B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, March 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, April 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Epidemic: Responding to America's Prescription Drug Abuse Crisis, 2011

Prescription drug abuse is the Nation’s fastest-growing drug problem. While there has been a marked decrease in the use of some illegal drugs like cocaine, data from the National Survey on Drug Use and Health (NSDUH) show that nearly one-third of people aged 12 and over who used drugs for the first time in 2009 began by using a prescription drug non-medically.1 The same survey found that over 70 percent of people who abused prescription pain relievers got them from friends or relatives, while approximately 5 percent got them from a drug dealer or from the Internet.2 Additionally, the latest Monitoring the Future study—the Nation’s largest survey of drug use among young people—showed that prescription drugs are the second most-abused category of drugs after marijuana.3 In our military, illicit drug use increased from 5 percent to 12 percent among active duty service members over a three-year period from 2005 to 2008, primarily attributed to prescription drug abuse.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, May 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

ABSTRACT In recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. However, these information sources are rarely used together. Therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, GIS and technical knowledge has been applied and evaluated. Two areas of study, in the State of São Paulo, Brazil, totaling approximately 28.000 ha were used for this work. First, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. Subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). Finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. The proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, June 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, July 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, August 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Iron deficiency in infancy: is an immigrant more at risk?

QUESTIONS UNDER STUDY: Iron deficiency with or without anaemia is the most common deficiency in the world. Its prevalence is higher in developing countries and in low socioeconomic populations. We aimed at determining and comparing the prevalence of iron deficiency in an immigrant and non-immigrant population. METHODS: Every child scheduled for a routine check-up at 12 months of age was allowed to participate in the study. Haemoglobin, ferritin, anthropometric data, familial and nutritional status were measured. RESULTS: 586 infants were eligible and 463 were included in the study as they had assessment data at 12 months. Children were divided into two groups: immigrants' children and non-immigrants' children. The global prevalence of iron deficiency was 5.7% at 12 months. A significant difference for iron deficiency was noticed between the groups at 12 months (p = 0.01). Among risk factors, immigration (odds ratio 2.91; 95% CI 1.05-8.04) and unemployment (odds ratio 6.08; 95% CI 1.18-31.30) had the higher odds in the multivariable analysis. CONCLUSION: The prevalence of iron deficiency in the immigrant population is higher than in non-immigrants. Immigration and the category of employment are risk factors for iron deficiency, as starting baby cereals before 9 months is a protective factor. Good socioeconomic conditions in Switzerland, the quality of food for pregnant women and young infants may be the explanation. A study up to five years of age is necessary before drawing general conclusions on infancy.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, September 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Prenatal detection of congenital renal malformations by fetal ultrasonographic examination: an analysis of 709,030 births in 12 European countries.

The study was performed to evaluate the prevalence of prenatal ultrasound diagnoses for renal anomalies in 20 registries of 12 European countries, and to compare the different prenatal scanning policies. Standardized data were acquired from 709,030 livebirths, stillbirths, and induced abortions during the study period of 2.5 years and transmitted for central analysis. At least one renal malformation was diagnosed in 1130 infants and fetuses. Prenatal diagnosis (PD) was given in 81.8% of all cases, 29% of these pregnancies were terminated. The highest detection rate was reported for unilateral multicystic dysplastic kidneys with 97% (102/105). An early diagnosis was documented for exstrophy of bladder at a mean gestational age of 18.5 weeks. Dilatations of the upper urinary tract were seen late in pregnancy at 28.3 weeks. Terminations of pregnancies (TOP) were performed in 67% (58/86) of the detected bilateral renal agenesis/dysgenesis, but only 4% of the unilateral multicystic dysplastic renal malformations (4/102). In about 1/3 of the cases, renal malformations are within the category of associated malformations, which include multiple non-syndromal malformations, chromosomal aberrations, and non-chromosomal syndromes. Renal malformations were detected in 2/3 of the associated category by the first prenatal ultrasound scan. Detection rates vary in the different countries of the European community due to diverse policies, ethical, and religious background. Countries with no routine ultrasound show the lowest rates in detection, and termination of pregnancy. Prenatally detected renal malformations should result in a careful examination for further anomalies. Prenatal ultrasound fulfills the needs of screening examinations and is a good tool in detecting lethal and severe renal malformations.

The Political Economy of Active Labor-Market Policy

Active labor-market policies (ALMPs) have developed significantly over the past two decades across Organization for Economic Cooperation and Development (OECD) countries, with substantial cross-national differences in terms of both extent and overall orientation. The objective of this article is to account for cross-national variation in this policy field. It starts by reviewing existing scholarship concerning political, institutional, and ideational determinants of ALMPs. It then argues that ALMP is too broad a category to be used without further specification, and it develops a typology of four different types of ALMPs: incentive reinforcement, employment assistance, occupation, and human capital investment. These are discussed and examined through ALMP expenditure profiles in selected countries. The article uses this typology to analyze ALMP trajectories in six Western European countries and shows that the role of this instrument changes dramatically over time. It concludes that there is little regularity in the political determinants of ALMPs. In contrast, it finds strong institutional and ideational effects, nested in the interaction between the changing economic context and existing labor-market policies.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, October 2012

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Low predictive value of elevated blood pressure during childhood and adolescence

Background: Screening of elevated blood pressure (BP) in children has been advocated to early identify hypertension. However, identification of children with sustained elevated BP is challenging due to the high BP variability. The value of an elevated BP measure during childhood and adolescence for the prediction of future elevated BP is not well described. Objectives: We assessed the positive (PPV) and negative (NPV) predictive value of high BP for sustained elevated BP in cohorts of children of the Seychelles, a rapidly developing island state in the African region. Methods: Serial school-based surveys of weight, height, and BP were conducted yearly between 1998-2006 among all students of the country in four school grades (kindergarten [G0, mean age (SD): 5.5 (0.4) yr], G4 [9.2 (0.4) yr], G7 [12.5 (0.4) yr] and G10 (15.6 (0.5) yr]. We constituted three cohorts of children examined twice at 3-4 years interval: 4,557 children examined at G0 and G4, 6,198 at G4 and G7, and 6,094 at G7 and G10. The same automated BP measurement devices were used throughout the study. BP was measured twice at each exam and averaged. Obesity and elevated BP were defined using the CDC (BMI_95th sex-, and age-specific percentile) and the NHBPEP criteria (BP_95th sex-, age-, and height specific percentile), respectively. Results: Prevalence of obesity was 6.1% at G0, 7.1% at G4, 7.5% at G7, and 6.5% at G10. Prevalence of elevated BP was 10.2% at G0, 9.9% at G4, 7.1% at G7, and 8.7% at G10. Among children with elevated BP at initial exam, the PPV of keeping elevated BP was low but increased with age: 13% between G0 and G4, 19% between G4 and G7, and 27% between G7 and G10. Among obese children with elevated BP, the PPV was higher: 33%, 35% and 39% respectively. Overall, the probability for children with normal BP to remain in that category 3-4 years later (NPV) was 92%, 95%, and 93%, respectively. By comparison, the PPV for children initially obese to remain obese was much higher at 71%, 71%, and 62% (G7-G10), respectively. The NPV (i.e. the probability of remaining at normal weight) was 94%, 96%, and 98%, respectively. Conclusion: During childhood and adolescence, having an elevated BP at one occasion is a weak predictor of sustained elevated BP 3-4 years later. In obese children, it is a better predictor.

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.