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Carolyn Ho

Srivarshini Cherukupalli1,2, Akansha Singh1, Chintan Pathak1, Jingran Ji12, Shreya Agarwal1, Apas Aggarwal1,2, Max Hockstein3, Irene Helenowski2, Ashish Bhalla4, M Shapiro2, Anagha Loharikar2, Mamta Swaroop2

1Northwestern University, Evanston, IL, USA; 2Northwestern University Feinberg School of Medicine, Chicago, IL, USA; 3Ross University School of Medicine, Miramar, FL; 4Postgraduate Institute of Medical Education and Research, Chandigarh, India

Objectives: This study conducted a needs assessment in rural Haryana, India to compare health-related perceptions and practices between two populations in the same location: migrant brick laborers (BL) and rural non-brick laborers (NBL).

Methods: Data was collected through 187 household interviews, which were randomly selected from throughout the Charnia village and three adjacent villages. The survey used in the interview included the following sections included demographics/education, income indicators, hygiene, general health/access to care and reproductive health.

Results: Sixty-six (35%) respondents classified themselves as BL, 102 (55%) as NBL and 19 (10%) gave no information in this regard. Most (76%) BL and 41% of NBL reported no education. Symptoms of illness such as cough, cold, and fever were significantly higher in BL children under 8 years old.

Conclusions: Socioeconomic, health and educational disparities exist within the same geographic location, as demonstrated by the significant differences between Charnia’s BL and NBL who reside in close proximity. As the BL population is mostly migratory, BL are unable to fully utilize local health and education infrastructure. Targeted health education programs designed to take place during the brick manufacturing season could help BL understand the consequences of any symptoms they may have, prevent chronic and infectious disease and improve the accuracy of self-reported data. Therefore, disparities must be targeted through a community-based approach that recognizes and addresses the varying population dynamics of BL and NBL in Charnia. Overall, health interventions in rural India must consider the characteristics of diverse population sub-groups in order to be effective and sustainable.


Despite ongoing progress, poverty persists in India with 29.5% of the population living below the poverty line.1 The World Health Organization (WHO) South-East Asia region, which includes India, bears 40% of the global poor.2,3 Poverty is especially prevalent in rural areas, where 77% of the Indian poor reside.4 Social factors such as gender, literacy and disparities in land ownership exacerbate poverty in rural India, and thus, females, illiterate individuals and unskilled laborers are at a higher risk for poverty.4 Studies have shown a strong association between poverty and ill health, which in itself perpetuates the cycle of poverty.5,6 As such, within a single community, groups with differing education levels and employment statuses may have varying health outcomes.

Household needs assessments can elicit knowledge of current health standards and living conditions within a community. Therefore, needs assessments can be used to understand the community’s level of health literacy and perceptions of its own health problems.7 This information can, in turn, influence policies to minimize health disparities and enhance healthcare infrastructure.8

We conducted a needs assessment in the area of Charnia, Haryana to identify similarities and differences in health-related perceptions and practices between two different populations living in the same geographic location: migrant brick laborers (BL) and a rural non-brick laborer (NBL) population. The Charnia area is unique in that it contains these two populations within the same geographical area. The overall goal of the needs assessment was not to compare and contrast the health practices of people of two different vocations (BL and NBL), but rather the different residence patterns of the two groups. BL are largely migrant workers who travel from Uttar Pradesh and Bihar to Charnia to work at the brick factory, and later leave Charnia to return to their homes at the end of the monsoon season. The NBL population is a more permanent population that resides in Charnia year-round. Therefore, the Charnia area is unique in that it offers the opportunity to compare a migrant population subgroup with a stationary subgroup in one specific location. Through this study, we hope to supplement the literature with an analysis of disparities between two different subpopulations living in geographic proximity to better understand how to design interventions targeting these communities. To our knowledge, this study is the first needs assessment of its kind in the region.



Charnia is a rural area in the North Indian state of Haryana (Figure 1), which has a population of 25 million. The Charnia area, which includes the Charnia village (with a population of 2600) as well as several geographically proximal villages such as Kiritpur, Kherawali, and Karanpor, has 13,600 people overall.9 The study groups were BL living in informal settlements of 50-100 laborers (known as “brick zones”) surrounding the brick factories, and NBL living in permanent villages. These groups were chosen because they live in the same geographical area (Figure 2).

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Survey Development

We designed a survey using the WHO model needs assessment.10 The survey included questions on education, literacy, family demographics, material possessions, access to healthcare facilities, income stability, access to food and clean water, sanitation and hygiene, general health of self and family members, chronic and infectious disease prevalence, immunizations, trauma, injury, wound care and reproductive health (Appendix 1). The needs assessment was exempt from the Northwestern University Institutional Review Board.

Sampling Selection and Data Collection

Data was collected from August 19 to September 6, 2012 through household-level interviews. Households were classified into two strata: BL and NBL. Households within the Charnia village and three adjacent villages, Kiritpur, Kherawali and Karanpor were then randomly sampled within each stratum. These three villages, which were the closest to the Charnia village, were included to obtain a larger sample size within one geographical region. The entire region surveyed was referred to as Charnia (Figure 3).



Surveys were administered by a pair of surveyors: a speaker fluent in Hindi and a recorder to transcribe in English. Assessments were conducted in Hindi after obtaining verbal consent, and queries were directed to the primary caregiver in the household. Responses were transcribed onto a standard data collection form. For the culturally sensitive portions of the survey, including reproductive and sexual health, the surveyor spoke with the individual in private.

Data Management and Analysis

Data was entered into a Microsoft Access 2010 (Microsoft Corporation, Redmond, Washington) database. Due to the small BL sample size, differences between the BL and NBL population of 15-20% could be detected at the 5% significance level with 80% power. Pearson Chi-Square analysis was conducted using SPSS Version 21 (IBM Corporation, Endicott, New York) to determine statistical differences in response proportions between BL and NBL populations. Poisson and logistic regression analyses were conducted in SAS v9.4 (SAS Institute, Cary, North Carolina).

Univariate analysis of categorical data between labor groups was conducted via the Fisher’s exact test and within groups using the one sample chi-square test. Univariate associations between groups of count data were reported as means and 95% confidence intervals (CI). Multivariate analyses involved Poisson regression when the response was count data and logistic regression when the response was binary. Least-square means and standard errors were obtained from Poisson regression, and odds ratios and their 95% CI were reported from logistic regression analyses, where standard errors and CI were presented as measures of variation. The relationship between the number of children in school and population type was determined by regressing the number of girls and boys in school on population type while controlling for children under five in the household, total number of children in the household and years of the parent’s education.

Logistic regressions were carried out to determine the effect of availability of electricity and population type on the ownership of television, refrigerator and mobile phones while controlling for self-reported income variability. In order to isolate the effect of the number of years of education and respondent group on the number of children per family, a Poisson regression was applied. The status of BL was treated as a dummy variable; age and gender were controls. Using Poisson regression, the frequency of diarrhea in children under 8 years of age was regressed on respondent type while controlling for total number of children in the household and number of years of education of the surveyed parent.


Demographics and Education

One hundred and eighty-seven household-level interviews were conducted. The survey was administered to the individual in the household who obtained the water, cared for the children and assisted any sick family members; 122 (65%) respondents were female, 65 (35%) respondents were male, and the median age of respondents was 35 (range 17-80 years). Sixty-six (35%) respondents classified themselves as BL, 102 (55%) as NBL, and 19 (10%) gave no information in this regard. Of NBL, 25% stated they were farmers (Table1; Figure 4).


Most (76%) BL reported no education, as compared to 41% of NBL (p<0.0001). There was no statistically significant difference in primary education between BLs and NBLs; however, 12% of BL received a secondary education (10th grade, 12th grade, and BA levels) as compared to 27% of NBL (p=0.03). Of BL respondents, 33% reported Hindi or Punjabi literacy vs. 49% of NBL (p=0.04).

Female BL reported fewer years of education than NBL females (a 95% CI = [0.575,3.07] for BL women vs. [4.42,7.06] for NBL women). Similarly, male BL had fewer years of education than male NBL (a 95% CI = [0.910,5.17] for BL men vs. [3.12,8.05] for NBL men).

Controlling for age and gender, BL had a significantly higher average number of children than NBL (2.0 vs. 2.8, p = 0.002). Education rates of children (dependents under 18 years) differed between groups; 51 (39%) BL male children were in school as compared to 80 (61%) NBL male children (p=0.01). Twenty-eight (33%) BL female children were in school as compared to 56 (67%) NBL female children (p=0.002).

Permanent Income Indicators

BL were less likely to own stoves (p<0.001), refrigerators (p<0.001), and televisions (p=0.048). Both BL and NBL had similar access to mobile phones with 80% of all households owning at least one mobile phone (p=0.85) (Table 2; Figure 5).


In both groups, availability of electricity was associated with an increase in odds of owning a refrigerator (Odds Ratio (OR) and 95% CI: 11.02, 1.27-95.58, p=0.03) and with an increase in odds of owning a television (OR and 95% CI: 11.98, 2.46-57.40, p=0.002). Adjusting for electricity and fixed income, BL were significantly less likely to own a refrigerator (OR and 95% CI: 0.11, 0.05-0.27, p<0.0001) or television (OR and 95% CI: 0.30, 0.12-0.76, p=0.01) than NBL. However, no statistically significant difference was found between groups for availability of electricity in correlation with possession of mobile phones.

Sanitation and Hygiene

There was no significant difference found in sanitary or hygienic practices between BL and NBL. Teeth brushing and hand washing were found to be similarly prevalent in both groups. However, significantly fewer BL showered daily (33% vs. 57%, p=0.02). In addition, sources of drinking water differed between the two groups. The primary source of drinking water for NBL was municipal tap water (70%), while only 17% of BL had access to tap water (p<0.0001) (Figure 6).picture6

General Health and Access to Care

While chronic disease rates were high in both groups, self-reported rates of anemia and hypertension were higher in NBL. Of all respondents, 24% reported that at least one household member suffered from anemia, 32% from hypertension, and 18% from hypotension. Self-reported rates of anemia (16% vs. 29%, p=0.04) and hypertension (20% vs. 37%, p=0.053) were lower in BL.

No significant differences were found for self-reported disease rates of tetanus or tuberculosis between the two groups. Of all respondents, 24% reported at least one household member suffered from typhoid fever. Self-reported malaria rates were higher among BL (21%) compared with NBL (12%) (p=0.13). Unfiltered cigarettes were smoked by 35% of BL and 20% of NBL (p=0.16) (Table 3; Figure 7).


Common symptoms of illness such as cough, cold and fever in children younger than 8 years were significantly higher in BL as indicated by the average number of symptoms (6.0 ± 0.3 in BL vs. 4.5 ± 0.3 in NBL, p = 0.001). For frequency of diarrhea in children under 8 years of age, a positive albeit insignificant effect of BL (1.49 + 0.18 in BL vs. 1.14 + 0.15 in NBL, p = 0.15) was observed. A significantly negative effect of the number of years of education of a parent was observed (p=0.045) as well. In other words, the population type did not have a significant effect on the frequency for diarrhea in children, but children whose parents had lower education levels demonstrated a higher frequency of diarrhea.

Reproductive Health

Females from the two groups demonstrated different prenatal care and delivery practices. Female BL (18%) were less likely to take iron supplementation during their pregnancies than NBL (33%) (p=0.02). Female BL (52%) were also more likely to deliver at home with a traditional birth attendant (33%) (p=0.005), while female NBL (36%) were found to be more likely to deliver in a hospital (18%) (p=0.004) (Table 4; Figure 8).


Menstruation cycle regularity differed between the respondent groups as well; 33% of female BL reported regular menstrual cycles vs. 44% of female NBL (p=0.01). Cloth usage was regressed on respondent type while controlling for age and years of education; a 20% decrease was evident with every year increase in education (OR and 95%, CI: 0.80, 0.70-0.92, p=0.001).

Contraception use varied by group. Sixty percent of male BL were aware of condoms compared to 78% of male NBL. Twenty percent of male BL who were aware of condoms had used them vs. 21% of male NBL (p=0.99). Of the female BL, 77% were aware of copper-T IUD and 7% of the total female BL population had used it. Of the female NBL, 86% knew of this contraceptive and 22% of all female NBL had used it (p=0.04).



This household-level needs assessment offers a cross-sectional perspective regarding the demographics and health of two different groups within the same geographic location: Charnia. Despite geographic proximity, the data indicates disparities in education, health, and socioeconomic status, in correlation with difference in employment. Surveying the migrant BL population allowed the study to uncover health disparities between two populations living in close proximity and strongly suggested that long-term and consistent access to education and healthcare play a role in the disparities that exist between BL and NBL.

Individuals were surveyed from multiple brick zones and residential villages within the Charnia area; the three adjacent villages had similar compositions to the Charnia village. Accordingly, the needs assessment provided a representative cross-sectional perspective of the Charnia area’s population subgroups of BL and NBL.

The study was limited by several factors. The absence of pre-existing literature means that there is no data with which the results of this study can be compared. In addition, the survey had to be revised several times during fieldwork to remove inapplicable questions and reword questions for better phrasing. Some questions had variable response rates, particularly when the male or female head of the household was not present to answer gender-specific reproductive health questions. Future studies may attempt to decrease respondent recall bias (systemic error due to differences in how survey respondents remember information) by reorganizing questions and including fewer questions in the survey. Finally, as the study population sample size was not conducted during the brick manufacturing season, the study population sample size was limited by the relatively small BL population present. The majority of the BL population comes from the surrounding states of Uttar Pradesh and Bihar. Thus, the BL population is highly migratory; individuals travel to the brick factories during the start of the brick manufacturing season and leave during the monsoon season. The same BL may not return to Charnia during the next season. The high BL population turnover could affect the results as the health indicators measured could vary from year to year as the population changes.

Data from Charnia are consistent with existing studies describing poor socioeconomic status in populations with lower education.11,12 The results of this study indicate that BL are less educated than NBL; both BL children and adults had fewer years of attendance of formal schooling than NBL children, contributing to lower literacy rates.  In addition, BL have fewer material possessions, indicating a difference in wealth.

Charnia’s BL demonstrated a need for health education. Many of the BLs are from Uttar Pradesh and Bihar, which have the highest rates of health and education disparities in India.13 As the BL population is mostly migratory, BL are also unable to fully utilize the health and education infrastructure in either their home or work states. Rates of chronic disease and infectious disease were similar between BL and NBL, but BL reported more symptoms of illness. Thus, BL may not be as knowledgeable about which diseases arise from those symptoms. Targeted health education programs designed to be completed before the end of the brick manufacturing season could help BL understand the consequences of various symptoms, prevent chronic and infectious disease and improve the accuracy of self-reported data.

Community-based health education, with a focus on available prenatal resources, may also encourage and increase utilization of existing resources.13 Although governmental programs exist to subsidize hospital deliveries and provide prenatal care by distributing Iron/Folic-Acid supplementation, few BL participate; this observation may be due to distrust in the public health system and their migratory behavior.14 These governmental programs rely on following up with patients in person and on a regular basis; however, currently, there is no centralized record of each follow-up visit. With the migrant BL population, continuous follow-up is difficult as the population moves. This issue can be alleviated through mobile health technologies and electronic medical record systems, which would allow government health workers to track patients and their health history as they change locations. In order to design an effective educational curriculum, follow-up studies should be designed to assess specific illnesses or conditions. For instance, a detailed survey on maternal/child health and nutritional behavior should include objective biomarkers like hemoglobin measurements, blood pressure and anthropometrics.

Studies have indicated the potential benefits of community health worker (CHW) programs, which can target educational and health disparitises.15,16 CHW programs work by recruiting, training and education community members to advocate for behavior change in their own communities. These studies have shown that CHW programs can increase the effectiveness of health interventions, especially those that target behavior change. For example, in Charnia, community members should advocate for preventive care and improve awareness, especially for programs targeting prenatal care in both the BL and NBL.  Community-level interventions may also help reduce other lifestyle differences between BL and NBL, such as increasing the rate of BL children attending school.

Moreover, mobile health technology can enable health workers to assess and track prevalent conditions such as cardiovascular disease, malnutrition and anemia.17,18,19  Mobile health technology has already been implemented in government health worker systems in India to help track patient health information and help improve communication between health workers in the field and government primary health centers. In this way, mobile technology can be used to bridge the gap between government health facilities and underserved rural areas. In Charnia, there was no difference in possession of mobile phones between BL and NBL despite differences in income. Research on technology usage has shown that mobile phones are ubiquitous throughout India regardless of socioeconomic status.20 Although further research must be conducted to analyze the reasons for this trend in Charnia, it may be hypothesized that BL utilize mobile phones to remain in contact with their family members in other parts of India.  As such, interventions that specifically utilize mobile phones could be successful in reducing the health disparities in Charnia. For example, SMS reminders for medication adherence could be implemented. Follow-up research should further explore the feasibility of mobile health technology and CHW programs in Charnia.


Disparities exist in the same geographical location, as demonstrated by the differences between Charnia’s BL and NBL, who reside in close proximity to each other.  Overall, BL had fewer material possessions, lower rates of education and lower rates of literacy compared to NBL. BL were also more likely to have a home birth delivery and less likely to have access to IFA supplementation during their pregnancies. Disparities are linked to socioeconomic, health and educational differences, which can largely be associated with the BL’s migratory nature. BL’s migratory behavior may pose challenges in obtaining medical care provided by the government, accessing continual health education, enrolling in schools, and maintaining a stable income.  These disparities must be targeted through a sustainable community-based approach that recognizes and addresses the varying population dynamics of BL and NBL in Charnia. Interventions targeting migrant populations specifically must be designed and implemented differently than interventions targeting more stable, permanent populations.


We thank the following individuals and groups for their contributions: Neelima Agrawal, Pooja Avula, Manisha Bhatia, Kancana Dasgupta, Sathwik Nandamuri, Smitha Sarma, Maitreyi Sistla, and Rajiv Varandani, Northwestern University Project RISHI (Rural India Social and Health Improvement); Ravi Menghani, Project RISHI National Board of Directors; Harbans Singla, Richa Singla, Arpit Singla, Arpna Singla, Swami Sureshwaranand Puri Ji, Param Seva Trust; Mary Poliwka and Gregory Buchanan, Northwestern University Office of International Program Development; Chandigarh Rotaract Club; J.S. Thakur, Postgraduate Institute of Medical Education and Research, Chandigarh, India. This study was funded by the Northwestern University Office of International Program Development.


  1. Planning Commision of India. (2015). Report of the Expert Group to Review the Methodology for Measurement of Poverty.
  2. Dhillon PK, et al. (2012) Status of epidemiology in the WHO South-East Asia region: burden of disease, determinants of health and epidemiological research, workforce and training capacity. International journal of epidemiology 41(3):847-60 doi:10.1093/ije/dys046
  3. WHO (2007) 11 Health Questions about the 11 SEAR Countries. World Health Organization, Regional Office for South-East Asia, Evidence and Health Information Unit, Department of Health Systems Development
  4. TWB (1997) India: Achievements and Challenges in Reducing Poverty. The World Bank
  5. McIntyre D, Thiede M, Dahlgren G, Whitehead M (2006) What are the economic consequences for households of illness and of paying for health care in low-and middle-income country contexts? Social science & medicine 62(4):858-865
  6. Wagstaff A (2002) Poverty and health sector inequalities. Bulletin of the World Health Organization 80(2):97-105
  7. Ahari SS, Habibzadeh S, Yousefi M, Amani F, Abdi R (2012) Community based needs assessment in an urban area: a participatory action research project. BMC public health 12:161 doi:10.1186/1471-2458-12-161
  8. Daivadanam M, et al. (2013) Lifestyle change in Kerala, India: needs assessment and planning for a community-based diabetes prevention trial. BMC public health 13:95 doi:10.1186/1471-2458-13-95
  9. Census of India (2011) Haryana Profile. CensusInfo India 2011
  10. WHO (2002) World Health Survey: 2002 A – Household Questionnaire. In: World Health Survey Instruments and Related Documents. World Health Organization. Accessed February 10th 2014
  11. Ghosh M (2013) Liberalization, growth and regional disparities in India. Springer India; Springer distributor, New Delhi, London
  12. Gupta K, Yesudian PP (2006) Evidence of women’s empowerment in India: A study of socio-spatial disparities. GeoJournal 65(4):365-380
  13. Gill, K. (2009). A primary evaluation of service delivery under the National Rural Health Mission (NRHM): findings from a study in Andhra Pradesh, Uttar Pradesh, Bihar and Rajasthan.Planning Commission of India, Government of India.
  14. Programme Evaluation Organisation PC, Government of India (2011) Evaluation Study of National Rural Health Mission (NRHM) In 7 States. New Dehli, India
  15. Berman PA, Gwatkin DR, Burger SE (1987) Community-based health workers: Head start or false start towards health for all? Social Science & Medicine 25(5):443-459 doi:
  16. Haines A, et al. Achieving child survival goals: potential contribution of community health workers. The Lancet 369(9579):2121-2131 doi:
  17. Cecchini S, Scott C (2003) Can information and communications technology applications contribute to poverty reduction? Lessons from rural India. Information Technology for Development 10(2):73-84
  18. Praveen D, et al. (2013) A multifaceted strategy using mobile technology to assist rural primary healthcare doctors and frontline health workers in cardiovascular disease risk management: protocol for the SMARTHealth India cluster randomised controlled trial. Implementation Science 8(1):137
  19. Ramachandran D, Canny J, Das PD, Cutrell E (2010) Mobile-izing health workers in rural India. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM CHI 1889-1898
  20. Singh, SK (2008). The diffusion of mobile phones in India. Telecommunications Policy 32(9):642-651


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