Percentage frequencies for usage of psychotropic medications.
\r\n\tWithin this scenario, special attention needs to be devoted to financial implications, due to their pervasiveness. Nobody would question the key role that finance plays to complement the real sphere of the economy and that has increasingly attracted both academics and practitioners. As a result, traditional pillars – such as financial markets, products, and institutions – have evolved significantly, with financial innovation fueling further progress over time. The global side of the coin features – among others – financially connected markets, international financial exchanges, and financial conglomerates that provide valuable opportunities in terms of international corporate finance. On the other side, recent advances have involved a wider recourse to ESG factors, allowed forward steps towards a more inclusive financial system, and have made digital finance a must, rather than an option, even though much remains to be accomplished, for instance, to facilitate access to formal financial channels in many underdeveloped regions.
\r\n\r\n\t
\r\n\tThis book aims to examine emerging trends, new perspectives, and empirical applications that deal with globalization and sustainability. The goal is to provide a comprehensive overview of these important concepts as valuable support to successfully meet the challenges and take on the opportunities ahead. At the same time, drawing upon empirical evidence can contribute to bridging the gap between theory and practice, which also fits within the scope of this book.
2018 was a period of interesting change in Malaysia. The General Election held on May 9 resulted in the ruling Barisan Nasional (BN) coalition, which has held power in the country ever since Malaysia’s independence in 1957, being voted out of power. In its place, the Pakatan Harapan (PH) coalition formed the new Malaysian government. It seemed that the unprecedented change of government also brought a new wave of hope for the Malaysian people. The Malaysian economy grew above market expectations in the fourth quarter of 2018, buoyed by strong domestic activities which grew 5.6% along with total external trade that grew at 6.9% [1].
\nThis was excellent news for the people, as earlier in the year in March 2018, there appeared to be a looming threat of a full-on trade war between the United States and China. China is Malaysia’s second largest trade partner at 13%, and 9% of Malaysian trade is with the United States, its third largest trade partner, respectively [2]. Worries about the trade war between these two countries had already caused Asian markets in general to trade lower—the KLCI (Kuala Lumpur Composite Index) dropped 0.6% to 1865.22 points, and Hong Kong’s Hang Seng Index dropped 2.5% to 30, 309.29 points after US and China announced tariffs on each other on 26 March 2018 [3]. Despite the threat of a trade war, there were potential benefits for Malaysia. It was thought that China may scale back purchases of US Treasury bonds, and divert capital flows to Ringgit assets, which will be beneficial to Malaysian Government Securities’ yields [2]. There is even the possibility that the price of oil, a key Malaysian export, will increase as a result of the trade war [2]. Indeed, by the fourth quarter of 2018, most Malaysians had a positive outlook on their personal finances and job prospects, with 70% of Malaysians believing that the state of personal finances in the next year would be excellent or good, and 71% had a positive view of their job prospects [4].
\nHowever, this positive mood had declined by the second quarter of 2019, with only 63% of Malaysians believing that the state of personal finances in the next year would be excellent or good, and 63% had a positive view of their job prospects [5]. In Ref., [5] also found that 70% of the Malaysian consumers already believed that the country was already in economic recession. In response, the central bank of Malaysia, Bank Negara Malaysia (BNM) signaled the banking industry that they should continue to give out more loans by reducing the Statutory Reserve Requirement (SRR) Ratio to 3% from 16 November 2019 [6]. This is the latest series of SRR ratio reduction by BNM, which started in 2016, when the central bank reduced the SRR from 4 to 3.5% [7]. This message was reinforced by BNM on 22 January 2020, when the Monetary Policy Committee of Bank Negara announced the reduction of the Overnight Policy Rate (OPR) to 2.75%, and considered the adjustment to the OPR as a pre-emptive measure to secure economic growth with price stability [8].
\nWhat had happened to Malaysia between 2018 and 2019 timeframe that had caused such a drastic change in its perceived economic fortunes is a point of conjecture at this time, but several external forces will be discussed here, which cumulatively, may explain the present situation that Malaysia has found herself in. Further, the recent shocking developments in the political scene in Malaysia on 24 February 2020 also add turmoil to the present situation of economic uncertainty. Through it all stands the Malaysian banker, duty bound to protect the interests of their respective employers, and at the same time fully aware that should they be too careful, they may just doom the country to another economic recession that they have tried so hard to avoid. Conversely, BNM is counting on the banking industry to continue to provide the necessary funds for economic activities despite the heightened risk of non-performing loans and the negative impacts this will have on the banking industry. It would almost seem that the BNM appears to be willing to expose the banking industry to more risks in order to protect the Malaysian economy.
\nGiving loans has always been an integral part of banking operations [9]. In 2018, CIMB Group, Malaysia’s second largest commercial bank by assets, recorded that its gross loans account for 63% of its total assets and Maybank, Malaysia’s largest commercial bank with over 20 million customers, recorded that its gross loans in 2018 is 62.8% over its total assets [10]. This shows the importance of lending to banks, as it is the most important income generating activity to banks [11]. Banks would evaluate potential customers, and then decide the level of risk such a loan would impose, charge the appropriate interest and disburse the amount requested, while ensuring that the capital borrowed is returned over the agreed time-frame of the loan. The interest charged will then yield interest income to the banks. However, there are instances when the borrower defaults on paying back the loan, and this causes the lending bank to suffer financial loss, as their income will be affected and potentially there is a danger that the capital loaned may be lost as well.
\nThis was the case in Malaysia during the Asian Financial Crisis of 1997–1998, when cases of Non-Performing Loans (NPLs)/Impared Loans (as expressed as the ratio of net impaired loans to net total loans in percentage) reached 8–9% [12]. Because of the Asian Financial Crisis of 1997, growth and innovation in Malaysia are constrained by banks whose capital has been eroded by accumulating non-performing loans [13]. This leads to the mergers of several banks that shaped the banking industry to the one that we see in Malaysia today [14].
\nTherefore, it is clear that anything that affects the banking sector’s lending activity will adversely affect the income of the banks. The factors that may affect such activities may be divided into two types of factors, internal factors (bank specific factors), and external factors like the economic climate or political climate. By its nature, external factors may not be readily controlled by the bank [14]. This makes it harder for banks to maintain profitable lending. Malaysian banks loan growth remained low at 5% as of June 2018, due to the poor performance of working capital loans [15] and loan growth was projected to remain flat at 5–6% level in 2019 [16]. The external factors are discussed next.
\nBrexit is a portmanteau for ‘Britain’ and ‘Exit’, following a referendum in the United Kingdom (UK) that resulted in London, Scotland and Northern Ireland electing to remain in the European Union (EU) while the rest of England and Wales deciding to leave the EU [17]. Following [18], there is little short-term impact of Brexit to Malaysia. The UK is not a large trading partner of Malaysia, and despite UK’s status as the financial center of Europe, any impact of Brexit will not have a big effect to Malaysian financial markets [18]. In fact, the weakened British Pound (GBP) will even make the UK a favourable destination for Malaysian students, and well-to-do Malaysians may even find it a good time to holiday there or to buy properties in the UK. This view is echoed by [19], despite the UK contributing RM 1.9 billion worth of Foreign Direct Investment (FDI) while the EU invested RM 30.3 billion in the same period in 2016, and estimated that Brexit will not affect FDI to Malaysia.
\nHowever, Brexit’s effect on the UK economy is expected to be not so benign. Following [20], a recession in the UK is a strong possibility. The Organization for Economic Cooperation and Development (OECD) has already predicted the UK economy to grow at only 1% in 2020. An impending economic recession in the UK will have a negative impact on the EU, since half of the UK’s imports come from the EU. In addition, with a general slowdown in the global economy caused by instability in global trade due to trade spats, the OEDC concludes that it is likely to dampen the EU’s growth rate to about 1% as well in 2020 [20].
\nGiven that Malaysian trade with the EU is about 10%, a weaker EU economy will impact the Malaysian economy in general. Further, with the expected weakening of the GBP against the Ringgit, Malaysian exports to the UK, already at a tiny 0.2% of total exports, will increasingly become less competitive in the UK market [20]. These events do not bode well for the Malaysian economy.
\nMalaysia has always been a trading nation, and international trade has consistently contributed to the Malaysian gross domestic product (GDP), and since 2017 its share of contribution to the GDP was higher than 130% [21]. Following [21], there is concern that the Malaysian economy will shift into lower gear on fears of a gloomier global economic outlook. The International Monetary Fund (IMF) had already reduced its 2019 forecast for the ASEAN region from 3.9 to 3.7%. Echoing a similar sentiment, the Asian Development Bank has put the region’s growth rate expectation at 5.8%. The sole underlying cause that contributed to these downward revisions is the US-China trade war.
\nAs discussed earlier, the US and China are two of Malaysia’s largest trade partners. Therefore, any trade policies that harm trade between these countries could affect Malaysia’s trade position, especially when Malaysia’s intermediate goods exports to China are significant [21]. Further, the weakening global markets will impact demand for electronics, one of the key Malaysian exports. Palm oil demand is also expected to fall, as does the price of oil [21].
\nThe slowdown in exports will result in the narrowing of the Malaysian current account surpluses [21] as well, giving the Malaysian government less money to spend on fiscal measures to stimulate the economy. Indeed, the 2020 Malaysian economic growth forecast had been continually downgraded, with Finch Solutions predicting only 3.7% expansion, down from an earlier projection of 4.7% [22]. To navigate the difficult economic situation, it is vital for the Malaysian government to establish forward-looking policies. Thus far, it appears to be contented to rely on BNM to apply monetary policies to shore up the economy. This may soon be insufficient.
\nWhen the Pakatan Harapan (PH) party took over the reins of power after the 2018 General Election, Tun Dr. Mahathir Mohamad was made Prime Minister with an understanding that there will be a transition plan for Datuk Seri Anwar Ibrahim to succeed him mid-term [23]. However, the PH government collapsed on Monday 24 February 2020 by Tun Dr. Mahathir’s shock resignation as Prime Minister. Tun Dr. Mahathir’s resignation was speculated to be caused by a power struggle between the Prime Minister and his planned successor [22]. Economists quickly opined that the political uncertainty this has caused is harmful to the Malaysian economy [22].
\nThe resignation by a country’s Prime Minister at any time during the tenure of a government has the effect of causing the entire cabinet to be dissolved [24]. Effectively, Malaysia does not have a government anymore. Unfortunately, the dissolution of the Malaysian government cannot come at a worse time, as in the past few months, an additional threat to the Malaysian economy had emerged in the form of a novel corona virus, called Covid-19 [25]. While Covid-19 is currently under control, the disease has already caused a huge impact on the global economy, and Malaysia’s economy has not been spared [25]. Accordingly, the Malaysian government was scheduled to announce an economic stimulus package to stimulate the economy on 27 February 2020. Unfortunately, the sudden resignation of the Prime Minister on 24 February 2020 has caused fears that the stimulus package has been put on hold [22, 25]. Indeed, this was stated by the former Finance Minister Lim Guan Eng on 25 February 2020 that Tun Dr. Mahathir will announce the economic stimulus at a later date [26].
\nUnease about the political uncertainty arising from a suddenly-defunct government and its possible effect on the Malaysian economy prompted BNM to announce on 25 February 2020 that it was closely monitoring the financial markets, and stated that there was sufficient liquidity in the financial markets [27]. The political situation had settled down somewhat by the 27th of February 2020, with Tun Dr. Mahathir appointed Interim Prime Minister. The irony of his actions now fully realized, Tun Dr. Mahathir had stated that he had intended to resign from his job, but now only he among his cabinet colleagues still had one.
\nAs such, Tun Dr. Mahathir was able to announce the anxiously awaited economic stimulus on 27 February 2020. The economic stimulus package has three strategies—to manage the impact of Covid-19, spur a people-centric growth and encourage quality investment. With immediate effect, government employees who are in medical profession will receive an extra allowance of RM 400 a month, while Immigration and other civil servants involved with the fight against Covid-19 will receive RM 200 extra monthly, until the outbreak is over [25].
\nThe effectiveness of the stimulus package to counter the effects of Covid-19 remains to be seen. Goldman Sachs warned that it does not expect US companies to generate earnings growth in 2020 due to the Covid-19 virus [28]. It also expected that a more severe pandemic would lead to a more prolonged disruption and even a US recession. Goldman Sachs also expected a severe decline in the Chinese economic activities as well [28]. Indeed, blue chip US companies like Microsoft, Apple, Booking Holdings and Anheuser-Busch InBev are already reporting that they no longer expect to meet revenue targets due to Covid-19 [28, 29].
\nWith Malaysia’s major trading partners expected to experience economic troubles, the Malaysian economy is expected to experience significant challenges going forward. Following [30], the Malaysian economic growth rate is expected to be 2.5% at the end of the first quarter of 2020. It is expected to slowly recover to 4.4% by the same time period next year [30]. How does this data on economic performance affect Malaysian bankers?
\nFollowing [11], economic and political factors have the potential to adversely affect the banking sector’s lending activity and this will adversely affect the income of the banks. Consumer lending risks can be moderated by banks using computerized credit reporting as provided by CTOS Data Systems Sdn Bhd and BNM. CTOS produces the MyCTOS Basic Report and MyCTOS Score Report by using information sourced from various public sources such as the National Registration Department, Registrar of Societies, the Malaysian Insolvency Department, Companies Commission of Malaysia and publication of legal proceedings or litigation records [31]. BNM also provides credit reports and this is managed by the Credit Bureau of BNM, known as the Central Credit Reference Information System (CCRIS). CCRIS gathers credit related information from all borrowers of participating financial institutions in Malaysia, and this includes all licensed commercial banks, Islamic banks, investment banks, development financial institutions, insurance companies, payment instrument issuers, rehabilitation companies, building societies, credit leasing companies and government agencies. These financial institutions are required to report on a regular basis information like profiles of borrowers (name, business or personal identification number, date of birth/registration, and address, among others) credit application details (amount of borrowing applied, date of application, type of facility) and credit account details (type of credit facilities, credit limit, outstanding balance, instalment amount, conduct of account and legal action status, if any) [31].
\nTherefore, it is possible for financial institutions in Malaysia to access up-to-date financial records for any potential individual borrower and assess the suitability of lending risk by obtaining CCRIS or CTOS financial data. Even so, despite such a system in place, banks continue to suffer cases of Non-Performing Loans (NPLs)/Impaired Loans. Data from BNM show the ratio of net impaired loans to net total loans in percentage [32] of a high of 9% in November 1998 to a low of 1.2% in January 2016, with this figure hovering around 1.2% throughout 2015. While the reduction of impaired loans since 1998 has been significant, the banking industry has seemingly been unable to completely eradicate impaired loans/NPLs. As a result of this inability, the collective impairment provisions that has to be made to account for the impaired loans totaled in the region of RM 16,354 million in January 2016 alone, and this figure increased to RM 23, 914.4 million in February 2019, even though the ratio of net impaired loans to net total loans in percentage has been reduced to 0.93% by February 2019. Credit scoring methods are by nature quantitative methods of evaluating credit and are only as good as the scoring algorithms that predict consumer behavior. If it is implemented properly, then cases of impaired loans should not have happened at all.
\nMore worryingly for bankers, such a system does not exist for another important consumer group of lending, small to medium sized Industries (SMEs). SMEs contribute one third of the country’s Gross Domestic Product (GDP). This has increased to 37.1% in 2018 [11] and comprises 97% of all formal business establishments in Malaysia. SMEs employ 66% of the Malaysian workforce as of 2018 and BNM also believes that SMEs have the potential to contribute more to the Malaysian economy. It is expected that by the year 2020, SMEs contribution to the Malaysian GDP will increase to 41% [11]. Also, financial institutions like banks are the main source of financing for SMEs, and lending to SMEs constitute almost half of the total sum of the financial institutions lending to the business sector [11]. The financing approval rate from financial institutions has exceeded 80%, with the financing approval rate reaching 91% in 2019. As of February 2018, there was still RM 3.3 billion still available to SMEs to use for working capital and for buying machinery [11]. However, the higher loan approval rates to SMEs have also had some unintended consequences to financial institutions, which showed an increase in NPLs from the business sector in 2019 [11] (Figure 1).
\nMalaysian banking NPL. source: BNM.
Following [33], an increase in NPLs will affect the bank’s profitability and liquidity which are the main components for the efficiency of the bank. There has been some research to find methods to mitigate this by using non-quantitative methods by analyzing the SME sales officers’ psychological factors [11, 12, 13, 14] as well as their past experiences [34]. Indeed, since there is no computerized credit analysis available for SMEs, banks have been forced to approach loan applications by SMEs using a case-by-case approach, where a personalized banking style is adopted. This makes the case for understanding how an individual bank sales officer approaches a lending case very relevant in order to minimize NPLs.
\nThe global economic events of Brexit and the US-China Trade War in the last few years along with the unanticipated Covid-19 pandemic are still influencing the Malaysian economy, and as a result, it poses great credit risks to Malaysian banks. One might even be tempted to call this a “perfect storm” for the banking industry. The surprise political events of 24 February 2020 had just magnified those risks. Faced with a lack of governmental fiscal policy, BNM must rely on the tools it has on hand to get on with the task of protecting the Malaysian economy. It is expected that BNM will soon be forced to further reduce the OPR yet again [35]. This will mean that Malaysian banks’ interest income, already under strain, will be further reduced. Even so, the BNM is fast running out of options. As an analogy, if BNM was an archer, its two quivers of OPR and SRR is running out of arrows. It is clear that BNM cannot prevent an economic disaster on its own. However, for Malaysian bankers, a time of hardship is just beginning.
\nThe authors acknowledge Dato’ Sia Cheong Lin, Business Partner, Bangun Usaha Sukses Sejahtera (BUSS) Malaysia Sdn. Bhd. for his informative views on Entrepreneurship of the Malaysian insurance industry and the way forward; Yvonne De Souza, Customer Service and Operations Manager, Hong Leong Bank Malaysia Bhd. for her valuable insights into customer sentiments and front-line banking staff alike; and Nicholas Ho Keng Yeong, District Sales Executive, Expeditors (M) Sdn. Bhd. for his insights on the Malaysian logistics industry.
\nThe authors thank Aiden Yong Ern Liang, for his help in clarifying economic concepts and sorting research documents and classifying them accordingly. Dr. Maizaitulaidawati Md Husin, for her knowledge, continuous support, and encouragement.
\nThis chapter illustrates a truism that pathways pursued in scientific investigation often deviate from linear trajectories. The research we report here evolved from concerns about ongoing practices in the continuing care of older people, a serendipitous convergence of people with compatible research needs and desires, and totally unanticipated findings in need of further investigation. This section of the chapter traces the earlier stages of that progression.
The research that follows evolved from participation by the lead author in unpublished work in the late 1990s commissioned by the Canadian Institute for Health Information (CIHI). The purpose was to analyze early Canadian data on version two of the Minimum Data Set (MDS 2.0), which is the former name of the RAI 2.0. In 1996, this tool became mandated for use in all chronic care hospitals, now known as complex community care (CCC) facilities in the Canadian Province of Ontario. The residents of these facilities are mainly older people in receipt of continuing care and/or rehabilitation associated with disabling chronic illness.
The findings from that work that was most troubling included high frequencies of physical restraint and chemical management in Canadian facilities compared to findings with the same tool in other countries. Although Canadian physical restraint levels lessened in frequency since that time, such is not the case for chemical management. Hence the enduring interest in chemical management by these authors.
The purpose of chemical management is to address symptoms that fall under the umbrella of behavioral and psychological symptoms of dementia (BPSD). The definition of the latter at a 1996 Consensus Conference of the International Psychogeriatric Association (IPA) is as follows: “symptoms of disturbed perception, thought content, mood or behavior that frequently occur in patients with dementia” [1]. The behavioral symptoms include physical aggression, loud vocalization, restlessness, agitation, and wandering. The psychological symptoms include anxiety, depressive mood, hallucinations, and delusions.
Current estimates suggest that over half the patients with dementia are at risk to such symptoms, which typically arise during the middle or later stages of the disease. Chemical management may include analgesics to lessen pain and discomfort, along with antidepressants, anti-anxiety medication and antipsychotics, all of which address behavioral and psychological symptoms [2]. The most frequent concerns about chemical management relate to antipsychotic medication. These are drugs first developed for the treatment of psychosis [3]. Presently, antipsychotic drugs fall within two categories. Typical antipsychotics include those initially developed to treat psychosis, while atypical antipsychotics were developed later to reduce adverse side effects of the former.
Concerns about harmful effects of antipsychotics in dementia patients are legitimate. The adverse effects of these drugs include high rates of cardiovascular events, cardiac arrhythmias, cerebrovascular events, cognitive decline, extrapyramidal symptoms, pneumonia, falls and fractures, and others [4]. However, the most serious concern is an elevated risk of mortality. Notice of such concerns began early this millennium with evidence that these adverse effects were over and above those associated with old age, an underlying dementia, and behavioral and psychological symptoms that might precipitate the use of antipsychotics [5].
In 2002, the manufacturer of a typical antipsychotic medication warned of an increased risk of adverse cardiovascular events [5]. Subsequently, the US Federal Food and Drug Administration (FDA) required “black box” warnings about the use of atypical antipsychotics (in 2005) and typical antipsychotics (in 2008). The warning states: “WARNING: Elderly patients with dementia-related psychosis treated with antipsychotic drugs are at an increased risk of death.” Health authorities in other countries subsequently expressed similar concerns.
Conclusions from the most recent and most extensive meta-analysis of studies that relate the use antipsychotic medication to the mortality of elderly people [6] are as follows. Mortality risk with antipsychotic medication (1) is twice that of people without such prescription, (2) comparable between typical and atypical antipsychotics, (3) highest during the initial half-year of use, (4) higher at higher dosage, and (5) comparable between people with or without diagnosed dementia. One interpretation of these five points is that people with more severe dementia-related psychosis are at greater risk of mortality, with that risk lessening 6 months after they begin that course of medication. However, findings from placebo-controlled trials indicate antipsychotic use is associated with an increase in mortality above and beyond the baseline dementia symptoms. The authors of this meta-analysis also recommended a restricted use of antipsychotic medication with older people and encouraged the deployment of de-prescribing practices.
Final thoughts by those authors concur with comments made earlier by authors of this chapter [7]. Ralph and Espinet [6] anticipate greater cultural disapproval about the sedation of older people through antipsychotic, anxiolytic, and hypnotic medications. They envision attitudinal changes within the health and legal professions to consider such practices examples of systemic elder abuse that requires legal reform. A current drive toward the de-prescribing of antipsychotic medication to elderly people is consistent with these beliefs [8].
When Sarah Worobetz, a doctoral student at Lakehead University, wanted to research relationships between antipsychotic medication and mortality in older people, after control for a wide range of variables we describe subsequently, faculty members Michael Stones and Peter Brink were happy to oblige. These researchers had a working familiarity with the RAI 2.0. They hoped to obtain census level data, with linkages to other mortality relevant datasets, to provide Sarah with the means to conduct her research. Peter Brink successfully submitted a proposal to CIHI for access to access these data.
The RAI 2.0 is a standardized assessment tool used routinely in LTCH and CCC facilities in Ontario and other settings across the world. The tool contains over 350 items relevant to medical diagnoses; physical, cognitive, social, and emotional functioning; and treatment categories that include medication use. It also indexes mortality within the relevant facilities. The trained health care professionals responsible for RAI 2.0 assessments obtain that information from multiple sources, such as direct observation, medical records, and communication with family members and other health care professionals. Objective scales on the RAI 2.0 consist of sets of items selected for relevance to a given construct. Evaluation of such scales may be against “gold standard” measures of the constructs (e.g., measures of activities of daily living, cognitive status, depression, aggression, and pain) or relevant outcomes (e.g., mortality risk). From a measurement perspective, previous findings on data quality and the reliability and validity of RAI 2.0 measures are positive [8, 9, 10].
The antipsychotic medication item on the RAI 2.0 falls within a psychotropic category that also contains items on antidepressant, analgesic, anxiolytic, and hypnotic medications. The wording of each of these items asks for the number of days during the past week that the resident received the medication. This form of measurement differs from that common to previous studies of psychotropic medication and mortality, which invariably report specific medications and dosages but not frequency of medication use.
The other databases provided by CIHI are the Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS). The DAD contains demographic, administrative, and clinical data for hospital discharges (inpatient acute, chronic, and rehabilitation) and day surgeries. The NACRS contains data for hospital-based and community-based emergency and ambulatory care (e.g., day surgery and outpatient clinics). Both datasets contain mortality data pertaining to their respective contexts. CIHI encrypted the personal and facility identifiers across datasets to ensure anonymity of residents. Brink merged these three datasets for purposes of analysis. Consequently, the merged file contains mortality data both within LTCH and CCC facilities and for those discharged to other health care settings.
The dataset analyzed by Worobetz includes all admission, quarterly and annual RAI 2.0 assessment for residents aged over 65 years in all LTCH and CCC facilities in Ontario during the financial years 2010–2011 and 2011–2012. The data are from 102,658 residents of approximately 760 facilities with a mean of 5.83 assessments per resident. Approximately 70% of residents are female and 30% male, with a mean age over all assessments of approximately 84 years. Approximately 86% of residents live in LTCH and 14% in CCC facilities. The mean length of stay prior to the first assessment was approximately 20 months, with the age at entry approximately 82 years. The distribution of antipsychotic prescriptions shows that approximately 69% of residents are without medication, 29% have daily prescriptions, and 2% have prescriptions of 1–6 days per week, which for purposes of this research we describe as PRN prescriptions. The total mortality rate during the 2-year period of data collection is approximately 32%.
The initial findings by Worobetz on relationships between mortality and type of antipsychotic prescription came as a big surprise. Her analysis by generalized linear mixed modeling (GLMM) appointed LTCH as a random effect variable (i.e., independent entities) with residents clustered (i.e., showing covariation) within their respective facilities. Such modeling is appropriate because of differences among facilities with respect to admission criteria, population size, staffing levels, types of programming, treatment protocols, etc., with localized interpersonal exchanges within facilities that foster covariation among residents. Compared to residents with no prescription for antipsychotic medication, her findings show attenuated mortality for those with daily prescription, but augmented mortality for those with PRN prescription. A possible interpretation is that these findings are consistent with earlier evidence of a protective effect of antipsychotics after 6 months but an increased mortality risk for residents prescribed antipsychotics on a PRN basis because they began to exhibit relevant symptoms.
Subsequent GLMM analyses by Worobetz included all residents, only those from LTCH, only those from CCC, new admissions, residents with dementia, and combinations of the preceding. The fixed effect variables in such analyses included not just prescriptions for antipsychotic medication but multivariate control for confounding variables such as demographics, scores on RAI 2.0 scales (e.g., activity limitation, cognitive status, aggression, depression, and mortality risk), temporal changes on those scales, and medical diagnoses (e.g., cancer, dementia, maniac depression, and schizophrenia [11]). The findings from all these analyses consistently show highest mortality among residents with PRN prescriptions on the final assessment.
The preceding findings provide reasons to broaden the scope of investigation to encompass mortality in relation to prescriptions of other types of psychotropic medication. We begin this section with brief discussion of prescribing practices and mortality associated with analgesics, antidepressants, anxiolytics and hypnotics, which in the RAI 2.0 fall within an item-set of psychotropic medications. Then follows discussion of problems associated with PRN prescribing practices. Finally, we report findings from separate analyses of mortality against these types of psychotropic medications.
Prescribing practices with analgesics show the following trends. Although rates for PRN prescription in elderly care services are generally low, some reports indicate highest levels for analgesics [12]. Worldwide, scheduled rates for analgesic use (that include acetaminophen and opioids) in LTCH show a historical increase, whereas rates for scheduled plus PRN rates show no such increase [13]. Recent findings from the Czech Republic suggest that a large proportion of LTCH residents with pain receive no analgesic medication and a moderate proportion of those that receive analgesic medication continue to report pain. These findings of analgesic under-prescription are consistent with those from North America and elsewhere in Europe. The lowest frequency of reported pain and lowest prevalence of analgesic administration are for residents with moderate-to-severe dementia [14], which suggests this group’s susceptibility to under-detection and under-prescribing of this medication.
Anti-depressant medications find frequent use in older people, with average prevalence rates of approximately 25% [15, 16]. Recent evidence suggests no association between antidepressant prescriptions and augmented risk of all-cause mortality [17, 18]. However, best practice guidelines recommend caution when prescribing because low adherence may increase risks of fatal cardiovascular and cerebrovascular injuries [19]. On the other hand, high adherence appears to lower mortality risk [20]. The findings give rise to hypotheses that intermittent use of antidepressants (e.g., comparable to PRN prescribing) has unfavorable implications for mortality whereas regular use (e.g., associated with daily prescription) has favorable implications. However, we will discuss other interpretations later in the chapter.
A recent review suggests that benzodiazepines are the most frequently prescribed anxiolytic medications for geriatric anxiety [21]. However, consensus is low about whether anxiolytic and hypnotic medications have unfavorable implications for mortality risk amongst older adults [22]. On the other hand, a large-scale retrospective cohort study of patients in UK primary care concluded that anxiolytic and hypnotic drugs were associated with significantly increased risk of mortality over a 7-year period, after adjusting for a range of potential confounders [23].
A number of studies and reviews examined PRN prescription in psychiatric and LTCH settings [24, 25, 26, 27]. Summary findings indicate higher PRN use for residents with lower care needs, frequent use alongside regularly scheduled medications, and recent entry into a facility. Contextual factors also have a strong influence on PRN prescribing. These include general levels of activity and disturbance on the ward, staffing level, perceived competence of staff, and familiarity of the staff with residents. The reports also indicate frequent omissions and errors in records of PRN usage. Findings that relate mortality to PRN usage appear to be absent in the literature.
Following the thesis research by Worobetz, subsequent analyses of the same dataset examined relationships of mortality to prescriptions of anesthetics (Jason Randle), antidepressants (Carlina Marchese and Shauna Fossum), anxiolytics (Michael Stones), and hypnotics (Dane Ostrom). We describe the sampling procedure in the following section. The main findings show significantly higher mortality with PRN prescriptions for each type of psychotropic medication compared with daily or null prescription. These findings persist even after statistical control of relevant confounding variables. They provide the impetus for the new analyses that follow.
We try here to expand the scope and level of precision beyond those present in previous analyses, each of which examined associations of mortality with a single psychotropic medication. First, we analyze the effects on mortality of all the psychotropic prescriptions within a series of multivariate analyses that includes the psychotropic medications. Second, we introduce a verified measure of mortality risk into the array of control variables. Third, we examine intervals for mortality of 90 days from the final RAI 2.0 assessment (i.e., the scheduled date of the next assessment) and shorter periods within that interval. Fourth, we examine predictors of daily and PRN prescriptions of psychotropic medications. Fifth, we examine the effects on mortality for residents receiving only daily, only PRN, or both daily and PRN prescriptions of psychotropic medication.
Section 1.2 described the three datasets linked and encrypted by CIHI. Here we report analyses that restrict data entry to (1) scheduled intake, quarterly and yearly assessments, (2) in LTCH settings, (3) for residents aged 65+ years, (4) with an intake assessment during the financial year 2010/2011, and (5) subsequent assessments that do not exceed first yearly assessment (i.e., <13 months after the intake assessment). Consequently, the data enable computation of a census level 1-year incidence rate for mortality among LTCH residents aged 65 years and older.
The analyses that follow begin at a descriptive level and proceed to an inferential level. The latter analyses were performed using the SPSS 25 GLMM program. The target variables in different analyses include mortality within 90 days of the final assessment, time to mortality within that interval, and frequencies of daily and PRN prescriptions for psychotropic medication categories. The random variable in all analyses identifies the LTCH at intake assessment. Evidence of statistical significance for the random variable would confirm that levels on the target variable differ across the LTCH spectrum.
The fixed effects in different analyses include the following RAI 2.0 measures: demographics (gender, age at assessment); scales (Activities of Daily Living Hierarchy, Cognitive Performance Scale, Depression Rating Scale, Aggressive Behavior Scale, Pain Scale and the Changes in Health, End-Stage Disease, and Symptoms and Signs Scale); diagnoses (insomnia and dementia), and medications (antipsychotic, antidepressant, analgesic, anxiolytic, and hypnotic). Convincing evidence from earlier and more recent publications [28] attest to good data quality and psychometric properties associated with RAI 2.0 measures.
Here we describe the sample with respect to demographics, psychotropic medication use, and mortality. The sample of 20,414 residents of 631 LTCH includes approximately 38% admissions from inpatient acute care, 28% from a private home, 13% from residential board and care, and 10% from home care. The remaining 11% of admissions are from residences with 24-hour nursing care, inpatient continuing care, inpatient rehabilitation, or inpatient psychiatry. The sample comprises 33.6% men and 66.4% women. The age distribution for men has a mean of 83.03 years and standard deviation of 7.37 years, with respective estimates for women of 85.29 and 7.19 years.
Table 1 shows percentage frequencies for residents prescribed psychotropic medication during the week before the final assessment. Daily prescription is highest for analgesics followed by antidepressants followed by antipsychotics. More than half the residents use analgesics daily, nearly half use antidepressants daily, and approximately 30% receive daily antipsychotics. The frequency of PRN usage is low. The highest frequencies are 7.5% for analgesics and 3.3% for anxiolytics.
Frequencies of daily prescriptions for different types of psychotropic medication are as follows: 33.4% of residents receive one medication; 31.5% receive two medications; 17.6% use three or four (i.e., 3+) medications; and 17.4% of residents have no daily psychotropic medication. Frequencies for PRN prescriptions of psychotropic medication are as follows: 10.9% of residents use one medication; 1.6% use two or three (i.e., 2+) medications; and 87.5 of residents have no psychotropic medication on a PRN basis. Frequencies for combinations of daily and PRN prescriptions are as follows: 74.6% of residents have only daily prescriptions for psychotropic medications; 4.5% have only PRN prescriptions; 8.0% have both daily and PRN prescriptions; and 12.9% have no prescription. These findings indicate that about half the residents receive two or more psychotropic medications daily, 12.5% receive one or more on a PRN basis, and 8.0% have both daily and PRN prescriptions.
Probabilistic mortalities within 90 days of the final assessment are 18.1% overall, 21.1% for males and 16.3% for females. The percentages of residents dying within different time periods after the final assessment are as follows: 2.2% mortality within 7 days after the final assessment, 6.1% within 8–30 days, and 9.8% within 31–90 days. The proportion of residents with no mortality within 90 days is 82%.
The purpose is to evaluate whether findings from separate analyses of relationships between mortality and types of psychotropic medication replicate in multivariate analysis that includes all such medications and potential confounders. The most significant findings from the earlier research indicate highest mortality with PRN prescription for each type of psychotropic medication. Replication in multivariate analysis would suggest that such relationships exist independently of any covariation in prescribing practices across types of medication.
The target variable in this analysis is mortality within 90 days of the final assessment, with 90 days being the time before the next scheduled assessment. The reference category for this variable is absence of mortality. The distribution of the target variable is binomial and related to a linear model by a complementary log-log link. SPSS 25 recommends such a linkage in survival analysis, where some observations have no termination event.
The fixed effects include demographic variables (i.e., men/women, age at the final assessment); measures of functional capability (i.e., Cognitive Performance Scale, Activities of Daily Living Hierarchy); and prescribed frequency of usage for each type of psychotropic medication (i.e., none, PRN, daily). The analysis accords with conventional GLMM practices that include centering of continuous measures on their grand mean and comparison of levels on a nominal variable with a reference category. For the present nominal measures, the respective reference categories are male gender and zero frequency of usage for a psychotropic medication.
Findings for the random variable in this and all subsequent analyses indicate significant differences (at
Prescription of psychotropic medication | |||
---|---|---|---|
Medication type | None (%) | PRN (%) | Daily (%) |
Antipsychotic | 69.2 | 1.4 | 29.4 |
Antidepressant | 50.9 | 1.4 | 47.7 |
Analgesic | 34.6 | 7.5 | 57.9 |
Anxiolytic | 84.9 | 3.3 | 11.8 |
Hypnotic | 93.7 | 0.7 | 5.6 |
Percentage frequencies for usage of psychotropic medications.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Intercept | 0.21 | 0.000 | 0.19 | 0.23 |
Female | 0.68 | 0.000 | 0.64 | 0.73 |
Male | ||||
Age at assessment | 1.03 | 0.000 | 1.03 | 1.04 |
Activities of daily living | 1.49 | 0.000 | 1.46 | 1.53 |
Cognitive performance | 0.98 | 0.144 | 0.96 | 1.01 |
Daily antipsychotic | 0.92 | 0.040 | 0.85 | 1.00 |
PRN antipsychotic | 1.59 | 0.000 | 1.28 | 1.96 |
No antipsychotic | ||||
Daily antidepressant | 0.78 | 0.000 | 0.72 | 0.83 |
PRN antidepressant | 1.37 | 0.004 | 1.11 | 1.69 |
No antidepressant | ||||
Daily analgesic | 0.98 | 0.555 | 0.91 | 1.05 |
PRN analgesic | 1.72 | 0.000 | 1.53 | 1.93 |
No analgesic | ||||
Daily anxiolytic | 0.99 | 0.899 | 0.89 | 1.11 |
PRN anxiolytic | 1.40 | 0.000 | 1.20 | 1.64 |
No anxiolytic | ||||
Daily hypnotic | 1.08 | 0.301 | 0.93 | 1.25 |
PRN hypnotic | 1.77 | 0.000 | 1.31 | 2.40 |
No hypnotic |
Fixed effect odds ratios including daily, PRN and No psychotropic predictors of mortality within 90 days of the final assessment.
Figures 1–5 illustrate the magnitude of these findings, which are present in both men and women.
Proportionate mortality by antipsychotic frequency in men and women.
Proportionate mortality by antidepressant frequency in men and women.
Proportionate mortality by analgesic frequency in men and women.
Proportionate mortality by anxiolytic frequency in men and women.
Proportionate mortality by hypnotic frequency in men and women.
The findings from this analysis replicate in a multivariate context those from separate analyses of each psychotropic medication. We conclude, therefore, that associations between PRN prescription and higher mortality are independent for each type of psychotropic medication. Compared to previous research, the findings of lower mortality with daily antipsychotic and antidepressant prescription are unusual for the former but not the latter. However, we have no reason to doubt their validity, based as they are on census level data from an entire province. It is possible that confounding factors that contribute to earlier reports of augmented mortality with antipsychotic medication include failures to distinguish daily from intermittent usage and to identify adherence to daily prescription regimens. The presence of either confound might overturn protective effects associated with antipsychotic medication.
The purpose of this analysis is to advance findings from the preceding analysis in two ways. The first is to replace the binary target variable with a nominal variable of time to death. The categories are 1–7, 8–30 and 31–90 days after the final assessment. We chose these categories to index mortality shortly, soon, and sometime after the final assessment but before the next scheduled assessment. The reference category is no recorded death. This analysis allows to use examine relationships between mortality and psychotropic prescriptions using a finer temporal scale.
The second advance is to include the CHESS scale [10] as a fixed effect. The CHESS is arguably the strongest current predictor of mortality for people within continuing care contexts. For example, mortality in the present database is <10% for residents with the lowest score but >85% for residents with the highest score. Consequently, inclusion of the CHESS helps us to test between two interpretations of PRN/mortality relationships. On the one hand, if nearness of death is the primary reason for PRN prescription (i.e., prescribed mainly for palliative reasons), its relationship to mortality should nullify after inclusion of the CHESS as a fixed effect. On the other hand, if PRN prescription increases the risk of subsequent mortality, a relationship should endure despite inclusion of the CHESS as a fixed effect.
The target variable in this analysis has a multinomial distribution with a generalized logit link. The random and fixed effects are the same as in the preceding analysis except for the addition of the CHESS as a fixed effect. The fixed effect coefficients for this analysis comprise a multi-layered table, wherein each layer represents a different time to death category. However, we present each layer as a separate table in order to improve ease of readability.
Tables 3–5 respectively show findings from 1–7, 8–30, 31–90 days from the final assessment. The findings for demographic, functional capability and mortality risk measures show the following trends. Over all three mortality intervals, mortality is lower in women than men, higher at older ages, higher with greater activity limitation and higher with higher CHESS scores. For the interval 8–30 days after the final assessment, mortality is lower with higher cognitive impairment (i.e., higher scores on the scale).
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Intercept | 0.017 | 0.000 | 0.013 | 0.022 |
Female | 0.533 | 0.000 | 0.432 | 0.659 |
Male | ||||
Age at assessment | 1.032 | 0.000 | 1.017 | 1.048 |
Activities of daily living | 1.625 | 0.000 | 1.484 | 1.780 |
Cognitive performance | 0.986 | 1.000 | 0.922 | 1.053 |
CHESS scale | 2.489 | 0.000 | 2.294 | 2.700 |
Daily antipsychotic | 0.859 | 0.222 | 0.670 | 1.102 |
PRN antipsychotic | 2.139 | 0.009 | 1.273 | 3.594 |
No antipsychotic | ||||
Daily antidepressant | 0.606 | 0.000 | 0.487 | 0.755 |
PRN antidepressant | 1.696 | 0.045 | 1.010 | 2.847 |
No antidepressant | ||||
Daily analgesic | 1.104 | 0.395 | 0.863 | 1.410 |
PRN analgesic | 2.655 | 0.000 | 1.916 | 3.681 |
No analgesic | ||||
Daily anxiolytic | 0.997 | 0.903 | 0.716 | 1.388 |
PRN anxiolytic | 1.006 | 0.942 | 0.617 | 1.640 |
No anxiolytic | ||||
Daily hypnotic | 1.104 | 0.586 | 0.711 | 1.716 |
PRN hypnotic | 1.592 | 0.318 | 0.666 | 3.806 |
No hypnotic |
Fixed effect odds ratios for predictors of mortality within 7 days after the final assessment.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Intercept | 0.089 | 0.000 | 0.077 | 0.103 |
Female | 0.596 | 0.000 | 0.525 | 0.677 |
Male | ||||
Age at assessment | 1.022 | 0.000 | 1.013 | 1.031 |
Activities of daily living | 1.495 | 0.000 | 1.423 | 1.572 |
Cognitive performance | 0.947 | 0.047 | 0.909 | 0.986 |
CHESS scale | 1.883 | 0.000 | 1.786 | 1.986 |
Daily antipsychotic | 0.881 | 0.084 | 0.761 | 1.020 |
PRN antipsychotic | 1.335 | 0.280 | 0.872 | 2.043 |
No antipsychotic | ||||
Daily antidepressant | 0.675 | 0.000 | 0.594 | 0.768 |
PRN antidepressant | 1.152 | 0.497 | 0.758 | 1.753 |
No antidepressant | ||||
Daily analgesic | 0.824 | 0.007 | 0.719 | 0.943 |
PRN analgesic | 1.574 | 0.000 | 1.274 | 1.946 |
No analgesic | ||||
Daily anxiolytic | 0.885 | 0.337 | 0.721 | 1.086 |
PRN anxiolytic | 1.482 | 0.006 | 1.113 | 1.974 |
No anxiolytic | ||||
Daily hypnotic | 1.049 | 0.624 | 0.802 | 1.370 |
PRN hypnotic | 1.585 | 0.148 | 0.872 | 2.880 |
No hypnotic |
Fixed effect odds ratios for predictors of mortality 8–30 days after the final assessment.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Intercept | 0.140 | 0.000 | 0.124 | 0.158 |
Female | 0.650 | 0.000 | 0.587 | 0.720 |
Male | ||||
Age at assessment | 1.039 | 0.000 | 1.031 | 1.046 |
Activities of daily living | 1.326 | 0.000 | 1.277 | 1.377 |
Cognitive performance | 0.986 | 0.901 | 0.953 | 1.020 |
CHESS scale | 1.542 | 0.000 | 1.474 | 1.614 |
Daily antipsychotic | 0.989 | 0.807 | 0.882 | 1.108 |
PRN antipsychotic | 1.436 | 0.092 | 1.003 | 2.056 |
No antipsychotic | ||||
Daily antidepressant | 0.822 | 0.000 | 0.743 | 0.909 |
PRN antidepressant | 1.226 | 0.269 | 0.854 | 1.758 |
No antidepressant | ||||
Daily analgesic | 0.894 | 0.052 | 0.803 | 0.995 |
PRN analgesic | 1.406 | 0.000 | 1.172 | 1.687 |
No analgesic | ||||
Daily anxiolytic | 1.011 | 0.689 | 0.866 | 1.181 |
PRN anxiolytic | 1.392 | 0.006 | 1.093 | 1.772 |
No anxiolytic | ||||
Daily hypnotic | 1.080 | 0.376 | 0.875 | 1.333 |
PRN hypnotic | 1.990 | 0.006 | 1.243 | 3.186 |
No hypnotic |
Fixed effect odds ratios for predictors of mortality 31–90 days after the final assessment.
Compared to a reference category of no medication, the findings for prescribed medications show the following. Table 3 shows mortality 1–7 days from the final assessment to be significantly lower with daily antidepressant prescription but significantly higher with PRN prescription of antipsychotics, analgesics and antidepressants.
Table 4 shows mortality 8–30 days after the final assessment is significantly lower with daily antidepressant and daily analgesic prescriptions but significantly higher with PRN anxiolytics and PRN analgesic prescriptions.
During the 31–90 day interval after the final assessment, Table 5 shows significantly lower mortality with daily antidepressant prescription but significantly higher mortality with PRN analgesic, anxiolytic, and hypnotic prescriptions.
At one or more levels of the target variable, the findings from this analysis replicate those from the preceding analysis that relate male gender, older age and greater activity limitation to significantly augmented mortality. They also replicate findings of augmented mortality with PRN prescription for all types of psychotropic medication. They further extend relationships of attenuated mortality to include daily prescription of both analgesic and antidepressant medication. The only failure of replication is the absence of significantly attenuated mortality with daily prescription of antipsychotic medication, despite a coefficient close to significance for the 8–30 day interval. Other significant findings include attenuated mortality with cognitive impairment (i.e., higher scores on the scale) at the 8–30 day interval and augmented mortality at each level of the target variable with higher estimates of mortality risk on the CHESS.
The inclusion of the CHESS provides a rationale to interpret findings of attenuated or augmented mortality associated with psychotropic prescriptions. The presence of such findings after control for the CHESS supports an interpretation that the medicinal prescriptions have direct or indirect effects on mortality beyond the levels of risk measured by the tool. An alternative interpretation that the findings on mortality are secondary to altered prescribing practices with perceived closeness to death seems less plausible.
LTCH residents may receive more than a single type of psychotropic medication on a daily or PRN basis. The purpose of these analyses is to identify variables that contribute to the number of such prescriptions. The target variable in both analyses is the number of psychotropic categories prescribed on a daily or PRN basis. We modeled the target as an ordinal measure with a cumulative logit link in mixed multinomial logistic regression models. Levels on the daily prescription distribution are 0, 1, 2, 3 and 4+ ordinal categories. Levels on the PRN prescription distribution are 0, 1, 2 and 3+ ordinal categories.
The fixed effects include the same demographic, functional capability and mortality risk measures as in the preceding analyses. Other measures are scales (i.e., Depression, Aggressive Behavior, and Pain scales), diagnoses (i.e., Anxiety Disorder, and Dementia) and conditions (i.e., Insomnia) that might influence psychotropic medication use. We added a final fixed effect of the total number of prescribed medications.
The findings in Table 6 show fixed findings for daily prescriptions of psychotropic medication. The table includes thresholds (i.e., intercepts) at different levels of the target variable and coefficients for the predictors. Predictors of significantly higher frequencies of daily prescription include categorical measures of female gender, anxiety disorder, insomnia, and dementia; higher scores on scales measuring activity limitation, cognitive impairment, depression, aggressive behavior, and pain; and the total number of medications. These findings suggest that residents prescribed more types of psychotropic medication have higher levels on multiple conditions that might benefit from psychotropic intervention. Findings that seem at odds with the preceding are relationships of younger age and lower mortality risk (i.e., on the CHESS) to higher frequencies of daily psychotropic prescriptions.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Threshold for Number = 0 | 0.225 | 0.000 | 0.214 | 0.237 |
Threshold for Number = 1 | 1.504 | 0.000 | 1.433 | 1.579 |
Threshold for Number = 2 | 9.442 | 0.000 | 8.970 | 9.939 |
Threshold for Number = 3 | 87.211 | 0.000 | 81.593 | 93.216 |
Female | 1.047 | 0.002 | 1.017 | 1.079 |
Male | ||||
Age at assessment | 0.973 | 0.000 | 0.971 | 0.974 |
Activities of daily living | 1.015 | 0.003 | 1.005 | 1.025 |
Cognitive performance | 1.118 | 0.000 | 1.105 | 1.131 |
Depression scale | 1.108 | 0.000 | 1.100 | 1.115 |
Aggressive behavior scale | 1.067 | 0.000 | 1.060 | 1.074 |
Pain scale | 1.208 | 0.000 | 1.187 | 1.230 |
CHESS scale | 0.915 | 0.000 | 0.900 | 0.929 |
Anxiety disorder | 2.744 | 0.000 | 2.599 | 2.896 |
No anxiety disorder | ||||
Insomnia | 1.124 | 0.000 | 1.079 | 1.170 |
No insomnia | ||||
Dementia | 1.393 | 0.000 | 1.348 | 1.439 |
No dementia | ||||
Total medications | 1.191 | 0.000 | 1.187 | 1.195 |
Fixed effect odds ratios for predictors of number of daily medications for the final assessment.
Table 7 shows fixed effect findings for PRN prescriptions of psychotropic medication. Significant predictors of higher levels of PRN prescribing are limited to the Aggressive Behavior, Pain, and CHESS scales, and the presence of insomnia. Age and diagnosed dementia have negative relationships with the number of PRN prescriptions.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Threshold for Number = 0 | 7.737 | 0.000 | 6.970 | 8.587 |
Threshold for Number = 1 | 73.344 | 0.000 | 63.257 | 85.040 |
Threshold for Number = 2 | 399.408 | 0.000 | 303.212 | 526.123 |
Female | 1.033 | 0.496 | 0.941 | 1.134 |
Male | ||||
Age at assessment | 0.993 | 0.029 | 0.988 | 0.999 |
Activities of daily living | 1.019 | 0.252 | 0.987 | 1.052 |
Cognitive performance | 0.977 | 0.194 | 0.942 | 1.012 |
Depression scale | 1.008 | 0.437 | 0.988 | 1.028 |
Aggressive behavior scale | 1.085 | 0.000 | 1.064 | 1.107 |
Pain scale | 1.334 | 0.000 | 1.268 | 1.404 |
CHESS scale | 1.214 | 0.000 | 1.165 | 1.266 |
Anxiety disorder | 0.991 | 0.917 | 0.838 | 1.173 |
No anxiety disorder | ||||
Insomnia | 1.618 | 0.000 | 1.444 | 1.812 |
No insomnia | ||||
Dementia | 0.789 | 0.000 | 0.710 | 0.877 |
No dementia | ||||
Total medications | 0.996 | 0.423 | 0.986 | 1.006 |
Fixed effect odds ratios for predictors of number of PRN medications for the final assessment.
The most revealing differences in outcome of the two preceding analyses relate to diagnosed dementia and the CHESS scale. Diagnosed dementia relates positively to the number of daily but negatively to the number of PRN prescriptions. Conversely, higher mortality risk relates negatively to the number of daily but positively to the number of PRN prescriptions.
The purpose of this analysis is to ascertain relationships between mortality within 90 days of the final assessment and the distribution of prescriptions of psychotropic medications at the final assessment. The target variable has a binomial distribution with a complementary log-log link. The fixed effect variables include the same demographic, functional capability and mortality risk measures as previous analyses. Categories within the distribution of prescriptions include (1) no prescriptions, (2) daily and PRN prescriptions, (3) PRN prescriptions only and (4) daily prescriptions only. The reference category is zero prescriptions.
The findings in Table 8 show significantly lower mortality for women than men and for residents with greater cognitive impairment (i.e., lower scores on the scale). Mortality is significantly higher at older ages and for residents scoring higher on activity limitation and the CHESS measure of mortality risk. Findings for the distribution of prescriptions show significantly attenuated mortality for residents with daily prescriptions only, significantly augmented mortality residents with PRN prescriptions only, and significantly augmented mortality for residents with both daily and PRN prescriptions. Paired comparisons with Bonferroni correction show significant differences across all four categories.
Model term | Odds ratio | Sig. | 95% confidence interval | |
---|---|---|---|---|
Lower | Upper | |||
Intercept | 0.21 | 0.000 | 0.19 | 0.23 |
Female | 0.67 | 0.000 | 0.63 | 0.72 |
Male | ||||
Age at assessment | 1.03 | 0.000 | 1.02 | 1.03 |
Activities of daily living | 1.36 | 0.000 | 1.32 | 1.40 |
Cognitive performance | 0.97 | 0.007 | 0.95 | 0.99 |
CHESS scale | 1.58 | 0.000 | 1.53 | 1.62 |
Only daily prescriptions | 0.76 | 0.000 | 0.69 | 0.84 |
Only PRN prescriptions | 1.42 | 0.000 | 1.21 | 1.66 |
Daily and PRN prescriptions | 1.17 | 0.025 | 1.02 | 1.34 |
No prescriptions |
Fixed effect odds ratios for mortality within 90 days of the final assessment.
We interpret these findings as follows. Antipsychotics, antidepressants and analgesics have the highest rates of daily prescription. Daily prescription of each of those psychotropics attenuates mortality in at least one of our analyses. Consequently, the present association between attenuated 90-day mortality in residents with only daily prescriptions is unsurprising. For similar reasons, findings of augmented mortality in residents with only PRN prescriptions are also unsurprising. The new ground breaking findings, however, concern residents with both daily
Proportionate mortality by prescription regimens and CHESS categories.
Because of the statistical significance of LTCH as a random effect in all the preceding analyses, we report here on additional analyses that explore whether particular differences among LTCH contribute to fixed effect relationships to mortality. Specifically, the analyses include facility-level covariates that correspond with previously significant, resident-level fixed effect terms. These centered facility-level covariates are LTCH means for age, level of activity limitation, and mortality risk (i.e., CHESS scores); and percentages of residents by gender and psychotropic prescription on a PRN basis. The findings indicate that these facility-level variables have significant zero-order relationships with mortality in the same directions as the corresponding resident-level measure. However, all are nonsignificant when added to the list of fixed effects described in the preceding section (i.e., Section 2.2.4), with the odds-ratios and significance levels for resident-level terms showing minimal change from those in Table 8. We conclude, therefore, that the findings with resident-level fixed effects show negligible influence due to distributions in LTCH.
This chapter charts a journey of scientific investigation with the following milestones:
Concern about excess mortality with the use of antipsychotic medication.
Findings that limit the preceding relationship to PRN prescription.
Findings that excess mortality with PRN prescription applies to all types of psychotropic medication.
Findings of attenuated mortality with daily prescription for some types of psychotropic medication but augmented mortality with PRN prescription for all types of medication.
Evidence to support the preceding after control for confounders that include a proven indicator of mortality risk.
Findings that diagnosed dementia relates positively to the number of daily but negatively to the number of PRN prescriptions, whereas higher mortality risk relates negatively to the number of daily but positively to the number of PRN prescriptions.
Evidence that a combination of daily and PRN prescriptions of psychotropics overturns protective effects against mortality associated with the former but lowers deleterious effects associated with PRN prescribing.
The journey began with concern about excess mortality with the use of antipsychotic medication but ends with evidence for minor protective effects of daily prescription. How do we account for this discrepancy? Consider, first, the quality of measurement and, second, the appropriateness of the analyses. We have confidence in the data quality and psychometric adequacy (i.e., reliability and validity) of the RAI 2.0 measures. For nearly 30 years, efforts by researchers from across the world tried to ensure good measurement quality. A recent example is research on 15 years of archived data in Canada. Those authors report favorable outcomes, concluding that the RAI 2.0 provides a “robust, high quality data source” ([28], p. 27). If our data possess unrecognized error in measurement, similar error likely contaminates findings from many hundred referred publications that rely on RAI 2.0 data.
Second, we consider the technical details of our methodology to be appropriate. The decision to restrict analysis to data from new admissions ensures that the findings from over 20,000 residents provide 1-year incidence rates (rather than prevalence rates) for mortality. Such restriction also ensures that the data from all residents begin with comparable intake assessments. Earlier in this chapter, we provide conceptual reasons to justify the appropriateness of GLMM analysis. In every analysis, findings of significant random effects for LTCH provide empirical justification. Our wish for the future is that more researchers adopt similar modeling in order to avoid incorrect assumptions that measurement error is uncorrelated in clustered data.
We also ought to mention here that the statistical models show reasonable data fit in every analysis. The final model in Table 8 provides an example. Compared to a simplified model that includes only demographics as fixed effects, the proportions of correct classification in the full model improve by 16.3% for predicted mortality (i.e., from 50 to 66.3%) and by 2.4% for the prediction of nonmortality (i.e., from 82.1 to 84.5%). These gains are fairly impressive given the lopsided skew in mortality data.
We conclude from the preceding is that our findings are trustworthy. How then can we account for findings from other studies of excessive mortality among elderly people prescribed with antipsychotic medication? We offer three possible interpretations. The first is a failure by many or most such studies to distinguish between daily and PRN prescribing. The second is a failure to provide information about PRN prescription of other psychotropic medications. Because our findings suggest associations between augmented mortality and PRN prescription of
The third interpretation relates to adherence. Anyone with work experience in LTCH settings knows that adherence to drug regimens is problematic. Residents that put pills into their mouths do not necessarily swallow them. Instead, some residents chose to hide their pills, others throw them away. Previous findings show that nonadherence to antidepressant medication has unfavorable implications for mortality [19]. Findings from psychiatry show frequent low adherence to antipsychotic medication [29]. That study reports rates of nonadherence to antipsychotic medication by psychiatric patients as high as 40% within a year. Because low adherence results in intermittent use of antipsychotic medication, the consequences in LTCH residents could well include excessive mortality.
Our scientific journey progressed past milestones that indicate consistency in findings with alternative indices of mortality and control of confounding variables. At the end of the journey, the metaphorical villain is no longer daily prescription of psychotropic medication but PRN prescription. Although relatively few residents receive PRN prescriptions on a per drug basis, one resident in eight (12.5%) has at least one such prescription. Although residents prescribed PRN medication have high mortality risk, as measured by the CHESS, Figure 6 shows elevated mortality rates at every level of mortality risk.
Issues that arise from our research include reasons for the high mortality associated with PRN prescriptions and implications for health care practice and policy. However, before such discussion, here are some words of caution.
The present research design is correlational rather that experimental. Only the latter assigns individuals randomly to the various conditions. Despite agreement that correlational designs provide less-compelling evidence about causality than experimental designs, the majority of studies on the effects of psychotropic medications are correlational. For example, most recent studies of the effects of antipsychotic medication on mortality have correlation designs that compare residents prescribed a given drug with residents without prescription or prescribed an alternative medication [6]. However, even in experimental studies, which are invariably of short duration [5], any differences in mortality between conditions do not necessarily imply direct causation. Such effects may arise because of changes to health stability or changes in likelihood of adverse health events. We make these points to warn against simplistic interpretation of complex phenomena.
Efforts within epidemiology to facilitate appropriate causal inferences include procedures to identify
However, our analyses clearly omit control for unmeasured sources of confounding. Also, our research lacks the rigor that random assignment brings to experimental forms of design. Consequently, we strongly recommend that future randomized controlled trials evaluate the relationships we report here.
Our cautious interpretation of effects on mortality associated with daily and PRN prescription of psychotropic medication rests heavily on findings from the final analysis. These findings indicate that PRN prescription overturns any protective effects associated with daily prescription, whereas daily prescription lowers adverse consequences of PRN prescribing. These findings suggest a mutually antagonistic relationship between daily and PRN prescriptions with respect to their effects on mortality. The clinical rationale for daily prescription is the alleviation of disturbance to a resident’s equilibrium, as exemplified by aggression, depression, pain, anxiety and insomnia. Successful treatment results in regained equilibrium after the resident adapts to regular ingestion of the drug. Conversely, the reasoning behind PRN prescription is to alleviate disturbances to equilibrium thought to be temporary. However, irregularity of ingestion is antagonistic to adaptation and may exacerbate instability through toxicity rather than help restore equilibrium. This interpretation appears to be consistent with our findings. Practical implications for health care practices and policy are consistent with current trends. Such trends include the de-prescribing of psychotropic medications for older people, with such medications to be used only for short durations and as a last resort [5, 6]. Similarly, a recent rewrite of Medicaid Long Term Care rules in the USA states that LTCH residents must not be prescribed antipsychotic, antidepressant, anxiolytic or hypnotic medications unless necessary to treat a specific condition diagnosed and documented in the clinical record. The new rules also normally limit PRN prescriptions to 14 days. Any extension requires an attending physician or prescribing practitioner to document the rationale in the resident’s medical record [31]. We especially applaud these latter rules, which tackle an issue hitherto neglected in health care practice and policy.
PRN prescriptions of psychotropic medications have more aversive effects on the mortality of LTCH residents than daily prescriptions. These findings apply to all types of psychotropic medication after control for major confounding variables. The findings also suggest that daily prescription may ameliorate effects on mortality associated the PRN prescriptions, whereas PRN prescriptions exacerbate effects on mortality associated with daily prescription.
All 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.
No author has any conflict of interest.
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In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. 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The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. Physiology research may be linked to development, aging, environment, regular and pathological processes, adaptation and evolution, exercise, or several other factors affecting, or involved with, animal physiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11406,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",institutionString:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",institutionString:null,institution:{name:"Alexandria University",institutionURL:null,country:{name:"Egypt"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",institutionString:"Kafkas University",institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}}]},onlineFirstChapters:{paginationCount:6,paginationItems:[{id:"82135",title:"Carotenoids in Cassava (Manihot esculenta Crantz)",doi:"10.5772/intechopen.105210",signatures:"Lovina I. 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