77 research outputs found

    The impact of actual and surrogate variegated cutworm stubble phytophagy on the growth and yield of alfalfa

    Get PDF
    The response of alfalfa regrowth to actual and simulated stubble damage by the variegated cutworm (VCW), Peridroma saucia (Hubner), was investigated. A laboratory study found that most larvae exhibited 7 larval stages. Development by these larvae took 32.8 days at 24(DEGREES)C, during which 442.2 mg of alfalfa foliage was consumed;The effect of the duration of a complete suppression of regrowth on alfalfa productivity was studied using a surrogate defoliation technique. Complete regrowth suppression for 1 and 3 days did not significantly affect alfalfa regrowth. Delays of 7 and 11 days retarded plant development and suppressed the crop growth rate (CGR) of subsequent regrowth. Most of the reduction in CGR was caused by a decline in the growth rate of support (stem) structures. Leaf-weight and leaf-area growth rates were not significantly affected. The differential response of component growth rates resulted in an increase in the leaf area ratio, leaf weight ratio, and specific leaf area of regrowth;A second study found that stubble damage by various densities of VCW larvae produced the same general effects on alfalfa growth and partitioning that simulated defoliation produced. The effects, however, were significant in all years only for densities of 6 and greater larvae/0.1 m(\u272). These densities were large enough to completely suppress regrowth in most years for 10 to 15 days;The results of both studies indicated that plants minimized the adverse effects of stubble injury by maintaining growth rates of leaf weight and area at the expense of support-structure growth. A hypothesis based on the depletion of stored carbohydrate reserves was proposed to explain the changes observed in alfalfa regrowth;A replacement-feed cost analysis was conducted for both studies. Depending on commodity price and control costs, stubble damage was economic for a harvest system based on cutting at first bloom when regrowth was suppressed completely for 4.4 to 7.5 days or when densities of newly-molted, last-stage larvae reached 2.8 to 4.8 larvae/0.1 m(\u272). Action levels were reduced by approximately one-half if cutting occurred based on calendar-date. A management program for VCW in alfalfa was developed using these results, and guidelines for the management of other stubble defoliators also were proposed

    The Effect of Medicare Eligibility on Spousal Insurance Coverage

    Get PDF
    A majority of married couples in the United States take advantage of the fact that employers often provide health insurance coverage to spouses. When the older spouses become eligible for Medicare, however, many of them can no longer provide their younger spouses with coverage. In this paper, we study how spousal eligibility for Medicare affects the health insurance and health care access of the younger spouse. We find spousal eligibility for Medicare results in the younger spouse having worse insurance coverage and reduced access to health care services

    Insect-Attracting and Antimicrobial Properties of Antifreeze for Monitoring Insect Pests and Natural Enemies in Stored Corn

    Get PDF
    Insect infestations in stored grain cause extensive damage worldwide. Storage insect pests, including the Indianmeal moth, Plodia interpunctella (Hubner) (Lepidoptera: Pyralidae); Sitophilus spp. (Coleoptera: Curculionidae); and their natural enemies [e.g., Cephalonomia tarsalis (Ashmead) (Hymenoptera: Bethylidae), and Anisopteromalus calandrae (Howard) (Hymenoptera: Pteromalidae)] inhabit a temporary, but stable ecosystem with constant environmental conditions. The objective of the present experiment was to assess the efficacy of using ethylene glycol antifreeze in combination with nutrient solutions to monitor storage insect pest and natural enemy populations in three bins of corn, Zea mays L. The treatments were deionized water, a diluted (1:5 antifreeze:water) antifreeze solution, 10% honey, 10% honey in the diluted antifreeze solution, 10% beer in the diluted antifreeze solution, 10% sucrose in the diluted antifreeze solution, and a commercial pheromone trap suspended in a 3.8-liter container filled with 300-ml of diluted antifreeze solution. The seven treatments captured storage insect pests and their natural enemies in the bins at 33-36°C and 51-55% RH. The pheromone trap in the container with the diluted antifreeze captured significantly more P. interpunctella than the other treatments, but a lower percentage (7.6%) of these captures were females compared with the rest of the treatments (\u3e40% females). All trapping solutions also captured Sitophilus spp. and other beetle species, but the captures of the coleopteran pests were not significantly different among the seven treatments (P \u3e 0.05). Two parasitoid wasps also were captured in the study. The number of A. calandrae was different among the seven treatments (P \u3c 0.05), whereas the number of C. tarsalis was not different among the treatments (P \u3e 0.05). Most A. calandrae adults were captured by the 10% honey in the diluted antifreeze, whereas the fewest were captured in the deionized water. Microbial growth was observed in the 10% honey solution, but no microbial growth occurred in the rest of the treatments, including 10% honey in the diluted antifreeze solution. The results of insect captures and microbial growth demonstrated that antifreeze could be used as a part of storage insect monitoring and/or control programs

    New record of Trissolcus solocis (Hymenoptera: Scelionidae) parasitising Halyomorpha halys (Hemiptera: Pentatomidae) in the United States of America

    Get PDF
    A parasitoid wasp, Trissolcus solocis Johnson, was recorded parasitising eggs of the invasive stink bug Halyomorpha halys (Stål), in the United States. This is the first record of this species parasitising eggs of H. halys. First record of Trissolcus solocis parasitising Halyomorpha halys eggs in the United States and first record of T. solocis in Alabama

    Spatial Patterns of Aflatoxin Levels in Relation to Ear-Feeding Insect Damage in Pre-Harvest Corn

    Get PDF
    Key impediments to increased corn yield and quality in the southeastern US coastal plain region are damage by ear-feeding insects and aflatoxin contamination caused by infection of Aspergillus flavus. Key ear-feeding insects are corn earworm, Helicoverpa zea, fall armyworm, Spodoptera frugiperda, maize weevil, Sitophilus zeamais, and brown stink bug, Euschistus servus. In 2006 and 2007, aflatoxin contamination and insect damage were sampled before harvest in three 0.4-hectare corn fields using a grid sampling method. The feeding damage by each of ear/kernel-feeding insects (i.e., corn earworm/fall armyworm damage on the silk/cob, and discoloration of corn kernels by stink bugs), and maize weevil population were assessed at each grid point with five ears. The spatial distribution pattern of aflatoxin contamination was also assessed using the corn samples collected at each sampling point. Aflatoxin level was correlated to the number of maize weevils and stink bug-discolored kernels, but not closely correlated to either husk coverage or corn earworm damage. Contour maps of the maize weevil populations, stink bug-damaged kernels, and aflatoxin levels exhibited an aggregated distribution pattern with a strong edge effect on all three parameters. The separation of silk- and cob-feeding insects from kernel-feeding insects, as well as chewing (i.e., the corn earworm and maize weevil) and piercing-sucking insects (i.e., the stink bugs) and their damage in relation to aflatoxin accumulation is economically important. Both theoretic and applied ramifications of this study were discussed by proposing a hypothesis on the underlying mechanisms of the aggregated distribution patterns and strong edge effect of insect damage and aflatoxin contamination, and by discussing possible management tactics for aflatoxin reduction by proper management of kernel-feeding insects. Future directions on basic and applied research related to aflatoxin contamination are also discussed

    The Effect of Medicare Eligibility on Spousal Insurance Coverage

    Get PDF
    A majority of married couples in the United States take advantage of the fact that employers often provide health insurance coverage to spouses. When the older spouses become eligible for Medicare, however, many of them can no longer provide their younger spouses with coverage. In this paper, we study how spousal eligibility for Medicare affects the health insurance and health care access of the younger spouse. We find spousal eligibility for Medicare results in the younger spouse having worse insurance coverage and reduced access to health care services

    Extended Sentinel Monitoring of Helicoverpa zea Resistance to Cry and Vip3Aa Toxins in Bt Sweet Corn: Assessing Changes in Phenotypic and Allele Frequencies of Resistance

    Get PDF
    Transgenic corn and cotton that produce Cry and Vip3Aa toxins derived from Bacillus thuringiensis (Bt) are widely planted in the United States to control lepidopteran pests. The sustainability of these Bt crops is threatened because the corn earworm/bollworm, Helicoverpa zea (Boddie), is evolving a resistance to these toxins. Using Bt sweet corn as a sentinel plant to monitor the evolution of resistance, collaborators established 146 trials in twenty-five states and five Canadian provinces during 2020–2022. The study evaluated overall changes in the phenotypic frequency of resistance (the ratio of larval densities in Bt ears relative to densities in non-Bt ears) in H. zea populations and the range of resistance allele frequencies for Cry1Ab and Vip3Aa. The results revealed a widespread resistance to Cry1Ab, Cry2Ab2, and Cry1A.105 Cry toxins, with higher numbers of larvae surviving in Bt ears than in non-Bt ears at many trial locations. Depending on assumptions about the inheritance of resistance, allele frequencies for Cry1Ab ranged from 0.465 (dominant resistance) to 0.995 (recessive resistance). Although Vip3Aa provided high control efficacy against H. zea, the results show a notable increase in ear damage and a number of surviving older larvae, particularly at southern locations. Assuming recessive resistance, the estimated resistance allele frequencies for Vip3Aa ranged from 0.115 in the Gulf states to 0.032 at more northern locations. These findings indicate that better resistance management practices are urgently needed to sustain efficacy the of corn and cotton that produce Vip3Aa

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

    Get PDF
    © 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
    corecore