DETERMINANTS OF MALARIA ENDEMICITY

Transcription

1 CHAPTER VI DETERMINANTS OF MALARIA ENDEMICITY In this chapter an attempt has been made to examine the determinants of malaria endemicity in the study area. The change in pattern of malaria epidemiology brought about by both physical and socio-economic factors has been statistically analysed. The components of physical determinants include temperature, rainfall and humidity, vegetation cover, land use pattern and waterbodies. The socio-economic determinants include education level, economic level, house type, human mobility and connectivity, tradition and culture etc. 6.1 Physical determinants Temperature Temperature is one of the important influencing factors responsible for transmission of malaria. Malaria is caused by mosquito and parasite and the existence and growth of both is determined by suitability of temperature. A temperature range between 20 C and 35 C is desirable for mosquito in which the mosquito proliferates continuously. The rainy season creates opportunity for the growth of mosquito during suitable temperature range. The average maximum and minimum temperatures prevailing in Sonitpur district during indicate the suitability for mosquito growth. The desirable temperature condition required for mosquito proliferation is observed in the district during the months from April to October. The number of mosquito gets 124

2 MALARIA CASES (numbers) TEMPERATURE ( C) DETERMINANTS OF MALARIA ENDEMICITY multiplied, once the ideal temperature condition is achieved (Fig. 6.1). However, the growth of mosquito beyond these months continues because the minimum temperature required for the mosquitoes to survive still prevails. This fact is supported by the number of cases registered in the district during winter. AVERAGE MAXIMUM AND MINIMUM TEMPERATURE AND MALARIA CASES IN SONITPUR DISTRICT ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Winter Pre Monsoon Monsoon Pos Monsoon Winter Avg Max Temp Avg Min Temp Malaria Cases Figure 6.1: Monthly average maximum and minimum temperature and number of malaria cases in Sonitpur district during The transmission of malaria was perennial and persistent in the district as revealed from the data of It is evident that malaria is prevalent in all the months of the year with distinct peak from May to July indicating a high transmission period (Fig. 6.1). A steep rise in the number of cases of malaria has been observed from April to June and thereafter a gradual decrease persists till October. Then again the average number of malaria cases slightly rises in 125

3 MALARIA CASES (numbers) HUMIDITY (%) and RAINFALL (mm) DETERMINANTS OF MALARIA ENDEMICITY November due to high number of cases registered in some years, especially It has been noted that when there is a peak transmission period, the difference between the maximum and minimum temperature becomes uniform, thereby rendering the environment conducive for vector proliferation and longevity Rainfall and humidity The rainfall pattern in the study area is influenced by the monsoon and the monthly rainfall pattern indicates variation with a peak in the month of June (Fig. 6.2). The period within the monsoon season from June to August receives highest rainfall. The gradual increase in rainfall is observed from the month of April to October during which the percentage of relative humidity also increases RAINFALL AND HUMIDITY AND MONTHLY AVERAGE MALARIA CASES, ( ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Winter Pre Monsoon Monsoon Pos Monsoon Winter Rainfall Malaria Cases Humudity Figure 6.2: Rainfall and humidity pattern and monthly average malaria cases, in Sonitpur district during

4 With the onset of monsoon, there is a gradual increase in the number of malaria cases beginning from March followed by steep rise during April-June. Subsequently, there appears a steady decline till October corresponding to the postmonsoon period. During the winter months there is further decline in the number of cases indicating low level of transmission (Fig. 6.2) due to decrease in both rainfall and humidity. The malaria incidence has a direct relation with the pattern of rainfall. The increase or decrease in malaria cases fully depends on the rainfall pattern. However, the yearly data available since 1986 for malaria cases and corresponding amount of rainfall indicate negative relationship probably due to irregularity and sometime due to abnormality of rainfall data. The rainfall pattern and number of malaria cases since 1991 indicate corelation between the two variables. The peak of rainfall amount well coincide the corresponding peak of malaria cases (Fig. 6.3). The simple linear correlation regression between the month wise malaria cases and the month wise amount of rainfall received since 1991 has been examined and it suggests a positive correlation coefficient of with coefficient of determination r 2 value of (11.68%) as shown in figure 6.4. The level of significance at 0.05% level is considered extremely significant with p value less than This supports the hypothesis that there is a positive significant relationship between monthly cases of malaria and rainfall. Moreover, 11.68% of the variability in malaria cases could be explained by rainfall. 127

5 MALARIA CASES (numbers) RAINFALL (mm) DETERMINANTS OF MALARIA ENDEMICITY It may be mentioned here that a close relationship between malaria cases and rainfall has been observed. The monthly changes in rainfall amount show a positive correlation with malaria incidence, but whenever the cumulative amount of rainfall in a year increases to a certain limit, the relationship becomes weak with a tendency to show negative relation. This indicates that there is a threshold level for rainfall and as it exceeds there occurs a decreasing trend in malaria cases. That is why it has been observed that the year with high amount of rainfall shows decrease in the number of malaria cases due to flushing out of mosquito larvae by high surface runoff or flash flow MONTHLY MALARIA CASES AND RAINFALL IN SONITPUR DISTRICT ( ) Rainfall Malaria Case Figure 6.3: Malaria cases and rainfall pattern in Sonitpur district during

6 MALARIA CASES (numbers) DETERMINANTS OF MALARIA ENDEMICITY 900 SIMPLE LINEAR REGRESSION BETWEEN MALARIA CASES AND MONTHLY RAINFALL ( ) y = 0.055x R² = Malaria RAINFALL (in mm) Figure 6.4: Simple linear regression between malaria cases and monthly rainfall Vegetation cover Epidemiological data of malaria were collected from 24 villages in the foothill areas of Sonitpur district (Assam) bordering Arunachal Pradesh since The malaria cases collected or reported to the Defence Research Laboratory, Tezpur and health sub-centres run by State Government were taken for the study. The survey was carried out by active fever surveillance being engaged in door to door collection of thick and thin blood smears on glass slides by finger prick method from persons having fever history for the past days. For convenience, the Slide Positivity Rate (SPR in %) is estimated from the sample villages which are categorised under six patches based on their location (Table 6.1). Patch I includes six villages, namely Hoograjuli, Sapai majgaon, Sapai 129

7 rawmari, Balisuti, Dipabasti and Pochabasti which are located in the western part of the study area (Fig. 6.5). Patch II includes a group of small resettlements in the foothill area situated in the north-western part of the study area. Patch III covers villages like Bengenajuli, Naharani, Gulai centre, Kalamati, Dighaljuli, Rikamari and Jiagabharu, whereas Ramnathpur, Belsiri, Nonkebelsiri, Barbeel, Dhankhona, Bandarhagi and Dhekipelua villages are included in patch IV. Patch V, which is located in the north-eastern part of the study area, includes Chatai and Gamani villages, while Charduar village is included in the Patch VI. Figure 6.5: Sample villages in different patches of the study area The malaria slide positivity rate (SPR) has been calculated from the collected data and used to reveal the malaria incidence pattern. Epidemiological surveys were carried out throughout the year even during the summer and monsoon season when malaria cases generally increase. 130

8 Table 6.1: Slide positivity rate (in %) in the sample villages under different patches during Years Surveyed areas (Patches with group of villages) Patch I - Hoograjuli, Sapai majgaon, Sapai Rawmari, Balisuti, Dipabasti and Pochabasti villages Patch II - Foothill settlements Patch III - Bengenajuli, Naharani, Gulai centre, Kalamati, Dighaljuli, Rikamari and Jiagabharu villages Patch IV - Ramnathpur, Belsiri, Nonkebelsiri, Barbeel, Dhankhona, Bandarhagi and Dhekipelua villages Patch V - Chatai and Gamani village Patch VI - Charduar village Source: The Slide Positivity Rates are calculated by the research student based on data collected from Defence Research Laboratory, Tezpur and State Health Subcentres of the district. 124

9 Topological maps on scale 1:50,000 of the study area published by the Survey of India are scanned and georeferenced in the GIS environment with the help of ArcMap TM 9.2 software. Base map representing different layers such as forest covers, water bodies, roads, villages, etc. of the study area has been prepared from the toposheets. Global Positioning System (GPS) survey is carried out with the help of a hand-held Garmin ique M5 GPS in order to locate the sample villages for mapping. To understand the impact of deforestation on distribution of malaria in the study area, satellite imageries are also used and Normalized Difference Vegetation Index (NDVI) was calculated from each of the satellite images to observe the change in the vegetation cover. Satellite imageries of Indian Remote Sensing Satellite (IRS) were taken from National Remote Sensing Centre (NRSC) and Defence Electronics Application Laboratory (DEAL), India. IRS 1D LISS-III digital data pertaining to years 2000, 2003 and 2005 of the study area were used to monitor the changes in forest cover. Based on the toposheets, the satellite images were georeferenced to rectify the images using more number of ground control points (GCP) with the help of PCI Geomatica v 9.0 software. Normalized Difference Vegetation Index (NDVI) has been calculated using the PCI Geomatica software. The NDVI which determines the density of vegetation is measured based on solar radiation in the near infrared (NIR) and visible (VIS) wavelength bands and it has been estimated using the following mathematical formula: NDVI = (NIR VIS) / (NIR + VIS) 132

10 In the software, NDVI for IRS 1D LISS III satellite data were calculated by NDVI = (Band 3 Band 2) / (Band 3 + Band 2) It is a non-linear function and the value of NDVI varies between 1 and +1, where + 1 value corresponds to dense vegetation cover. The total area covered by forests during the years 2000, 2003 and 2005 has been calculated by counting pixels and presented in square kilometres. However, the forest covered areas before 2000 could not be calculated and mapped due to non-availability of satellite images. The Slide Positivity Rate (SPR) estimated during the years under study has been compared using analysis of variance (ANOVA) and chi-square tests. The forest covered area is compared using chi- square test, whereas regression analysis has been carried out to find out the correlation and trends. Figure 6.6: Malaria incidence in the study patches during

11 Twenty-four villages are taken to analyse the relationship between vegetation cover and degree of malaria incidence. These villages occupy sq km of area inhabited by majority ethnic tribes. The malaria epidemiological data from 1994 to 2005 show that the entire study area is endemic with perennial malaria infection. The SPR obtained in respect of all the 24 sample villages over the years has been presented in figure 6.6. The overall SPR among the patches ranges from 5.1% in 1997 to 44.4% in 2005 (Table 6.1). There is a significant increase in the SPR over the period under study and maximum SPR (28.23% ± Standard Error of Mean) was recorded in 2005 (F = 2.536; df = 11; p < 0.012). Initially the SPR recorded was lower than 20% in most of the villages. However, the increasing pattern of SPR was observed over the subsequent years under the study. A positive linear trend has been found among the SPR and forest cover recorded during the study years (slope = ; r 2 = ; p < 0.002). Table 6.2: Slide positivity rate (SPR) and forest cover of the study area Study year SPR Forest cover (km 2 ) ±3.5 ( ) 367 (23.6%) ±3.3 ( ) 289 (18.6%) ±5.1 ( ) 239 (15.4%) Source: The Slide Positivity Rates are calculated by the research student based on data collected from Defence Research Laboratory, Tezpur and State Health Sub-centres of the district. Forest cover change calculated by research student based on IRS data of NRSA, Hyderabad. 134

12 Since the satellite images are available for the years 2000, 2003 and 2005, therefore forest covered areas are calculated for these years only and compared with the SPR of the same years to find out the correlation. The values of SPR during 2000, 2003 and 2005 (Table 6.2) show that there appears a gradual increase. On the other hand, the forest covered areas of study area have been found reduced from 2000 to 2005 (Table 6.2 and Figure 6.7) to the tune of 23.6%, 18.6% and 15.4% in 2000, 2003 and 2005 respectively. The decrease in forest cover in 2005 was found to be statistically significant (p < ; df = 2; X 2 = ). The correlation obtained between the SPR and forest cover during 2000, 2003 and 2005 was significant (r 2 = ; p > 0.09). The deforestation in an area influences the local ecology and biodiversity, which in turn influences the transmission of disease and behavioural changes among the vector species (Baruah et al. 2004). The deforested land has been found associated with a higher risk of malaria transmission in many endemic areas (Olson et al. 2010). Alteration in the landform by deforestation changes the behavioral characteristic of anopheline vectors. The shrub landcover developed in the form of cultivated crop land along with irrigation channels and paddy fields has significantly greater abundance of An. minimus, An. philippinensis, An. nivipes and An. culicifacies larvae than in forested land (Das et al. 2004). These mosquito species have been incriminated as malaria vectors and they appear to be establishing themselves as major vectors in addition to A. dirus in the area (Das et al. 2007; Bhattacharyya et al. 2010). 135

13 Figure 6.7: NDVI derived from the satellite imagery showing the depletion of forest cover over the years 2000, 2003 and Area delimited by red line indicates dense forest. 136

14 Local agricultural practice, which involves shifting cultivation, has resulted in parasite reservoir in the untreated asymptomatic individuals. This practice makes the control programme difficult leading to year round malaria transmission in the region (Dev et al. 2010). The NDVI analysis suggests that the north-western part of the study area has been undergoing massive reduction in forest cover from 2000 to The forest cover has been decreased by 450% during the last 35 years. The deforested areas have relatively higher temperature and humidity, which can increase the pace at which mosquitoes develop into adults, the frequency of their blood feeding rate at which parasite is acquired and the incubation of the parasite within the mosquitoes (Walsh et al. 1993). It is a fact that malaria transmission and distribution is endemic among population groups living in poverty. The people inhabiting the forest fringed areas and foothills of interstate border suffer more, and need priority in interventions (Das et al. 2004). The district health authority under the guidelines and funding from National Vector Borne Disease Control Programme (NVBDCP) undertake malaria control programme to reduce the malaria incidence and deaths due to malaria infection. In addition to the treatment using anti-malarial drugs, indoor residual spray using DDT has been carried out regularly in the district. Insecticide impregnated bed nets are also supplied by the health authority. The results of the present investigation indicate that despite the malaria control activities, the study areas are conducive to the persistent malaria transmission without any interruption. 137

15 The GIS mapping of the study area using the epidemiological data since 1994 shows an increasing trend in the incidence of malaria among the people of the 24 highly malaria endemic villages located in the forest fringed areas. The SPR depicted on map using GIS application may be helpful to the health policy makers which will enforce them to review the current control strategies. Further, the decreasing forest cover is also a serious ecological issue to be taken into account by the concerned authorities Land use The land use pattern too is a factor that greatly determines the endemicity of malaria in a region. The various types of land use in a region regulate the breeding pattern of mosquito which, in turn may determine disease spread by them. The lands put to various uses and cover like local vegetation, dense forest, agricultural field, swamps, marshy land etc. influence the density and diversity of mosquito variants. Land use and land cover appeal mosquitoes in determining the preference for breeding that varies as per species. As discussed earlier, the dense vegetation types and pattern attract Anopheles mosquito for proliferation. Anopheles dirus prefers to live in dense forest, but any change in vegetation cover alters the habitat and ecosystem thereby forcing the species to migrate to other suitable habitat. The change in the morphology of landforms also attracts other mosquito species that prefers the new landform type. Thus, the land use pattern influences the behavioral aspect of mosquito variety for their breeding and development that may determine the disease pattern in the region. 138

16 As malaria is disseminated by Anopheles mosquito, its species prefer to live and breed in different types of land uses. There is no such hard and fast rule for mosquito in the case of breeding in specific types of habitat. Although they breed in all types of available habitats, they also prefer some particular type of habitat governed by some specific land use type. As regards the spread of malaria by Anopheles primary and secondary vectors, the table 6.3 shows the correlations between mosquito vectors and land use types in the study area. Table 6.3: Correlations between mosquito vectors and land use types in Sonitpur district Coefficient of correlation values (r) Mosquito Anopheles Anopheles Anopheles Land Anopheles Primary Secondary Use Land non vector vector vector Cover Wetlands * * Marshy lands * * River Vegetation Dense forest * * * Agriculture fields * * * Damp area Sand area Note: Mosquitoes are captured by CDC light trap *. Correlation values are significant at 0.05 level (2-tailed). The correlation coefficient values calculated between different types of LULC with Anopheles mosquito indicate positive as well as negative correlations. The number of Anopheles collected from different mosquito survey sites suggests positive correlation with wetlands, local vegetation and dense 139

17 forest. The primary and secondary vectors of Anopheles mosquitoes which spread malaria, also show positive correlation with these three variables types thereby indicating proliferation of mosquitoes. On the other hand, the non vectors only show positive correlation with wetlands and dense forest. The dense forests show high positive correlation with the numbers of Anopheles and its primary and secondary vectors with correlation coefficient values of , and Strong correlation (r =+0.799) exists with between secondary vectors and dense forest at significance level of The value of coefficient of determination (r 2 ) being 63.87% for secondary vectors indicates high chances of infection due to Anopheles in dense forest. However, all types of vectors show a negative correlation indicating less chances of their incidence in marshy lands, agricultural fields, damp area and sandy area Water bodies Water availability is an important factor that determines the degree of mosquito breeding. Any water body or wetland is a low-lying area where the land is saturated with water. A wetland covers a bigger area as compared to a water body. There are a number of water bodies in the study area mostly in form of man-made ponds. Wetland is an important land use feature in the district which provides a favourable breeding ground for the mosquitoes. The correlation analysis carried out between wetlands and available mosquito vectors suggest a positive correlation of r = in the case of Anopheles mosquitoes (Table 6.3). 140

18 In order to evaluate the distribution pattern of wetlands in the study area, satellite images with resolution of 5.8 metre panchromatic LISS III data are used. The man-made ponds found in the settlement areas are identified from the satellite imagery. However, the water bodies identified are too small to be visualized in the map of Sonitpur district. A buffer of 1 kilometre has been created around the water bodies. The buffer of 1 kilometer is created considering the fact that a mosquito can fly up to a radius of 2 kilometres in search of blood and also that there is availability of water bodies in the study area. Buffer zone of 1 km is made to see the minimum reach of mosquito within its range (Fig. 6.8). Figure 6.8: Buffer around water bodies From the study it is found that a major part of the district comes within the reach of mosquito between settlement areas and water bodies. A 1 kilometer buffer around water bodies has covered almost all parts of the district. In the study, water bodies with less than 5.8 metre size could not be considered due to 141

19 resolution factor of satellite image which is limited only to 5.8 metre. It is really difficult to identify the small pools of water. But, mosquito can breed in small pools of water, e.g. elephant foot marks. Many such water accumulated pools are available which are good breeding grounds for the mosquitoes. 6.2 Socio-economic determinants A field study with sample design and data collection works on malaria incidence pattern was carried out in the district. A sample design was prepared for assessing the socio-economic factors associated with malaria incidence in Sonitpur district. As many as 288 sub-centres are in health service of the people in the district. From the jurisdiction of each Primary Health Centre (PHC) as many as 10% sub-centres are selected for the sample survey (Table 6.4). In some cases, based on the areal size and the degree of endemicity in the PHC 4 more sub-centres are taken into consideration for the survey. In this way altogether 36 numbers of sub-centres are randomly selected from the district. PHC Table 6.4: Sub-centres selected for the survey Total SC SC selected (10%) Reason for selecting additional number of SC Dhekiajuli = 5 +1 Bigger region Bihaguri = 4 Rangapara = 2 +1 PHC with high endemicity Balipara = 4 +2 Bigger region North Jamuguri = 4 Biswanath Charali = 3 Behali = 3 Gohpur = 7 Total Note: PHC Primary Health Centre, SC Sub-centres 142

20 All the sub-centres from the PHCs of the study area are selected and numbered. Every 10 th random number from the Random Number Table by taking last two digit numbers are picked up until the pre decided desired number is achieved from each of the PHCs. This sampling procedure is done for all the eight PHCs of the district. It was so done following random sampling technique in order to cover all PHCs under study giving equal chance to all the sub-centres for being selected (Fig. 6.9). Figure 6.9: Study sites (sub-centres) selected for survey Further 50% of the total villages under each sub-centre are selected for the survey which was again done randomly. Following the random sampling method a total of 81 villages are selected from the entire district (Appendix II). From each of the villages 20 households are selected randomly and thus a total of

21 households are sampled for which equal number of questionnaires were filled up by the surveyor. As the main objectives are to find out the socio-economic determinants, the questionnaire has been designed to identify the factors involved in causing malaria incidences in the district (Appendix I). Socio-economic status of the people as well as household details is supposed to determine the spatial variation of malaria situation in the district. The information on village includes its location with coordinates, slope pattern, altitude, population nearby sub-centres etc. The personal information include name, sex, body complexion, skin texture, skin and hair types, earlier malaria history of the family etc. The socio-economic information includes number of persons in the family, children below 15 years, number of educated people and education level, mother s education, number of earning members, average income, occupation type, wealth assets, house type, cooking fuel, food habit, knowledge on malaria, livestock, facilities of electricity and source of water, use of bed nets etc. The questionnaires filled up for each village were sorted and clubbed together sub-centres wise. In order to find out the difference between each and every sub-centre with varying socio-economic conditions, the sub-centres are divided into two groups, i.e. Low and High malaria risk sub-centres based on malaria epidemiological data of The high risk sub-centres are identified on the basis of malaria cases >50, API > 2, Pf % >30 and SPR > 2. The sub-centres not fitted in this criterion are termed as low risk sub-centres. A clear cut difference in the location of the high and low risk sub-centres has been observed which can be vividly delineated by a line called as 144

22 risk line (Fig. 6.10). However, further analysis is carried out based on these two groups. The survey was conducted on 1620 families (740 from low and 880 from high risk sub-centres) with a population of 8463 persons (4564 and 3899 from low and high risk sub-centre areas respectively). The male and female respondents covered in the study are 4230 and 4233 respectively. In order to examine the association of variables with malaria incidence, analysis is carried out from the data collected during household survey from different areas under sub-centres of the district. The chi square test has been conducted to see if there is any intergroup association among the potential risk factor for malaria at 95% confidence level (Table 6.5). Figure 6.10: Malaria risk sub-centres in study sites 145

23 Table 6.5: Results of the analysis of the potential risk factors for malaria (based on data from 1620 respondent families) Characteristic Investigated Low risk area High risk area Chi square value P value Row column association Total families (54.32) 740(45.68) Population (53.93) 3899(46.07) Male (54.85) 1910(45.15) Female (53.01) 1989(46.99) Children below 15 years Educated persons Educated Below primary Educated Above primary 2671 (31.56)* 1400(52.41) 1271(47.59) (41.59) * 1989(56.51) 1531(43.49) 2004 (23.68) * 1102(54.99) 902(45.01) 1516 (17.91) * 887(58.51) 629(41.49) < significant Uneducated persons 4943 (58.41) * 2575(52.09) 2368(47.91) Earning members Average income /family 2109 (24.92) * 1117(52.96) 992(47.04) 6567/- 6848/- 6216/- Occupation 1388 Primary (65.81) * 267 Secondary (12.66) * 454 Others (21.53) * Wealth assets index Poor 993 (61.30) * 730(52.59) 658(47.41) 139(52.06) 128(47.94) (54.63) 206(45.37) 525(52.87) 468(47.12) not significant Medium (54.34) 226(45.66) significant 146 Contd.

24 (30.56) * Rich 132 (8.15) * 86(65.15) 46(34.85) House type Bamboo & thatch Half pacca Brick and concrete Cooking fuel Wood Kerosene LPG Knowledge about malaria Yes No 868 (53.58) * 566 (34.94) * 186 (11.48) * 1426 (77) * 358 (19.33) * 68 (3.67) * 1112 (68.64) * 508 (31.36) * 429(49.42) 439(50.58) 332(58.66) 234(41.34) < significant 119(63.98) 67(36.02) 738(51.75) 688(48.25) 228(63.69) 130(36.31) < significant 56(82.35) 12(17.64) 599(53.87) 513(46.13) (55.31) 227(44.68) not significant Electricity Yes No 628 (38.77) * 992 (61.23) * 372(59.24) 256(40.76) 508(51.21) 484(48.79) significant Source of water Well Tube well Govt. water Supply 833 (51.42) * 671 (41.42) * 116 (7.16) * 488(58.58) 345(41.42) 362(53.95) 309(46.05) < significant 30(25.86) 86(74.14) News/Infor mation Newspaper 28 12(42.86) 16(57.14) 147 Contd.

25 TV Radio (3.57) * 369 (47.07) * 387 (49.36) * 222(60.16) 147(39.84) (55.04) 174(44.96) not significant Bed net categories No net Untreated nets Use of Insecticide Treated Nets (ITN) Preventative measures Traditional method Mosquito coils Electric repellants 527 (32.53) * 739 (45.62) * 354 (21.85) * 873 (53.66) * 585 (35.96) * 169 (10.39) * 256(48.58) 271(51.42) 451(61.03) 288(38.97) < significant 173(48.87) 181(51.13) 494(56.59) 379(43.41) 347(59.32) 238(40.68) significant 113(66.86) 56(33.13) *Value in brackets represents percentage within group. Percentage is being calculated out of total respondents Education level Education is considered as an important factor that determines the occurrence of malaria in a population. It is found that the knowledge about malaria and the measures taken by literate families have significantly reduced the degree of malaria incidence. Illiteracy or ignorance about malaria is one of the major causes for spread of malaria. The number of literates among the respondents of the surveyed area is 3520 persons (41.59%), with 2004 persons below primary level (23.68%) and 1516 persons above primary level (17.91%). 148

26 Among the literates 1531 persons (43.49%) are in the high risk area and 1989 persons (56.51%) in the low risk area. It gives an idea how the literacy level influences the occurrence pattern of malaria in the study area. However, many other influencing factors along with low literacy level may result in increase of malaria incidence. Figure 6.11: Literacy rate among respondents of the survey sites (subcentres) The figure 6.11 shows the locations of the catchment areas of the subcentres along with their literacy levels. The sub-centres representing less than 25% of literacy rate are six in number of which four are located in the high risk zone. The sub-centres with literacy rate ranging from 25 to 50% are situated both in the high and low risk zones, whereas sub-centres with higher rate of literacy rate are mostly located in the low risk area (7 numbers). The chi square test 149

27 carried out to examine the risk associated with literacy level in the study area indicates that literacy level significantly determines the occurrence of malaria in the region. The χ 2 value calculated is at df = 2 with p value less than at significance level of The result suggests significance of literacy in determining risk for malaria incidence Economic level The economic level is also one of the important key factors that affect the degree of incidence of malaria in a region. The standard of living of the people determines the disease occurrence pattern. In the study area, the data collected from the socio-economic survey indicate variation in occupation, income and the wealth assets of the people. Income pattern: As regards income pattern, out of the total population surveyed in the study area, the number of persons involve in economic activities was 2109 (24.92%). The rest 75.08% of population were economically inactive and they fully depend upon the active population. It is the reason for low per capita income of the families. The average annual income per family of the study area is Rs. 6567/-. This income is estimated from all activities related to primary, secondary and tertiary occupations. The average income per family in low risk belt is Rs. 6848/-, whereas it is lower in high risk belt being a sum of Rs. 6216/- per family. The numbers of earning family members in high and low risk belt are 992 (47.04%) and 1117(52.96%) respectively. The range of income of the families, calculated in respect of different sub-centres of the surveyed area, 150

28 indicates variation in income pattern. It is also found that there is a close association of income level with malaria occurrence. The number of sub-centres with average family income below Rs. 5000/- is more in high risk belt than that in the low risk belt (Fig. 6.12). Out of the five sub-centres with average income above Rs. 10,000/-, three sub-centres are located in high risk belt. This association however shows a reverse pattern due to some families earning high income, where persons are mostly employed in highly paid central and state government services. Figure 6.12: Average annual income per family in survey sites Occupation pattern: So far the occupation pattern of the study area is concerned, out of the total 2109 earning members, 1388 (65.81%) persons are engaged in primary activities related to agriculture, forestry, animal rearing and fishing. Most of the people in the study area either have their own agricultural lands or they work as casual labourers in agricultural fields owned by others. As many as 267 (12.66%) persons are involved in secondary type of activities that 151

29 include weaving, handlooms, pottery, carpentry etc. But the number of population involved in secondary activities is low. The persons involved in other categories of activities like business, small scale industries, services in private and government sectors etc. account for 21.53% of the total. While looking into persons engaged in different occupational activities in high and low risk belt, it has been observed that 52.06% to 54.63% persons are of the low risk belt. On the other hand, 45.37% to 47.94% persons are of the high risk belt. Based on the economic status and wealth asset index, the families are categorized into poor, medium and rich ones. The index is calculated by assessing the land holding size, house type, per capita income and standard of living of the families. It has been observed that 993 families (61.30%) of the total 1620 families belong to poor category of which 52.87% families are in the low risk belt and 47.12% families in the high risk belt. On the other hand, 495 (30.56%) and 132 families (8.15%) of the total families of the surveyed areas are put into the groups of medium and rich categories respectively where 65.15% of the families belonging to rich category are found to be in the low risk belt. The chi square test is calculated taking different categories of occupation and wealth assets index. The χ 2 value of with P = at 95% confidence level indicates no significant impact of occupation on malaria. This may due to the homogeneity in peoples occupational activities in the district and as such no specific pockets of any occupational activity have been observed from the surveyed area. But the χ 2 value of with p value of suggests significance of data at 95% confidence level, which indicates the association of 152

30 economic status of the families in determining the degree of malaria incidence in the study area House type The house type is also an important factor that affects the occurrence pattern of malaria. It is found that houses with open ventilation are more prone to mosquito attack than the houses with closed ventilation, as mosquito can easily come into the houses through these holes or ventilations. A house made up of bamboo and thatch is more prone to malaria attack than a house made up of bricks and concrete. As revealed from the survey all total 868 houses are made of bamboo and thatch which account for 53.58% of the total housing types followed by 566 houses (34.94%) and 186 houses (11.48%) under half pacca and brick and concrete category respectively. The housing type in high risk belt under category of bamboo and thatch houses constitutes 50.58%, followed by half pacca houses of 41.34% and brick and concrete houses of 36.02%. But, it shows a inverse picture in low risk belt, where the houses made of brick and concrete constitute 62.98% followed by half pacca and bamboo & thatches with 58.66% and 49.42% respectively. The chi square test carried out indicates the significance of house type with malaria risk with χ 2 value of and p value less than at 0.05 significance level. This test shows the role of house type in influencing the malaria occurrence pattern in the study area under field survey. 153

31 6.2.4 Knowledge and information Knowledge and awareness about malaria held by an individual is always considered and thought as an important precautionary measure in combating malaria. But from the study area it is revealed that the people are not so much aware of the malaria disease. A total of 1112 (68.64%) families replied to the questions pertaining to the knowledge about malaria and the causes responsible for its spread. The rest of the families do not have knowledge about the causes responsible for malaria. In the high risk belt a total of 513 (46.13%) of total families know about malaria. In the same belt, 599 families (53.87%) are found to have no knowledge about malaria. The information on malaria aired on television or radio or published in newspapers too cannot help reduce malaria. Only 784 families of total respondents have access to mass communication, where 3.57% people read newspaper, 47% to 49% have access to TV and radio. The chi square test of both knowledge on malaria and access to TV, radio and newspaper shows insignificance of variable with malaria. The χ 2 value of with p value of suggests non significance of knowledge about malaria in the high and low risk malaria belts. Similarly the chi square value of with p value of suggests insignificance of news and information to influence malaria incidence Other basic needs The other basic needs of human life like cooking fuel and electricity as observed from the questionnaire data collected in the study area indicate their 154

32 significance in deciding malaria incidence pattern. The information on the use of different types of cooking fuel in the surveyed area suggests wood as the dominant one (77%) being used followed by kerosene (19.33%) and LPG (3.67%). About 51.75% people use wood as fuel in the low risk belt as compared to 48.25% in the high risk belt. The use of kerosene and LPG is similarly high in the low risk belt representing 63.69% and 82.35% respectively. The chi square test carried out to find any relationship among the variables suggests the significance of cooking fuel in the disease occurrence pattern. The χ 2 value with and p value less than at 0.05 confidence level suggests the significance of cooking fuel to malaria incidence. Similarly the presence of electricity in the study area shows its association with malaria incidence pattern. The chi square test to find the association relationship among variable indicates the significance of electricity with the incidence pattern of malaria. The χ 2 value of with p value of indicates the statistical significance of the relationship at 95% confidence level which shows the importance of electricity in determining the degree of malaria incidence in the study area. The use of mosquito bed nets is an important aspect which determines the degree of malaria incidence in a region. The use of mosquito bed nets is not so popular habit of individuals in the tribal villages. The importance of bed net is being disregarded due to lack of education. The use of mosquito bed net in the study area decides the degree of malaria endemicity. The use of insecticide treated mosquito bed net is strongly recommended by World Health Organization which is one of the effective preventive measures. The use of Long Lasting 155

33 Insecticide Net (LLIN) is presently distributed freely to the people living below poverty line by the health officials of the state government. But, its proper use is hardly noticed. The chi square test carried out to find any relationship among different bed net categories suggests its significance. The χ 2 value of with p value of less than at 95% confidence level suggests the significance of mosquito bed nets to affect the incidence of malaria in the study area Human mobility and connectivity The variation in human mobility and connectivity of people to health centres located in different parts of the district influences the disease occurrence pattern. A health centre with better accessibility by road in the surrounding areas helps in reducing diseases in the region. Both health centre facilities and good roads are required for better service and lack of any one of them will change the service quality. Figure 6.13: Buffer around sub-centres 156

34 In order to examine the accessibility of a health centre in serving health facility to the public, a buffer of 3km around each sub-centre identified by GPS has been prepared and plotted in a map. A 3km distance is considered as the desirable range of mobility of the people to health centres during unwellness and also for services provided by health centres towards local people. The malaria cases since 2006 to 2010 are also plotted in three groups i.e cases, cases and above 100 cases. While superimposing these data it is found that large areas of the district particularly the northern part of the district is uncovered by health centres, where there is high incidence of malaria and people reside there at high risk (Fig. 6.13). These areas are basically adjacent to the foot hill region of the district, where people live in forest fringe villages. All these villages are non revenue villages and these are built up in the forest area by clearing the dense forest and converting it into agricultural lands. The establishment of new health sub-centres in these new villages with formal government approval is problematic due to non recognition of these non revenue villages Community tradition and culture The tradition and culture of a society in terms of fooding, clothing, shelter, art and language, knowledge etc. generally pass on from one generation to another. The information that is transferred is very valuable and important which has been inherited from generation to generation. The knowledge on disease prevention is one of them which help the people in surviving and lasting the human race till now among all odds. Earlier, when the modern medicine has not been developed, the traditional methods helped them survive. Malaria is an 157

35 old disease. Many measures like fumes generated from some tree barks, leaves etc. that spread scents were used as repellant. These traditional methods along with modern scientific techniques used to repel mosquito help in reducing malaria. The preventative measures for malaria adopted by different people were observed during the survey. It was found that around 53.66% of the families still use traditional methods followed by mosquito repellant coils by 35.96% and electric repellants by 10.39%. As the use of electric repellant is fully dependent on the accessibility of electricity and also because of its high cost, all people cannot use it. The chi square value of with p value of at 0.05 confidence level indicates the statistical significance of the use of preventive measures in determining the pattern of malaria incidence. Thus it can be stated that traditional customs and culture are well used that has been transferred from generation to generation among the communities. 158