BackgroundAn economic crisis can widen health inequalities between individuals. The aim of this paper is to explore differences in the effect of socioeconomic characteristics on Spaniards’ self-assessed health status, depending on the Spanish economic situation.MethodsData from the 2006–2007 and 2011–2012 National Health Surveys were used and binary logit and probit models were estimated to approximate the effects of socioeconomic characteristics on the likelihood to report good health.ResultsThe difference between high and low education levels leads to differences in the likelihood to report good health of 16.00–16.25 and 18.15–18.22 percentage points in 2006–07 and 2011–12, respectively. In these two periods, the difference between employees and unemployed is 5.24–5.40 and 4.60–4.90 percentage points, respectively. Additionally, the difference between people who live in households with better socioeconomic conditions and those who are in worse situation reaches 5.37–5.46 and 3.63–3.74 percentage points for the same periods, respectively.ConclusionsThe magnitude of the contribution of socioeconomic characteristics to health inequalities changes with the economic cycle; but this effect is different depending on the socioeconomic characteristics indicator that is being measured. In recessive periods, health inequalities due to education level increase, but those linked to individual professional status and household living conditions are attenuated. When the joint effects of individuals’ characteristics are considered, the economic crisis brings about a slight increase in the inequalities in the probability of reporting good health between the two extreme profiles of individuals. The design of public policies aimed at preventing any worsening of health inequalities during recession periods should take into account these differential effects of socioeconomic characteristics indicators on health inequalities.

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Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups—healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.

Highlights Mycobacterium genavense infection, a non-tuberculous mycobacteria, should be considered in immunosuppressed patients. Disseminated infection by Mycobacterium genavense is a clinical and microbiological diagnostic challenge. There are no standardized treatment guidelines for Mycobacterium genavense, but schemes with clarithromycin are favoured.