Tuberculosis types and its characteristics in Dindigul ... · PDF filein Pott's disease of the spine. ... however many clinical case reports and case series ... To study the tuberculosis
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 265
2. Study area
Dindigul District is situated in the western part of Tamil Nadu. Dindigul District was carved
out of the composite Madurai District on 15.09.1985. It extends between 10º05 and 10º09
north latitude and 77º30 and 78º20 east longitude.
Figure 1: Location map of the study area
2.1. Aims and Objectives
1. To analyses the spatial distribution of the tuberculosis
2. To study the various types of life style and social aspects of the disease and to identify
the major dimensions and to explain each dimension and discuss the role of each one.
3. To study the tuberculosis disease pattern and demographic characteristics.
2.1.2. Methodology
The main source of study is the secondary data. It is collected from the government hospital
in Dindigul. The study area map particulars were collected from collectrate office. For the
preparation of maps GIS software and ERDAS image 8.5 and SPSS 7.5 Software packages
are used.
3. Distribution of tuberculosis in India
In India Tuberculosis evaluated more than 24 million people are evaluated with suspected TB,
examined more than 100 million sputum slides, treated more than 6 million patients, and
probably prevented more than a million TB deaths. In Tamilnadu there are 10 lakh people
infected with TB in Tamilnadu. This state about 1.4 lakh persons develop with tuberculosis,
among 48,000 have TB bacilli in their sputum.
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 266
Distribution of Tuberculosis in Dindigul District
DISTRIBUTION OF TUBERCULOSIS IN DINDIGUL DISTRICT
Dindigul
Athoor
Pappampatty
Eriodu
Perumalamalai
Figure 2: Distribution of Tuberculosis in Dindigul District
The distribution of Tuberculosis for the 2009 in (Fig No: 2) Dindigul district was divided into
392 villages as per 2001 census. The major Tuberculosis Unit division viz. Dindigul unit,
Athoor unit, Pappampatty unit, Eriodu unit, Perumalmalai unit, Dindigul District altogether
reported a total of 1199592 tuberculosis patients.
The incidence was generally found to be very highest in the Dindigul Unit. (397716) which
has the highest concentration of TB Secondly the TB unit of Athoor unit (313016) followed
by other Unit such as Eriodu (241925), Pappampatty (122028), Perumalmalai (124907).
Coolies, Mill workers, Tannery workers, Load man and others were the occupational groups
who were largely affected by TB. The age group of the affected belong to 30-45 years.
3.1 Distribution of pulmonary tuberculosis in Dindigul district
The distribution of pulmonary tuberculosis is that it is very high in male patients in Athoor
unit (389) and very low in Perumalmalai unit (42). The number of female patients are high in
the Pappampatty unit (139) and low in Perumalmalai unit (28).
3.2 Distribution of extra pulmonary tuberculosis in Dindigul district
The distribution of Extra pulmonary tuberculosis is that it is very high male patients in
Dindigul unit (138) and very low in Perumalmalai unit (13). Number of female patients are
high in the Dindigul unit (123) and the low level in Perumalmalai unit (23).
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 267
Figure 3: Total cases in Pulmonary Tuberculosis in Dindigul District
Figure 4: Total cases in Extra Pulmonary Tuberculosis in Dindigul District
4. Factor structure and factor solution
The four major dimensions of tuberculosis were analyzed with help of 54 variables drawn
from the secondary data. Many scholars already discussed the procedure and advantages of
the factor analysis Shanmuganandan .S (1994) incorported ‘‘Medical Geography, HIV Risk
behaviours knowledge and sero status of STD patients in Madurai city. Tamil Nadu, India,
and presented a paper in the Second International Congress on AIDS in Asia Pacific –New
Delhi”. Saravanabavan .V (1995, 1998, 2014) have elaborate The Geomedical analysis of
Leprosy: Epidemiological and Ecological Aspects of Tamil Nadu. The variables despict the
characteristics of tuberculosis in relation to their case history, treatment and characteristics,
mortality , detection rate, treatment failure variables. The first and the foremost factor
dimension Case History and Demographic Characteristics and Nature of Treatment with an
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 268
eigen value of 42.5 explained a total variance 52.1and from 2, 3 and 4 explained a total
variance of 72%, 86.9%, 88%.
The variable structure justified the selection of variable to reveal the characteristics of the
tuberculosis patient in the best possible manner. The factor analysis was used in the present
study to identify the major four determinants of the disease. The factor analysis resolved to
identify only the major dimensions but also the important variable that were associated with
each dimension.
Table 1: Percentage of variance
Table 2: Dimension – I: Case History and Demographic Characteristics and Nature of
Treatment
Variable
number Variable name Factor loading
3 Age group 15-24 male 0.77
4 Age group 15-24 female 0.77
5 Age group 25-34 male 0.91
7 Age group 35-44 male 0.96
8 Age group 35-44 female 0.85
9 Age group 45-54 male 0.80
10 Age group 45-54 female 0.63
11 Age group 55-64 male 0.79
13 Age group >65 male 0.85
15 New smear positive pulmonary Tb male 0.79
16 New smear positive pulmonary Tb female 0.90
18 New smear negative pulmonary Tb female 0.89
19 New Extra pulmonary Tb male 0.92
20 New Extra pulmonary Tb female 0.79
21 Relapses male 0.93
23 Failures male 0.87
25 Others female 0.86
26 Cured male 0.85
27 Cured female 0.91
28 Treatment completed male -0.76
29 Treatment completed female 0.94
30 Died male 0.91
32 Failure female 0.81
34 Defaulted male 0.69
S.No Name of the dimension Eigen
Value
Percentage
of Variance
Cumulative
Percentage
1
Case History and Demographic
Characteristics and Nature of
Treatment
42.5 49.1 52.1
2 Tuberculosis with Mortality Status 11.4 17 72
3 Roll of treatment and preventive
measure 8.1 11.9 86.9
4 The Importance of Treatment and
Detection Rate 5.4 10 88
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 269
38 Total new adult op 0.65
39 Sputum diagnosed 0.82
40 New smear positive cases 0.92
41 New smear negative cases 0.72
42 New Extra pulmonary tuberculosis 0.92
43 Total treated cases 0.85
48 Annualized sputum positive detection rate 0.81
50 Three month conversation rate -0.81
51 Sputum positive cases 0.92
52 Total New smear positive cases 0.81
53 Total Extra pulmonary Tuberculosis cases 0.79
54 Death cases 0.97
Eigen Value – 42.5 Percentage of Total Variance – 52.1
4.1 Dimension – I Case history and demographic characteristics and nature of
treatment
The primary factor highlights the case history and demographic. Characteristics and nature
of treatment. This factor contains as many as 34 variables with very high positive loading
and 2 variables with negative loading explaining as much as 52.1 percent of the total variance
with an eigen value 42.5 (Table 2). The highlight variable of age group is 35-45 male. 0.96
In this variable associated with the death cases. Very low factor in treatment completed (-
0.761). So this variable affected in the 35-45 male age group.
Figure 5: Case History and Demographic Characteristics and Nature of Treatment in
Dindigul District
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 270
Table 3: Dimension – II: Tuberculosis with mortality status
Variable Number Variable name Factor loading
3 Age group 15-24 male 0.71
4 Age group 15-24 female 0.71
12 Age group 55-64 female 0.83
14 >65 female 0.89
24 Treatment after default – male 0.79
34 Defaulted – male 0.67
36 Transfer to another district – male 0.71
38 Total new adult op 0.66
45 Success rate 0.62
46 Death rate -0.99
47 Default rate 0.78
Eigen Value – 11.4 Percentage of Total Variance – 19.9
4.2 Dimension – II Tuberculosis with mortality status
The second dimension is an important factor that highlights age group of <65 female. This
factor contains 13 variables (Table. 3) with the positive and negative loading explaining as
much as 19.9 percent of the total variance with an Eigen value 11.4. The very high positive
factor loading were seen in <64 female age group high in negative death rate -0.98. This
factor is named as TB with mortality status.
Figure: 6 Tuberculosis with Mortality Status in Dindigul District
Table 4: Dimension – III: Roll of treatment and preventive measure
Variable
number Variable name
Factor
loading
1 Age group 0-14 male 0.68
2 Age group 0-14 female -0.69
6 Age group 25-34 female -0.87
17 New smear negative pulmonary tuberculosis – male 0.79
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 271
22 Relapses – female 0.99
35 Defaulted – female 0.85
37 Transfer to another district – female 0.79
45 Success rate -0.78
Eigen Value – 8.1 Percentage of Total Variance – 14.9
4.3 Dimension – III Roll of treatment and preventive measure
The third dimension is knowledge on preventive measure and roll of treatment (Table 4).
This factor contains variables with the positive and negative loading explaining as much as
14.9 percentage of total variance with an Eigen value 8.1.
In 0.99 value indicates the very high positive loading were seen in relapses female variable
and the second negative loadings can be seen in 0-14 male age group in (-0.684), relapses
female is very high so the 0-14 male age group is very low level loading.
Figure : Roll of treatment and preventive measure in Dindigul District
Table 5: Dimension IV: The importance of treatment and detection rate
Variable number Variable name Factor loading
1 Age group 0-14 male -0.63
2 Age group 0-14 female -0.64
10 Age group 45-54 female 0.76
28 Treatment completed – male 0.61
31 Died - female 0.95
33 Failure – female 0.8
44 Cure rate 0.78
49 Detection rate 0.91
Eigen Value – 5 Percentage of Total Variance –13
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 272
4.4 Dimension IV the Importance of Treatment and Detection Rate
The fourth dimension is on the importance of treatment and detection rate. These factors
contains as many as 8 variables (Table 5) with positive a negative loading explaining as
much as 13 percentage of total variance with an eigen vale 5.4.
0-14 male, female age group is low rate of loading variable and then detection rate and failure
female variable is high on the female detection rate is high.
Figure 8: The Importance of Treatment and Detection Rate in Dindigul District
5. Conclusion
The studies done in Dindigul show a high TB prevalence incidence ratio among males based
on the age range analysed in the study. Tuberculosis is largely a preventable one and hence
scientific knowledge is essential to create a world in which an occurrence of a tuberculosis
disease can be prevented. Dindigul district was chosen and spatial distribution of
Tuberculosis in relation to the socioeconomic determinants, life style variation associated
with physical activities and living environmental conditions were analyzed The factor
analysis (multivariate analysis) was used in the present study to identify the major dimensions under the factor reduction process. According to factor solution, similarity and
factor loading matrix were used to derive the intercorrelation matrix, Eigen value and the
factor loading matrices.
6. References
1. Andrew, Learmonth (1985), Patterns of Disease and Hunger, some apparently Non-
infectious Disease, pp 92-126.
2. Baveja CP, Gumma VN, Jain M, Jha H (2010), Foot ulcer caused by multidrug-
resistant Mycobacterium tuberculosis in a diabetic patient, Journal of Medical
Microbiology, 59(Pt 10), pp 1247-9.
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 273
3. Cegielski JP, Chin DP, Espinal MA, et al., (2002), The global tuberculosis situation:
progress and problems in the 20th century, prospects for the 21st century. Infectious
Disease Clin North Am 2002; 16, pp 1–58.
4. Cliff.A.D. and P. Haggett (1988), Atlas of Disease Distributions: Analytic
Approaches to Epidemiological Data. Blackwell, Oxford.
5. Cliff.A.D. et al (2000), Island Epidemics. Oxford, Oxford University Press.
6. Corbett EL, Watt CJ, Walker N, et al. The growing burden of tuberculosis: global
trends and interactions with the HIV epidemic. Arch Intern Med, 2003; 163, pp 1009–
1021
7. Dye C, Scheele S, Dolin P, Pathania V, Raviglione MC, for the WHO Global
Surveillance and Monitoring Project. Global burden of tuberculosis: Estimated
incidence, prevalence, and mortality by country, 282(7), pp 677–86.
8. Gould. P. (1993), The Slow Plague: A Geography of the AIDS Epidemic. Oxford,
Blackwell Publishers.
9. Hyde,Walker and Margaret.O (1994), Know About Tuberculosis, Company New
York.
10. Joseph. A. E. and D. R Phillips (1984), Accessibility and Utilization: Geographical
Perspectives on Health Care Delivery, Journal of the Royal College of General
Practitioners, London.
11. Kriki P, Thodis E, Deftereos S, Panagoutsos S, Theodoridis M, Kantartzi K, et al.
(2009), A tumor-like manifestation of extrapulmonary tuberculosis in a hemodialysis
patient. Clinical Nephrology, 71(6), pp 714-8.
12. May, J.M. (1950), Medical Geography its Methods and Objectives Geographical
Review, 51Pp 9-41
13. Mayer. J. (1986), Ecological associative analysis, in Pacione, M., ed. Progress in
Medical Geography. Beckenham, Croom Helm, pp 64-83.
14. Park. K (2009), Preventive and Social Medicine Banarsides, Bhanot Publisher
Jabalpur. Tuberculosis, p.159
15. Saravanabavan V (1998), Leprosy and Multidrug Therapy in Tamil Nadu, India: A
Factor Analysis, The Indian Geographic Journal, 73(1) pp 41-50
16. Saravanabavan.V et al., (2014), Patient’s Perception and Epidemiological
Characteristics of Dengu in Madurai City- Using Factor Analysis. International
Journal of Mosquito Research, 1 (2), pp 19-26.
17. Shanmuganandan. S (1994), Medical Geography, HIV Risk Behaviours Knowledge
and Sero Status of STD Patients in Madurai City. Tamil Nadu, India, Paper presented
in The Second International Congress on AIDS in Asia Pacific –New Delhi.
Tuberculosis types and its characteristics in Dindigul District – A Geomedical study using GIS
Vimala Vinnarasi J and Saravanabavan.V
International Journal of Geomatics and Geosciences
Volume 7 Issue 3 2017 274
18. Shannon. G. W. and A. Dever. (1974), Health Care Delivery: Spatial Perspectives.
New York, McGraw Hill.
19. Thomas. R. W. (1992), Geomedical Systems: Intervention and Control. London,
Routledge.
20. Tufariello JM, Chan J, Flynn JL, (2003), Latent tuberculosis: mechanisms of host and
acillus that contribute to persistent infection. Lancet Infect Disease,3, pp 578–590
21. Vimal Vinnarasi J and V. Saravanabavan (2015), A Geo-Medical analysis of Smear
Positive Pulmonary Tuberculosis in Dindigul district Using GIS. Research Paper
published in the book Volume on ‘Geospatial Technologies for Resource Evaluation
and Management’,Published in Jayalakshmi Publications, 140 VPM Towers , TPK
Main Road, Vasanthanagar, Madurai.
22. Wiler JL, Shalev R, Filippone L. (2010), Case report and review: Potts disease and
epididymal tuberculosis presenting as back pain and scrotal mass. Am J Emerg Med.