Designing strategies to reach the maximum number of women for comprehensive knowledge of Human Immunodeficiency Virus (HIV)

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This applies equally to stigma and discrimination related to HIV awareness and treatment. India has the second largest HIV epidemic in the world with 2.467 million people living with HIV in 2023. India shares 6.3% of global cases of people living with HIV. The biggest challenge is not only to reach all HIV-infected people but also to reach the maximum number of people for counseling and testing to avoid future transmission. There is a need to frame cost-effective, rapid, and confidential awareness strategies that will eventually encourage people to HIV testing. Design: Anonymized, publicly available data of the India National Family Health Survey (NFHS-5) and ASHAs per state is collected from the Ministry of Health and Family Welfare, India. The sample consisted of 724,115 women of 15–49 years of age and were sub-grouped as urban and rural women. Descriptive statistical analysis, linear regression analysis, and Pearson correlation coefficient analysis were done for the data. Results: The multiple linear regression equation for women with comprehensive HIV knowledge (%) Y is ŷ = -0.19433 X 1 + 0.32387 X 2 + 12.32505 where X1 is the percentage of ASHAs per state and X2 is the percentage of women with Internet access. It shows an R square value of 0.2338 for an overall p-value of 0.0123. Pearson correlation indicated that there is a non-significant medium negative relationship between ASHAs per state (%) and women with knowledge of HIV (%) ( r = -0 .315, p = 0.061). Whereas, the results of the Pearson correlation indicated that there is a significant medium-positive relationship between the percentage of women with internet access and the percentage of women with comprehensive knowledge of HIV, ( r = 0 .481, p = 0.003). Conclusion: More urban women have access to the Internet as compared to rural women, which may be the reason why the knowledge of HIV is higher in urban women as compared to their rural counterparts. Internet access to women is more beneficial in states where the rate of literacy is high. In areas where internet access and understanding content in English is an issue, community health workers can provide better support to spread awareness about HIV. Health Policy HIV India Internet use population NFHS-5 SDGs Figures Figure 1 Figure 2 Research highlights The percentage of women with comprehensive knowledge of HIV is very low (21.6%) at the national level. There is need for rapid action to spread awareness. The percentage of women with comprehensive knowledge of HIV can drop over the period of time so regular monitoring of it and the determinants behind it is necessary. Urban-rural divide is seen for access of women to the internet. Community health workers can provide better help to illiterate women, and to the women who do not have access to the internet. But in the case of literate women, the Internet is far more beneficial if compared to community health workers. 1. Introduction HIV remains a major global public health concern which claimed 40.4 million (32.9–51.3 million) lives so far with ongoing transmission in all countries globally [ 1 ]. As per WHO, some countries are reporting increasing trends in new infections when previously on the decline. India has the third highest absolute burden of HIV in the world with 2.3 million people living with HIV in 2021 of which 63 thousand are newly infected [ 2 ]. Women represent more than half (51%) of the global PLHIV [ 3 ]. Women are more likely than men to get HIV during vaginal sex because the vagina has a larger surface area compared with the penis which can be exposed to HIV-infected semen [ 4 , 5 ] along with other sex-specific acquisition risks [ 6 ]. Women are at high risk of HIV infection due to social and cultural context also [ 7 , 8 , 9 ]. Gender-based discrepancies between knowledge and HIV prevalence are found among global youth [ 10 , 11 ]. Comprehensive knowledge about HIV can help mitigate the risk-taking behaviors that contribute to HIV and other STD infections. The gender-based study may help to design gender-based interventions. The study conducted for the period,1997–2019 in the Indian population showed a declining trend in HIV infections [ 12 ]. NACO reports for 2019, and 2023 have also shown declining trends, and nationally 2.35 million people and 2.4 million people are living with HIV respectively. Even though there are reports on declining trends, the number of people living with HIV is still high in India [ 13 , 14 ]. Burgeoning studies show that the Internet plays an important role in public health awareness and improvement [ 15 , 16 , 17 , 18 ]. A study shows that being female is positively associated with a preference for internet health information-seeking behaviors [ 19 , 20 ]. Over fifty percent of Indians (759 million) are active Internet users and this user base is expected to grow to 900 million by 2025. Out of 759 million active internet users in India for 2022, 399 million are from rural India while 360 million are urbans. Out of the total internet users, 54% are male users. However, it is interesting to know that 57% of all new users in 2022 were females [ 21 ]. The COVID-19 pandemic has unearthed our reliance on broadband internet, not as a luxury but as an essential utility such as water and electricity [ 22 ]. Broadband internet access is a super determinant of health because many other social determinants like education, health care, food, and income hinge on it [ 23 ]. ASHAs (Accredited Social Health Activists) are trained female health workers in India who sensitize people about public health services and provide basic health care. One of her roles is counseling women on contraception and common infections like Reproductive Tract Infection/Sexually Transmitted Infections (RTIs/STIs). They also provide oral pills and condoms. There is a need to understand factors like target audiences and multiple channels for information dissemination before designing effective awareness strategies. This study is designed to understand the comprehensive knowledge of HIV in urban as well as rural women. As well as to understand how ASHAs and the Internet can be effectively used to spread awareness against HIV. The results may be used to frame strategic policies to provide targeted information in regional languages to these women. The results obtained and the policies framed can be used for betterment in sub-Saharan and other Asian countries also. 2. Methods 2.1 Data: We used anonymized, publicly available secondary data from the India National Family Health Survey- 5 (NFHS-5), the Ministry of Health and Family Welfare (MoHFW), Government of India. The data related to ASHAs per state or union territories for the National Health Mission (NHM) was obtained from the Ministry of Health and Family Welfare. The survey work of the NFHS-5 for a total of 707 districts, 28 states, and 8 Union territories (UTs) was planned in two phases. The first phase was carried out for 17 states and 5 Union territories from 17 June 2019 to 30 January 2020 and the second phase has been completed in 11 States and 3 UTs from 2 January 2020 to 30 April 2021. A uniform sample design, which is representative at the national, state/union territory, and district levels was adopted for each round of the survey. As per the data, the NFHS-5 survey protocol was reviewed and approved by the International Institute for Population Sciences (IIPS) Institutional Review Board. 2.2 Sample Design: A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98%. In the interviewed households, 747,176 eligible women aged 15–49 were identified for individual women’s interviews. Interviews were completed with 724,115 women for a response rate of 97%. 2.3 Study population: Our sample consisted of 724,115 women in the age group of 15–49 years. The majority of India’s population growth comes from rural and underprivileged areas, while the rise in income comes from the urban privileged population. So, studying both populations, urban as well as rural women were considered to help better understand the issue. For better analysis of data, the country is divided into regions- 1)The northern region- has six states- Himachal Pradesh, Punjab, Uttarakhand, Haryana, Delhi, and Uttar Pradesh; 2) The southern contains five states- Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and Telangana; 3)Eastern region- is consisting of the states of Bihar, Jharkhand, Odisha and West Bengal, 4)Western region states are Rajasthan, Maharashtra Gujarat and Goa; 5) Central region-It consists of two states- Madhya Pradesh and Chhattisgarh 6)North-East region includes-eight States viz. Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. Union territories (Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, Puducherry, Jammu & Kashmir, Ladakh) are grouped as per their geographical locations. 2.4 Study variables: The primary outcome variable or dependent variable in this study is the percentage of women with comprehensive knowledge of HIV. The independent variable or explanatory variable is the number of ASHA workers per state (%) and the percentage of women getting Internet Access. The information on the percentage of women with comprehensive knowledge of HIV, women getting Internet access and ASHAs was obtained by asking ‘yes’ or ‘no’ questions orally. As per the survey protocol, comprehensive knowledge means knowing that consistent use of condoms every time they have sex and having just one uninfected faithful sex partner can reduce the chance of getting HIV/AIDS, knowing that a healthy-looking person can have HIV/AIDS, and rejecting two common misconceptions about transmission or prevention of HIV/AIDS 2.5 Statistical analysis: Descriptive statistical analysis, mean, median, and range were calculated for the data. Pearson correlation coefficient analysis, simple linear regression, and multiple linear regression were carried out for independent variables, ASHAs per state (%) and women with Internet access (%), and dependent variable women with comprehensive knowledge of HIV (%). Social Science Statistics ( www.socscistatistics.com ) tools are used for the above analysis. 3. Results When simple linear regression analysis was done for the independent variable, ASHAs per state (%), and dependent variable knowledge of HIV in women (%) it has shown β = -1.1, p = 0.061, α = 28.9, p < .001. R-squared (R 2 ) equals 0.09939. This means that 9.9% of the variability of the dependent variable is explained by the independent variable. Correlation (R) equals − 0.3153 which means that there is a weak inverse relationship between the variables. Simple linear regression for our data has shown a moderate direct relationship between women with Internet access (%) and women with comprehensive knowledge of HIV (%). The slope: b₁=0.347 CI [0.1268, 0.5673] means that when you increase the independent variable internet access to women (%) by 1, the value of women with HIV knowledge (%) increases by 0.347. For our data, the multiple linear regression equation for women with comprehensive HIV knowledge (%) Y is ŷ = -0.19433 X 1 + 0.32387 X 2 + 12.32505 where X1 is the percentage of ASHAs per state and X2 is the percentage of women with Internet access. It shows an R square value of 0.2338 for an overall p-value of 0.0123. Results of the Pearson correlation indicated that there is a non-significant medium negative relationship between ASHAs per state (%) and women with knowledge of HIV (%) ( r = -0 .315, p = 0.061). Whereas, the results of the Pearson correlation indicated that there is a significant medium-positive relationship between the percentage of women with internet access and the percentage of women with comprehensive knowledge of HIV, ( r = 0 .481, p = 0.003). In the northern region, the state of Uttar Pradesh has shown the highest number (163407) of ASHA workers compared to any other state of the country. Still, only 13.1% of women had comprehensive knowledge of HIV in Uttar Pradesh which is lowest in the entire northern region. In the same region, the state of Himachal Pradesh has 32376 ASHA workers but 36.2% of women with comprehensive knowledge of HIV, which is highest in the northern region. In every state of northern India, the percentage of urban women with internet access is higher than in rural areas. Similarly, the percentage of women with knowledge of HIV is far higher in urban areas than in rural ones. Compared to NFHS-4 data, in every state, the percentage of women with knowledge of HIV has dwindled (Table 1). The mean of women with internet access is 63.58 in urban areas as compared to 45.35 in rural. The average of total women with internet access is 51.92 for the country's northern region. Table-1-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Northern region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Jammu & Kashmir (UT) 12356 55.0 38.9 43.3 17.4 15.2 15.8 18.9 Himachal Pradesh 32376 78.9 45.2 49.7 46.1 34.7 36.2 30.9 Punjab 21470 64.1 48.8 54.8 24.0 18.4 20.6 49.3 Uttarakhand 12212 58.4 39.4 45.1 33.6 20.6 24.5 28.6 Haryana 20115 60.2 42.8 48.4 22.0 18.7 19.7 31.1 Delhi 6035 63.7 69.2 63.8 29.4 38.0 29.5 32.7 Uttar Pradesh 163407 50.2 24.5 30.6 18.3 11.5 13.1 17.5 Chandigarh (UT) 18 75.2 -- 75.2 20.3 -- 20.3 41.1 Ladakh (UT) 000 66.5 54.0 56.4 15.6 26.4 24.3 26.4 Mean 33498.63 63.58 45.35 51.92 25.19 22.93 22.67 30.72 Median 16235.5 63.7 44.0 49.7 22 19.65 20.6 30.9 Range 163389 28.7 44.7 44.6 30.5 26.5 23.1 31.8 When compared to comprehensive knowledge of HIV in the northern region, urban women (25.19%) are more in number as compared to rural women (22.93%). When the total number of women with comprehensive knowledge of HIV is compared for NFHS5 data (22.67%) with NFHS4 data (30.72%); the number shows a reduction in several women with HIV knowledge. As far as internet access to women is concerned in all the states of southern India, urban women are more in number than rural women (Table 2). More urban women have comprehensive knowledge of HIV as compared to rural women. Compared to NFHS4 data states like Karnataka, Tamil Nadu, and Telangana have shown improvement in the number of women having comprehensive knowledge of HIV. Table-2-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the southern region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Andhra Pradesh 42346 33.9 15.4 21.0 29.1 22.6 24.6 29.0 Karnataka 43500 50.1 24.8 35.0 30.0 20.8 24.5 9.5 Kerala 30113 64.9 57.5 61.1 35.5 34.2 34.8 43.1 Tamil Nadu 3965 55.8 39.2 46.9 24.6 22.8 23.6 16.0 Telangana 32575 43.9 15.8 26.5 36.9 26.9 30.7 29.5 Puducherry (UT) 206 66.9 50.4 61.9 30.2 30.2 30.2 25.4 Andaman & Nicobar (UT) 422 44.1 27.9 34.8 10.1 18.4 14.9 29.3 Lakshadweep (UT) 110 61.8 36.0 56.4 50.2 34.8 46.9 22.0 Mean 19154.625 52.68 33.38 42.95 30.83 26.34 28.78 25.48 Median 17039 52.95 31.95 40.95 30.1 24.85 27.4 27.2 Range 43390 33 42.1 40.9 40.1 16.4 32.0 33.6 The state of Karnataka has the highest number of ASHA workers in the southern region whereas the Union Territory of Puducherry has the highest (61.9%) percentage of women with Internet access and Lakshadweep has the highest number (46.9%) of women with comprehensive knowledge of HIV. In Lakshadweep, there are only 110 ASHAs but 56.4% of women with Internet access. In the Eastern region of India, in every state, more urban women have access to the Internet as compared to the rural one (Table 3). Jharkhand (31.4%) has the highest percentage of women with internet access in the region compared to the other regional states. It is followed by West Bengal (25.5%), Odisha (24.9%), and Bihar (20.6%). In every state of the Eastern region, more urban women have comprehensive knowledge of HIV as compared to rural women of that state. If compared to NFHS4, the state of Odisha has shown slight improvement in the number of women with comprehensive knowledge of HIV. In the eastern region, though Bihar has the highest number (89437) of ASHA workers, the state has the lowest (10.3%) number of women with comprehensive knowledge of HIV. Table-3-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Eastern region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Bihar 89437 38.4 17.0 20.6 13.5 9.7 10.3 10.1 Jharkhand 41312 57.8 22.7 31.4 21.7 11.2 13.8 15.8 Odisha 46566 39.7 21.3 24.9 25.1 20.5 21.4 20.3 West Bengal 61545 48.1 14.0 25.5 30.8 12.3 18.5 18.6 Mean 59715 46 18.75 25.6 22.775 13.425 16.00 16.20 Median 54055.5 43.9 19.15 25.2 23.4 11.75 16.15 17.2 Range 48125 19.4 8.7 10.8 17.3 10.8 11.1 10.2 In the Western region of India, urban as well as rural women of Goa are more in number for access to the internet (Table-4). Goa is followed by Maharashtra (38.0%), Rajasthan (36.9%), and Gujrat (30.8%) for total women who got internet access. Goa is the only state where rural women have more knowledge of HIV as compared to the urban one. Compared to NFHS4 data, women in every state of this region of India have shown improvement in their knowledge of HIV. In western region, Maharashtra has highest number of ASHA workers (70282) but 34.4% of women with comprehensive knowledge of HIV whereas as there is no data available for ASHAs in the state of Goa. Table-4-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Western region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Rajasthan 64243 56.1 30.8 36.9 32.1 25.1 26.8 19.1 Maharashtra 70282 54.3 23.7 38.0 39.2 30.1 34.4 30.0 Gujrat 46287 48.9 17.5 30.8 36.3 22.8 28.5 18.4 Goa 000 78.1 68.3 73.7 47.7 50.6 49.0 34.6 Dadra & Nagar Haveli, Diu & Daman 676 49.4 23.8 36.7 31.4 19.0 25.3 16.4 Mean 36297.6 57.36 32.82 43.22 37.34 29.52 32.8 23.7 Median 46287 54.3 23.8 36.9 36.3 25.1 28.5 19.1 Range 70282 29.2 50.8 42.9 16.3 31.6 23.7 18.2 In Chhattisgarh and Madhya Pradesh of Central India, double the urban women have access to the internet as compared to rural women (Table 5). In both the states of central India, urban women are more in number as compared to rural ones as far as their knowledge of HIV and in both states the comprehensive knowledge of HIV has improved in women as compared to NFHS4 data of 2015-16. Chhattisgarh has 69515 and Madhya Pradesh has 77531 ASHA workers. Table-5-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Central region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Chhattisgarh 69515 44.5 20.8 26.7 23.9 22.8 23.1 20.7 Madhya Pradesh 77531 46.5 20.1 26.9 26.7 16.0 18.7 18.1 Mean 73523 45.5 20.45 26.8 25.30 19.40 20.90 19.40 Median 73523 45.5 20.45 26.8 25.3 19.4 20.9 19.4 Range 8016 2 0.7 0.2 2.8 6.8 4.4 2.6 In north-east region of India, Assam has the highest number (32256) of ASHA workers whereas Sikkim has the lowest number (656) of ASHA workers. In North-Eastern India, urban Sikkim (90%), followed by Mizoram (83.8%) and Arunachal Pradesh (70.7%) have the highest number of women with internet access, not only in the North-East region but also in the other parts of the country (Table-6). In rural parts of the states, this number is lower than in urban parts of the region. The state of Tripura has the lowest number of women with internet access in urban (36.6%) as well as in rural (17.7%) parts. Table-6-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the North-Eastern region of India- States No. of ASHAs Women with Internet Access (%) Women with knowledge of HIV (%) NFHS-5 Women with knowledge of HIV (%) NFHS-4 (2015-16) Urban Rural Total Urban Rural Total Total Arunachal Pradesh 3880 70.0 49.6 52.9 11.1 12.5 12.3 16.0 Assam 32256 49.0 24.4 28.2 24.1 18.3 19.2 9.4 Manipur 4009 50.8 40.4 44.8 54.8 47.5 50.6 40.7 Meghalaya 6697 57.8 28.0 34.7 23.8 11.8 14.5 13.3 Mizoram 1170 83.8 48.0 67.6 70.8 56.0 64.1 66.2 Nagaland 1992 66.5 40.3 49.9 25.5 25.7 25.6 12.5 Sikkim 656 90.0 68.1 76.7 34.1 17.2 23.9 25.5 Tripura 8044 36.6 17.7 22.9 19.7 13.7 15.4 28.0 Mean 7338 63.06 39.562 47.212 32.998 25.337 28.200 26.450 Median 3944.5 62.15 40.35 47.35 24.8 17.75 21.55 20.75 Range 31600 53.4 50.4 53.8 59.7 44.2 51.8 56.8 In all the states of the North-east barring Arunachal Pradesh, urban women are more in number with comprehensive knowledge of HIV as compared to their rural counterparts. When compared with NFHS4 data, states like Arunachal Pradesh, Mizoram, Sikkim, and Tripura have shown a decline in the number of women with HIV knowledge. States like Assam, Manipur, Meghalaya, and Nagaland have shown an increase in the population of women with HIV knowledge. Table 7 presents national-level data on internet access to women (%) and the % of women with comprehensive knowledge of HIV (Fig. 1 ). Table-7- Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in India National level data % of women with internet access Women with knowledge of HIV (%) Urban women 51.8 28.6 Rural Women 24.6 18.2 Total 33.3 21.6 NFHS-4 (2015-16) -- 20.9 Mean/average were compared for the regional situation for a percentage of women with internet access and comprehensive knowledge of HIV. The region of north India has shown the highest percentage of women (51.92%) in India with internet access. It is followed by north-east India (47.21%), western India (43.22%), south India (42.95%), central India (26.8%) and eastern India (25.6%). On average highest number of women with comprehensive knowledge of HIV is observed in western India (32.8%) followed by South India (28.78%), North-East India (28.20%), North India (22.67%), central India (20.90%), and eastern India (16.00) (Fig. 2 ). When comparing the results for the range of access to the internet for women and comprehensive knowledge of HIV, the variation is observed among the various regions of India. North-East India has shown a higher value for the range (53.8%) of Internet access to women. This means in North-East India; some states have a higher percentage of women with Internet access whereas some states have a very low number of women with Internet access. The declining range was followed by north India (44.6), west India (42.9), south India (40.9), east India (10.8) and central India (0.2). As far as comprehensive knowledge of HIV is concerned, the highest value is noticed for north-east India (51.8) followed by south India (32.0), West India (23.7), North India (23.1), east India (11.1), and central India (4.4). 4. Discussion Our study shows more urban women have access to the Internet as compared to rural women. The knowledge of HIV is higher in urban women as compared to their rural counterparts. The regression analysis done for the data shows a weak inverse relationship between ASHAs per state and the percentage of women with comprehensive knowledge of HIV whereas there is a positive relationship between Internet access to women and comprehensive knowledge of HIV. Similar results are obtained for the correlation coefficient analysis. A similar study was carried out in four countries of the sub-Saharan region- Ghana, Guinea Bissau, Malawi, and Zimbabwe [ 24 ]. Variation in the prevalence of accessing computers and the internet across regional and socio-economic groups and its association with knowledge of HIV was studied. Participants who reported ever using computers and the internet were more likely to have higher knowledge regarding the transmission of HIV compared to those who did not. Factors such as area of residence, educational attainment, and household wealth status were significantly associated with the usage of computers and the Internet. Characteristic of the study population shows Urban women are more educated than their rural counterparts. More than one-fourth (27%) of rural women have never attended school compared with (13%) of urban women. 20% of women in rural areas have completed 12 or more years of schooling compared with 39% in urban areas. Preliminary data shows 33% of women (15–49 years) in India have ever used the internet. More than half (52%) of women in urban areas have ever used the internet compared with only one-fourth of women in rural areas. The data used for the study shows, that ever use of the internet increases with education with 72% of women with 12 or more years of education ever using the internet, compared with 8% of women with less than 5 years of schooling. Other studies also show women’s access to education is a strong determinant of Internet use. Controlling for other variables, urban poor women with at least some kind of secondary education were six times more likely to be online than urban poor women with lower levels of schooling [ 25 ]. The findings of the study in the Malawi women population show that wealth status and education are the primary determinants of HIV knowledge [ 26 ]. It supports our finding as the characteristic of the study population also shows, that more women in the highest wealth quantile have ever used the internet (69%) than those in the lowest wealth quantile (9%). Wealth and education status might be the reason for access to the Internet and HIV knowledge acquired through the Internet. There are studies carried out in Bangladesh [ 27 ], Tajikistan [ 28 ], and Vietnam [ 29 ] which also support that education is an important determinant of women’s knowledge of HIV. The better financial status of the urban women of the study population might be due to the opportunities for paid work in the urban areas. The NFHS5 data used for the study shows urban women are most likely to be employed as production workers (28%) and professional workers (22%) whereas in rural areas (61%) women are agricultural workers. Having good socioeconomic status improves media exposure or educational achievement which increases the likelihood of knowledge about HIV/AIDS [ 30 ]. Our result is in agreement with the study done in populations of Nigeria and the Republic of Congo [ 31 ], Ghana [ 32 ], and populations of three East African countries [ 33 ]. A study carried out on Ethiopian women for the determinants of comprehensive knowledge of HIV is in tandem with our results where some of the observed determinants are education, wealth, and having a mobile phone [ 34 ]. The results of this study pointed out that a health education program on AIDS for women has significantly improved their knowledge of AIDS transmission. One of the significant predictors for comprehensive knowledge of HIV/AIDS is a method of contraceptive use. This might be due to those women who use traditional contraceptive methods may be literate and then prone to information than non-users. Our study supports the observation that education and financial status have a direct role in access to technology for women and consequently in their knowledge of HIV. Whether it is urban or rural if the women have the opportunity to earn; they can avail technology for their improvement. Even in developed countries, low access to digital technologies is noticed among the socio-economically disadvantaged communities and in areas with poor supply of electricity and internet [ 35 ]. Brazil is a country like India with economic inequality and uneven population distribution and faces challenges in achieving internet access to all. A study carried out on the Brazilian population has also shown urban houses, women, and higher income behind internet access and healthcare [ 36 ]. This study also shows, that living in cities with population 100,000 to 499,999 residents, higher education, and being female are the factors associated with the use of the internet for health purposes. In that case, providing internet access on a priority basis to the target areas can, not only help to reduce child mortality and improve maternal health but to improve overall public health in these cities. India has 40 cities with more than a million population, 396 cities with between 1,00,000 and 1 million population, and 2500 cities with between 10,000 and 1,00,000 population [ 37 ]. On a priority basis, the internet and electricity should be provided in these areas. India has shown rapid advancement in internet penetration over the last decade but our study highlights, that there are still significant gender differences, regional differences, state-level differences, and urban and rural differences. Studying reasons behind the non-acceptance of mobile/internet in certain areas should be studied. There are studies carried out that show these reasons might be the effect of electromagnetic radiation emitting from mobile phones and towers on multiple organs/organ systems of the human body [ 38 ], as well as its effect on the environment [ 39 ]. The COVID-19 pandemic has compounded the challenge of HIV/AIDS elimination, creating difficulties in accessing HIV care services such as early testing and treatment [ 40 ] shows a study carried out to evaluate online interest in HIV care services related search terms before and during the pandemic. This study supports our observation that during COVID times the knowledge about HIV has lowered as compared to the study period of NFHS4. This global study has shown that resource-poor countries with a high prevalence of HIV have a high online interest in HIV/AIDS. It emphasizes the need to improve internet access, the quality of HIV-related health information, and online health literacy to improve health-seeking behaviour, especially in areas with high disease burden. India is committed to UN SDG 3.3 to end the HIV epidemic by 2030 [ 41 , 42 ]. Using this study one can frame targeted strategies to reach the maximum women for HIV awareness. Community health workers and the Internet should be used strategically. In rural areas and in urban slums where due to less or no education and economic opportunities, women have limited access to internet health information. Even if they have access to technology, information in the English language is another barrier. This group should be counselled by community health workers. The government should provide health information in regional languages. In the geographical regions where literacy rates are high in women, the government should focus on increasing internet penetration among women by bringing down the cost of technology. Limitations of study- There is no data available for the internet access to women in NFHS-4 for comparison with data of NFHS-5. There are many factors affecting the comprehensive knowledge of HIV in the women population. Only two factors are controlled in this paper, and there is no way to comprehensively consider the impact of other factors on the results in this paper, such as social and cultural barriers. These are the focus of our next research. Secondly, this study is based on the data collected by the Ministry of Health and Family Welfare, Government of India for NFHS-5. The limitations which are related to any secondary data are also related to this data. Conclusion and policy implications In conclusion, India is a large country with geographical inequality in access to the internet for women population. Socio-economic factors and urban-rural divide decide the access to health technology. Although the role of technology in improving public health is widely accepted in the country there is a need to improve the access of women to internet technology. There is a need for uniform distribution of technology throughout the country understanding the potential of bridging the gap of health care. Our findings reveal that the number of years of education of women, and the financial status of women are determining factors for internet-based health knowledge. Thus, the targeted measures are necessary to provide health information in regional languages as well, and improving the quality of formal education is also important. In areas where the illiteracy of women and access to information in regional language is a problem, community health workers can provide better help if compared to Internet technology. Declarations Acknowledgements - Not Applicable Ethics approval and consent to participate - Not applicable. Conflict of interest/ Competing interests - The authors declare no competing interests. Authors Contribution -1) Conceptualization, methodology, analysis, and investigation, writing, reviewing & editing of the draft - J. S 2) Supervision, statistical analysis, original draft preparation/writing, review, and editing- C. S. All authors have agreed to the submission of this manuscript. Funding sources - Not Applicable References WHO. HIV & AIDS Available at https://www.who.int/news-room/fact-sheets/detail/hiv-aids; 2023 (Accessed on 12th September 2023). Malik M, Girotra S, Roy D, Basu S. 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Fostering accurate HIV/AIDS knowledge among unmarried youths in Cameroon: do family environment and peers matter? BMC Public Health. 2011;11(1):348. doi: 10.1186/1471-2458-11-348, PMID 21595931. Gebremedhin S, Wang Y, Tesfamariam E. Predictors of HIV/AIDS knowledge and attitude among young women of Nigeria and Democratic Republic of Congo: cross-sectional study. J AIDS Clin Res. 2017;8(3):677. Fenny AP, Crentsil AO, Asuman D. Determinants and distribution of comprehensive HIV/AIDS knowledge in Ghana. Glob J Health Sci. 2017;9(12):32. doi: 10.5539/gjhs.v9n12p32. Teshome R, Youjie W, Habte E, Kasm N. Comparison and association of comprehensive HIV/AIDS knowledge and attitude towards people living with HIV/AIDS among women aged 15-49 in three East African countries: Burundi, Ethiopia and Kenya. J AIDS Clin Res. 2016;7(559):2. Agegnehu CD, Geremew BM, Sisay MM, Muchie KF, Engida ZT, Gudayu TW et al. Determinants of comprehensive knowledge of HIV/AIDS among reproductive age (15-49 years) women in Ethiopia: further analysis of 2016 Ethiopian demographic and health survey. AIDS Res Ther. 2020;17(1):51. doi: 10.1186/s12981-020-00305-z, PMID 32787881. Bender MS, Choi J, Arai S, Paul SM, Gonzalez P, Fukuoka Y. Digital technology ownership, usage, and factors predicting downloading health apps among Caucasian, filipino, korean, and latino americans: the digital link to health survey, Filipino, Korean. JMIR Mhealth Uhealth. 2014;2(4):e43. doi: 10.2196/mhealth.3710, PMID 25339246. Nakayama LF, Binotti WW, Link Woite N, Fernandes CO, Alfonso PG, Celi LA et al. The digital divide in Brazil and barriers to telehealth and equal digital health care: analysis of Internet access using publicly available data. J Med Internet Res. 2023 Jul 21;25: e42483. doi: 10.2196/42483, PMID 37477958. Sarkar J. Are you staying in age ready city? 2022. Science Reporter pg 20-23. Available from: https://nopr.niscpr.res.in/bitstream/123456789/60624/1/SR%2059%2810%29%2020-23.pdf. “RF-EMR – Effects on human and environmental health” Biju B, Sarkar C. OSF Prepr. 2020. doi:10.31219/osf.io/fexc3 Sarkar J. Wildlife around communication towers. News Curr Sci. 2011;101(11, 10). Ornos EDB, Tantengco OAG, Abad CLR. Global Online Interest in HIV/AIDS care Services in the time of COVID-19: A Google Trends Analysis. AIDS Behav. 2023 June;27(6):1998-2004. doi: 10.1007/s10461-022-03933-w, PMID 36441409. /1. Transforming our world: the 2030 Agenda for Sustainable Development. October 2015. THE 17 GOALS. Sustainable Development. Available from: https://sdgs.un.org/goals. (Accessed on 18th October 2023). Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4393566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300515260,"identity":"e5ff09ec-a061-4fdc-8aa7-6136850094ba","order_by":0,"name":"Jaimini Sarkar","email":"","orcid":"https://orcid.org/0000-0002-5477-3047","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Jaimini","middleName":"","lastName":"Sarkar","suffix":""},{"id":300516043,"identity":"51222866-6239-411d-98d9-083707477de4","order_by":1,"name":"Chiradeep Sarkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSklEQVRIie2PTUvDMBjHn1JoLym5pky2rxAJdCuO9ausFPSmgjAGigsU6mXotaJfQZgXwVtGYbuU7RrRy9h1yEAYevClm6/brF4F+yOEJM/z458HICPjL0IUDqC93wQABlBVgNJrdbZ+U0w+U8gPynT7qlAxryyBT/xgfF8r54ugD+7qcWWL9c8itr1HHMzV9jWC0uZiyE3bP2721pnNETNj6dkX8tbzwg5xQ6F5awjIzoJCieuDEURuS6BkjLFKLRmzCGmkCgJZuURx+bKiPAbPjZbQhw983KAsnCpPxCkIPElTVCMQ1WRqy+QyohQ3mWcERElyte8UIhNlpeetXvrIsnncpUQixoxD4p5HGrNP6ZKCw42hMqpVCkX9YHjFO7sUH8UshyZlJ9/1B3JU319UPn+ofgRX3w6zF5rSP1fCIr0rIyMj41/yAgbtbOvrXPL8AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1537-4609","institution":"Department of Biotechnology, G. N. Khalsa College (Autonomous), University of Mumbai, Mumbai-19, India.","correspondingAuthor":true,"prefix":"","firstName":"Chiradeep","middleName":"","lastName":"Sarkar","suffix":""}],"badges":[],"createdAt":"2024-05-09 08:04:02","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4393566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4393566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56282201,"identity":"660bde01-b7e4-44ec-aa01-f23cf0478429","added_by":"auto","created_at":"2024-05-10 21:27:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNational-level data on internet access to women (%) and the % of women with comprehensive knowledge of HIV\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4393566/v1/8682be4e4fb74981c8c11a3c.png"},{"id":56282160,"identity":"e54c9f17-d25b-471a-a376-4a14eb147941","added_by":"auto","created_at":"2024-05-10 21:26:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical Distribution of Average of women with Internet Access (%) and average of women with HIV knowledge (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4393566/v1/e7d35afaf1d905a395e190ed.png"},{"id":56281648,"identity":"00c5d217-5ed2-4e1d-9149-f6e9e07ffd7d","added_by":"auto","created_at":"2024-05-10 21:18:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1173954,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4393566/v1/673f3a0d-258b-4a91-a7bd-c93a41e66b81.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDesigning strategies to reach the maximum number of women for comprehensive knowledge of Human Immunodeficiency Virus (HIV)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Research highlights","content":"\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe percentage of women with comprehensive knowledge of HIV is very low (21.6%) at the national level. There is need for rapid action to spread awareness.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe percentage of women with comprehensive knowledge of HIV can drop over the period of time so regular monitoring of it and the determinants behind it is necessary.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eUrban-rural divide is seen for access of women to the internet.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCommunity health workers can provide better help to illiterate women, and to the women who do not have access to the internet. But in the case of literate women, the Internet is far more beneficial if compared to community health workers.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eHIV remains a major global public health concern which claimed 40.4\u0026nbsp;million (32.9\u0026ndash;51.3\u0026nbsp;million) lives so far with ongoing transmission in all countries globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As per WHO, some countries are reporting increasing trends in new infections when previously on the decline. India has the third highest absolute burden of HIV in the world with 2.3\u0026nbsp;million people living with HIV in 2021 of which 63 thousand are newly infected [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen represent more than half (51%) of the global PLHIV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Women are more likely than men to get HIV during vaginal sex because the vagina has a larger surface area compared with the penis which can be exposed to HIV-infected semen [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] along with other sex-specific acquisition risks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Women are at high risk of HIV infection due to social and cultural context also [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGender-based discrepancies between knowledge and HIV prevalence are found among global youth [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Comprehensive knowledge about HIV can help mitigate the risk-taking behaviors that contribute to HIV and other STD infections. The gender-based study may help to design gender-based interventions.\u003c/p\u003e \u003cp\u003eThe study conducted for the period,1997\u0026ndash;2019 in the Indian population showed a declining trend in HIV infections [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. NACO reports for 2019, and 2023 have also shown declining trends, and nationally 2.35\u0026nbsp;million people and 2.4\u0026nbsp;million people are living with HIV respectively. Even though there are reports on declining trends, the number of people living with HIV is still high in India [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBurgeoning studies show that the Internet plays an important role in public health awareness and improvement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A study shows that being female is positively associated with a preference for internet health information-seeking behaviors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver fifty percent of Indians (759\u0026nbsp;million) are active Internet users and this user base is expected to grow to 900\u0026nbsp;million by 2025. Out of 759\u0026nbsp;million active internet users in India for 2022, 399\u0026nbsp;million are from rural India while 360\u0026nbsp;million are urbans. Out of the total internet users, 54% are male users. However, it is interesting to know that 57% of all new users in 2022 were females [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has unearthed our reliance on broadband internet, not as a luxury but as an essential utility such as water and electricity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Broadband internet access is a super determinant of health because many other social determinants like education, health care, food, and income hinge on it [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eASHAs (Accredited Social Health Activists) are trained female health workers in India who sensitize people about public health services and provide basic health care. One of her roles is counseling women on contraception and common infections like Reproductive Tract Infection/Sexually Transmitted Infections (RTIs/STIs). They also provide oral pills and condoms.\u003c/p\u003e \u003cp\u003eThere is a need to understand factors like target audiences and multiple channels for information dissemination before designing effective awareness strategies. This study is designed to understand the comprehensive knowledge of HIV in urban as well as rural women. As well as to understand how ASHAs and the Internet can be effectively used to spread awareness against HIV. The results may be used to frame strategic policies to provide targeted information in regional languages to these women. The results obtained and the policies framed can be used for betterment in sub-Saharan and other Asian countries also.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data:\u003c/h2\u003e \u003cp\u003eWe used anonymized, publicly available secondary data from the India National Family Health Survey- 5 (NFHS-5), the Ministry of Health and Family Welfare (MoHFW), Government of India. The data related to ASHAs per state or union territories for the National Health Mission (NHM) was obtained from the Ministry of Health and Family Welfare.\u003c/p\u003e \u003cp\u003eThe survey work of the NFHS-5 for a total of 707 districts, 28 states, and 8 Union territories (UTs) was planned in two phases. The first phase was carried out for 17 states and 5 Union territories from 17 June 2019 to 30 January 2020 and the second phase has been completed in 11 States and 3 UTs from 2 January 2020 to 30 April 2021.\u003c/p\u003e \u003cp\u003eA uniform sample design, which is representative at the national, state/union territory, and district levels was adopted for each round of the survey. As per the data, the NFHS-5 survey protocol was reviewed and approved by the International Institute for Population Sciences (IIPS) Institutional Review Board.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Design:\u003c/h2\u003e \u003cp\u003eA total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98%. In the interviewed households, 747,176 eligible women aged 15\u0026ndash;49 were identified for individual women\u0026rsquo;s interviews. Interviews were completed with 724,115 women for a response rate of 97%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study population:\u003c/h2\u003e \u003cp\u003eOur sample consisted of 724,115 women in the age group of 15\u0026ndash;49 years. The majority of India\u0026rsquo;s population growth comes from rural and underprivileged areas, while the rise in income comes from the urban privileged population. So, studying both populations, urban as well as rural women were considered to help better understand the issue.\u003c/p\u003e \u003cp\u003eFor better analysis of data, the country is divided into regions- 1)The northern region- has six states- Himachal Pradesh, Punjab, Uttarakhand, Haryana, Delhi, and Uttar Pradesh; 2) The southern contains five states- Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and Telangana; 3)Eastern region- is consisting of the states of Bihar, Jharkhand, Odisha and West Bengal, 4)Western region states are Rajasthan, Maharashtra Gujarat and Goa; 5) Central region-It consists of two states- Madhya Pradesh and Chhattisgarh 6)North-East region includes-eight States viz. Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. Union territories (Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, Puducherry, Jammu \u0026amp; Kashmir, Ladakh) are grouped as per their geographical locations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study variables:\u003c/h2\u003e \u003cp\u003eThe primary outcome variable or dependent variable in this study is the percentage of women with comprehensive knowledge of HIV. The independent variable or explanatory variable is the number of ASHA workers per state (%) and the percentage of women getting Internet Access. The information on the percentage of women with comprehensive knowledge of HIV, women getting Internet access and ASHAs was obtained by asking \u0026lsquo;yes\u0026rsquo; or \u0026lsquo;no\u0026rsquo; questions orally.\u003c/p\u003e \u003cp\u003eAs per the survey protocol, comprehensive knowledge means knowing that consistent use of condoms every time they have sex and having just one uninfected faithful sex partner can reduce the chance of getting HIV/AIDS, knowing that a healthy-looking person can have HIV/AIDS, and rejecting two common misconceptions about transmission or prevention of HIV/AIDS\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis:\u003c/h2\u003e \u003cp\u003eDescriptive statistical analysis, mean, median, and range were calculated for the data. Pearson correlation coefficient analysis, simple linear regression, and multiple linear regression were carried out for independent variables, ASHAs per state (%) and women with Internet access (%), and dependent variable women with comprehensive knowledge of HIV (%). Social Science Statistics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.socscistatistics.com\" target=\"_blank\"\u003ewww.socscistatistics.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.socscistatistics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e tools are used for the above analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eWhen simple linear regression analysis was done for the independent variable, ASHAs per state (%), and dependent variable knowledge of HIV in women (%) it has shown β = -1.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061, α\u0026thinsp;=\u0026thinsp;28.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. R-squared (R\u003csup\u003e2\u003c/sup\u003e) equals 0.09939. This means that 9.9% of the variability of the dependent variable is explained by the independent variable. Correlation (R) equals \u0026minus;\u0026thinsp;0.3153 which means that there is a weak inverse relationship between the variables.\u003c/p\u003e \u003cp\u003eSimple linear regression for our data has shown a moderate direct relationship between women with Internet access (%) and women with comprehensive knowledge of HIV (%). The slope: b₁=0.347 CI [0.1268, 0.5673] means that when you increase the independent variable internet access to women (%) by 1, the value of women with HIV knowledge (%) increases by 0.347.\u003c/p\u003e \u003cp\u003eFor our data, the multiple linear regression equation for women with comprehensive HIV knowledge (%) \u003cem\u003eY\u003c/em\u003e is ŷ = -0.19433\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.32387\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;12.32505 where X1 is the percentage of ASHAs per state and X2 is the percentage of women with Internet access. It shows an R square value of 0.2338 for an overall p-value of 0.0123.\u003c/p\u003e \u003cp\u003eResults of the Pearson correlation indicated that there is a non-significant medium negative relationship between ASHAs per state (%) and women with knowledge of HIV (%) (\u003cem\u003er\u003c/em\u003e = -0 .315, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061). Whereas, the results of the Pearson correlation indicated that there is a significant medium-positive relationship between the percentage of women with internet access and the percentage of women with comprehensive knowledge of HIV, (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 .481, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eIn the northern region, the state of Uttar Pradesh has shown the highest number (163407) of ASHA workers compared to any other state of the country. Still, only 13.1% of women had comprehensive knowledge of HIV in Uttar Pradesh which is lowest in the entire northern region. In the same region, the state of Himachal Pradesh has 32376 ASHA workers but 36.2% of women with comprehensive knowledge of HIV, which is highest in the northern region.\u003c/p\u003e \u003cp\u003eIn every state of northern India, the percentage of urban women with internet access is higher than in rural areas. Similarly, the percentage of women with knowledge of HIV is far higher in urban areas than in rural ones. Compared to NFHS-4 data, in every state, the percentage of women with knowledge of HIV has dwindled (Table\u0026nbsp;1). The mean of women with internet access is 63.58 in urban areas as compared to 45.35 in rural. The average of total women with internet access is 51.92 for the country's northern region.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-1-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Northern region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\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 (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.9\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e32376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.9\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e21470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.3\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e12212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.6\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e20115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelhi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.7\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e163407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChandigarh (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLadakh (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33498.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16235.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.8\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\u003eWhen compared to comprehensive knowledge of HIV in the northern region, urban women (25.19%) are more in number as compared to rural women (22.93%). When the total number of women with comprehensive knowledge of HIV is compared for NFHS5 data (22.67%) with NFHS4 data (30.72%); the number shows a reduction in several women with HIV knowledge.\u003c/p\u003e \u003cp\u003eAs far as internet access to women is concerned in all the states of southern India, urban women are more in number than rural women (Table\u0026nbsp;2). More urban women have comprehensive knowledge of HIV as compared to rural women. Compared to NFHS4 data states like Karnataka, Tamil Nadu, and Telangana have shown improvement in the number of women having comprehensive knowledge of HIV.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-2-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the southern region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndhra Pradesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.0\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e43500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.5\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e30113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43.1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e3965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.0\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e32575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePuducherry (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndaman \u0026amp; Nicobar (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLakshadweep (UT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19154.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33.6\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 state of Karnataka has the highest number of ASHA workers in the southern region whereas the Union Territory of Puducherry has the highest (61.9%) percentage of women with Internet access and Lakshadweep has the highest number (46.9%) of women with comprehensive knowledge of HIV. In Lakshadweep, there are only 110 ASHAs but 56.4% of women with Internet access.\u003c/p\u003e \u003cp\u003eIn the Eastern region of India, in every state, more urban women have access to the Internet as compared to the rural one (Table\u0026nbsp;3). Jharkhand (31.4%) has the highest percentage of women with internet access in the region compared to the other regional states. It is followed by West Bengal (25.5%), Odisha (24.9%), and Bihar (20.6%). In every state of the Eastern region, more urban women have comprehensive knowledge of HIV as compared to rural women of that state. If compared to NFHS4, the state of Odisha has shown slight improvement in the number of women with comprehensive knowledge of HIV. In the eastern region, though Bihar has the highest number (89437) of ASHA workers, the state has the lowest (10.3%) number of women with comprehensive knowledge of HIV.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-3-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Eastern region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%) NFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBihar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e41312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.8\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e46566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.3\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e61545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54055.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.2\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\u003eIn the Western region of India, urban as well as rural women of Goa are more in number for access to the internet (Table-4). Goa is followed by Maharashtra (38.0%), Rajasthan (36.9%), and Gujrat (30.8%) for total women who got internet access. Goa is the only state where rural women have more knowledge of HIV as compared to the urban one. Compared to NFHS4 data, women in every state of this region of India have shown improvement in their knowledge of HIV. In western region, Maharashtra has highest number of ASHA workers (70282) but 34.4% of women with comprehensive knowledge of HIV whereas as there is no data available for ASHAs in the state of Goa.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-4-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Western region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%) NFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRajasthan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e70282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGujrat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.4\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDadra \u0026amp; Nagar Haveli, Diu \u0026amp; Daman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36297.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.2\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\u003eIn Chhattisgarh and Madhya Pradesh of Central India, double the urban women have access to the internet as compared to rural women (Table\u0026nbsp;5). In both the states of central India, urban women are more in number as compared to rural ones as far as their knowledge of HIV and in both states the comprehensive knowledge of HIV has improved in women as compared to NFHS4 data of 2015-16. Chhattisgarh has 69515 and Madhya Pradesh has 77531 ASHA workers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-5-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the Central region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%) NFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e69515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.7\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\u003e77531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.6\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\u003eIn north-east region of India, Assam has the highest number (32256) of ASHA workers whereas Sikkim has the lowest number (656) of ASHA workers. In North-Eastern India, urban Sikkim (90%), followed by Mizoram (83.8%) and Arunachal Pradesh (70.7%) have the highest number of women with internet access, not only in the North-East region but also in the other parts of the country (Table-6). In rural parts of the states, this number is lower than in urban parts of the region. The state of Tripura has the lowest number of women with internet access in urban (36.6%) as well as in rural (17.7%) parts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-6-Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in the North-Eastern region of India-\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of ASHAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWomen with Internet Access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWomen with knowledge of HIV (%) NFHS-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003cp\u003eNFHS-4\u003c/p\u003e \u003cp\u003e(2015-16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArunachal Pradesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.0\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e32256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.4\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e4009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.7\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e6697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.3\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.2\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.5\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.5\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e8044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3944.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56.8\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\u003eIn all the states of the North-east barring Arunachal Pradesh, urban women are more in number with comprehensive knowledge of HIV as compared to their rural counterparts. When compared with NFHS4 data, states like Arunachal Pradesh, Mizoram, Sikkim, and Tripura have shown a decline in the number of women with HIV knowledge. States like Assam, Manipur, Meghalaya, and Nagaland have shown an increase in the population of women with HIV knowledge.\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 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents national-level data on internet access to women (%) and the % of women with comprehensive knowledge of HIV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cb\u003eTable-7- Women with Internet Access (%) and women who have comprehensive knowledge of HIV (%) in India\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational level data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% of women with internet access\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen with knowledge of HIV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban women\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural Women\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNFHS-4 (2015-16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.9\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\u003eMean/average were compared for the regional situation for a percentage of women with internet access and comprehensive knowledge of HIV. The region of north India has shown the highest percentage of women (51.92%) in India with internet access. It is followed by north-east India (47.21%), western India (43.22%), south India (42.95%), central India (26.8%) and eastern India (25.6%). On average highest number of women with comprehensive knowledge of HIV is observed in western India (32.8%) followed by South India (28.78%), North-East India (28.20%), North India (22.67%), central India (20.90%), and eastern India (16.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing the results for the range of access to the internet for women and comprehensive knowledge of HIV, the variation is observed among the various regions of India. North-East India has shown a higher value for the range (53.8%) of Internet access to women. This means in North-East India; some states have a higher percentage of women with Internet access whereas some states have a very low number of women with Internet access. The declining range was followed by north India (44.6), west India (42.9), south India (40.9), east India (10.8) and central India (0.2). As far as comprehensive knowledge of HIV is concerned, the highest value is noticed for north-east India (51.8) followed by south India (32.0), West India (23.7), North India (23.1), east India (11.1), and central India (4.4).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study shows more urban women have access to the Internet as compared to rural women. The knowledge of HIV is higher in urban women as compared to their rural counterparts. The regression analysis done for the data shows a weak inverse relationship between ASHAs per state and the percentage of women with comprehensive knowledge of HIV whereas there is a positive relationship between Internet access to women and comprehensive knowledge of HIV. Similar results are obtained for the correlation coefficient analysis.\u003c/p\u003e \u003cp\u003eA similar study was carried out in four countries of the sub-Saharan region- Ghana, Guinea Bissau, Malawi, and Zimbabwe [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Variation in the prevalence of accessing computers and the internet across regional and socio-economic groups and its association with knowledge of HIV was studied. Participants who reported ever using computers and the internet were more likely to have higher knowledge regarding the transmission of HIV compared to those who did not. Factors such as area of residence, educational attainment, and household wealth status were significantly associated with the usage of computers and the Internet.\u003c/p\u003e \u003cp\u003eCharacteristic of the study population shows Urban women are more educated than their rural counterparts. More than one-fourth (27%) of rural women have never attended school compared with (13%) of urban women. 20% of women in rural areas have completed 12 or more years of schooling compared with 39% in urban areas. Preliminary data shows 33% of women (15\u0026ndash;49 years) in India have ever used the internet. More than half (52%) of women in urban areas have ever used the internet compared with only one-fourth of women in rural areas.\u003c/p\u003e \u003cp\u003eThe data used for the study shows, that ever use of the internet increases with education with 72% of women with 12 or more years of education ever using the internet, compared with 8% of women with less than 5 years of schooling.\u003c/p\u003e \u003cp\u003eOther studies also show women\u0026rsquo;s access to education is a strong determinant of Internet use. Controlling for other variables, urban poor women with at least some kind of secondary education were six times more likely to be online than urban poor women with lower levels of schooling [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of the study in the Malawi women population show that wealth status and education are the primary determinants of HIV knowledge [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It supports our finding as the characteristic of the study population also shows, that more women in the highest wealth quantile have ever used the internet (69%) than those in the lowest wealth quantile (9%). Wealth and education status might be the reason for access to the Internet and HIV knowledge acquired through the Internet. There are studies carried out in Bangladesh [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Tajikistan [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and Vietnam [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] which also support that education is an important determinant of women\u0026rsquo;s knowledge of HIV.\u003c/p\u003e \u003cp\u003eThe better financial status of the urban women of the study population might be due to the opportunities for paid work in the urban areas. The NFHS5 data used for the study shows urban women are most likely to be employed as production workers (28%) and professional workers (22%) whereas in rural areas (61%) women are agricultural workers. Having good socioeconomic status improves media exposure or educational achievement which increases the likelihood of knowledge about HIV/AIDS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our result is in agreement with the study done in populations of Nigeria and the Republic of Congo [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Ghana [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and populations of three East African countries [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA study carried out on Ethiopian women for the determinants of comprehensive knowledge of HIV is in tandem with our results where some of the observed determinants are education, wealth, and having a mobile phone [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The results of this study pointed out that a health education program on AIDS for women has significantly improved their knowledge of AIDS transmission. One of the significant predictors for comprehensive knowledge of HIV/AIDS is a method of contraceptive use. This might be due to those women who use traditional contraceptive methods may be literate and then prone to information than non-users.\u003c/p\u003e \u003cp\u003eOur study supports the observation that education and financial status have a direct role in access to technology for women and consequently in their knowledge of HIV. Whether it is urban or rural if the women have the opportunity to earn; they can avail technology for their improvement. Even in developed countries, low access to digital technologies is noticed among the socio-economically disadvantaged communities and in areas with poor supply of electricity and internet [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBrazil is a country like India with economic inequality and uneven population distribution and faces challenges in achieving internet access to all. A study carried out on the Brazilian population has also shown urban houses, women, and higher income behind internet access and healthcare [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This study also shows, that living in cities with population 100,000 to 499,999 residents, higher education, and being female are the factors associated with the use of the internet for health purposes. In that case, providing internet access on a priority basis to the target areas can, not only help to reduce child mortality and improve maternal health but to improve overall public health in these cities.\u003c/p\u003e \u003cp\u003eIndia has 40 cities with more than a million population, 396 cities with between 1,00,000 and 1\u0026nbsp;million population, and 2500 cities with between 10,000 and 1,00,000 population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. On a priority basis, the internet and electricity should be provided in these areas.\u003c/p\u003e \u003cp\u003eIndia has shown rapid advancement in internet penetration over the last decade but our study highlights, that there are still significant gender differences, regional differences, state-level differences, and urban and rural differences. Studying reasons behind the non-acceptance of mobile/internet in certain areas should be studied. There are studies carried out that show these reasons might be the effect of electromagnetic radiation emitting from mobile phones and towers on multiple organs/organ systems of the human body [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], as well as its effect on the environment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has compounded the challenge of HIV/AIDS elimination, creating difficulties in accessing HIV care services such as early testing and treatment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] shows a study carried out to evaluate online interest in HIV care services related search terms before and during the pandemic. This study supports our observation that during COVID times the knowledge about HIV has lowered as compared to the study period of NFHS4. This global study has shown that resource-poor countries with a high prevalence of HIV have a high online interest in HIV/AIDS. It emphasizes the need to improve internet access, the quality of HIV-related health information, and online health literacy to improve health-seeking behaviour, especially in areas with high disease burden.\u003c/p\u003e \u003cp\u003eIndia is committed to UN SDG 3.3 to end the HIV epidemic by 2030 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Using this study one can frame targeted strategies to reach the maximum women for HIV awareness. Community health workers and the Internet should be used strategically. In rural areas and in urban slums where due to less or no education and economic opportunities, women have limited access to internet health information. Even if they have access to technology, information in the English language is another barrier. This group should be counselled by community health workers. The government should provide health information in regional languages. In the geographical regions where literacy rates are high in women, the government should focus on increasing internet penetration among women by bringing down the cost of technology.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations of study-\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere is no data available for the internet access to women in NFHS-4 for comparison with data of NFHS-5. There are many factors affecting the comprehensive knowledge of HIV in the women population. Only two factors are controlled in this paper, and there is no way to comprehensively consider the impact of other factors on the results in this paper, such as social and cultural barriers. These are the focus of our next research. Secondly, this study is based on the data collected by the Ministry of Health and Family Welfare, Government of India for NFHS-5. The limitations which are related to any secondary data are also related to this data.\u003c/p\u003e "},{"header":"Conclusion and policy implications","content":"\u003cp\u003eIn conclusion, India is a large country with geographical inequality in access to the internet for women population. Socio-economic factors and urban-rural divide decide the access to health technology. Although the role of technology in improving public health is widely accepted in the country there is a need to improve the access of women to internet technology. There is a need for uniform distribution of technology throughout the country understanding the potential of bridging the gap of health care. Our findings reveal that the number of years of education of women, and the financial status of women are determining factors for internet-based health knowledge. Thus, the targeted measures are necessary to provide health information in regional languages as well, and improving the quality of formal education is also important. In areas where the illiteracy of women and access to information in regional language is a problem, community health workers can provide better help if compared to Internet technology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e- Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e - Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/ Competing interests\u0026nbsp;\u003c/strong\u003e- The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u0026nbsp;\u003c/strong\u003e-1) Conceptualization, methodology, analysis, and investigation, writing, reviewing \u0026amp; editing of the draft - J. S 2) Supervision, statistical analysis, original draft preparation/writing, review, and editing- C. S. \u0026nbsp;All authors have agreed to the submission of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u0026nbsp;\u003c/strong\u003e- Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. HIV \u0026amp; AIDS Available at https://www.who.int/news-room/fact-sheets/detail/hiv-aids; 2023 (Accessed on 12th September 2023).\u003c/li\u003e\n\u003cli\u003eMalik M, Girotra S, Roy D, Basu S. Knowledge of HIV/AIDS and its determinants in India: findings from the National Family Health Survey-5 (2019-2021). Popul Med. 2023;5(May):1-12. doi: 10.18332/popmed/163113.\u003c/li\u003e\n\u003cli\u003eGilleece Y, Krankowska D. ART in pregnant women living with HIV. Lancet. 2021;397(10281):1240-1. doi: 10.1016/S0140-6736(21)00626-7.\u003c/li\u003e\n\u003cli\u003eWhat every woman needs to know about HIV and AIDS; March 2023. 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OSF Prepr. 2020. doi:10.31219/osf.io/fexc3\u003c/li\u003e\n\u003cli\u003eSarkar J. Wildlife around communication towers. News Curr Sci. 2011;101(11, 10).\u003c/li\u003e\n\u003cli\u003eOrnos EDB, Tantengco OAG, Abad CLR. Global Online Interest in HIV/AIDS care Services in the time of COVID-19: A Google Trends Analysis. AIDS Behav. 2023 June;27(6):1998-2004. doi: 10.1007/s10461-022-03933-w, PMID 36441409.\u003c/li\u003e\n\u003cli\u003e/1. Transforming our world: the 2030 Agenda for Sustainable Development. October 2015.\u003c/li\u003e\n\u003cli\u003eTHE 17 GOALS. Sustainable Development. Available from: https://sdgs.un.org/goals. (Accessed on 18th October 2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Not applicable","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":"HIV, India, Internet use, population, NFHS-5, SDGs","lastPublishedDoi":"10.21203/rs.3.rs-4393566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4393566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eGlobally women are the major victims of socio-economic and political inequalities. This applies equally to stigma and discrimination related to HIV awareness and treatment. India has the second largest HIV epidemic in the world with 2.467\u0026nbsp;million people living with HIV in 2023. India shares 6.3% of global cases of people living with HIV. The biggest challenge is not only to reach all HIV-infected people but also to reach the maximum number of people for counseling and testing to avoid future transmission. There is a need to frame cost-effective, rapid, and confidential awareness strategies that will eventually encourage people to HIV testing.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e \u003cp\u003eAnonymized, publicly available data of the India National Family Health Survey (NFHS-5) and ASHAs per state is collected from the Ministry of Health and Family Welfare, India. The sample consisted of 724,115 women of 15\u0026ndash;49 years of age and were sub-grouped as urban and rural women. Descriptive statistical analysis, linear regression analysis, and Pearson correlation coefficient analysis were done for the data.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe multiple linear regression equation for women with comprehensive HIV knowledge (%) \u003cem\u003eY\u003c/em\u003e is ŷ = -0.19433\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.32387\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;12.32505 where X1 is the percentage of ASHAs per state and X2 is the percentage of women with Internet access. It shows an R square value of 0.2338 for an overall p-value of 0.0123. Pearson correlation indicated that there is a non-significant medium negative relationship between ASHAs per state (%) and women with knowledge of HIV (%) (\u003cem\u003er\u003c/em\u003e = -0 .315, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061). Whereas, the results of the Pearson correlation indicated that there is a significant medium-positive relationship between the percentage of women with internet access and the percentage of women with comprehensive knowledge of HIV, (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 .481, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eMore urban women have access to the Internet as compared to rural women, which may be the reason why the knowledge of HIV is higher in urban women as compared to their rural counterparts. Internet access to women is more beneficial in states where the rate of literacy is high. In areas where internet access and understanding content in English is an issue, community health workers can provide better support to spread awareness about HIV.\u003c/p\u003e","manuscriptTitle":"Designing strategies to reach the maximum number of women for comprehensive knowledge of Human Immunodeficiency Virus (HIV)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 21:09:07","doi":"10.21203/rs.3.rs-4393566/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":"b20ab91c-5bf9-4240-809c-d7ea99e26bdd","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31708100,"name":"Health Policy"}],"tags":[],"updatedAt":"2024-05-10T21:09:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-10 21:09:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4393566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4393566","identity":"rs-4393566","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}