables: gender, age, educational attainment, area of residence, country of birth, household composition, and current ongoing relationship status. In unadjusted analyses, telephone status was also associated with women's participation rates, participants' number of other-sex sexual partners in the previous year, and women's lifetime sexual experience. However, after controlling for significant demographic factors, telephone status was only independently related to women's participation rates. Post hoc analyses showed that significant, between-group differences for all other sexual behavior outcomes could be explained by demographic covariates. Results also suggested that telephone status may be associated with participation bias in research on sexual behavior. Taken together, these findings highlight the importance of sampling both landline and mobile phone users to improve the representativeness of sexual behavior data collected via telephone interviews.

Including mobile-only telephone users in a statewide preventive health survey-Differences in the prevalence of health risk factors and impact on trends

The Queensland preventive health survey is conducted annually to monitor the prevalence of behavioural risk factors in the north-east Australian state. Prompted by domestic and international trends in mobile telephone usage, the 2015 survey incorporated both mobile and landline telephone numbers from a list-based sampling frame. Estimates for landline-accessible and mobile-only respondents are compared to assess potential bias in landline-only surveys in the context of public health surveillance. Significant differences were found in subcategories of all health prevalence estimates considered (alcohol consumption, body mass index, smoking, and physical activity) from 2015 survey results. Results from Australian and international studies that have considered mobile telephone non-coverage bias are also summarised and discussed. We find that adjusting for sampling biases of telephone surveys by weighting does not fully compensate for the differences in prevalence estimates. However, predicted trends from previous years' surveys only differ significantly for the 2015 prevalence estimates of alcohol consumption. We conclude that the inclusion of mobile telephones into standard telephones surveys is important for obtaining valid, reliable and representative data to reduce bias in health prevalence estimates. Importantly, unlike some international experiences, the addition of mobiles telephones into the Queensland preventive health survey occurred before population trends were significantly affected.

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There is a rapidly growing proportion of adults who are contactable via mobile-only.

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Exclusion of mobile-only group leads to biases in health prevalence estimates.

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Weighting and controlling for age differences does not correct for these biases.

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A list-based survey yields representative statistics for public health policy.

Effect of the inclusion of mobile phone interviews to Vigitel

OBJECTIVE

To evaluate the impact on the prevalence changes of risk factors for chronic diseases, published in the Surveillance System of Risk and Protection Factors for Chronic Diseases by Telephone Survey (Vigitel), after the inclusion of data from the population only with mobile phone.

METHODS

Our study used data from the 26 State capitals and Federal District of Brazil obtained by the National Survey on Health (PNS) and Vigitel, both held in 2013. In each capital, we added a subsample of 200 adults living in households with only mobile phones, extracted from PNS, to the Vigitel 2013 database, with approximately 1,900 households, named Vigitel dual frame.

RESULTS

Vigitel results showed absolute relative biases between 0.18% and 14.85%. The system underestimated the frequency of adult smokers (10.77%), whole milk consumption (52.82%), and soft drink consumption (22.22%). Additionally, it overestimated the prevalence of hypertension (25.46%). In the simulations using Vigitel dual frame, with inclusion of the sample of adults living in households with only mobile phones, the bias of estimates was reduced in five out of eight analyzed indicators, with greater effects in regions with lower rates of landline coverage. In comparing regions, we observed negative correlation (ρ = -0.91) between the percentage of indicators with presence of bias and the percentage of households with only mobile phone.

CONCLUSIONS

The results of this study indicate the benefits of including a subsample of 200 adults with only mobile phone on the Vigitel sample, especially in the capitals of the North and Northeast regions.

OBJETIVO

Avaliar o impacto nas mudanças das prevalências de fatores de risco de doenças crônicas, divulgadas no Vigitel, após a inclusão de dados provenientes da população com somente telefone celular.

MÉTODOS

O estudo utilizou os dados das capitais obtidos da Pesquisa Nacional de Saúde e do Vigitel, que foram realizados em 2013. Em cada capital, acrescentou-se uma subamostra de 200 adultos residentes em domicílios com somente celular, extraída da PNS, à base de dados do Vigitel 2013, com aproximadamente 1.900 domicílios, denominado Vigitel cadastro duplo.

RESULTADOS

Os resultados do Vigitel mostraram vícios relativos absolutos entre 0,18% e 14,85%. O sistema subestimou a frequência de adultos fumantes (10,77%), o consumo de leite com teor integral de gordura (52,82%) e o consumo de refrigerante (22,22%). Adicionalmente, superestimou a prevalência de hipertensão (25,46%). Nas simulações utilizando o Vigitel cadastro duplo, com inclusão da amostra de adultos residentes em domicílios com somente celular, o vício das estimativas foi reduzido em cinco de oito indicadores analisados, com maiores efeitos nas regiões com menores taxas de cobertura de telefonia fixa. Na comparação entre as regiões, observa-se correlação negativa (ρ = -0,91) entre o percentual de indicadores com presença de vício e o percentual de cobertura de domicílios com somente celular.

CONCLUSÕES

Os resultados do presente estudo indicam os benefícios da inclusão de uma subamostra de 200 adultos com somente celular na amostra do Vigitel, especialmente nas capitais das regiões Norte e Nordeste.

Moving the Agenda on Noncommunicable Diseases: Policy Implications of Mobile Phone Surveys in Low and Middle-Income Countries

The growing burden of noncommunicable diseases (NCDs), for example, cardiovascular diseases and chronic respiratory diseases, in low- and middle-income countries (LMICs) presents special challenges for policy makers, due to resource constraints and lack of timely data for decision-making. Concurrently, the increasing ubiquity of mobile phones in LMICs presents possibilities for rapid collection of population-based data to inform the policy process. The objective of this paper is to highlight potential benefits of mobile phone surveys (MPS) for developing, implementing, and evaluating NCD prevention and control policies. To achieve this aim, we first provide a brief overview of major global commitments to NCD prevention and control, and subsequently explore how countries can translate these commitments into policy action at the national level. Using the policy cycle as our frame of reference, we highlight potential benefits of MPS which include (1) potential cost-effectiveness of using MPS to inform NCD policy actions compared with using traditional household surveys; (2) timeliness of assessments to feed into policy and planning cycles; (3) tracking progress of interventions, hence assessment of reach, coverage, and distribution; (4) better targeting of interventions, for example, to high-risk groups; (5) timely course correction for suboptimal or non-effective interventions; (6) assessing fairness in financial contribution and financial risk protection for those affected by NCDs in the spirit of universal health coverage (UHC); and (7) monitoring progress in reducing catastrophic medical expenditure due to chronic health conditions in general, and NCDs in particular. We conclude that MPS have potential to become a powerful data collection tool to inform policies that address public health challenges such as NCDs. Additional forthcoming assessments of MPS in LMICs will inform opportunities to maximize this technology.

The impact of the mode of survey administration on estimates of daily smoking for mobile phone only users

Background

Over the past decade, there have been substantial changes in landline and mobile phone ownership, with a substantial increase in the proportion of mobile-only households. Estimates of daily smoking rates for the mobile phone only (MPO) population have been found to be substantially higher than the rest of the population and telephone surveys that use a dual sampling frame (landline and mobile phones) are now considered best practice. Smoking is seen as an undesirable behaviour; measuring such behaviours using an interviewer may lead to lower estimates when using telephone based surveys compared to self-administered approaches. This study aims to assess whether higher daily smoking estimates observed for the mobile phone only population can be explained by administrative features of surveys, after accounting for differences in the phone ownership population groups.

Methods

Data on New South Wales (NSW) residents aged 18 years or older from the NSW Population Health Survey (PHS), a telephone survey, and the National Drug Strategy Household Survey (NDSHS), a self-administered survey, were combined, with weights adjusted to match the 2013 population. Design-adjusted prevalence estimates and odds ratios were calculated using survey analysis procedures available in SAS 9.4.

Results

Both the PHS and NDSHS gave the same estimates for daily smoking (12%) and similar estimates for MPO users (20% and 18% respectively). Pooled data showed that daily smoking was 19% for MPO users, compared to 10% for dual phone owners, and 12% for landline phone only users. Prevalence estimates for MPO users across both surveys were consistently higher than other phone ownership groups. Differences in estimates for the MPO population compared to other phone ownership groups persisted even after adjustment for the mode of collection and demographic factors.

Conclusions

Daily smoking rates were consistently higher for the mobile phone only population and this was not driven by the mode of survey collection. This supports the assertion that the use of a dual sampling frame addresses coverage issues that would otherwise be present in telephone surveys that only made use of a landline sampling frame.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0342-4) contains supplementary material, which is available to authorized users.

Australia's plain tobacco packs: anticipated and actual responses among adolescents and young adults 2010-2013

BACKGROUND

In December 2012, Australia introduced world-first legislation mandating plain packaging for all tobacco products. To date, there is very little evidence on youth responses to the changed packs.

AIM

To assess attitudes towards, and responses to, tobacco plain packs preimplementation and postimplementation.

METHODS

The Tobacco Promotion Impact Study (TPIS) was a yearly cross-sectional telephone survey of adolescents and young adults (12-24 years) from the states of New South Wales (NSW) and Queensland, conducted at three time points preimplementation (June 2010; June 2011; June 2012) and one time point postimplementation (June 2013; total n=8820).

RESULTS

There were significant increases in support for plain packaging from preimplementation to postimplementation for: never smokers (56% in 2012 vs 63% in 2013; OR=0.77, 95% CI 0.65 to 0.90, p=0.001), experimenters/ex-smokers (55% in 2012 vs 72% in 2013; OR=0.51, 95% CI 0.38 to 0.68, p<0.001) and current smokers (35% in 2012 vs 55% in 2013; OR=0.49, 95% CI 0.32 to 0.75, p=0.001). At postimplementation, 16% of never smokers reported that plain packaging made them less likely to try smoking and 18% of experimenters/ex-smokers reported that plain packaging made them less likely to smoke again. Youth were significantly less likely to have anticipated these responses preimplementation (never smokers: 8% in 2011; OR=0.43, 95% CI 0.28 to 0.65, p<0.00; experimenters/ex-smokers: 11%; OR=0.65, 95% CI 0.52 to 0.82, p<0.001). At postimplementation, 34% of smokers reported a quitting-related response to plain packaging (tried to quit or thought about quitting); the proportion who anticipated such a response preimplementation was significantly less (14% in 2011; OR=0.33, 95% CI 0.20 to 0.53, p<0.001). 28% of smokers reported a social denormalisation response at postimplementation (hid their pack from view, used a case to cover their pack, felt embarrassed); the proportion who anticipated such a response preimplementation was significantly less (9% in 2011; OR=0.24, 95% CI 0.14 to 0.42, p<0.001).

CONCLUSIONS

The actual response of youth to plain packaging was greater than anticipated prior to their introduction, and support for plain packaging increased from preimplementation to postimplementation among all groups of youth. Jurisdictions planning to implement plain tobacco packaging should be encouraged by these findings.

Profiling the mobile-only population in Australia: insights from the Australian National Health Survey

BACKGROUND

The Australian population that relies on mobile phones exclusively has increased from 5% in 2005 to 29% in 2014. Failing to include this mobile-only population leads to a potential bias in estimates from landline-based telephone surveys. This paper considers the impacts on selected health prevalence estimates with and without the mobile-only population.

METHODS

Using data from the Australian Health Survey - which, for the first time, included a question on telephone status - we examined demographic, geographic and health differences between the landline-accessible and mobile-only population. These groups were also compared to the full population, controlling for the sampling design and differential non-response patterns in the observed sample through weighting and benchmarking.

RESULTS

The landline-accessible population differs from the mobile-only population for selected health measures resulting in biased prevalence estimates for smoking, alcohol risk and private health insurance coverage in the full population. The differences remain even after adjusting for age and gender.

CONCLUSIONS

Using landline telephones only for conducting population health surveys will have an impact on prevalence rate estimates of health risk factors due to the differing profiles of the mobile-only population from the landline-accessible population.

Exposure to Internet-Based Tobacco Advertising and Branding: Results From Population Surveys of Australian Youth 2010-2013

Background

Since legislation prohibiting tobacco advertising in traditional media, online communication platforms and social media have become one of the few avenues for the tobacco industry to promote its products to Australians. Little is currently known about the exposure of young people to these new media promotions.

Objective

To measure exposure to Internet-based tobacco advertising and branding among Australian youth, identify common formats of branding encountered, and examine the association between exposure and smoking susceptibility.

Methods

The Tobacco Promotion Impact Study is a repeat cross-sectional telephone survey of young people (12-24 years) in 2 Australian states, conducted yearly from 2010 to 2013 (total n=8820). The survey included questions about past-month exposure to Internet-based tobacco advertising and tobacco company branding. Changes in levels of exposure, characteristics of exposed youth, and the association between exposure and smoking susceptibility were explored.

Results

Past-month exposure to Internet-based tobacco advertising and branding among young people increased over the years of the survey (advertising: 21% in 2010 to 29% in 2013; branding: 20% in 2010 to 26% in 2013). The participants who were younger, female, from lower socioeconomic status, and never-smokers were more likely to report exposure. Facebook was the most commonly cited platform for encountering tobacco branding in 2013 (22% of all branding). Compared with young people interviewed in 2013, participants in 2010 were significantly less likely to report exposure to tobacco branding on social media (odds ratio [OR] 0.26, 95% CI 0.20-0.33, P<.001) or 2011 (OR 0.46, 95% CI 0.37-0.57, P<.001). Among never-smokers aged 12-17 years, exposure to online advertising and branding (OR 1.32, 95% CI 1.11-1.57, P=.002) or branding alone (OR 1.39, 95% CI 1.10-1.77, P=.007) were significant predictors of smoking susceptibility.

Conclusions

Ensuring tobacco advertising bans are inclusive of Internet-based media is essential. Given the global nature of Internet-based content, cooperation among signatory nations to the World Health Organization Framework Convention Alliance on Tobacco Control will be necessary.

Bias of health estimates obtained from chronic disease and risk factor surveillance systems using telephone population surveys in Australia: results from a representative face-to-face survey in Australia from 2010 to 2013

BACKGROUND

Emerging communication technologies have had an impact on population-based telephone surveys worldwide. Our objective was to examine the potential biases of health estimates in South Australia, a state of Australia, obtained via current landline telephone survey methodologies and to report on the impact of mobile-only household on household surveys.

METHODS

Data from an annual multi-stage, systematic, clustered area, face-to-face population survey, Health Omnibus Survey (approximately 3000 interviews annually), included questions about telephone ownership to assess the population that were non-contactable by current telephone sampling methods (2006 to 2013). Univariable analyses (2010 to 2013) and trend analyses were conducted for sociodemographic and health indicator variables in relation to telephone status. Relative coverage biases (RCB) of two hypothetical telephone samples was undertaken by examining the prevalence estimates of health status and health risk behaviours (2010 to 2013): directory-listed numbers, consisting mainly of landline telephone numbers and a small proportion of mobile telephone numbers; and a random digit dialling (RDD) sample of landline telephone numbers which excludes mobile-only households.

RESULTS

Telephone (landline and mobile) coverage in South Australia is very high (97%). Mobile telephone ownership increased slightly (7.4%), rising from 89.7% in 2006 to 96.3% in 2013; mobile-only households increased by 431% over the eight year period from 5.2% in 2006 to 27.6% in 2013. Only half of the households have either a mobile or landline number listed in the telephone directory. There were small differences in the prevalence estimates for current asthma, arthritis, diabetes and obesity between the hypothetical telephone samples and the overall sample. However, prevalence estimate for diabetes was slightly underestimated (RCB value of -0.077) in 2013. Mixed RCB results were found for having a mental health condition for both telephone samples. Current smoking prevalence was lower for both hypothetical telephone samples in absolute differences and RCB values: -0.136 to -0.191 for RDD landline samples and -0.129 to -0.313 for directory-listed samples.

CONCLUSION

These findings suggest landline-based sampling frames used in Australia, when appropriately weighted, produce reliable representative estimates for some health indicators but not for all. Researchers need to be aware of their limitations and potential biased estimates.

Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey: An Experimental Study

Mobile telephone numbers are increasingly being included in household surveys samples. As approach letters cannot be sent because many do not have address details, alternatives approaches have been considered. This study assesses the effectiveness of sending a short message service (SMS) to a random sample of mobile telephone numbers to increase response rates. A simple random sample of 9000 Australian mobile telephone numbers: 4500 were randomly assigned to be sent a pre-notification SMS, and the remaining 4500 did not have a SMS sent. Adults aged 18 years and over, and currently in paid employment, were eligible to participate. American Association for Public Opinion Research formulas were used to calculated response cooperation and refusal rates. Response and cooperation rate were higher for the SMS groups (12.4% and 28.6% respectively) than the group with no SMS (7.7% and 16.0%). Refusal rates were lower for the SMS group (27.3%) than the group with no SMS (35.9%). When asked, 85.8% of the pre-notification group indicated they remembered receiving a SMS about the study. Sending a pre-notification SMS is effective in improving participation in population-based surveys. Response rates were increased by 60% and cooperation rates by 79%.

Health Estimates Using Survey Raked-Weighting Techniques in an Australian Population Health Surveillance System

A challenge for population health surveillance systems using telephone methodologies is to maintain representative estimates as response rates decrease. Raked weighting, rather than conventional poststratification methodologies, has been developed to improve representativeness of estimates produced from telephone-based surveillance systems by incorporating a wider range of sociodemographic variables using an iterative proportional fitting process. This study examines this alternative weighting methodology with the monthly South Australian population health surveillance system report of randomly selected people of all ages in 2013 (n = 7,193) using computer-assisted telephone interviewing. Poststratification weighting used age groups, sex, and area of residence. Raked weights included an additional 6 variables: dwelling status, number of people in household, country of birth, marital status, educational level, and highest employment status. Most prevalence estimates (e.g., diabetes and asthma) did not change when raked weights were applied. Estimates that changed by at least 2 percentage points (e.g., tobacco smoking and mental health conditions) were associated with socioeconomic circumstances, such as dwelling status, which were included in the raked-weighting methodology. Raking methodology has overcome, to some extent, nonresponse bias associated with the sampling methodology by incorporating lower socioeconomic groups and those who are routinely not participating in population surveys into the weighting formula.

Recruiting 9126 Primary Care Patients by Telephone: Characteristics of Participants Reached on Landlines, Basic Cell Phones, and Smartphones

In primary care, collecting information about patient health behaviors between appointments can be advantageous. Physicians and researchers who embrace phone-based technology may find valuable ways to monitor patient-reported outcome measures of health (PROM). However, the level of phone technology sophistication should be tailored to the phone use of the population of interest. Despite the growing use of telephones as a means to gather PROM, little is known about phone use among primary care patients. As part of an ongoing study, the authors recruited primary care patients (N = 9126) for a health behavior screening study by calling them on the primary contact number listed in their medical record. The current study evaluated the frequency with which individuals were reached on landlines, basic cell phones, and smartphones, and examined participant characteristics. The majority of participants (63%) used landlines as their primary contact. Of the 37% using cell phones on the recruitment call, most (71%) were using smartphones. Landline users were significantly older than cell phone users (61.4 vs. 46.2 years; P = .001). Cell phone use did not differ significantly between participants with a college education and those without (37% vs. 38%; P = .82); however, smartphone use did differ (61% vs. 77%; P = .01). The majority of participants sampled used landlines as their primary telephone contact. Researchers designing phone-based PROM studies for primary care may have the broadest intervention reach using interactive voice response telephone technology, as patients could report health outcomes from any type of phone, including landlines. (Population Health Management 2016;19:212-215).

A Widening Gap? Changes in Multiple Lifestyle Risk Behaviours by Socioeconomic Status in New South Wales, Australia, 2002-2012

Background

Socioeconomic inequalities in health outcomes have increased over the past few decades in some countries. However, the trends in inequalities related to multiple health risk behaviours have been infrequently reported. In this study, we examined the trends in individual health risk behaviours and a summary lifestyle risk index in New South Wales, Australia, and whether the absolute and relative inequalities in risk behaviours by socioeconomic positions have changed over time.

Methods

Using data from the annual New South Wales Adult Population Health Survey during the period of 2002–2012, we examined four individual risk behaviours (smoking, higher than recommended alcohol consumption, insufficient fruit and vegetable intake, and insufficient physical activity) and a combined lifestyle risk indicator. Socioeconomic inequalities were assessed based on educational attainment and postal area-level index of relative socio-economic disadvantage (IRSD), and were presented as prevalence difference for absolute inequalities and prevalence ratio for relative inequalities. Trend tests and survey logistic regression models examined whether the degree of absolute and relative inequalities between the most and least disadvantaged subgroups have changed over time.

Results

The prevalence of all individual risk behaviours and the summary lifestyle risk indicator declined from 2002 to 2012. Particularly, the prevalence of physical inactivity and smoking decreased from 52.6% and 22% in 2002 to 43.8% and 17.1% in 2012 (p for trend<0.001). However, a significant trend was observed for increasing absolute and relative inequalities in smoking, insufficient fruit and vegetable consumption, and the summary lifestyle risk indicator.

Conclusions

The overall improvement in health behaviours in New South Wales, Australia, co-occurred with a widening socioeconomic gap.

Implications

Governments should address health inequalities through risk factor surveillance and combined strategies of population-wide and targeted interventions.

Summary of the Impact of the Inclusion of Mobile Phone Numbers into the NSW Population Health Survey in 2012

Background

Although it was estimated that 20% of the population in Australia were mobile-only phone users in 2010, the inclusion of mobile numbers into computer-assisted telephone interviews (CATI) behavioural risk factor surveys did not occur until 2012.

Methods

Three papers have been published describing the methods, weighting strategy and the impact in detail of including mobile numbers into the NSW Population Health Survey (NSWPHS). This paper identifies the important components of those papers and summarises them for a broader audience.

Results

In the 2012 NSWPHS, 15,214 (15,149 with weights) interviews were completed (64% landline frame; 36% mobile frame). Response, cooperation and contact rates were 37%, 65% and 69% respectively. The inclusion of mobile phone numbers resulted in a sample that was closer to the NSW population profile and impacted on the time series of estimates for alcohol drinking, recommended fruit consumption, current smoking, and overweight or obesity.

Conclusions

The papers found that including mobile phone numbers into NSWPHS did not impact negatively on response rates or data collection, but it did cost more and affect the time series for some behavioural risk factors, in that it corrected the estimates that had been produced from a sample frame that was progressively getting less representative of the population.

Impact of Australia's introduction of tobacco plain packs on adult smokers' pack-related perceptions and responses: results from a continuous tracking survey

OBJECTIVES

To investigate the impact of Australia's plain tobacco packaging policy on two stated purposes of the legislation--increasing the impact of health warnings and decreasing the promotional appeal of packaging--among adult smokers.

DESIGN

Serial cross-sectional study with weekly telephone surveys (April 2006-May 2013). Interrupted time-series analyses using ARIMA modelling and linear regression models were used to investigate intervention effects.

PARTICIPANTS

15,745 adult smokers (aged 18 years and above) in New South Wales (NSW), Australia. Random selection of participants involved recruiting households using random digit dialling and selecting the nth oldest smoker for interview.

INTERVENTION

The introduction of the legislation on 1 October 2012.

OUTCOMES

Salience of tobacco pack health warnings, cognitive and emotional responses to warnings, avoidance of warnings, perceptions regarding one's cigarette pack.

RESULTS

Adjusting for background trends, seasonality, antismoking advertising activity and cigarette costliness, results from ARIMA modelling showed that, 2-3 months after the introduction of the new packs, there was a significant increase in the absolute proportion of smokers having strong cognitive (9.8% increase, p=0.005), emotional (8.6% increase, p=0.01) and avoidant (9.8% increase, p=0.0005) responses to on-pack health warnings. Similarly, there was a significant increase in the proportion of smokers strongly disagreeing that the look of their cigarette pack is attractive (57.5% increase, p<0.0001), says something good about them (54.5% increase, p<0.0001), influences the brand they buy (40.6% increase, p<0.0001), makes their pack stand out (55.6% increase, p<0.0001), is fashionable (44.7% increase, p<0.0001) and matches their style (48.1% increase, p<0.0001). Changes in these outcomes were maintained 6 months postintervention.

CONCLUSIONS

The introductory effects of the plain packaging legislation among adult smokers are consistent with the specific objectives of the legislation in regard to reducing promotional appeal and increasing effectiveness of health warnings.

Developing a weighting strategy to include mobile phone numbers into an ongoing population health survey using an overlapping dual-frame design with limited benchmark information

BACKGROUND

In 2012 mobile phone numbers were included into the ongoing New South Wales Population Health Survey (NSWPHS) using an overlapping dual-frame design. Previously in the NSWPHS the sample was selected using random digit dialing (RDD) of landline phone numbers. The survey was undertaken using computer assisted telephone interviewing (CATI). The weighting strategy needed to be significantly expanded to manage the differing probabilities of selection by frame, including that of children of mobile-only phone users, and to adjust for the increased chance of selection of dual-phone users. This paper describes the development of the final weighting strategy to properly combine the data from two overlapping sample frames accounting for the fact that population benchmarks for the different sampling frames were not available at the state or regional level.

METHODS

Estimates of the number of phone numbers for the landline and mobile phone frames used to calculate the differing probabilities of selection by frame, for New South Wales (NSW) and by stratum, were obtained by apportioning Australian estimates as none were available for NSW. The weighting strategy was then developed by calculating person selection probabilities, selection weights, applying a constant composite factor to the dual-phone users sample weights, and benchmarking to the latest NSW population by age group, sex and stratum.

RESULTS

Data from the NSWPHS for the first quarter of 2012 was used to test the weighting strategy. This consisted of data on 3395 respondents with 2171 (64%) from the landline frame and 1224 (36%) from the mobile frame. However, in order to calculate the weights, data needed to be available for all core weighting variables and so 3378 respondents, 2933 adults and 445 children, had sufficient data to be included. Average person weights were 3.3 times higher for the mobile-only respondents, 1.3 times higher for the landline-only respondents and 1.7 times higher for dual-phone users in the mobile frame compared to the dual-phone users in the landline frame. The overall weight effect for the first quarter of 2012 was 1.93 and the coefficient of variation of the weights was 0.96. The weight effects for 2012 were similar to, and in many cases less than, the effects found in the corresponding quarter of the 2011 NSWPHS when only a landline based sample was used.

CONCLUSIONS

The inclusion of mobile phone numbers, through an overlapping dual-frame design, improved the coverage of the survey and an appropriate weighing procedure is feasible, although it added substantially to the complexity of the weighting strategy. Access to accurate Australian, State and Territory estimates of the number of landline and mobile phone numbers and type of phone use by at least age group and sex would greatly assist in the weighting of dual-frame surveys in Australia.

Inclusion of mobile telephone numbers into an ongoing population health survey in New South Wales, Australia, using an overlapping dual-frame design: impact on the time series

BACKGROUND

Since 1997, the NSW Population Health Survey (NSWPHS) had selected the sample using random digit dialing of landline telephone numbers. When the survey began coverage of the population by landline phone frames was high (96%). As landline coverage in Australia has declined and continues to do so, in 2012, a sample of mobile telephone numbers was added to the survey using an overlapping dual-frame design. Details of the methodology are published elsewhere. This paper discusses the impacts of the sampling frame change on the time series, and provides possible approaches to handling these impacts.

METHODS

Prevalence estimates were calculated for type of phone-use, and a range of health indicators. Prevalence ratios (PR) for each of the health indicators were also calculated using Poisson regression analysis with robust variance estimation by type of phone-use. Health estimates for 2012 were compared to 2011. The full time series was examined for selected health indicators.

RESULTS

It was estimated from the 2012 NSWPHS that 20.0% of the NSW population were mobile-only phone users. Looking at the full time series for overweight or obese and current smoking if the NSWPHS had continued to be undertaken only using a landline frame, overweight or obese would have been shown to continue to increase and current smoking would have been shown to continue to decrease. However, with the introduction of the overlapping dual-frame design in 2012, overweight or obese increased until 2011 and then decreased in 2012, and current smoking decreased until 2011, and then increased in 2012. Our examination of these time series showed that the changes were a consequence of the sampling frame change and were not real changes. Both the backcasting method and the minimal coverage method could adequately adjust for the design change and allow for the continuation of the time series.

CONCLUSIONS

The inclusion of the mobile telephone numbers, through an overlapping dual-frame design, did impact on the time series for some of the health indicators collected through the NSWPHS, but only in that it corrected the estimates that were being calculated from a sample frame that was progressively covering less of the population.