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UNEMPLOYMENT, GOVERNMENT HEALTHCARE SPENDING AND
CEREBROVASCULAR MORTALITY, WORLDWIDE 1981-2009:
AN ECOLOGICAL STUDY
Dr Mahiben Maruthappu, MA BM BCh1,2
Dr Joseph Shalhoub, BSc MBBS MRCS FHEA PhD 1
Ms Zoon Tariq, BA 3
Mr Callum Williams, BA 3,4
Professor Thomas Zeltner, MD LLM 5,6
Professor Alun H Davies, MA DM FRCS FHEA FACPh FEBVS 1
Professor Rifat Atun, FRCP MBA, FFPH 1,2
1
Imperial College London, London SW7 2AZ, UK
2
3
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5
Harvard University, MA 02138, USA
University of Oxford, Oxford OX1 2JD, UK
The Economist, 25 St James’s Street, London SW1A 1HG, UK
Special Envoy for Financing to the Director General of the World Health Organization (WHO),
Avenue Appia 20, 1211 Geneva 27, Switzerland
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University of Bern, Gerechtigkeitsgasse 31, Bern, CH 3011, Switzerland
1
Please address correspondence to:
Dr Mahiben Maruthappu
Senior Fellow
Chair & Chief Executive’s Office, NHS England
Skipton House, 80 London Road
London
SE1 6LH
UK
[email protected]
Financial Disclosure and Products Statement
None of the participating authors has a conflicting financial interest related to the work detailed
in this manuscript; nor do any of the authors maintain a financial stake in any product, device or
drug cited in this report. We declare no support from any organization for the submitted work; no
financial relationships with any organizations that might have an interest in the submitted work
in the previous three years, and no other relationships or activities that could appear to have
influenced the submitted work. Information on data used in this report is available upon request.
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ABSTRACT
Background: The global economic downturn has been associated with unemployment rises,
reduced health spending and worsened population health. This has raised the question of how
economic variations affect health outcomes. We sought to determine the effect of changes in
unemployment and government healthcare expenditure on cerebrovascular mortality globally.
Methods: Data were obtained from the World Bank and World Health Organization.
Multivariate regression analysis was used to assess the effect of changes in unemployment and
government healthcare expenditure on cerebrovascular mortality. Country-specific differences in
infrastructure and demographics were controlled for. One to five year lag analyses and
robustness checks were conducted.
Findings: Across 99 countries worldwide, between 1981-2009, every 1% increase in
unemployment was associated with a significant increase in cerebrovascular mortality
(coefficient 187 deaths/100,000, CI: 86.6-288, p=0.0003). Every 1% rise in government
healthcare expenditure, across both genders, was associated with significant decreases in
cerebrovascular deaths (coefficient 869 deaths/100,000, CI: 383-1354, p=0.0005). The
association between unemployment and cerebrovascular mortality remained statistically
significant for at least five years subsequent to the 1% unemployment rise, while the association
between government healthcare expenditure and cerebrovascular mortality remained significant
for two years. These relationships were both shown to be independent of changes in gross
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domestic product per capita, inflation, interest rates, urbanization, nutrition, education and outof-pocket spending.
Interpretation: Rises in unemployment and reductions in government healthcare expenditure
are associated with significant increases in cerebrovascular mortality globally. Clinicians may
also need to consider unemployment as a possible risk factor for cerebrovascular disease
mortality.
Funding: None.
Key words: cerebrovascular disease; stroke; mortality; unemployment; economic recession;
austerity; government spending; public health expenditure.
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INTRODUCTION
Stroke is the second leading cause of global mortality, accounting for 6.7 million deaths in
2012(1). In upper-middle income countries, cerebrovascular events are the leading killer,
responsible for 126 deaths per 100,000 population(1). Such statistics have been the impetus for
public health strategies to reduce cerebrovascular mortality.
A number of studies have reported an association between socioeconomic status and the
development of cerebrovascular disease in addition to increased stroke mortality(2-7). In recent
years, the role of economic changes in healthcare has been accentuated. The global population
has faced an economic crisis, presenting unexpected socioeconomic challenges to healthcare. A
number of governments have adopted policies aimed to reduce budget deficits through a series of
tax rises and spending cuts, often directly leading to rising unemployment rates, and in the
context of healthcare, reduced health system spending(8, 9). In countries where particularly
aggressive fiscal austerity measures have been implemented – including Greece, Cyprus,
Portugal and Spain – unemployment has risen sharply(10). Studies have attempted to evaluate
the impact of economic changes on population health outcomes(11, 12), however these have yet
to be translated to policy development. Further, these studies have also almost exclusively
focused on all-cause mortality or suicide, rather than the mortality attributed to specific
conditions. This raises the question of how economic variations, both within and outside of
economic crises, affect disease-specific health outcomes, including cerebrovascular mortality.
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We sought to determine whether there is an association between two key economic variables:
unemployment and government healthcare expenditure, and cerebrovascular mortality,
worldwide, between the years 1981 and 2009. We hypothesized that increased unemployment,
and reduced government healthcare expenditure, would be associated with rises in
cerebrovascular deaths, attributable to a variety of factors including reduced access to care.
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METHODS
Cerebrovascular mortality data for the years 1981 to 2009 were obtained from the WHO
mortality database(13), which is updated annually from civil registration systems of member
states. The quality of the data has been evaluated by the WHO(14). Cerebrovascular mortality
was defined as annual deaths from cerebrovascular diseases (under the International
Classification of Diseases (ICD) codes: ICD-9 430-438; ICD-10 I60-I69, G45) per 100,000
population(15). Age standardized death rates (ASDR) were employed as the basis of the
analysis. The ASDR, as defined by the WHO, is the weighted average of age-specific mortality
rate per 100,000 population, where the weights are the proportions of persons in the
corresponding age groups of the WHO standard population(16). ASDR was selected as it
controls for differences in age distribution within populations by incorporating age-specific
mortality rates(16).
Socioeconomic data were obtained from the World Bank’s Development Indicators and Global
Development Finance 2013 edition for the period 1981 to 2009(10). Government debt, as a
percentage of gross domestic product (GDP), data were obtained from the International
Monetary Fund (IMF) Historical Public Debt Database(17). Data were available for 99 countries
for each of the two analyses, namely unemployment and government healthcare expenditure
(Table A, see Appendix). There was some variation in the 99 states included in each analysis.
This was due to different data availability for unemployment and government healthcare
expenditure respectively. Unemployment, as defined by the World Bank(18), was taken to be the
share of the labour force that is without work but available and seeking employment.
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Government healthcare expenditure was measured as a percentage of GDP; it was taken to
include recurrent and capital spending from government budgets, external borrowings and
grants, and social insurance funds(19).
Multivariate regression analysis was used to separately assess the relationship between
cerebrovascular mortality (dependent variable), and either unemployment (independent variable
– Table B, see Appendix) or government healthcare expenditure (independent variable – Table
C, see Appendix). To ensure that results were not driven by extreme observations for certain
countries, a fixed-effects approach was used in the regression models, including 99 dummy
variables for the 99 countries in each dataset. Doing this meant that models evaluated mortality
changes within individual countries while holding constant time-invariant differences between
countries, including higher predispositions to cerebrovascular mortality as well as political,
healthcare, cultural, and structural differences. The demographic structure of the selected
countries was controlled for by incorporating total population size, in addition to the percentage
of the population over 65 years of age and less than 15 years old into the model. This is because
country demographics can affect both cerebrovascular incidence and mortality rates. A CookWeisberg test(20) was used to assess for and to confirm heteroskedasticity (where sub-samples
have different distributions) in the data used. Therefore, robust standard errors were included in
the regression models; accounting for the heterogeneity in the unemployment or government
expenditure datasets due to, for example, differences in the way that countries measured
unemployment rates or government healthcare expenditure. Due to the inclusion of over 100
control variables (in turn losing degrees of freedom and reducing sample size), the approach was
conservative. This methodology has been widely used in similar health-economic studies, and is
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regarded as a statistically robust and conservative approach(12, 21-23).
The basic linear fixed effects statistical model was therefore:
ΔHi,t–ΔHi=α+β×(ΔIi,t–ΔUi) +η×t+εi,t
Where i is country and t year; H is the dependent variable health metric (cerebrovascular
mortality); I is the independent variable (either unemployment or public health spending); α
represents the population structure of the country being analyzed, η is a dummy variable for each
country included in the regression model, and ɛ is the error term.
One, 2, 3, 4 and 5-year time-lag multivariate analyses were conducted to quantify the long-term
effects of changes in unemployment on cerebrovascular mortality. Robustness checks were also
conducted to control for economic factors, urbanisation and nutrition, education and out of
pocket expenses.
Stata SE version 12 was used for the analysis (Stata Corporation, Texas, USA).
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RESULTS
The results of the regression models are displayed in Tables 2 and 3 and are adjusted for
population size, demographic structure and inter-country differences including higher
predispositions to cerebrovascular mortality as well as political, healthcare, cultural, and
structural differences.
The results (Table 1) show that a 1% rise in unemployment was associated with a statistically
significant rise in cerebrovascular mortality in both men and women (coefficient for men 76.8
deaths/100,000, CI: 28.8-125, p=0.0017; coefficient for women 111 deaths/100,000, CI: 55.2166, p=0.0001). Between genders, the effect was greater for women than for men.
The results (Table 2) demonstrate that a 1% rise in government healthcare expenditure
(measured as a percentage of GDP) was associated with a statistically significant decrease in
cerebrovascular mortality in both men and women (coefficient for men -466 deaths/100,000, CI:
-708 to -224, p=0.0002; coefficient for women -402 deaths/100,000, CI: -671 to -133, p=0.0034).
Between genders, the effect was more pronounced for men than for women.
Lag analysis
Further analysis was performed to investigate whether this association lasted in the long-term.
The results displayed in Table 1 show that following a 1% rise in unemployment, associated
cerebrovascular mortality continued to increase, in both men and women, to a peak at 4 years
(coefficient for both genders 208 deaths/100,000, CI: 98.4-317,p=0.0002, Figure 1). The
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increased cerebrovascular mortality remained significantly raised at year five (coefficient for
both genders 175 deaths/100,000, CI: 72.2-278,p=0.0009, Figure 1). The results displayed in
Table 2 show that following a 1% rise in government healthcare expenditure, associated
cerebrovascular mortality continued to fall significantly for two years in males, but for women
the association with cerebrovascular mortality was limited to the year of the rise in government
healthcare expenditure. Overall, across both genders, increases in government healthcare
expenditure were associated with significantly decreased cerebrovascular mortality for two years
after the expenditure change (coefficient -479 deaths/100,000, CI: -948 to -9.67, p=0.0455,
Figure 2).
Robustness checks
In order to adjust for confounding factors, the analysis was re-run with additional economic and
infrastructure controls included. Firstly, to control for economic factors, variables to account for
changes to GDP per capita, inflation and interest rates were introduced. Secondly urbanisation
and nutrition (mean calorie intake) were controlled for. Next, education (percentage of children
with progression to secondary school), and finally out-of-pocket expenses were controlled for.
Results are displayed in Table 3 and show that broadly, across both sexes, the association
between unemployment and increased cerebrovascular mortality remained statistically
significant when each of these variables were controlled for. This was also the case for
government healthcare expenditure; the relationship between reduced spending and increased
cerebrovascular mortality remained statistically significant when each of the economic,
infrastructure, nutritional and educational variables were controlled for, with males, females, and
both genders combined.
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The significant associations described for both unemployment and cerebrovascular mortality,
and government healthcare expenditure and cerebrovascular mortality, were maintained when
analyses were re-run, controlling for changes in crude death rate. This suggests that the
associations seen are specific for cerebrovascular deaths, beyond the increased mortality risk
inherent to the unemployed and those in countries with reduced government healthcare spending.
To ensure that the analysis had not been impacted upon by suboptimal data quality, a further
robustness check was performed whereby the analysis was repeated using only high quality data
(WHO level 1 and 2 surveillance data quality(24)). Again, the findings remained significant for
both the unemployment and government healthcare expenditure analyses, and for both genders.
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DISCUSSION
Principal findings
This study has shown that across 99 countries worldwide between 1981 and 2009, there is an
association between unemployment and cerebrovascular mortality, and government healthcare
expenditure and cerebrovascular mortality. The association between unemployment and
cerebrovascular mortality remained statistically significant for at least five years subsequent to
the 1% unemployment rise, whilst the overall link between government healthcare expenditure
and cerebrovascular mortality remained significant for two years. These relationships were
shown to be independent of GDP per capita changes, inflation, interest rates, urbanisation,
nutrition and education.
Causal mechanisms
Although our analyses only demonstrate associations, there are a number of potential
intermediate mechanisms by which unemployment may result in increased risk of stroke and,
subsequently, a higher cerebrovascular mortality. These mechanisms include, but are not limited
to, traditional cardiovascular risk factors: smoking(25), diet, psychological disturbances(26) –
including depression(27)– hypertension, and poor diet(28) resulting in the metabolic
syndrome(29). Recently, in addition to mean blood pressure, variability in blood pressure has
been highlighted as an independent risk factor for stroke(30). There is consequently biological
plausibility for links between unemployment, periods of stress, variations in blood pressure and
cerebrovascular mortality. Such changes however occur in the long-term, possibly explaining the
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results of the lag analysis, however they do not account for the short-term associations between
unemployment and cerebrovascular mortality.
In the short-term, unemployment and government healthcare expenditure are both likely to
impair individuals’ access to care, specifically in terms of treatment of acute stroke with its
respective sequelae. Consistent with this, higher income has been associated with higher rates of
stroke unit admission, neurology consultations, referrals to secondary prevention clinics, and
physician visits after hospital discharge(31). Congruently, low socioeconomic status has been
associated with a lower chance of receiving optimal acute stroke care(32).
In this study, unemployment had a greater effect on women, whilst government healthcare
expenditure was more relevant to males. The cause of this is likely to be a complex interplay
between biological, social and economic factors. Gender differences have previously been
identified in the context of cerebrovascular mortality. In a US study investigating the association
between employment status and mortality among 7361 middle-aged women, after adjusting for
sociodemographic factors and selected risk factors for mortality, employed women had a lower
risk of mortality than homemakers, including circulatory system-related deaths(33).
Policy implications
Policy implications can be considered at a system level, or at the level of individuals.This work
furthers existing evidence of the association between government healthcare expenditure and
health outcomes, reinforcing the importance of government spending in minimizing
cerebrovascular mortality. This is of particular significance given the recent economic crisis
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where many governments have implemented austerity measures; either due to government
policy, such as in the UK, or under compulsion as a result of accepting financial assistance
packages following problems with bound monetary policy, such as in Greece and Ireland(34).
Austerity measures have in turn been associated with reduced public spending(34). The findings
of this study therefore suggest that such policy interventions may be exacerbating the adverse
health effects of the global economic downturn, specifically with regard to cerebrovascular
mortality, rather than ameliorating them. Outside of economic crises, the findings of this study
have relevance in debates over cost-control in healthcare and times when governments attempt to
reduce healthcare spending in favour of alternative initiatives. The results suggest that caution
must be taken with regard to cost-control and budget restrictions of healthcare; if cost reductions
are not achieved as a result of efficiency improvements, they may entail worse quality care and
in turn greater cerebrovascular mortality.
From the perspective of unemployment, the association between increased unemployment and
worsened cerebrovascular mortality reinforces arguments against austerity measures from a
public health perspective, which have been associated with markedly increased unemployment
rates(10). Further, this work supports policies promoting return-to-work, social welfare and those
that prevent further job losses. This is of particular importance as forecasts predict that in some
countries unemployment may only return to pre-recession levels in 2017(35).
For clinicians, it is important to recognise that macro-level multinational policy can affect
mortality for specific conditions at the individual patient level, affecting day-to-day clinical
practice. Clinicians should be aware that unemployment could be a risk factor for
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cerebrovascular mortality. Furthermore, unemployed patients should receive particular attention
to their traditional cardiovascular risk factors.
Limitations
Our study has several limitations that should be considered: firstly, public health outcomes and
economic trends were examined at a national and international level, without consideration of
local discrepancies; such trends however, are likely of relevance to multinational policy
development by agencies such as the IMF, World Bank, and WHO. Second, information
regarding the distribution of unemployment throughout different social classes could not be
incorporated; this is notable as economic changes have been shown to impact those of lower
socioeconomic status the most. Thirdly, whilst a number of controls were included in robustness
checks, healthcare factors – such as access to primary care – were not incorporated into the
regression models. Additionally, although this study highlights important associations, it does
not demonstrate causality. Potential confounding factors linking socioeconomic indices and
cardiovascular diseases, including stroke, are stress, depression (especially when untreated)(27),
sleep patterns, cigarette smoking(25) and alcohol consumption(36), environmental factors, and
obesity(29). Furthermore, low socioeconomic status and psychological distress have been
demonstrated as synergistic predictors of stroke mortality(26). Sufficiently high-resolutiondata
for these variables were not available for integration into the multivariate statistical models. This
may have resulted in individual-level residual confounding. Our fixed-effects approach, although
removing time-invariant differences between countries, did not control for time-varying
confounding at the country level. Moreover, there are limitations regarding the quality of the
multinational cerebrovascular mortality data acquired from the WHO, as previous studies have
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shown both over- and under-diagnosis of acute stroke as a cause of death in some countries(37).
Finally, with regard to government healthcare spending, it is worth noting that we did not
account for changes in efficiency; indeed, it is feasible that a country spends less on healthcare
but achieves greater outcomes due to the efficiency of its system.
Nevertheless, our study used worldwide data, taken from high-quality, objective, and centralized
databases, avoiding selection and recall bias. The volume of data used allowed for high statistical
powering and multiple robustness checks. By focusing on countries at a global level over a 30year period, this study permitted consideration of macroscopic trends. Notably, our study used a
conservative, fixed-effects multivariate regression analysis model. This model along with the
multiple robustness checks addresses many criticisms levelled at other studies looking at the
relationship between health outcomes and economic changes, including the omission of potential
confounders. By using this panel-data approach, we also controlled for inter-country timeinvariant heterogeneity; something that aggregate, time-series analyses fail to do. Further all data
used are publicly accessible, supporting the reproducibility of our study.
Conclusions
This study has shown that unemployment and public spending on healthcare are both associated
with significant changes in cerebrovascular mortality worldwide. Current policy responses to the
economic crisis, which may increase unemployment and decrease public spending, are of
concern, and may present additional barriers to the management of cerebrovascular disease,
possibly increasing cerebrovascular mortality. At a policy level, initiatives that bolster
employment and maintain public expenditure may decreaselikelihood of cerebrovascular
17
mortality. Clinicians should also consider unemployment as a possible risk factor when assessing
patients in the context of cerebrovascular disease.
18
REFERENCES
1.
World Health Organization. The top 10 causes of death. World Health Organization;
2014 [updated May 2014; Fact sheet N°310; cited 19/09/2014]; Available from:
http://who.int/mediacentre/factsheets/fs310/en/.
2.
Hanchate AD, Schwamm LH, Huang W, Hylek EM. Comparison of ischemic stroke
outcomes and patient and hospital characteristics by race/ethnicity and socioeconomic status.
Stroke2013;44(2):469-76.
3.
Chen R, McKevitt C, Rudd AG, Wolfe CD. Socioeconomic deprivation and survival after
stroke: findings from the prospective South London stroke register of 1995 to 2011. Stroke
2014;45(1):217-23.
4.
Brown AF, Liang LJ, Vassar SD, et al. Neighborhood socioeconomic disadvantage and
mortality after stroke. Neurology 2013;80(6):520-7.
5.
Lindmark A, Glader EL, Asplund K, Norrving B, Eriksson M. Socioeconomic disparities
in stroke case fatality - Observations from Riks-Stroke, the Swedish stroke register. Int J Stroke
2013.
6.
Sergeev AV. Stroke mortality disparities in the population of the Appalachian Mountain
region. Ethn Dis 2013;23(3):286-91.
7.
Jaja BN, Saposnik G, Nisenbaum R, et al. Effect of socioeconomic status on inpatient
mortality and use of postacute care after subarachnoid hemorrhage. Stroke; a journal of cerebral
circulation 2013;44(10):2842-7.
8.
Stuckler D, Basu S. The Body Economic: Why Austerity Kills. First ed. New York: Basic
Books; 2013.
19
9.
Martin A, Lassman D, Whittle L, Catlin A. Recession contributes to slowest annual rate
of increase in health spending in five decades. Health Aff (Millwood) 2011;30(1):11-22.
10.
World Bank. Employment. 2014 [updated 2014; cited 19/09/2014]; Available from:
http://datatopics.worldbank.org/jobs/topic/employment.
11.
Stuckler D, Meissner CM, King LP. Can a bank crisis break your heart? Global Health
2008;4:1.
12.
Stuckler D, Basu S, Suhrcke M, Coutts A, McKee M. The public health effect of
economic crises and alternative policy responses in Europe: an empirical analysis. Lancet
2009;374(9686):315-23.
13.
World Health Organization. WHO Mortality Database. 2012 [updated 2012; cited
19/09/2014]; Available from: http://www.who.int/healthinfo/mortality_data/en/.
14.
Mathers CD, Fat DM, Inoue M, Rao C, Lopez AD. Counting the dead and what they died
from: an assessment of the global status of cause of death data. Bulletin of the World Health
Organization 2005;83(3):171-7.
15.
World Health Organization. International Classification of Diseases (ICD) [cited
19/09/2014]; Available from: http://www.who.int/classifications/icd/en/.
16.
World Health Organisation. Indicator and Measurement Registry (version 1.7.0) [cited
19/09/2014]; Available from: http://www.who.int/gho/indicator_registry/en/.
17.
International Monetary Fund. Historical Public Debt Database [cited 19/09/2014];
Available from: http://www.worldbank.org/qpsd.
18.
World Bank. Umemployment, total [cited 19/09/2014]; Available from:
http://data.worldbank.org/indicator/SL.UEM.TOTL.ZS.
19.
World Bank [cited 19/09/2014]; Available from: http://data.worldbank.org.
20
20.
Cook RD, Weisberg S. Diagnostics for heteroscedasticity in regression. Biometrika
1983;70(1):1-10.
21.
Davidson R, Mackinnon JG. Estimation and inference in econometrics. New York ;
Oxford: Oxford University Press; 1993.
22.
Stuckler D, King L, McKee M. Mass privatisation and the post-communist mortality
crisis: a cross-national analysis. Lancet 2009;373(9661):399-407.
23.
Gerdtham UG, Ruhm CJ. Deaths rise in good economic times: evidence from the OECD.
Econ Hum Biol 2006;4(3):298-316.
24.
World Health Organization. Mortality and burden of disease estimates for WHO Member
States in 2004. Geneva; 2009 [updated 2009; cited 19/09/2014]; Available from:
http://www.who.int/healthinfo/global_burden_disease/estimates_country/en/.
25.
Shinton R, Beevers G. Meta-analysis of relation between cigarette smoking and stroke.
BMJ 1989;298(6676):789-94.
26.
Lazzarino AI, Hamer M, Stamatakis E, Steptoe A. Low socioeconomic status and
psychological distress as synergistic predictors of mortality from stroke and coronary heart
disease. Psychosom Med 2013;75(3):311-6.
27.
Rahman I, Humphreys K, Bennet AM, Ingelsson E, Pedersen NL, Magnusson PK.
Clinical depression, antidepressant use and risk of future cardiovascular disease. Eur J Epidemiol
2013;28(7):589-95.
28.
Mejean C, Droomers M, van der Schouw YT, et al. The contribution of diet and lifestyle
to socioeconomic inequalities in cardiovascular morbidity and mortality. Int J Cardiol
2013;168(6):5190-5.
21
29.
Xu G, Ma M, Liu X, Hankey GJ. Is there a stroke belt in China and why? Stroke; a
journal of cerebral circulation 2013;44(7):1775-83.
30.
Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visit-to-visit
variability, maximum systolic blood pressure, and episodic hypertension. Lancet
2010;375(9718):895-905.
31.
Huang K, Khan N, Kwan A, Fang J, Yun L, Kapral MK. Socioeconomic status and care
after stroke: results from the Registry of the Canadian Stroke Network. Stroke; a journal of
cerebral circulation 2013;44(2):477-82.
32.
Langagergaard V, Palnum KH, Mehnert F, et al. Socioeconomic differences in quality of
care and clinical outcome after stroke: a nationwide population-based study. Stroke; a journal of
cerebral circulation 2011;42(10):2896-902.
33.
Rose KM, Carson AP, Catellier D, et al. Women's employment status and mortality: the
atherosclerosis risk in communities study. J Womens Health (Larchmt) 2004;13(10):1108-18.
34.
Karanikolos M, Mladovsky P, Cylus J, et al. Financial crisis, austerity, and health in
Europe. Lancet 2013;381(9874):1323-31.
35.
Office for Budget Responsibility. Economic and fiscal outlook. 2011 [updated 2011;
cited 19/09/2014]; Available from: http://budgetresponsibility.org.uk/economic-and-fiscaloutlook-march-2011/.
36.
Brenner MH. Relation of economic change to Swedish health and social well-being,
1950-1980. Soc Sci Med 1987;25(2):183-95.
37.
Asplund K, Bonita R, Kuulasmaa K, et al. Multinational comparisons of stroke
epidemiology. Evaluation of case ascertainment in the WHO MONICA Stroke Study. World
22
Health Organization Monitoring Trends and Determinants in Cardiovascular Disease. Stroke
1995;26(3):355-60.
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TABLES
TABLE 1: Multiple regression and lag analysis examining the association between unemployment and cerebrovascular mortality
The association between a 1% annual increase in unemployment and cerebrovascular mortality, controlling for population size, population structure (proportion of population
below 14 years of age, proportion of population above 65 years of age) and controlling for inter-country differences in healthcare infrastructure in addition to political,
cultural and structural differences (by introducing dummy variables for each of the 99 countries).
Number of years after
Cerebrovascular Mortality
Cerebrovascular Mortality
Cerebrovascular Mortality
per 100,000 Population – Males
per 100,000 Population – Females
per 100,000 Population – Total
1% rise in
unemployment
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Year 0
76.8017
0.0017
28.7677
124.8357
110.6915
0.0001
55.2027
166.1804
187.4933
0.0003
86.6132
288.3733
Year 1
73.5142
0.0000
38.8693
108.1590
113.0801
0.0000
65.6415
160.5188
186.5943
0.0000
107.3886
265.8000
Year 2
76.3933
0.0000
42.9474
109.8391
114.9787
0.0000
63.0931
166.8644
191.3720
0.0000
107.9746
274.7694
Year 3
83.2844
0.0001
42.2016
124.3671
119.3565
0.0001
61.6646
177.0484
202.6409
0.0000
105.7971
299.4846
Year 4
89.5167
0.0003
41.5960
137.4374
118.3769
0.0003
54.4908
182.2629
207.8936
0.0002
98.3657
317.4214
Year 5
77.4850
0.0013
30.3998
124.5702
97.5798
0.0012
38.7557
156.4040
175.0648
0.0009
72.2281
277.9015
(year of change in
unemployment)
24
TABLE 2: Multiple regression and lag analysis examining the association between government healthcare expenditure and cerebrovascular mortality
The association between a 1% annual increase in government healthcare expenditure (measured as a percentage of gross domestic product) and cerebrovascular mortality,
controlling for population size, population structure (proportion of population below 14 years of age, proportion of population above 65 years of age) and controlling for
inter-country differences in healthcare infrastructure in addition to political, cultural and structural differences (by introducing dummy variables for each of the 99 countries).
Number of years after
Cerebrovascular Mortality
Cerebrovascular Mortality
Cerebrovascular Mortality
per 100,000 Population – Males
per 100,000 Population – Females
per 100,000 Population – Total
1% rise in public
health spending
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Co-efficient
P value
Lower
Upper
confidence
confidence
interval
interval
Year 0
-466.3814
0.0002
-708.3203
-224.4424
-402.1509
0.0034
-670.8491
-133.4527
-868.5323
0.0005
-1,354.0851
-382.9795
Year 1
-424.4079
0.0009
-673.7293
-175.0865
-276.8732
0.0579
-563.0360
9.2896
-701.2811
0.0055
-1,195.6212
-206.9410
Year 2
-338.0197
0.0014
-544.8691
-131.1704
-141.2928
0.3638
-446.4759
163.8902
-479.3126
0.0455
-948.9538
-9.6714
Year 3
-191.2689
0.0985
-418.2631
35.7254
102.3362
0.6186
-300.9594
505.6317
-88.9327
0.7755
-700.8505
522.9851
Year 4
-47.5893
0.7587
-351.5746
256.3960
348.7724
0.2280
-218.7609
916.3058
301.1831
0.4913
-557.5444
1,159.9107
Year 5
176.1068
0.4212
-253.5085
605.7221
633.9594
0.1011
-124.1933
1,392.1121
810.0662
0.1789
-372.1113
1,992.2438
(year of change in
unemployment)
25
TABLE 3: Robustness checks
Multiple regression analyses were re-run using the controls in the original analysis (population size, proportion of population above 65 years of age, proportion below 15
years of age, and 99 country controls), in addition to those mentioned in the tables below, for males (A), females (B), and both males and females (C). The data shows the
association between a 1% rise in unemployment (above) and a 1% rise in government healthcare expenditure (below) with cerebrovascular mortality, using the mentioned
controls.
A. MALES
Unemployment Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
P value
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
Lower
Upper
confidence
confidence
interval
interval
105
88.2482
0.0014
34.0193
142.4772
104
46.8630
0.0110
10.7440
82.9820
107
47.4382
0.0334
3.7398
91.1367
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
80.8182
0.0009
33.1875
128.4489
Original analysis controls
102
80.2045
0.0021
29.1685
131.2404
Lower
Upper
P value
confidence
confidence
interval
interval
WHO data quality
check
Public Health Spending Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
105
-343.8167
0.0155
-622.0187
-65.6147
104
-559.1992
0.0003
-858.9808
-259.4177
107
-489.3470
0.0083
-852.5544
-126.1395
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
-303.3863
0.0038
-508.4560
-98.3165
Original analysis controls
102
-493.3691
0.0002
-752.8275
-233.9106
WHO data quality
check
26
B. FEMALES
Unemployment Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
P value
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
Lower
Upper
confidence
confidence
interval
interval
105
125.1431
0.0001
62.5004
187.7857
104
75.4905
0.0016
28.6402
122.3408
107
76.7294
0.0034
25.3724
128.0864
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
116.2260
0.0000
61.6872
170.7648
Original analysis controls
102
118.4280
0.0001
59.3504
177.5056
Lower
Upper
P value
confidence
confidence
interval
interval
WHO data quality
check
Public Health Spending Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
105
-278.5776
0.0367
-539.9429
-17.2124
104
-565.4826
0.0007
-890.1044
-240.8608
107
-517.6799
0.0010
-825.9609
-209.3989
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
-191.7352
0.1381
-445.2797
61.8093
Original analysis controls
102
-421.3254
0.0044
-710.7370
-131.9137
WHO data quality
check
27
C. BOTH MALES & FEMALES
Unemployment Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
P value
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
Lower
Upper
confidence
confidence
interval
interval
105
213.3913
0.0003
98.703
328.0791
104
122.3535
0.0021
44.5702
200.1368
107
124.1677
0.0073
33.5950
214.7403
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
197.0442
0.0001
97.5528
296.5355
Original analysis controls
102
198.6325
0.0003
91.4299
305.8351
Lower
Upper
P value
confidence
confidence
interval
interval
WHO data quality
check
Public Health Spending Analysis
Total number of
Robustness check
Controls used in multiple regression
controls in
Coefficient
regression
Economic controls
Original analysis controls and: changes in GDP per capita, inflation,
interest rates
Infrastructure
Original analysis controls and: urbanisation, nutrition (mean calorie
controls
intake)
105
-622.3943
0.0193
-1,143.3612
-101.4275
104
-1,124.6818
0.0002
-1,712.9558
-536.4078
107
-1,007.0269
0.0025
-1,658.9321
-355.1217
Economic and
Original analysis controls and: GDP per capita, inflation, interest rates,
infrastructure controls
urbanisation, nutrition (mean calorie intake)
Crude death rate
Original analysis controls and: crude death rate
103
-495.1215
0.0257
-930.0318
-60.2111
Original analysis controls
102
-914.6944
0.0006
-1,436.7352
-392.6537
WHO data quality
check
28
FIGURE LEGENDS
Additional Cerebrovascular Deaths
per 100,000 Population
220
200
****
***
****
***
****
***
180
160
140
120
100
80
60
40
20
0
ar
Ye
0
r1
a
Ye
r2
a
Ye
r3
a
Ye
ar
Ye
4
r5
a
Ye
Year Since 1% Increase in Unemployment
FIGURE 1: Multiple regression and lag analysis examining the association between unemployment and
cerebrovascular mortality
Results for both genders for change in cerebrovascular mortality (per 100,000 population) for 5 years following
a 1% increase in unemployment (at year 0). ***p<0.001; ****p<0.0001.
29
NS
NS
NS
*
**
Ye
ar
5
Ye
ar
4
Ye
ar
3
Ye
ar
2
Ye
ar
1
***
Ye
ar
0
Additional Cerebrovascular Deaths
per 100,000 Population
900
800
700
600
500
400
300
200
100
0
-100
-200
-300
-400
-500
-600
-700
-800
-900
-1000
Year Since 1% Increase in Public Health Spending
FIGURE 2: Multiple regression and lag analysis examining the association between government
healthcare expenditure and cerebrovascular mortality
Results for both genders for change in cerebrovascular mortality (per 100,000 population) for 5 years following
a 1% increase in public health spending (at year 0). *p<0.05; **p<0.01; ***p<0.001; NS, non-significant.
30