Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-13 (of 13 Records) |
Query Trace: Monti M[original query] |
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Temporal variation in indoor radon concentrations using environmental public health tracking data
Manono Fotso Kamgang SL , Monti MM , Salame-Alfie A . Health Phys 2023 124 (4) 342-347 Indoor radon is the second leading cause of lung cancer in the United States (US) after smoking and the number one for lung cancer in non-smokers. Understanding how indoor radon varies during the year reveals the best time to test to avoid underestimating exposure. This study looks at the temporal variation in 13 years of radon concentrations in buildings located in 46 US states and the District of Columbia (DC). In the dataset, radon concentration varies from 3.7 Bq m-3 (Becquerels per cubic meter) to 52,958.1 Bq m-3, with an overall mean of 181.4 Bq m-3. About 35.4% of tests have a radon concentration level equal to or greater than the US Environmental Protection Agency (US EPA) action level 4.0 pCi L-1 (148 Bq m-3).3 Temporal variation in radon concentrations was assessed using the overall monthly mean radon concentration. The highest concentrations were found in January (203.8 Bq m-3) and the lowest in July (129.5 Bq m-3). Higher monthly mean indoor radon concentrations were found in January, February, and October, and lower in July, August, and June. This result is consistent with findings from other studies and suggests continuing to encourage radon testing throughout the year with an emphasis on testing during the colder months. |
Spread of Sporothrix spp. through respiratory droplets from infected cats: a potential route of transmission
de Andrade Galliano Daros Bastos F , Raimundo Cognialli RC , de Farias MR , Dos Santos Monti F , Wu K , Queiroz-Telles F . Med Mycol 2022 60 (11) Cat-transmitted sporotrichosis is a zoonosis in geographic expansion from Brazil to other Latin American countries, and considered a public health problem. Data suggest that transmission can occur through the sneeze of an infected cat. The One Health approach is necessary to control the disease. | eng |
Environmental Public Health Tracking, an untapped resource for occupational health
Namulanda G , Monti M , Werner A , Nogueira I , Solomon G , English P , Karlsson N , Cosser A , Bush K , Mitchell C . J Occup Environ Hyg 2022 19 1-9 The cornerstone of the Centers for Disease Control and Prevention’s (CDC) Environmental Public Health Tracking Program (Tracking Program) is the Environmental Public Health Tracking Network (Tracking Network)—a web-based system with components at the local, state, and national levels (Qualters et al. Citation2015). The Tracking Network brings together standardized data on environmental hazards, exposures to these hazards, potentially related health effects, and other data such as socioeconomic and risk factors (CDC Citation2021). The Tracking Program uses these data to perform environmental public health surveillance activities, such as identifying and assessing the distribution of hazards in the environment and the health effects resulting from exposure to these hazards, to provide information that can be used to improve the public’s health (Qualters et al. Citation2015; Eatman and Strosnider Citation2017). The CDC’s National Institute for Occupational Safety and Health (NIOSH) surveillance programs perform similar activities but with workers as their target population, and with the goal to improve worker safety and health (Thomsen et al. Citation2007; NIOSH Citation2022a). |
Co-occurrence of metal contaminants in United States public water systems in 2013-2015
Thompson AK , Monti MM , Gribble MO . Int J Environ Res Public Health 2021 18 (15) The United States Environmental Protection Agency monitors contaminants in drinking water and consolidates these results in the National Contaminant Occurrence Database. Our objective was to assess the co-occurrence of metal contaminants (total chromium, hexavalent chromium, molybdenum, vanadium, cobalt, and strontium) over the years 2013-2015. We used multilevel Tobit regression models with state and water system-level random intercepts to predict the geometric mean of each contaminant occurring in each public water system, and estimated the pairwise correlations of predicted water system-specific geometric means across contaminants. We found that the geometric means of vanadium and total chromium were positively correlated both in large public water systems (r = 0.45, p < 0.01) and in small public water systems (r = 0.47, p < 0.01). Further research may address the cumulative human health impacts of ingesting more than one contaminant in drinking water. Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
Improving Delayed Antibiotic Prescribing for Acute Otitis Media
Frost HM , Monti JD , Andersen LM , Norlin C , Bizune DJ , Fleming-Dutra KE , Czaja CA . Pediatrics 2021 147 (6) OBJECTIVES: Acute otitis media (AOM) is the most-common indication for antibiotics in children. Delayed antibiotic prescribing for AOM can significantly reduce unnecessary antibiotic use and is recommended by the American Academy of Pediatrics for select children. We sought to improve delayed prescribing for AOM across 8 outpatient pediatric practices in Colorado. METHODS: Through a collaborative initiative with American Academy of Pediatrics and the Centers for Disease Control and Prevention, we implemented an economical 6-month antimicrobial stewardship intervention that included education, audit and feedback, online resources, and content expertise. Practices used The Model for Improvement and plan-do-study-act cycles to improve delayed antibiotic prescribing. Generalized estimating equations were used to generate relative risk ratios (RRRs) for outcomes at the intervention end and 3- and 6-months postintervention. Practice surveys were evaluated. RESULTS: In total, 69 clinicians at 8 practice sites implemented 27 plan-do-study-act cycles. Practices varied by size (range: 6-37 providers), payer type, and geographic setting. The rate of delayed antibiotic prescribing increased from 2% at baseline to 21% at intervention end (RRR: 8.96; 95% confidence interval [CI]: 4.68-17.17). Five practices submitted postintervention data. The rate of delayed prescribing at 3 months and 6 months postintervention remained significantly higher than baseline (3 months postintervention, RRR: 8.46; 95% CI: 4.18-17.11; 6 months postintervention, RRR: 6.69; 95% CI: 3.53-12.65) and did not differ from intervention end (3 months postintervention, RRR: 1.12; 95% CI: 0.62-2.05; 6-months postintervention, RRR: 0.89; 95% CI: 0.53-1.49). CONCLUSIONS: Baseline rate of delayed prescribing was low. A low-cost intervention resulted in a significant and sustained increase in delayed antibiotic prescribing across a diversity of settings. |
A Learning Collaborative to Improve Antibiotic Prescribing in Primary Care Pediatric Practices
Norlin C , Fleming-Dutra K , Mapp J , Monti J , Shaw A , Bartoces M , Barger K , Emmer S , Dolins JC . Clin Pediatr (Phila) 2021 60 99228211001623 An American Academy of Pediatrics State Chapter organized a 6-month, mostly online quality improvement learning collaborative to improve antibiotic prescribing and patient education for upper respiratory infection (URI) and acute otitis media (AOM). Practices submitted data on quality measures at baseline, monthly, and 4 months post-project. Fifty-three clinicians from 6 independent, private primary care pediatric practices participated. Use of first-line antibiotics for AOM increased from 63.5% at baseline to 80.4% 4 months post-project. Use of safety-net antibiotic prescriptions (SNAP) for AOM increased from 4.5% to 16.9%. Educating patients about management for URI increased from 66.1% to 88.0% and for AOM from 20.4% to 85.6%. Practices maintained high performance for not prescribing antibiotics for URI (94.4% to 96.2%). Leveraging local relationships and national resources, this replicable antibiotic stewardship project engaged independent private practices to improve patient education for URI and AOM and prescribing and use of SNAP for AOM. |
Community drinking water data on the National Environmental Public Health Tracking Network: a surveillance summary of data from 2000 to 2010
Monti MM , David F , Shin M , Vaidyanathan A . Environ Monit Assess 2019 191 (9) 557 This report describes the available drinking water quality monitoring data on the Centers for Disease Control and Prevention (CDC) National Environmental Public Health Tracking Network (Tracking Network). This surveillance summary serves to identify the degree to which ten drinking water contaminants are present in finished water delivered to populations served by community water systems (CWS) in 24 states from 2000 to 2010. For each state, data were collected from every CWS. CWS are sampled on a monitoring schedule established by the Environmental Protection Agency (EPA) for each contaminant monitored. Annual mean and maximum concentrations by CWS for ten water contaminants were summarized from 2000 to 2010 for 24 states. For each contaminant, we calculated the number and percent of CWS with mean and maximum concentrations above the maximum contaminant level (MCL) and the number and percent of population served by CWS with mean and maximum concentrations above the MCL by year and then calculated the median number of those exceedances for the 11-year period. We also summarized these measures by CWS size and by state and identified the source water used by those CWS with exceedances of the MCL. The contaminants that occur more frequently in CWS with annual mean and annual maximum concentrations greater than the MCL include the disinfection byproducts, total trihalomethanes (TTHM), and haloacetic acids (HAA5); arsenic; nitrate; radium and uranium. A very high proportion of exceedances based on MCLs occurred mostly in very small and small CWS, which serve a year-round population of 3,300 or less. Arsenic in New Mexico and disinfection byproducts HAA5 and TTHM, represent the greatest health risk in terms of exposure to regulated drinking water contaminants. Very small and small CWS are the systems' greatest difficulty in achieving compliance. |
Prevalence of substandard and falsified artemisinin-based combination antimalarial medicines on Bioko Island, Equatorial Guinea
Kaur H , Allan EL , Mamadu I , Hall Z , Green MD , Swamidos I , Dwivedi P , Culzoni MJ , Fernandez FM , Garcia G , Hergott D , Monti F . BMJ Glob Health 2017 2 (4) e000409 INTRODUCTION: Poor-quality artemisinin-containing antimalarials (ACAs), including falsified and substandard formulations, pose serious health concerns in malaria endemic countries. They can harm patients, contribute to the rise in drug resistance and increase the public's mistrust of health systems. Systematic assessment of drug quality is needed to gain knowledge on the prevalence of the problem, to provide Ministries of Health with evidence on which local regulators can take action. METHODS: We used three sampling approaches to purchase 677 ACAs from 278 outlets on Bioko Island, Equatorial Guinea as follows: convenience survey using mystery client (n=16 outlets, 31 samples), full island-wide survey using mystery client (n=174 outlets, 368 samples) and randomised survey using an overt sampling approach (n=88 outlets, 278 samples). The stated active pharmaceutical ingredients (SAPIs) were assessed using high-performance liquid chromatography and confirmed by mass spectrometry at three independent laboratories. RESULTS: Content analysis showed 91.0% of ACAs were of acceptable quality, 1.6% were substandard and 7.4% falsified. No degraded medicines were detected. The prevalence of medicines without the SAPIs was higher for ACAs purchased in the convenience survey compared with the estimates obtained using the full island-wide survey-mystery client and randomised-overt sampling approaches. Comparable results were obtained for full island survey-mystery client and randomised overt. However, the availability of purchased artesunate monotherapies differed substantially according to the sampling approach used (convenience, 45.2%; full island-wide survey-mystery client, 32.6%; random-overt sampling approach, 21.9%). Of concern is that 37.1% (n=62) of these were falsified. CONCLUSION: Falsified ACAs were found on Bioko Island, with the prevalence ranging between 6.1% and 16.1%, depending on the sampling method used. These findings underscore the vital need for national authorities to track the scale of ineffective medicines that jeopardise treatment of life-threatening diseases and value of a representative sampling approach to obtain/measure the true prevalence of poor-quality medicines. |
Rural and urban differences in air quality, 2008-2012, and community drinking water quality, 2010-2015 - United States
Strosnider H , Kennedy C , Monti M , Yip F . MMWR Surveill Summ 2017 66 (13) 1-10 PROBLEM/CONDITION: The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality. REPORTING PERIOD: 2008-2012 for air quality and 2010-2015 for water quality. DESCRIPTION OF SYSTEM: Since 2002, CDC's National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008-2012 and one water-quality measure for 26 states during 2010-2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM2.5) levels greater than the U.S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM2.5 (PM2.5 days); 2) mean annual average ambient concentrations of PM2.5 in micrograms per cubic meter (mean PM2.5); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant. RESULTS: Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM2.5 concentration decreased from 11.15 mug/m3 in large central metropolitan counties to 8.87 mug/m3 in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties. INTERPRETATION: Noncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties. PUBLIC HEALTH ACTION: Understanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality. |
Introduction to the Summary of Notifiable Noninfectious Conditions and Disease Outbreaks - United States
Coates RJ , Stanbury M , Jajosky R , Thomas K , Monti M , Schleiff P , Singh SD . MMWR Morb Mortal Wkly Rep 2016 63 (55) 1-4 With this 2016 Summary of Notifiable Noninfectious Conditions and Disease Outbreaks - United States, CDC is publishing official statistics for the occurrence of nationally notifiable noninfectious conditions and disease outbreaks for the second time in the same volume of MMWR as the annual Summary of Notifiable Infectious Diseases and Conditions. As was the case for the 2015 Summary of Notifiable Noninfectious Conditions and Disease Outbreaks, this joint publication is the result of a request by the Council of State and Territorial Epidemiologists (CSTE) to provide readers with information on all nationally notifiable conditions and disease outbreaks in a single publication. |
Acute nonoccupational pesticide-related illness and injury - United States, 2007-2011
Namulanda G , Monti MM , Mulay P , Higgins S , Lackovic M , Schwartz A , Prado JB , Waltz J , Mitchell Y , Calvert GM . MMWR Morb Mortal Wkly Rep 2016 63 (55) 5-10 CDC's National Institute for Occupational Safety and Health (NIOSH) collects data on acute pesticide-related illness and injury reported by 12 states (California, Florida, Iowa, Louisiana, Michigan, North Carolina, Nebraska, New Mexico, New York, Oregon, Texas, and Washington). This report summarizes the data on illnesses and injuries arising from nonoccupational exposure to conventional pesticides that were reported during 2007-2011. Conventional pesticides include insecticides, herbicides, fungicides, and fumigants. They exclude disinfectants (e.g., chlorine and hypochlorites) and biological pesticides. This report is a part of the Summary of Notifiable Noninfectious Conditions and Disease Outbreaks - United States, which encompasses various surveillance years but is being published in 2016. The Summary of Notifiable Noninfectious Conditions and Disease Outbreaks appears in the same volume of MMWR as the annual Summary of Notifiable Infectious Diseases. In a separate report, data on illnesses and injuries from occupational exposure to conventional pesticides during 2007-2011 are summarized. |
Genome wide identification of new genes and pathways in patients with both autoimmune thyroiditis and type 1 diabetes.
Tomer Y , Dolan LM , Kahaly G , Divers J , D'Agostino RB Jr , Imperatore G , Dabelea D , Marcovina S , Black MH , Pihoker C , Hasham A , Hammerstad SS , Greenberg DA , Lotay V , Zhang W , Monti MC , Matheis N . J Autoimmun 2015 60 32-9 Autoimmune thyroid diseases (AITD) and Type 1 diabetes (T1D) frequently occur in the same individual pointing to a strong shared genetic susceptibility. Indeed, the co-occurrence of T1D and AITD in the same individual is classified as a variant of the autoimmune polyglandular syndrome type 3 (designated APS3v). Our aim was to identify new genes and mechanisms causing the co-occurrence of T1D + AITD (APS3v) in the same individual using a genome-wide approach. For our discovery set we analyzed 346 Caucasian APS3v patients and 727 gender and ethnicity matched healthy controls. Genotyping was performed using the Illumina Human660W-Quad.v1. The replication set included 185 APS3v patients and 340 controls. Association analyses were performed using the PLINK program, and pathway analyses were performed using the MAGENTA software. We identified multiple signals within the HLA region and conditioning studies suggested that a few of them contributed independently to the strong association of the HLA locus with APS3v. Outside the HLA region, variants in GPR103, a gene not suggested by previous studies of APS3v, T1D, or AITD, showed genome-wide significance (p < 5 x 10-8). In addition, a locus on 1p13 containing the PTPN22 gene showed genome-wide significant associations. Pathway analysis demonstrated that cell cycle, B-cell development, CD40, and CTLA-4 signaling were the major pathways contributing to the pathogenesis of APS3v. These findings suggest that complex mechanisms involving T-cell and B-cell pathways are involved in the strong genetic association between AITD and T1D. |
U.S. census unit population exposures to ambient air pollutants
Hao Y , Flowers H , Monti MM , Qualters JR . Int J Health Geogr 2012 11 3 BACKGROUND: Progress has been made recently in estimating ambient PM(2.5) (particulate matter with aerodynamic diameter < 2.5 mcm) and ozone concentrations using various data sources and advanced modeling techniques, which resulted in gridded surfaces. However, epidemiologic and health impact studies often require population exposures to ambient air pollutants to be presented at an appropriate census geographic unit (CGU), where health data are usually available to maintain confidentiality of individual health data. We aim to generate estimates of population exposures to ambient PM(2.5) and ozone for U.S. CGUs. METHODS: We converted 2001-2006 gridded data, generated by the U.S. Environmental Protection Agency (EPA) for CDC's (Centers for Disease Control and Prevention) Environmental Public Health Tracking Network (EPHTN), to census block group (BG) based on spatial proximities between BG and its four nearest grids. We used a bottom-up (fine to coarse) strategy to generate population exposure estimates for larger CGUs by aggregating BG estimates weighted by population distribution. RESULTS: The BG daily estimates were comparable to monitoring data. On average, the estimates deviated by 2 mcg/m(3) (for PM(2.5)) and 3 ppb (for ozone) from their corresponding observed values. Population exposures to ambient PM(2.5) and ozone varied greatly across the U.S. In 2006, estimates for daily potential population exposure to ambient PM(2.5) in west coast states, the northwest and a few areas in the east and estimates for daily potential population exposure to ambient ozone in most of California and a few areas in the east/southeast exceeded the National Ambient Air Quality Standards (NAAQS) for at least 7 days. CONCLUSIONS: These estimates may be useful in assessing health impacts through linkage studies and in communicating with the public and policy makers for potential intervention. |
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