RESEARCH ARTICLE

The Malaria Situation among the Baiga Tribe in Kabirdham District, Chhattisgarh

Dakeshwar*, Shailendra Kumar

School of Studies in Anthropology, Pt. Ravishankar Shukla University, Raipur, 492010 (C.G.) India

*Corresponding Author Email- dakeshwar1994janghel@gmail.com

Introduction

At the global level, an estimated 249 million malaria cases and 608,000 malaria-related deaths occurred in 85 countries in 2023. The WHO African Region continues to bear a disproportionately high share of the global malaria burden. In 2023, the region accounted for 94 percent of malaria cases (233 million) and 95 percent of malaria deaths (580,000) worldwide. Approximately 80 percent of all malaria deaths in this region occurred among children under five years of age (WHO, 2023).

Malaria is a bio-social phenomenon involving both biological and social dimensions. The biological aspect is that malaria is caused by the Plasmodium parasite, which is transmitted through the bite of infected mosquitoes. The social aspect includes factors such as the unavailability of mosquito nets, poor housing conditions, and inadequate healthcare services in tribal areas, all of which influence the prevalence and spread of malaria (Farmer, 2003).

Malaria is a febrile disease caused by infection with specific Plasmodium sporozoan parasites. The disease is transmitted to humans through the bite of infected female Anopheles mosquitoes. Malaria fever is characterized by distinct clinical stages. During the eighteenth century, malaria was commonly known as “bad air” (Mal Aria). At present, malaria has become a major public health concern. Four species of Plasmodium parasites are recognized as responsible for malaria infection in humans (Swarnkar, 2016).

• Plasmodium vivax (P. vivax): This species is widely distributed throughout the world and is responsible for nearly 70 percent of malaria cases. If left untreated, it may cause prolonged episodes of fever.

• Plasmodium falciparum (P. falciparum): This parasite causes cerebral malaria and is responsible for approximately 25–30 percent of malaria cases. The fever caused by this species is associated with a higher risk of mortality.

• Plasmodium quartan (P. quartan): This parasite accounts for about 1 percent of malaria cases in certain regions. In India, its presence has been reported in tribal areas of the Hassan and Tumakuru districts of Karnataka.

• Plasmodium ovale (P. ovale): This species is responsible for a very small proportion of malaria cases, and its occurrence is largely confined to regions of Africa and Vietnam.

Malaria affects individuals of all age groups, although infants possess a certain degree of natural resistance against infection. Compared to women, men are generally more affected by malaria due to differences in clothing and occupational activities. Malaria is more prevalent in underdeveloped regions, overcrowded settlements, areas with inadequate housing, and among lower socioeconomic groups. Individuals who sleep outdoors are at greater risk of contracting malaria.

The disease spreads more rapidly during the rainy season, in conditions of high humidity, and in areas located near water sources. Moisture, darkness, unhygienic surroundings, ponds, stagnant water bodies, kitchens, piles of wood and scrap materials provide favorable breeding grounds for mosquitoes and thereby contribute to the spread of malaria. In India, approximately nine species of Anopheles mosquitoes are known to be responsible for malaria transmission (Swarnkar, 2016).

Baiga Tribe

Among the 42 Scheduled Tribes of the state of Chhattisgarh, the Government of India, based on prescribed criteria, had classified the Kamar, Birhor, Abujhmadia, Pahari Korwa, and Baiga tribes as Primitive Tribal Groups (PTGs). In 2006, the Government of India officially replaced the term Primitive Tribal Groups (PTGs) with Particularly Vulnerable Tribal Groups (PVTGs).

In Chhattisgarh, the Baiga are categorized as a Particularly Vulnerable Tribal Group (PVTG) and are geographically classified under the Central Tribal Region. They are primarily found in the districts of Kabirdham, Korea, Bilaspur, Rajnandgaon, and Mungeli. From a demographic perspective, the Baiga constitute the largest population among the Particularly Vulnerable Tribal Groups in Chhattisgarh. The Baiga tribe is considered one of the most ancient indigenous communities in India (Abidi, 2020).

Kabirdham district of Chhattisgarh is a tribal-dominated region where the Baiga tribe resides in significant numbers. Classified by the Government of India as a Particularly Vulnerable Tribal Group (PVTG), the Baiga community continues to remain socially and economically marginalized (Ministry of Tribal Affairs, 2018). The tribe inhabits forested and hilly regions, maintaining a traditional way of life, with limited access to educational resources and modern healthcare facilities (Census of India, 2011).

According to the Census of India (2011), the total population of the Baiga tribe in Chhattisgarh was 88,317, comprising 44,402 males (50.28%) and 43,915 females (49.72%). The highest concentration of the Baiga population was found in Kabirdham district, accounting for 49.80 percent (43,979 individuals) of the total Baiga population in the state. The lowest population was recorded in Chhuikhadan Development Block of Rajnandgaon district (presently part of Khairagarh-Chhuikhadan-Gandai), representing 4.97 percent (4,385 individuals) of the total Baiga population. Of these, 2,206 were males and 2,179 were females, resulting in a sex ratio of 988 females per 1,000 males, which is lower than the overall sex ratio of Chhattisgarh (991) (Census of India, 2011).

Review of Literature

The available literature highlights that malaria among tribal communities, particularly the Baiga tribe, is shaped by a complex interaction of socio-economic, environmental, cultural, and healthcare-related factors. Elwin (1939), in his classic work The Baiga, documented the Baiga tribe’s unique cultural traditions and indigenous healthcare practices, emphasizing their dependence on medicinal plants, herbal remedies, and traditional healers due to their remote forest-based lifestyle. Similar observations were made by Mishra et al. (2006), who studied malaria among Primitive Tribal Groups in Kandhamal district, Odisha, and found that ecological conditions, socio-cultural beliefs, and magical-religious practices strongly influenced malaria perception and treatment. The authors emphasized the need to integrate modern healthcare interventions with an understanding of local cultural practices. Daash et al. (2009) further reported that, in Koraput district, Odisha, malaria prevalence is not determined solely by environmental factors but is also significantly influenced by socio-economic conditions, community behavior, and cultural practices.

Several studies have examined the epidemiology and seasonal distribution of malaria in tribal regions. Singh et al. (2009) analyzed more than 15,000 blood reports from Jharkhand and observed that malaria incidence increased substantially during the monsoon months (June–October), with Plasmodium falciparum accounting for nearly one-third of all malaria cases. Chand et al. (2015) demonstrated that malaria transmission dynamics vary across tribal regions of Madhya Pradesh due to differences in vector species and ecological conditions, concluding that site-specific malaria control strategies are essential. Kiszewski and Darling (2010) contributed by developing a probability model showing that the widespread acceptance and effective use of mosquito repellents can substantially reduce malaria transmission at the community level.

Research has consistently identified poor socio-economic conditions as major contributors to malaria vulnerability among tribal populations. Shrivastava (2013) reported that poverty, illiteracy, poor sanitation, unsafe drinking water, and limited awareness make tribal communities highly susceptible to communicable diseases, including malaria. Sharma et al. (2021) similarly demonstrated that housing quality, sanitation, water availability, income, and environmental conditions significantly influence malaria incidence among the Baiga population in Central India. Meravi (2022) observed that the Baiga community in Balaghat district lives in geographically isolated and underdeveloped areas characterized by poor infrastructure, inadequate education, dependence on agricultural labor, and limited awareness of government welfare programmes, all of which contribute to the persistence of malaria, particularly during the rainy season.

Healthcare-seeking behaviour and traditional beliefs have also emerged as important themes in previous studies. Sonowal and Konch (2021) noted that Particularly Vulnerable Tribal Groups (PVTGs) continue to rely heavily on traditional healing systems because modern healthcare services remain inaccessible or culturally incompatible. Similarly, Babu and Panda (2016) found that the Baiga possess a distinct healthcare tradition based on medicinal herbs while maintaining a close relationship with forests and natural resources. Singh (2017) reported that traditional beliefs, superstition, and dependence on priests or shamans often delay the utilization of modern healthcare facilities, contributing to the continued burden of infectious diseases. Ranjha (2019), in a study conducted in Surajpur district of Chhattisgarh, found that despite government efforts, awareness regarding malaria remained inadequate, community members frequently preferred traditional healers over public health facilities, and recommended strengthening malaria education, Indoor Residual Spraying (IRS), and the distribution of Long-Lasting Insecticidal Nets (LLINs). Likewise, Panday and Maheshwari (2018) reported limited health awareness, inadequate healthcare infrastructure, long distances to health facilities, and irregular availability of medical personnel among the Baiga community.

Overall, the reviewed literature consistently demonstrates that malaria among the Baiga and other tribal communities is not merely a biomedical problem but is closely linked with poverty, environmental conditions, cultural beliefs, educational status, and limited access to healthcare services. While several studies have explored malaria epidemiology, traditional healthcare practices, socio-economic determinants, and seasonal transmission patterns, there remains a need for region-specific investigations that integrate socio-demographic, environmental, behavioural, and healthcare-related factors. Such evidence is essential for designing culturally appropriate and community-based malaria control strategies, particularly in vulnerable tribal populations such as the Baiga of Chhattisgarh.

Objectives of the study:

1. To study the prevalence of malaria infection among the Baiga tribe in the Kabirdham district of Chhattisgarh.

2. To analyze the physical, social, cultural, and economic impacts of malaria on the Baiga tribe in the Kabirdham district of Chhattisgarh.

3. To study seasonal malaria patterns among the Baiga tribe.

Research Methodology

The present study employed an exploratory and descriptive research design. Under purposive sampling, 65 respondents aged between 5 and 60 years, who had been affected by malaria, were selected from Baiga-dominated villages of the Bodla Development Block in Kabirdham district, Chhattisgarh.

For the collection of primary data, various tools and techniques were used, including semi-structured interview schedules, anthropometric measurements (height and weight), blood pressure measurements (systolic and diastolic), non-participant observation, interview guides, group interviews, photography, and audio recordings. In addition, case studies of selected key informants such as Baiga traditional healers (Baiga and Guniya), healthcare workers, and some respondents suffering from malaria were conducted whenever necessary.

For the collection of secondary data, sources included population reports, records from the Tribal Development Department, Outpatient Department (OPD) registers of Community Health Centres, malaria-related reports and pathology reports, research articles, dissertations, books, newspapers, and, where required, photographs and audio-visual materials.

The collected data were analyzed using SPSS software (Version 16.0) and Microsoft Excel (2007).

Results and Discussion

The table 1 presents the gender, age group, educational background, and occupational status of the respondents. The data reveals that 43.1% of the females and 56.9% of the males tested positive for malaria, indicating a higher prevalence of the disease among men compared to women. The study found the highest number of patients (38.5%) in the 6–15 age group, while the lowest number (4.6%) was recorded in the 46–55 age group. The individuals who tested positive for malaria had an average age of 23.87 years. More than 80.0% of the patients in the study were under the age of 35, indicating that the impact of malaria infection was predominantly observed among children, adolescents, and young adults. Notably, the highest percentage of patients fell within the 6–15 age group, suggesting that this demographic may be more susceptible to malaria infection. Regarding educational status, the highest proportion (38.5%) of respondents studied up to the primary level, while the lowest (1.5%) studied up to high school; notably, 29.2% of respondents have received no education and are unable to sign their names. In terms of occupation, 95.4% rely on agricultural labor and the collection of forest produce, 3.1% engage solely in wage labor, and 1.5% sustain themselves through farming and the collection of forest produce. Regarding household composition, the most common household size is five members (26.2%), with a range extending from a minimum of two members to a maximum of eight; the average household size is 5.2 members.

Table No. 1 Socio-demographic profile of the respondents

The table 2 illustrates the housing conditions of the respondents, showing that 67.7% live in *Kachcha* (mud/temporary) houses. While 4.6% use handpumps and tap water, 95.4% rely on well water for drinking; notably, 63.1% of the latter do not treat the well water, a practice that can foster mosquito breeding and increase the risk of malaria. Wood is used as the fuel source for cooking. Motorcycles are used for commuting in the households of 73.8% of the respondents, while 24.6% travel on foot. Mobile phones are available in 87.7% of the households for communication purposes.

Table No. 2 Information about the condition of house of the respondents

The table 3 presents data regarding the cleanliness of the respondents' homes and their waste disposal locations; it reveals that 16.9% of the households exhibit poor or unsanitary conditions in their immediate surroundings, while 50.8% dispose of waste behind or to the side of the house. This practice increases the likelihood of filth and mosquito breeding near the home, creating a potential risk for malaria.

Table No. 3 Information about the Cleanliness around the House of the respondents

The table 4 outlines the substance use status of the respondents; 44.6% of them consume intoxicants, a factor that could potentially delay their health recovery.

Table No. 4 Addiction/Substance Use Status of Respondents

The table 5 presents the health and BMI status of the respondents, showing that 6.2% had health problems. Regarding BMI status, 30.7% of the respondents were underweight and 3.1% were overweight; such nutritional deficiencies or excesses could delay recovery from malaria.

Table No. 5 Health and BMI Status of Respondents

The table 6 presents data on the respondents regarding malaria type, symptoms, and seasonal occurrence; it reveals that 50.7% of cases were *Plasmodium vivax*, 43.1% were *Plasmodium falciparum*, and 6.2% were mixed infections, indicating a higher prevalence of *Plasmodium vivax* in the region. Regarding the season (month) of transmission, the highest incidence (43.1%) was observed in July, while the lowest (1.5%) occurred in September and November. Observed symptoms included increased body temperature and severe chills (43.1%), as well as fatigue, increased body temperature, and severe chills (56.9%).

Table No. 6 Malaria Status, Symptoms, Season, and Treatment Practices of Respondents

According to the table 7, 81.5% of the respondents sought malaria treatment through a combination of Baiga practitioners and the Community Health Centre (CHC), while 18.5% utilized a combination of the CHC, Baiga practitioners, and *Vaidyas* (traditional healers). Regarding treatment methods, 90.7% relied on allopathic medicine combined with faith healing (*Jhad-Phunk*), and 3.1% used forest-based herbal medicine combined with faith healing; this indicates that traditional folk medicine remains prevalent and in use in this region. The highest proportion of malaria patients (40%) recovered within five days.

Table No. 7 Treatment and Recovery Status of Malaria-Affected Respondents

The table 8 outlines the use of mosquito nets and awareness regarding malaria prevention among respondents; it reveals that mosquito nets are not used in 61.5% of households, which is a primary cause of malaria. Additionally, 29.2% of respondents did not receive information on malaria prevention from Mitanins (community health workers).

Table No. 8 Use of Mosquito Nets and Awareness on Malaria Prevention among Respondents

The table 9 illustrates the distribution of malaria types (PF, PV, and PFR) across different months. A total of 65 malaria patients were identified during the study period; PV (*Plasmodium vivax*) cases were the most frequent, accounting for 33 (50.8%) cases, followed by 28 (43.1%) cases of PF (*Plasmodium falciparum*) and 4 (6.2%) cases of PFR. The highest number of cases—30 (46.2%)—was recorded in July, comprising 18 (60.0%) PV cases and 12 (40.0%) PF cases. In June, 23 (35.4%) cases were recorded, with PV (47.8%) and PF (39.1%) being the predominant types. In May, 10 (15.4%) cases were identified, with PF (60.0%) representing the highest proportion. In contrast, only one case each was recorded in the months of September and November. Correlation analysis yielded an r-value of 0.728, indicating a highly positive correlation. This implies the existence of a strong positive relationship between the two variables used in the study; that is, an increase in one variable was associated with a tendency for the other variable to increase as well. This value of the correlation coefficient indicates that the distribution of malaria cases is significantly associated with the factors studied. The results show that the *P. vivax* (PV) type of malaria was the most prevalent, with the highest incidence of cases observed during the months of June and July—likely due to increased mosquito activity and breeding during the rainy season. Furthermore, the obtained value of r = 0.728 confirms a strong positive correlation between the study variables.

The table 10 presents the distribution of various types of malaria (PF, PV, and PFR) according to age group. A total of 65 malaria patients were included in the study, comprising 33 (50.8%) cases of PV (*Plasmodium vivax*), 28 (43.1%) cases of PF (*Plasmodium falciparum*), and 4 (6.2%) cases of PFR. Analysis by age group revealed that the 6–15 years age group was the most affected, with 25 (38.5%) cases recorded. Within this age group, 14 (56.0%) cases of PV, 9 (36.0%) cases of PF, and 2 (8.0%) cases of PFR were identified. A total of 16 (24.6%) cases were found in the 16–25 years age group, comprising 10 (62.5%) cases of PF and 6 (37.5%) cases of PV. In the 26–35 age group, 11 (16.9%) cases were found, comprising 6 (54.5%) cases of PF and 5 (45.5%) cases of PV. Similarly, a total of 5 (7.7%) cases were found in the 36–45 age group, consisting of 3 (60.0%) cases of PV, 1 (20.0%) case of PF, and 1 (20.0%) case of PFR. Only 3 (4.6%) cases were found in the 46–55 age group—comprising 2 (66.7%) cases of PF and 1 (33.3%) case of PFR—while no cases of PV were detected. In the age group of 56 years and above, a total of 5 (7.7%) cases were recorded, all of which were PV (100.0%). PV (50.8%) was the most prevalent type of malaria, while PF (43.1%) ranked second. The highest incidence of malaria infection was observed in the 6–15 age group, suggesting that children and adolescents may be more susceptible to infection. Correlation analysis yielded an r-value of 0.335, indicating a low positive correlation; this implies that while a positive relationship exists between age and malaria infection, the association is weak. In other words, while some changes in malaria infection are observed with increasing age, the impact of other factors influencing the distribution of malaria may be relatively greater. Thus, although age is partially associated with malaria infection, it does not appear to be the primary determinant.

Table No. 9 Correlation between Month and Malaria

Table No. 10 Correlation between Age and Malaria

The table 11 presents the distribution of the recovery period from malaria across different age groups. The largest proportion of patients (36.9%) recovered in 5 days, while 1.5% recovered in 2 days and 6.2% took more than 7 days to recover. Analysis by age group revealed that there were 25 patients in the 6–15 age group; the majority of them recovered in 5 days (32.0%) and 4 days (28.0%). Similarly, among the 16 patients in the 16–25 age group, the highest number recovered in 5 days (37.5%) and 4 days (31.3%). In the 26–35 age group, among 11 patients, 45.5% recovered in 5 days and 27.3% recovered in 6 days. In the 36–45 age group, 80.0% of the 5 patients recovered in 5 days, which was the highest rate for this age group. Among the 5 patients in the 56 years and above age group, 40.0% recovered in 4 days, 40.0% in 7 days, and 20.0% took more than 7 days to recover. Correlation analysis yielded an r-value of 0.213, indicating a low positive correlation. This indicates that only a weak positive correlation was found between age and the recovery period from malaria. In other words, while there appears to be a tendency for the recovery period to increase with age, this relationship is not very strong. This suggests that the recovery period does not depend solely on age; rather, factors such as the severity of the disease, availability of treatment, the patient's immune system, and other health-related aspects also play a significant role.

Table No. 11 Correlation between Age and Recovery form Malaria