Analyzing Health Trends in Thailand

The assignment delves into a study examining the association between diabetes and pesticide use among Thai farmers. It involves analyzing research papers and understanding epidemiological patterns related to hepatitis B and C, coronary artery disease, and survival analysis in the context of these health concerns.

Running head: CRITICAL APPRAISAL
Critical Appraisal of 2 articles using the CASP tool
Name of the Student
Name of the University
Author Note

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Introduction
Critical appraisal refers to the systematic process that identifies strengths and weaknesses
of an article for evaluating its validity and usefulness of the research findings (12). This report
will critically analyse two articles, using the CASP tool for appraisal. Questions in the CASP
tool will facilitate determination of the relevance and trustworthiness of the aforementioned
articles.
Article 1
Juntarawijit C, Juntarawijit Y. Association between diabetes and pesticides: a case-
control study among Thai farmers. Environmental Health and Preventive Medicine. 2018
January; 23:3.
The issue addressed in the study was quite relevant to the context of the research.
Diabetes is a chronic health disorder prevalent globally with an incidence of 1 in 3 adults (5).
According to research studies, a dramatic increase has been observed in the number of diabetic
patients in Thailand from 2009 to 2014 (17). Furthermore, there are several evidences that
suggest correlation between exposure to pesticides and incidence of type 2 and gestational
diabetes (8). Moreover, the sudden increase in the application of pesticides in Thailand
establishes selection of the issue as a correct procedure (19).
An appropriate method was used to address the research question that can be validated
by the fact that the outcomes were beneficial for the target population. The research method
applied to address the question suggested that an overexposure to pesticides is responsible for
increasing the susceptibility to diabetes among farmers. Consistency with previous findings
suggests that the method was correct (3).

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Lack of adequate information on association between diabetes and pesticide in Thailand
might create negative impacts on both the case and the control by providing inaccurate results
during analysis of the responses. Apart from mentioning that the cases were diagnosed with
diabetes, they were not defined in a precise way. The time frame of pesticide exposure and
number of cases were not sufficient. Reliable system for selecting the cases was based on
diagnosis of diabetes. There was no specialty of the sample and no power calculation. However,
the sample represented a defined population of diabetic rice farmers in Thailand. However, the
selection of participants for the study was based on prevalence of diabetes and pesticide
exposure, collected from hospital data and structured questionnaire respectively,and a reliable
system was used (randomisation). Thus, the cases not recruited in an acceptable way.
Although the controls represented rice farmers without diabetes, which represented the
population, their specialty was that they belonged to same gender and age that increased
likelihood of pesticide exposure, due to similar environmental factors. They were matched but
more in number than the sample. The authors failed to provide information on the response rate.
Thus, the controls were not selected in an acceptable way.
Structured questionnaires, in addition to similar measurement in the sample and control
were used. Temporal relation was correct as pesticide exposure preceded diabetes. However,
there was recall bias due to collection of information based on questionnaire that might affect the
responses owing to differences in recall ability of the participants. Moreover, the researchers did
not blind the participants to the study, which might affect their responses and give misleading
results (6). Construct validity was also not measured. Thus, the measures did not reflect true to
what was claimed.

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The confounding factors that were taken into account include gender, BMI, cigarette
smoking, alcohol consumption, occupation, family history and age.
The researchers had used logistic regression to analyse the adjusted odds ratio showing
association between exposure to pesticide and its outcome (diabetes). Thus, use of regression
analysis help in estimating the correlation between the independent variable and outcome,
while holding other variables as constant (10).
Statistically significant association between rodenticide exposure and diabetes prevalence
was established by the findings (OR = 1.35; 95%CI 1.04-1.76). Presence of odds ratio > 1 for the
use of fungicides (OR = 2.08; 95%CI 1.03–4.20), organophosphate (OR = 2.22; 95%CI 1.17–
4.19), carbamate (OR = 1.50; 95%CI 1.02–2.19), and organoclorine (OR = 1.40; 95%CI 1.01–
1.95) establishes strong correlation of exposure to outcome. Thus, adjustment of the results
using regression has made a big difference.
Low p-values for BMI, occupation, smoking, alcohol consumption, and family history of
diabetes signifies strong evidence against for the association between pesticide exposure and
diabetes incidence (16). Presence of 95% CI for all findings suggests that sampling the same
population numerous times will result in similar results and sound statistical findings.
Although high odds ratio suggest positive association between exposure and the outcome,
it is essential to remove selection bias and recall bias and use a larger sample size for
confirming and believing the results.
The findings can be applied in local population based on the fact that about 2 million
tones of pesticides are used every year, worldwide (15). Owing to the high exposure to pesticides

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by people and the high prevalence of diabetes patients, worldwide, the results can prove effective
in the local population.
Results show consistency with previous findings that correlated type 2 and gestational
diabetes with pesticide exposure.
Article 2
Kuo SH, Hung WT, Tang PL, Huang WC, Yang JS, Lin HC, Mar GY, Chang HT, Liu
CP. Impact of hepatitis C virus infection on long-term mortality after acute myocardial
infarction: a nationwide population-based, propensity-matched cohort study in Taiwan. BMJ
open. 2018 January; 8:e017412.
The issue addressed by the authors was relevant owing to the high mortality rates due to
acute myocardial infarction (AMI) in Taiwan. Moreover, presence of previously conducted
cohort studies and systematic review that correlated risks of myocardial infarction with chronic
infection of hepatitis C also establishes the fact that the authors addressed a clearly focused issue
(4) (13).
The cohort was recruited in an appropriate way owing to the fact that of the 186112
patients diagnosed with AMI, 4666 patients were identified with HCV infection. Moreover, the
researchers displayed. Thus, the cohort represented the defined population.
The authors used appropriate methods to classify the subjects into particular groups.
One-to-one matching among all participants regarding some factor such as, hypertension, age,
sex, diabetes mellitus, peripheral vascular disease and heart failure demonstrates correct

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methodology in minimizing bias. Moreover, the NHIRD data on AMI had been validated by
previous studies (2).
The subjects and the assessors were not blinded to the study, which might contribute to
bias in the outcomes. Although the measures reflected the expected outcomes, lack of adequate
information on minimizing bias makes it difficult to tell if all the outcomes were accurately
measured.
Although the database did not include information on potential confounding factors, the
researchers used a propensity score matching procedure for controlling the essential confounding
variables such as, age, sex, hypertension, previous stroke, peripheral vascular disease, and
dyslipidemia, which might affect the outcomes among AMI patients. Thus, all important
confounding factors were identified.
The authors had accurately analysed the results using a Cox proportional hazard
regression method. This helped in adjusting the confounding factors that were taken into account
(14). Furthermore, use of a propensity score matching method also helped in minimizing the
factors or variables. Thus, all confounding factors were taken into account in the research
design.
The follow-up of the subjects was complete owing to the fact that all patients were
followed for a time period of 12 years, until the outcomes of the disease was accurately
observed. The fact that the authors did not mention loss of patients from the groups, it can be
considered that the study began and ended with similar number of patients.
The fact that the study evaluated impacts of Hepatitis C infection on the mortality rate of
patients with AMI in Taiwan for 12 years, establishes on the follow-up being long enough.

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Conducting the study for a period of 12 years helps to develop a valid scenario of the extent of
the outcome being studied.
The results showed that mortality rate after 12 years was significantly higher among
AMI patients with cirrhosis and HCV infection compared to HCV infected subjects without
cirrhosis (P<0.0001) and controls (P<0.0001). Furthermore, the hazard ratio (HR) was
significantly large among patients with HCV infection (HR 1.12; 95% CI 1.06 to 1.18). Presence
of HR>1 indicates that the risk of mortality among AMI patients with HCV infection is greater
(18).
The results were also precise due to presence of 95% CI in the role of HCV infection on
the long term mortality of all patients with AMI (HR 1.12; 95% CI 1.06-1.18). Presence of 95%
CI indicates that similar results will be obtained on performing the research for innumerable
times on the same population.
Although utilization of data from the NHIRD database that contains more information for
more than 23,000,000 patients and reliability of the AMI data by previous studies suggest that
the results are true, it cannot be believed completely (2). This occurs due to the fact that the
retrospective design of the study, did not provide information on atherosclerosis burden, and
used a database which did not include information on the major confounding factors such as,
family history, body weight, height, glucose levels, lipid and viral load, and actual reason for
death. Apart from consistency with previous studies that established correlation between HCV
and coronary atherosclerosis, no other principles of the Bradford Hill criteria.
The study can be applied to the local population due to the fact that millions of cases of
AMI are observed globally. Furthermore, high incidence and a higher prevalence of hepatitis C

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throughout the world accounts for the fact a similar cohort study can be conducted in the
population to estimate effects of the infection on morality due to AMI (20).
The results of the study fit with previous findings in that antiplatelets and statins are less
used among HCV group than control group. Similarity is also observed in previous reports on the
link between carotid atherosclerosis and HCV seropositivity and AMI mortality among liver
cirrhosis patients (7) (1).
Results of the study can have positive implications in that it can create awareness among
healthcare professionals for considering the impacts of Hepatitis C infection while administering
therapeutic treatments to AMI patients. However, more robust evidences are required to establish
the findings.

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References
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myocardial infarction mortality among patients with liver cirrhosis: a nationwide analysis
across a decade. Journal of clinical gastroenterology. 2015 Oct 1;49(9):778-83.
2. Cheng CL, Lee CH, Chen PS, Li YH, Lin SJ, Yang YH. Validation of acute myocardial
infarction cases in the national health insurance research database in taiwan. Journal of
epidemiology. 2014 Nov 5;24(6):500-7.
3. Evangelou E, Ntritsos G, Chondrogiorgi M, Kavvoura FK, Hernández AF, Ntzani EE,
Tzoulaki I. Exposure to pesticides and diabetes: a systematic review and meta-analysis.
Environment international. 2016 May 1;91:60-8.
4. Forde KA, Haynes K, Troxel AB, Trooskin S, Osterman MT, Kimmel SE, Lewis JD, Re
VL. Risk of myocardial infarction associated with chronic hepatitis C virus infection: a
population‐based cohort study. Journal of viral hepatitis. 2012 Apr 1;19(4):271-7.
5. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global
estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes research and
clinical practice. 2014 Feb 1;103(2):137-49.
6. Hróbjartsson A, Thomsen AS, Emanuelsson F, Tendal B, Hilden J, Boutron I, Ravaud P,
Brorson S. Observer bias in randomised clinical trials with binary outcomes: systematic
review of trials with both blinded and non-blinded outcome assessors. Bmj. 2012 Feb
27;344:e1119.
7. Ishizaka N, Ishizaka Y, Yamkado M. Atherosclerosis as a possible extrahepatic
manifestation of chronic hepatitis C virus infection. Clinical Medicine Insights:
Cardiology. 2014 Jan;8:CMC-S17069.

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8. Jaacks LM, Staimez LR. Association of persistent organic pollutants and non-persistent
pesticides with diabetes and diabetes-related health outcomes in Asia: A systematic
review. Environment international. 2015 Mar 1;76:57-70.
9. Juntarawijit C, Juntarawijit Y. Association between diabetes and pesticides: a case-
control study among Thai farmers. Environmental Health and Preventive Medicine. 2018
January; 23:3.
10. Knol MJ, Le Cessie S, Algra A, Vandenbroucke JP, Groenwold RH. Overestimation of
risk ratios by odds ratios in trials and cohort studies: alternatives to logistic regression.
Canadian Medical Association Journal. 2012 May 15;184(8):895-9.
11. Kuo SH, Hung WT, Tang PL, Huang WC, Yang JS, Lin HC, Mar GY, Chang HT, Liu
CP. Impact of hepatitis C virus infection on long-term mortality after acute myocardial
infarction: a nationwide population-based, propensity-matched cohort study in Taiwan.
BMJ open. 2018 January; 8:e017412.
12. Morse JM. Critical analysis of strategies for determining rigor in qualitative inquiry.
Qualitative health research. 2015 Sep;25(9):1212-22.
13. Pothineni NV, Rochlani Y, Vallurupalli S, Kovelamudi S, Ahmed Z, Hakeem A, Mehta
JL. Comparison of angiographic burden of coronary artery disease in patients with versus
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(Online). 2013 Aug 9;347.
15. Shorette K. Outcomes of global environmentalism: longitudinal and cross-national trends
in chemical fertilizer and pesticide use. Social Forces. 2012 Jul 25;91(1):299-325.

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16. Sullivan GM, Feinn R. Using effect size—or why the P value is not enough. Journal of
graduate medical education. 2012 Sep;4(3):279-82.
17. Thai NCD network. Situation of NCDs (2nd report):“Kick off to the Goals.” Nontaburi:
The International Health Policy Program, Thailand (IHPP); 2016.
18. Uno H, Claggett B, Tian L, Inoue E, Gallo P, Miyata T, Schrag D, Takeuchi M, Uyama
Y, Zhao L, Skali H. Moving beyond the hazard ratio in quantifying the between-group
difference in survival analysis. Journal of clinical Oncology. 2014 Jun 30;32(22):2380-5.
19. Wanwimolruk S, Kanchanamayoon O, Phopin K, Prachayasittikul V. Food safety in
Thailand 2: Pesticide residues found in Chinese kale (Brassica oleracea), a commonly
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20. Zidan A, Scheuerlein H, Schüle S, Settmacher U, Rauchfuss F. Epidemiological pattern
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Appendix 1

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Appendix 2

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