Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. In this study, we estimated the genetic correlations of eight pain phenotypes (defined by the UK Biobank, n = 151,922-226,683) with depressive symptoms, major depressive disorders and neuroticism using the the crosstrait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. We also used the LDSC software to calculate the genetic correlations among pain phenotypes. All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h 2 = 0.31, standard error = 0.072). Many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Our results suggest that pain, neuroticism and depression share partially overlapping genetic risk factors.
Increasing coprevalence of diabetes mellitus (DM) and tuberculosis (TB) in low-income and middle-income countries (LMICs) indicates a rising threat to the decades of progress made against TB and requires global attention. This systematic review provides a summary of type 2 diabetes and tuberculosis coprevalence in various LMICs. We searched PubMed, Ovid Medline, Embase, and PsychINFO databases for studies that provided estimates of TB-DM coprevalence in LMICs published between 1990 and 2016. Studies that were non-English and exclusively conducted in multidrug resistant-tuberculosis or type 1 diabetes and inpatient settings were excluded. We reviewed 84 studies from 31 countries. There were huge diversity of study designs and diagnostic methods used to estimate coprevalence, and this precluded pooling of the results. Most studies (n = 78) were from small, localized settings. The DM prevalence among TB patients in various LMICs varied from 1.8% to 45%, with the majority (n = 44) between 10% and 30%. The TB prevalence among people with DM ranged from 0.1% to 6.0% with most studies (n = 9) reporting prevalences less than 2%. Coprevalence of TB-DM was higher than general population prevalence of either diseases in these countries. This study underscores the need for intervention and more focused research on TB DM bidirectional screening programs in low-income and middle-income countries as well as integrated chronic disease management.
Background: India was one of the countries to institute strict measures for Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control in the early phase. Since, then, the epidemic growth trajectory was slow before registering an explosion of cases due to local cluster transmissions. Methods: We estimated the growth rate and doubling time of SARS-CoV-2 for India and high burden states using crowdsourced time series data. Further, we also estimated the Basic Reproductive Number (R0) and Time-dependent Reproductive number (Rt) using serial intervals from the data. We compared the R0 estimated from five different methods and R0 from SB was further used in the analysis. We modified standard Susceptible-Infectious-Recovered (SIR) models to SIR/Death (SIRD) model to accommodate deaths using R0 with the sequential Bayesian method for simulation in SIRD models. Results: On average, 2.8 individuals were infected by an index case. The mean serial interval was 3.9 days. The R0 estimated from different methods ranged from 1.43 to 1.85. The mean time to recovery was 14 ± 5.3 days. The daily epidemic growth rate of India was 0.16 [95% CI; 0.14, 0.17] with a doubling time of 4.30 days [95% CI; 3.96, 4.70]. From the SIRD model, it can be deduced that the peak of SARS-CoV-2 in India will be around mid-July to early August 2020 with around 12.5% of the population likely to be infected at the peak time. Conclusion: The pattern of spread of SARS-CoV-2 in India is suggestive of community transmission. There is a need to increase funds for infectious disease research and epidemiologic studies. All the current gains may be reversed if air travel and social mixing resume rapidly. For the time being, these must be resumed only in a phased manner and should be back to normal levels only after we are prepared to deal with the disease with efficient tools like vaccines or medicine.
BACKGROUNDIndia was one of the countries to institute strict measures for SARS-CoV-2 control in early phase. Since, then, the epidemic growth trajectory was slow before registering an explosion of cases due to local cluster transmissions. METHODSWe estimated growth rate and doubling time of SARS-CoV-2 for India and high burden states using crowd sourced time series data. Further, we also estimated Basic Reproductive Number (R0) and time dependent reproductive number (Rt) using serial intervals from the data. We compared the R0 estimated from five different methods and R0 from SB was further used in analysis. We modified standard SIR models to SIRD model to accommodate deaths using R0 with the Sequential Bayesian method (SBM) for simulation in SIRD models. RESULTSOn an average, 2.8 individuals were infected by an index case. The mean serial interval was 3.9 days. The R0 estimated from different methods ranged from 1.43 to 1.85. The mean time to recovery was 14 ± 5.3 days. Daily epidemic growth rate of India was 0.16 [95%CI; 0.14, 0.17] with a doubling time of 4.30 days [95%CI; 3.96, 4.70]. From the SIRD model, it can be deduced that the peak of SARS-CoV-2 in India will be around mid-July to early August 2020 with around 12.5% of population likely to be infected at the peak time. CONCLUSIONSAll rights reserved. No reuse allowed without permission.
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