Prevalence of Hypertension and Its Determinants Among the Female Informal Workers (15 to 49 Years) in India with Reference to NFHS 5

Prevalence of Hypertension and Its Determinants Among the Female Informal Workers (15 to 49 Years) in India with Reference to NFHS 5 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence of Hypertension and Its Determinants Among the Female Informal Workers (15 to 49 Years) in India with Reference to NFHS 5 Ganesh Chandra Gan, Akash Mallick, Sachita Nanda Sa, Sunil Kumar Padhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7053757/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction The informal job sectors in India, comprising the majority of the national workforce, pose a greater hindrance to their workers, both economically in terms of wage discrepancies and income volatility, and health-wise in terms of a poor work environment, lack of access to healthcare facilities, and exposure to various occupational health hazards. Female informal workers, in particular, are at greater risk of developing critical health complications due to the multifaceted nature of their livelihood and health barriers. In this view, while incidences of cardiovascular complications such as hypertension are surging, the present cross-sectional study attempts to examine the trend of hypertension and identify its determinants among the female workers in the informal sectors in India. Methods The present study took an analytical sample of 23,655 female informal workers of 29 Indian states from the latest National Family Health Survey (NFHS-5) data. Data analyses were performed with descriptive statistics and tests of association for the explanatory and outcome variables. The variables at the individual-level, household-level, community-level, and occupational level were considered for multi-level binary logistic regression to identify the determinants of hypertension. Results Among the female informal sector workers, the majority belonged to the poorest section (49.8%) of the society, who also mostly occupied agricultural (66.0%) and construction (12.6%) work. The prevalence of hypertension among the workers was 12.6%, the state of Sikkim (45.0%) showing the highest, and Bihar (7.0%) showing the lowest prevalence rate. The workers who are older [OR=7.322; 95% CI 6.165-8.696; p<0.001] and perform occasional work [OR=1.228; 95% CI 1.005-1.500; p<0.001] have a significantly greater susceptibility to develop hypertension. In contrast, the workers from rural sectors [OR=0.885; 95% CI 0.788-0.995; p<0.001] showed a lower odds for the same. Conclusion The major highlights of the study were to identify the effects of age, religion, place of living, and even mode of employment to be significant contributors to hypertension among the female informal workers. It strongly suggests implementing strategic interventions at both the community and workplace levels to mitigate the rise of hypertension among female informal workers. Informal sector NFHS-5 Hypertension Women workers Informal Employment Figures Figure 1 Figure 2 Introduction Globally, the informal job sector is recognized as the major workforce behind economic development of a nation. It consists of both unregistered enterprises and firms, informal wage employees and self-employed workers without any state regulated formal labour protection and social or legal rights [ 1 – 3 ]. The sector is primarily unorganized and generate a wide range of occupational avenues [ 4 , 5 ]. In several Low- and Middle-income countries (LMICs) like India, these unorganized job sector (90% of total workforce) exist in many forms, ranging from street vending, to domestic work, small manufacturing, daily wage works etc. [ 6 – 9 ]. In both urban and rural sectors alike, this sector is predominantly occupied by many of the socially and economically marginalised groups [ 9 ]. They work under no certain security, experience income volatility, that further result in economic, food and health insecurity and compromised well-being [ 10 , 11 ]. The multi-faceted constraints of the informal workers’ livelihood have been methodically discussed in earlier literatures, that showcased their persisting livelihood challenges. Beside the constant exposure to unhygienic working conditions [ 12 ], issues like unsophisticated workplace infrastructure, prolonged working hours, absence of social protection [ 13 , 14 ] play a crucial role behind their overall experience in the informal jobs. Their health and well-being are also regulated by the level of exposure to various occupational health hazards [ 15 , 16 ]. Particularly in India, the situation remains more complex due to the presence of huge economic discrepancy, lack of minimum wage enforcement or any specific policy, the exploitative workplace culture in terms of wage disparity and further issues [ 17 , 18 ]. Especially the female informal workers, in comparison to males, further lacks income opportunity and social benefits as they majorly occupy different kind of home-based or farm-based casual low-paying jobs like crop processing, fishing, and beedi rolling [ 19 – 21 ]. Hence, the female informal workers are often compelled to gender discrimination in the workplace, earn less, and poorly access the medical care [ 21 ]. It certainly does shape their health in an unimaginable way. The informal workers, for instance, are exposed to a number of occupational health stressors such as prolonged working hour [ 22 ], unavailability of health-care facilities [ 23 ] and extreme working conditions [ 24 ]. The female informal workers frequently experience gender-based discrimination, deal with lower wage, take multiple caregiving responsibilities and yet, they lack adequate social security [ 25 , 26 ]. Previous literatures have showed that the female informal workers had developed various health problems such as musculoskeletal problems [ 20 , 27 , 28 ], respiratory health complications like asthma [ 29 , 30 ], infections [ 30 ] and cardio-vascular problems like hypertension [ 26 , 31 ], due to such risk factors. On that note, the livelihood and health burdens of the informal workers strictly undermine the journey of the United Nations, in achieving good health and well-being (Goal 3) and inclusive economic growth and decent work (Goal 8) through Sustainable Development Goals (SDGs). Although in India, several policies has been implemented to address the persisting issue of informal workers, their effectiveness is limited due to the unjustly discrimination to the workers regarding legal, social and health securities [ 9 , 17 , 32 , 33 ]. In India, the dearth of such insights calls for a systematic investigation taking into account the health burden of female informal workers to explore the incidence of health burdens among the workers and their determinants [ 31 , 34 ]. In this purview, the present study systematically investigated the prevalence of hypertension among the female workers in informal sectors of India, extending its approach to identify potential determinants of hypertension with reference to the latest National Family Health Survey of India. Materials and methods 2.1. Data Source The National Family Health Survey (NFHS) is a multi-round survey, directed by the Ministry of Health and Family Welfare (MoHFW), Government of India. The technical and financial aid were provided by ICF of United States of America and the United States Agency for International Development (USAID), respectively. The survey is presently managed by the International Institute of Population Sciences (IIPS), Mumbai. In the present study, the latest National Family Health Survey-5 (2019–2021) data were used that primarily included 636699 households from 29 Indian states and 9 union territories. The survey performed a two-stage stratified sampling method. In the first stage, the sampling process was brought off separately for urban and rural sectors. In the urban sector, the census enumeration blocks (CEBs) were used as Primary Sampling Units (PSUs), selected using probability proportional to size (PPS). While in the rural areas, villages were used as primary sampling units (PSUs), which were selected using probability proportional to size (PPS). In the second stage, in selected urban and rural clusters, households were selected with systematic random sampling after mapping and household listing. The detailed methodology is published elsewhere [ 35 ]. 2.2. Analytical sample The NFHS-5 survey was conducted to address the child and maternal health issues in India. Therefore, the survey was able to obtain a large female sample aged 15–49 years (n = 724115). However, as per the objective of the present study, the final analytical sample was extracted only for the female workers in the informal sector, after excluding individuals with missing data for several variables (See Fig. 1 ). At the initial stage, a total of 72,344 individuals, who had no data on blood pressure, were excluded. At the subsequent stages, a total of 532,653 individuals (missing data on occupation), 64,270 individuals (data of unemployed females), 5,251 individuals (employed female workers of the formal sector), 16,603 individuals (females taking medication for hypertension), and 7,250 individuals (missing data on age) were excluded. At the next stage, 2089 individual data from the Union Territories were excluded, as the study considered the data only from Indian states. The final analytical sample of female workers in the informal sector included data from 23,655 individuals. 2.3. Variables The data from NFHS-5 ‘ Females recodes ’ file (IAIR7AFL) were used for the present study. The variables ‘Systolic blood pressure’ and ‘Diastolic blood pressure’ were considered to calculate the ‘Hypertension status’ outcome variable. Hypertension (HTN) was defined as an individual having either systolic blood pressure (SBP) > 140 mmHg or having diastolic blood pressure (DBP) > 90 mmHg or both taking the JNC-8 guidelines into account [ 36 ]. Individuals having such traits were categorised as ‘HTN’ whereas individuals having no such traits were categorised as ‘Non-HTN’. A total of 12 variables were considered as potential predictor variables of hypertension status. The variable ‘Age’ was categorized into four age cohorts (15 to 25 years, 26 to 35 years, 36 to 45 years, and 45 years and above), variable ‘Educational attainment’ was grouped into four categories (No education, Upto primary level, Upto secondary level, Above secondary level), and the variable ‘Marital status’ was grouped into three categories (Unmarried, Married, and Widowed/Divorced/Separated). Again, the variable ‘Religion’ was categorised into four (Hindus, Muslims, Christians, and Others), the ‘Ethnicity’ was grouped into three categories (General, Scheduled Caste and Other Backward Classes, and Scheduled Tribe), and the ‘Wealth stratum’ was grouped into five quintiles (Poorest, Poorer, Middle, Richer, and Richest). The variables ‘Place of residence’ was categorised into two (Urban and rural) and the ‘Region’ variable was grouped into six categories (Southern, Western, Central, Eastern and Northern and North-eastern). The 29 states were categorised into six regions viz. Southern, Western, Central, Eastern, Northern, and North-eastern [ 37 ]. Under the ‘ Southern ’ region, came the states Karnataka, Kerala, Andhra Pradesh, Tamil Nadu, and Telangana. The ‘ Western ’ region included states of Maharashtra, Gujarat, and Goa. Thirdly, the ‘ Eastern ’ region included Bihar, Jharkhand, Odisha and West Bengal. The states of Madhya Pradesh, Chhattisgarh, and Uttar Pradesh were grouped under the ‘ Central ’ region. In the ‘ Northern ’ region category, states like Jammu and Kashmir, Himachal Pradesh, Haryana, Punjab, Rajasthan, Uttarakhand were included. Lastly, the ‘ North-eastern ’ region included the states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. Again, the variable ‘Type of occupation’ was grouped into six categories (Agriculture, Forestry and Fishing, Manufacturing, Construction and Infrastructure, Trade and Retail, Domestic and Hospitality scheme, Personal Care, Art and Entertainment) following the National Classification of Occupation guidelines [ 38 ]. The ‘Pattern of earning’ was grouped into four categories (Unpaid, Paid in Cash, Paid in kind only and Paid in Cash and kind), and the variable ‘Mode of employment’ was grouped into three categories viz. Annual, Seasonal and Occasional). 3. Statistical Analysis The descriptive statistics were performed to explore the background characteristics of the female workers in the informal sector. The percentage distribution was assessed for the individual-level, household-level, and community-level characteristics of the workers. Furthermore, the workers were also distributed based on their occupational categories and other occupation-specific variables. The association of potential predictor variables with the nature of occupation was also assessed through the chi-square test of association to examine the diversification in occupational avenues based on the socio-demographic, economic, household, and community-level traits. State-wise prevalence of hypertension among the female informal workers were shown in percentage distribution and spatial visualization. The multi-level Binary Logistic Regression (BLS) models were carried out to assess the effects of all the potential predictor variables on the probability of hypertension among the female workers in informal sector. The outcome/dependent variable of status of hypertension was binary in nature ( hypertensive was denoted as ‘HTN’ and was coded as 1, and non-hypertensive was denoted as ‘Non-HTN’ and was coded as 0). As the study adopted a multi-level binary logistic model-building procedure, a total of four models were established to assess the prediction strength of the explanatory variables. The explanatory variables were selected after assessing the multicollinearity among them. The variables with a Variance Inflation Factor (VIF) value of 2.000 or less was considered for the BLR model, as it indicated from ‘no’ to ‘low’ multicollinearity among the variables [ 40 ]. In the Model 1, only the ‘Age group’ and ‘Highest Educational Attainment’ were included. In the Model 2, the household-level predictor variables such as ‘Religion’ and ‘Wealth stratum’ were included along with the variables from the Model 1. Simultaneously, the Model 3 included both individual-level and household-level predictor variables along with ‘place of residence’ and ‘region’ variables. In the last model (Model 4), the occupation-specific variables, such as ‘Mode of employment’ and ‘Pattern of earning’ were successively included with all the variables from the previous models. Hence, all the models generated distinct predictive equations for the outcome variable. The Omnibus Tests of Model Coefficient, Pseudo R-Square, Hosmer-Lemeshow Test, correct classification percentage, and odds ratio with 95% CI were calculated to evaluate the model fit and predictive power of the independent variables [ 39 ]. Findings The distribution of female informal workers in India based on their socio-demographic, economic and occupational characteristics is given in Table 1 . In present findings, the majority (32.5%) of the respondents belonged to the 36–45 age group, and 32.4% were under the 26–35 age groups. Educational attainments of the respondents were found poor. Around 36% had no formal education while around 43% had education only up to secondary level. Majority of the females in present study were married (76.9%). Most of the study participants belonged to the Hindu religion (80.6%). The proportion of workforce participation in the informal sector was predominated by scheduled caste and other backward class (SC & OBC) communities (76.9%) and from the rural sectors of India (86.2%). Economically, the finding in wealth stratum reflects that almost half of the respondents belonged to the poorest (26.4%) and poorer (23.4%) section of the society. The table also demonstrated the distribution of study respondents based on their occupational avenues. It revealed that most of the respondents were engaged in agricultural work and forestry (66.0%), followed by construction and infrastructural job (12.6%). Interestingly, almost half of the respondents were engaged in seasonal job, while the other half were engaged in annual job. The cash only payment was predominant (65.5%) in the informal sector. The Table 1 also showed that majority of the female informal workers were from the Central region (24.5%), followed by the Southern region (23.3%), Northern region (14.2%), Western region (13.9%), North-eastern region (13.7%) and the least in the Eastern region (13.4%). Table 1 Background characteristics of the female informal workers based on socio-demographic, economic and occupational traits Background characteristics Categories Total (n = 23655) 95% confidence interval Age group 15 to 25 years 5575 (23.6) 23.0-24.1 26 to 35 years 7669 (32.4) 31.8–33.0 36 to 45 years 7697 (32.5) 31.9–33.1 45 years and above 2714 (11.5) 11.0-11.8 Educational attainment No formal education 8626 (36.5) 35.8–37.0 Up to primary level 3852 (16.3) 15.8–16.7 Up to secondary level 10084 (42.7) 42.0-43.2 Above secondary level 1093 (4.6) 4.3–4.9 Marital status Unmarried 3644 (15.4) 14.9–15.8 Married 18180 (76.9) 76.3–77.3 Widowed/Divorced/Separated 1831 (7.7) 7.4-8.0 Religion Hindus 19068 (80.6) 80.1–81.1 Muslims 1494 (6.3) 6.0-6.6 Christians 1968 (8.3) 7.9–8.6 Others 1125 (4.8) 4.4-5.0 Ethnicity General 628(2.7) 2.4–2.9 Scheduled Caste & Other Backward Classes 18194(76.9) 76.3–77.6 Scheduled Tribe 4833(20.4) 19.7–21.2 Place of residence Urban 3271 (13.8) 13.3–14.2 Rural 20384 (86.2) 85.7–86.6 Wealth stratum Poorest 6254 (26.4) 25.8–27.0 Poorer 5524 (23.4) 22.8–23.9 Middle 4977 (21.0) 20.5–21.5 Richer 4258 (18.0) 17.5–18.5 Richest 2642 (11.2) 10.7–11.5 Type of occupation Agriculture, forestry and fishing 15610 (66.0) 65.3–66.5 Manufacturing 2489 (10.5) 10.1–10.9 Construction and infrastructure 2969 (12.6) 12.1–12.9 Trade and retail 955 (4.0) 3.7–4.3 Domestic and hospitality service 1478 (6.2) 5.9–6.5 Personal care, art and entertainment 154 (0.7) 0.5–0.7 Mode of employment Annual 11776 (49.8) 49.1–50.4 Seasonal 10846 (45.9) 45.2–46.4 Occasional 1033 (4.4) 4.1–4.6 Earning type Unpaid 4769 (20.2) 19.6–20.6 Paid in cash 15505 (65.5) 64.9–66.1 Paid in kind 787 (3.3) 3.1–3.5 Paid in cash and kind 2594 (11.0) 10.5–11.3 Geographical Region Southern 4810 (23.3) 19.8–20.8 Western 3285 (13.9) 13.4–14.3 Central 5784 (24.5) 23.9–25.0 Eastern 3177 (13.4) 13.0-13.8 Northern 3350 (14.2) 13.7–14.6 North-Eastern 3249 (13.7) 13.3–14.1 The association between the types of occupation of female informal workers and their background characteristics is presented in Table 2 . It reveals that the majority of the respondents in the age group 15–25 years were in manufacturing industry (31.3%), followed by services in art and entertainment. Around 35% of the workers in 25–36 years age group were engaged in manufacturing industry, agricultural and forestry work (31.5%) and in construction and infrastructure (33.6%). The females in the older age group (36–45 years) showed similar results as well. Age of the respondents was significantly associated with their occupation (χ2 = 220.15, df = 15, p < 0.001). Interestingly, almost half of the workers without any formal education were highly engaged in construction and infrastructural jobs (44.5%), followed by agricultural and forestry-based works (41.0%). The association between educational attainment and occupation of the workers were found significant (χ2 = 1881.77, df = 20, p < 0.001). It was observed that the proportion of female informal workers in various works was significantly occupied by married women (χ2 = 413.01, df = 10, p < 0.001). The Table 2 also showed that in each of the occupational categories, the participation of females from SC and OBC communities were significantly higher (χ2 = 491.332, df = 10, p < 0.001). Since the proportion of workers of rural sectors was high in the present study, it was observed that in the agricultural, fishing, and forestry sectors, their participation was also quite high (95.2%). Nevertheless, around 76% of the respondents in construction and infrastructure, followed by around 67% in the manufacturing sector, were also from the rural part of India. The association between place of residence and occupation of the female informal workers was significant as well (χ2 = 3459.32, df = 5, p < 0.001). The table also highlighted that the occupational involvement of the workers was significantly associated with their wealth index quintile (χ2 = 897.96, df = 20, p < 0.001). It was fascinating to observe that in agricultural and forestry sector, majority of the females were seasonal workers (54.8%), however in all other sectors such as manufacturing (69.0%), trade and retails (74.7%), domestic services (76.0%) as well as in art and entertainment (69.5), female workers were engaged in annual/regular basis. Moreover, the earning type (χ2 = 2165.50, df = 15, p < 0.001) and their region (χ2 = 1417.99, df = 25, p < 0.001) were also significantly associated with occupation of the female workers. Table 2 Distribution of female informal workers based on their type of occupation Background characteristics Type of occupation among the female informal workers (n = 23655) Chi-square value, df ( p -value) Agriculture, forestry and fishing Manufacturing Construction and infrastructure Trade and retail Domestic and hospitality service Personal care, art and entertainment Sample size (n = 15610) (n = 2489) (n = 2969) (n = 955) (n = 1478) (n = 154) Age group 15 to 25 years 3543 (22.7) 779 (31.3) 631 (21.3) 242 (25.3) 326 (22.1) 54 (35.1) 220.15*, 15 (< 0.001) 26 to 35 years 4911 (31.5) 881 (35.4) 999 (33.6) 345 (36.1) 481 (32.5) 52 (33.8) 36 to 45 years 5193 (33.3) 665 (26.7) 1015 (34.2) 276 (28.9) 506 (34.2) 42 (27.3) 45 years and above 1963 (12.6) 164 (6.6) 324 (10.9) 92 (9.6) 165 (11.2) 6 (3.9) Educational Attainment No formal education 6402 (41.0) 314 (12.6) 1320 (44.5) 157 (16.4) 421 (28.5) 12 (7.8) 1881.77*, 20 (< 0.001) Upto primary level 2692 (17.2) 295 (11.9) 544 (18.3) 94 (9.8) 215 (14.5) 12 (7.8) Upto secondary level 6330 (38.7) 1596 (64.2) 1032 (34.7) 581 (60.9) 737 (49.9) 97 (63.0) Above secondary level 475 (3.0) 284 (11.4) 73 (2.5) 123 (12.9) 105 (7.1) 33 (21.4) Marital Status Unmarried 2116 (13.6) 635 (25.5) 402 (13.5) 191 (20.0) 251 (17.0) 49 (31.8) 413.01*, 10 (< 0.001) Married 12426 (79.6) 1697 (68.2) 2226 (75.0) 675 (70.7) 1057 (71.5) 99 (64.3) Widowed/Divorced/Separated 1068 (6.8) 157 (6.3) 341 (11.5) 89 (9.3) 170 (11.5) 6 (3.9) Religion Hindus 12968 (83.1) 1811 (72.8) 2417(81.4) 688(72.0) 1063(71.9) 121(78.6) 892.65*, 15 (< 0.001) Muslims 617 (4.0) 396 (15.9) 183 (6.2) 74 (7.7) 209 (14.1) 15(9.70) Christians 1371 (8.8) 117 (4.7) 188 (6.3) 161 (16.9) 121 (8.2) 10 (6.5) Others 654 (4.2) 165 (6.6) 181 (6.1) 32 (3.4) 85 (5.8) 8 (5.2) Ethnicity General 333 (2.1) 102 (4.1) 87 (2.9) 29 (3.0) 71 (4.8) 6 (3.9) 491.332*, 10 (< 0.001) Scheduled Caste & Other Backward Classes 11583 (74.2) 2210 (88.8) 2354 (79.3) 705 (73.8) 1204 (81.5) 138 (89.6) Scheduled Tribe 3694 (23.7) 177 (7.1) 528 (17.8) 221 (23.1) 203 (13.7) 10 (6.5) Place of Residence Urban 744 (4.8) 817 (32.8) 700 (23.6) 395 (41.4) 547 (37.0) 68 (44.2) 3459.32*, 5 (< 0.001) Rural 14866 (95.2) 1672 (67.2) 2269 (76.4) 560 (58.6) 931 (63.0) 86 (55.8) Wealth Stratum Poorest 4310 (27.6) 336 (13.5) 1086 (36.6) 166 (17.4) 338 (22.9) 18 (11.7) 897.96*, 20 (< 0.001) Poorer 3714 (23.8) 497 (20.0) 726 (24.5) 240 (25.1) 319 (21.6) 28 (18.2) Middle 3348 (21.4) 549 (22.1) 520 (17.5) 209 (21.9) 332 (22.5) 19 (12.3) Richer 2780 (17.8) 547 (22.0) 420 (14.1) 199 (20.8) 274 (18.5) 38 (24.7) Richest 1458 (9.3) 560 (22.5) 217 (7.3) 141 (14.8) 215 (14.5) 51 (33.1) Mode of Employment Annual 6506 (41.7) 1717 (69.0) 1610 (54.2) 713 (74.7) 1123 (76.0) 107 (69.5) 1792.23*, 10 (< 0.001) Seasonal 8559 (54.8) 594 (23.9) 1200 (40.4) 181 (19.0) 277 (18.7) 35 (22.7) Occasional 545 (3.5) 178 (7.2) 159 (5.4) 61 (6.4) 78 (5.3) 12 (7.8) Type of Earning Unpaid 4052 (26.0) 123 (4.9) 92 (3.1) 83 (8.7) 409 (27.7) 10 (6.5) 2165.50*, 15 (< 0.001) Paid in cash 8838 (56.6) 2225 (89.4) 2645 (89.1) 774 (81.0) 888 (60.1) 135 (87.7) Paid in kind 659 (4.2) 33 (1.3) 26 (0.9) 17 (1.8) 51 (3.5) 1 (0.6) Paid in cash and in-kind 2061 (13.2) 108 (4.3) 206 (6.9) 81 (8.5) 130 (8.8) 8 (5.2) Region Southern 3175 (20.3) 568 (22.8) 469 (15.8) 280 (29.3) 291 (19.7) 27 (17.5) 1417.99*, 25 (< 0.001) Western 2612 (16.7) 230 (9.2) 166 (5.6) 63 (6.6) 179 (12.1) 35 (22.7) Central 4201 (26.9) 480 (19.3) 625 (21.1) 166 (17.4) 287 (19.4) 25 (16.2) Eastern 1824 (11.7) 353 (14.2) 751 (25.3) 88 (9.2) 150 (10.1) 11 (7.1) Northern 1730 (11.1) 552 (22.2) 584 (19.7) 103 (10.8) 339 (22.9) 42 (27.3) North-eastern 2068 (13.2) 306 (12.3) 374 (12.6) 255 (26.7) 232 (15.7) 14 (9.1) *Significant at 0.001 level The Table 3 demonstrates the prevalence of hypertension among the female informal workers in various Indian states. The nation-wide prevalence rate was found to be 12.6%. Across the states, Sikkim (45.0%) showed the highest prevalence of hypertension. States like Arunachal Pradesh, Nagaland, Punjab, and Odisha showed around 15–20% prevalence of hypertension among the female informal workers. On the contrary, states like Bihar, Jharkhand in the Eastern region, Uttar Pradesh in the Northern region, Rajasthan in Western region, and Tripura in North-eastern region showed lower prevalence rate of hypertension of as low as 7–10%. The detailed representation of state-wise prevalence of hypertension among the female informal workers is shown in Fig. 2 . Table 3 State-wise prevalence of hypertension among the female informal workers in India State Hypertension Status Total Non-hypertensive Hypertensive Jammu & Kashmir 436 (86.5) 68 (13.5) 504 (100%) Himachal Pradesh 245 (88.8) 31 (11.2) 276 (100%) Punjab 392 (82.4) 84 (17.6) 476 (100%) Uttarakhand 278 (86.1) 45 (13.9) 323 (100%) Haryana 403 (87.2) 59 (12.8) 462 (100%) Rajasthan 1178 (90.0) 131 (10.0) 1309 (100%) Uttar Pradesh 1864 (90.9) 18 6(9.1) 2050 (100%) Bihar 798 (93.0) 60 (7.0) 858 (100%) Sikkim 33 (55.0) 27 (45.0) 60 (100%) Arunachal Pradesh 626 (79.7) 159 (20.3) 785 (100%) Nagaland 261 (80.6) 63 (19.4) 324 (100%) Manipur 369 (84.1) 70 (15.9) 439 (100%) Mizoram 255 (91.4) 24 (8.6) 279 (100%) Tripura 137 (89.5) 16 (10.5) 153 (100%) Meghalaya 450 (88.8) 57 (11.2) 507 (100%) Assam 629 (89.6) 73 (10.4) 702 (100%) West Bengal 398 (85.4) 68 (14.6) 466 (100%) Jharkhand 757 (91.8) 68 (8.2) 825 (100%) Odisha 861 (83.8) 167 (16.2) 1028 (100%) Chhattisgarh 1515 (85.3) 262 (14.7) 1777 (100%) Madhya Pradesh 1720 (87.9) 237 (12.1) 1957 (100%) Gujarat 1460 (89.0) 180 (11.0) 1640 (100%) Maharashtra 1434 (88.1) 194 (11.9) 1628 (100%) Andhra Pradesh 521 (88.9) 65 (11.1) 586 (100%) Karnataka 1063 (86.0) 173 (14.0) 1236 (100%) Goa 15 (88.2) 2 (11.8) 17 (100%) Kerala 171 (86.6) 26 (13.2) 197 (100%) Tamil Nadu 1032 (87.2) 151 (12.8) 1183 (100%) Telangana 1372 (85.3) 236 (14.7) 1608 (100%) Total 20673 (87.4) 2982 (12.6) 23655 (100%) The Multilevel binary logistic regression for assessing the predictive strength of explanatory variables on the likelihood of hypertension among the female informal workers is given in Table 4 . A total of four models are given, which include individual-level (Model 1), household-level (Model 2), community-level (Model 3), and occupation-level (Model 4) predictor variables. The Model 1 depicts that the likelihood of hypertension significantly increased with age progression among the workers (26 to 35 years - OR:2.395 [2.047–2.803]), (36 to 45 years - OR:4.997 [4.290–5.819]), (45 years and above - OR:7.511 [6.351–8.882]). Again, the Model 2 shows that both age (26 to 35 years - OR:2.373 [2.026–2.779]), (36 to 45 years - OR:4.923 [4.221–5.743]), (45 years and above - OR:7.351 [6.203–8.712]) and religion (Christians - OR:1.199 [1.044–1.377]), others - OR: 1.457 [1.235–1.719]) were significant predictors of the likelihood of hypertension among the female informal workers. The Model 3, along with the age and religion showed that the development of hypertension significantly varied across the geographical region (Western - OR:0.827 [0.702–0.975]), (Central - OR:0.780 [0.654–0.930]), (Northern - OR:0.779 [0.655–0.927]) and across place of residence (Rural - OR:0.885 [0.788–0.995]). The last Model 4 depicted that along with age, religion, and geographical region, the mode of employment (Occasional - OR:1.228 [1.005-1.500]) also significantly predicted the likelihood of hypertension among the female informal workers. Discussion Worldwide, the incidence of hypertension is increasing manifold across the population, emerging as a major health concern, particularly in the Low- and Middle-income countries like India. The exposed individuals are often compelled to experience some associated health complications and compromise with their health and well-being. Nevertheless, the occurrence of hypertension varies across gender, ethnicity, age, as well as additional socio-economic layers, especially across occupation. In context of Indian subcontinent, the diversity in occupation can be categorised under the broader concept of formal and informal sectors, where the latter faces major livelihood and health challenges compared to the former. Female informal workers, precisely, face greater discrepancies in many spheres, which places them at greater risk of developing various cardio-vascular health problems, such as hypertension. The present study, therefore, took an attempt to explore the prevalence of hypertension among the female informal workers in India (15 to 49 years) and unveil its determinants. The major findings are elaborately discussed in the subsequent sections. While exploring the socio-economic background of the female informal workers in India, the present study found that the majority of the workers were economically deprived since they belonged to the poorest section of the society. Almost half of them came within the lowest wealth quintile, as the study finding suggested. This finding corroborated with some previous studies in India, who showed that the informal workers were always at greater economic deprivation due to persisting wage inequality and other social barriers [ 40 , 41 ]. Perhaps the cause, as stated by an earlier study, is that the female informal workers, often face gender-based discrimination in getting wage or salary [ 42 ]. The report of Periodic Labour Force Survey, 2017-18 also re-confirms the same [ 43 ]. The majority of the female informal workers were engaged in agricultural works, either as a cultivator or as a labour, as well as in construction work, primarily as a helper. This is, perhaps, due to the constant motivation generated through the availability of such kind of low-wage jobs, demanding only the low-skilled workers [ 43 , 44 , 45 ]. Nevertheless, the scenario is fairly different between urban and rural sectors, where the workers in urban region were primarily engaged in domestic work, street vending, and home-based services and workers in rural region mostly occupy the agricultural work [ 42 , 46 ]. Interestingly, the present NFHS data showed a huge worker participation of marginalized communities in the informal sector, found by earlier literatures as well [ 41 , 44 ]. The findings regarding the likelihood of hypertension unveiled a few interesting ones. It stated that the likelihood of hypertension increased with various socio-demographic and occupation-specific factors such as age, education, place of living, and type of job regularity. As seen in the previous studies [ 47 – 49 ], the present study showed a strong positive association between age and hypertension, where the former augmented the latter. It was also evident that low education was a crucial factor behind the occurrence of hypertension among the female workers in the informal sector. This finding was also in line with several literature [ 50 , 51 ], which cited that education regulated the knowledge and awareness about health issues, that could in turn influence the health status as well. Nevertheless, the present study findings contradicted with a previous work [ 52 ]. Present study also highlighted that the female workers in the informal sector did seasonal work as per their skills and capabilities. As shown previously, the female informal workers are often engaged in a wide range of works such as planting and harvesting [ 45 ], brick-kiln works [ 44 ], agricultural works [ 9 ], and so on. Often, the workers are unwillingly committed to unpaid works such as home-based work activities [ 46 ]. However, in Indian context, the scenario is opposite in urban sectors, where informal workers are paid either in cash or kind [ 41 ]. The study also unveiled that the female informal workers in urban sector had a greater susceptibility for developing hypertension. It is, perhaps, due to degraded working condition, unhealthy lifestyle and so on [ 49 ]. However, some other studies contrastingly showed higher odds of hypertension prevalence in the rural sectors of India [ 53 , 54 ]. It was fascinating to observe that the likelihood of hypertension among the female workers, also depended whether their job was regular or not. Similar to the earlier studies [ 35 , 54 ], female workers with irregular job showed a greater vulnerability to develop hypertension, in present study. Overall, the study highlights that the female workers in informal sector are always at a greater risk of developing hypertension, with a combined effects of various individual-level, household and community level, as well as occupation-specific factors. Conclusion and recommendation The study unveiled a major health concern regarding the incidence of hypertension among the female informal workers in India. The current study highlights that not only the socio-demographic, but also the occupation-specific factors played a crucial role in determining hypertension. The major issues like irregularity in job, wage inequality, and others were significant aspects, that need an urgent intervention. The management and treatment of hypertension is a continuous process and can often be expensive. Therefore, targeted intervention for hypertension among the workers is the need of the hour such as improved working conditions, balance diet and physical activity, and ensure job and economic security. Appropriate implementation of various programmes is of utmost necessity at both community and workplace levels to ensure accessible and affordable utilisation of health service and to mitigate health burdens. Strengthening the medical infrastructure, lifestyle modification, and strategic plan regarding health awareness can lower the current trend of hypertension occurrence. Limitations Although the present study is based on a nationally representative sample, it had its own limitations. The large-scale sample of NFHS 5 strengthens the robustness of the study findings. Besides, the sampling strategies and statistical approaches, followed in the survey, strictly avoids the election bias. However, since the study focused only on the female informal workers, it would have been better to include the male workers and workers from the formal sectors as well. The gender-based and occupation-based comparative approach could unveil deeper insights. The present study findings strongly calls for an effective policy making at both community and administration level, to mitigate the livelihood and health burdens of the informal workers. Moreover, the study considered only the female workers of 15 to 49 years, though the informal sector comprises the older women (49 + years) as well. It is noteworthy that the survey data lacks on various occupational stressors such as physical stress, income, duration of working hour, and so on, which could have been incorporated to reveal valuable insights. Declarations Ethics and Guidelines The NFHS-5 survey received the ethical clearance from the Ethical Review Board of the International Institute for Population Science, prior to the commencement of the survey. The respondents also provided the informed consents prior to the conduct of interviews to the research personnel. Therefore, the present study was exempted from any ethical consideration. Consent to Participate The consent to participate is not applicable as the study is based on secondary data. Consent for publication The authors provided consent for the publication of the manuscript without any conflict of interest. Availability of data and materials The National Family Health Survey (NFHS) data are available at Demographic and Health Survey (DHS) website and can be accessed at https://dhsprogram.com. The data used in the present study may be obtained from the corresponding author upon reasonable request. Clinical Trial Number Not applicable. Competing interests The authors declare no competing interest. Funding The authors did not receive any specific grant from any funding agency in the public, commercial, and not-for-profit sectors. Authors' contributions Both the authors S.K.P and S.N.S initiated the study design and supervised in the preparation of the manuscript. A.M and G.C.G extracted the relevant data, and conducted the literature review. A.M analysed and interpreted the data. Both A.M, G.C.G and S.N.S participated in drafting the manuscript. S.K.P critically revised the manuscript. All the Authors approved the manuscript prior to its submission. Acknowledgement The authors are indebted to the respective authorities of the NFHS-5 survey and the International Institute for population Science (IIPS), Mumbai for providing the accessibility to the latest survey data for research purpose. The authors are also indebted to Mr. Rajkumar Guria, Department of Geography, Fakir Mohan University for his assistance in preparing the figures in QGIS. Conflict of Interest: The authors declare no competing interests. References Hussmanns, R. (2001, September). Informal sector and informal employment: elements of a conceptual framework. In Fifth Meeting of the Expert Group on Informal Sector Statistics (Delhi Group), New Delhi (pp. 19-21). Chen, M. A. (2012). The informal economy: Definitions, theories, and policies. 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Mandal, B., & Pradhan, K. C. (2025). Association between rural-to-urban migration and the onset of hypertension among middle-aged and older population: evidence from India. BMC Public Health, 25(1), 1-11. Virk, A., Samdarshi, N., Saini, P., Mohapatra, A., Sahoo, S., & Goel, S. (2022). Prevalence and determinants of hypertension and associated comorbidities in non-pregnant women of reproductive age group (15–49 years): Evidence from National Family Health Survey (NFHS-4), India. Journal of Family Medicine and Primary Care, 11(9), 5865-5873. Tripathi, V., Talukdar, D., Tripathi, M., & Teelucksingh, S. (2024). Prevalence and associated factors of undiagnosed hypertension among women aged 15–49 years in India: an analysis of National Family Health Survey-4 data. Journal of Human Hypertension, 38(3), 245-256. Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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16:50:34","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175572,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7053757/v1/83aef4e2a90932df2c5c0556.html"},{"id":92887707,"identity":"459d10ba-a27c-4dcb-9230-14eaa842a2ce","added_by":"auto","created_at":"2025-10-06 16:58:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100515,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of final analytical sample from NFHS-5 (2019-2021)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7053757/v1/46915b71044b3b34a70a3340.png"},{"id":92887034,"identity":"40e65c30-7be8-4496-b41c-d133a75ef87b","added_by":"auto","created_at":"2025-10-06 16:50:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373624,"visible":true,"origin":"","legend":"\u003cp\u003eState-wise prevalence of hypertension among the female (15-49 years) informal workers\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7053757/v1/cb24ac66cb762f806678951f.png"},{"id":93101204,"identity":"137c9ad4-f81b-4121-97b3-28a27544096e","added_by":"auto","created_at":"2025-10-09 05:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7053757/v1/a62e83e5-cb96-4a96-b03c-236887029849.pdf"},{"id":92887030,"identity":"2c5a6c35-aacb-4d2f-baa2-95de2e5bd113","added_by":"auto","created_at":"2025-10-06 16:50:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20690,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7053757/v1/b45e1ae6d635e7ccfa4e042f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of Hypertension and Its Determinants Among the Female Informal Workers (15 to 49 Years) in India with Reference to NFHS 5","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, the informal job sector is recognized as the major workforce behind economic development of a nation. It consists of both unregistered enterprises and firms, informal wage employees and self-employed workers without any state regulated formal labour protection and social or legal rights [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The sector is primarily unorganized and generate a wide range of occupational avenues [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In several Low- and Middle-income countries (LMICs) like India, these unorganized job sector (90% of total workforce) exist in many forms, ranging from street vending, to domestic work, small manufacturing, daily wage works etc. [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In both urban and rural sectors alike, this sector is predominantly occupied by many of the socially and economically marginalised groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. They work under no certain security, experience income volatility, that further result in economic, food and health insecurity and compromised well-being [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe multi-faceted constraints of the informal workers\u0026rsquo; livelihood have been methodically discussed in earlier literatures, that showcased their persisting livelihood challenges. Beside the constant exposure to unhygienic working conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], issues like unsophisticated workplace infrastructure, prolonged working hours, absence of social protection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] play a crucial role behind their overall experience in the informal jobs. Their health and well-being are also regulated by the level of exposure to various occupational health hazards [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Particularly in India, the situation remains more complex due to the presence of huge economic discrepancy, lack of minimum wage enforcement or any specific policy, the exploitative workplace culture in terms of wage disparity and further issues [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Especially the female informal workers, in comparison to males, further lacks income opportunity and social benefits as they majorly occupy different kind of home-based or farm-based casual low-paying jobs like crop processing, fishing, and \u003cem\u003ebeedi\u003c/em\u003e rolling [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hence, the female informal workers are often compelled to gender discrimination in the workplace, earn less, and poorly access the medical care [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It certainly does shape their health in an unimaginable way.\u003c/p\u003e\u003cp\u003eThe informal workers, for instance, are exposed to a number of occupational health stressors such as prolonged working hour [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], unavailability of health-care facilities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and extreme working conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The female informal workers frequently experience gender-based discrimination, deal with lower wage, take multiple caregiving responsibilities and yet, they lack adequate social security [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Previous literatures have showed that the female informal workers had developed various health problems such as musculoskeletal problems [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], respiratory health complications like asthma [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], infections [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and cardio-vascular problems like hypertension [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], due to such risk factors. On that note, the livelihood and health burdens of the informal workers strictly undermine the journey of the United Nations, in achieving good health and well-being (Goal 3) and inclusive economic growth and decent work (Goal 8) through Sustainable Development Goals (SDGs). Although in India, several policies has been implemented to address the persisting issue of informal workers, their effectiveness is limited due to the unjustly discrimination to the workers regarding legal, social and health securities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn India, the dearth of such insights calls for a systematic investigation taking into account the health burden of female informal workers to explore the incidence of health burdens among the workers and their determinants [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this purview, the present study systematically investigated the prevalence of hypertension among the female workers in informal sectors of India, extending its approach to identify potential determinants of hypertension with reference to the latest National Family Health Survey of India.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Source\u003c/h2\u003e\u003cp\u003eThe National Family Health Survey (NFHS) is a multi-round survey, directed by the Ministry of Health and Family Welfare (MoHFW), Government of India. The technical and financial aid were provided by ICF of United States of America and the United States Agency for International Development (USAID), respectively. The survey is presently managed by the International Institute of Population Sciences (IIPS), Mumbai. In the present study, the latest National Family Health Survey-5 (2019\u0026ndash;2021) data were used that primarily included 636699 households from 29 Indian states and 9 union territories. The survey performed a two-stage stratified sampling method. In the first stage, the sampling process was brought off separately for urban and rural sectors. In the urban sector, the census enumeration blocks (CEBs) were used as Primary Sampling Units (PSUs), selected using probability proportional to size (PPS). While in the rural areas, villages were used as primary sampling units (PSUs), which were selected using probability proportional to size (PPS). In the second stage, in selected urban and rural clusters, households were selected with systematic random sampling after mapping and household listing. The detailed methodology is published elsewhere [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Analytical sample\u003c/h2\u003e\u003cp\u003eThe NFHS-5 survey was conducted to address the child and maternal health issues in India. Therefore, the survey was able to obtain a large female sample aged 15\u0026ndash;49 years (n\u0026thinsp;=\u0026thinsp;724115). However, as per the objective of the present study, the final analytical sample was extracted only for the female workers in the informal sector, after excluding individuals with missing data for several variables (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the initial stage, a total of 72,344 individuals, who had no data on blood pressure, were excluded. At the subsequent stages, a total of 532,653 individuals (missing data on occupation), 64,270 individuals (data of unemployed females), 5,251 individuals (employed female workers of the formal sector), 16,603 individuals (females taking medication for hypertension), and 7,250 individuals (missing data on age) were excluded. At the next stage, 2089 individual data from the Union Territories were excluded, as the study considered the data only from Indian states. The final analytical sample of female workers in the informal sector included data from 23,655 individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Variables\u003c/h2\u003e\u003cp\u003eThe data from NFHS-5 \u0026lsquo;\u003cem\u003eFemales recodes\u003c/em\u003e\u0026rsquo; file (IAIR7AFL) were used for the present study. The variables \u0026lsquo;Systolic blood pressure\u0026rsquo; and \u0026lsquo;Diastolic blood pressure\u0026rsquo; were considered to calculate the \u0026lsquo;Hypertension status\u0026rsquo; outcome variable. Hypertension (HTN) was defined as an individual having either systolic blood pressure (SBP)\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg or having diastolic blood pressure (DBP)\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg or both taking the JNC-8 guidelines into account [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Individuals having such traits were categorised as \u0026lsquo;HTN\u0026rsquo; whereas individuals having no such traits were categorised as \u0026lsquo;Non-HTN\u0026rsquo;. A total of 12 variables were considered as potential predictor variables of hypertension status. The variable \u0026lsquo;Age\u0026rsquo; was categorized into \u003cem\u003efour\u003c/em\u003e age cohorts (15 to 25 years, 26 to 35 years, 36 to 45 years, and 45 years and above), variable \u0026lsquo;Educational attainment\u0026rsquo; was grouped into \u003cem\u003efour\u003c/em\u003e categories (No education, Upto primary level, Upto secondary level, Above secondary level), and the variable \u0026lsquo;Marital status\u0026rsquo; was grouped into \u003cem\u003ethree\u003c/em\u003e categories (Unmarried, Married, and Widowed/Divorced/Separated). Again, the variable \u0026lsquo;Religion\u0026rsquo; was categorised into \u003cem\u003efour\u003c/em\u003e (Hindus, Muslims, Christians, and Others), the \u0026lsquo;Ethnicity\u0026rsquo; was grouped into \u003cem\u003ethree\u003c/em\u003e categories (General, Scheduled Caste and Other Backward Classes, and Scheduled Tribe), and the \u0026lsquo;Wealth stratum\u0026rsquo; was grouped into \u003cem\u003efive\u003c/em\u003e quintiles (Poorest, Poorer, Middle, Richer, and Richest).\u003c/p\u003e\u003cp\u003eThe variables \u0026lsquo;Place of residence\u0026rsquo; was categorised into \u003cem\u003etwo\u003c/em\u003e (Urban and rural) and the \u0026lsquo;Region\u0026rsquo; variable was grouped into \u003cem\u003esix\u003c/em\u003e categories (Southern, Western, Central, Eastern and Northern and North-eastern). The 29 states were categorised into six regions viz. Southern, Western, Central, Eastern, Northern, and North-eastern [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Under the \u0026lsquo;\u003cem\u003eSouthern\u003c/em\u003e\u0026rsquo; region, came the states Karnataka, Kerala, Andhra Pradesh, Tamil Nadu, and Telangana. The \u0026lsquo;\u003cem\u003eWestern\u003c/em\u003e\u0026rsquo; region included states of Maharashtra, Gujarat, and Goa. Thirdly, the \u0026lsquo;\u003cem\u003eEastern\u003c/em\u003e\u0026rsquo; region included Bihar, Jharkhand, Odisha and West Bengal. The states of Madhya Pradesh, Chhattisgarh, and Uttar Pradesh were grouped under the \u0026lsquo;\u003cem\u003eCentral\u003c/em\u003e\u0026rsquo; region. In the \u0026lsquo;\u003cem\u003eNorthern\u003c/em\u003e\u0026rsquo; region category, states like Jammu and Kashmir, Himachal Pradesh, Haryana, Punjab, Rajasthan, Uttarakhand were included. Lastly, the \u0026lsquo;\u003cem\u003eNorth-eastern\u003c/em\u003e\u0026rsquo; region included the states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. Again, the variable \u0026lsquo;Type of occupation\u0026rsquo; was grouped into \u003cem\u003esix\u003c/em\u003e categories (Agriculture, Forestry and Fishing, Manufacturing, Construction and Infrastructure, Trade and Retail, Domestic and Hospitality scheme, Personal Care, Art and Entertainment) following the National Classification of Occupation guidelines [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The \u0026lsquo;Pattern of earning\u0026rsquo; was grouped into \u003cem\u003efour\u003c/em\u003e categories (Unpaid, Paid in Cash, Paid in kind only and Paid in Cash and kind), and the variable \u0026lsquo;Mode of employment\u0026rsquo; was grouped into \u003cem\u003ethree\u003c/em\u003e categories viz. Annual, Seasonal and Occasional).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThe descriptive statistics were performed to explore the background characteristics of the female workers in the informal sector. The percentage distribution was assessed for the individual-level, household-level, and community-level characteristics of the workers. Furthermore, the workers were also distributed based on their occupational categories and other occupation-specific variables. The association of potential predictor variables with the nature of occupation was also assessed through the chi-square test of association to examine the diversification in occupational avenues based on the socio-demographic, economic, household, and community-level traits. State-wise prevalence of hypertension among the female informal workers were shown in percentage distribution and spatial visualization.\u003c/p\u003e\u003cp\u003eThe multi-level Binary Logistic Regression (BLS) models were carried out to assess the effects of all the potential predictor variables on the probability of hypertension among the female workers in informal sector. The outcome/dependent variable of status of hypertension was binary in nature (\u003cem\u003ehypertensive\u003c/em\u003e was denoted as \u0026lsquo;HTN\u0026rsquo; and was coded as 1, and \u003cem\u003enon-hypertensive was\u003c/em\u003e denoted as \u0026lsquo;Non-HTN\u0026rsquo; and was coded as 0). As the study adopted a multi-level binary logistic model-building procedure, a total of four models were established to assess the prediction strength of the explanatory variables. The explanatory variables were selected after assessing the multicollinearity among them. The variables with a Variance Inflation Factor (VIF) value of 2.000 or less was considered for the BLR model, as it indicated from \u0026lsquo;no\u0026rsquo; to \u0026lsquo;low\u0026rsquo; multicollinearity among the variables [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the Model 1, only the \u0026lsquo;Age group\u0026rsquo; and \u0026lsquo;Highest Educational Attainment\u0026rsquo; were included. In the Model 2, the household-level predictor variables such as \u0026lsquo;Religion\u0026rsquo; and \u0026lsquo;Wealth stratum\u0026rsquo; were included along with the variables from the Model 1. Simultaneously, the Model 3 included both individual-level and household-level predictor variables along with \u0026lsquo;place of residence\u0026rsquo; and \u0026lsquo;region\u0026rsquo; variables. In the last model (Model 4), the occupation-specific variables, such as \u0026lsquo;Mode of employment\u0026rsquo; and \u0026lsquo;Pattern of earning\u0026rsquo; were successively included with all the variables from the previous models. Hence, all the models generated distinct predictive equations for the outcome variable. The Omnibus Tests of Model Coefficient, Pseudo R-Square, Hosmer-Lemeshow Test, correct classification percentage, and odds ratio with 95% CI were calculated to evaluate the model fit and predictive power of the independent variables [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eThe distribution of female informal workers in India based on their socio-demographic, economic and occupational characteristics is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In present findings, the majority (32.5%) of the respondents belonged to the 36\u0026ndash;45 age group, and 32.4% were under the 26\u0026ndash;35 age groups. Educational attainments of the respondents were found poor. Around 36% had no formal education while around 43% had education only up to secondary level. Majority of the females in present study were married (76.9%). Most of the study participants belonged to the Hindu religion (80.6%). The proportion of workforce participation in the informal sector was predominated by scheduled caste and other backward class (SC \u0026amp; OBC) communities (76.9%) and from the rural sectors of India (86.2%). Economically, the finding in wealth stratum reflects that almost half of the respondents belonged to the poorest (26.4%) and poorer (23.4%) section of the society. The table also demonstrated the distribution of study respondents based on their occupational avenues. It revealed that most of the respondents were engaged in agricultural work and forestry (66.0%), followed by construction and infrastructural job (12.6%). Interestingly, almost half of the respondents were engaged in seasonal job, while the other half were engaged in annual job. The cash only payment was predominant (65.5%) in the informal sector. The Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also showed that majority of the female informal workers were from the Central region (24.5%), followed by the Southern region (23.3%), Northern region (14.2%), Western region (13.9%), North-eastern region (13.7%) and the least in the Eastern region (13.4%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBackground characteristics of the female informal workers based on socio-demographic, economic and occupational traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBackground characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23655)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% confidence interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAge group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 to 25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5575 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0-24.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 to 35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7669 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.8\u0026ndash;33.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 to 45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7697 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9\u0026ndash;33.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2714 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.0-11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducational attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8626 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.8\u0026ndash;37.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUp to primary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3852 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.8\u0026ndash;16.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUp to secondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10084 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.0-43.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove secondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1093 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3\u0026ndash;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3644 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.9\u0026ndash;15.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18180 (76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.3\u0026ndash;77.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1831 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.4-8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHindus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19068 (80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.1\u0026ndash;81.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslims\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1494 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.0-6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChristians\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1968 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.9\u0026ndash;8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1125 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4-5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e628(2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4\u0026ndash;2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScheduled Caste \u0026amp; Other Backward Classes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18194(76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.3\u0026ndash;77.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScheduled Tribe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4833(20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.7\u0026ndash;21.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3271 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3\u0026ndash;14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20384 (86.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.7\u0026ndash;86.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eWealth stratum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6254 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.8\u0026ndash;27.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5524 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.8\u0026ndash;23.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4977 (21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5\u0026ndash;21.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4258 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.5\u0026ndash;18.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2642 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.7\u0026ndash;11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eType of occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture, forestry and fishing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15610 (66.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.3\u0026ndash;66.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManufacturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2489 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.1\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstruction and infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2969 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.1\u0026ndash;12.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrade and retail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e955 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.7\u0026ndash;4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomestic and hospitality service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1478 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u0026ndash;6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersonal care, art and entertainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026ndash;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMode of employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11776 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u0026ndash;50.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10846 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.2\u0026ndash;46.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccasional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1033 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.1\u0026ndash;4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEarning type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnpaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4769 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6\u0026ndash;20.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaid in cash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15505 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.9\u0026ndash;66.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaid in kind\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e787 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaid in cash and kind\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2594 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.5\u0026ndash;11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eGeographical Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4810 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.8\u0026ndash;20.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3285 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.4\u0026ndash;14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5784 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.9\u0026ndash;25.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3177 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.0-13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3350 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.7\u0026ndash;14.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorth-Eastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3249 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3\u0026ndash;14.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe association between the types of occupation of female informal workers and their background characteristics is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It reveals that the majority of the respondents in the age group 15\u0026ndash;25 years were in manufacturing industry (31.3%), followed by services in art and entertainment. Around 35% of the workers in 25\u0026ndash;36 years age group were engaged in manufacturing industry, agricultural and forestry work (31.5%) and in construction and infrastructure (33.6%). The females in the older age group (36\u0026ndash;45 years) showed similar results as well. Age of the respondents was significantly associated with their occupation (χ2\u0026thinsp;=\u0026thinsp;220.15, df\u0026thinsp;=\u0026thinsp;15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, almost half of the workers without any formal education were highly engaged in construction and infrastructural jobs (44.5%), followed by agricultural and forestry-based works (41.0%). The association between educational attainment and occupation of the workers were found significant (χ2\u0026thinsp;=\u0026thinsp;1881.77, df\u0026thinsp;=\u0026thinsp;20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was observed that the proportion of female informal workers in various works was significantly occupied by married women (χ2\u0026thinsp;=\u0026thinsp;413.01, df\u0026thinsp;=\u0026thinsp;10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also showed that in each of the occupational categories, the participation of females from SC and OBC communities were significantly higher (χ2\u0026thinsp;=\u0026thinsp;491.332, df\u0026thinsp;=\u0026thinsp;10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Since the proportion of workers of rural sectors was high in the present study, it was observed that in the agricultural, fishing, and forestry sectors, their participation was also quite high (95.2%). Nevertheless, around 76% of the respondents in construction and infrastructure, followed by around 67% in the manufacturing sector, were also from the rural part of India. The association between place of residence and occupation of the female informal workers was significant as well (χ2\u0026thinsp;=\u0026thinsp;3459.32, df\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The table also highlighted that the occupational involvement of the workers was significantly associated with their wealth index quintile (χ2\u0026thinsp;=\u0026thinsp;897.96, df\u0026thinsp;=\u0026thinsp;20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was fascinating to observe that in agricultural and forestry sector, majority of the females were seasonal workers (54.8%), however in all other sectors such as manufacturing (69.0%), trade and retails (74.7%), domestic services (76.0%) as well as in art and entertainment (69.5), female workers were engaged in annual/regular basis. Moreover, the earning type (χ2\u0026thinsp;=\u0026thinsp;2165.50, df\u0026thinsp;=\u0026thinsp;15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and their region (χ2\u0026thinsp;=\u0026thinsp;1417.99, df\u0026thinsp;=\u0026thinsp;25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly associated with occupation of the female workers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of female informal workers based on their type of occupation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBackground characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eType of occupation among the female informal workers\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23655)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChi-square value, df (\u003cem\u003ep\u003c/em\u003e-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture, forestry and fishing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eManufacturing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConstruction and infrastructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTrade and retail\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDomestic and hospitality service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePersonal care, art and entertainment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15610)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2489)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;955)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1478)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eAge group\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15 to 25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3543 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e779 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e631 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e242 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e326 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e220.15*, 15 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26 to 35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4911 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e881 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e999 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e345 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e481 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52 (33.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36 to 45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5193 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e665 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1015 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e276 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e506 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42 (27.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1963 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e324 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 (3.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eEducational Attainment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6402 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e314 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1320 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e421 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1881.77*, 20 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpto primary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2692 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e544 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e215 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (7.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpto secondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6330 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1596 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1032 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e581 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e737 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97 (63.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove secondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e475 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e284 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e105 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33 (21.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2116 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e635 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e402 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e251 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e413.01*, 10 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12426 (79.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1697 (68.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2226 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e675 (70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1057 (71.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99 (64.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1068 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 (3.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12968 (83.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1811 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2417(81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e688(72.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1063(71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e121(78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e892.65*, 15 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslims\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e617 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e183 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e209 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15(9.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristians\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1371 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e188 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e121 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e654 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e491.332*, 10 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScheduled Caste \u0026amp; Other Backward Classes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11583 (74.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2210 (88.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2354 (79.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e705 (73.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1204 (81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e138 (89.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScheduled Tribe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3694 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e528 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e221 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e203 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePlace of Residence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e744 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e817 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e700 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e395 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e547 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3459.32*, 5 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14866 (95.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1672 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2269 (76.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e560 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e931 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86 (55.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eWealth Stratum\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4310 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1086 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e338 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e897.96*, 20 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3714 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e497 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e726 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e240 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e319 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28 (18.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3348 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e549 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e520 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e332 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19 (12.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2780 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e547 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e199 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e274 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38 (24.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1458 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e560 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e217 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e141 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e215 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51 (33.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMode of Employment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6506 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1717 (69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1610 (54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e713 (74.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1123 (76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107 (69.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1792.23*, 10 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeasonal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8559 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e594 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1200 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e181 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e277 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35 (22.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccasional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e545 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e159 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (7.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eType of Earning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnpaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4052 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e409 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2165.50*, 15 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaid in cash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8838 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2225 (89.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2645 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e774 (81.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e888 (60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e135 (87.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaid in kind\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e659 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaid in cash and in-kind\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2061 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3175 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e568 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e469 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e280 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e291 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e1417.99*, 25 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2612 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e166 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e179 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35 (22.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4201 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e480 (19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e625 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e287 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25 (16.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1824 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e353 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e751 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1730 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e552 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e584 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e339 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42 (27.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-eastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2068 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e306 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e374 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e255 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e232 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14 (9.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Significant at 0.001 level\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the prevalence of hypertension among the female informal workers in various Indian states. The nation-wide prevalence rate was found to be 12.6%. Across the states, Sikkim (45.0%) showed the highest prevalence of hypertension. States like Arunachal Pradesh, Nagaland, Punjab, and Odisha showed around 15\u0026ndash;20% prevalence of hypertension among the female informal workers. On the contrary, states like Bihar, Jharkhand in the Eastern region, Uttar Pradesh in the Northern region, Rajasthan in Western region, and Tripura in North-eastern region showed lower prevalence rate of hypertension of as low as 7\u0026ndash;10%. The detailed representation of state-wise prevalence of hypertension among the female informal workers is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eState-wise prevalence of hypertension among the female informal workers in India\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eState\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHypertension Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hypertensive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJammu \u0026amp; Kashmir\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e436 (86.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e504 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHimachal Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245 (88.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e276 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePunjab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e392 (82.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e476 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUttarakhand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e278 (86.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e323 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaryana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403 (87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e462 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRajasthan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1178 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1309 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUttar Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1864 (90.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 6(9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2050 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBihar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e798 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e858 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSikkim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArunachal Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e626 (79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e785 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNagaland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e261 (80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e324 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e369 (84.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e439 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMizoram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e255 (91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e279 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTripura\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e137 (89.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e153 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeghalaya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e450 (88.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e507 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e629 (89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e702 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Bengal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e398 (85.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e466 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJharkhand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e757 (91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e825 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOdisha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e861 (83.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1028 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChhattisgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1515 (85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e262 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1777 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMadhya Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1720 (87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e237 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1957 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGujarat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1460 (89.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1640 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaharashtra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1434 (88.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1628 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndhra Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e521 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e586 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarnataka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1063 (86.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e173 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1236 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKerala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e171 (86.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e197 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTamil Nadu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1032 (87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1183 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTelangana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1372 (85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e236 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1608 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20673 (87.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2982 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23655 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Multilevel binary logistic regression for assessing the predictive strength of explanatory variables on the likelihood of hypertension among the female informal workers is given in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A total of four models are given, which include individual-level (Model 1), household-level (Model 2), community-level (Model 3), and occupation-level (Model 4) predictor variables. The Model 1 depicts that the likelihood of hypertension significantly increased with age progression among the workers (26 to 35 years - OR:2.395 [2.047\u0026ndash;2.803]), (36 to 45 years - OR:4.997 [4.290\u0026ndash;5.819]), (45 years and above - OR:7.511 [6.351\u0026ndash;8.882]). Again, the Model 2 shows that both age (26 to 35 years - OR:2.373 [2.026\u0026ndash;2.779]), (36 to 45 years - OR:4.923 [4.221\u0026ndash;5.743]), (45 years and above - OR:7.351 [6.203\u0026ndash;8.712]) and religion (Christians - OR:1.199 [1.044\u0026ndash;1.377]), others - OR: 1.457 [1.235\u0026ndash;1.719]) were significant predictors of the likelihood of hypertension among the female informal workers. The Model 3, along with the age and religion showed that the development of hypertension significantly varied across the geographical region (Western - OR:0.827 [0.702\u0026ndash;0.975]), (Central - OR:0.780 [0.654\u0026ndash;0.930]), (Northern - OR:0.779 [0.655\u0026ndash;0.927]) and across place of residence (Rural - OR:0.885 [0.788\u0026ndash;0.995]). The last Model 4 depicted that along with age, religion, and geographical region, the mode of employment (Occasional - OR:1.228 [1.005-1.500]) also significantly predicted the likelihood of hypertension among the female informal workers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWorldwide, the incidence of hypertension is increasing manifold across the population, emerging as a major health concern, particularly in the Low- and Middle-income countries like India. The exposed individuals are often compelled to experience some associated health complications and compromise with their health and well-being. Nevertheless, the occurrence of hypertension varies across gender, ethnicity, age, as well as additional socio-economic layers, especially across occupation. In context of Indian subcontinent, the diversity in occupation can be categorised under the broader concept of formal and informal sectors, where the latter faces major livelihood and health challenges compared to the former. Female informal workers, precisely, face greater discrepancies in many spheres, which places them at greater risk of developing various cardio-vascular health problems, such as hypertension. The present study, therefore, took an attempt to explore the prevalence of hypertension among the female informal workers in India (15 to 49 years) and unveil its determinants. The major findings are elaborately discussed in the subsequent sections.\u003c/p\u003e\u003cp\u003eWhile exploring the socio-economic background of the female informal workers in India, the present study found that the majority of the workers were economically deprived since they belonged to the poorest section of the society. Almost half of them came within the lowest wealth quintile, as the study finding suggested. This finding corroborated with some previous studies in India, who showed that the informal workers were always at greater economic deprivation due to persisting wage inequality and other social barriers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Perhaps the cause, as stated by an earlier study, is that the female informal workers, often face gender-based discrimination in getting wage or salary [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The report of Periodic Labour Force Survey, 2017-18 also re-confirms the same [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe majority of the female informal workers were engaged in agricultural works, either as a cultivator or as a labour, as well as in construction work, primarily as a helper. This is, perhaps, due to the constant motivation generated through the availability of such kind of low-wage jobs, demanding only the low-skilled workers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Nevertheless, the scenario is fairly different between urban and rural sectors, where the workers in urban region were primarily engaged in domestic work, street vending, and home-based services and workers in rural region mostly occupy the agricultural work [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Interestingly, the present NFHS data showed a huge worker participation of marginalized communities in the informal sector, found by earlier literatures as well [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe findings regarding the likelihood of hypertension unveiled a few interesting ones. It stated that the likelihood of hypertension increased with various socio-demographic and occupation-specific factors such as age, education, place of living, and type of job regularity. As seen in the previous studies [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], the present study showed a strong positive association between age and hypertension, where the former augmented the latter. It was also evident that low education was a crucial factor behind the occurrence of hypertension among the female workers in the informal sector. This finding was also in line with several literature [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which cited that education regulated the knowledge and awareness about health issues, that could in turn influence the health status as well. Nevertheless, the present study findings contradicted with a previous work [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePresent study also highlighted that the female workers in the informal sector did seasonal work as per their skills and capabilities. As shown previously, the female informal workers are often engaged in a wide range of works such as planting and harvesting [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], brick-kiln works [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], agricultural works [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and so on. Often, the workers are unwillingly committed to unpaid works such as home-based work activities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, in Indian context, the scenario is opposite in urban sectors, where informal workers are paid either in cash or kind [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The study also unveiled that the female informal workers in urban sector had a greater susceptibility for developing hypertension. It is, perhaps, due to degraded working condition, unhealthy lifestyle and so on [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, some other studies contrastingly showed higher odds of hypertension prevalence in the rural sectors of India [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. It was fascinating to observe that the likelihood of hypertension among the female workers, also depended whether their job was regular or not. Similar to the earlier studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], female workers with irregular job showed a greater vulnerability to develop hypertension, in present study. Overall, the study highlights that the female workers in informal sector are always at a greater risk of developing hypertension, with a combined effects of various individual-level, household and community level, as well as occupation-specific factors.\u003c/p\u003e"},{"header":"Conclusion and recommendation","content":"\u003cp\u003eThe study unveiled a major health concern regarding the incidence of hypertension among the female informal workers in India. The current study highlights that not only the socio-demographic, but also the occupation-specific factors played a crucial role in determining hypertension. The major issues like irregularity in job, wage inequality, and others were significant aspects, that need an urgent intervention. The management and treatment of hypertension is a continuous process and can often be expensive. Therefore, targeted intervention for hypertension among the workers is the need of the hour such as improved working conditions, balance diet and physical activity, and ensure job and economic security. Appropriate implementation of various programmes is of utmost necessity at both community and workplace levels to ensure accessible and affordable utilisation of health service and to mitigate health burdens. Strengthening the medical infrastructure, lifestyle modification, and strategic plan regarding health awareness can lower the current trend of hypertension occurrence.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eAlthough the present study is based on a nationally representative sample, it had its own limitations. The large-scale sample of NFHS 5 strengthens the robustness of the study findings. Besides, the sampling strategies and statistical approaches, followed in the survey, strictly avoids the election bias. However, since the study focused only on the female informal workers, it would have been better to include the male workers and workers from the formal sectors as well. The gender-based and occupation-based comparative approach could unveil deeper insights. The present study findings strongly calls for an effective policy making at both community and administration level, to mitigate the livelihood and health burdens of the informal workers. Moreover, the study considered only the female workers of 15 to 49 years, though the informal sector comprises the older women (49\u0026thinsp;+\u0026thinsp;years) as well. It is noteworthy that the survey data lacks on various occupational stressors such as physical stress, income, duration of working hour, and so on, which could have been incorporated to reveal valuable insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and Guidelines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NFHS-5 survey received the ethical clearance from the Ethical Review Board of the International Institute for Population Science, prior to the commencement of the survey. The respondents also provided the informed consents prior to the conduct of interviews to the research personnel. Therefore, the present study was exempted from any ethical consideration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe consent to participate is not applicable as the study is based on secondary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors provided consent for the publication of the manuscript without any conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Family Health Survey (NFHS) data are available at Demographic and Health Survey (DHS) website and can be accessed at https://dhsprogram.com. The data used in the present study may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any specific grant from any funding agency in the public, commercial, and not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the authors S.K.P and S.N.S initiated the study design and supervised in the preparation of the manuscript. A.M and G.C.G extracted the relevant data, and conducted the literature review. A.M analysed and interpreted the data. Both A.M, G.C.G and S.N.S participated in drafting the manuscript. S.K.P critically revised the manuscript. All the Authors approved the manuscript prior to its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are indebted to the respective authorities of the NFHS-5 survey and the International Institute for population Science (IIPS), Mumbai for providing the accessibility to the latest survey data for research purpose. The authors are also indebted to Mr. Rajkumar Guria, Department of Geography, Fakir Mohan University for his assistance in preparing the figures in QGIS.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHussmanns, R. (2001, September). 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Boca Raton: CRC Press; 2014.\u003c/li\u003e\n\u003cli\u003ePandoh, A., \u0026amp; Singh, A. (2025). Examining wage inequality among women in India: A multidimensional analysis of socio-economic disparities. PloS one, 20(4), e0320940.\u003c/li\u003e\n\u003cli\u003eSahu, P. R., \u0026amp; Behera, D. K. (2025). Barriers to women\u0026rsquo;s empowerment in India\u0026rsquo;s informal sector: structural and socio-economic constraints. Discover Sustainability, 6(1), 1-16.\u003c/li\u003e\n\u003cli\u003eNikore, M. (2022). Building India\u0026apos;s economy on the backs of women\u0026apos;s unpaid work: a gendered analysis of time-use data. In Building India\u0026apos;s economy on the backs of women\u0026apos;s unpaid work: a gendered analysis of time-use data: Nikore, Mitali. New Delhi, India: ORF, Observer Research Foundation.\u003c/li\u003e\n\u003cli\u003eMinistry of Labour and Employment. (2022). Periodic Labour Force Survey (PLFS) 2017-18. Government of India.\u003c/li\u003e\n\u003cli\u003eSumalatha, B. S., \u0026amp; Roy, V. N. (2024). Gender Disparity in the Informal Sector Employment in India. In Informal Economy and Sustainable Development Goals: Ideas, Interventions and Challenges (pp. 359-371). Emerald Publishing Limited.\u003c/li\u003e\n\u003cli\u003eRaveendran, G., \u0026amp; Vanek, J. (2020). Informal workers in India: A statistical profile. Women in Informal Employment: Globalizing and Organizing (WEIGO): Manchester, UK, 1-16.\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez, C. (2023). A Statistical Portrait of the Indian Female Labor Force.\u003c/li\u003e\n\u003cli\u003eBhimarasetty, M. D., Pamarthi, K., Kandipudi, K. L. P., Padmasri, Y., Nagaraja, S. B., Khanna, P., \u0026amp; Goel, S. (2022). Hypertension among women in reproductive age in India: Can we predict the risk? An analysis from National Family Health Survey (2015\u0026ndash;2016). Journal of Family Medicine and Primary Care, 11(9), 5857-5864.\u003c/li\u003e\n\u003cli\u003eDhamodharan, S., Megala, M., Duraimurugan, M., \u0026amp; Ganapathi, K. C. (2020). Prevalence of hypertension and its risk factors among transport workers in South India. Int J Community Med Public Health, 7(4), 1329.\u003c/li\u003e\n\u003cli\u003eMohammad, R., \u0026amp; Bansod, D. W. (2024). Hypertension in India: a gender-based study of prevalence and associated risk factors. BMC Public Health, 24(1), 2681.\u003c/li\u003e\n\u003cli\u003eReddy, K. S., Prabhakaran, D., Jeemon, P., Thankappan, K. R., Joshi, P., Chaturvedi, V., ... \u0026amp; Ahmed, F. (2007). Educational status and cardiovascular risk profile in Indians. Proceedings of the National Academy of Sciences, 104(41), 16263-16268.\u003c/li\u003e\n\u003cli\u003eGoyal, P., Goyal, G. K., Yadav, K., Bhatt, A., Nassa, K., Raushan, S. K., ... \u0026amp; Dagar, R. (2024). Attributes of hypertension among industrial workers in Northern India-An alarming signal. Journal of Family Medicine and Primary Care, 13(1), 330-335.\u003c/li\u003e\n\u003cli\u003eMandal, B., \u0026amp; Pradhan, K. C. (2025). Association between rural-to-urban migration and the onset of hypertension among middle-aged and older population: evidence from India. BMC Public Health, 25(1), 1-11.\u003c/li\u003e\n\u003cli\u003eVirk, A., Samdarshi, N., Saini, P., Mohapatra, A., Sahoo, S., \u0026amp; Goel, S. (2022). Prevalence and determinants of hypertension and associated comorbidities in non-pregnant women of reproductive age group (15\u0026ndash;49 years): Evidence from National Family Health Survey (NFHS-4), India. Journal of Family Medicine and Primary Care, 11(9), 5865-5873.\u003c/li\u003e\n\u003cli\u003eTripathi, V., Talukdar, D., Tripathi, M., \u0026amp; Teelucksingh, S. (2024). Prevalence and associated factors of undiagnosed hypertension among women aged 15\u0026ndash;49 years in India: an analysis of National Family Health Survey-4 data. Journal of Human Hypertension, 38(3), 245-256.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Informal sector, NFHS-5, Hypertension, Women workers, Informal Employment","lastPublishedDoi":"10.21203/rs.3.rs-7053757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7053757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\n\u003cp\u003eThe informal job sectors in India, comprising the majority of the national workforce, pose a greater hindrance to their workers, both economically in terms of wage discrepancies and income volatility, and health-wise in terms of a poor work environment, lack of access to healthcare facilities, and exposure to various occupational health hazards. Female informal workers, in particular, are at greater risk of developing critical health complications due to the multifaceted nature of their livelihood and health barriers. In this view, while incidences of cardiovascular complications such as hypertension are surging, the present cross-sectional study attempts to examine the trend of hypertension and identify its determinants among the female workers in the informal sectors in India.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eThe present study took an analytical sample of 23,655 female informal workers of 29 Indian states from the latest National Family Health Survey (NFHS-5) data. Data analyses were performed with descriptive statistics and tests of association for the explanatory and outcome variables. The variables at the individual-level, household-level, community-level, and occupational level were considered for multi-level binary logistic regression to identify the determinants of hypertension.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eAmong the female informal sector workers, the majority belonged to the poorest section (49.8%) of the society, who also mostly occupied agricultural (66.0%) and construction (12.6%) work. The prevalence of hypertension among the workers was 12.6%, the state of Sikkim (45.0%) showing the highest, and Bihar (7.0%) showing the lowest prevalence rate. The workers who are older [OR=7.322; 95% CI 6.165-8.696; p\u0026lt;0.001] and perform occasional work [OR=1.228; 95% CI 1.005-1.500; p\u0026lt;0.001] have a significantly greater susceptibility to develop hypertension. In contrast, the workers from rural sectors [OR=0.885; 95% CI 0.788-0.995; p\u0026lt;0.001] showed a lower odds for the same.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eThe major highlights of the study were to identify the effects of age, religion, place of living, and even mode of employment to be significant contributors to hypertension among the female informal workers. It strongly suggests implementing strategic interventions at both the community and workplace levels to mitigate the rise of hypertension among female informal workers.\u003c/p\u003e","manuscriptTitle":"Prevalence of Hypertension and Its Determinants Among the Female Informal Workers (15 to 49 Years) in India with Reference to NFHS 5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 16:50:29","doi":"10.21203/rs.3.rs-7053757/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d2b8d5c-9977-4f74-b60e-8c8579e78561","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T05:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 16:50:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7053757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7053757","identity":"rs-7053757","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}