Long-Term Mortality in Patients with Tuberculous Meningitis: A Danish Nationwide Cohort Study

Background: With high short-term mortality and substantial excess morbidity among survivors, tuberculous meningitis (TBM) is the most severe manifestation of extra-pulmonary tuberculosis (TB). The objective of this study was to assess the long-term mortality and causes of death in a TBM patient population compared to the background population. Methods: A nationwide cohort study was conducted enrolling patients notified with TBM in Denmark from 1972–2008 and alive one year after TBM diagnosis. Data was extracted from national registries. From the background population we identified a control cohort of individuals matched on gender and date of birth. Kaplan-Meier survival curves and Cox regression analysis were used to estimate mortality rate ratios (MRR) and analyse causes of death. Findings: A total of 55 TBM patients and 550 individuals from the background population were included in the study. Eighteen patients (32.7%) and 107 population controls (19.5%) died during the observation period. The overall MRR was 1.79 (95%CI: 1.09–2.95) for TBM patients compared to the population control cohort. TBM patients in the age group 31–60 years at time of diagnosis had the highest relative risk of death (MRR 2.68; 95%CI 1.34–5.34). The TBM patients had a higher risk of death due to infectious disease, but not from other causes of death. Conclusion: Adult TBM patients have an almost two-fold increased long-term mortality and the excess mortality stems fro

Revised Lithostratigraphy of the Sonsela Member (Chinle Formation, Upper Triassic) in the Southern Part of Petrified Forest National Park, Arizona

BACKGROUND: Recent revisions to the Sonsela Member of the Chinle Formation in Petrified Forest National Park have presented a three-part lithostratigraphic model based on unconventional correlations of sandstone beds. As a vertebrate faunal transition is recorded within this stratigraphic interval, these correlations, and the purported existence of a depositional hiatus (the Tr-4 unconformity) at about the same level, must be carefully re-examined. METHODOLOGY/PRINCIPAL FINDINGS: Our investigations demonstrate the neglected necessity of walking out contacts and mapping when constructing lithostratigraphic models, and providing UTM coordinates and labeled photographs for all measured sections. We correct correlation errors within the Sonsela Member, demonstrate that there are multiple Flattops One sandstones, all of which are higher than the traditional Sonsela sandstone bed, that the Sonsela sandstone bed and Rainbow Forest Bed are equivalent, that the Rainbow Forest Bed is higher than the sandstones at the base of Blue Mesa and Agate Mesa, that strata formerly assigned to the Jim Camp Wash beds occur at two stratigraphic levels, and that there are multiple persistent silcrete horizons within the Sonsela Member. CONCLUSIONS/SIGNIFICANCE: We present a revised five-part model for the Sonsela Member. The units from lowest to highest are: the Camp Butte beds, Lot's Wife beds, Jasper Forest bed (the Sonsela sandstone)/Rainbow Forest Bed, Jim Camp Wash beds, and Martha's Butte beds (including the Flattops One sandstones). Although there are numerous degradational/aggradational cycles within the Chinle Formation, a single unconformable horizon within or at the base of the Sonsela Member that can be traced across the entire western United States (the "Tr-4 unconformity") probably does not exist. The shift from relatively humid and poorly-drained to arid and well-drained climatic conditions began during deposition of the Sonsela Member (low in the Jim Camp Wash beds), well after the Carnian-Norian transition

Barriers and progress in the treatment of low back pain

Low back pain is a common and costly condition and for most people is likely to be a recurrent problem throughout their lifetime. The management of patients with low back pain has been positively influenced by the rise in high quality clinical trials and systematic reviews in recent decades, and this body of evidence, synthesized in many clinical practice guidelines, has improved our knowledge about which treatments for low back pain are useful and which are not. For the largest group of patients, those with non-specific low back pain for whom a clear diagnosis cannot be given, the reality is that the treatments we have to offer tend to produce small effects, often only in the short term and none appear to effectively change long-term prognosis. This commentary summarizes the array of treatments currently available, notes the results of recent trials and guidelines and considers alternative approaches that may prove more valuable in achieving better patient outcomes in the future

Acute graft versus host disease

Acute graft-versus-host disease (GVHD) occurs after allogeneic hematopoietic stem cell transplant and is a reaction of donor immune cells against host tissues. Activated donor T cells damage host epithelial cells after an inflammatory cascade that begins with the preparative regimen. About 35%–50% of hematopoietic stem cell transplant (HSCT) recipients will develop acute GVHD. The exact risk is dependent on the stem cell source, age of the patient, conditioning, and GVHD prophylaxis used. Given the number of transplants performed, we can expect about 5500 patients/year to develop acute GVHD. Patients can have involvement of three organs: skin (rash/dermatitis), liver (hepatitis/jaundice), and gastrointestinal tract (abdominal pain/diarrhea). One or more organs may be involved. GVHD is a clinical diagnosis that may be supported with appropriate biopsies. The reason to pursue a tissue biopsy is to help differentiate from other diagnoses which may mimic GVHD, such as viral infection (hepatitis, colitis) or drug reaction (causing skin rash). Acute GVHD is staged and graded (grade 0-IV) by the number and extent of organ involvement. Patients with grade III/IV acute GVHD tend to have a poor outcome. Generally the patient is treated by optimizing their immunosuppression and adding methylprednisolone. About 50% of patients will have a solid response to methylprednisolone. If patients progress after 3 days or are not improved after 7 days, they will get salvage (second-line) immunosuppressive therapy for which there is currently no standard-of-care. Well-organized clinical trials are imperative to better define second-line therapies for this disease. Additional management issues are attention to wound infections in skin GVHD and fluid/nutrition management in gastrointestinal GVHD. About 50% of patients with acute GVHD will eventually have manifestations of chronic GVHD

Phylogeny in Aid of the Present and Novel Microbial Lineages: Diversity in Bacillus

Bacillus represents microbes of high economic, medical and biodefense importance. Bacillus strain identification based on 16S rRNA sequence analyses is invariably limited to species level. Secondly, certain discrepancies exist in the segregation of Bacillus subtilis strains. In the RDP/NCBI databases, out of a total of 2611 individual 16S rDNA sequences belonging to the 175 different species of the genus Bacillus, only 1586 have been identified up to species level. 16S rRNA sequences of Bacillus anthracis (153 strains), B. cereus (211 strains), B. thuringiensis (108 strains), B. subtilis (271 strains), B. licheniformis (131 strains), B. pumilus (83 strains), B. megaterium (47 strains), B. sphaericus (42 strains), B. clausii (39 strains) and B. halodurans (36 strains) were considered for generating species-specific framework and probes as tools for their rapid identification. Phylogenetic segregation of 1121, 16S rDNA sequences of 10 different Bacillus species in to 89 clusters enabled us to develop a phylogenetic frame work of 34 representative sequences. Using this phylogenetic framework, 305 out of 1025, 16S rDNA sequences presently classified as Bacillus sp. could be identified up to species level. This identification was supported by 20 to 30 nucleotides long signature sequences and in silico restriction enzyme analysis specific to the 10 Bacillus species. This integrated approach resulted in identifying around 30% of Bacillus sp. up to species level and revealed that B. subtilis strains can be segregated into two phylogenetically distinct groups, such that one of them may be renamed

Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool

© 2014 Vivas-Consuelo et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background Pharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending. Methods Descriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts. Results The risk adjustment tool proved to have an acceptable level of prediction (R2 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established. Conclusion The model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037). The authors would like to thank members (Juan Bru and Inma Saurf) of the Pharmacoeconomics Office of the Valencian Health Department. The opinions expressed in this paper are those of the authors and do not necessary reflect those of the afore-named. Any errors are the authors' responsibility. We would also like to thank John Wright for the English editing.Vivas Consuelo, DJJ.; Usó Talamantes, R.; Guadalajara Olmeda, MN.; Trillo Mata, JL.; Sancho Mestre, C.; Buigues Pastor, L. (2014). Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool. BMC Health Services Research. 14:462-472. https://doi.org/10.1186/1472-6963-14-462S46247214Hux JE, Naylor CD: Drug prices and third party payment: do they influence medication selection?. Pharmacoecon. 1994, 5 (4): 343-350. 10.2165/00019053-199405040-00008.Sicras-Mainar A, Serrat-Tarres J, Navarro-Artieda R, Llopart-Lopez J: [Prospects of adjusted clinical groups (ACG’s) in capitated payment risk adjustment]. Rev Esp Salud Publica. 2006, 80 (1): 55-65. 10.1590/S1135-57272006000100006.Mossey JM, Roos LL: Using insurance claims to measure health-status - the illness scale. J Chronic Dis. 1987, 40: S41-S50.Newhouse JP, Manning WG, Keeler EB, Sloss EM: Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ Rev. 1989, 10 (3): 41-54.Ash A, Porell F, Gruenberg L, Sawitz E, Beiser A: Adjusting Medicare capitation payments using prior hospitalization data. Health Care Financ Rev. 1989, 10 (4): 17-29.Ellis RP, Pope GC, Iezzoni L, Ayanian JZ, Bates DW, Burstin H, Ash AS: Diagnosis-based risk adjustment for Medicare capitation payments. Health Care Financ Rev. 1996, 17 (3): 101-128.Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J: Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004, 25 (4): 119-141.Starfield B, Weiner J, Mumford L, Steinwachs D: Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res. 1991, 26 (1): 53-74.Weiner JP, Starfield BH, Steinwachs DM, Mumford LM: Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991, 29 (5): 452-472. 10.1097/00005650-199105000-00006.Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC: Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004, 42 (1): 81-90. 10.1097/01.mlr.0000102367.93252.70.Berlinguet M, Preyra C, Dean S: Comparing the Value of Three Main Diagnostic-Based Risk-Adjustment Systems (DBRAS). 2005, Ottawa, Ontario: Edited by Foundation CHSRVon Korff M, Wagner EH, Saunders K: A chronic disease score from automated pharmacy data. J Clin Epidemiol. 1992, 45 (2): 197-203. 10.1016/0895-4356(92)90016-G.Malone DC, Billups SJ, Valuck RJ, Carter BL: Development of a chronic disease indicator score using a Veterans Affairs Medical Center medication database. IMPROVE Investigators. J Clin Epidemiol. 1999, 52 (6): 551-557. 10.1016/S0895-4356(99)00029-3.Clark DO, Von Korff M, Saunders K, Baluch WM, Simon GE: A chronic disease score with empirically derived weights. Med Care. 1995, 33 (8): 783-795. 10.1097/00005650-199508000-00004.Lamers LM: Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs. Med Care. 1999, 37 (8): 824-830. 10.1097/00005650-199908000-00012.Lamers LM: Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?. Inquiry. 2001, 38 (4): 423-431.Lamers LM, van Vliet RC: The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation. Health Policy. 2004, 68 (1): 113-121. 10.1016/j.healthpol.2003.09.001.Lamers LM, Vliet RC: Health-based risk adjustment Improving the pharmacy-based cost group model to reduce gaming possibilities. Eur J Health Econ. 2003, 4 (2): 107-114. 10.1007/s10198-002-0159-9.Johnson RE, Hornbrook MC, Nichols GA: Replicating the chronic disease score (CDS) from automated pharmacy data. J Clin Epidemiol. 1994, 47 (10): 1191-1199. 10.1016/0895-4356(94)90106-6.Zhao Y, Ellis RP, Ash AS, Calabrese D, Ayanian JZ, Slaughter JP, Weyuker L, Bowen B: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data. Health Serv Res. 2001, 36 (6 Pt 2): 180-193.Stam PJ, van Vliet RC, van de Ven WP: Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models. Med Care. 2010, 48 (5): 448-457. 10.1097/MLR.0b013e3181d559b4.Hanley GE, Morgan S, Reid RJ: Explaining prescription drug use and expenditures using the adjusted clinical groups case-mix system in the population of British Columbia, Canada. Can Med Care. 2010, 48 (5): 402-408. 10.1097/MLR.0b013e3181ca3d5d.Aguado A, Guino E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care. BMC Health Serv Res. 2008, 8 (4): 11.Garcia-Goni M, Ibern P: Predictability of drug expenditures: An application using morbidity data. Health Econ. 2008, 17 (1): 119-126. 10.1002/hec.1238.Garcia-Goni M, Ibern P, Inoriza JM: Hybrid risk adjustment for pharmaceutical benefits. Eur J Health Econ. 2009, 10 (3): 299-308. 10.1007/s10198-008-0133-2.Vivas-Consuelo D, Uso-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martinez I, Buigues-Pastor L: Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 2014, 116 (2–3): 188-195.Robst J, Levy JM, Ingber MJ: Diagnosis-based risk adjustment for medicare prescription drug plan payments. Health Care Financ Rev. 2007, 28 (4): 15-30.Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L: Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Med Care. 2005, 43 (1): 34-43.Buchner F, Goepffarth D, Wasem J: The new risk adjustment formula in Germany: implementation and first experiences. Health Policy. 2013, 109 (3): 253-262. 10.1016/j.healthpol.2012.12.001.Inoriza JM, Coderch J, Carreras M, Vall-Llosera L, Garcia-Goni M, Lisbona JM, Ibern P: [Measurement of morbidity attended in an integrated health care organization]. Gac Sanit. 2009, 23 (1): 29-37. 10.1016/j.gaceta.2008.02.003.Orueta JF, Mateos Del Pino M, Barrio Beraza I, Nuno Solinis R, Cuadrado Zubizarreta M, Sola Sarabia C: [Stratification of the population in the Basque Country: results in the first year of implementation.]. Aten Primaria. 2012, 45 (1): 54-60.Sicras-Mainar A, Navarro-Artieda R: [Validating the Adjusted Clinical Groups [ACG] case-mix system in a Spanish population setting: a multicenter study]. Gac Sanit. 2009, 23 (3): 228-231. 10.1016/j.gaceta.2008.04.005.Omar RZ, O’Sullivan C, Petersen I, Islam A, Majeed A: A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study. BMJ. 2008, 337: a238-10.1136/bmj.a238.Caballer-Tarazona M, Buigues-Pastor L, Saurí- Ferrer I, Uso-Talamantes R, Trillo-Mata JL: [A standardized amount indicator by equivalent patient to control outpatient pharmaceutical expenditure, Spain]. Rev Esp Salud Publica. 2011, 86: 371-380.De la Poza-Plaza E, Barrachina I, Trillo-Mata J, Uso-Talamantes R: Sistema de Prescripción y dispensación electrónica en la Agencia Valenciana de Salud. El Prof de la Inf. 2011, 20: 9.Vivas D, Guadalajara N, Barrachina I, Trillo JL, Uso R, De-la-Poza E: Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. Health Policy. 2011, 103 (1): 9-15. 10.1016/j.healthpol.2011.08.014.Buntin MB, Zaslavsky AM: Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004, 23 (3): 525-542. 10.1016/j.jhealeco.2003.10.005.Duan N: Smearing estimate - a nonparametric retransformation method. J Am Stat Assoc. 1983, 78 (383): 605-610. 10.1080/01621459.1983.10478017.Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A: Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res. 2010, 10: 22-10.1186/1472-6963-10-22