Contoh Format Full Paper

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RISK FACTORS OF LYMPHOMA AT DR. SOEBANDI HOSPITAL

IN JEMBER DISTRICT – EAST JAVA

Annisa Reykaningrum1

1Department of Epidemiology, Biostatistics and Population, Public Health Faculty, Jember University,

Jember

Correspondence: Jl. Kalimantan I/93 Jember. Telp (0331-337878). Fax (0331-322995). Mobile phone

085730607719. email : [email protected]

ABSTRACT

Background: In the world, the incidence of malignant lymphoma was rising but the

exact cause of this disease is unknown. Indonesia had not accurate data about malignant

lymphoma, but its predicted ranked 6th highest cancer incidence.

Objective: This research aims to analyze the risks factors of malignant lymphoma in

Jember District Hospital.

Methods: This research is an analytical study with cross sectional approach. The study

population was 97 patients in General Surgery Clinic in dr. Soebandi Jember District

Hospital. This research was analyzed using Chi-square test and multivariable analysis

using logistic regression technique.

Results: The relationship between age and race with lymphoma were not significant.

Risks Factors for lymphoma that significant were gender (male) (p-value=0,019;

OR=5,7; 95% CI=1,16-27,8), familial predisposition (p-value=0,012; OR=5,4; 95%

CI=1,44-20,34), history of illness (p-value=0,001, OR=10,8; 95% CI=2,59-45,19), and

exposure to pesticides (p-value=0,021, OR=9,1; 95% CI=1,12-74,31).

Conclusion: The factors that have the stronger relationship with lymphoma are familial

predisposition, history of illness and exposure to chemicals (pesticides). Base on this

research, developing multi sector approach between hospital, official health, department

of agriculture and Organization of Cancer in Indonesia must be built, to prevent and

decrease the incidence of lymphoma’.

Keywords: Pesticides, Familial Predisposition, History of Illness, Lymphoma

Introduction

Lymphoma is a malignancy that occurs due to activation of the abnormal gene

(mutated lymph cells or lymphocytes) specific to the lymphatic system (lymph nodes,

spleen, spinal marrow, and the thymus gland in the neck) which leads to the

uncontrolled growth of lymph node cells (1)

. Lymphoma is one rapidly increased cancer

incidence over the last 30 years, which increased 80% from 1973 to 1999. World Health

Organization (WHO) estimates that approximately 1.5 million people worldwide

currently live with lymphoma and 300 thousand people die from this disease each year

(2).

mailto:[email protected]

2

In 2007, the incidence of lymphoma in the United States was as many as 71,380

people and it was included five major cancers in men and women. In 2010, there were

65,540 people (35,380 men and 30,160 women) to be diagnosed with lymphoma. Of the

overall population suffering from lymphoma, 20,210 people died from this cancer

(10,710 men and 9,500 women) (2)

. Data on lymphoma incidence in Indonesia is still not

accurate. It is estimated that this cancer is ranked the sixth highest cancer incidence (3)

.

The results of preliminary study on the medical record at Poly Chemotherapy of dr.

Soebandi Hospital, Jember, stated that lymphoma was included in one of 2 major

cancers in the poly. Each year the number of lymph nodes of cancer patients has

increased. In the year 2008 it was recorded 124 patient visits to the hospital and in 2009

it reached 151 visits.

The exact cause of this disease remains unknown. Several previous

epidemiological studies stated that age, gender, exposure to chemicals, and a history of

infectious disease or other types of cancer may increase the risk of lymphoma (2)

.

Increased incidence of lymphoma was associated with age, especially in the age group >

55 years. As for gender, lymphoma was more in men than women, which was equal to

2:1. Race also may increase the risk of this cancer. The results of research in the United

States stated that lymphoma was found in 40-70% whites compared to blacks. As for

the continent of Asia, the race that had a high risk of this cancer was Chinese (4)

. History

of non-communicable diseases such as Diabetes Mellitus proved to have a significant

relationship to the incidence of lymphoma (5)

.

History of infections and diseases associated with immune system, such as

congenital immunodeficiency, AIDS, use of immunosuppressive drugs after organ

transplants, Sicca syndrome, Rheumatoid arthritis, Celiac disease, EBV, HTLV-1,

Helicobacter pylori and Campylobacter, hepatitis C virus, Chlamydia psittaci and

exposure to chemicals such as pesticides, especially those containing 2,4-

dichlorophenoxyacetic acid, hexachlorocyclo hexane and benzene could also increase

the risk of developing lymphoma (4)

. The results of a study in California stated that

smoking had an influence on some incidence of lymphoma. Genetic factors or family

history of lymphoma or other types of cancer might also increase the risk of developing

lymphoma, but the increased risk was also influenced by exposure to carcinogenic

chemicals or ingredients (6-7)

.

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Based on the high number of patients with lymphoma in dr. Soebandi Hospital,

Jember, which was included in the 2nd

highest cancer incidence after breast cancer at

chemotherapy Poly, it was necessary to do research to find out more about the factors

associated with the incidence of lymphoma in Jember District, especially at Dr.

Soebandi Hospital of Jember. This was needed so that preventive action could be done

in the community to expect to reduce the prevalence and risk factors in susceptible

people suffering from lymphoma.

Method

This study was an observational and analytical study with a cross-sectional

approach. The research was conducted at the General Surgery Outpatient Poly of dr.

Soebandi Hospital, Jember, because the poly is referral poly to identify the type of

lymphadenopathy. As for the research time, it was conducted during the months of

March to June 2011.

The population in this study was all patients who performed examinations or

outpatients at General Surgery Outpatient Poly of Dr. Soebandi diagnosed with

lymphoma or other types of disease, in order to obtain a sample size of 97 patients.

Patients diagnosed with other types of cancer were excluded. Sampling in this study

used systematic random sampling technique.

The dependent variable in this study was the status of lymphoma and the

independent variables were the age at diagnosis, gender, race, family history of the

respondents that had cancer, history of illnesses suffered by respondents (diabetes

mellitus, HIV / AIDS, Hepatitis C, Rheumatoid arthritis, another type of cancer) prior to

suffering lymphoma as well as other diseases, and exposure to chemicals (pesticides).

The needed data in this study were collected with a structured interview method

directly to the respondent with the help of a questionnaire to investigate the

characteristics of respondents consisting of age, gender, race, family history of cancer,

history of other diseases (diabetes mellitus, HIV / AIDS, Hepatitis C, Rheumatoid

arthritis, other types of cancer), and exposure to chemicals (pesticides). In addition, the

method of review of documents in this case medical records was also conducted to

obtain data on the results of laboratory examinations that included the staging of

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respondents’ lymphoma and respondents’ age when diagnosed with lymphoma, and the

status of HIV / AIDS.

The collected data were analyzed with univariable, bivariable and multivariable

analysis with the help of SPSS data processing program and then presented in the form

of frequency distribution tables and contingency tables with narrative interpretation.

The univariable analysis was performed to describe the frequency distribution and the

proportion of each variable studied, both independent variables and the dependent

variable. The bivariable analysis was conducted to determine the relationship between

each independent variable with the dependent variable by using the Chi square test at

95% confidence interval (α = 0.05). The multivariable analysis was performed to

determine the variables most closely associated with the incidence of lymphoma by

using Logistic Regression test 95% confidence interval (α = 0.05).

Results and Discussion

Univariable Analysis

Table 1 explains that the majority of respondents did not suffer from lymphoma.

The majority of respondents who had cancer (81.8%) were diagnosed at stage III or IV

(advanced stage) and 18.2% were diagnosed at stage I or II (early stage). In the

respondents who suffered from cancer and non cancer, most chose to do self-medication

with drugs bought at pharmacies and traditional medicine, as well as alternative

treatments. Most respondents were aged ≤ 55 years (52.6%) and female (51.5%). In this

study, racial/ethnic categorization was also examined by Chinese and non-Chinese (Java

or Madura). The majority of respondents (94.8%) were classified as non-Chinese Race

(Java or Madura), while the Chinese respondents were only 5.1%. In the group of non-

Chinese race there were 39 Javanese and 53 Maduranese.

Table 1. Description of research variables

Respondent’s characteristics N Percentage (%)

Status

Cancer 11 11. 3

non Cancer 86 88. 7

Age

>55 years 46 47. 4

≤55 years 51 52. 6

Gender

Male 47 48. 5

Female 50 51. 5

Race

5

Chinese 5 5. 2

Non Chinese 92 94. 8

Family history of cancer

Yes 28 28. 9

No 69 71. 1

History of illness

Yes 25 25. 8

No 72 74. 2

Exposure to pesticides

Yes 55 56. 7

No 42 43. 3

Source: Processed Primary Data (2011)

Family cancer history was derived from the father’s parents, mother’s parents,

both father and mother. Only 28.9% of respondents had a family history of cancer. The

disease of cancer included breast cancer, uterine cancer, colon cancer and liver cancer.

History of illnesses associated with a decrease in the immune system affected only a

minority of respondents (25.8%). The illnesses were Rheumatoid arthritis (60%),

Diabetes Mellitus (40%), Hepatitis C (8%), and breast cancer and colon cancer (8%). In

this study the status of the respondents on pesticide exposure was viewed through

participation in agricultural or plantation activities; the level of exposure risk to

pesticides which was classified as moderate or high, the use of self equipment

protection which was not complete, ≥ 5 working hours in a day, and period of

employment in agricultural or plantation activity ≥ 5 years (made at least 3 of them). In

this research, it was noted that more respondents were exposed to pesticides (56.7%)

than those not exposed (43.3%).

Bivariable analysis

The bivariable analysis was conducted to determine the relationship between

each independent variable with the dependent variable. The independent variables

consisted of age, gender, race, family illness history, history of illness and exposure to

pesticides. In detail they can be observed in Table 2.

Table 2. Percentage, Odd’s Ratio and 95% Confidence Interval to the incidence of lymphoma

by age, gender, family history of cancer, history of illness and exposure to pesticides

Variabel

Lymphoma

p-value Odd’s

Ratio 95% CI Yes No

n % n %

Age

>55 years 7 7. 2 39 40. 2

0. 253

2. 1 0. 58-7. 74

≤55 years 4 4. 1 47 48. 5 1

Gender

Male 9 9. 2 38 39. 2 0. 019 5. 7 1. 16-27. 88

Female 2 2. 1 48 49. 5 1

Race

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Chinese 1 1. 0 4 4. 1 0. 460 2. 1 0. 21-20. 19

Non Chinese 10 10. 3 82 84. 6 1

Family history of

cancer

Yes 7 7. 2 21 21. 7 0. 012 5. 4 1. 44-20. 34

No 4 4. 1 65 67. 0 1

History of illness

Yes 8 8. 2 17 17. 6 0. 001 10. 8 2. 59-45. 19

No 3 3. 1 69 71. 1 1

Exposure to pesticides

Yes 10 10. 3 45 46. 4 0. 021 9. 1 1. 12-74. 31

No 1 1. 0 41 42. 3 1

Source: Processed primary data of dr. Soebandi Hospital, Jember (2011)

Based on Table 2, more respondents who suffered from lymphoma were aged >

55 years (7.2%) and those who did not suffer were aged ≤ 55 years (48.5%). The

bivariable analysis results using Chi-square test showed p-value = 0.253 with OR 95%

CI (2.1; 0.58-7.74). This suggested that the age group > 55 years had a risk 2.1 times

more likely to have lymphoma than those aged ≤ 55 years. The results of this analysis

showed that the relationship of age with the incidence of lymphoma was insignificant

statistically.

Age is one major characteristic of an individual. Age has an influence to the

level of exposure, magnitude of risk and resistance. Different experiences of health

problems/diseases and decision-making are also influenced by the individual's age (8)

. In

general, the risk of non-Hodgkin's lymphoma increases with advancing age while

Hodgkin's lymphoma in the elderly is associated with a poorer prognosis than that

observed in younger patients. Increased incidence of lymphoma was associated with

age, especially in the age group> 55 years (4, 9-10)

.

Gender is a grouping of respondents based on genital traits, ie males and

females. Some diseases sometimes tend to infect a specific gender. That is because of

differences in physiological function, hormonal activity, and the need for certain

nutrients between men and women. Table 2 describes the respondents who suffered

from lymphoma that were more in male sex group (9.2%), while those not suffering

from lymphoma were more in female sex group (49.5%). Chi-square test results

showed that the sex variable had a p-value = 0.019 with OR, 95% CI (5.7; 1.16-27.88).

This suggests that men had a 5.7 time greater risk of suffering from lymphoma than

women. Thus, gender was significantly related to the incidence of lymphoma.

The results of this study were in line with previous research which stated that

most patients with lymphoma were male with a comparison between men and women

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by 2:1. Risk difference was probably caused by other factors such as exposure to

carcinogenic materials received between men and women, including the exposure to

chemicals (pesticides), the lifestyle associated with consumption patterns, as well as

susceptibility to certain infectious diseases. Other causes of these differences were

caused by different physiological functions between men and women who led in a

particular gender which was more prone to suffer from a disease (4)

. Risk differences of

developing lymphoma in men and women were also caused by hormonal functions in

the body. In his research, Nelson found that most women suffering from lymphoma

took oral contraceptives or hormone replacement therapy (11)

.

Race is a category for a group of individuals / human hereditary with similar

physical features and biological characteristics. Table 2 shows that the majority of

respondents who suffered from lymphoma (10.3%) were non-Chinese Race (Java or

Madura). Similarly, the respondents who did not suffer from lymphoma were also the

majority of non-Chinese race (84.6%). Chi-square test results prevailed that the race

variable had a p-value = 0.460 with OR, 95% CI (2.1; 0.21-20.19). The results of the

analysis indicated that the Chinese race had 2.1 times greater risk of developing

lymphoma than non-Chinese race, but race was not significantly associated with

incidence of lymphoma statistically.

The results of this research were in line with the previous research which stated

that in the continent of Asia, the race that had a high risk of cancer was the Chinese

race. That is because in Chinese patients it is found that many of them were infected

with Epstein-Barr virus and Human T-cell leukemia / lymphoma virus type 1 (HTLV-1)

which are one of the infectious causes of lymphoma (4)

.

Table 2 shows that most respondents who suffered from lymphoma (7.4%) had a

family history of cancer, whereas the respondents who did not have lymphoma (67.0%)

did not have a family history of cancer. Chi-square test results prevailed that the family

cancer history variable had a p-value = 0.012 with OR, 95% CI (5.4; 1.44-20.34). The

analysis results showed that people who had a history of family cancer risk were 5.4

times more likely to have lymphoma than those not having a family history of cancer.

Statistically, a family history of cancer was also significantly associated with incidence

of lymphoma.

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Genetic factors or family history of cancer may increase the risk of developing

lymphoma, but the increased risk is also influenced by exposure to chemicals or

materials that are carcinogenic. That is because there is a gene (DNA) derived by

certain parents who can be a cancer trigger in the offspring (7)

. People who have an

inherited immune disorder from their parents are at risk of suffering from lymphoma.

This risk will increase if the person is also exposed to carcinogenic materials on a

continuous basis, either through work, lifestyle, and diet which are a predisposing factor

in people who have a genetic abnormality in the immune system. But until now there

have still been no studies that examine what genes are found in patients with lymphoma

(12).

History of previous illness has an influence on the occurrence of lymphoma,

especially diseases associated with decreased immunity or disruption of the immune

system in acute and chronic. Based on Table 2, the majority of respondents who

suffered from lymphoma (8.2%) had a history of illness, whereas the respondents who

did not suffer from lymphoma were known mostly (71.1%) without history of illness.

Chi-square test results prevailed that the variable of the history of the disease had a p-

value = 0.001 with OR, 95% CI (10.8; 2.59-45.19). The analysis results showed that

people who had a history of illness (Diabetes Mellitus, HIV / AIDS, Hepatitis C and

Rheumatoid arthritis, as well as other types of cancer) had a risk 10.8 times likely to

suffer from lymphoma than those with no history of illness. Statistically, the history of

illness was also related significantly to the incidence of lymphoma.

Diabetes Mellitus Patients who routinely take insulin are at increased risk of

developing lymphoma. The risk is also influenced by genetic (family history of cancer),

environmental, and infectious agent factors. Diabetes Mellitus Patients also often

experience complications resulting in physiology function that they cannot work

optimally which often causes to be infected with multiple diseases (5).

Patients with

Hepatitis C virus has the risk of suffering from this disease due to chronic infection

caused by a virus that can cause changes in B cells, causing mutations and B cells

become malignant and proliferate uncontrollably (13-16)

.

Rheumatoid arthritis is a disease associated with the incidence of lymphoma. It

is linked to the impact or side effects of Methoxtrexate (MTX) and Anti-Tumor

Necrosis Factor (Anti-TNF) therapy often given to patients with Rheumatoid arthritis

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(Wolfe and Michaud, 2004). In congenital immunodeficiency, it is known that the

immune system of patients with the disease is not able to fight viral and bacterial

pathogens in the respiratory and digestions that cause the immune system disorders

continuously which can cause mutations in B cells (12)

. In people with HIV/AIDS, the

immune system has a significant reduction. Eighty-two percent of HIV / AIDS patients

suffer from lymphoma . Therefore, the diagnosis in patients with lymphoma should also

be conducted along with tests to determine HIV infection (17)

. Chronic diseases

associated with immune system can reduce lymphokine and lower natural killer cell

activity (NK). It triggers B cells mutation to become malignant and proliferate

uncontrollably (18-19)

.

Table 2 describes the respondents who suffered from lymphoma who were

exposed more to chemicals (pesticides), as well as those who were not suffering from

lymphoma. Chi-square test results prevailed that the variable of exposure to chemicals

(pesticides) had a p-value = 0.021 with OR, 95% CI (9.1; 1.12-74.31). The analysis

results showed that people exposed to chemicals (pesticides) had a 9.1 times greater risk

of suffering from lymphoma than those not exposed to chemicals (pesticides).

Statistically, exposure to chemicals (pesticides) was significantly associated with the

incidence of lymphoma.

Exposure to chemicals such as pesticides, especially those containing 2.4-

dichlorophenoxyacetic acid, hexachlorocyclohexane and benzene can increase the risk

of lymphoma (20-22)

. Pesticides can enter the human body through various routes,

including through the skin, respiratory system and digestive system. Pesticide residues

pose an indirect effect on humans, but in the long run lead to health problems including

neurological disorders and metabolic enzymes (23)

.

Chemicals from the content of pesticides can poison the body cells or affect

specific organs that may be related to the nature of the chemical or chemicals associated

with a place to enter the body or also known as target organs (24)

. Pesticides contain

carcinogenic; the carcinogenic substances can cause or increase the risk of cancer

because exposure to chemicals continuously for long periods can lead to mutations

(changes) of genes (DNA) from cells of the body so that it continues to develop into

abnormal proliferation uncontrollably. The mechanism of the effect of pesticides on the

body is through two stages, namely the pharmacokinetics and pharmacodynamics. Thus,

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in work or activities with the use of pesticides, preferably one uses Self Protective

Equipment (PPE) which consists of work clothes, mask (goggles or face shield), hat,

gloves, and boots (25)

.

Multivariable Analysis

The independent variables included to logistic regression test were those that

had a p-value <0.25. The results of the bivariable analysis using chi-square test included

gender, family cancer history, history of illness and exposure to chemicals (pesticides).

Selection of the best logistic regression model was performed with backward LR

method. The results of logistic regression test analysis with backward LR method

produced some value at each stage, namely:

a. Value for goodness of fit test 0.128 means that logistic regression model was proper

to be used for further analysis, since there was no real difference between the

predicted classification and the observed classifications.

b. The coefficient value of Negelkerke determination indicated that 4 predictors in step

1 were able to explain 45.3% of the total diversity of the response variable. In step 2

they could explain 44.4%. This means that the regression model for the coefficient

determination was good enough to predict the incidence of lymphoma.

c. Assessment of the feasibility of regression model in predicting performed used Chi-

square test of Hosmer and Lameshow. Hosmer and Lameshow value presented a

significance value of 0.01 in step 1 and of 0.214 in step 2. Hosmer and Lameshow

value in step 2 was greater than the p-value of 0.05, so that Ho was accepted, or in

other words, the estimated model was in accordance with the actual data used.

d. Percentage of the overall value of each step was in the range of 91.8%. The high

overall percentage value indicated that the accuracy of prediction had been very good

to use to predict the incidence of lymphoma at Dr. Soebandi Hospital, Jember.

e. Significance value of the multivariable analysis results (regression test with LR

backward method) revealed that there were 3 variables related significantly to the

incidence of lymphoma at Dr. Soebandi Hospital, Jember, namely family history of

cancer, history of illness, and exposure to chemicals (pesticides).

Based on the results of multivariable analysis (logistic regression test with LR

backward method), there were three variables that had a significant relationship

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(statistically significant) on the incidence of lymphoma, namely family history of

cancer, history of illness, and exposure to chemicals (pesticides). It was based on the p-

value of the 3 variables that was less than α = 0.05. The variable having the most

powerful relationship to the incidence of lymphoma was a history of illnesses suffered

by the respondents. Here were alternative models generated from multivariable analysis:

In logistic regression, the value of E(Y/X) would always be between zero and

one (0 ≤ E (Y / X) ≤ 1). Constant value of 0.962 stated that if there was no family

history of cancer, no history of illness and no exposure to chemicals (pesticides), the

incidence of lymphoma was 0.32%. It can be seen from the following calculation:

The logistic regression model alternative could be used to predict the likelihood

of a person or a respondent to suffer from lymphoma based on the most influential

factors, namely family history of cancer, history of illness, and exposure to chemicals

(pesticides) by entering a code categorization of each variable to in the logistic

regression model as follows:

Some respondents probably had lymphoma:

a. Likelihood of the respondents to suffer from lymphoma occurred if there were a

family history of cancer, a history of illness, but no exposure to pesticides.

Family history of cancer = 1, illness history = 1, and exposure to pesticides = 0

The possibility or chance of suffering from lymphoma that occurred if they had a

family history of cancer, a history of illness, but no exposure to pesticides was 4.3%.

b. Likelihood of the respondents to suffer from lymphoma occurred if there were no

family history of cancer, a history of illness, and no exposure to pesticides.


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The possibility or chance of suffering from lymphoma that occurred if the

respondents did not have a family history of cancer, a history of illness, and no

exposure to pesticides was 28%.

c. Likelihood of respondents to suffer from lymphoma occurred if there were a family

history of cancer, no history of illness, and exposure to pesticides.



respondents had a family history of cancer, no history of illness, and exposure to

pesticides was 2.1%.

d. Likelihood of the respondents to suffer from lymphoma occurred if there were no

family history of cancer, a history of illness, and exposure to pesticides.



respondents did not have a family history of cancer, had a history of the disease, and

had exposure to pesticides was 2.6%.

e. Likelihood of the respondents to suffer from lymphoma occurred if there were no

family history of cancer, no history of illness, and exposure to pesticides.



respondents did not have a family history of cancer, history of illness, and had

exposure to pesticides was 15.5%.

f. Likelihood of the respondents to suffer from lymphoma occurred if there were a

family history of cancer, no history of illness, and no exposure to pesticides.


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respondents had a family history of cancer, no history of illness, and no exposure to


g. Likelihood of the respondents to suffer from lymphoma occurred if there were a

family history of cancer, a history of illness, and exposure to pesticides.



respondents had a family history of cancer, a history of illness, and exposure to


The logistic regression model alternative could be used to predict the likelihood

of a person or a respondent to suffer from lymphoma based on the factors having the

most powerful relationship, namely family history of cancer, history of previous illness,

and exposure to chemicals (pesticides).

Conclusion

Based on the results of data analysis and discussion, it can be concluded as

follows: 1) There was a significant relationship between gender, family history of

cancer, illness history, and exposure to pesticide and the incidence of lymphoma, but it

did not happen for the variables of age and race, 2) Factors that could predict the

incidence of lymphoma were a family history of cancer, history of illness, and exposure

to chemicals (pesticides).

Recommendation

There is a need for increasing cooperation across sectors including hospitals,

Indonesia Cancer Foundation, health office, Ministry of Agriculture and the business

industry in developing prevention programs and reducing cancer incidence through

increased use of personal protective equipment from exposure to chemicals primarily in

people who have a history of cancer in their family and have a history of illness in their

immune system.

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