Mechanisms that may underlie a potential causal link between unemployment and HIV mortality. 

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... findings, we conducted a series of further statistical analyses on the associations of unemployment and PSEH on HIV mortality in both sexes, taking into consideration several additional control variables. First, we controlled for GDP per capita, inflation and national debt (as a percentage GDP). These markers of national economic well–being are commonly used as indicators for the standard of living and also influence national health care budgets. Second, we controlled for urbanisation, calorific intake and access to clean water. Our third robustness check combined the controls from the previous two. Fourth, we controlled for out of pocket health care expenses. Fifth, private health care expenditure (as a percentage GDP) was controlled for. Sixth, we controlled for changes in crude death rate; this accounted for mortality risk inher- ent to the unemployed and in countries with reduced government health care spending, allowing us to determine HIV–specific trends. Seventh, we re–ran the original mul- tivariate regressions using data classified as either Level 1 or Level 2 in quality by the WHO. Finally, we reran the PSEH analysis with changes in PSEH measured in purchasing power parity (PPP) per capita rather than GDP . The association between both unemployment and PSEH and HIV mortality remained statistically significant ( P < 0.05) throughout all of our robustness checks ( Table 2 ). Stata SE version 12 was used for the analysis (Stata Corpo- ration, Texas, USA). The results of our regression analyses evaluating the effects of unemployment and PSEH on HIV mortality per 100 000, 1981–2009, controlling for inter–country differences in infrastructure and demographics, are shown in Table 3 . A 1% rise in unemployment was found to be associated with a statistically significant immediate rise in HIV mortality in both males (coefficient 0.1861, 95% CI: 0.0977 to 0.2744, P < 0.0001) and females (coefficient 0.0383, 95% CI: 0.0108 to 0.0657, P = 0.0064). As of 2012, the combined populations of the 74 countries in our analysis was in excess of 2.19 billion individuals. Lag analysis showed that unemployment rises were associated with significantly increased HIV mortality for several years following the initial change ( Table 3 ). In males, there is a significant association for 3 years after the rise in unemployment. In year 1, coefficient 0.1523, 95% CI: 0.0636 to 0.2411, P = 0.0008. In year 2, coefficient 0.1436, 95% CI: 0.0603 to 0.2270, P = 0.0008. And in year 3, coefficient 0.0964, 95% CI: 0.231 to 0.1697, P = 0.0100. After this interval, the association becomes non–significant. In females, the association remains statistically significant for at least 5 years. In year 1, coefficient 0.0345, 95% CI: 0.0082 to 0.0607, P = 0.0101. In year 2, coefficient 0.0446, 95% CI: 0.0190 to 0.0702, P = 0.0007. In year 3, coefficient 0.0395, 95% CI: 0.0141 to 0.0649, P = 0.0123. In year 4, coefficient 0.0352, 95% CI: 0.0077 to 0.0628, P = 0.0123. And in year 5, coefficient 0.0377, 95% CI: 0.0045 to 0.0709, P = 0.0260. The combined population of the 75 countries included in our PSEH analysis exceeded 2.22 billion individuals in 2012. A 1% rise in PSEH was found to be associated with a significant reduction in HIV mortality. Within the first year following a 1% increase in PSEH, the ASDR of HIV changed by a coefficient of –0.5015, 95% CI: –0.7432 to –0.2598, P = 0.0001 in males, and by a coefficient of –0.1562, 95% CI: –0.2404 to –0.0720, P = 0.0003 in females. Lag analysis of the PSEH data showed that these associations with HIV mortality persisted for at least 5 years in males. For year 1, coefficient –0.5398, 95% CI: –0.8537 to –0.2258), P = 0.0008. In year 2, coefficient –0.4704, 95% CI: 0.7518 to –0.1890, P = 0.0011. In year 3, coefficient –0.5063, 95% CI: –0.7916 to –0.2210, P = 0.0005. In year 4, coefficient –0.4674, 95% CI: –0.7705 to –0.1642, P = 0.0026. And in year 5, coefficient –0.3511, 95% CI: –0.6507 to –0.0514, P = 0.0218. In females, the statistically significant association persisted for 4 years following the change in PSEH. For year 1, coefficient –0.2105, 95% CI: –0.3460 to –0.0749, P = 0.0024. In year 2, coefficient –0.1623, 95% CI: 0.2853 to –0.0393, P = 0.0098. In year 3, coefficient –0.1881, 95% CI: –0.3080 to –0.0682, P = 0.0022. In year 4, coefficient –0.1599, 95% CI: –0.2906 to –0.0292, P = 0.0165. This study demonstrates that both increased unemployment and decreased PSEH are associated with increased HIV mortality on a global scale. Changes in these two pa- rameters have an immediate association with changes HIV mortality which continues into the medium–term. The sig- nificance of these findings persisted even after consideration of a variety of potential confounders, including demographic, economic, infrastructure, health care, and data quality related factors. We propose a number of mechanisms that may underlie a potential causal link between unemployment and HIV mortality ( Figure 1 ). First, unemployment may contribute towards the reduced socioeconomic status of HIV–infected individuals. A number of studies have previously shown that low socioeconomic status is associated with an increased risk of HIV mortality [36-39]. Some have conclud- ed that this the result of reduced health care access which in turn results in delayed diagnosis and treatment [36]. Others suggest that an association remains even after consideration of such factors and instead propose that low socioeconomic status acts as an independent risk factor for HIV mortality [38]. Unemployment contributes towards the perceived barriers to health care access [40], and there is reduced utilisation of health care services by the unemployed compared to their employed counterparts [41]. Whether employment status impacts upon HIV mortality through delayed health care access [39] or is an independent risk factor is current- ly unclear [25]. Our study does, however, confirm this association across a global data set. The influence of unemployment on impaired mental well- being [14, 16] and increased suicidal tendencies has been well described [11, 18, 19]. The psychological sequelae associated with unemployment may also contribute towards increased HIV mortality. Regarding PSEH, mechanisms are likely to focus on the availability of health care resources, which may be reduced during times of decreased PSEH. The era of highly active antiretroviral therapy (HAART) has seen vast improvements in HIV survival [42]. However, despite a gradual reduction in price, HAART remains an expensive therapeutic intervention. Importantly, better treatment outcomes of HAART are associated with the provision of free medica- tion [43, 44]. It has also been shown that ineffective pro- phylaxis and treatment of co–morbidities, such as tuber- culosis or opportunistic infections, can also contribute to higher HIV–mortality in low–income countries [44]. As a result both HIV–specific and general PSEH can have a di- rect impact upon HIV mortality. It is likely that different mechanisms predominate in high–income and low–income settings. In high–income settings, the state tends to contribute towards the great majority to health care provision via PSEH, during times of recession there is also reduced long–term growth in private health insurance and out–of–pocket expenditure [7]. In lower–income countries, the contribution of the state is comparatively small and in the context of minimal private insurance cover, health care is funded primarily by out–of–pocket expenses [45]. As a result, economic crises can be particularly detrimental to health care access in such countries. The introduction of bias was minimised from this study by only using data from high–quality, objective, centralised databases. Sufficient data was collected to allow us to capture multinational associative trends. We recognise, however, that there are potential limitations to our study. Our evaluation of annual national data would have limited our ability to capture variations at the subnational level or within intra–year timeframes. HIV mortality served as the endpoint of our study; as a result we will have overlooked the influence of unemployment and PSEH on other health measures. We were unable to stratify our study by socioeconomic class – a factor which is known to have a significant influence on health care outcomes [36-39]. While we show a statistical association between unemployment and PSEH with HIV mortality, a causal link cannot be established. While we did intend for our study to have a truly global scope, a number of countries were omitted due to inadequate data for HIV mortality, unemployment and PSEH. In particu- lar, only two countries in sub–Saharan Africa (Mauritius and South Africa) were included in our analysis despite the disproportionate burden of HIV in this region of the world. We also recognise that unemployment may not serve as an accurate barometer of individual financial well–being in less–developed countries. The poorest sub–strata within these populations may en- gage in work within the informal sector or in small–scale agriculture on subsistence farms. In such settings, government census data on employment status may be less mean- ingful. Our study is a retrospective, observational ecological study and so lacks the reliability of a prospective, experi- mental study. It may be subject to the influence of unknown or inadequately controlled confounders and cannot give strong evidence for causal attribution. Additional considerations, such as, indicators of political changes, occurrence of conflict or war, educational levels, and others, may per- mit any underlying mechanisms to be better determined. Further statistical techniques, such as interaction analyses could also provide insight into specific causal mechanisms. However, we believe this to be outside the immediate scope of this study which aimed to determine whether associations existed between ...