Descriptive analysis of CF registry variables.
\r\n\tSynthetic zeolites can be formed from different raw materials and among these many wastes represent some interesting sources due to their chemical and mineralogical composition. Today, a large number of different types of waste resulting from many human activities are produced in the world (e.g. industrial, municipal, agricultural waste) and most of them are deposed of in landfills thus determining a great environmental problem.
\r\n\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art on the possibility to transform the different types of waste materials into useful products, zeolites, through conventional processes and innovative methods. The aim is to demonstrate that waste can be a problem or a resource depending on how it is managed.
",isbn:"978-1-80356-426-5",printIsbn:"978-1-80356-425-8",pdfIsbn:"978-1-80356-427-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"3ed0dfd842de9cd1143212415903e6ad",bookSignature:"Dr. Claudia Belviso",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11561.jpg",keywords:"Structure, Properties, Natural Material, Synthetic Product, Type, Composition, Production, Disposal, Hydrothermal Method, Pre-fusion Process, Sonication, Multiple Steps",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 25th 2022",dateEndSecondStepPublish:"March 25th 2022",dateEndThirdStepPublish:"May 24th 2022",dateEndFourthStepPublish:"August 12th 2022",dateEndFifthStepPublish:"October 11th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"5 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Since 2002, Dr. Claudia Belviso has been carrying out research activity in the field of mineralogy and geochemistry aimed at environmental protection. She is responsible for the research activity on zeolite synthesis from waste materials and natural sources which has allowed her to be the inventor of an International Patent, publish numerous scientific articles in peer-reviewed journals, and carry out scientific research in national and international projects.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"61457",title:"Dr.",name:"Claudia",middleName:null,surname:"Belviso",slug:"claudia-belviso",fullName:"Claudia Belviso",profilePictureURL:"https://mts.intechopen.com/storage/users/61457/images/system/61457.jpg",biography:"Claudia Belviso is a researcher at the Institute of Methodologies of Environmental Analysis (IMAA) of CNR. After graduating in Geological Sciences and qualifying as a professional geologist, she earned a Ph.D. in Earth Sciences. Since 2002 has been carrying out her research activity in the field of mineralogy and geochemistry aimed at environmental protection. She is responsible for the research activity on zeolite synthesis from waste materials and natural sources as well as their application to solving environmental problems and as new raw material. These research activities have allowed her to be the inventor of an International Patent, publish numerous scientific articles in peer-reviewed journals, participate in national and international conferences, take part in the organization of international congresses, and carry out scientific research in national and international projects.",institutionString:"National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"National Research Council",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"8",title:"Chemistry",slug:"chemistry"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453622",firstName:"Tea",lastName:"Jurcic",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"tea@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"5306",title:"Zeolites",subtitle:"Useful Minerals",isOpenForSubmission:!1,hash:"eec7f864baf093058440c0f56072a7cf",slug:"zeolites-useful-minerals",bookSignature:"Claudia Belviso",coverURL:"https://cdn.intechopen.com/books/images_new/5306.jpg",editedByType:"Edited by",editors:[{id:"61457",title:"Dr.",name:"Claudia",surname:"Belviso",slug:"claudia-belviso",fullName:"Claudia Belviso"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Registries and other non-intervention studies are often referred to as
Treatment and disease registries play a vital role in the advancement of patient-centered outcomes research. These patient registries often include data arising from patient surveillance in observational settings. Numerous epidemiologic studies have used patient registries to characterize disease progression. In more recent years, patient registries have been used for a variety of health-related inquiries, ranging from comparative effectiveness studies to informing clinical decision making at the point of care (see [2], for an example). The maturity of so many patient registries and recent national focuses on their potential to improve patient-centered outcomes have led to the establishment of guidelines for the conduct of registry data analyses [1]. Although these guidelines are recent, the statistical challenges posed in these observational settings were noted decades ago in epidemiology and public health research [3]. Indeed, registry analyses are plagued with methodological challenges, such as confounding, missing data, time varying treatment and/or covariates, and treatment-by-selection bias.
Despite these challenges, registry studies are essential for clinical effectiveness research. They reflect real-world clinical practice and allow for evaluating patient outcomes in a realistic clinical environment. A registry encompasses the general patient population, including those who are severely ill or less likely to adhere with assigned treatment. These patients commonly are excluded from the randomized controlled trials, and are likely to have very different treatment responses. Further, registry study offers the opportunity to examine important factors such as physician’s practice behavior, prescription preference and other covariates pertaining to quality of care, which are impossible to assess in an experimental study. Registry studies commonly include long-term observation and therefore can reflect change of treatment practices, in order to provide a timely assessment of emerging research questions. The use of registry data to evaluate outcomes is of mutual benefit to both patients and clinicians, and it facilitates management of patient care, thereby improving the health care system.
Throughout the chapter, we will refer to an example from a retrospective longitudinal cohort study, which used the Cystic Fibrosis Foundation Patient Registry (CFFPR) to evaluate the clinical effectiveness of a treatment for lung function decline [4]. Cystic fibrosis (CF) is a lethal autosomal disease in which respiratory failure is the primary cause of death.
In this chapter, we focus on the design and statistical analyses of patient registry studies. We begin in Section 2 by describing processes to design a study involving registry data, in accordance with the aforementioned guidelines from Gliklich and colleagues. We follow this section with overviews of inferential analyses methods that can be used in registry study to combat selection bias, missing data, time varying treatment or covariates in Section 3. In Section 4, we describe details of the application to the aforementioned patient registry. We discuss the utility of existing methods and remaining analytic challenges in Section 5. Finally, we provide an appendix in Appendix A with implementation of the statistical analyses in our illustrative application.
Registries may be organized around conditions or exposures (e.g., a cystic fibrosis registry, stroke registry); a healthcare service (e.g., procedure); or a product (drug or device) and can address questions ranging from treatment effectiveness and safety to the quality of care delivered. Registries vary in complexity from simply recording product use as a requirement for reimbursement to more systematic efforts to collect prospective data on many types of treatment, risk factors, and clinical events in a defined population. Follow-up can be retrospective, prospective, or a combination of both. The mode and duration of follow-up can range from days (e.g., hospital admission registry) to decades (e.g., orthopedic implant registry). Constructing and maintaining a large registry requires substantial resources, collaborative effort, and often requires a multi-center or inter-institutional agreement, and a governing body that oversees and coordinates all activities. Typically, there are standard guidelines or written procedures in place that help researchers to gain familiarity and/or access to the registry study.
Before utilizing data from any registry, it is imperative to define the research question and develop a study protocol. Clinical or public health questions of interest should be stated as research questions. Each research question should correspond to a testable hypothesis, which may be assessed using an approach fully described in the statistical considerations (this is particularly important for comparative effectiveness studies).
Finding a registry that is appropriate to answer the research question of interest will require us to review preliminary information about each of the prospective registries, particularly regarding the data elements. For example, consider the following two studies. In each study, it is of interest to determine treatment effectiveness for cystic fibrosis (CF) lung disease. The first study utilized the Cystic Fibrosis Foundation Patient Registry (hereafter, CFFPR) [5] to examine the association between ibuprofen and lung function decline [6, 7]. In a subsequent study, Konstan et al. [8] assessed the relationship between a different treatment, dornase alfa, and lung function decline using registry data from the Epidemiologic Study of Cystic Fibrosis (ESCF) [9]. Although both studies examined treatment effectiveness on the same outcome (lung function decline), each study required distinct data elements to answer the research questions regarding treatment effectiveness. The CFFPR includes data collected on ibuprofen usage; however, the ECSF does not include information for this treatment, eliminating this database as an option for the first study. On the other hand, the ECSF has detailed information on pulmonary symptoms (e.g., coughing), which are known predictors of more rapid lung function decline [7] and therefore need to be considered as potential confounders to assessing treatment effectiveness. Although both registries include data elements to measure dornase alfa usage, which are necessary to answer the research question in the second study, the ECSF enabled the authors to consider detailed pulmonary symptoms as potential confounders. If our research question involves a newly diagnosed condition or rare disorder, we may be limited to a single patient registry. In those instances, the research question may need additional refinement.
In the study protocol, we will need to state the specific objectives. The objective of our CF study is to evaluate the effect of tobramycin on lung function decline. Once the objectives are clarified, we consider the most appropriate study design. In registry analyses, the selection of our study design often depends on how the registry was structured. Registries constructed to capture natural histories are often amenable to studies with longitudinal cohort designs. We can identify the population of interest at this point in the study protocol. Acquiring the subset of data which best reflects the population of interest, exposure variables, and primary and secondary endpoints may include some manipulation of the original registry data files. In our CF example, it is of interest to limit our cohort to individuals chronically infected with
Characteristics | Treated with tobramycin | Not treated with tobramycin | |
---|---|---|---|
Age, mean ± SD ( | 12.82 ± 4.68 (6451) | 12.78 ± 4.59 (6255) | 0.84 |
Male sex, % ( | 47.2% (3046) | 53.5% (3346) | <0.0001 |
FEV1, mean ± SD ( | 74.46 ± 25.33 (6451) | 83.69 ± 22.68 (6255) | <0.0001 |
Weight-for-age percentile, mean ± SD ( | 30.05 ± 26.08 (6446) | 33.92 ± 26.88 (6252) | <0.0001 |
CF-related diabetes, % ( | 2.3% (150) | 1.5% (96) | 0.0012 |
Pancreatic insufficiency, % ( | 95.3% (6145) | 94.8% (5932) | 0.27 |
No or state/federal insurance, % ( | 32.3% (2082) | 30.5% (1910) | 0.0348 |
Prior hospitalizationsb | — | — | — |
None, % ( | 57.2% (3360) | 75.7% (4448) | <0.0001 |
1, % ( | 23.9% (1401) | 16.0% (940) | |
2, % ( | 9.3% (546) | 4.6% (273) | |
3 or more, % ( | 9.6% (566) | 3.7% (219) | |
Dornase alfa, % ( | 79.3% (5116) | 49.4% (3087) | <0.0001 |
Descriptive analysis of CF registry variables.
P-values from Wilcoxon Mann-Whitney or chi-square test.
Number of hospitalizations in the year before baseline.
Abbreviations: CF, cystic fibrosis; FEV1, percentage predicted of forced expiratory volume in 1 s.
For many different types of research, particularly comparative effectiveness research or research involving children and/or rare disease conditions, no single institution has a large enough patient population to perform a proper study. This, along with the growing infrastructures of electronic medical records, has led to an increased effort to create distributed research networks. The widespread adoption of electronic health records (EHRs) has enabled them to become a main source for registry data, capable of capturing the necessary elements as part of routine clinical care, and the ever-changing clinical practices.
The number of data elements and scope of collection often increase over the life of the registry. Well-maintained registries typically include data dictionaries, but verifying data quality specific to our study is essential. In our CF example, we had to calculate specific variables for analysis. Understanding how the data have been collected over time and to what extent (e.g., every clinical encounter) will help determine the appropriate subset of data to extract from the registry. For example, the CFFPR data are collected at every clinical encounter and hospitalization, as well as on an annual basis, on each patient and provided to the CF Foundation. Using descriptive statistics, such as the 5-number summary, mean and standard deviation for each variable, and histograms or boxplots can highlight data discrepancies in continuous variables. Similarly, computing the frequency and percentage of each category in a nominal or ordinal variable may identify variables with questionable entries. Furthermore, summary statistics stratified by calendar year can inform selection of an optimal time frame from natural history registries. In our example, CF-related diabetes, a known predictor of lung function decline that should be included in the analysis, was not collected in earlier calendar years in the CFFPR.
Access to most registries requires approval by a local institutional review board (IRB) prior to data release, and this approval is often necessary to have results of the study peer-reviewed and published. In our experience, developing a protocol that is in accordance with the aforementioned guidelines is sufficient for the IRB review. Although registries rarely contain patient names or medical record numbers, they often include clinical encounter and/or discharge dates. Having this type of protected health information in the data often requires IRB approval.
Statistical analyses in the registry data setting are subject to the statistical challenges previously described for analyses of observational studies [10]. Registries are often established for the purpose of evaluating the effects of interventions. The statistical analysis plan should include appropriate methods to test each hypothesis, methods to address biases and confounding arising from various sources, and sample size/power considerations.
Regardless of the research question, a registry study will likely be plagued with numerous sources of bias. Selection bias, although inevitable, is typically the most concerning. This type of bias distorts the results for the association of interest and may yield misleading results. Failure to sample from the correct target population and loss to follow-up due to death or some other event are types of selection bias.
A pervasive type of selection bias is confounding by indication, arising from nonrandomized treatment assignment that is often related to the patient’s risk to experience poor outcomes. This treatment-by-selection bias creates distinctions between the risk profiles of treated and comparator groups and may violate statistical assumptions in our analyses. In our CF example, treatment selection bias may be more pronounced because the drug in question should only be prescribed to individuals with CF who have a specific chronic infection. Narrowing the cohort to “sicker” individuals can intensify the aforementioned risk profile imbalance between Tobi and non-Tobi groups.
Statistical methods to combat treatment selection bias have been applied in previous studies. Approaches to adjust for treatment selection bias include multivariable regression, propensity score methods, matching and instrumental variables analysis. Stukel et al. [11] applied each of these four approaches to examine the association between cardiac catheterization and long-term acute myocardial infarction mortality. The authors found that the results differed according to the choice of statistical approach. Next, we describe and outline each approach in the context of our CF example.
In the absence of randomization, intervention and comparator groups may exhibit large differences with respect to observed covariates recorded in the registry. This approach, sometimes referred to as covariate adjustment, attempts to account for such differences that may distort estimates of intervention effects (Figure 1). Most biomedical studies employ ordinal least squares (OLS) regression to adjust the association between the treatment indicator variable
Causal diagram. The multivariable regression in Model (1) examines the treatment-outcome association, after adjustment for measured confounders. The propensity score methods outlined in Model (2) use the measured confounders to balance the treatment groups (exposure). The IV regression from Model (3) examines the treatment-outcome association, to the extent that the exposure is associated with the instrument. The instrument should not be related the measured confounders; therefore no arrow is drawn for this relationship.
where
The propensity score (PS) is a summary balancing score indicating the likelihood for a patient to receive the active treatment
where
The second and the third approaches often categorize patients into five groups using propensity score quintiles. The stratified analyses will perform the regression model of
One of our primary analysis goals in the registry setting is to identify potential sources of confounding and make the appropriate adjustments in our statistical analysis. Failure to identify sources of measured confounding results in residual confounding. This type of unaddressed confounding goes into the error term,
Inferential results can also be impacted by what is known as unmeasured confounding. McClellan et al. [16] propose a technique known as instrumental variables (IV) to combat both measured and unmeasured confounding. We introduce the following notation for IV regression. From Model (1), recall that the variables
In the typical clinical setting, a provider does not flip a coin to determine whether she will prescribe her patient treatment A, as opposed to some alternative. By construction,
where
We continue this approach, often referred to as two-stage least squares regression, by substituting
In this regression, the same method of estimation is used; however, we use distinct notation because parameter estimates and residual error will differ from Model (1). Finally, we use the estimate of
Incorporating time-varying treatment and/or covariate effects is a pervasive issue in registry data analyses. The fundamental challenge arising from the change in treatment and covariates over time often results from a patient’s responses and/or experiences with the previous treatment assignment. Thus, simply including the time varying treatment or covariate in such cases could induce bias in estimating treatment effect. Special attention is needed to address this issue when analyzing registry data. Relatively few statistical approaches are available to assess time-varying treatment effects or intermediate outcomes. Hogan and Lancaster [18] proposed inverse probability weighting and instrumental variables as time-varying treatment approaches; another population-based approach is the G-computation formula [19].
Completing this process implies that we have carefully considered the hypothesis test and analysis variables, ultimately arriving at a statistical model that will rigorously address the research question. Sample size assessments will differ according to the statistical approach proposed to test the hypothesis, and should incorporate previously established public health or clinical information.
If the statistical approach entails adjustment for confounding and other sources of bias, the sample size calculation is often straightforward. Suppose we plan to test the significance of the treatment effect,
We now reconsider the importance of sample size justification for analyses involving a large registry. Statistical significance depends on the sample size and is typically declared if the
Missing data can occur in the registry setting for a variety of reasons. Simply put, a missing data point is an observation that should have been recorded; however, for some reason, it was not recorded. It is our desire, as analysts, to understand the reason for this “missingness.” In this section, we outline practical analytic approaches to identify potential sources attributable to missing data and methods to combat the resulting bias. We begin with a brief description of the three fundamental missing data mechanisms. For an elegant mathematical treatment of the distinctions among the mechanisms, we refer the reader to the original work by Rubin [23].
If the registry data are MCAR, then the reason for missingness is not related to the data that we were able to observe or to the data that we were not able to observe. We now consider the CF example. MCAR could correspond to the following. The probability of a lung function observation (the outcome variable) being missing from the registry does not depend on any of the observed data (e.g., patient’s age) or any of the unobserved data (e.g., having lower lung function does not alter the risk of the observation being missing). Our analysis results from this subset of data will be no different (aside from larger standard errors) than if we had been able to perform the analysis on the entire dataset.
This assumption is more relaxed than MCAR but still has specific requirements. For MAR to hold, the missingness cannot be related to unobserved data, given what we have been able to observe. In other words, the missingness can depend upon data that we have already observed (i.e., data entries that were recorded in the registry). Referring again to our CF example, the probability of a lung function observation being missing does not depend upon the actual lung function value, provided that we have the other covariate data. In this case, missingness can depend upon characteristics that have been recorded in the CFFPR (e.g., gender).
We are more likely to encounter this mechanism in registry data, compared to the other mechanisms. If data are MNAR, then the missingness is related to unobserved data (unlike MAR). The missing observation follows a different distribution than the observed data, regardless of whether the two types of data have other characteristics that are the same. Despite the fact that we have registry data, the data that we are able to observe are not representative of the entire population. Within the CFFPR example, consider the longitudinal data. According to CF Foundation guidelines, patients are supposed to have at least one pulmonary function test per quarter [5]. Suppose there is a subset of patients who do not have lung function data recorded at every clinical encounter. There are many plausible explanations for why these data are missing. For an individual patient, there may be a lack of interest in managing his disease progression, or it could be an entry error. In general, we may lack relations to observed values or those relations may be irrelevant.
In practice, we do not have the information necessary to declare the reason for the missingness. Even thoughtfully developed, well-maintained registries will have missing data; therefore, sensitivity analyses are needed as part of the statistical considerations. As a preliminary step, we recommend creating an indicator (dummy) variable to indicate whether the observation is missing (=1) or otherwise (=0). Regress this dichotomous variable on the other variables to determine whether the missing indicator is associated with observed characteristics. If no association is found, we may conclude that the data are MCAR; however, we still encourage caution when making the MCAR assumption for statistical models using registry data. Although small sample size may produce this result, it is not a likely culprit in settings with large data sources. It is possible that the extent of the missingness may be too low (e.g., 5% of observations are missing) to substantially alter results, but having a low proportion of missing observations is also unlikely in a registry setting. If there is a significant association from our preliminary regression with the indicator variable, then we can rule out the MCAR assumption and more intently investigate the MAR and MNAR assumptions.
We can further examine the MAR assumption by checking for variables that are often missing simultaneously or other potential patterns of missingness. Whenever possible, we recommend performing the analysis under the MAR assumption. The two most common approaches under this mechanism are direct modeling and multiple imputation. Direct modeling implies that we will consider all available data points in our parameter estimation. This method is sometimes referred to as “available case analysis” [24]. In other words, the analysis will not exclude the records of any individual subject who has at least one observed entry. There is a second approach, multiple imputation [25], which has gained favor among analysts with the expansion of computing resources. To perform this approach, several data points for each missing data point are generated, resulting in several distinct dataset. We employ our proposed statistical model separately on each dataset and obtain parameter estimates. The estimates are combined to produce an aggregate estimate. The aggregate estimate and standard error are used to make interpretations of results. This technique is available in many software packages (e.g., SAS proc mi, proc mianalyze).
Unfortunately, there is no way to know whether the data are MAR or MNAR. Previous work by experts in the analysis of missing data has shown that any model we develop under the MNAR assumption will have an equivalent MAR counterpart [26]. Developing an MNAR model requires technical steps that are beyond the scope of our current chapter. Dmitrienko et al. [27] provide an applied approach to investigating MNAR assumptions in the context of sensitivity analyses. Although their text focuses on analyses for data from clinical trials, their approach and accompanying SAS implementation may be adapted to registry data analyses.
To simplify interpretation and improve accuracy of the results, sources of potential confounding (measured or unmeasured) should be considered as much in advance as possible. Propensity score regression offers an effective method to further balance the treatment and non-treatment groups. Like multivariable regression, this approach accounts for treatment selection bias [28] only for measured confounders (e.g., measured comorbidities and severity of illness). The propensity score could utilize measured confounders to remove treatment-selection bias. However, when there are unmeasured confounders that determine treatment-selection bias, the propensity-score approach will be limited. In analyzing registry data, IV analyses should be considered when unmeasured confounders are suspected.
Although the IV analysis is a powerful approach, this method has some noteworthy constraints. Large sample size is essential for performing IV analysis, but this issue may not be a challenge in the registry setting. The IV must only affect treatment assignment and have no direct association with outcome. If these assumptions are satisfied, then the IV analysis will yield a consistent estimate of the average causal effect [29]. Assumption (i) is directly testable, but making a heuristic argument for assumption (ii) is a common approach. See Kahn et al. [30] for an example. A weak IV will produce larger standard errors and may lead to incorrect inferential results. This approach is ideal in the presence of small/moderate confounding but becomes less reliable in the presence of large confounding. Admittedly, this is a limitation of the IV analysis in the registry setting. On the other hand, an appropriate IV minimizes the potential impacts of measured and unmeasured confounding [31].
Sensitivity analyses should be performed to examine potential impacts of missing data and particular subgroups that may drive inferential results. Analyses corresponding to the missing at random assumption should be explored in the registry setting. Subgroup analyses are essential to identify heterogeneous treatment effects, particularly in the IV analysis. These sensitivity analyses should be performed regardless of the statistical model that we choose to employ.
The CFFPR contains data on individuals receiving care from any CF center in the United States, which has been accredited by the CF Foundation. Like many registries, we underwent an application process to receive the data. The CFFPR data that we received were in separate databases. We used the following two databases. The encounter-level database had one record per patient, per clinical encounter. The annual-level database contained one record per patient, per year. We merged these data to extract the information necessary to determine whether there is a significant association between the use of inhaled tobramycin and lung function in individuals with CF who are chronically infected with
We considered the following restrictions to target the study cohort of interest. We requested CFFPR data ranging from January 1, 1998 to December 31, 2009, in order to capture the time at which inhaled tobramycin (Tobi) was recorded in the registry on a consistent basis. We did not consider study records with individuals <6 years of age, due to limitations of modality to measure lung function in young children. We limited the maximum age to 21 years, in an effort to focus on first occurrence of chronic
Diagram of study population in the illustrative CF example, showing inclusion and exclusion steps to obtain an analysis cohort from the registry. CFFPR, Cystic Fibrosis Foundation Patient Registry;
We identified potential confounders by looking at previous literature (see [6], for example). These variables, measured in the CFFPR, included gender, baseline measurements for age, FEV1, weight-for-age percentile, insurance coverage, CF-related diabetes (with or without fasting hyperglycemia), dornase alfa use, pancreatic insufficiency (defined as taking pancreatic enzymes) and number of hospitalizations in the preceding year. We can compare Tobi and non-Tobi groups with respect to each of these variables using basic inferential testing (i.e., nonparametric test for continuous variables and Chi-square test for categorical variables). Results of the descriptive analysis are presented in Table 1. Our descriptive analysis reveals that Tobi and non-Tobi groups differed by several demographic and clinical characteristics. We note that the groups did not differ according to age or being pancreatic insufficient. Next, we utilize the aforementioned statistical models to test this association.
We use Model (1) to test the association between lung function and tobramycin use, adjusting for potential confounders as covariates, represented as
Type of model | ||
---|---|---|
Multiple linear regressiona | Propensity score regressionb | |
Covariates | Coefficient (SE), ( | Coefficient (SE), ( |
Patient tobramycin use | ||
Treated | −1.74 (0.31) (<0.0001) | −1.71 (0.30) (<0.0001) |
Not treated | 0 | 0 |
Age | −0.87 (0.04) (<0.0001) | −0.86 (0.04) (<0.0001) |
Baseline FEV1 | −0.27 (0.01) (<0.0001) | −0.27 (0.01) (<0.0001) |
Sex | ||
Female | −1.16 (0.30) (<0.0001) | −1.15 (0.31) (0.0002) |
Male | 0 | 0 |
Weight-for-age percentile | 0.06 (0.01) (<0.0001) | 0.05 (0.01) (<0.0001) |
CF-related diabetes | ||
Yes | 2.06 (1.44) (0.15) | 2.19 (1.36) (0.11) |
No | 0 | 0 |
Pancreatic insufficiency | — | — |
Yes | 0.52 (0.83) (0.54) | 0.44 (0.83) (0.60) |
No | 0 | 0 |
Insurance | — | — |
None or state/federal | −1.66 (0.34) (<0.0001) | −1.66 (0.34) (<0.0001) |
Other | 0 | 0 |
Baseline hospitalizations+ | — | — |
None | 5.05 (0.70) (<0.0001) | 4.63 (0.69) (<0.0001) |
1 | 2.74 (0.74) | 2.26 (0.74) |
2 | 0.40 (0.87) | 0.37 (0.87) |
3 or more | 0 | 0 |
Dornase alfa use | — | — |
Yes | −0.46 (0.39) (0.25) | −0.38 (0.40) (0.34) |
No | 0 | 0 |
Multiple linear regression and propensity score method to predict lung function decline.
For each categorical variable in the first-stage model, the coefficient is the difference in patient tobramycin use between the indicated category and the reference category (labeled as coefficient = 0). For each continuous variable, it is the change in patient tobramycin use when the variable is increased by 1 unit. A negative value implies decreased patient tobramycin use.
Predicted treatment obtained in Stage 1 serves as propensity score in Stage 2. For each categorical variable in the second-stage model, the coefficient is the difference in FEV1 decline between the indicated group and the reference group (labeled as coefficient = 0).
significant at 2-sided p value < 0.05
Abbreviations: CF, cystic fibrosis; FEV1, percentage predicted of forced expiratory volume in 1 s.
For each continuous variable, it is the change in FEV1 when the variable is increased by 1 unit. A negative value implies greater FEV1 decline.
The patient characteristics at the baseline, which are known to impact FEV outcomes, are considered into the multivariable logistic regression model (Eq. (2)) for estimating propensity scores. Figure 3 presented the histograms of propensity score for the Tobi treated and not-treated patient groups, showing different but overlapping propensity scores between the two groups. Propensity scores are grouped into five groups by quintiles. The distribution of propensity scores are compared between the Tobi treated and not treated patients within each of the five PS categories; as one could see from Figure 4, within each quintile categories, the two patient groups present comparable patterns in their likelihood of receiving Tobi. To check for propensity score balance, we compared the Tobi treated and not treated patients on their baseline covariates, the standardized differences between the treated and not treated groups are presented in Table 3. The results show that there is a significant difference between the treated and not treated patients groups according to their gender, baseline FEV1, CF-related diabetes, pancreatic insufficiency, insurance status, prior hospitalization and dornase alfa use. After matching patients on their PS categories, as well as after adjusting by inverse propensity score weighting, we are able to achieve balance between the Tobi treated and not treated groups. Subsequently, we proceed with the propensity score analyses using the inverse propensity score weighted approach. The results are presented in Table 4, which can be contrasted with the results from the multivariable regression analyses in Table 2. The results from these two approaches are very similar; both are suggesting negative Tobi treatment effect on the improvement of FEV. The results from randomized clinical trials, however, all suggest a positive Tobi treatment effect. Such differences might be explained by unmeasured confounding that is related to treatment selection bias but not recorded in the registry. We further proceed with IV analyses to examine the Tobi treatment effect.
Histogram of the propensity score distributions by Tobi use (red) and not-group groups (blue). Related the measured confounders; therefore no arrow is drawn for this relationship.
Box-Whisker plots of the distribution of propensity scores by Tobi use (red) and not use (blue) groups stratified by the quintiles.
Characteristics | Tobi | Level | Before PS matching | After PS matching | After IPW weighting | |||
---|---|---|---|---|---|---|---|---|
Mean | Mean | Mean | ||||||
Sex | Treated | Male | 47.8% | <0.01 | 49.1% | 0.44 | 50.8% | 0.91 |
Not treated | 54.0% | 48.2% | 50.9% | |||||
FEV1% predicted | Treated | 76.38 | <0.01 | 81.68 | 0.86 | 81.26 | 0.85 | |
Not treated | 85.25 | 81.75 | 81.17 | |||||
Age | Treated | 12.10 | 0.51 | 11.98 | 0.73 | 12.01 | 0.94 | |
Not treated | 12.05 | 12.01 | 12.01 | |||||
Weight-for-age percentile | Treated | 30.24 | <0.01 | 30.34 | 0.80 | 32.33 | 0.79 | |
Not treated | 33.78 | 30.18 | 32.19 | |||||
CF-related diabetes | Treated | Yes | 1.5% | <0.01 | 1.3% | 0.53 | 1.2% | 0.49 |
Not treated | 1.0% | 1.2% | 1.4% | |||||
Pancreatic insufficiency, % ( | Treated | Yes | 96.0% | 0.94 | 96.6% | 0.85 | 96.5% | 0.72 |
Not treated | 96.0% | 96.7% | 96.6% | |||||
No or state/federal insurance | Treated | None or state/federal | 30.7% | 0.53 | 30.2% | 0.62 | 30.7% | 0.81 |
Not treated | 30.2% | 30.7% | 30.5% | |||||
Prior hospitalizations | Treated | None | 58.5% | <0.01 | 69.1% | 0.12 | 67.4% | 0.95 |
1 | 23.8% | 18.9% | 19.8% | |||||
2 | 9.4% | 6.3% | 6.9% | |||||
3 or more | 8.4% | 5.7% | 5.9% | |||||
Not treated | None | 75.9% | 70.2% | 67.4% | ||||
1 | 16.3% | 19.5% | 19.7% | |||||
2 | 4.6% | 5.8% | 6.9% | |||||
3 or more | 3.3% | 4.6% | 6.0% | |||||
Dornase alfa | Treated | Yes | 77.6% | <0.01 | 68.7% | 0.24 | 63.2% | 0.88 |
Not treated | 49.3% | 67.4% | 63.3% |
Standardized difference (T-val) between Tobi treated and untreated patients.
Abbreviations: CF, cystic fibrosis; FEV1, percentage predicted of forced expiratory volume in 1 s; PS, propensity score. Calculations for standardized differences are described in Section 4.3.
Stage 1 (predicts patient tobramycin use)a | Stage 2 (predicts change in lung function)b | |
---|---|---|
Covariates | Coefficient (SE), ( | Coefficient (SE), ( |
Patient tobramycin use | — | — |
Treated | — | 2.55 (1.22), (0.0366) |
Not treated | — | 0 |
Age | −0.013 (0.003), (0.0002) | −0.86 (0.04), (<0.0001) |
Baseline FEV1 | −0.010 (0.001), (<0.0001) | −0.27 (0.01), (<0.0001) |
Sex | — | — |
Female | 0.112 (0.027), (<0.0001) | −1.23 (0.30), (<0.0001) |
Male | 0 | 0 |
Weight-for-age percentile | −0.000 (0.001), (0.74) | 0.06 (0.01), (<0.0001) |
CF-related diabetes | — | — |
Yes | 0.112 (1.27), (0.38) | 1.93 (1.44), (0.18) |
No | 0 | 0 |
Pancreatic insufficiency | — | — |
Yes | 0.064 (0.074), (0.39) | 0.52 (0.83), (0.54) |
No | 0 | 0 |
Insurance | — | — |
None or State/Federal | −0.128 (0.030), (<0.0001) | −1.58 (0.34), (<0.0001) |
Other | 0 | 0 |
Baseline hospitalizations+ | — | — |
None | −0.598 (0.064), (<0.0001) | 5.44 (0.69), (<0.0001) |
1 | −0.251 (0.068) | 2.89 (0.74) |
2 | −0.148 (0.080) | 0.48 (0.87) |
3 or more | 0 | 0 |
Dornase alfa use | — | — |
Yes | 0.224 (0.036), (<0.0001) | 0.28 (0.40), (0.48) |
No | 0 | 0 |
Instrumental variable analysis to predict lung function decline*.
Each model is adjusted for measured confounders by including each listed variable as a covariate. For each categorical variable, the coefficient is the difference in FEV1 decline between the indicated category and the reference category (labeled as coefficient = 0). For each continuous variable, it is the change in FEV1 decline when the variable is increased by 1 unit. A negative value implies greater FEV1 decline.
Multivariable analysis with standard adjustment for confounding by including characteristics as covariates.
Multivariable analysis weighted using propensity scores.
Abbreviations: CF, cystic fibrosis; FEV1, percentage predicted of forced expiratory volume in 1 s.
It is possible that the discrepancy between the previously described registry analysis and clinical trial findings of the treatment effect are due to unmeasured confounding. It is common in observational settings to encounter confounding by indication bias that is not recorded in registries. In this application, we selected a preference-based instrument, center-level prescribing patterns, to combat this bias. The CFFPR includes more than 240 centers. For each center, we calculated the tobramycin-prescribing rate during the time frame of the study. This rate was calculated as the number of times the center prescribed tobramycin to the patient when eligible divided by the total number of times the center should have prescribed tobramycin. We considered a patient to be eligible for the treatment once he met the CFF guidelines for its use.
We had to determine whether the IV met the previously mentioned criteria to be a valid instrument. We began by performing the first-stage analysis outlined in Model (4). We include all potential confounders as explanatory variables, and we include the IV. The response variable in this equation is the tobramycin use. The first-stage results are presented in Table 4 and reflect what we found in the exploratory analysis from Table 1. The IV included in this regression was a highly significant predictor of tobramycin use. The corresponding
Assumption (ii) is not directly testable, but we examine it through sensitivity analyses of heterogeneous treatment effects. These effects may be caused by confounding from other medication use or differences in quality of care received across centers. We performed three different types of sensitivity analyses. First, we extracted quality of care markers through the CF Foundation Annual Report (1) and calculated them for each center. We correlated each marker with our IV and found no significant association. Second, we used subgroup analyses to determine the impact of dornase alfa use on tobramycin effectiveness. We divided the cohort into two distinct groups according to whether they reportedly used dornase alfa. We performed the IV analysis separately on each group. The two sets of results were similar with regard to first- and second-stage analyses. Third, we performed a secondary analysis of patients with
Registry data plays an increasingly important role in health care research. Appropriate design and careful statistical approaches to the analyses of registry data are essential. In this chapter, we have described a step-by-step approach to formulating and implementing a registry data analysis. Understanding the research question, selecting the appropriate data source and identifying potential sources of bias are necessary before beginning to construct an analytic plan. The statistical considerations should include data quality assessments and descriptive analyses, and it is critically important to address selection bias due to both measured and unmeasured confounding. This is because selection bias is ubiquitous; failure to adequately address selection bias will lead to biased conclusions. Multivariable regression has been the primary means to combat selection bias. While this technique can help to minimize differences between groups, it is limited to relatively fewer covariates in the adjustment process. Propensity scores, which correspond to the probability of treatment assignment given pre-treatment characteristics, provide a way to summarize multiple covariates into a single score for each individual. Therefore, this approach is capable of handling a large dimension of confounders, which is particularly useful in registry studies when confounders are measured. Another advantage of PS is that it allows one to check between the treatment groups when conditioning on propensity score whether the confounding factors is balanced out. However, when important confounders are not measured, the PS method is limited. One solution is to perform sensitivity analyses by evaluating how estimated treatment effectiveness might change if there exists an unmeasured confounder with varying levels of prevalence. Such practice will allow one to gauge the impact of unmeasured confounders to the treatment effect.
In this example, the likelihood of tobramycin use depends on unmeasured characteristics at the patient, family or care level. The adjustment of unmeasured confounding that is possible through IV analysis may have led to more intuitive conclusions regarding treatment effect. Since CF care is organized by care center, it was reasonable to examine the validity of a preference-based instrument to combat treatment-selection bias. Thorough sensitivity analyses are necessary to examine the robustness of the IV. We limit our illustrative application to a single instrument. It is possible to include multiple instruments and gain more formal properties to testing assumption (ii).
When designing and analyzing registry data, it is critically important to address biases and confounding that are inherent in this type of study. Although we have focused, in this chapter, on describing methods for controlling selection biases, registry data are often subject to other types of biases related to measurement and miss-classification error, immortal time bias, loss to follow up, and missing data. We encourage use of sensitivity analyses to understand the impacts of these potential biases to the study conclusions. There are rich literature sources and several guidelines for design and analysis of registry data. In addition to the literature referenced in this chapter, a very useful resource is the recent report on standards in the conduct of registry studies for patient centered outcomes research and the references therein [33].
In addressing selection bias, most often, treatment effects are examined using multiple linear regression with measured confounders included as covariates [34]. Increasingly, PS methods are employed. However, existing statistical methods to address unmeasured confounding may be underutilized in registry settings. The models that we have presented are by no means exhaustive. There is room to develop more methodology, particularly to combat time-varying treatment effects and utilize time-varying instruments [12]. It is possible that preference-based instruments will provide a feasible approach to interrogating registries [14]. Admittedly, there are some situations, such as the IV regression specified in Model (3), where the sample size/power analysis calculation is not straightforward. There are approaches to simulate power for this model, but additional assumptions are necessary. Furthermore, in most controlled studies, we can follow up with subjects who drop out. We rarely have this capability in registry settings, which further limits our ability to diagnose the missing data mechanism.
We are grateful to the Cystic Fibrosis Foundation Patient Registry Committee for dispensing the data utilized in the illustrative application. We thank Laurie Kahill, M.S., for information regarding the process of center-specific reporting for this registry. Tables 1, 2 and 4 reprinted with permission of the American Thoracic Society. Copyright © 2014 American Thoracic Society [4].
The authors have no relevant conflicts of interest to report.
Below, we present code from SAS 9.3 (SAS Institute, Cary, NC) to implement the statistical analyses for the application in Section 5.4. See Leslie and Ghomrawi [35] for additional details on the implementation of instrumental variables regression using the QLIM procedure in SAS.
/*For each implementation below, we begin with
title ‘Unadjusted Analysis’;
proc ttest data=analysis_data;
class Tobi;
var dfev1;
run;
/*The code below performs a multivariable linear regression to determine the association between tobramycin and change in lung function, with adjustment for the previously described measured confounders. The variables below correspond to sex (
title ‘Model (1): Traditional Regression’;
proc glm data=analysis_data;
class Tobi inscat cfrd dnase pancr numhosp gender;
model dfev1=Tobi base_fev1 wtpct age inscat cfrd dnase pancr numhosp gender/ cl solution;
lsmeans Tobi/pdiff cl;
run;
/*Next, we implement the propensity score regression model previously described. First, we use logistic regression to estimate propensity scores for each subject.*/
title ‘Model (2): Propensity Score Regression’;
proc logistic data=analysis_data;
class inscat cfrd dnase pancr numhosp gender;
model Tobi=base_fev1 wtpct age inscat cfrd dnase pancr numhosp gender/ link=logit;
output out=props pred=ps;
run;
/*We use the commands below to assign a subject-specific weight that corresponds to his or her propensity score from the logistic regression above. Since the propensity score, denoted
data props2;
set props;
if Tobi=1 then ps_weight=1/ps;
if Tobi=0 then ps_weight=1/(1-ps);
run;
/*We now implement the weighted multivariable regression. The commands are similar to our previous regression, except for our use here of the
proc glm data=props2;
class Tobi inscat cfrd dnase pancr numhosp gender;
model dfev1=Tobi base_fev1 wtpct age inscat cfrd dnase pancr numhosp gender/ cl solution;
lsmeans Tobi/pdiff cl;
weight ps_weight;
run;
/*Finally, we present commands for the instrumental variables regression. The first model statement performs the first-stage regression of the treatment indicator
title ‘Model (3): Instrumental Variables Regression’;
proc qlim data=analysis_data;
class inscat cfrd dnase pancr numhosp gender;
model Tobi=cid_iv base_fev1 wtpct age inscat cfrd dnase pancr numhosp gender /discrete;
model dfev1=base_fev1 wtpct age inscat cfrd dnase pancr numhosp gender /select(Tobi=1);
output out=Tobi prob proball predicted residual;
run;
The global pandemic of COVID-19 has an impact on people of all age levels [1]. Similar to other countries, older adults are more likely to have a chronic illness [1]. Over the world reported that 66% of people aged 70 and over have at least one underlying condition that increased the risk of the severe impact of COVID-19 infection and its long sufferings [1].
The Ministry of Public Health (MOPH), the leader of the Department of Disease Control, nongovernmental organizations, and local organizations have an active role and follow the World Health Organization’s (WHO’s) eight pillars of COVID-19 response, which are a good guide for strengthening surveillance, case investigation, and the laboratory system, institutionalizing, the mechanism of coordination, and strengthening communication between stakeholders [1]. Medical pluralism (MP) is used for the treatment, prevention, and control of the COVID-19 pandemic in Thailand since 2019 [2, 3, 4, 5].
The World Health Organization (WHO) acknowledges that Thailand shows significant progress on overall population health indicators, as seen in the relatively low COVID-19 cases number (less than one death per million population) and improving capacity for pandemic response [1]. WHO has recognized Thai Village Health Volunteers (VHVs) as “unsung heroes” who have made a great effort to fight COVID-19 [6]. The VHVs formulated “Socio-politics networks” or can be seen as a “Pluralistic network” based on a “collaborative system” between numerous agents/stakeholders in the community, including VHV groups, villagers, families/households level politicians’ officials, and private sector actors [6, 7].
In the Northeast of Thailand, research and project produced by the staff of Mahasarakham University (MSU), Chaiyaphum Rajabhat University (CPRU), and Khon Kaen University (KKU) presented medical pluralism, development of herb medicine, health-seeking behaviors of older adults for treatment, prevention, and control of COVID-19 infection and its long sufferings [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22].
This study aimed to synthesize knowledge about successful cases of pluralism medical treatment, prevention, and control of COVID-19 infection and its long sufferings among older adults.
The objective of this study was to synthesize knowledge about the successful pluralism of medical treatment, prevention, and control of COVID-19 infection and its long sufferings among the older population in the Northeast of Thailand from 2019 to 2022.
The study reviewed the results of the author’s research and project in four steps as follows:
Step 1: Synthesize contents from the staff of Mahasarakham University (MSU), Chaiyaphum Rajabhat University (CPRU), and Khon Kaen University (KKU)‘s research studies and projects from 1987 to 2022.
Step 2: Reported herbal medicine in pluralism medical treatment, prevention, and control of COVID-19 infection and its long COVID-19 sufferings.
Step 3: Reported cases of success in treatment, prevention, and control of COVID-19 infected.
Step 4: Summarized the organization chart related to the pluralism medical treatment in the Northeast of Thailand.
The research review was approved by the Ethics Committee for Human Research at Mahasarakham University (MSU), Chaiyaphum Rajabhat University (CPRU), and Khon Kaen University (KKU) (HE591125).
Most of the research studies were based on secondary data. Those who volunteered had signed the consent form.
There are four steps to this research result as follows:
Study No. | Title | Ref. | Study design | Age group (Years old) | Sample size (sex) (Data from) | Findings | Year of pubn. |
---|---|---|---|---|---|---|---|
1. [N] | The causes of unpopularity of the traditional medicine | [8] |
| Most of them were older adults |
| Five factors affecting unpopularity of the Traditional medicine are as follows:
| 1987 |
2. [N] | The promotion of herbal planting for medical use and increasing household income to the villagers | [9] |
| Most of them were older populations | 20 community leaders and health volunteers (VHVs) 20 Villagers 4 Households at Mooban Bausimma Tambon Non-udom Amphur Chumpae Khon Kaen province |
| 1992 |
3. [N] | The promotion of health care and health environment of target populations women, children, and aging people) at Mooban sum, Amphur Mahachanachai, Yasothorn | [10] |
|
|
| After 1 month, 3 months, and 1-year follow-up found that the target populations, increase of
| 1992 |
4. [N] | The factors affecting an acceptance of herbal treatment | [11] |
| 18-85 years old mean age 51 74 Males 127 Females | OPD cases at Phon hospital 112 used modern Medicine and 89 cases used Thai Traditional Medicine (TTM) | 77.1% present TTM equal to modern biomedicine 12.9% herbs better than modern biomedicine 76.6% herbs cheaper than modern drugs, factors affecting are as follows:
| 1995 |
5. [N] | The problems of seeking continuous medical care of patients with paralysis in Nonmuang Village, T. Sila, A. muang, Khon Kaen Province. | [12] | Descriptive study | All of them were older adults | 11 cases living in Nonmuang village, Khon Kaen 5 Males 6 Females With hemiplegia caused by stroke, and had spinal cord disease | 6 cases get better 2 cases stable 3 cases worsened
| 2005 |
6. [N] | Proportion of outpatient’s perception in Srinagarind hospital about drugs after receiving from central dispensary | [13] |
| 18-84 years old median (45.6, IQR 18) 39.9% were educated as Bachelor’s degree | 143 cases at our outpatients at Srinagarind Hospital 44 Males 99 Females |
| 2016 |
7. [I] | Effect of institution-based management for elderly health promotion programs in Northeastern Thailand | [14] |
| Older adults 60 years old and over | 60 cases 55 controls | Cases demonstrated improvements in perceived self-efficacy, received social support, health promotion behavior, and HDL-C level (p < 0.05) than control | 2016 |
8. [N] | Health promotion behaviors of elderly living in an urban community of Khon Kaen Province | [15] | Descriptive study | 60-79 years old | 545 older adults 218 Males 327 Females | Factors related to health promotion behaviors were as follows:
| 2018 |
9. [N] | Frailty and associated factors of elderly Buddhist monks in Chiang Mai Province, Thailand | [16] |
| Age 60 and over | 135 older Buddhist monks |
| 2019 |
10. [I] | Health problems and health care outcomes of older patients admitted to intensive care units in the low and middle-income counties: A systematic and review meta-analysis | [17] | Systematic and review meta-analysis published from 2010 to 2019 |
| 10 out of 1486 observational studies from 4915 from 6 general and 13 specialty ICUs in the 7 LMICS |
| 2020 |
Summary of medical pluralism (MP) from MSU, CPRU, and KKU staffs’ publications and presentations from 1987 to 2022.
I: index journal; and N: non-index journal.
Study No. | Title | Ref. | Study Design | Age group (years old) | Sample size (sex) (Data from) | Findings | Year of pubn. |
---|---|---|---|---|---|---|---|
1 [P] | Poster presentation: “Maelong volunteer for long-term care” | [18] |
| Most of them are adults and aging who work as VHVs at Maelong village |
|
| 2018 |
2(AC) | The 1st Academic Conference “COVID-19 Situation Nursing Challenge”, during 1-2 June 2021 at Faculty of Nursing, Chaiyaphum Rajabhat University, Chaiyaphum Province | [19] | Zoom Conference | Participants were 25 years old and over 60 years old | 300 participants were Registered nurses who work in Thailand and abroad. | Most of them get more knowledge about the prevention and control of people and people infected with COVID-19 in terms of policy and practice | During 1-2 June 2021 |
3(AC) | The 2nd Academic Conference “COVID-19 Situation Leadership: Nursing Challenge”, during 7-9 April 2022 at Faculty of Nursing, Chaiyaphum Rajabhat University, Chaiyaphum Province | [20] | Zoom Conference | Participants were 25 years old and over 60 years old | 250 participants were Registered nurses who work in Thailand and abroad. | Most of them developed nursing leadership potential in the situation of COVID-19, covering services, administration, research, and nursing education. | During 7-9 April 2022 |
Summary of project related to the older populations and Medical Pluralism (MP) from MSU, CPRU, and KKU staffs’ presentation during 2018-2022.
P: poster presentation; and AC: academic conference.
For older adults, these three universities had worked with this problem to prepare health personnel to face aging as advancement in medical technology has resulted in the Thai population having a longer life expectancy, leading Thailand to the “Aging Society”. Such changes affect the quality of life of the elderly and the working-age population who are direct caregivers. Ministry of Public Health prepares to step into a quality elderly society. The policy to promote the health of the elderly in the issue of “Long-term care for the elderly” to create understanding and ability to implement the policy appropriately.
The health promotion policy “Long-term care for the elderly” uses an analysis through cultural sensitivity aspects. The formulation process was divided into the five stages of policy formation, namely: (1) Policy Agenda Setting, (2) Policy Formulation, (3) Policy Implementation, (4) Evaluation Stage Policy, and (5) Policy Implementation.
The results of the analysis revealed that: (1) the policy arose from two major currents, namely the mainstream and the policy stream; (2) it was the government’s policy with cultural sensitivity due to the policy transformation, taking into account the classification of the elderly into three groups (the home group, the social group, and the bed group). The policy implementation strategy is open to each sub-district to be able to develop an innovative long-term care system that takes into account the local context, community potential, and social costs [21]. Besides this, Chaiyaphum Rajabhat University (CPRU)‘s staff has the arrangement of teaching nursing student’s computer-assisted instruction (CAI).
The computer-assisted instruction (CAI) was selected for gerontological nursing lessons on Depression, Dementia, Parkinson’s, and Geriatric assessment. Thirty third-year nursing students of the Faculty of Nursing at Chaiyaphum Rajabhat University were attending the gerontological nursing course. The lessons on Depression, Dementia, Parkinson, and Geriatric assessment were taught via Tutorial Instruction Pattern. The constructed CAI efficiency was 87/84 with an E.I. value of 0.7, higher than the expected criteria. It was found that the mean score of the students’ knowledge at post-test (x = 9.00) was higher than those at pre-test (x = 6.00), with a significant level of p < 0.001. Moreover, the score of satisfaction toward the CAI was high on every item. The computer-assisted instruction results in the student’s acquiring knowledge on nursing care of gerontological nursing [22].
For giving knowledge of COVID-19 infected during the pandemic in 2019–2022, Chaiyaphum Rajabhat University (CPRU) had organized two academic conferences to educate professional nurses about the prevention and control of people and people infected with COVID-19 in terms of both policy and practice. Those professional nurses were provided with knowledge and understanding of situations and trends for the COVID-19 management. At the conference, the Director-General of the Department of Medical Sciences gave a talk on treating and caring for COVID-19 patients by medical professionals, effects and infection control in Asian countries including Japan and Indonesia, management for nurses in hospitals, roles of professional nurses in hospitals and communities, and application of nursing theories and processes. The conference was continually organized as the second meeting to develop nursing leadership potential in the situation of COVID-19. This second conference covered services, administration, research, and nursing education. The conference was paid an honor by the Dean of the Faculty of Nursing at UCLA and was attended by scholars from all the regions of Thailand and professional nurses who work in Thailand and abroad (Table 2).
To promote herbal medicine, the staff of Khon Kaen University (KKU), Mahasarakham University (MSU), and Phon Hospital gave knowledge and practice to the villagers in Khon Kaen province. The results showed that the Community leaders, Health Volunteers (VHVs), and the villagers gain more knowledge and experience of herbal planting for medical use and increase household income (Table 1).
The medical practice guidelines, diagnosis, treatment, and prevention of infection in the hospital in case of Coronavirus infection in 2019 (COVID-19) from the Department of Thai Traditional and Alternative Medicine, Ministry of Public Health, the treatment of COVID-19 are as below:
Probable case person with test results positive for Rapid Antigen Test or Antigen Test Kit (ATK per SAR-CoV-2), and total confirmed cases, both those who have symptoms and asymptomatic person separate group according to the severity of the disease and risk factors can be in four cases as follows:
Asymptomatic COVID-19
Out-patients with self-isolation, home isolation, or state locations are provided as appropriate
Provide symptomatic care
Do not give antiviral drugs such as Favipiravir due to most of the patients’ s symptoms decreasing on their own.
Consider giving Andrographis paniculate for treatment
Do not give Andrographis paniculate with an antiviral drug, because there may be side effects from the medicine.
Symptomatic COVID-19 without pneumonia, and no risk factors for severe disease.
May consider giving Favipiravir by starting the drug as soon as possible.
If the infection is detected when the patient has symptoms for more than 5 days and the patient is asymptomatic, or the patient had few symptoms, may not need to give the antiviral drug, because the patient may heal by themselves without the complications.
COVID-19 with mild symptoms, but has risk factors for severe disease or having comorbidity or mild pneumonia, any of the risk factors are as follows:
Older than 60 years
Chronic Obstructive Pulmonary Disease (COPD), includes another Chronic Lung Disease
Chronic Kidney Disease (CKD)
Cardiovascular disease, including congenital heart disease
Cerebrovascular disease
Uncontrollable Diabetes
Obesity (weight more than 90 Kg, or BMI > 30 Kg/Square meter)
Liver Cirrhosis
Low immunity, and lymphocytes less than 1000 cells/cubic millimeter, or
Patients without risk factors, but tends to the severity of the disease increased
It is recommended to use only one antiviral drug as below, considering congenital disease; contraindications to the drug against each other of antivirus drug, and original medicine (drug-drug interaction), bed management, ease of drug administration, and reserve dose of drugs.
Nirmatrelvir/ritonavir for 5 days (Medication is not recommended, if systems last more than 5 days), or.
Molnupiravir for 5 days (This medication is not recommended for use if symptoms persist for more than 7 days).
Remdesivir for 3 days (This medication is not recommended for use if symptoms persist for more than 7 days).
Favipiravir for 5–10 days (This medication is not recommended for use if symptoms persist for more than 4 days).
Confirmed patients with pneumonia, who have hypoxia (resting oxygen saturation <94%), or have hypoxia (SPO2 > 3%) of measured value while exercising (exercise-induced hypoxemia) or chest radiograph has the progression of pulmonary infiltrates.
Recommend Remdesivir for 5–10 days, in the patients who require oxygen, depending on the clinical symptom, the patients should closely follow-up for the symptoms.
First choice, in the case of mild pneumonia at SpO2 during 94-96%, or no oxygen receives, may consider giving Molnupiravir for 5 days, which should start using the drug within 5 days, after symptoms or Remdesivir, which gives within 7 days after symptoms.
Consider giving Remdesivir for 5–10 days as follows:
Patients with mild symptoms but their risk factors for the severe disease, or have major comorbidities or patients with pneumonia, also do not need oxygen.
Patients with severe pneumonia no later than 10 days after symptoms, and receive cannula >1 liter/min, and level of SpO2 < 95%, or receive HFNC/NIVHFNC or use a ventilator (if wear a breathing apparatus may benefit from this drug is not fully).
Pregnant woman with pneumonia (has more details on the topic of treatment COVID-19 in pregnant women).
There are contraindications to the administration of the drug by mouth or absorption problems.
Choose to use antiviral drugs, kind to eat or Remdesivir, either not shared due to medicine active in the same position when giving Remdesivir until the recommended date.
No recommend Corticosteroid in case of mild symptoms (No additional oxygen is required), or asymptomatic pneumonia.
The Department of Thai Traditional and Alternative Medicine presented the restored health after COVID-19 infection with herbal medicine as shown in Table 3.
Herbal medicine name | Properties |
---|---|
Andrographis panicolata | Reduce fever, anti-inflammatory |
Benjalokwichian Medicine | Cure a fever, make poison out of the body |
Prasacanthr Daeng Medicine (Dracaena loureiroi Gagnep) | Reduce fever, hot fix, cure thirst |
Reduce Fever Medicine namely Junleera | Relieve symptoms of fever, seasonal Fever |
Aromatic Medicine namely Na Wa Kot | Cure wind dizziness, Squeamish, vomit, fix the wind, late fever |
Triphala | Cough relief, expectorant, Elemental balance |
Cough Medicine namely Makhampom | Expectorant, cough relief |
Ginger pill | Relieve flatulence, and indigestion, expel cure heartburn |
Cannabidial (CBD oil) | Cure insomnia, headache, and appetizing |
Muscle Relaxants | Joint pain relief, muscle pain chest pain, stomach ache |
Ya SUK SAI YAI | Cure insomnia, for mode changes, alleviate exhaustion |
Restore health after COVID-19 Infection with herbal medicine (Available from: https://web.facebook.com/informationcovid19/posts/498540561764273?_rdc=1&_rdr).
Source: Department of Thai Traditional and Alternative Medicine, 2022.
Since 2019, the elderly who get COVID-19 received treatment in the hospital and home isolation. The older adults who used medical pluralism (MP) during treatment were our case studies.
We followed the treatment of Coronavirus with the phone who was admitted to the University Hospital of KKU and MSU. Those cases who did not use MP and died from their complication did not report in this study.
We could only review report the number of cases with COVID-19 on May 31, 2022 at the Area Health District, which includes seven provinces in the Northeast of Thailand such as Udon Thani, Sakon Nakhon, Nakhon Phanom. Loei, Nong Khai, Nong Bua Lamphu, and Bueng Kan reported cases of COVID-19 as +264 new cases; 123,760 cumulative patients, 21,956 hospitalized, and 120,195 healed [23].
Our cases reported from 2019 to 2022 are divided into four groups as follows:
Case of unable to COVID vaccination
Case of COVID-19 infected
Case of COVID-19 infection and its long-sufferings
Case of the older adults’ health-seeking behavior in the Northeast of Thailand during COVID-19 Outbreaks
One Thai male, age 64 years old who cannot vaccinate COVID vaccine since 2019, because he has health problems of chronic illness and heart diseases. He needs to insert three catheters entering the heart and used much medicine to protect against embolism. His life is very difficult during the COVID-19 pandemic in Thailand.
He changes his lifestyle by quitting smoking, taking medicine and food as prescribed by the doctor, taking some supplements and herbs, exercising according to the doctor’s orders, living in a well-ventilated environment, getting enough rest, and following Thai policies to prevent COVID-19 infection. He insisted that pluralism of medical treatment prevention and control of COVID-19 infection was very good for him.
One Thai female, age 70 years old who had controllable diabetes mellitus, got COVID-19 infected with test results positive for Rapid Antigen Test. She was asymptomatic COVID-19 and received Andrographis Paniculate for treatment and home isolation. She takes this medicine and food as prescribed by the doctor, exercises according to the doctor’s order, lives in a well-ventilated environment, and gets enough rest.
Nowadays, she takes some supplements and herbs, also has health practices as above, and follows Thai policies to prevent long Covid-19 suffering.
One Thai female, age 59 years old, got COVID-19 infected in 2021 with symptomatic COVID-19 without pneumonia and no risk factors for severe disease. Her doctor gave Favipiravir by starting the drug as soon as possible. But she needs to work hard and not get enough rest.
At present, she still has a persistent cough. She uses cough medicine, namely Makhampom for cough relief, takes some supplement and herbs, and follows Thai policies to prevent COVID-19 infected to other people nearby.
One Thai female, age 60 years old, she fell in the bathroom and ruptured blood vessels in the brain that paralyzed her. Her husband is famous for Thai traditional treatment, but her daughter believes in treatment with modern medicine because she works in one private hospital in Khon Kaen province. During the COVID-19 outbreak in Thailand, patients have difficulty going to the hospital.
Her family members always quarreled about treatment. One female health volunteer in this village recommended her family and other patients to the treatment of Thai traditional medicine and modern medicine. After that, this older adult with hemiplegia gradually got better, and her family is happy.
Medical pluralism (MP) plays a role in many people’s lives over the world. Older people in the Northeast of Thailand are familiar with the herb, Thai government policies have implemented pluralism medicine for treatment, prevention, and control of COVID-19 infected Thai people as summarized by the organization as present in Figure 1.
Summarized the organization chart related to pluralism medical treatment in Northeastern, Thailand, since 2019.
Medical pluralism (MP) is used in many countries over the world during the COVID-19 outbreak. Our review articles found that pluralism in medical treatment, prevention, and control of COVID-19 infection and its long sufferings was found in most older adults worldwide, it presented that most countries have medical pluralism (MP) for care sickness of the populations [24].
The government of Thailand focuses on the older adults and classified them as a risk group, by giving it the name 608 groups (groups of people who need to get vaccinated with the most COVID are older people aged 60 years and over and those with underlying disease, including chronic respiratory disease, cardiovascular disease, chronic renal disease, cerebrovascular disease, obesity, cancer, and diabetes) that will be vaccinated in the first priority group. Similar to other countries, which are the aging society, they also focus on the older populations, they arranged at least three vaccinations of COVID-19 vaccine for those older adults [25].
Most Thai older adults are familiar with herbs because it’s grown for food and medicine [9, 11]. During the COVID-19 outbreak in 2019, Thai older people in rural communities used herbs, such as Andrographis paniculate to build immunity for preventing COVID-19 infection [26]. Similar to other studies presented, herbal medicine is a class of natural substances and is also used as adjuvant therapy for COVID-19. These herbal medicines are psoralidine, silverstrol, quwrrectin, myricellin, flavonoids, and polyphenols [27, 28, 29].
Health interventions have been implemented, reducing the rate of the COVID-19 infection, including a face mask, hand hygiene, COVID vaccination, home isolation, and social distancing, similar to the prior studies [30, 31].
Our review of research, and projects produced at Mahasarakham University (MSU), Chaiyaphum Rajabhat University (CPRU), and Khon Kaen University (KKU) presented that Modern Biomedicine and Thai Traditional Medicine can help reduce the severity of the infection and long sufferings of the COVID-19 during 2019–2022, among the older adults in the Northeast of Thailand.
The medical pluralism (MP) between modern biomedicine and Thai traditional medicine is needed to remedy COVID-19 cases among the older adults because most of them are familiar with herbs used in their household for food and medicine.
The promotion of herbal planting for medical use, which is increasing household income for the villagers in the Northeast of Thailand, should be widely developed and safe for all people in Thailand.
The authors thank (a) all the respondents for their valuable contribution to this study, (b) The Faculty of Medicine of Mahasarakham University and Khon Kaen University for data and financial support, (c) The Faculty of Nursing of Chaiyaphum Rajabhat University for data and financial support, and (d) Dr. Thawalsak Ratanasiri, Dr. Bangonsri Jindawong, Dr. Chanchanok Aramrat, and Mrs. Sompong Chantakram for help search.
All authors declare that they have no conflicts of interest.
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This change influences one another at various temporal and spatial scales; however, improper land uses are the primary causal factor on climate change. It studies relevant literature and Nepal’s case to assess the relationship between land use and climate change. Similarly focuses on how land-use impacts climate change and vice versa. In recent centuries land-use change significant effects on ecological variables and climate change. Likewise, understanding the research on both topics will help decision-makers and conservation planners manage land and climate.",book:{id:"10754",slug:"the-nature-causes-effects-and-mitigation-of-climate-change-on-the-environment",title:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment",fullTitle:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment"},signatures:"Pawan Thapa",authors:[{id:"349566",title:"M.Sc.",name:"Pawan",middleName:null,surname:"Thapa",slug:"pawan-thapa",fullName:"Pawan Thapa"}]},{id:"50282",title:"Relation Between Land Use and Transportation Planning in the Scope of Smart Growth Strategies: Case Study of Denizli, Turkey",slug:"relation-between-land-use-and-transportation-planning-in-the-scope-of-smart-growth-strategies-case-s",totalDownloads:4667,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"In the decision-making process of planning residential areas in developing countries, importance of the commercial areas and need for a sustainable urban transportation infrastructure have generally been ignored based on several sociopolitical reasons. Meanwhile, decision-making periods of location choice and determining areal densities are conducted without quantitative spatial/technical analyses. Those urban matters bring along new planning paradigms like smart growth (SG) and new urbanism. SG is a land use planning paradigm which indicates that traffic problems should be minimized by transit alternatives, effective demand management and providing a balance between land use and transportation planning. This study aims to apply SG strategies to the land use planning process and evaluate the accuracy of land use planning decisions in the perspective of sustainable transportation. In order to reveal the effects of land use planning decisions on the available transportation infrastructure, two scenarios are investigated for 2030. In the first scenario “do nothing” option is considered, while the residential area densities and trip generation rates are regulated based on SG strategies in the second scenario. The results showed that the land use and traffic impact analyses should simultaneously be conducted before land use configuration process.",book:{id:"5235",slug:"sustainable-urbanization",title:"Sustainable Urbanization",fullTitle:"Sustainable Urbanization"},signatures:"Gorkem Gulhan and Huseyin Ceylan",authors:[{id:"182126",title:"Dr.",name:"Gorkem",middleName:null,surname:"Gulhan",slug:"gorkem-gulhan",fullName:"Gorkem Gulhan"},{id:"185555",title:"Dr.",name:"Huseyin",middleName:null,surname:"Ceylan",slug:"huseyin-ceylan",fullName:"Huseyin Ceylan"}]},{id:"42926",title:"Disaster Risk Management and Social Impact Assessment: Understanding Preparedness, Response and Recovery in Community Projects",slug:"disaster-risk-management-and-social-impact-assessment-understanding-preparedness-response-and-recove",totalDownloads:10045,totalCrossrefCites:3,totalDimensionsCites:11,abstract:null,book:{id:"3364",slug:"environmental-change-and-sustainability",title:"Environmental Change and Sustainability",fullTitle:"Environmental Change and Sustainability"},signatures:"Raheem A. Usman, F.B. Olorunfemi, G.P. Awotayo, A.M. Tunde and\nB.A. Usman",authors:[{id:"156875",title:"Dr.",name:"Usman A",middleName:null,surname:"Raheem",slug:"usman-a-raheem",fullName:"Usman A Raheem"},{id:"166449",title:"Dr.",name:"A.M",middleName:null,surname:"Tunde",slug:"a.m-tunde",fullName:"A.M Tunde"},{id:"167886",title:"Dr.",name:"F.B.",middleName:null,surname:"Olorunfemi",slug:"f.b.-olorunfemi",fullName:"F.B. Olorunfemi"},{id:"167887",title:"Dr.",name:"G.P.",middleName:null,surname:"Awotayo",slug:"g.p.-awotayo",fullName:"G.P. Awotayo"}]}],onlineFirstChaptersFilter:{topicId:"136",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82644",title:"Climate-Driven Temporary Displacement of Women and Children in Anambra State, Nigeria: The Causes and Consequences",slug:"climate-driven-temporary-displacement-of-women-and-children-in-anambra-state-nigeria-the-causes-and-",totalDownloads:28,totalDimensionsCites:0,doi:"10.5772/intechopen.104817",abstract:"With increasing periods of extreme wet seasons, low lying geographic position, with socioeconomic, and political factors; some communities in Anambra State, Nigeria experience heightened floods annually resulting in loss of shelter, displacement of people with breakdown of livelihoods, particularly in rural communities worsening their risks and vulnerabilities. In 2012, a major flood event in the state temporarily displaced about 2 million people. In this chapter, we used a community-based adaptation approach to investigate the causes and consequences of climate-related temporary displacement on community members in Ogbaru LGA, Anambra State following flood events. We used global positioning system to obtain the community’s ground control points and gathered our data via field observation, transects walks, focus group discussions, photography, and in-depth interviews. Our findings reveal a heightened magnitude of flood related disasters with decreased socio-economic activities, affecting their health and well-being. Also, the community members have a practice of returning to their land, after flood events, as a local mitigating risk management strategy. For multilevel humanitarian responses at the temporary shelter camps, it becomes imperative to meaningfully engage the community members on the challenging risks and vulnerabilities they experience following climate-driven temporary displacement to inform adaptation and resilience research, policy change and advocacy.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Akanwa Angela Oyilieze, Ngozi N. Joe-Ikechebelu, Ijeoma N. Okedo-Alex, Kenebechukwu J. Okafor, Fred A. Omoruyi, Jennifer Okeke, Sophia N. Amobi, Angela C. Enweruzor, Chinonye E. Obioma, Princess I. Izunobi, Theresa O. Nwakacha, Chinenye B. Oranu, Nora I. Anazodo, Chiamaka A. Okeke, Uwa-Abasi E. Ugwuoke, Uche M. Umeh, Emmanuel O. Ogbuefi and Sylvia T. Echendu"},{id:"79637",title:"Evaluation of the Spatial Distribution of the Annual Extreme Precipitation Using Kriging and Co-Kriging Methods in Algeria Country",slug:"evaluation-of-the-spatial-distribution-of-the-annual-extreme-precipitation-using-kriging-and-co-krig",totalDownloads:54,totalDimensionsCites:0,doi:"10.5772/intechopen.101563",abstract:"In this chapter, we have conducted a statistical study of the annual extreme precipitation (AMP) for 856 grid cells and during the period of 1979–2012 in Algeria. In the first step, we compared graphically the forecasts of the three parameters of the generalized extreme value (GEV) distribution (location, scale and shape) which are estimated by the Spherical model. We used the Cross validation method to compare the two methods kriging and Co-kriging, based on the based on some statistical indicators such as Mean Errors (ME), Root Mean Square Errors (RMSE) and Squared Deviation Ratio (MSDR). The Kriging forecast error map shows low errors expected near the stations, while co-Kriging gives the lowest errors on average at the national level, which means that the method of co-Kriging is the best. From the results of the return periods, we calculate that after 50 years the estimated of the annual extreme precipitation will exceed the maximum AMP is observed in the 33-year.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Hicham Salhi"},{id:"77854",title:"Flooding and Flood Modeling in a Typhoon Belt Environment: The Case of the Philippines",slug:"flooding-and-flood-modeling-in-a-typhoon-belt-environment-the-case-of-the-philippines",totalDownloads:164,totalDimensionsCites:0,doi:"10.5772/intechopen.98738",abstract:"Flooding is a perennial world-wide problem and is a serious hazard in areas where the amount of precipitable water has potential to dump excessive amount of water. The warming of the Earth’s climate due to the increase in greenhouse gases (GHGs) increases the availability of water vapor and hence, of extreme precipitation as observed and forecasted by researchers. With rainfall intensity too high, the torrential rains coupled with weather systems that enhances its effects, flooding not only submerges anything low-lying, it also washes away living and non-living things along the course of the river and the floodplain. The flooding is even worsened by the increase in velocity of flow caused by unsustainable urbanization and denudation of the watershed at the headwaters. Nature’s strength is an order of a magnitude that is way beyond that of the strength of men but human ingenuity enables us to transform our living environment into models that could help us better understand it. Flood modeling provides us decision support tools to deal better with nature. It also enables us to simulate the future especially nowadays that changes in our climate is imminent and even happening already in many parts of the world. Therefore, strategies on how to cope with our ever changing environment is very important particularly to countries that are at more risk to climate change such as the archipelagic Philippines.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Fibor J. Tan"},{id:"77797",title:"Adapting to Climatic Extremes through Climate Resilient Industrial Landscapes: Building Capacities in the Southern Indian States of Telangana and Andhra Pradesh",slug:"adapting-to-climatic-extremes-through-climate-resilient-industrial-landscapes-building-capacities-in",totalDownloads:99,totalDimensionsCites:0,doi:"10.5772/intechopen.98732",abstract:"There is now greater confidence and understanding of the consequences of anthropogenic caused climate change. One of the many impacts of climate change, has been the occurrence of extreme climatic events, recent studies indicate that the magnitude, frequency, and intensity of hydro-meteorological events such as heat waves, cyclones, droughts, wildfires, and floods are expected to increase several fold in the coming decades. These climatic extremes are likely to have social, economic, and environmental costs to nations across the globe. There is an urgent need to prepare various stakeholders to these disasters through capacity building and training measures. Here, we present an analysis of the capacity needs assessment of various stakeholders to climate change adaptation in industrial parks in two southern states of India. Adaptation to climate change in industrial areas is an understudied yet highly urgent requirement to build resilience among stakeholders in the Indian subcontinent. The capacity needs assessment was conducted in two stages, participatory rural appraisal (PRA) and focus group discussion (FGD) were conducted among various stakeholders to determine the current capacities for climate change adaptation (CCA) for both, stakeholders and functional groups. Our analysis indicates that in the states of Telangana and Andhra Pradesh, all stakeholder groups require low to high levels of retraining in infrastructure and engineering, planning, and financial aspects related to CCA. Our study broadly supports the need for capacity building and retraining of functionaries at local and state levels in various climate change adaptation measures; likewise industry managers need support to alleviate the impacts of climate change. Specific knowledge, skills, and abilities, with regard to land zoning, storm water management, developing building codes, green financing for CCA, early warning systems for climatic extremes, to name a few are required to enhance and build resilience to climate change in the industrial landscapes of the two states.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Narendran Kodandapani"},{id:"77460",title:"Changing Climatic Hazards in the Coast: Risks and Impacts on Satkhira, One of the Most Vulnerable Districts in Bangladesh",slug:"changing-climatic-hazards-in-the-coast-risks-and-impacts-on-satkhira-one-of-the-most-vulnerable-dist",totalDownloads:211,totalDimensionsCites:0,doi:"10.5772/intechopen.98623",abstract:"Changes in the climate due to anthropogenic and natural variation are indicated by parameters including temperature and rainfall. Climate change variability with changing trends of the two have been unpredictable and unprecedented globally leading to changing weather patterns, natural disasters, leading to sectoral impacts on food and water security, livelihood, human health among others. This research analyses the changing patterns of these parameters over the last 35/37 years of Satkhira district of Bangladesh to assess the state and trend across spatial and temporal dimensions. Such, the study validates to rationalize the observed seasonal changes that persist in Satkhira of Bangladesh. Both in terms of intensity and frequency of the occurrences of natural disasters, the series of natural events have been triangulated, with impacts and vulnerability being assessed from temperature variations, erratic rainfall, cyclone, flood and water logging etc. The study’s prime contribution remains in attribution of climate change in relation contextual circumstances in the region including sea level rise, salinity intrusion. Therefore, the risk and climatic hazards and its resulting impacts over time has been assessed to draw deeper connection between theoretical and practical values. The series of analyses also draw conclusion that assets are at risk from changing climatic condition.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Md. Golam Rabbani, Md. Nasir Uddin and Sirazoom Munira"},{id:"76915",title:"The Impacts of Climate Change in Lwengo, Uganda",slug:"the-impacts-of-climate-change-in-lwengo-uganda",totalDownloads:102,totalDimensionsCites:0,doi:"10.5772/intechopen.97279",abstract:"Climate Change has become a threat worldwide. Vulnerable communities are at foremost risk of repercussions of climate change. The present study aimed at highlighting a case study of climate change impacts on Lwengo District of Uganda. Out of the total geographical area of the district, 85% hectares are under cultivation and most of its population depends majorly on the rain- fed agriculture sector to meet the food requirement and as a major income source. With the changing climatic conditions, agriculture is the major sector which is being impacted. The region has experienced disasters from some time, usually the second seasons rains used to result in such disasters but since 2016 both seasons have occurred disasters, which majorly include hailstorm, strong wind, long dry spells, pests and diseases. The situation became more severe due to shortage of availability of skilled human resources, quality equipment for disaster management, limited financial resources and weak institutional capacity, which resulted in increasing vulnerability of small farm holders. Some of the adaptation strategies are being taken up by the government but there is a need to understand prospects of decision-making that are site specific and more sustainable for smallholder communities. Climatic changes possess many obstacles to farming communities which require sustainable adaptation to enhance the adaptive capacities of the communities through continued production systems, which are more resilient to the vagaries of weather. Farmers are practising such options which are location specific, governed by policy framework and dependent on dynamism of farmers. This study investigated how these drivers influence farmers’ decision- making in relation to climate change adaptations.",book:{id:"7724",title:"Climate Change in Asia and Africa - Examining the Biophysical and Social Consequences, and Society's Responses",coverURL:"https://cdn.intechopen.com/books/images_new/7724.jpg"},signatures:"Shyamli Singh and Ovamani Olive Kagweza"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:124,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 16th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. 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},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",slug:"jose-gutierrez-fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",slug:"maria-teresa-mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",slug:"monica-alexandra-sousa-oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. 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