Antipsychotic prescription frequencies combined with frequencies for other antipsychotic medications on the final assessment.
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"68551",title:"Agricultural Management Strategies for Countering Drought Conditions in Eastern Croatia",doi:"10.5772/intechopen.88503",slug:"agricultural-management-strategies-for-countering-drought-conditions-in-eastern-croatia",body:'\nDrought is commonly defined as below-usual water availability [1, 2, 3]. Even though drought is commonly associated with arid regions [4], it can also occur in more humid regions [5], which has been confirmed by climate models from various authors [6, 7]. Short-term drought periods (lasting for days or months) usually do not cause permanent or substantial environmental issues in humid areas; however, they can still be reflected on a seasonal agricultural production. Furthermore, if drought periods would last longer (for years or even decades), a negative impact on both the environment and the socioeconomic circumstances of the region can undoubtedly be expected. Agricultural crop production is particularly dependent on precipitation and therefore sensitive to the appearance of drought. Climate change predictions include uncharacteristic drought periods which besides the limited water availability for plants can also have a detrimental effect on other soil organisms (e.g., microbes) [8] and indirectly influence plant growth and development by restricting nutrient availability in soil. Thus, drought periods may lead to an overall decreased fertility of soils. Furthermore, prolonged dry conditions in soil may increase the susceptibility of soil to wind erosion, that is, frequent and severe drought periods can reduce the plant cover and expose the soil to wind, resulting in erosion and desertification [9].
\nAlthough drought periods are not unusual for the coastal parts of Croatia, in the last several decades, they have become more frequent and long-lasting, for example [10] and recently are even recorded in the continental parts of the country [11]. This is also confirmed by this hydrological study in the area of the Biđ-Bosut field, an area of 7200 ha of agricultural soil for which the construction of the Biđ field irrigation canal is planned (Figure 1). During the monitoring (2003–2018), significant changes of water regime, as well as soil and water management difficulties were recorded in the agricultural part of the studied area. For example, agricultural soils showed a noticeable lowering of groundwater levels, and this negative trend showed a tendency of becoming even more rapid in the future.
\nThe monitoring area (2003–2018) of the Biđ-Bosut field presented with the indication of used field equipment.
In the studied area, the unfavorable annual distribution of precipitation, the absence of snow cover during winter, and the rising of air temperature, all are contributing to drying conditions in agricultural soils, thus already negatively affecting local agricultural production, that is, crop rotation is becoming more and more simplified, the germination and sprouting of crops are impaired by the lack of precipitation, the yield is weather-dependent and unstable, and economic projections are frequently unreliable. Understanding of the changes in the soil water regime is of major importance for selecting the appropriate strategies for the drought risk management in agricultural systems and countering the harmful effects of climate change [12].
\nIn this chapter, climatic and hydrological data from the Biđ-Bosut field agricultural area are presented, with a description of practical strategies which could, at least to a certain extent, alleviate the negative impact of drought on the agricultural production. One of such strategies is the installation of irrigation systems, which imposes as a relatively obvious or a simple solution, but it is not traditionally applied in agricultural production in the eastern continental Croatia, mostly because until recently drought was non-occurring or the occurrence was mostly of a relatively short duration and/or of mild intensity and because the initial cost of implementing the irrigation systems may be considered high. However, if drought periods should occur during sensitive crop developmental stages (e.g., sprouting) or extend during prolonged periods, implementing the irrigation systems could prove to be extremely beneficial for the local agriculture, as well as cost-effective in the aftermath of plant production. The fundamental basis for introducing the irrigation systems in this area was met by proceeding with the construction of the irrigation canal in 2018. However, even though the use of irrigation is a possible solution for plant production under drought conditions [13], it is by no means the only action which should be taken, especially considering that water is not an unlimited resource. Thus, the existing network of drainage canals, built in the 1960s and 1970s, could with certain modifications be used to maintain the groundwater level and contribute to the total amount of water available for irrigation. Also, the proper selection of crops and management techniques can help to facilitate plant production and, keeping the before-mentioned in mind, contribute to alleviating the negative impacts of drought on agricultural production.
\nClimate and water regime monitoring at the Biđ-Bosut field started in 2003, with the aim of determining the impact of the irrigation canal construction on the groundwater dynamics and the surrounding agroecosystem. From the preliminary tracking of initial conditions, the monitoring evolved into a very valuable source of information with the majority of relevant agroecological data regarding the surface water, groundwater, leachate, as well as the agricultural soils in the studied area recorded. For tracking the groundwater dynamics, 50 shallow (up to 4 m of depth) and 5 deep (up to 15 m of depth) piezometers and 5 limnigraphs (up to 4 m of depth) were used. Measurements using the piezometers were done manually (by measuring tape) every 10 days, while the limnigraphs (Orpheus Mini) recorded groundwater levels daily.
\nThe monitoring area is located at intersect between a semiarid into a semi-humid moderate continental climate [14]. Meteorological data were analyzed for 2003–2018 and collected from the nearest meteorological station, the national meteorological station Gradište (45°09′ N; 18°42′ E). Data regarding the Sava river, to which the irrigation canal is connected, were taken from the Sava-Slavonski Šamac national measurement station for the years 2014–2018. The values are given on a monthly basis and are also transformed into annual values for easier following.
\nFigure 2 shows the monthly values of precipitation during the observed period. The monthly precipitation amounts (2003–2018) show a positive but nonsignificant upward trend (0.010 mm per month, that is, 0.12 mm per year), which is in accordance with the multiannual findings from similar studies [15]. In the last 5 years, a mildly negative but also nonsignificant trend (−0.40 mm per year) is visible. Moreover, the average annual sum of precipitation exhibits a mild but constant rise (682.7 mm for 1981–2018; 688.6 mm for 2003–2018; 728.8 mm for 2014–2018). Also, irregular precipitation extremes have been recorded (e.g., in June 2018; Figure 2). However, it should be noted here that high amounts of precipitation in a very short period actually have an extremely low effective value for crops (as explained in detail in Section 3.2).
\nThe dynamics of average monthly precipitation (mm) at the monitored area of the Biđ-Bosut field.
The trend of increased average monthly air temperatures by 0.084°C per year (2003–2018) was recorded (Figure 3). If the determined value is used for the prediction of an air temperature after a longer period, data suggest that an increase of as much as 4.7°C may be expected in 50 years. The average multiannual temperature sequences also exhibit an increase (11.8°C for 1981–2018; 12.3°C for 2003–2018; 12.7°C for 2014–2018). Although these data are not sufficiently long-term in nature for solid conclusions, it is still may be considered as indicative of a general increase in air temperature.
\nThe dynamics of average monthly air temperatures (°C) at the monitored area of the Biđ-Bosut field.
Through detailed hydro-pedological research of the monitored area, a pedological characterization survey was completed with five soil-systematic units defined [16]. The classification was done according to [17], and the determined pedological units were semigleyic, hypogleyic, humogleyic, amphygleyic, and hydromeliorated soil. Figure 4 shows the average monthly values of groundwater levels obtained by shallow piezometers located at 4 m from the soil surface, with a regard to the before-mentioned soil-systematic units.
\nThe dynamics of average monthly groundwater levels in the shallow piezometers (located at 4 m from the soil surface) at the monitored area of the Biđ-Bosut field (left, groundwater levels in period 2003–2018; right, groundwater levels in period 2014–2018).
A relatively slight negative trend of groundwater levels (2003–2018) in the agricultural soils of the monitored area was recorded (6–10 cm per year). However, it should be noted that by observing only the last 5 years of monitoring (2014–2018), a negative trend was much more pronounce, ranging from 18 to 71 cm per year, that is, from 200% in semigleyic soil (negative trend for period 2003–2018 = 9 cm per year; period 2014–2018 = 19 cm per year) to 700% in hypogleyic soil (negative trend for period 2003–2018 = 10 cm per year; period 2014–2018 = 71 cm per year), depending on the soil type. Although the groundwater level was occasionally recorded below 4 m from the soil surface during the studied period, such occurrences were short-lasting (days) and irregular. However, in July 2017 and 2018, the groundwater level at the entire monitoring area lowered below 4 m from the soil surface and remained unchanged until the end of the year. The extremely low groundwater level which occurred in the second half of the last two research years is undoubtedly suggesting the need for further monitoring in the studied area.
\nThe more frequent lowering of groundwater levels below 4 m from the soil surface was the reason for adding 5 deeper piezometers at 15 m from the soil surface during 2014. The average monthly groundwater level data obtained by deep piezometers are presented in Figure 5. The values were only slightly increased in comparison to the values measured by the shallow piezometers, which can be explained by a mild difference in pressure between the shallow soil aquifer and the deep water-bearing aquifer. The negative trend of a decreased groundwater level ranged from 26 to 77 cm per year, which is in agreement with the trends obtained by using shallow piezometers. However, data obtained from deep piezometers are for a relatively short period (4 years), and it is expected that after a longer research period, these values could even be rising.
\nThe dynamics of average monthly groundwater levels in the deep piezometers (located at 15 m from the soil surface) at the monitored area of the Biđ-Bosut field.
All described climatic and water regime parameters suggest that in the studied Biđ-Bosut area, the agroecosystem changes are becoming more prominent. These changes are usually slow in progressing thus are hard to observe within shorter periods. However, field measurements and alterations of climatic and water regime parameters recorded during this study are contributing to the global predictions in which these changes in the agroecosystem are increasing in importance for the agricultural production. Finally, further continuous observation (monitoring) of climatic, water regime, and soil parameters should result in reliable databases, thus providing a foundation for the selection of appropriate site-specific strategies to counter the occurring changes and their possible negative impact on the agricultural production.
\nThe measures proposed herein primarily focus not only on the eastern continental Croatia example but can also be applied to other agroecosystems with similar agroecological conditions [18]. Namely, according to the recent analyses for a 50-year period (1961–2010), it confirmed an evidence of increase in drought seasons (defined as consecutive dry days—CDD with daily precipitation <1–10 mm) notably in the eastern continental Croatia (e.g., Slavonia region) by 4%/decade to 7%/decade during summer [19]. In general, the studied agricultural area in continental Croatia characterizes relatively flat arable therein (with fluvisols, gleysols, and cambisols), positioned in between of Sava and Drava rivers, cultivated mostly with cereals and oil crops, with average annual effective precipitation of 521–890 mm and potential evapotranspiration (ET0) of 690–820 mm, as well as high irrigation demands, either in average (81–260 mm/annually) or dry (168–383 mm/annually) vegetation season [20].
\nFrequent periods during which groundwater level lowers below 4 m from the soil surface are imposing substantial limitations in the last decades for the agricultural production which lacks an irrigation system (such is the case in the Biđ-Bosut area). Agricultural production in the study (monitoring) area, although located in a traditionally agricultural region, so far does not rely on irrigation as a possible solution for alleviating occasional negative drought effects. The possible reason for that is because local agricultural production in this area is mostly located on hydromorphic soils, characterized by occasional or permanent moisturization by groundwater within 1 m from the soil surface [18]. Thus, the issue of lacking soil moisture which can last for several months has been an occurrence noted in this area only for the last 10 or so years, while before the main problem was the opposite: excess surface and groundwater amounts.
\nThe completion of the irrigation canal in 2018 (Figure 6) was the main prerequisite for irrigation of the surrounding agricultural soils. The canal is connected to the Sava river, and, with the proper regulation of water levels in the canal, it could provide necessary and sustainable amount of irrigation water. It was projected that, during high water levels of the Sava river, water will be pumped into the melioration canal, from which it would then be channeled to the surrounding highly arable agricultural fields during the most of vegetation season given on negative water balance, for example [20]. More precisely, considering the amount of water in the Sava river [18], this hydrotechnical solution could help to ensure adequate amount of water for irrigation of the approximately 10,000 ha of surrounding agricultural soil. As for the quality of the water, studies from various authors have made it clear that the water from the Sava river is of ample quality to irrigate the local crops, for example [18]. However, water quality is an important factor when considering its use for crop irrigation; thus, if canal water is used for irrigation, it is necessary to implement permanent water quality control.
\nOpening of the Biđ-Bosut field irrigation canal.
Application of appropriate water management strategies for the usage of Sava river water for irrigation of crops is of major importance. Such strategies include application of the modern low-pressurized/low-energized (fert)irrigation system, adaptation of cropping pattern (e.g., to give advantage to winter over spring cereals/cultivars and to those with shorter vegetation period when water balance is the most negative), modernization of conveyance systems (e.g., channel overlying or replacing with pipelines), conduct irrigation management on real time data measurements, application of conservation agriculture practices, and many others [20, 21]. Some of the most recent studies have confirmed that almost all crops cultivated on the studied areas are exposed to water stress (negative water balance) with significant yield losses even in normal (average) sessions. For instance, in Brodsko-posavska County (overspread on the most of elaborated area), an average annual (for 1963–2005 period) effective precipitation reaches 690 mm, while potential evapotranspiration (ET0) is 718 mm, causing the negative water balance during vegetation period for almost −200 mm [22]. According to the same study, irrigation requirements in average climate season for the most cropped cultures yield from 82 mm (corn) up to 160 mm (sugar beet) and over 200 mm (lucerne), while in dry seasons water requirements are higher by 1.8–1.9-fold (lucerne and sugar beet) up to 2.6-fold (corn). The yield reduction in the case of nonirrigated conditions on this area is also significant for the most of crops, even in normal (from 11% in corn and soybean up to 25% in sugar beet on texture-lighter soils) and especially in dry seasons (from 25% in soybean up to 47% in sugar beet on texture-lighter soils).
\nAlthough Sava river can provide the required amount of water for irrigation, excessive (unsustainable) management measures could possibly create additional (agro)ecological issues regarding water levels of Sava river and even question the sustainability of such practice. This possibility is confirmed by the trend of lower levels of Sava river by 0.51 m per year for the period from 2014 to 2018 (Figure 7). Using Sava river water for irrigation should therefore be applied with the utmost rationality, that is, taking into consideration the optimal water regime within the river-soil-plant-atmosphere system, for example [23]. Additionally, the education of local farmers should be included as an important step in the planning and implementation of any irrigation system which is depending on a natural system, such as (Sava) river.
\nThe dynamics of Sava river daily levels at the Sava-Slavonski Šamac measuring station for the period from 2014 to 2018.
During the 1960s and 1970s, the main issue for the agricultural production in the studied region was the excess amount of surface and groundwater. That is why the area has an abundance of drainage canal networks through which excessive water was channeled into the recipient—the Sava river.
\nHowever, in June 2018, 257.4 mm of precipitation was recorded (Figure 2), which exceeds the average monthly precipitation in this area by several times (320%). These extremes were usually accompanied by storms and hail, which is why the authorities declared a state of emergency for the years 2010, 2014, and 2018. As mentioned before, such high amount of precipitation in a very short time has a very small effective value for crops because in such conditions water cannot infiltrate in the soil, usually resulting in (sub) surface runoff. In the studied area, most of the water from surface runoff streams firstly into the drainage canals, then toward the Sava river, and finally reaching the Danube river and ultimately the Black Sea. Thus, water from surface runoff is basically lost from this area and does not have any effect on the water regime of the soils, although the possibilities and sustainability of some on-farm water storage systems (e.g., surface accumulations, public reservoirs) should be also evaluated. This was confirmed by field measurements (Figures 2-5), from which it is clear that even the abundant rainfall in June 2018 did not lead to a noticeable rise in groundwater levels. Moreover, for the whole first half of 2018, the groundwater did not exceed the level of 3.7 m from the soil surface, and in July of the same year, groundwater level lowered below 4 m from the soil surface, remaining at stated level until the end of the year 2018.
\nThe network of drainage canals was up until 10 years ago used exclusively for drainage of the excess water from the area. During the last 10 or so years, the appearance of excess water became increasingly rare, and in 2017 and 2018, no such occurrence was recorded, except for a few days in June 2018 (data not shown). What is more, in the last several years, the lack of moisture in soil has become especially noticeable and culminated in 2018. One possible hydrotechnical solution for such issue would be to modify the existing canal network by implementing the controlled drainage canal system (where water flow is controlled and limited by a regulating system) at the main drainage canals. This way, in cases of an extreme precipitation, the drainage canals would preserve their primary drainage function, but in case of lower precipitation (when no excessive water is present in soil but before the drought conditions), by closing the canal release point, the same canals could be used as a form of a precipitation retention system. This proposed system of a branched-out canal network could, with an adequate regulation of canal water release points, prove to be very useful when additional amount of water for the agricultural plant production is necessary, that is, under drought conditions. Using these drainage waters as a potentially valuable “resource” rather than considering them as a “waste” can contribute to the alleviation of water scarcity, thus the negative effects of drought conditions [24], which is also in accordance with the widely accepted and nowadays preferential concept of sustainability in agricultural production.
\nAdditionally, if subsurface drainage systems are installed, there is also the possibility of implementing the subsurface drainage water regulation system which could control the groundwater level according to the soil moisture. According to [25], controlled drainage, also known as drainage water management, is a practice of using the water control structure at the drainage outlet in order to raise the groundwater level and thus retain water in soil during periods when drainage is not needed, but a deficiency of soil water is present. The implementation of controlled systems ($120 or $50–100 per ha if upgrading from conventional drainage systems) is relatively inexpensive [26] and therefore should be taken into account when designing an agricultural systems. However, considering the initial cost of installing such system, its introduction should be accompanied by a sufficiently profitable agricultural production that would presumably justify the additional investment.
\nAgricultural production without an irrigation system is completely depending on climate and available soil moisture (weather-dependent). In the context of increasingly important climate change, such production will presumably encounter more and more stressful conditions (i.e., plant water stress). In order to maintain the productivity, drought- and heat-tolerant crops/cultivars/hybrids must become the product of choice, as must the application of techniques to maintain the soil moisture by reducing evaporation [27, 28]. More precisely, evaporation occurs when moist soil is exposed to the atmosphere. In theory, to reduce the evaporation, it is necessary to reduce the exposed soil surface as much as possible and/or to shorten the time of the soil exposure to the atmosphere. In practice, mulching with plant residues and/or polyethylene foils can be used for this purpose [29]. Also, certain probiotic soil enhancers which have become available on the market recently can be used for the same purpose of reducing the evaporation [30]. These soil enhancers enrich the soil with beneficial microbes which accelerate decomposition of soil organic matter into smaller compounds capable to retain more water in the soil and further to plant-available nutrients, which increases the overall soil fertility but also improves soil capacity to retain moisture. Additionally, if the irrigation systems are applied in the studied area of Biđ-Bosut field, the appropriate irrigation systems are those with the localized water distribution (e.g., micro-sprinklers or drip irrigation), which distribute water only alongside the crops and thus reduce water losses and evaporation (in comparison with, e.g., irrigation boom).
\nAt the end, the important viewpoint of the drought-alleviating management techniques is also from the economical aspect. Generally, adequate agricultural management includes the cost-benefit ratio regarding the crop value. Higher input into the agricultural production should be justified by investing into profitable crops, which will presumably pay out the initial investment. In this context, replacing the less profitable crops with crops for which the market demand is higher could be an appropriate action. However, this agricultural management strategy is not an easy task as it is not grounded on a permanent aspect but strongly relies on the current supply and demand market circumstances. Thus, additional economic analyses which include supplementary perspectives such as estimations of future market opportunities and trends may be of major importance.
\nClimatic and soil water regime data (2003–2018) suggest that the agroecosystem changes are becoming more prominent in the studied Biđ-Bosut area, and thus the future agricultural production may be exposed to the greater pressures regarding the insufficient amount of water in the soil. Also, some of the most recent midterm climate scenarios (models) performed for the studied and wider area support our theses. For instance, modern climate models from local to global scales employ relatively different horizontal resolutions from 10 to 300 km [19] and predict wide range of climate parameters, that is, scenarios. At the European scale (notably in its central part), it is expected that average seasonal near-surface temperature (Ta) is going to increase in the period 2011–2040 by 0.2–2°C [19]. According to the same authors and for the same midterm period, in Croatia the largest changes in Ta can be expected in the mid of vegetation session (summer) with an increase of Ta by 0.8–1°C in the central part of Croatia and around 0.8°C in eastern (Slavonia) region. As regards the average precipitation, a decrease of precipitation between 2 and 8% is predicted over the larger part of Croatia [19]. Consequently, higher evapotranspiration demands (over increasing average vegetation air temperature) and reduced average effective precipitations might further exacerbate water imbalances in the agroecosystems on the elaborated area.
\nInstallation of the irrigation systems is a possible solution for countering the negative impact of drought, but other management strategies should also be implemented in order to achieve the sustainability of agricultural production. In this context, the education of local farmers should be included as an important step in the planning and implementation of any drought countering techniques, in order to achieve the highest success rate by adhering the rules and instructions referring to the rational and responsible water use. Finally, this study has shown that multiannual climate and soil water regime data may provide a good basis for the decision-making process in creating sustainable agricultural management policies (construction of the appropriate irrigation systems and use of the existing irrigation infrastructure for the purpose of collecting precipitation, use of drought- and heat-tolerant crops/cultivars/hybrids, and application of techniques to maintain the soil moisture by reducing evaporation, for example, mulching with plant residues and/or polyethylene foils and use of probiotic soil enhancers) focused on countering the negative impact of drought on the agricultural production.
\nThis chapter builds upon findings from retrospective studies described in a previous chapter by Stones, Worobetz, Randle, Marchese, Fossum, Ostrum and Brink [1]. Those studies examine associations between mortality in long-term care home (LTHC) residents in the Canadian province of Ontario and the reported use of psychotropic medications. Regulations in Section 155 of the Canadian province of Ontario’s Long-Term Care Homes Act of 2007 specifies that the residents of LTCH should be (1) 18+ years of age; (2) insured under the Health Insurance Act; (3) in need of 24-hours on-site nursing care, or (4) frequent daily assistance with activities of daily living (ADL), or (5) on-site monitoring or supervision in order to ensure safety and well-being. Alternative terms for LTCH in other dominions and countries include nursing homes and homes for the aged. Such homes contrast with supportive housing and continuing care hospitals that respectively provide lesser or greater levels of health care provision.
\nThe impetus for what became our research program concerns the allegedly harmful effects of antipsychotic medication on mortality and medical conditions that potentially precipitate mortality. Although the research we describe in this chapter relates specifically to the effects of antipsychotics, our overall research program evolved to focus more generally on associations between mortality and the reported usage of any type of psychotropic medication. The latter includes not only antipsychotics but also anxiolytics, analgesics, antidepressants and hypnotics. The primary instrumentation deployed in these studies is the Resident Assessment Instrument 2.0 (RAI 2.0). This tool provides standardized clinical assessment and good data quality [2], with widespread adoption throughout the world. The measure of psychotropic usage on the RAI 2.0 is the number of days of delivery during the week preceding an assessment.
\nThe findings described in the earlier chapter indicate strongest associations with mortality for intermittent usage of 1–6 days per week when compared with no use or daily use. These findings are significant for each type of psychotropic medication in both univariate and multivariate analyses, where the latter attempts to control for potentially confounding effects and interactions. We refer to intermittent prescribing as pro re nata (PRN, or ‘as needed’) prescription in order to be consistent with recent regulatory initiatives to curb hazardous effects associated with ‘as needed’ prescribing practices [3].
\nIn contrast to augmented mortality with PRN prescribing, our earlier findings indicate lower mortality associated with daily usage of antidepressant and antipsychotic medications when compared to an absence of usage. The findings on daily usage of antipsychotic medication depart from expectations in the existing literature of its hazardous effects on mortality. This finding is also surprising because the database is among the most all-encompassing of any used in previous studies. It includes consensus level, yearly incidence data on all new admissions to all LTCHs in Ontario (i.e., over 20,000 new admissions to over 600 LTCHs during a given year, with each resident followed up for 1-year). The purpose of the research in this chapter is to explore reasons for this discrepancy.
\nThe context of our research concerns caregiving for residents of long-term care homes with behaviors that generally fall under the rubric of behavioral and psychological symptoms of dementia (BPSD). A consensus conference of the International Psychogeriatric Association in 1976 defined BPSD as “symptoms of disturbed perception, thought content, mood or behavior that frequently occur in patients with dementia” [4]. These symptoms include physical aggression, loud vocalization, restlessness, agitation, wandering, anxiety, depressive mood, hallucinations, and delusions [5]. Not all residents with dementia exhibit such symptoms, which usually emerge during the middle and later stages of the illness. Previous estimates indicate that BPSD characterizes nearly 40% of residents in Ontario’s LTCHs [1].
\nBy far the most frequent treatment for residents of LTCHs is chemical management. Of the five types of psychotropic medication, the main purposes are to alleviate pain and discomfort (i.e., analgesics), depression (i.e., antidepressants), anxiety (i.e., anxiolytics), sleeplessness (i.e., hypnotics) and BPSD (i.e., antipsychotics). The two categories of antipsychotic medication are termed typical and atypical. The latter were introduced in attempt to intent reduce adverse side-effects associated with the former [6].
\nThe first columns in \nFigure 1\n, which is adapted from our previous chapter [1], shows more than double the usage of antipsychotic medication for male and female LTCH residents with than without diagnosed dementia. The findings for no other form of psychotropic medication approach this level of discrepancy. Consequently, antipsychotic medication is the most frequently used psychotropic medication specifically used with demented individuals, which is a conclusion consistent with that reported in previous publications [7].
\nPercentage of PRN or daily use of psychotropic medications for residents with or without diagnosed dementia.
Concerns arose early in this millennium about adverse effects associated with the use of antipsychotic medications for the management of dementia. Such effects include cardiovascular and cerebrovascular events, cardiac arrythmia, cognitive decline, extrapyramidal symptoms, falls, fractures, pneumonia and elevated mortality [8]. Levels of the preceding for people with antipsychotic prescriptions exceed those among elderly people in general, people with dementia but without antipsychotic prescriptions, and those exhibiting BPSD without antipsychotic prescriptions [9].
\nFrom 2002 onwards, manufacturers of antipsychotic medications issued warnings about health and mortality risks when prescribed for elderly people. In 2005, the USA’s Federal Food and Drug Administration required “black box” warnings on packages of atypical antipsychotics, which in 2008 was extended to typical antipsychotics. This warning reads: “WARNING: Elderly patients with dementia-related psychosis treated with antipsychotic drugs are at an increased risk of death.” Health authorities in other countries subsequently issued comparable warnings.
\nRecent meta-analysis [10] and scoping reviews [11] draw the following conclusions about mortality and antipsychotic usage in elderly people. The mortality risks are comparable between typical and atypical antipsychotics, and approximately twice that of people without such usage. The risks are comparable between individuals with or without dementia; they increase with dosage, and are highest during with first month(s) of usage. The latter suggests to authors of the scoping review [11] that factors other than antipsychotic medication may contribute to findings of elevated mortality. The authors of the meta-analytic study [10] recommend restriction and de-prescribing of antipsychotics with older people.
\nThe findings discussed in our earlier chapter [1] indicate that, after control for variables that include gender, age, activities of daily living, level of cognition and mortality risk, mortality was (1) significantly elevated with PRN use for each type of psychotropic medication (2) significantly attenuated with daily use of antipsychotic and antidepressant medications, (3) significantly elevated for combinations of psychotropic medications that include PRN use and (4) significantly attenuated for combinations of psychotropic medications that include their daily use. These findings are consistent with and build upon unpublished thesis research on antipsychotic medication use by Worobetz [12]. Differences and confounds that might relate to discrepancies between our findings and previous reports of excessive mortality associated with antipsychotic medication use include the following.
\nFirst, the analyses in our chapter use generalized linear mixed modeling (GLMM) procedures. Such modeling includes a random variable that encompasses clustering of observations within that variable. This structure is appropriate for the analysis of LTCH data, with the individual homes assumed to be a random variable (i.e., the homes are independent and uncorrelated entities). In contrast, observations of residents living within a given home have commonalities because of localized admission practices, treatment preferences that differ in content and/or frequency from those in other homes, the mutual interactions of residents, etc. Traditional regression and survival analyses fail to account for such commonalities, thereby violating assumptions of independence of observations of residents, which adds to correlated error, potentially with adverse implications for the correctness of analytic outcomes. Unfortunately, it appears that the majority of studies of LTCH residents fail to address this problem. The specific form of mixed modeling used in our earlier [1] and present studies is interval censored survival (i.e., a binomial distribution with a complementary log–log link), which is appropriate for analysis of clustered observations, some of which are without a terminal event.
\nSecond, the majority of studies of relationships between antipsychotic medication and mortality report the type and dosage of medication but not the frequency of usage [11]. Although a few studies treat PRN use as an exclusionary criterion [11], it is more likely grouped with daily use in the majority of studies. The implications of such inclusion include augmented mortality beyond that associated with daily use.
\nThird, our earlier study indicates augmented mortality associated with PRN use of any psychotropic but ameliorated mortality associated with daily use of certain psychotropics (e.g., antidepressants) [1]. Consequently, combinations that include PRN or daily use of other psychotropics have respective implications for increased or decreased mortality levels associated with antipsychotic use.
\nFourth, compliance and adherence to medication regimens are problematic among older people with chronic illness [13]. Anyone with work experience in long-term care settings knows that “residents who put pills into their mouths do not necessarily swallow them” [1]. Some residents chose to hide those pills, others throw them away. In effect, such ‘hidden’ non-compliance transforms daily prescriptions to intermittent usage, potentially with adverse effects on the estimated risk of mortality.
\nThe motivation that underlies the present research is to explain our earlier finding that daily use of antipsychotic medication ameliorates mortality, which contradicts conclusions reported in the majority of previous studies [9, 10, 11]. The research that follows analyzes the same database as our earlier chapter [1] to answer questions about the frequencies of concurrent combinations of antipsychotic with other psychotropic usage and their associations with subsequent mortality. As in the earlier study, the target variable is mortality within 90 days following the final RAI 2.0 assessment. The reason for this duration is that successive RAI 2.0 assessments occur at approximately 90-day intervals.
\nTo simplify the presentation of results, we limit the control variables in analyses of mortality to the Changes in Health, End-Stage Disease, Signs, and Symptoms Scale (CHESS), which is an established indicator of mortality risk [14]. Although preliminary analyses also included demographic measures of age, gender and objective scales from the RAI 2.0 that include the Cognitive Performance Scale, the Activities of Daily Living Hierarchy and the Aggressive Behavior Scale (ABS), their inclusion fails to add appreciably to an interpretation of effects associated with the primary predictor variable. The latter is represented in \nTable 1\n by concurrent combinations of antipsychotic use and other psychotropic use.
\nThe 1st and 2nd columns in \nTable 1\n represent combinations of concurrent usage of antipsychotic and other psychotropic medications. The frequencies for antipsychotics include no use, PRN use and daily use. The inclusive frequencies all other types of psychotropic are no use, PRN and daily use, only PRN use and only daily use. The 3rd, 4th, 5th and 6th columns represent possible combinations of antipsychotics with antidepressant, analgesic, anxiolytic or hypnotic medications, respectively. The possible frequencies for each of the latter are no use, PRN use and daily use.
\nThe main hypotheses derive from our previous findings that, after control of the major risk factor for mortality, daily use of psychotropic medication ameliorates risk, whereas PRN use exacerbates risk. Consequently, we anticipate that combinations of antipsychotics with the daily use of other psychotropics ameliorate mortality to levels below that associated with absence of psychotropic use. In contrast, we predict augmented mortality associated with combinations of antipsychotic and other psychotropics that involve PRN use.
\nThe participants are all new admissions, aged 65 years and older, to LTCHs in the Canadian province of Ontario during the financial year April 1st 2010 to March 31st 2011. They include 20,414 residents from 631 LTCHs. The distribution of men to women is 33.6% to 66.4%. The mean age of men is 83.03 years with a standard deviation of 7.37 years. The mean age of women is 85.29 years with a standard deviation of 7.19 years.
\nThe main assessment tool used here is the RAI 2.0, which, to the authors’ knowledge, (1) is used in more countries, (2) has a more thorough psychometric evaluation, and (3) is more comprehensive than any other geriatric assessment tool. The RAI 2.0 requires trained heath care professionals to score quantifiable assessment items relevant to medical diagnoses, levels of functioning, behavioral and emotional problems, forms of treatment, etc. The information is from medical records, clinical observations, and communication with residents, their family members and the facility’s staff members. As already indicated, the RAI also contains objective scales that are evaluated against ‘gold standard’ measures from the relevant literature. The measures in the present analyses are the CHESS and items on antipsychotic, analgesic, antidepressant, anxiolytic and hypnotic medication use. The latter items record the number of days of usage during the week preceding an assessment. We report here on three usage categories: no use, PRN (i.e., intermittent) use, and daily use.
\nThe RAI 2.0 also provides information on the mortality of residents in a LTCH. Other databases linked to the RAI 2.0 are the Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS). The DAD reports mortality data for hospital discharges and the NACRS reports mortality in settings for emergency and ambulatory care. Consequently, our data encompasses mortality throughout the health care system. We are grateful to the Canadian Institute for Health Information (CIHI) for the provision of the data with encrypted personal and facility level identifiers.
\nResidents receive RAI 2.0 assessments upon admission and thereafter at quarterly intervals. The maximal follow-up period in the present study is 1-year. We report here on data from the final assessment, with mortality indexed by its absence or presence during 90 days following that assessment (i.e., a period that precedes the scheduled date of any subsequent assessment).
\nThe statistical analyses relate to three issues. The first concerns the types of concurrent combination of usage frequencies between antipsychotics with other psychotropics. These analyses begin graphic and tabular statistics that relate to concurrent relationships between frequencies antipsychotic usage with frequencies for other types of psychotropic usage. Then follows findings from Statistical Package for the Social Sciences (SPSS Version 25) GLMM multinomial analysis of frequencies of antipsychotic usage (i.e., the target variable) against corresponding frequencies for each other psychotropic (i.e., the fixed effect variables). The random variable for this and every subsequent GLMM analysis are LTCHs.
\nThe second issue concerns mortality within 90 days of the final assessment. The primary analysis is a GLMM interval censored survival model (i.e., a binomial distribution with a complementary log–log link). The CHESS (i.e., centered on its grand mean) and concurrent combinations of frequencies for antipsychotic and other psychotropic usage comprise the fixed effects. Then follows GLMM interval censored survival models that attempt to clarify implications of the preceding by analyzing summative categories that respectively relate to antipsychotic use and other psychotropic use. Next, we analyze models that examine combinations of antipsychotics with each type of psychotropic. The purpose is to ascertain the types of antipsychotic that may ameliorate of exacerbate risk of mortality at different levels of usage. All the latter models include the CHESS as a measure of mortality risk.
\nThe final issue concerns the effects on mortality of changes in health condition and prescribing practices from the penultimate to final assessment. This GLMM analysis examines whether changes in the CHESS and PRN prescriptions have independent implications for survival. In contrast, an alternative hypothesis suggests that changes in PRN prescription are a consequence of changes in health condition, with the former having with no direct implications for survival.
\nThe following graph and table illustrate relationships between frequencies of usage for antipsychotic medication with corresponding usage of all other psychotropic medications. \nFigure 2\n shows 95% confidence intervals for the totality of any other psychotropic use against no use, PRN, and daily use for antipsychotic medication. The mean use of other psychotropic medication is significantly lower with no use of antipsychotic medication than for PRN and daily use, as evidenced by non-overlapping confidence intervals. \nTable 2\n shows percentages of residents with a given frequency of antipsychotic medication combined with the use of 1, 2, 3 or 4 other psychotropic medications. The statistical mode (i.e., the most frequent value) within columns of this table indicates that residents without antipsychotics most frequently receive one other psychotropic, whereas those with PRN and daily antipsychotic use most frequently use two other psychotropic medications.
\n95% confidence intervals for summative Co-medication frequencies for other psychotropics against frequencies for antipsychotic medication.
Antipsychotic prescription | \n\nPrescriptions for other psychotropics\n | \n||||
---|---|---|---|---|---|
All psychotropics | \nAntidepressant | \nAnalgesic | \nAnxiolytic | \nHypnotic | \n|
None | \nNone | \nNone | \nNone | \nNone | \nNone | \n
None | \nPRN & Daily | \n. | \n. | \n. | \n. | \n
None | \nPRN | \nPRN | \nPRN | \nPRN | \nPRN | \n
None | \nDaily | \nDaily | \nDaily | \nDaily | \nDaily | \n
PRN | \nNone | \nNone | \nNone | \nNone | \nNone | \n
PRN | \nPRN & Daily | \n. | \n. | \n. | \n. | \n
PRN | \nPRN | \nPRN | \nPRN | \nPRN | \nPRN | \n
PRN | \nDaily | \nDaily | \nDaily | \nDaily | \nDaily | \n
Daily | \nNone | \nNone | \nNone | \nNone | \nNone | \n
Daily | \nPRN & Daily | \n. | \n. | \n. | \n. | \n
Daily | \nPRN | \nPRN | \nPRN | \nPRN | \nPRN | \n
Daily | \nDaily | \nDaily | \nDaily | \nDaily | \nDaily | \n
Antipsychotic prescription frequencies combined with frequencies for other antipsychotic medications on the final assessment.
Number of other of psychotropics | \nPercentage of residents | \n||
---|---|---|---|
Antipsychotic use | \n|||
None | \nPRN | \nDaily | \n|
0 | \n18.6% | \n13.6% | \n13.2% | \n
1 | \n41.4% | \n35.6% | \n36.6% | \n
2 | \n31.8% | \n38.6% | \n38.9% | \n
3 | \n7.7% | \n11.2% | \n10.4% | \n
4 | \n0.5% | \n1.0% | \n1.0% | \n
Percentage of residents with No, PRN or daily use of antipsychotics 1, 2, 3 or 4 other psychotropics.
The following figures illustrate frequencies of use of specific psychotropics that accompany no, PRN or daily use of antipsychotics. \nFigure 3\n shows findings associated with antidepressant medication. The findings indicate that approximately 60% of residents with daily antipsychotics and just over 40% of those with no antipsychotics receive antidepressants on a daily basis. Of those residents with PRN use of antipsychotics, the majority show either PRN (18%) or daily (35%) use of antidepressants.
\nPercentage antidepressant frequency against antipsychotic frequency.
Levels of analgesic medication are uniformly high. \nFigure 4\n shows PRN or daily use among approximately 65–70% of residents regardless of frequency of usage of antipsychotic medication. Consistent with finding for antidepressants and anxiolytics (see below), the highest PRN use of analgesics corresponds with PRN use antipsychotic medication (approximately, 22%).
\nPercentage analgesic frequency against antipsychotic frequency.
\n\nFigure 5\n indicates a low overall use of anxiolytic medication. The levels of daily use are approximately 7–10% regardless of frequency of use for antipsychotics. However, among residents with PRN use of antipsychotics, PRN use of anxiolytics is approximately 14%, which is considerably higher than daily use for this subgroup of residents.
\nPercentage Anaxiolytic frequency against antipsychotic frequency.
\n\nFigure 6\n shows hypnotic use to be lower than for any of other psychotropic (i.e., approximately 6.3% of residents). The highest PRN use of hypnotics occurs in combination with PRN use of antipsychotics. Daily use of hypnotics has approximately similar levels among residents with no of daily use of antipsychotic medication.
\nPercentage hypnotic frequency against antipsychotic frequency.
Inferences from the preceding graphs and table include the following. First, residents with PRN or daily use of antipsychotics have higher concurrent use of other psychotropic medications than those without antipsychotic use. \nTable 2\n shows that over 80% of residents without antipsychotic use receive at least one other psychotropic. The psychotropics that appear most frequently in these combinations are antidepressants and analgesics, which have the highest overall frequency of usage. Second, PRN use of antipsychotics combines with the highest PRN in each other psychotropic category. This finding suggests a clustering of PRN prescribing that encompasses all types of psychotropic medication.
\nThe final analysis this section is a GLMM multinomial analysis. This analysis includes LTCHs as a random variable and fixed effect predictors that evaluate the independent contributions by other psychotropics to frequencies of antipsychotic use. The target and predictor variables are on nominal scales of no use, PRN use and daily use, respectively, with the former designated as reference category.
\nThe findings in \nTable 3\n include the regression coefficients, standard errors, levels of statistical significance and 95% confidence intervals. \nTable 3\n also includes derivative exponential coefficient for readers that prefer odds ratios over regression coefficients. Positive or negative regression coefficients respectively indicate mean values above or below those associated with the reference category, with odds ratios greater or less than unity having comparable meaning. The overall findings for the model include significant random effects of LTCHs at p < .001. Findings for the fixed effect terms are as follows.
\nAntipsych. frequency | \nModel term | \nCoefficient | \nStd. error | \nSig. | \n95% confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|---|
Lower | \nUpper | \n\n | |||||
Daily | \nIntercept | \n−1.119 | \n.0372 | \n.000 | \n−1.192 | \n−1.046 | \n.327 | \n
Antidepress. Daily | \n.682 | \n.0329 | \n.000 | \n.617 | \n.746 | \n1.977 | \n|
Antidepress. PRN | \n−.242 | \n.1705 | \n.155 | \n−.576 | \n.092 | \n.785 | \n|
Antidepress. None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Analgesic Daily | \n−.134 | \n.0347 | \n.000 | \n−.201 | \n−.066 | \n.875 | \n|
Analgesic PRN | \n−.384 | \n.0701 | \n.000 | \n−.521 | \n−.246 | \n.681 | \n|
Analgesic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Anxiolytic Daily | \n.207 | \n.0484 | \n.000 | \n.112 | \n.302 | \n1.230 | \n|
Anxiolytic PRN | \n.561 | \n.0863 | \n.000 | \n.392 | \n.730 | \n1.753 | \n|
Anxiolytic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Hypnotic Daily | \n−.033 | \n.0694 | \n.634 | \n−.169 | \n.103 | \n.967 | \n|
Hypnotic PRN | \n−.302 | \n.2145 | \n.160 | \n−.722 | \n.119 | \n.740 | \n|
Hypnotic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
PRN | \nIntercept | \n−4.151 | \n.1246 | \n.000 | \n−4.395 | \n−3.907 | \n.016 | \n
Antidepress. Daily | \n.054 | \n.1330 | \n.683 | \n−.206 | \n.315 | \n1.056 | \n|
Antidepress. PRN | \n2.378 | \n.1898 | \n.000 | \n2.006 | \n2.750 | \n10.788 | \n|
Antidepress. None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Analgesic Daily | \n−.120 | \n.1398 | \n.389 | \n−.394 | \n.154 | \n.887 | \n|
Analgesic PRN | \n.703 | \n.1777 | \n.000 | \n.355 | \n1.052 | \n2.020 | \n|
Analgesic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Anxiolytic Daily | \n−.312 | \n.2373 | \n.188 | \n−.777 | \n.153 | \n.732 | \n|
Anxiolytic PRN | \n1.399 | \n.1907 | \n.000 | \n1.025 | \n1.773 | \n4.050 | \n|
Anxiolytic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Hypnotic Daily | \n−.392 | \n.3289 | \n.234 | \n−1.036 | \n.253 | \n.676 | \n|
Hypnotic PRN | \n.986 | \n.3710 | \n.008 | \n.259 | \n1.713 | \n2.680 | \n|
Hypnotic None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effects coefficients and odds ratios for prediction of antipsychotic frequency by frequencies of all other antipsychotic categories.
Daily antidepressants, daily anxiolytics and PRN anxiolytics are all positive predictors of daily antipsychotic use (all p < .001). Daily analgesics and PRN analgesics are negative predictors (both p < .001). These findings suggest that psychotropics purportedly relevant to mood improvement and anxiety reduction are likely to accompany daily antipsychotic use, whereas medications purportedly relevant to pain relief are less likely to occur in combination with daily antipsychotic medication.
\nPRN use of antidepressants (p < .001), anxiolytics (p < .001), analgesics (p < .001) and hypnotics (p < .001) are positive predictors of PRN use of antipsychotics. There are no significant relationships between daily use of other psychotropics and PRN use of antipsychotics. These findings indicate a clustering of PRN prescribing.
\nMortality during the 1-year follow-up period of data collection is 18.1% overall. The mortality rates for men and women are 21.1% and 16.3% respectively. The distribution of mortality across assessments indicates that 45% of residents died within 90 days of the admission assessment, with a decreasing proportion of deaths at each subsequent assessment.
\nThe primary interval censored survival analysis shows a significant random effect for LTCHs p < .001. Because the same level of significance is present in all subsequent GLMM analyses, we need not report them henceforth. \nTable 4\n shows findings for the fixed effects. Unsurprisingly, the positive coefficient for the CHESS indicates higher mortality for residents at greater risk of mortality. The reference category for combinations of medications is the daily use of both antipsychotics and other psychotropics, which numerically is associated with the lowest level of mortality. This combination has significantly lower mortality (p < .005 or beyond) than any other combination except for those that combine no antipsychotics with daily psychotropics and PRN use of antipsychotics with daily psychotropics. An implication is that daily use of psychotropics ameliorates mortality associated with antipsychotics to levels below that associated with no use of the latter.
\nFixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n|||||
Intercept | \n−1.938 | \n.0412 | \n.000 | \n−2.019 | \n−1.858 | \n.14 | \n
CHESS | \n.569 | \n.0136 | \n.000 | \n.543 | \n.596 | \n1.77 | \n
AP, None: PT, None | \n.366 | \n.0600 | \n.000 | \n.248 | \n.483 | \n1.44 | \n
AP, None: PT, Mixed | \n.552 | \n.0776 | \n.000 | \n.400 | \n.704 | \n1.74 | \n
AP, None: PT, PRN | \n.643 | \n.0782 | \n.000 | \n.490 | \n.797 | \n1.90 | \n
AP, None: PT, Daily | \n.084 | \n.0459 | \n.066 | \n−.005 | \n.174 | \n1.09 | \n
AP, PRN: PT, None | \n.999 | \n.2851 | \n.000 | \n.441 | \n1.558 | \n2.72 | \n
AP, PRN: PT, Mixed | \n.818 | \n.2480 | \n.001 | \n.332 | \n1.304 | \n2.27 | \n
AP, PRN: PT, PRN | \n.910 | \n.1978 | \n.000 | \n.522 | \n1.298 | \n2.48 | \n
AP, PRN: PT, Daily | \n.346 | \n.1739 | \n.047 | \n.005 | \n.687 | \n1.41 | \n
AP, Daily: PT, None | \n.273 | \n.0944 | \n.004 | \n.088 | \n.458 | \n1.31 | \n
AP, Daily: PT, Mixed | \n.421 | \n.1036 | \n.000 | \n.218 | \n.624 | \n1.52 | \n
AP, Daily: PT, PRN | \n.549 | \n.1537 | \n.000 | \n.248 | \n.850 | \n1.73 | \n
AP, Daily: PT, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and other psychotropic (PT) frequencies in prediction of mortality.
A Bonferroni multiple comparison with the combination that includes neither antipsychotic nor any other psychotropic provides further support for this inference. The only other combination with significantly lower mortality than zero use of any psychotropic is that of no antipsychotics but daily use of other psychotropics (p < .001). Consequently, the latter ameliorates mortality below the level associated with zero psychotropic medications.
\nThe next two analyses condense the preceding array of combinations into those associated with antipsychotic use (i.e., none, PRN and daily) and other psychotropic use, respectively (i.e., none, mixed, PRN and daily). Both analyses include the CHESS, with daily use as the reference category for the combinational variable. The findings in \nTable 5\n show the findings from the analysis of psychotropic use. In addition to significance for the CHESS, daily use of antipsychotics is associated with significantly lower mortality than no use or PRN use (p < .005 or beyond). Moreover, a Bonferroni multiple comparison shows that no use has a significantly lower level of mortality than PRN use. These findings replicate the trends for antipsychotic use reported in our earlier publication [1].
\nFixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n|||||
Intercept | \n−1.846 | \n.0355 | \n.000 | \n−1.915 | \n−1.776 | \n.16 | \n
CHESS | \n.577 | \n.0134 | \n.000 | \n.550 | \n.603 | \n1.78 | \n
No Antipsychotics | \n.113 | \n.0380 | \n.003 | \n.039 | \n.188 | \n1.12 | \n
PRN Antipsychotics | \n.566 | \n.1095 | \n.000 | \n.352 | \n.781 | \n1.76 | \n
Daily Antipsychotics | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for combined categories of antipsychotic frequencies with other psychotropic medications.
\n\nTable 6\n shows findings from the analysis of the use of other psychotropics. With daily use as the reference category, no use, mixed use and PRN use are associated with higher levels of mortality (p < .005 or beyond). Sequential Bonferroni multiple comparisons of no, mixed and PRN use reveal higher mortality for PRN than no use (p < .001), with no comparison that involves mixed use significant at p < .01 level. These findings suggest that daily use of other psychotropics has ameliorative effects on mortality. \nFigure 7\n provides a graphic portrayal of the combined finding from last two analyses, indicating inverted-V or inverted-U structures corresponding to frequencies of no, mixed, PRN and daily use, with lowest frequencies associated with daily use of other psychotropics.
\nFixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n|||||
Intercept | \n−1.879 | \n.0262 | \n.000 | \n−1.930 | \n−1.827 | \n.15 | \n
CHESS | \n.572 | \n.0135 | \n.000 | \n.546 | \n.598 | \n1.77 | \n
No Psychotropics | \n.294 | \n.0455 | \n.000 | \n.205 | \n.383 | \n1.34 | \n
Mixed Psychotropics | \n.457 | \n.0578 | \n.000 | \n.344 | \n.570 | \n1.58 | \n
PRN Psychotropics | \n.589 | \n.0626 | \n.000 | \n.466 | \n.712 | \n1.80 | \n
Daily Psychotropics | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for combined categories of other psychotropic medication use with antipsychotic use.
Percentage mortality for frequency combinations of antipsychotic with other psychotropic medication.
The following GLMM interval censored survival analyses examine combinations of antipsychotic with separate types of other psychotropic. These combinations correspond to frequencies of usage outlined in last four columns of \nTable 1\n. All these analyses include the CHESS among the fixed effects, with daily usage of both an antipsychotic and the other specified psychotropic as the reference category for combinations.
\n\n\nTables 7\n–\n10\n show fixed effect findings for combinations that include antidepressants, analgesics, anxiolytics and hypnotics respectively. \nTables 7\n and \n8\n show coefficients for the combinations that include the most frequently used psychotropics. \nTable 7\n shows significantly lower mortality for a combination of daily antipsychotic with antidepressant use than for two of three combinations without antipsychotics (p < .001); the exception being a combination of no antipsychotics with daily antidepressants. \nTable 8\n shows comparable findings for the combination of daily antipsychotics with analgesic use. Also, every combination that includes PRN use of an antipsychotic and/or another psychotropic has significantly higher mortality than the reference category.
\nFixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n|||||
Intercept | \n−1.984 | \n.0464 | \n.000 | \n−2.075 | \n−1.893 | \n.14 | \n
CHESS | \n.575 | \n.0135 | \n.000 | \n.549 | \n.601 | \n1.78 | \n
AP, None: AD, None | \n.370 | \n.0510 | \n.000 | \n.270 | \n.470 | \n1.45 | \n
AP, None: AD, PRN | \n.718 | \n.1374 | \n.000 | \n.448 | \n.987 | \n2.05 | \n
AP, None, AD, Daily | \n.069 | \n.0547 | \n.207 | \n−.038 | \n.176 | \n1.07 | \n
AP, PRN: AD, None | \n.819 | \n.1541 | \n.000 | \n.517 | \n1.121 | \n2.27 | \n
AP, PRN: AD, PRN | \n1.040 | \n.2203 | \n.000 | \n.608 | \n1.472 | \n2.83 | \n
AP, PRN, AD, Daily | \n.301 | \n.2127 | \n.157 | \n−.116 | \n.718 | \n1.35 | \n
AP, Daily: AD, None | \n.320 | \n.0647 | \n.000 | \n.193 | \n.447 | \n1.38 | \n
AP, Daily: AD, PRN | \n.625 | \n.2955 | \n.034 | \n.046 | \n1.204 | \n1.87 | \n
AP, Daily, AD, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and antidepressant (AD) use in prediction of mortality.
Fixed Effects | \nCoefficient | \nStd. Error | \nSig. | \n95% Confidence Interval | \nExponential Coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n\n | ||||
Intercept | \n−1.891 | \n.0450 | \n.000 | \n−1.979 | \n−1.803 | \n.15 | \n
CHESS | \n.573 | \n.0136 | \n.000 | \n.546 | \n.599 | \n1.77 | \n
AP, None: AN, None | \n.225 | \n.0551 | \n.000 | \n.117 | \n.333 | \n1.25 | \n
AP, None: AN, PRN | \n.566 | \n.0714 | \n.000 | \n.426 | \n.706 | \n1.76 | \n
AP, None, AN, Daily | \n.055 | \n.0501 | \n.276 | \n−.044 | \n.153 | \n1.06 | \n
AP, PRN: AN, None | \n.701 | \n.2015 | \n.001 | \n.306 | \n1.096 | \n2.02 | \n
AP, PRN: AN, PRN | \n.721 | \n.2139 | \n.001 | \n.302 | \n1.140 | \n2.06 | \n
AP, PRN, AN, Daily | \n.510 | \n.1579 | \n.001 | \n.200 | \n.819 | \n1.67 | \n
AP, Daily: AN, None | \n.012 | \n.0705 | \n.860 | \n−.126 | \n.151 | \n1.01 | \n
AP, Daily: AN, PRN | \n.546 | \n.1117 | \n.000 | \n.327 | \n.764 | \n1.73 | \n
AP, Daily, AN, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and analgesic (AN) use in prediction of mortality.
\n\nTables 9\n and \n10\n show findings for combinations of antipsychotic use with anxiolytic and hypnotic use, respectively. Neither psychotropic has a high prevalence of usage in LTCHs. The findings mainly indicate non-significant differences in mortality against the reference category. The significant differences include higher mortality than for the reference category for combinations that include PRN use of antipsychotic or another psychotropic (p < .001).
\nFixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n|||||
Intercept | \n−1.925 | \n.0886 | \n.000 | \n−2.099 | \n−1.751 | \n.15 | \n
CHESS | \n.574 | \n.0134 | \n.000 | \n.548 | \n.601 | \n1.78 | \n
AP, None: AX, None | \n.199 | \n.0898 | \n.027 | \n.023 | \n.375 | \n1.22 | \n
AP, None: AX, PRN | \n.537 | \n.1322 | \n.000 | \n.278 | \n.797 | \n1.71 | \n
AP, None, AX, Daily | \n.042 | \n.1078 | \n.695 | \n−.169 | \n.254 | \n1.04 | \n
AP, PRN: AX, None | \n.546 | \n.1503 | \n.000 | \n.251 | \n.840 | \n1.73 | \n
AP, PRN: AX, PRN | \n1.143 | \n.2503 | \n.000 | \n.652 | \n1.633 | \n3.14 | \n
AP, PRN, AX, Daily | \n.649 | \n.4058 | \n.110 | \n−.146 | \n1.444 | \n1.91 | \n
AP, Daily: AX, None | \n.081 | \n.0941 | \n.390 | \n−.104 | \n.265 | \n1.08 | \n
AP, Daily: AX, PRN | \n.302 | \n.1580 | \n.056 | \n−.007 | \n.612 | \n1.35 | \n
AP, Daily, AX, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and anxiolytic (AX) use in prediction of mortality.
Fixed effects | \nCoefficient | \nStd. error | \nSig. | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n\n | ||||
Intercept | \n−1.707 | \n.1230 | \n.000 | \n−1.948 | \n−1.466 | \n.18 | \n
CHESS | \n.576 | \n.0134 | \n.000 | \n.550 | \n.602 | \n1.78 | \n
AP, None: HY, None | \n−.027 | \n.1237 | \n.828 | \n−.269 | \n.216 | \n.97 | \n
AP, None: HY, PRN | \n.657 | \n.2081 | \n.002 | \n.249 | \n1.065 | \n1.93 | \n
AP, None, HY, Daily | \n−.123 | \n.1495 | \n.411 | \n−.416 | \n.170 | \n.88 | \n
AP, PRN: HY, None | \n.450 | \n.1633 | \n.006 | \n.130 | \n.770 | \n1.57 | \n
AP, PRN: HY, PRN | \n.001 | \n.5223 | \n.999 | \n−1.023 | \n1.025 | \n1.00 | \n
AP, PRN, HY, Daily | \n.350 | \n.5583 | \n.531 | \n−.744 | \n1.444 | \n1.42 | \n
AP, Daily: HY, None | \n−.149 | \n.1264 | \n.239 | \n−.397 | \n.099 | \n.86 | \n
AP, Daily: HY, PRN | \n−.009 | \n.4182 | \n.982 | \n−.829 | \n.810 | \n.99 | \n
AP, Daily, HY, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and hypnotic (HY) use in prediction of mortality.
A final analysis in this section relates mortality to the duration of residence in a LTCH. Because previous reviews indicate higher mortality during the beginning phase of antipsychotic use, we would be remiss not to examine such effects [10, 11]. We report at the beginning of this section that nearly half the deaths occurred within 90 days of the admission assessment. Consequently, the following GLMM multinomial analysis uses as the target variable categories of (1) death after the admission assessment, (2) death after subsequent assessments, with (3) absence of mortality as the reference category. Findings in \nTable 11\n for death after the initial assessment indicate significantly lower mortality for the daily antipsychotic with other psychotropic use combination than for any other combination (p < .005 and beyond). Bonferroni multiple comparison also shows that the no antipsychotic but other daily psychotropic use combination has lower mortality than the combination with neither antipsychotic nor other psychotropic use (p < .001). These findings are comparable to those reported for mortality over the full range of assessments. However, the findings for mortality after the admission assessment show no significant effects. We conclude, therefore, that effects associated with the medicinal combinations are stronger for mortality that occurs shortly after the admission assessment.
\nAssessment | \nModel term | \nCoefficient | \nStd. error | \nSig | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|---|
Lower | \nUpper | \n\n | |||||
Initial | \nIntercept | \n−2.932 | \n.0717 | \n.000 | \n−3.072 | \n−2.791 | \n.053 | \n
CHESS | \n.557 | \n.0230 | \n.000 | \n.512 | \n.602 | \n1.745 | \n|
AP, None: PT, None | \n.906 | \n.0961 | \n.000 | \n.717 | \n1.094 | \n2.474 | \n|
AP, None: PT, Mixed | \n1.211 | \n.1223 | \n.000 | \n.971 | \n1.450 | \n3.356 | \n|
AP, None: PT, PRN | \n1.423 | \n.1187 | \n.000 | \n1.190 | \n1.655 | \n4.148 | \n|
AP, None: PT, Daily | \n.420 | \n.0802 | \n.000 | \n.263 | \n.577 | \n1.522 | \n|
AP, PRN: PT, None | \n2.079 | \n.3858 | \n.000 | \n1.323 | \n2.835 | \n7.995 | \n|
AP, PRN: PT, Mixed | \n1.605 | \n.4089 | \n.000 | \n.803 | \n2.406 | \n4.977 | \n|
AP, PRN: PT, PRN | \n1.714 | \n.3130 | \n.000 | \n1.100 | \n2.327 | \n5.549 | \n|
AP, PRN: PT, Daily | \n.819 | \n.2808 | \n.004 | \n.268 | \n1.369 | \n2.268 | \n|
AP, Daily: PT, None | \n.688 | \n.1474 | \n.000 | \n.399 | \n.977 | \n1.991 | \n|
AP, Daily: PT, Mixed | \n.839 | \n.1701 | \n.000 | \n.506 | \n1.172 | \n2.314 | \n|
AP, Daily: PT, PRN | \n.768 | \n.2735 | \n.005 | \n.232 | \n1.304 | \n2.156 | \n|
AP, Daily: PT, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n|
Subsequent | \nIntercept | \n−2.315 | \n.0558 | \n.000 | \n−2.424 | \n−2.206 | \n.099 | \n
CHESS | \n.773 | \n.0214 | \n.000 | \n.731 | \n.815 | \n2.166 | \n|
AP, None: PT, None | \n.006 | \n.0908 | \n.949 | \n−.172 | \n.184 | \n1.006 | \n|
AP, None: PT, Mixed | \n.152 | \n.1253 | \n.225 | \n−.094 | \n.397 | \n1.164 | \n|
AP, None: PT, PRN | \n.015 | \n.1361 | \n.912 | \n−.252 | \n.282 | \n1.015 | \n|
AP, None: PT, Daily | \n−.140 | \n.0636 | \n.028 | \n−.265 | \n−.015 | \n.869 | \n|
AP, PRN: PT, None | \n.206 | \n.5757 | \n.720 | \n−.922 | \n1.334 | \n1.229 | \n|
AP, PRN: PT, Mixed | \n.626 | \n.4299 | \n.145 | \n−.216 | \n1.469 | \n1.871 | \n|
AP, PRN: PT, PRN | \n.882 | \n.3216 | \n.006 | \n.252 | \n1.513 | \n2.416 | \n|
AP, PRN: PT, Daily | \n.058 | \n.2753 | \n.834 | \n−.482 | \n.597 | \n1.059 | \n|
AP, Daily: PT, None | \n.075 | \n.1397 | \n.589 | \n−.198 | \n.349 | \n1.078 | \n|
AP, Daily: PT, Mixed | \n.331 | \n.1522 | \n.030 | \n.033 | \n.629 | \n1.392 | \n|
AP, Daily: PT, PRN | \n.483 | \n.2271 | \n.033 | \n.038 | \n.928 | \n1.621 | \n|
AP, Daily: PT, Daily | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and combinations of antipsychotic (AP) and other psychotropic (PT) frequencies in the prediction of mortality after the first and later assessments.
The preceding analyses relate mortality to CHESS scores and prescription profiles on the final assessment. Questions raised in our preceding chapter concern issues about causality with respect to relationships between health and medicinal prescriptions, with potential implications for subsequent mortality [1]. One hypothesis is that changes toward higher PRN prescribing explains both worsening in health condition and subsequent mortality.
\nA second hypothesis is that worsening of health condition results in higher PRN prescribing and subsequent mortality, such that any relationship between PRN and mortality is artifactual rather than actual. A third hypothesis is that changes toward higher PRN prescribing and changes in health conditions make independent contributes to levels of mortality.
\nThe following GLMM analysis tests these hypotheses with the data necessarily restricted to the penultimate and final assessments among residents with two or more assessments. With mortality as the target variable, the fixed effects include binary scores of (1) high-risk scores versus low risk on the CHESS (i.e., high risk scores are 4 or 5 on a 5-point scale) (2) the presence or absence of any PRN prescription on the penultimate assessment; and (3) changes in the CHESS index and (4) the PRN index from the penultimate to final assessment. \nTable 12\n shows the findings.
\nModel term | \nCoefficient | \nStd. error | \nSig | \n95% Confidence interval | \nExponential coefficient | \n|
---|---|---|---|---|---|---|
Lower | \nUpper | \n\n | ||||
Intercept | \n−2.249 | \n.0308 | \n.000 | \n−2.309 | \n−2.188 | \n.053 | \n
CHESS Preceding, High | \n1.958 | \n.2346 | \n.000 | \n1.499 | \n2.418 | \n1.745 | \n
CHESS Preceding, Low | \n0 | \n. | \n. | \n. | \n. | \n2.474 | \n
CHESS Change, Worse | \n2.015 | \n.0756 | \n.000 | \n1.867 | \n2.163 | \n3.356 | \n
CHESS Change, Better | \n−1.052 | \n.2904 | \n.000 | \n−1.621 | \n−.482 | \n4.148 | \n
CHESS Change, None | \n0 | \n. | \n. | \n. | \n. | \n1.522 | \n
PRN Preceding, Present | \n.349 | \n.1005 | \n.001 | \n.152 | \n.545 | \n7.995 | \n
PRN Preceding, Absent | \n0 | \n. | \n. | \n. | \n. | \n4.977 | \n
PRN Change, Increase | \n.648 | \n.0731 | \n.000 | \n.505 | \n.791 | \n5.549 | \n
PRN Change, Decrease | \n−.123 | \n.1247 | \n.325 | \n−.367 | \n.122 | \n2.268 | \n
PRN Change, None | \n0 | \n. | \n. | \n. | \n. | \n1.00 | \n
Fixed effect coefficients for the CHESS and PRN levels on penultimate assessment and their changes from the penultimate to final assessments in the prediction of mortality after the final assessment.
Levels of mortality are significantly higher for high risk scores on the CHESS and the presence of PRN prescription. Changes on the CHESS toward worsening health are associated with significantly higher mortality, whereas changes toward lower risk scores are associated with significantly lower mortality, when compared an absence of change on the CHESS index. Compared to no change on the PRN index, an increased frequency of PRN prescription is associated with significantly increased mortality. Consequently, the findings indicate that detrimental levels and detrimental changes on the CHESS and PRN indexes contribute independently to higher levels of mortality.
\nOur previous research with this database [1] includes a number of resident-level and facility-level control variables from the RAI 2.0. The analyses reported here simplify the presentation of results by inclusion of only the CHESS as a control variable. The justification is that unreported analyses, which included a wider range of fixed effect predictors of mortality, did not substantially alter the present findings. We should also mention findings from unreported analyses with Cox regression, which is a common form of survival analysis that takes no account for correlated error in SPSS 25. Despite this limitation, the findings with Cox regression are otherwise comparable to those reported here.
\nThe present findings indicate that approximately 30% of residents are in receipt of antipsychotic medication, with more than 99% of those residents in receipt of at least one other psychotropic medication. The most frequently used among the latter are antidepressants and analgesics. The GLMM analysis in \nTable 3\n indicates that psychotropics with positive effects on mood and anxiety are frequently combined with daily use of antipsychotics, whereas analgesics are more frequent in residents without antipsychotic usage. PRN use of other types of psychotropic significantly predicts PRN use of antipsychotics, which indicates that residents typically receive PRN prescription for multiple types of psychotropic medication.
\nTo our knowledge, the study presented here is the first to examine how concurrent prescriptions of other psychotropics can affect elevated mortality among the elderly, which is attributed in many previous studies to the use of antipsychotics. Although limitations in present data includes absence of information on the types and dosages of psychotropics, a limitation common to previous studies is an absence of information on the frequencies of usage. Although prior evidence indicates the good overall quality of RAI 2.0 data [2], a limitation for present purposes is an absence of information about medicinal use prior to admission. A consequence is uncertainty about whether high mortality shortly after admission reflects effects associated with short-term antipsychotic use, relocation to a LTCH, or other unknown effects. However, the findings reported in \nTable 12\n on residents with at least two RAI 2.0 assessments indicate that changes in prescribing practices do have effects on mortality beyond those associated with changes in high risk health conditions measured by the CHESS. Consequently, we conclude that the relationship between PRN usage and mortality is one of primary determination, rather than secondary to the relationship between declining health and mortality.
\nThe overall findings on mortality support our hypotheses that daily use of other psychotropics may ameliorate mortality levels associated with antipsychotic use, whereas PRN use of other psychotropics augments that mortality. \nFigure 7\n provides a cogent illustration of the supportive findings. The specific psychotropics that support amelioration with daily use are antidepressants and analgesics, whereas concurrent PRN use of analgesics, anxiolytics and hypnotics are associated with augmented mortality. However, despite the high percentage of death among LTCH residents with PRN prescriptions on the final assessment, it must be remembered that only 12.9% are in receipt of such prescription.
\nImplications of the findings are that retrospective studies may incorrectly estimate the mortality associated with antipsychotic prescriptions by failure to take account of the deleterious effects of PRN usage and the beneficial effects of daily usage of other psychotropics. We reasoned in our previous chapter that the clinical rationale for psychotropic prescription is to renormalize disturbances to a resident’s equilibrium (e.g., aggression, depression, pain, anxiety, insomnia), with such disequilibrium considered a risk to wellbeing and mortality [1]. Successful treatment is associated with regained equilibrium after adaptation to regular prescription of the requisite medication. However, intermittent medication usage is antagonistic to adaptation, may exacerbate disequilibrium, with an elevation of mortality risk. Consequently, implications for caregiving of residents with BPSD may include daily antipsychotic and other daily psychotropic usage if non-pharmaceutical intervention fails to bring relief, but should avoid PRN usage of any form of psychotropic medication.
\nBehavioral disturbance is common among residents with dementia in LTCH. Such disturbance is associated with poor quality of life, caregiver burden and adverse health care outcomes. Although non-pharmacological procedures are recommended as the first line of treatment [15], the usual treatment in LTCHs includes the use of antipsychotics despite limited evidence for effectiveness and health outcomes reported to include elevated mortality. The research described here suggests that daily use of antipsychotics with daily use of other psychotropics (particularly antidepressants and analgesics) attenuate mortality whereas concurrent combinations that include PRN usage exacerbate mortality. The implications for caregiving include avoidance of PRN prescriptions of psychotropic medications.
\nAll the authors contributed to the research and manuscript preparation, and verified their authorship of this chapter. The authors wish to thank the editors, Robert Reynolds and Steven Day, for very helpful comments on an earlier draft.
\nNo author has any conflict of interest.
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