**5. Discussion**

Young adults from 35 years and above predominated the study group. Similarly, younger age adults were more prevalent among the HIV, tuberculosis, and schizophrenia groups than the group with hypertension and cancer. The dementia group was composed of entirely younger age adults. Infectious diseases both sexually and nonsexually infections were predominantly represented by patients in early adulthood [4, 7]. Similarly, tuberculosis is an infective disease, and exposure to it may be more in adults who work in healthcare facilities and are mostly caregivers close to already infected persons [7–9]. Tuberculosis is also common in individuals whose immunity may have waned due to poor nutrition, alcohol use, stress, and other emotional illnesses, in most times occasioned by the medical illness, as well as other infective diseases [7–9]. HIV had the most of youngest population with age group 30–39 years forming 60%. This is in line with the earlier finding that HIV is more prevalent among young persons [24, 27]. Similarly, most cases of schizophrenia usually begin in early adulthood except paraphrenia that occurs among the elderly [21]. Hebephrenia, characteristically, one of the most early-onset and worst prognoses was excluded the same way as those with AIDS-defined illness that were excluded among the HIV group. Paranoid schizophrenia was the most common of the schizophrenic group.

Apart from the influence of genetic predisposition, diabetic mellitus and hypertension are largely a disease of lifestyle including unhealthy eating habits, alcohol intake, and sedentary lifestyle, which would be mostly displayed among adults [4, 13, 42, 48–50]. Hypertension is typically a disease of older adults but can occur in younger ages [12–14]. In this study, the majority of patients with hypertension were in the 40–49 age group. Cancers can occur in any age group [93–97], and those in this study ranged from lung, prostate, breast, cervical, blood, and bone cancers to cancers of the gastrointestinal tract. Dementia is generally a disease of advanced age but could present as presenile [98–102]. Even though all dementia participants sampled in this study were within the age group of 40–49, a majority were presenile. Patients with vascular dementia were excluded to reduce the influence of high blood pressure on this group. Overall, all the chronic illnesses in this study span from young to older ages. Therefore the choice of a fairly mid-age as inclusion criteria for respondents was to reduce the impact of extremes of age on the study variable particularly quality of life and life span. However, this may have contributed to the low mortality rate in this study.

In the study, female gender was slightly higher among all subjects with 56%. Females generally have better healthcare-seeking behavior and tend to have lower mortality rate at all ages [103]. So, even though hypertension has higher prevalence in community studies [6, 13, 17], most hospital-based studies show slightly higher prevalence in females. This is in addition to biological and cultural vulnerability of HIV infection [104]. Cancer was slightly higher among females, and this could also be due to the same reason that females report symptoms more readily than do males [103, 104].

The majority of the respondents were married. However, illnesses that have predominantly onset in early adulthood like schizophrenia or common among young adult like HIV had a high percentage of single people [21, 24, 27]. In addition to being predominant among young people, HIV and schizophrenia are associated with a high level of stigma [1, 77, 105], although stigma in the former seems to have reduced significantly over the years. Persons affected with schizophrenia particular early-onset type tend to have difficulty forming or sustaining relationship or even get married, and even those who are already married may face a high risk of separation due to fear of transmission.

**Medical/mental**

**68**

DM Hypertension

HIV

TB Schizo

HIV Cancer Dementia Statistical analysis *DM, diabetes mellitus; HPT, hypertension;*

**Table 2.**

*Quality of life using the*

*WHOQOL-Bref*

 *of subjects and controls with diabetes mellitus,*

 *HIV, human*

*immunodeficiency*

 *virus; TB, tuberculosis;*

 *GHF, general health facet.*

*hypertension,*

 *HIV, tuberculosis,*

*schizophrenia,*

 *cancer, and dementia.*

Control

Subjects

Subjects

Control

Subjects

Control

Subjects

Control

Subject

Control

Subject

Control

Subject

Control

Subject

Control

 58.69 P = 0.001

 13.17

57.28

P = 0.001

 56.39

 41.70

 75.111

 66.46

 12.788

 41.98

 83.062

 67.36

 17.614

 49.23.

 13.072

 62.46

 12.644

 41.22

 13.015

 61.26

 13.428

 43.18

 12.044

 66.36

 13.698

 53.70

 10.103

 60.46

 12.788

61.05

48.67

67.85

44.60

62.05

53.40

59.06

59.63

68.85

42.60

67.05

45.67

 31.075

 13.362

 74.215

 38.833

 24.152

 53.362

 22.813

 11.122

 32.823

 27.870

 15.016

 13.362

 45.98.

 13.064

 44.98

 13.064

56.60

46.60

 24.914

 24.914

 51.97

 14.77

56.20

 22.19

57.51

48.06

53.06

66.80

46.84

57.09

44.06

65.71

46.16

62.80

52.06

56.09

47.06

66.74

45.84

58.49

P = 0.002

 53.44

 91.923

 28.414

 76.877

 17.755

 15.345

 27.378

 37.213

 23.241

 37.141

 14.888

 21.032

 21.378

 26.114

 26.114

 26.13

 **condition**

**Respondents** **Domain 1 (physical)**

 **Domain 2**

**(psychological)**

 **Domain 3 (social**

**relationship)**

 **Domain 4**  52.01

44.95

56.95

59.62

50.33

67.50

45.85

56.71

45.39

57.62

58.95

63.50

45.95

62.62

45.33

54.33

P = 0.004

 29.83

 60.62 P = 0.24

 34.71

 17.411

 47.83

 29.384

 19.411

 64.91

 23.744

 25.837

 43.98

 84.819

 34.107

 65.61

 29.411

 14.511

 53.15

 21.414

 16.503

 54.91

 23.100

 14.622

 43.15

 21.533

 15.412

 52.78

 14.271

 11.236

 46.98

 23.493

 24.102

 66.61

 22.418

 10.456

 63.83

 20.349

 16.503

 51.91

 23.319

 14.831

 50.98

 21.896

*Aging - Life Span and Life Expectancy*

 14.831

 47.98

 21.896

 16.91

 48.34

 22.44

**(environment)**

 **GHF**

**Domains of quality of life**

A majority of the respondents were engaged in middle-class occupation followed by the lower cadre occupation. Hypertension and dementia which are conditions seen common in advance age were most common among the first-class occupations, while cancer, TB, HIV, and schizophrenia were more prevalent among those with lower cadre occupation. Similarly, about 95% of the respondents fall within lowand medium-income earners. A rewarding and satisfying job or occupation is key to good quality of life and by extension prolongs life span [78–84]. Because of the stigma associated with HIV, tuberculosis, schizophrenia, and to some extent cancer [1, 105, 106], there is reduced opportunity to secure sustained employment, so most of these individual settle for menial jobs and petty trading. Even those who had better jobs are sometimes laid off due to chronic illnesses especially in the private sector. Diabetes, hypertension and pre-senile dementia, though without risk from job discrimination, can be the cause of job dissatisfaction due to poor functioning and performance following disabling symptoms.

The cost of continuing treatment in chronic medical and mental disorders is usually huge on sufferers and their families [30]. This often is a major reason for poor drug compliance and in some cases treatment discontinuation [60–62] and may largely contribute to mortality [51, 58]. A majority of the respondents were low- and medium-income earners and may have difficulty in financing the management of their illness. This may reduce quality of life as well as life span. From the study, the dropout rates were high among dementia patients, followed by diabetes mellitus and schizophrenia. This may be due to financial difficulty or poor insight.

Quality of life in people living with HIV (PLWHIV), dementia, schizophrenia, and tuberculosis was significantly affected, more on the psychological and social domains compared with diabetes mellitus, hypertension, and cancer diseases [92, 107–112]. This suggests that the stigma and social rejection associated with the communicable disease may play a significant role in the development of psychological illness. This also implies that even though psychological burden is equally common in the diabetes mellitus and hypertension and affecting quality of life generally, the presence of psychological burden and trauma that may be associated with PLWHIV and tuberculosis tended to have more severe negative impact on quality of life [65, 66]. Again, there is a possibility that there may have been existing psychological illnesses either undiagnosed or untreated that may have made them engage in risky sexual behaviors that may have made them vulnerable to infectious

The presence of symptoms of tuberculosis, cancers, hypertension, and diabetes mellitus alone appears to be more disabling than those in PLWHIV, dementia, and schizophrenia bearing in mind that acute cases were excluded. Moderate to severe cases of diabetes mellitus, hypertension, and tuberculosis cause more symptoms, and they are more disabling. This may account for the better quality of life among PLWHIV on the physical domain and the lower quality of life scores on both the physical and environment domains, among them, than the PLWHIV in this study. HIV not complicated with AIDS is most of the time symptom free or stable on medication, and this stability is often less sensitive to adverse environmental factors unlike in diabetes mellitus, hypertension, and tuberculosis where little adverse changes in the environment could affect profoundly the patients who had hitherto remained stable on medications [12, 22, 30, 39, 43, 52]. Such changes may include change in income level, employment, marital status (prolonged difficulty, disharmony, separation, divorce, or widowhood), and poor drug adherence with imme-

A majority of the patients in all medical conditions fared well on most domains. The possible reasons are their focus on physical strength (e.g., evident physical health, absence of symptoms, ability to work around, available family support, and a strong religious belief) than on their weaknesses (e.g., social discrimination). On Domains 1 and 4, PLWHIV had better performance on quality of life, followed by hypertension and diabetes mellitus, while tuberculosis had the least in similar domains. Furthermore, PLWHIV also scored higher on GHF than the other medical

diseases.

**Table 3.**

conditions.

**71**

diate exacerbation of symptoms.

**Condition No. of alive at**

*Record of death and survival of respondents.*

**beginning of study**

*DOI: http://dx.doi.org/10.5772/intechopen.90756*

**Average quality of life**

*Impact of Chronic Medical and Neuropsychiatric Illnesses on Quality of Life and Life…*

Hypertension 20 49.84 26.124 2 (10%) 1 (5%) 2.31 0.844 Diabetes 20 45.31 71.012 3 (15%) 2 (10%) 2.15 1.395 Tuberculosis 20 41.92 13.021 4 (20%) 0 (0%) 1.21 3.306 Schizophrenia 20 53.43 22.081 1 (5%) 2 (10%) 3.42 0.292 HIV 20 47.51 31.411 2 (10%) 1 (5%) 2.83 0.707 Cancer 20 42.61 78.033 4 (20%) 1 (5%) 1.85 2.162 Dementia 20 48.19 85.151 2 (10%) 3 (15%) 3.21 0.623 Control 35 67.22 52.81 0 (0%) 1 (5%) — —

**No. of deaths** **Dropout Mean**

**personyears lived**

**Crude mortality rate**

Quality of life among all respondents was below average on almost all domains and was statistically significant on all domains except the general health facet. The controls all had better quality of life as they all scored above average. Cancer and tuberculosis patients scored lowest on quality of life particularly both on physical and psychological domains and on social domains for the later. Apart from specific symptoms of these two medical conditions, they are usually associated with weight loss and extreme weakness, and as such the patient may have difficulty carrying out daily activities. In this case, physical domain of quality of life may be impaired. Psychological domains of quality of life of people suffering from chronic illnesses are usually first to be affected [64–73]. Cancers are associated with some level of stigma but not as high as seen in tuberculosis. The high level of stigma and discrimination associated tuberculosis usually affects the social domain of quality of life [82, 83, 106, 107]. Diabetes mellitus was the next with poor quality of life especially on physical and environmental domains, giving credence to a number of studies [107–110]. Diabetes presents with a lot of physical symptoms and risk of systemic damage and requires strict drug and dietary compliance. This may have contributed to the high rate of dropout and poor quality of life.

Hypertension had the best quality of life on all domains followed by HIV. These two conditions were equally found to have lower crude mortality rates. Generally, the study found that better quality of life directly correlated with higher personyears lived and inversely correlated with crude mortality rates (**Table 3**). Hypertension equally presents with a number of physical symptoms, the risk of systemic damage, and the need for strict drug compliance. However, it is not associated with any form of stigma, and a majority of respondents were average-income earners. The level of stigma and discrimination that was associated with HIV two decades ago has drastically reduced following massive public awareness. Also, governments of different countries and the WHO have continued to embark on different intervention strategies including free antiretroviral medications. These effects may have combined to reduce the financial and social burden including stigma and discrimination among HIV-positive individuals.

*Impact of Chronic Medical and Neuropsychiatric Illnesses on Quality of Life and Life… DOI: http://dx.doi.org/10.5772/intechopen.90756*

#### **Table 3.**

A majority of the respondents were engaged in middle-class occupation followed by the lower cadre occupation. Hypertension and dementia which are conditions seen common in advance age were most common among the first-class occupations, while cancer, TB, HIV, and schizophrenia were more prevalent among those with lower cadre occupation. Similarly, about 95% of the respondents fall within lowand medium-income earners. A rewarding and satisfying job or occupation is key to good quality of life and by extension prolongs life span [78–84]. Because of the stigma associated with HIV, tuberculosis, schizophrenia, and to some extent cancer [1, 105, 106], there is reduced opportunity to secure sustained employment, so most of these individual settle for menial jobs and petty trading. Even those who had better jobs are sometimes laid off due to chronic illnesses especially in the private sector. Diabetes, hypertension and pre-senile dementia, though without risk from job discrimination, can be the cause of job dissatisfaction due to poor functioning

The cost of continuing treatment in chronic medical and mental disorders is usually huge on sufferers and their families [30]. This often is a major reason for poor drug compliance and in some cases treatment discontinuation [60–62] and may largely contribute to mortality [51, 58]. A majority of the respondents were low- and medium-income earners and may have difficulty in financing the management of their illness. This may reduce quality of life as well as life span. From the study, the dropout rates were high among dementia patients, followed by diabetes mellitus and schizophrenia. This may be due to financial difficulty or poor

Quality of life among all respondents was below average on almost all domains and was statistically significant on all domains except the general health facet. The controls all had better quality of life as they all scored above average. Cancer and tuberculosis patients scored lowest on quality of life particularly both on physical and psychological domains and on social domains for the later. Apart from specific symptoms of these two medical conditions, they are usually associated with weight loss and extreme weakness, and as such the patient may have difficulty carrying out daily activities. In this case, physical domain of quality of life may be impaired. Psychological domains of quality of life of people suffering from chronic illnesses are usually first to be affected [64–73]. Cancers are associated with some level of stigma but not as high as seen in tuberculosis. The high level of stigma and discrimination associated tuberculosis usually affects the social domain of quality of life [82, 83, 106, 107]. Diabetes mellitus was the next with poor quality of life especially on physical and environmental domains, giving credence to a number of studies [107–110]. Diabetes presents with a lot of physical symptoms and risk of systemic damage and requires strict drug and dietary compliance. This may have contributed

Hypertension had the best quality of life on all domains followed by HIV. These two conditions were equally found to have lower crude mortality rates. Generally, the study found that better quality of life directly correlated with higher personyears lived and inversely correlated with crude mortality rates (**Table 3**). Hypertension equally presents with a number of physical symptoms, the risk of systemic damage, and the need for strict drug compliance. However, it is not associated with any form of stigma, and a majority of respondents were average-income earners. The level of stigma and discrimination that was associated with HIV two decades ago has drastically reduced following massive public awareness. Also, governments of different countries and the WHO have continued to embark on different intervention strategies including free antiretroviral medications. These effects may have combined to reduce the financial and social burden including stigma and discrimi-

and performance following disabling symptoms.

*Aging - Life Span and Life Expectancy*

to the high rate of dropout and poor quality of life.

nation among HIV-positive individuals.

**70**

insight.

*Record of death and survival of respondents.*

Quality of life in people living with HIV (PLWHIV), dementia, schizophrenia, and tuberculosis was significantly affected, more on the psychological and social domains compared with diabetes mellitus, hypertension, and cancer diseases [92, 107–112]. This suggests that the stigma and social rejection associated with the communicable disease may play a significant role in the development of psychological illness. This also implies that even though psychological burden is equally common in the diabetes mellitus and hypertension and affecting quality of life generally, the presence of psychological burden and trauma that may be associated with PLWHIV and tuberculosis tended to have more severe negative impact on quality of life [65, 66]. Again, there is a possibility that there may have been existing psychological illnesses either undiagnosed or untreated that may have made them engage in risky sexual behaviors that may have made them vulnerable to infectious diseases.

The presence of symptoms of tuberculosis, cancers, hypertension, and diabetes mellitus alone appears to be more disabling than those in PLWHIV, dementia, and schizophrenia bearing in mind that acute cases were excluded. Moderate to severe cases of diabetes mellitus, hypertension, and tuberculosis cause more symptoms, and they are more disabling. This may account for the better quality of life among PLWHIV on the physical domain and the lower quality of life scores on both the physical and environment domains, among them, than the PLWHIV in this study. HIV not complicated with AIDS is most of the time symptom free or stable on medication, and this stability is often less sensitive to adverse environmental factors unlike in diabetes mellitus, hypertension, and tuberculosis where little adverse changes in the environment could affect profoundly the patients who had hitherto remained stable on medications [12, 22, 30, 39, 43, 52]. Such changes may include change in income level, employment, marital status (prolonged difficulty, disharmony, separation, divorce, or widowhood), and poor drug adherence with immediate exacerbation of symptoms.

A majority of the patients in all medical conditions fared well on most domains. The possible reasons are their focus on physical strength (e.g., evident physical health, absence of symptoms, ability to work around, available family support, and a strong religious belief) than on their weaknesses (e.g., social discrimination). On Domains 1 and 4, PLWHIV had better performance on quality of life, followed by hypertension and diabetes mellitus, while tuberculosis had the least in similar domains. Furthermore, PLWHIV also scored higher on GHF than the other medical conditions.

Chronic medical and mental diseases account for multiple burdens for patients, including the necessity to deal with pain, suffering, reduced quality of life, premature mortality, financial costs, and familial emotional trauma [12, 30, 32, 72, 73]. The risk factors for mental health problems among patients suffering from chronic medical illnesses are complex [72, 73, 113]. Usually, the more serious the somatic disease and symptoms are, the more probable it will be to be accompanied by mood and/or anxiety symptoms of variable severity [72, 73]; conditions arising after the somatic disease are diagnosed. In other words, even if those with dual diagnoses were excluded from the study, it clearly understood that most chronic medical conditions tend to be associated with some emotional disturbance. Failure to manage such mental health problems increases the patients' probability of suffering from complications, even lethal.

population and also because those with poor prognosis like hebephrenia and disorganized were excluded from the study and only paranoid and catatonic types which carry better prognosis were included. This finding however differs from the earlier finding that any psychiatric diagnosis was associated with a 65% higher than expected total mortality in a case register study in a British primary care

*Impact of Chronic Medical and Neuropsychiatric Illnesses on Quality of Life and Life…*

*DOI: http://dx.doi.org/10.5772/intechopen.90756*

making life years lost an important outcome measure in this population. Life expectancy is a commonly used indicator for how longevity may be impaired by specific long-term exposures (e.g., smoking, obesity, ethnicity, and socioeconomic status) or chronic conditions of ill health or risk (e.g., diabetes mellitus) [48, 49] and provides an alternative measure to determine the influences of different exposures for the purpose of highlighting premature mortality at younger ages in potentially vulnerable groups. It is therefore primarily a measure of *impact* and should be seen as complementary to more elaborate studies using measures of *effect*. As a measure, life expectancy analyses offer an important means of communicating impact on survival to policy makers. Current smoking is associated

with around 4 to 5 life years lost for both genders [48, 49].

Coronary heart disease accounts for threefold elevated mortality in young adults with severe mental illness [120] and diabetes, while stroke is usually a complication of long-standing hypertension. Long-term antipsychotic use and adverse lifestyle choices (e.g., obesity, smoking, poor diet, illicit drug use, and physical inactivity) are implicated in increased risk of cardiovascular events in these populations [1, 51, 120–122] and clearly need higher levels of consideration in order to improve health and survival, as well as the better-known risks of suicide and violent deaths. The causal pathways between mental disorder and premature mortality are multiple,

Clearly the mechanisms through which medical and mental disorders are associated with premature mortality will include the effects of these individual risk factors (e.g., smoking behavior, risk of diabetes, etc.) as well as other factors (such as risk of suicide or accidents and direct effects of mental distress on cardiovascular risk). Excess mortality associated with mental disorders has been demonstrated to be predominantly due to "natural" causes [18, 119, 120] although mental health service provision is often focused on preventing more rare outcomes of suicide and violent death [115–117]. If improving overall survival is to be considered as an alternative priority, much more efforts are clearly required to address the challenges of improving general health in people with mental disorders through medical services, socioeconomic support, and physical health promotion strategies [51]. It is important to note that a good number of psychosocial and clinical factors, like increased age, marital status (married), later age of onset of illness, education, employment, average to high monthly income, shorter duration of illness, longer duration of treatment, and emotional stability, may affect the quality of life and other outcome of the medical conditions. The implication of this is that these factors have to be addressed in the holistic management of these and indeed other chronic

The findings of this study support the impression that chronic medical conditions are associated with reduced quality of life, which, together with a number of sociodemographic and clinical factors, in turn affect life expectancy. The results support the call that the management of patients with these medical conditions should necessarily include attention to the mental health status of the sufferers.

cohort [119].

medical conditions.

**6. Conclusion**

**73**

In chronic medical conditions, functionality may be severely impaired due to chronic psychogenic and somatic pain, frequent hospital admissions, and dependency from medical and nursing personnel. These are all markers of poor quality of life and well-being. It is important to mention that most of the mortalities in chronic medical conditions may not be due to the direct complication of the disease, rather a cumulative outcome of social and psychological dissatisfaction of the condition. Sufferers maintain the feeling that they have come to the end of the road and seek the easiest escape rooting out of the problem which is suicide [114–117]. In addition, research has pointed out a relationship between sustained emotional disturbance especially depression and reduced immunity. This may be worse among chronically ill patients, and this makes them more vulnerable to recurrent infections or reinfections. Good quality of life and well-being are a measure of satisfaction in major areas of life including mood stability and affording basic nutrition which will sustain immunity.

Most chronic illnesses particularly HIV, cancer, and tuberculosis in sub-Saharan Africa are classic examples of diseases with both medical and social dimensions, characterized by its close relation to poor socioeconomic conditions [27–31]. For instance, in tuberculosis, a higher risk of acquiring active disease occurs with alcoholism, smoking [48, 49], intravenous drug abuse [48, 49, 58, 74], diabetes mellitus, HIV infection, overcrowding, and other factors. The abovementioned risk factors are very prevalent among populations with reduced quality of life and well-being and increase risk of having HIV and progression from latent TB to active TB [77].

From the study, mortality was the highest among tuberculosis and cancer patients, followed by patients with diabetes mellitus. Schizophrenia had the lowest mortality after 5 years. There was no mortality among the control within the period. Correspondingly, mortally was the highest among the groups with the lowest quality of life. Quality of life is an indicator of total well-being and optimal health; therefore, if it is low, then it is an indication that the individual may not be enjoying good health. The finding among the diabetes group is in line with the earlier report that reduced life expectancy at age 15 by 1.3 years for men and 2.0 years for women in Canada [47] and a BMI of 40–45 kg/m<sup>2</sup> were associated with a 10-year reduction of life expectancy at age 35 compared to a BMI of 22.5–25 kg/m<sup>2</sup> [118]. Causes of mortality may be due to organ damage, complication of medication, systemic damage, or hemodynamic changes. Actual or direct causes of the deaths could not be ascertained as many of the deaths did not occur in the hospital. Mortality from tuberculosis and cancer tends to be high with a low rate of survival. Mortality in severe chronic mental illness is recognized to be raised, and underlying causes may be multiple. However, the death rate was lowest among schizophrenics in this study. This may be due to the fact the schizophrenics were predominantly young

## *Impact of Chronic Medical and Neuropsychiatric Illnesses on Quality of Life and Life… DOI: http://dx.doi.org/10.5772/intechopen.90756*

population and also because those with poor prognosis like hebephrenia and disorganized were excluded from the study and only paranoid and catatonic types which carry better prognosis were included. This finding however differs from the earlier finding that any psychiatric diagnosis was associated with a 65% higher than expected total mortality in a case register study in a British primary care cohort [119].

Coronary heart disease accounts for threefold elevated mortality in young adults with severe mental illness [120] and diabetes, while stroke is usually a complication of long-standing hypertension. Long-term antipsychotic use and adverse lifestyle choices (e.g., obesity, smoking, poor diet, illicit drug use, and physical inactivity) are implicated in increased risk of cardiovascular events in these populations [1, 51, 120–122] and clearly need higher levels of consideration in order to improve health and survival, as well as the better-known risks of suicide and violent deaths. The causal pathways between mental disorder and premature mortality are multiple, making life years lost an important outcome measure in this population.

Life expectancy is a commonly used indicator for how longevity may be impaired by specific long-term exposures (e.g., smoking, obesity, ethnicity, and socioeconomic status) or chronic conditions of ill health or risk (e.g., diabetes mellitus) [48, 49] and provides an alternative measure to determine the influences of different exposures for the purpose of highlighting premature mortality at younger ages in potentially vulnerable groups. It is therefore primarily a measure of *impact* and should be seen as complementary to more elaborate studies using measures of *effect*. As a measure, life expectancy analyses offer an important means of communicating impact on survival to policy makers. Current smoking is associated with around 4 to 5 life years lost for both genders [48, 49].

Clearly the mechanisms through which medical and mental disorders are associated with premature mortality will include the effects of these individual risk factors (e.g., smoking behavior, risk of diabetes, etc.) as well as other factors (such as risk of suicide or accidents and direct effects of mental distress on cardiovascular risk). Excess mortality associated with mental disorders has been demonstrated to be predominantly due to "natural" causes [18, 119, 120] although mental health service provision is often focused on preventing more rare outcomes of suicide and violent death [115–117]. If improving overall survival is to be considered as an alternative priority, much more efforts are clearly required to address the challenges of improving general health in people with mental disorders through medical services, socioeconomic support, and physical health promotion strategies [51].

It is important to note that a good number of psychosocial and clinical factors, like increased age, marital status (married), later age of onset of illness, education, employment, average to high monthly income, shorter duration of illness, longer duration of treatment, and emotional stability, may affect the quality of life and other outcome of the medical conditions. The implication of this is that these factors have to be addressed in the holistic management of these and indeed other chronic medical conditions.


**3. Drivers of traceability**

The drivers of traceability can be broadly classified into two main types – hard driver, and soft driver traceability requirements [5]. Hard traceability criteria are

*Role of Traceability Systems for Food Safety within Post-Harvest Systems: Indian Context DOI: http://dx.doi.org/10.5772/intechopen.110192*

those that either local or foreign marketers of perishable goods must adhere to in order to fulfill legal or contractual responsibilities under international trade agreements [6]. While soft requirements may not restrict commerce, they can have a big impact on the economics of specific supply chains. These can involve enhancing supply chain efficiency, adapting to shifting consumer demands, or addressing demands from other parties like importers or merchants. The following are the key drivers of traceability:


**2. Conjugate gradient algorithm (CG)**

It is an algorithm which is used because it converges very quickly. It is based on a classical descent method. Its formula of iterative updating can be, roughly, given by the following expression [7]:

$$f^{n+1} = f^n + \alpha^n d^n \tag{25}$$

We can verify that the correction is not multiplicative as for EM algorithm, but it is additive. This formula is characterized by a descent direction d (Eq. 26) and a descent speed α (Eq.27) that are recalculated in a conjugated manner for each iteration and this to optimize the speed of convergence [6].

$$\alpha^{\prime \prime} = \frac{\left\| R^{\prime} p - R^{\prime} R f^{\prime \prime} \right\|^2}{(R^{\prime} p - R^{\prime} R f^{\prime \prime})^{\prime} R (R t \text{ p } - R t \text{ R fm})} \tag{26}$$

p1 p2

Mathematics and Physics of Computed Tomography (CT): Demonstrations and Practical Examples

1 / 4 3 / 4 (E.1)

http://dx.doi.org/10.5772/52351

103

2 (E.3)

*n* + (*pk* − *pk n*) / *Rii* (E.5)

*n updating. The results obtained are shown n the following matrix:*

*n from the equation of P1(Eq.E.3(2)) for each*

(E.2)

(E.4)

f 1 f 2

*For each projected line, the projector matrix is modeled by considering a value proportional the area of the pixel covered by the ray (projection line).For example for p1 the projection ray cover approximately ¾ of the area of the first pixel (f1) and ¼ of the area of the second pixel (p2). Therefore, the projection operator (matrix) can is given by: R* = 3 / 4 1 / 4

*Supposing that the object function in known (f1=2, f2=3). In this case the projection values are equal to:*

4 *f* 1 + 1

4 *f* 1 + 3

4 *f* 2= 9 4 (1)

4 *f* 2= 11 4 (2)

> *Rki* ∑*j Rkj*

*During the iteration process just one ray equation (p1 or p2) is used per iteration alternatively. Results of the iteration and*

*n* 2.70 3.26 2.25 2.45 2.20 2.25 2.06 2.10 2.02 2.02 2.02 2.02 2.00

*n* 0.90 2.58 2.24 2.84 2.75 2.90 2.84 2.96 2.93 2.98 2.97 2.99 2.98

*n* / 3.09 / 2.54 / 2.41 / 2.31 / 2.27 / 2.26 /

*n* 1.35 / 2.24 / 2.61 / 2.64 / 2.70 / 2.73 / 2.73

*We can easily verify that this algorithm gives a solution which approximate well the real one (2,3) after 12 iterations*

*We try know to compute the object function data (f1, f2) using some practical iterative algorithms and to compare the values obtained to real ones (f1=2, f2=3). For all of the following algorithms, the initial iteration conditions are:*

*n* + (*pk* − *pk n*)

Iteration(n) 1 2 3 4 5 6 7 8 9 10 11 12 13

*p*1 =3

*p*2 =1

*f i n*+1 = *f i*

> *f i n*+1 = *f i*

*n is calculated from the equation of P1(Eq.E.3(1)) and f1*

*n and p2*

*Fig. E.1. Object (2 pixels) and projection model*

*f1 0=f2*

*0=0="/> p1*

*0= p2 0=0.*

f1

f2

p1

p1

*2. SIRT algorithm with Jacobi method*

*iteration by taking into consideration p1*

*In this method, the iteration updating is given by:*

*(2.02, 2.99)*

*In this method f1*

*1. Additive ART algorithm with Kackzmarz method In this method, the iteration updating is given by:*

*p1, p2 updating are shown on the following matrix.*

$$\begin{aligned} \text{Attention.1}: \qquad &d^1 = \mathcal{R}^t p - \mathcal{R}^t \mathcal{R} f^0\\ \text{Attention.2}: \quad &d^n = (\mathcal{R}^t p - \mathcal{R}^t \mathcal{R} f^n) + b^n d^{n-1} \end{aligned} \tag{27}$$

Through this process, the error between the measured projections and those calculated is minimized progressively. This error evaluation formula is given by:

$$e = \left\| p - Rf \right\|^2\tag{28}$$

#### **3. ART algorithm**

The formula of the iterative updating of this algorithm is given by the following expression:

$$f\_i^{(n+1)} = f\_i^{(n)} + \mathcal{R}\_{ji} \frac{p\_j - \mathcal{R}\_j f^{(n)}}{\left\| \mathcal{R}\_j \right\|^2} \tag{29}$$

The correction in this algorithm can be additive (e=pk-pk n) or multiplicative (e= pk/pk n).

#### **4. SIRT algorithm**

The formula of the iterative updating of this algorithm is given by the following expression:

$$f\_i^{(n+1)} = f\_i^{(n)} + \frac{\sum\_j p\_j}{\sum\_j \sum\_i R\_{ji}} - \frac{\sum\_j R\_j f^{(n)}}{\sum\_j \left\| R\_j \right\|^2} \tag{30}$$

#### *Practical example 1*

*To illustrate the difference between the iterative algorithms, let consider the example of figure (E.1). In this example we consider an object (image) composed by 2 pixels which are projected as shown on this figure.*

#### *Fig. E.1. Object (2 pixels) and projection model*

that are recalculated in a conjugated manner for each iteration and this to optimize the speed

( ) (Rt p - Rt R fn)

*t t nt*

.1 :

minimized progressively. This error evaluation formula is given by:

( 1) ( )

The correction in this algorithm can be additive (e=pk-pk

( 1) ( )

*consider an object (image) composed by 2 pixels which are projected as shown on this figure.*

*i i*

*R p R Rf R*

*iteration d R p R Rf iteration d R p R Rf b d* -

*n*

a

*t tn*

*R p R Rf*

102 Imaging and Radioanalytical Techniques in Interdisciplinary Research - Fundamentals and Cutting Edge Applications

.2 : ( )

2

1 0

= - =- +

Through this process, the error between the measured projections and those calculated is

The formula of the iterative updating of this algorithm is given by the following expression:

The formula of the iterative updating of this algorithm is given by the following expression:

å å

*Practical example 1 To illustrate the difference between the iterative algorithms, let consider the example of figure (E.1). In this example we*

*ji j i J j*

*p Rf*

*j j n n j j*

*f f R R* + =+ -

*n n j j i i ji*

*p Rf f fR*

( )

2 *n*

*j*

*R*

*t t n t t n nn*

1

2 *e p Rf* = - (28)

+ - = + (29)

( )

2 *n*

å å å (30)

n) or multiplicative (e= pk/pk

n).

(26)

(27)

of convergence [6].

**3. ART algorithm**

**4. SIRT algorithm**

*For each projected line, the projector matrix is modeled by considering a value proportional the area of the pixel covered by the ray (projection line).For example for p1 the projection ray cover approximately ¾ of the area of the first pixel (f1) and ¼ of the area of the second pixel (p2). Therefore, the projection operator (matrix) can is given by:*

$$\mathcal{R} = \begin{bmatrix} \mathbb{B} \ \mathbb{A} & \mathbb{1} \ \mathbb{A} & \mathbb{1} \ \mathbb{A} \\ \mathbb{1} \ \mathbb{A} & \mathbb{B} \ \mathbb{A} \end{bmatrix} \tag{E.1}$$

*Supposing that the object function in known (f1=2, f2=3). In this case the projection values are equal to:*

$$\begin{aligned} \rho\_1 &= \frac{3}{4} \int\_4 \, f\_1 + \frac{1}{4} \int\_4 \, f\_2 = \frac{9}{4} \{1\} \\ \rho\_2 &= \frac{1}{4} \int\_4 \, f\_1 + \frac{3}{4} \int\_4 \, f\_2 = \frac{11}{4} \{2\} \end{aligned} \tag{5.2}$$

*We try know to compute the object function data (f1, f2) using some practical iterative algorithms and to compare the values obtained to real ones (f1=2, f2=3). For all of the following algorithms, the initial iteration conditions are: f1 0=f2 0=0="/> p1 0= p2 0=0.*

#### *1. Additive ART algorithm with Kackzmarz method*

*In this method, the iteration updating is given by:*

$$f\_{l}^{n+1} = f\_{l}^{n} + (\mathfrak{p}\_{k} - \mathfrak{p}\_{k}\,^{n})^{\mathcal{R}\_{kl}} \Big| \sum\_{j} \,^{}\_{k\dot{j}} \mathcal{R}\_{kj} \,^{2} \tag{\text{E.3}}$$

*During the iteration process just one ray equation (p1 or p2) is used per iteration alternatively. Results of the iteration and p1, p2 updating are shown on the following matrix.*

*We can easily verify that this algorithm gives a solution which approximate well the real one (2,3) after 12 iterations (2.02, 2.99)*

#### *2. SIRT algorithm with Jacobi method*

*In this method, the iteration updating is given by:*

$$\{f\_i^{n+1} = f\_i^n + (\mathfrak{p}\_k - \mathfrak{p}\_k \iota^n) \mid \mathcal{R}\_k\} \tag{\mathbb{E}.5}$$

*In this method f1 n is calculated from the equation of P1(Eq.E.3(1)) and f1 n from the equation of P1(Eq.E.3(2)) for each iteration by taking into consideration p1 n and p2 n updating. The results obtained are shown n the following matrix:*

introduction to integral computed tomography. We then go over to projection and inversion algorithms. We give a detailed analysis of the filtered back-projection algorithm in the light of the sampling theorem. We also describe the convergence properties of iterative algorithms.

Mathematics and Physics of Computed Tomography (CT): Demonstrations and Practical Examples

http://dx.doi.org/10.5772/52351

105

Good reconstructions from the interlaced lattice can also be obtained by using the direct algebraic reconstruction algorithm, or by increasing the amount of data through the interpo‐ lation according to the sampling theorem. As we have seen, the interpolation step can introduce significant errors in certain cases. It has also been shown that the interpolation can be avoided by choosing the points x where the reconstruction is computed on a polar grid rather than on a square Cartesian grid, and interchanging the order of the two summations. This algorithm should work well for the interlaced lattice and is particularly beneficial in case of the fan-beam sampling geometry, since the method also avoids the homogeneous approx‐

The cone and fan beam scanning are the standard scanning modes in present day's clinical practice. The methods described above assumed that the geometry of the acquisition was a parallel geometry, like that of first generation systems. In the case a conical geometry (or fan),

**1.** The first is to ignore the discrepancy. The error induced by this approximation is consid‐ ered negligible if the beam angle is low (typically below 15 degrees). This method is applicable to systems of second generation, but more to the following where the beam

**2.** The second method is to rearrange the data in parallel projections, mainly by interpolation. **3.** The last method is to reformulate the problem completely. It becomes apparent that the projection theorem cannot be generalized, which does not have direct inversion method. The projection theorem can be adapted to different geometries, and then the algorithm is the same as for a parallel geometry, with the same calculations. Finally, the filtered back projection formulas can be corrected and result in a slightly different algorithms. The algebraic methods have some fewer additional problems, but the flexibility in the choice of basic functions and thus the coefficients of the matrix R, allows these methods to be

Finally, I want just to add that the purpose of this chapter is to give an introduction to the studied topic and treat some related aspects in more detail. The reader interested in a broader overview of the field, its relation to various branches of pure and applied mathematics, and

its development over the years may wish to consult the appropriate bibliography.

Department of Physics, Faculty of Science, University of Ferhat Abbas-Sétif, Algeria

must cover the whole section of the patient to remove the translation.

We shortly mention Fourier based algorithms used in CT.

three methods are possible:

adapted to different geometries.

**Author details**

Faycal Kharfi

imation, whose influence on the reconstruction is difficult to estimate.

*With this algorithm and method the best solution is obtained after 9 iterations.*

#### *3. SIRT algorithm with Gauss-Seidel method*

*This method is similar to the Jacobi method and use the same updating function (E.5) but just for the first iteration which allows the determination of f1 1 value. After that f2 1 is estimated from p2 equation considering the obtained value f1 1 which itself will be used to determinate f1 2 from p1 equation and so on (by matching alternatively between p1 and p2) until obtaining the best results. The results obtained are shown on the following matrix:*

$$\begin{array}{ccccccccc}\text{Attention}(n) & 1 & 2 & 3 & 4\\\text{ $f$ }\_{1}n & 3 & 1.18 & 1.90 & 1.98\\\text{ $f$ }\_{2}n & 2.66 & 3.27 & 3.03 & 3.00\\\end{array} \tag{5.7}$$

*We remark that this algorithm and method gives good results just after 4 iterations; so it is the very fast one in term of convergence.*

#### *End of practical example 1*

#### **2.3. Iteration stopping rules**

The iterative process for image reconstruction must be stopped at the end of the conver‐ gence phase before it starts diverging. This can be done using some regularization meth‐ ods. Indeed, well selected regularization parameter can controls the amount of stabilization imposed on the solution. In iterative methods one can use the stopping index as regulariza‐ tion parameter. When an iterative method is employed for image reconstruction, the user can also study on-line adequate visualizations of the iterates as soon as they are computed, and simply halt the iteration when the approximations reach the desired quality. This may actually be the most appropriate stopping rule in many practical applications, but it requires a good intuitive imagination of what to expect. If this is not the case, a computer can give us some aid to determine the optimal approximation. The stopping rules are divided into two categories: rules which are based on knowledge of the norm of the errors, and rules which do notrequire such information. If the error norm is known within reasonable accuracy, the perhaps most well known stopping rule is the discrepancy principle Morozov [8]. Examples of the second category of methods are the L-curve criterion [9], and the general‐ ized cross-validation criterion [10].

### **3. Conclusions**

After a substantial effort, major breakthroughs have been achieved in the last fourteen years in the mathematical modeling of CT. The aim of this chapter is to survey this progress and to describe the relevant models, mathematical problems and reconstruction procedures used in CT. We give a summary on the mathematics of computed tomography. We start with a short introduction to integral computed tomography. We then go over to projection and inversion algorithms. We give a detailed analysis of the filtered back-projection algorithm in the light of the sampling theorem. We also describe the convergence properties of iterative algorithms. We shortly mention Fourier based algorithms used in CT.

Good reconstructions from the interlaced lattice can also be obtained by using the direct algebraic reconstruction algorithm, or by increasing the amount of data through the interpo‐ lation according to the sampling theorem. As we have seen, the interpolation step can introduce significant errors in certain cases. It has also been shown that the interpolation can be avoided by choosing the points x where the reconstruction is computed on a polar grid rather than on a square Cartesian grid, and interchanging the order of the two summations. This algorithm should work well for the interlaced lattice and is particularly beneficial in case of the fan-beam sampling geometry, since the method also avoids the homogeneous approx‐ imation, whose influence on the reconstruction is difficult to estimate.

The cone and fan beam scanning are the standard scanning modes in present day's clinical practice. The methods described above assumed that the geometry of the acquisition was a parallel geometry, like that of first generation systems. In the case a conical geometry (or fan), three methods are possible:

Finally, I want just to add that the purpose of this chapter is to give an introduction to the studied topic and treat some related aspects in more detail. The reader interested in a broader overview of the field, its relation to various branches of pure and applied mathematics, and its development over the years may wish to consult the appropriate bibliography.


*3.8. Microstructure*

The scanning electron microscopy (SEM) images of the dried potato slices under different pretreatments are shown in Figure 6. The microscopic results of the different pretreated samples and untreated samples differed greatly. As shown in Figure 6a, the untreated samples had both dense and porous structures, which may be caused by the nonuniform shrinkage of the material structure. From Figure 6b, we also found intact starch granules, indicating that the starch did not swell and gelatinize during the drying process.

**Figure 6.** Microstructures of dried potato slices under different pretreatments in different magnifications. (**a**,**b**) Untreated potato samples; (**c**,**d**) blanching pretreatment for 90 s; (**e**,**f**) saline immersion under solution concentration of 20%; and (**g**,**h**) ultrasound pretreatment for 60 min.

The microstructure of the dried samples after blanching pretreatment are shown in Figure 6c,d. The tissue structure of the blanched dried potato slices was uniform and dense, and no obvious pore structure was found. This may be caused by the collapse of the cellular structure after the blanching and drying process. In addition, starch granules were not found in the micrograph field, indicating that the blanching treatment resulted in starch breakage and gelatinization [46]. This was similar to the results of a study on sweet potato bars [57].

The samples from the saline immersion pretreatment had a relatively loose and porous structure (Figure 6e,f). The cytoskeletal structure became coarse as compared to the untreated samples, and starch granules were no longer visible in the samples. This may be due to the internal modification of the starch particles by the components of osmotic solution during processing [67]. After the ultrasound pretreatment, the boundaries of the cells were fuzzy, while the starch granules could be also clearly seen (Figure 6g,h). This was because

the ultrasound pretreatment caused changes in the cell structure and formed microchannels on the surface of the potato samples, and the microchannels were combined with the original pore structure, which may be due to the cavitation and sponge effects of the ultrasound waves [68]. The observation of the microstructure of the material was helpful in understanding the effects of pretreatment on the drying rate and appearance deformation.