Development of a richer measure of health outcomes incorporating the impacts of income inequality, ethnic diversity, and ICT development on health

The determinants of health outcomes include well-known factors such as income, medical technology and education. Some recent empirical studies have focused on the important role of information and communication technology (ICT) [1], income inequality [2], ethnic diversity [1, 3, 4] and public healthcare expenditure [5] to explain the observed health outcomes of countries. However, most previous research (both time-series and panel data estimation) have used a single variable, such as life expectancy or mortality rates (infant, maternal or under-five) to measure health outcomes [5–7]. Yet other studies have used outcome variables such as the prevalence or incidence of diseases or infections, the number of visits and readmissions to hospitals, hospital length of stay, work absences due to illness, perceived health status, quality-adjusted life years and disability-adjusted life years. Each of these measures and some recently developed health outcome indices (including the ‘better life index published’ by the Organisation for Economic Co-operation and Development (OECD) and ‘urban health index’ published by the World Health Organisation (WHO) is justifiable to answer individual research questions (often with cross-sectional data). Although reliable, these measures are inadequate for time series and panel data analysis. This is because continuous data on some of the indicators (e.g. self-reported health, disability, healthcare access) used in these indices are unavailable. For example, none of the health outcome indices provide data for an adequate number of time periods to enable statistically robust estimation to be undertaken. Also, studies estimating health outcomes with panel data are often obliged to use a single variable (mostly mortality variables) for estimation. Unfortunately, even for the OECD countries, which have rich sources of data for both mortality and morbidity variables, a comprehensive health outcome index suitable for panel data estimation is non-existent.

The current study constructs a health outcome index that incorporates both mortality (e.g. life expectancy and infant mortality) and morbidity (e.g. perceived health status) variables. The main objective of this study is to re-examine the factors that determined health outcomes for 30 OECD countries over the period 2004 to 2015. Along with some conventional determinants (e.g. income, education and technology), the estimated model also incorporates the influence of income inequality, ethnic diversity, development in ICT, and public health expenditure on health outcomes into the estimation process.

Past empirical findings have showed that societies with unequal incomes often experience poorer health outcomes [8–11]. However, the hypothesis that income inequality contributes to poorer health outcomes is not definitively determined with mixed empirical findings. Some studies have found a significant relationship between health outcomes (mortality, longevity) and income inequality [2, 8, 9, 12, 13] and others have concluded no significant association [11, 14, 15]. Furthermore, Rözer and Volker [16] found that the impact of income inequality on health reduces to zero after the age of 36 and Li and Zhu [10] reported a U shaped relationship, which means that at lower-levels of inequality, health outcomes improve with rising inequality and vice-versa. Hence, despite the large volume of literature, it is seemingly impossible to definitively determine the causality of income inequality on public health outcomes [17]. However, these past studies have mostly used cross-sectional data and a single variable to measure health outcomes. The health impacts of inequality will develop over a long period of time as the negative social consequences of that inequality will gradually take account of an individual’s health status [18]. Therefore, a representative health outcome index and panel data of 12 continuous years have been used to generate a better understanding of the issue.

Another key variable of interest in this study is ethnic diversity. Previous researchers have primarily focused on whether there are disparities in healthcare access, use and health status amongst majority and ethnic minority population [3, 4, 19, 20]. Most of the empirical findings indicated that ethnic minorities experience poorer access to healthcare services and their health status is significantly inferior to the majority of the population [3, 21]. Nonetheless, it is still unclear as to whether having higher levels of ethnic diversity reduces the overall health status of a country or not. Moreover, no study has yet examined the influence of public health expenditure on the potential adverse effects of higher levels of ethnic diversity on health outcomes.

Better health outcomes are also firmly linked with ICT development. ICT can support and promote efficient disease management and prevention [1], effective communication and reduction in the knowledge gap between patient and provider [22, 23], facilitate long-distance medical consultations [24], underpin the prevention of and supervision of public health risks [25, 26] and boost the performance and administration of the health system [27]. On the contrary, some empirical studies reported adverse effects of ICT on health outcomes. For example, Kim et al. [28] found that higher usage of the internet is associated with irregular food intake; Rosell et al. [29] indicated an association between mental distress and ICT use; Booth et al. [30] linked ICT use with less physical activity; and Punamäki et al. [31] concluded that ICT use is related to lower perceived health status. The contradictory findings may arise because previous studies used variables of choice that most suited the study purpose- to scale health outcome (mortality or morbidity) or ICT development (adoption or diffusion). Therefore, the overall impact of ICT developments on health outcomes is still unconfirmed [24]. In particular, what is the impact on overall public health when there is increased access to and use of ICT?

In light of the above discussion, the primary objective of this study is to examine the impact of income inequality, ethnic diversity and ICT development on health outcomes in the OECD countries utilising a large panel data. The secondary objective is to construct a health outcome index with indicators representing both mortality and morbidity variables. This study, therefore, investigates the following key questions: (i) does income inequality significantly affect health outcomes? (ii) do countries with greater levels of ethnic diversity have lower health status than countries with lower levels of diversity? (iii) what impact does ICT development (access, use and skill level) have on health outcomes? and (iv) does public health expenditure significantly improve health outcomes? These questions are empirically investigated by developing a health outcome index and using a panel dataset of OECD countries over the period of 2004 to 2015 (Additional file 1: Appendix A).

This study will make the following contributions to the existing literature. First, instead of using a single variable to measure health outcome, a health outcome index is constructed using both morbidity and mortality variables in a partial least square estimator based on a structural equation model (PLS-SEM). To the best of the authors’ knowledge this is the first study that has used perceived health status as an indicator of health outcome in panel data analysis. In the OECD database perceived health status was measured with the question “How is your health in general” and the responses were recorded in a Likert scale (from very bad to very good). Second, given that the adverse effects of income inequality on health occurs over time, this study assembled income inequality data and constructed a health outcome index over 12 continuous years. Third, the relationship between higher ethnic diversity and overall health outcomes was examined. Previous studies have investigated health care access of ethnic minorities but no previous study has examined health outcome variances across countries with different levels of ethnic diversity. Fourth, the relationship between ICT development and health outcome was re-examined using credible ICT development index data from the International Telecommunication Union (ITU). Furthermore, this study also investigated which aspect of ICT (access, use or skills) development is most strongly related to population health outcomes. Fifth, unlike previous studies, this study adopted a heterogeneous panel study method, rather than use homogenous panel data analysis. This enables control of cross-section dependence in the panel data by using OLS estimation with panel-corrected standard errors.

The paper consists of five sections. Section “Methods” outlines the model variables and research method. Section “Results” shows the results and section “Discussion and Policy Implications” consists of the discussion and finally, section “Conclusions” draws conclusions of the study.

The results of the Pooled OLS DKSE estimation indicate similar signs for almost all the coefficients, however, there are some notable differences. The variable income inequality is not only negatively related to health outcome but is also significant. Growth in hours of work is positive but not significant and the suicide rate significantly impacts health outcomes. The only variable that has a different sign is growth in hours of work but it is not significant. For model 1, a one unit increase in income inequality and the suicide rate reduces the health outcome index by 0.012 and 0.011, respectively.

The diagnostic test results showed that the panel dataset exhibits heterogeneity and the errors are cross-sectional dependent. Serial correlation and multicollinearity were not detected and the errors are normally distributed [55]. All the results were significant at 95% confidence interval.

All estimated coefficients have the a priori anticipated signs. This confirms the validity of the health outcome index constructed in this study. Thus the method was then used investigate the key determinants of health outcome using longitudinal data while controlling for heteroskedasticity and cross-section dependence.

The results of the Pooled OLS with DKSE and PCSE estimation showed that income inequality negatively impacts health outcome for the OECD countries. However, this outcome is significant only in the Pooled OLS test. One observation is that to establish the relationship beyond doubt, maybe, time series data on a longer time scale is required [56] as income inequality often affects health through other components such as harmful health behaviour [57] and stress-related diseases [58]. Nonetheless, this study supports the income inequality hypothesis that the more unequal the society, the higher the probability of poorer health controlling for per capita GDP. Moreover, past empirical works often produced mixed conclusions, where some researchers supported this hypothesis [2, 8, 9, 13, 59] and others refuted it [15, 60].

In recent years, many organisations and governments have promoted the use and access of ICT in the health sector [24]. However, the findings of this study suggested that increasing ICT access and use in society has an adverse effect on health outcomes. A number of previous studies have discussed the potential health benefits of ICT development, access and use. Their findings concluded that ICT improves the diffusion and communication of health information and knowledge, therefore, facilitates increased health literacy and healthier lifestyles [26, 61]. As a result, increasing access and use of ICT can improve a country’s public health outcomes [62, 63]. Conversely, Punamäki et al. [31] found that ICT use is connected with lower perceived health status and yet others concluded that excessive usage of ICT can cause musculoskeletal problems [64, 65]. Other, empirical findings also associated ICT access and use with unhealthy lifestyles and obesity [28], mental health disorders such as lack of sleep and depression [58] and development of health compromising-behaviour in both men and women [66]. Therefore, ICT use and access have both positive and negative impacts on health. On balance, the results of this study show that the negative impacts outweigh the benefits. Not surprisingly, improvement in ICT skills (measured by the literacy rate) has been found to promote better health.

The level of ethnic diversity in most OECD countries is relatively low and the study findings show that countries with lower levels of diversity enjoy higher health status. This conclusion is consistent with previous empirical works which showed that ethnic minorities are subject to unequal access to health care services, and poorer health status [3, 4, 19, 64, 67, 68]. Nevertheless, the detrimental consequences of higher ethnic diversity on health outcome can be compensated in part by devoting a larger share of publicly funded health care expenditure to minority groups.

A common point of argument among researchers and policymakers is the utility of public welfare expenditure, especially in the healthcare sector. In this context, the present study provides strong evidence that where health care is generously financed by government better health outcomes are evidenced. The study results support previous findings of [5, 69–71] and contradicts Self and Grabowski [72] who found no such relationship in developed countries.

Finally, growth in per capita GDP, advancement in technology and increasing levels of tertiary education are pivotal components of better health outcomes in OECD countries. On the contrary, obesity plays a major role in deteriorating health status. The percentage of the obese population (male) has increased by 31% (authors own calculation) over the period 2004 to 2015, which can be a considerable cause of concern for future public health policies in these countries.

Some policy implications can be derived from the findings of this study.

First, this study examined the relationship between the development of ICT access, use and skills with overall public health outcomes. This study supports the hypothesis that overuse of ICT (by the general population) can have a negative impact on public health. Governments should create public awareness regarding the ill-effects of ICT use on health and recommend necessary actions to avoid future health problems.

Second, policymakers should recognise that having a population of greater ethnic diversity makes it more challenging to promote better health outcomes for all. This may be part of the answer to the puzzle as to why some countries could achieve better health outcomes with lower per capita expenditure on health. Populous countries such as the USA with higher levels of ethnic diversity may find it difficult to achieve the health status of Japan (a country with lower ethnic diversity). Again, policymakers should realise that improving health care access for ethnic minorities with publicly funded health care can improve the overall health of a country.

Third, understanding the effectiveness of public health expenditure in producing a healthy nation is very important. The findings of the study may be an important indication for governments especially those with comparatively lower health status. Even with the burden of rising health expenditure, governments should continue to fund the health system adequately.

Last, the issue of rising obesity in OECD countries should be urgently addressed. Governments must actively promote healthy lifestyles and discourage or control social and individual practices that cause obesity.

A number of empirical studies have examined the factors affecting health outcome in OECD countries. These studies either used cross-section data or a singular variable to measure health outcome. This study constructed a health outcome index with indicators comprising both mortality and morbidity data for 30 OECD countries over the period of 2004 to 2015. The index represented overall health status in individual countries. Development of the index gave the opportunity to investigate the impact of income inequality, ethnic diversity and ICT development on health with a panel dataset. In addition, the estimated method controlled for heterogeneity and cross-section dependence which improved the reliability of the estimated outcomes.

The study results indicate that income inequality, ethnic diversity and developments in ICT access and use have a significant negative relationship with health outcome in the OECD countries. On the other hand, improvements in ICT skills level and a higher levels of publicly funded health expenditure show statistically significant and positive associations with better health outcomes. Rising national incomes, developments in technology and higher education levels also improve a country’s health outcomes. On the contrary, rising obesity amongst the adult population has serious negative consequences for health.

There are some limitations of the study. The panel dataset is restricted to only 12 years due to unavailability of data for some of the variables such as perceived health status and income inequality. However, the time frame (2004–2015) is important because during this time ICT development accelerated rapidly (Table 2) and ethnic diversity increased in the OECD countries. In addition, use of more morbidity indicators would have increased the credibility of the health outcome index. The panel data has shown significant heterogeneity and cross-section dependence. The results of this study, therefore, should be interpreted with caution.