How do we measure up? A comparison of lifestyle-related health risk factors among sampled employees in South African and UK companies

Abstract

The need to address the growing prevalence of non-communicable diseases through changing the lifestyle behaviours that contribute to them has become a global priority. Settings-based health promotion strategies such as workplace health promotion programmes are growing in an attempt to start meeting this need. In order for settings-based health promotion programmes to be successful, they need to be based on the specific risk profiles of the population for whom they are designed. Workplace health promotion programmes are becoming popular in South Africa, but there are currently few data available about the health risks and lifestyle behaviours of the South African employed population. In order to obtain such data and reward workplace health promotion initiatives, Discovery Health initiated healthy company campaigns in South Africa and the UK. These campaigns took the form of a competition to assess the healthiest companies in each country. Through these campaigns, an extensive data set was collected encompassing UK and South African employees’ lifestyle behaviours and health risks. In this article, we used these data to compare self-reported physical activity levels, self-reported fruit and vegetable consumption, calculated BMI, self-reported smoking, mental health indicators, and health screening status of the UK and South African employee samples. We found significant differences across all measures, with the exception of self-reported fruit and vegetable consumption. The findings emphasise the importance of using local data to tailor workplace health promotion programmes for the population for which the programmes have been designed.

Keywords

chronic disease / non-communicable disease workplace health promotion nutrition obesity/overweight mental health physical activity health promotion

Introduction

Health promotion activities have become increasingly popular globally as they have the potential to improve people’s lives. This potential comes from evidence that many of the leading causes of ill health and premature death are potentially avoidable or controllable, particularly those due to non-communicable diseases (NCDs) such as diabetes, certain cancers, and some forms of heart disease (1). Until fairly recently it was assumed that the burden of such NCDs was predominantly a concern of the developed world, associated with the higher levels of affluence which have enabled a way of life associated with increased risk. It has become evident that developing countries are catching up swiftly. Urbanisation, globalisation, and access to international media and advertising have led to changes to nutritional and physical activity patterns in developing countries. These changes have resulted in NCDs becoming significant sources of disability and premature death in developing and newly developed countries (2). This trend is clearly evident in South Africa, which faces a quadruple health burden – a combination of poverty-related illnesses, injuries due to societal problems of violence and crime, HIV/AIDS, and the simultaneous urbanisation and Westernisation of South African society – leading to the emergence of chronic diseases previously more typically associated with highly developed economies (3).

This multiple burden of disease, coupled with the health inequalities that need to be addressed as a legacy of the apartheid health system, create demands on the health services of South Africa far beyond those experienced in developed countries. The competing priorities created by the multiple burden of disease compel South African officials to use limited resources optimally and implement cost-effective health promotion interventions to prevent NCDs in the face of all the other health issues that need to be addressed (3).

Against this background, workplace health promotion (WHP) appears to be a possible avenue for NCD prevention in South Africa. This is because it does not rely on scarce state resources for implementation and it provides fairly easy access to large numbers of people in one setting. There are health promotion challenges and opportunities in other settings, including schools and community-based settings, but the workplace is currently viewed by the World Health Organization (WHO) and the World Economic Forum as an important setting for advancing public health (4,5). The WHO Programme for Occupational Health (4) specifies that the workplace is a priority setting for health promotion in the 21st century: ‘The concept of the health promoting workplace (HPW) is becoming increasingly relevant as more private and public organisations recognize that future success in a globalizing marketplace can only be achieved with a healthy, qualified and motivated workforce…For nations, the development of HPW will be a pre-requisite for sustainable social and economic development’ (4). Reasons for focussing on the workplace as a site for health promotion include access to participants, greater opportunities on the part of employers for attracting and retaining talented employees, and potential for improving organisational productivity and health (5). Globally, organisations are starting to rise to this health challenge. The prevalence of WHP programmes currently is dominated by the US (76%), followed by Asia (47%) with Africa/Middle East trailing at 33% (6). Recent South African surveys, however, show that there is evidence of the development of a WHP presence in South Africa (7,8).

Despite the increasing prevalence of WHP in South Africa and the role it can play in addressing some of the country’s health issues, much of the empirical literature related to workplace health emanates from outside the country, primarily the US. This is problematic in that it assumes that cultural/national differences are not relevant to WHP initiatives in different countries. Such an assumption runs counter to prevailing models of health promotion, notably the social ecological model (9). Traditionally, WHP programmes have focussed on an individual level of analysis, with little acknowledgement of the broader context in which health promotion efforts takes place (10). The social ecological approach (9) counters this narrow focus. As a theoretical framework, it foregrounds the notion that individuals are embedded within larger social systems. It emphasises a multi-level approach which takes into account context-based influences on health and health interventions. Levels of influence on health from a social ecological perspective extend outwards, from the individual him/herself, to the community in which he/she lives and works, to the workplace itself, through to the country in which he/she resides. All of these levels include norms, values, opportunities, and resources (or lack of resources) which can act to facilitate or impede health, health behaviour, and health promotion interventions. Within the workplace health promotion field, there is a substantial body of research which has assessed the prevalence of health promotion programmes at work (11,12), the health and cost benefits of health promotion programmes at work (13) and there have also been some researchers who have looked at WHP from a more global perspective. One example of such global, comparative research is the International Association of Workplace Health Promotion (IAWHP) report in which 21 countries were profiled on prevailing health issues and health risk behaviours, key drivers for establishing WHP programmes, programme examples, good practices, and outcome and success indicators (6). While this research is promising, it tends to rely on secondary data or focuses on health promotion initiatives and programmes rather than looking at the specific health risks and health behaviours within the specific population for whom WHPs are directed. Kirsten et al. (6) argued that programmes specifically designed for local conditions are still lacking: ‘Not much has been done on a global scale aside from translations, spelling adjustments, (e.g. labor versus labour), and measurement conversions (e.g. pounds versus kilograms)’ (p.xxi). In the current study, we begin to address this concern through obtaining primary data comparing a sample of British and South African companies and employees in relation to self-reported employee health risks, health behaviours, and health-related attitudes.

The aim of the study was to compare similarities and differences in risk factors across these two samples so as to tailor WHP programmes more effectively for the needs of the populations for which they were designed. More specifically, the purpose of the research was to establish the particular effect that contextual differences (South Africa versus the UK) may have on self-reported employee health risk factors and health-related behaviours.

Methods

The data used for this research were collected as part of a WHP campaign initiated by Discovery Health, the administrator to the largest open medical scheme in South Africa. The campaigns were named the Healthy Company Index South Africa (HCISA) and the British Healthy Company Survey (BHCS). These were national projects conducted in both South Africa and the UK to encourage and reward employer health promotion activities and employee health behaviourⁱ and obtain much needed data about employee health. The campaigns were conducted a year apart but the same data-collection procedure, methodology, and measures were used for both.

The first HCISA was conducted during 2010 with a follow-up in 2012. In 2013 the Britain’s Healthiest Company (BHCS) campaign was launched in the UK with similar aims in the British marketplace. The methodology for conducting the HCISA and BHCS received clearance from the Human Research Ethics Committee (Medical) of the University of the Witwatersrand, Johannesburg South Africa. The University of the Witwatersrand subscribes to international research integrity standards as set out in the Singapore Statement (14).

Procedure

In both the UK and South Africa, companies were recruited through various media including web-based advertising, direct marketing to the sponsors’ client base, and word of mouth, to participate in the campaign. In both cases (HCISA and BHCS), company representatives were invited to enrol their company via a dedicated survey website. They then received an email from the research team, confirming participation and confirming the confidentiality and voluntary nature of the survey. Employees were reassured that their employers and Discovery Health would not have any access to their data, as all results were collated with no personal identifiers.

An independent data analysis company was contracted to email a link to the questionnaire to all eligible employees. Data were then collected online and the data analysis company removed all links to the employee email addresses before providing the researchers access to the data to ensure that anonymity and confidentiality of individual responses were maintained. In the case of HCISA, the company representative provided a list of eligible email addresses for validation purposes, while for BHCS the survey relied on individuals identifying the company in which they were employed. This meant that the eligibility count was more accurate for the HCISA. The same data analysis company was used to collect the BHCS data and, as in South Africa, data were anonymised before the researchers were provided access to them. The data analysis company provided all participating employees with an automated Health Risk Assessment report which was generated immediately on completion of the employee questionnaire.

The employee questionnaire was accessed using a protected online domain. This questionnaire was primarily a self-report health-risk assessment which included questions about demographic characteristics, overall health status, health behaviours, whether they had undergone basic health screenings, and biometric measures. Examples of the questions used in the analyses for this paper are included in Appendix A (available online). An employer questionnaire was also used to assess which interventions were available in the workplace. The manager responsible for the company’s workplace health management programme completed the online employer questionnaire, which included sections about current health promotion initiatives, onsite facilities, company health-related policy, and leadership support. The survey was largely based on the National Worksite Health Promotion Survey (NWHPS) (11).

In order to ensure a minimum level of statistical credibility, only companies with 50 or more employees with internet access were eligible to participate in the HCISA or BHCS. Those employers where the response rate was the minimum of 24 responses or 30% received a report benchmarking their health promotion activities and facilities as well as their employees’ health status and attitudes, against other participating companies (normalised for the age and sex distribution of eligible employees). In other words, companies with fewer than 80 employees required a 30% response rate and for the balance of companies, a minimum of 24 responses were required to be eligible for a report.

There was no charge for companies to participate in either campaign.

Sample

Table 1 contains a summary depiction of the South African and UK samples. The South African sample size and response rate was higher than the UK; however, the study had 100% power (15) to detect a difference between the response rates for South Africa and the UK. Therefore, the samples in both countries were sufficiently large to make meaningful comparisons. As far as demographics are concerned, the samples were similar. Both samples had a higher proportion of female respondents (59% and 55% in South Africa and the UK, respectively). The mean age of the South African respondents was 36.5 and of the UK respondents was 39. All respondents in both countries were white collar employees. Reliable demographic data on the total white collar workforce by company was not available for all the companies surveyed, so it is not possible to state how these demographics compare with the total workforce in each company.

Table 1.

Description of the UK and South Africa (SA) samples.

	SA	UK
Number of participating companies	107	38
Size of participating companies
50–100 employees	31	1
101–1000 employees	63	19
1001–2500 employees	8	7
2501–5000 employees	2	7
5001+ employees	3	4
Number of eligible employees	80,565	72,734
Number of respondents	18,902	9559
Response rate	23.46%	13.14%
% male	40.90%	45.10%
Age:
Average	36.48	38.99
Range	18–72	18–73
SD	11	10
Companies with onsite wellness programme	76%	32%

Measures

Data for the healthy company index in both countries were collected at two levels – employer and employee. Each participating company completed an employer questionnaire. Company details including number of employees, industry, demographic breakdown of employees (in terms of age and sex), and provision of WHP facilities were assessed through the employer questionnaire. The employee questionnaire included questions relating to various risk factors that have been shown to affect an individual’s health status (16,17). These included the following:

- BMI defined as weight in kilograms divided by the square of the height in metres. The BMI calculation itself was done by the researchers, based on the self-reported height and weight data provided by the respondents;

- Exercise measured as self-reported number of exercise sessions per week at moderate intensity;

- Smoking status, defined as current smoker, past smoker or never smoked;

- Nutrition measured in terms of self-reported fruit and vegetable servings per day and number of sugary drinks consumed per day; and

- Mental health based upon responses to the Kessler scale (18).

Examples of the questions used to assess the above factors are included in Appendix A.

Analysis

The online data collected via the two healthy company index campaigns were combined into a single data set. Data were analysed using the SAS 9.4 for Windows (SAS Institute Inc., Cary, NC, USA) statistical package. Frequency counts were utilised to describe the two samples’ risk factors. Cross-tabulation of respondents’ self-reported risk factors and country were obtained and chi square tests of association were undertaken to assess the significance of the results. Finally, multinomial regression models were estimated using the risk factors as dependent variables with country, sex, and age as independent variables.

Results

The results of the analyses comparing the South African and UK participants on the different health-related lifestyle factors are presented in Table 2. Statistically significant differences in self-reports were found for physical activity levels χ² (2, N = 28,570) = 490.83, p<0.001; number of sugary drinks consumed per day χ² (2, N = 28,570) = 919.06, p<0.001; BMI χ² (2, N=28,570) = 905.75, p<0.001; mental health status χ² (2, N = 28,570) = 398.66, p<0.001; and smoking (cigarettes) χ² (2, N = 28,570) = 385.36, p<0.001. In all of the above-mentioned results, the UK scores were significantly better than the South African scores. The scores on self-reported health checks (screenings) were also significantly different: BP screening χ² (1, N = 28,570) = 1064.51, p<0.001; glucose screening χ² (1, N = 28,570) = 244.997, p<0.001; cholesterol screening χ² (1, N = 28,570) = 598.89, p<0.001 and all three screenings completed χ² (1, N = 28,570) = 159.675, p<0.001, but South African employees scored higher on these latter measures. Post-hoc power analysis gave a power of 100% to detect the main associations.

Table 2.

Comparison between South Africa (SA) and United Kingdom (UK) samples on exercise, fruit, vegetable consumption, sugary drinks, smoking status, mental health and health checks.

		Country		Total
		SA	UK	Total
Number of times of exercise per week	0	4862 (25.6%)	1408 (14.7%)	6270 (21.9%)
	1–3	8570 (45.1%)	4535 (47.4%)	13105 (45.9%)
	4 or more	5579 (29.3%)	3616 (37.8%)	9195 (32.2%)
Servings of fruit and vegetables per day	0–2	7175 (37.7%)	2892 (30.3%)	10,067 (35.2%)
	3–4	6881 (36.2%)	4169 (43.6%)	11,050 (38.7%)
	5+	4955 (26.1%)	2498 (26.1%)	7453 (26.1%)
Number of sugary drinks per day	0 servings	8755 (46.1%)	6212 (65%)	14,967 (52.4%)
	1–2 servings	8990 (47.3%)	2880 (30.1%)	11,870 (41.5%)
	3 or more	1266 (6.7%)	467 (4.9%)	1733 (6.1%)
BMI (Body Mass Index)	Low risk (18.5–24.9)	6982 (36.7%)	4819 (50.4%)	11,801 (41.3%)
	Medium risk (25.0–29.9)	5755 (30.3%)	3133 (32.8%)	8888 (31.1%)
	High risk (18.5 or 30+)	6274 (33%)	1607 (16.8%)	7881 (27.6%)
Mental health	Mild risk (6–14)	14,755 (77.6%)	8351 (87.4%)	23,106 (80.9%)
	Moderate risk (15–18)	2642 (13.9%)	810 (8.5%)	3452 (12.1%)
	High risk (19–30)	1614 (8.5%)	398 (4.2%)	2012 (7%)
Smoking	Never smoked	11603 (61%)	6071 (63.5%)	17,674 (61.9%)
	Ex-smoker	3540 (18.6%)	2360 (24.7%)	5900 (20.7%)
	Current smoker	3868 (20.3%)	1128 (11.8%)	4996 (17.5%)
Health checks (screenings) completed	Blood pressure	13466 (70.8%)	4897 (51.2%)	18,363 (64.3%)
	Glucose	6028 (31.7%)	2182 (22.8%)	8210 (28.7%)
	Cholesterol	6250 (32.9%)	1822 (19.1%)	8072 (28.3%)
	All three*	2830 (14.9%)	912 (9.5%)	3742 (13.1%)

All three health checks completed (blood pressure, glucose and cholesterol).

Once the chi square analyses were completed, responses were categorised as ‘outside the healthy range’ or ‘within the healthy range’ so that further analyses could be undertaken based on these categories. The definitions used for these categorisations are presented in Table 3.

Table 3.

Healthy range definitions.

	Healthy range	Outside the healthy range
	Low risk	Medium risk	High risk
Exercise (sessions/week)	4+	1–3	0
Fruit and vegetable servings per day	5+	3–4	0–2
Sugary drinks consumed per day	0	1–2	3+
BMI (kg/m²)	18.5–24.9	25–29.9	<18.5 or 30+
Mental health	Mild risk (6–14)	Moderate risk (15–18)	High risk (19–30)
Smoking	Non-smoker	Previous smoker	Current smoker

Odds ratios (OR) and 95% confidence intervals (95% CI) from multinomial regression models using country, age, and sex to predict the likelihood of belonging to the ‘outside the healthy range’ compared with ‘Healthy range’ group are presented in Table 4. The results generally corroborated those presented in Table 2. That is, if age and sex were held constant, South African participants were more likely than the United Kingdom respondents to be in the high-risk group relative to the low-risk group for the risk factors: exercise [OR: 2.22; 95% CI: (2.06;2.39)], fruit and vegetable consumption [OR: 1.19; 95% CI: (1.12;1.28)], sugary drink consumption [OR: 1.81; 95% CI: (1.62;2.03)], mental health [OR: 2.1; 95% CI: (1.87;2.35)], smoking status [OR: 1.75; 95% CI: (1.62;1.88)] and BMI [OR: 2.06; 95% CI: (1.92;2.21)].

Table 4.

Odds ratios (OR) and 95% confidence intervals (95% CI) for the multinomial regression models.

			OR and (95% CI)
Number of times of exercise per week+			0 †	1–3
	Country	SA	2.22 (2.06;2.39)***	1.21 (1.15;1.28)***
	Age	<30	0.79 (0.71;0.88)***	0.96 (0.88;1.05)+
		30–40	1.11 (0.99;1.23)+	1.09 (1;1.19)+
		40–50	0.92 (0.82;1.04)+	0.97 (0.89;1.07)+
	Gender	Female	1.83 (1.71;1.96)***	1.28 (1.22;1.35)***
Servings of fruit and vegetables per day			0–2 †	3–4
	Country	SA	1.19 (1.12;1.28)***	0.81 (0.76;0.86)***
	Age	<30	2.34 (2.11;2.59)***	1.57 (1.43;1.73)***
		30–40	1.79 (1.62;1.97)***	1.39 (1.27;1.53)***
		40–50	1.38 (1.24;1.54)***	1.23 (1.12;1.36)***
	Sex	Female	0.59 (0.55;0.63)***	0.78 (0.73;0.82)***
Number of sugary drinks per day			1–2 servings	3 or more servings †
	Country	SA	2.12 (2.01;2.24)***	1.81 (1.62;2.03)***
	Age	<30	3.17 (2.9;3.46)***	4.45 (3.63;5.45)***
		30–40	2.28 (2.09;2.49)***	2.84 (2.31;3.49)***
		40–50	1.53 (1.39;1.69)***	1.33 (1.05;1.68)*
	Sex	Female	0.61 (0.58;0.64)***	0.47 (0.43;0.52)***
Mental health			Moderate risk (15–18)	High risk (19–30) †
	Country	SA	1.72 (1.59;1.88)***	2.1 (1.87;2.35)***
	Age	<30	2.13 (1.86;2.45)***	2.72 (2.24;3.29)***
		30–40	1.66 (1.45;1.91)***	2.09 (1.72;2.53)***
		40–50	1.28 (1.1;1.49)**	1.41 (1.14;1.75)**
	Sex	Female	1.49 (1.38;1.61)***	1.98 (1.79;2.19)***
Smoking			Ex-smoker	Current smoker †
	Country	SA	0.81 (0.76;0.86)***	1.75 (1.62;1.88)***
	Age	<30	0.65 (0.59;0.72)***	1.63 (1.45;1.84)***
		30–40	0.85 (0.77;0.93)***	1.53 (1.36;1.72)***
		40–50	0.77 (0.7;0.85)***	1.17 (1.03;1.33)*
	Sex	Female	0.74 (0.7;0.79)***	0.68 (0.64;0.73)***
BMI			Low risk	High risk †
	Country	SA	0.70 (0.66;0.75) ***	2.06 (1.92;2.21) ***
	Age	<30	2.21 (2.01;2.43) ***	1.12 (1.01;1.23)*
		30–40	1.44 (1.31;1.58) ***	1.07 (0.97;1.18)+
		40–50	1.10 (0.99;1.21)+	1.08 (0.97;1.20)+
	Sex	Female	1.92 (1.81;2.03) ***	1.73 (1.63;1.84) ***

Note: +p<0.1, *p<0.05, *p<0.01, ***p<0.00. †High-risk group. χ² (df) – Number of times of exercise per week: 904.90 (10)***; Servings of fruit and vegetables per day: 792.79 (10)***; Number of sugary drinks per day 2434.19 (10)***; Mental Health 1023.43 (10)***; Smoking 828.87 (10)*** and BMI 2085.62 (10)***. Reference Groups: Number of times of exercise per week: 4+; Servings of fruit and vegetables per day: 5+; Number of sugary drinks per day: 0; Mental health: Mild risk (6 –14); Smoking: non-smoker and BMI: medium risk; Country: UK; Age: >50, Sex: Male.

Discussion

As expected, the findings of this research revealed different patterns of health risks between the South African and UK participants. On average, the South African participants tended to report fewer healthy nutritional habits (fruit and vegetable consumption differences were not significant but consumption of sugary drinks were) and also reported less physical activity than their UK counterparts. Only 30.8% of the South African participants achieved healthy guideline levels of physical activity compared with 40.7% of the UK participants. In both countries there is evidently room for improvement, but in South Africa, more concerted effort is needed.

Given the evidence of poorer self-reported nutritional habits and lower levels of self-reported physical activity, it is not surprising that the South African sample’s BMI was higher on average than that of the UK participants, with a higher proportion of respondents in South Africa falling into the obese range. A further area of concern evident in the data was the relatively high number of self-reported smokers in South Africa. South African legislation banning smoking in the workplace and other public places has been in place for longer than in most Western countries, including the UK, yet the number of people who identified themselves as ‘current smokers’ⁱⁱ among the South African participants was higher. Smoking cessation clearly remains an important area of intervention for WHP initiatives.

On a more positive note for South Africa, the number of people who reported they had been screened for glucose, blood pressure, and cholesterol levels was higher than in the UK. Furthermore, the proportion of sampled companies offering WHP programmes was higher in South Africa than in the UK. These more positive results indicate that South Africa does have a strong base of knowledge among staff as well as a physical infrastructure in place in organisations to start addressing the areas of concern identified through the present research. To fully capitalise on this opportunity, however, there is an urgent need for more research to evaluate the appropriateness and effectiveness of WHP interventions in South Africa. One implication of this study is that rigorous longitudinal monitoring and evaluation studies are needed to establish whether the increasing numbers of WHP programmes in South Africa are having any effect.

The results also lend some support to the social ecological model (9). The multinomial regression results that established country as a significant predictor of health behaviour taking sex and age into account clearly reflected the importance of context in health research. One practical implication of this is that multinational organisations have to decide what types of WHP programmes they should implement across multiple worksites and multiple countries. In an increasingly globalised business world, the social ecological model serves as an important framework for recognising contextual influences on and differences in employee health. Obtaining employee-level data about employee health and health risks across countries represents an opportunity for acknowledging contextual influences on health, in line with the precepts of the social ecological model. Unfortunately, one of the limitations of this study was that we did not collect more detailed data about the differences between the broader environmental factors outside the workplace that may affect smoking, nutrition, and physical activity. Such factors include availability of sugary drinks, quality of water, safety for walking and exercise, use of public transport, and social norms around smoking, weight, and healthy eating. Such details may have provided insight into why we found the differences we did, and may be a fruitful area for future research. Similarly, the implications of our findings for multinational companies warrant further investigation. It would be interesting to replicate this research within a multinational company and establish what differences emerge within one company across different countries. Another area for future research would be to focus more specifically on demographic variables. The results of the multinomial regression models presented in this paper revealed that if age and sex were held constant, South African participants were still more likely than the United Kingdom respondents to be in the high-risk group rather than the low-risk group for most of the risk factors. The focus here was on ensuring that the results we obtained were not due to any age and sex differences across the two samples (South Africa and UK). However, given that cultural norms around issues such as exercise, use of public transport, and social norms around smoking, weight, and healthy eating may vary by sex and age, further research looking specifically at sex and age is warranted.

The aim of the present study was to compare similarities and differences in self-reported lifestyle risk factors across international samples in order to tailor WHP programmes more effectively for the needs of the population they are serving. It is important to note that the research presented in this paper did not use representative samples of the populations surveyed, as the data collection method did not allow for representative samples to be drawn. Rather, the samples were dependent on both the organisation and the individual employee volunteering to participate. This also meant that the response rates across the two countries diverged quite widely, with South Africa having a higher response rate. The Healthy Company Campaign had been run previously in South Africa, which may account for the higher response rate and sample size in South Africa. Companies participated in the survey to ‘win’ the healthiest company title as well as obtain information about the overall health and wellbeing of their employees. The competitive nature of the campaign may have resulted in some in-company pressure for employees to participate. However, the companies were never granted access to information about who had actually completed the employee questionnaires so no pressure on an individual, employee level could be exerted. A further limitation of the study is the self-reported nature of the data which may have been subject to biases of recall and impression management. It was, however, not possible to obtain objective measures in a study of this nature. Nevertheless, the data collection method was innovative in enabling the researchers to access large, multi-firm samples from two countries. Such data contribute to the accumulation of evidence needed to ensure that relevant and appropriate cross-cultural workplace health interventions are developed (6), interventions that are tailored to the specific needs and risk factors of the populations they are addressing.

Supplemental Material

Online_Appendix – Supplemental material for How do we measure up? A comparison of lifestyle-related health risk factors among sampled employees in South African and UK companies

Supplemental material, Online_Appendix for How do we measure up? A comparison of lifestyle-related health risk factors among sampled employees in South African and UK companies by Karen Milner, Roseanne da Silva, Deepak Patel and Sulaiman Salau in Global Health Promotion

Footnotes

Declaration of conflicting interests

None declared.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

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