Abstract
Objectives
Poor menstrual hygiene can contribute significantly to female morbidity, yet the use of hygienic absorbents during menstruation is considerably low among rural adolescent women. This study examines the spatial patterns of hygienic absorbent use during menstruation among rural adolescent women to identify the districts of India where it is significantly clustered. Further, it examines the factors responsible for the spatial disparity in the exclusive use of hygienic absorbents.
Methods
We applied Global Moran's I to estimate the degree of spatial heterogeneity. In addition, we ran Cluster and Outlier Analysis (Anselin Local Moran's I) to locate clusters and outliers in the exclusive use of hygienic absorbents across the districts. Ordinary least squares, spatial lag, and spatial error models were used to identify the determinants of exclusive use of hygienic absorbents.
Results
The Global Moran's I value for the outcome variable was 0.51, indicating a positive spatial autocorrelation in the exclusive use of hygienic absorbents. Central Indian districts had statistically significant cold spots, whereas statistically significant hot spots were discovered in south Indian districts. Our study identified that women's education, religion, social group, household wealth, mass media exposure, and women's employment status were significant determinants of the exclusive use of hygienic absorbents across Indian districts.
Conclusion
There is a substantial spatial disparity in the exclusive use of hygienic absorbents among rural adolescent women in India. The use of hygienic absorbents by rural adolescent women could be encouraged through mass media campaigns, rural healthcare personnel, and free or subsidized delivery of hygienic absorbents in the identified cold spots.
Keywords
1. Introduction
Menstruation is a normal biological event that causes significant physiological changes in girls during adolescence and marks the beginning of physiological maturity.
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Despite being a natural phenomenon, menstruation is associated with several social and religious taboos, superstitions, and hurdles, particularly in the developing countries.1
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For example, in India, especially in rural areas, it is regarded as a filthy and unclean process that renders the girl impure.1
As a result, they are often forbidden from visiting religious places, consuming sour food, washing their hair, and wearing clean clothes; in urban slums, they are refused access to the kitchen and forced to sit separately.5
, - Wagh R.V.
- Upadhye A.J.
- Upadhye J.J.
Menstrual hygiene practices in young girls of urban India.
Int J Reprod Contraception Obstet Gynecol. 2018; 7: 1897-1902https://doi.org/10.18203/2320-1770.ijrcog20181925
6
Due to such a negative attitude towards menstruation, girls often hesitate to talk about menstruation-related issues, leading to various misconceptions and misinformation regarding menstrual hygiene management.1
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Women generally use sanitary napkins, tampons, clothes, or other products to prevent blood stains from being visible during menstruation. A substantial proportion of girls in developing countries such as India use rags (old and tattered garments) as absorbent, potentially dirty, and infectious. Their use can result in several unfavourable health outcomes, including reproductive tract rnfections (RTIs).
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Evidence suggests that RTIs are widespread among adolescent girls in India, and poor menstrual hygiene is one of the primary causes of RTIs, contributing significantly to female morbidity.1
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Thus, access to safe menstruation is a fundamental necessity for women. Nonetheless, there is enough evidence to suggest that a significant number of girls, particularly in rural regions, are unable to manage their menstruation with basic sanitation and dignity, leaving them exposed to RTIs and other diseases.1
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Despite various government efforts, such as the Rashtriya Kishor Swasthya Karyakram, which aim to promote menstrual hygiene among adolescent girls in rural areas by providing subsidized sanitary napkins,
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the use of hygienic absorbents such as sanitary napkins and tampons is still extremely low in rural areas, and this varies greatly across India.1
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Correct information and education about menstruation among adolescents remains a major challenge in India. Thus, a better understanding of the factors that lead to lower demand for hygienic absorbents in rural areas, as well as how this varies across India's districts, is critical from a policy standpoint to achieve safe menstrual hygiene, which is linked to several Sustainable Development Goals.- Bhattacharya S.
- Singh A.
How effective is the menstrual hygiene scheme? An evaluation study from north India.
Int J Community Med Public Heal. 2016; 3: 2584-2586https://doi.org/10.18203/2394-6040.ijcmph20163077
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Although various studies have been conducted at the local, regional, and national levels in India
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and around the world17
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to understand menstrual hygiene practices better and the socioeconomic and demographic aspects that influence them, none of the earlier studies has attempted to examine geospatial pattern and spatial association in the use of hygienic absorbents among rural adolescent girls at the district level in India.1
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Thus, this study differs from previous studies in three key aspects. Firstly, most previous studies in India have focused on women aged 15 to 24; however, this study solely examines adolescent women (15–19 years) because of the unique challenges that adolescent girls face, such as a lack of autonomy in decision-making, particularly in rural areas. Secondly, this study aims to identify the districts of rural India where the exclusive use of hygienic absorbents is clustered and where it is deficient. Thirdly, this study systematically investigates the factors shaping such a regional dispari in the exclusive use of hygienic absorbents among rural adolescent women in India.Therefore, the present study systematically analyses geospatial pattern, spatial association, and correlates of exclusive use of hygienic absorbents among rural adolescent women in India.
2. Data and methods
2.1 Data source
We used data from the fourth round of the National Family Health Survey (NFHS-4) conducted during 2015–16. It is a large-scale, nationally representative survey that provides data on India's population, health, and nutrition.
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The NFHS-4 national report contains detailed information on the sampling procedures used in the survey.20
With a response rate of 97.0%, the survey interviewed 601,509 households and 699,686 women aged 15 to 49. Among these 699,689 women, only 247,833 women aged 15–24 years were interviewed for the menstrual hygiene module, so the remaining women were excluded from this study. The final sample used in this study included 91,655 rural adolescent women (see Fig. 1).
Fig. 1Flow chart showing the process of selection of rural adolescent women (15–19 years) sample.
2.2 Ethical statement
This study relied solely on an anonymous public-use dataset provided by the International Institute for Population Sciences (IIPS), for which no ethical approval was required.
2.3 Statistical analysis
In this study, we used descriptive statistics, calculated Global Moran's I, performed cluster and outlier analysis (Anselin Local Moran's I), and ran a set of regression models.
We first prepared a district-level map to examine the signs of spatial clustering in the exclusive use of hygienic absorbents among rural adolescent women in India. Then, we attempted to determine the degree of spatial autocorrelation using Global Moran's I. Moran's I values range between −1 and +1. A value of zero denotes a random spatial pattern. A positive Moran's I shows geographic clustering and positive spatial autocorrelation (i.e., nearby places have similar values) and vice versa.
Furthermore, we used Anselin Local Moran's I statistics, also known as cluster and outlier analysis, to identify and locate the concentration of high and low values and spatial outliers. The following types of clusters and outliers emerge from this analysis:
- 1.High-High clusters: districts with high values surrounded by other districts with comparable high values, also known as hot spots.
- 2.Low-Low clusters: districts with low values surrounded by other districts with comparable low values, also known as cold-spots.
- 3.High-Low outliers: high-value districts surrounded by low-value neighbours.
- 4.Low-High outliers: low-value districts surrounded by high-value neighbours.
We used polygon contiguity edges and corners weight for preparing spatial weights in ArcGIS 10.5.
Finally, we used various regression models to examine the correlates of exclusive use of hygienic absorbents. We began with an Ordinary Least Squares (OLS) regression. We employed Lagrange Multiplier (LM) and Robust Lagrange Multiplier (LM) diagnostic tests to discover spatial dependence in our dataset while performing OLS. Since both LM and Robust LM tests were significant for both Spatial Lag Model (SLM) and Spatial Error Model (SEM), we identified which model had the lowest Akaike Information Criterion (AIC) and Schwarz Criterion (SC) value and the highest log-likelihood value.
21
As SEM had the lowest AIC and SC values and the highest log-likelihood value, we considered SEM the best model among all three models. We used ArcGIS 10.5, GeoDa, GeoDaSpace, and Stata16 to analyze the data in this study.2.4 Dependent variable
NFHS-4 asks a multiple-response question to respondents to understand which protection methods they use during their menstrual period to prevent blood stains from becoming evident. Response options included six categories, i) cloth, ii) locally prepared napkins, iii) sanitary napkins, iv) tampons, v) nothing, and vi) others. Based on these responses, we constructed a binary outcome variable ‘exclusive use of hygienic absorbents’. It was defined as using one or more of the following products: sanitary napkins, locally made napkins, and tampons (coded as 1). The second category of this binary variable, the 'non-exclusive use of hygienic absorbents', was defined as the use of non-hygienic materials such as cloths, the use of both hygienic and non-hygienic absorbents, or no use of any form of menstrual absorbents (coded as 0).
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2.4 Independent variables
We considered a range of socioeconomic and demographic predictors such as religion, household wealth, education level of respondents, social group, exposure to mass media, and interaction with healthcare workers. The variable choice was guided by existing literature on menstrual hygiene management.
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The explanatory variables used in this study, their categories, and definitions are listed in Table 1.Table 1Operational definition of variables used in the study.
| Variables | Description |
|---|---|
| Religion | Religion is recoded into binary categories. ‘Muslim’ 1 and ‘non-Muslim’ (0) (including Hindu, Christian, Sikh, Buddhist/Neo-Buddhist, Jain, Jewish, Parsi/Zoroastrian, no religion, and other) |
| Household wealth | The wealth index is a composite index of household amenities and assets; it indicates the socioeconomic condition. In NFHS 4, every household is given a score based on the number of consumer goods they own. Total 33 assets and housing characteristics were taken into consideration to prepare a factor score using Principal Component Analysis. After that this factor score is divided into five equal categories, - ‘poorest' 1 , 23 ; ‘poorer'
Intimate partner violence among scheduled caste women in India: a cross-sectional study. Vict. Offenders. 2022; https://doi.org/10.1080/15564886.2022.2069897 2 , 24 ; ‘middle'
Economic inequality in intimate partner violence among forward and backward class women in India: a decomposition analysis. Vict. Offenders. 2022; https://doi.org/10.1080/15564886.2022.2080312 3 , 25 ; ‘richer'
Short birth interval and associated factors in rural India: a cross-sectional study. J. Biosoc. Sci. 2022; https://doi.org/10.1017/S0021932022000256 4 ; ‘richest',5 ,
Menstrual hygiene practices in young girls of urban India. Int J Reprod Contraception Obstet Gynecol. 2018; 7: 1897-1902https://doi.org/10.18203/2320-1770.ijrcog20181925 26 each with 20% of the population.
Decomposing the gap in intimate partner violence between Scheduled Caste and general category women in India: An analysis of NFHS-5 data. SSM - Popul. Health. 2022; https://doi.org/10.1016/j.ssmph.2022.101189 Further, we grouped the household's wealth into two categories: poor (included poorest and poorer) and non-poor (had middle, richer, and richest). |
| Education level of respondent | Education level indicates the highest education level of a respondent. It is divided into ‘no education’ (0) and ‘educated’ 1 by combining primary, secondary and higher education. |
| Social groups | Social groups are divided into two categories- ‘non-SC/ST’ (0) and ‘SC/ST’, 1 We have combined scheduled caste and scheduled tribe as ‘SC/ST’, and combined other backward castes and others as ‘non-SC/ST’ |
| Exposure to mass media | Three questions were asked to women in NFHS-4 survey. They are i) how often they read newspaper/magazines, ii) how often they watch television, and iii) how often they listen to radio. The responses are ‘almost every day’, at least once a week, less than once a week and not at all. Based on these responses, we have formed a dichotomous variable. women were considered ‘have’ 1 mass media exposure if they had exposure to any of these sources and as ‘no exposure’ (0) if they responded with ‘not at all for all three sources of media |
| Interaction with healthcare workers (in the last 3 months) | Two questions were asked to the respondents in NFHS-4 they are- i) in the last three months, if the respondent has met with any health worker- including an Auxiliary Nurse-Midwife (ANM), Accredited Social Health Activist (ASHA), Anganwadi Worker (AWW), also known as Integrated Child Development Services Worker, Multipurpose Worker (MPW), or any other community health worker; and ii) if they have discussed menstrual hygiene during the meeting. By combining these two, a new variable was generated with two categories - ‘did not meet with healthcare workers’ (0); ‘met with healthcare workers’ 1 |
3. Results
3.1 Sample characteristics
Table 2 shows the characteristics of sampled adolescent women. About 91% of the sampled women were educated. Only 8.1% of women never went to school. Approximately 60% of the women in the sample were SC/ST, and about 13% were Muslim. A significant proportion of rural adolescent women did not meet with healthcare workers three months before the survey. Around 43% of women were impoverished and about 20% had no media exposure.
Table 2Percentage distribution of rural adolescent women by background characteristics, NFHS-4 (2015–16), India.
| Background characteristics | N (91,655) | % |
|---|---|---|
| Religion | ||
| Non-Muslim | 79,513 | 86.75 |
| Muslim | 12,142 | 13.25 |
| Household Wealth | ||
| Poor | 39,330 | 42.91 |
| Non-poor | 52,325 | 57.09 |
| Education level of respondent | ||
| No education | 7,382 | 8.05 |
| Educated | 84,273 | 91.95 |
| Social Groups | ||
| Non-SC/ST | 55,327 | 60.36 |
| SC/ST | 36,328 | 39.64 |
| Mass media exposure | ||
| No | 18,653 | 20.35 |
| Yes | 73,002 | 79.65 |
| Interaction with healthcare workers | ||
| No | 77,184 | 84.21 |
| Yes | 14,471 | 15.79 |
Note: N = number of rural adolescent women, % = weighted percentage of N.
a Interaction with healthcare workers in last three months.
3.2 Exclusive use of hygienic absorbents by background characteristics
Table 3 presents the proportion of rural adolescent women who reported exclusive use of hygienic absorbents by their background characteristics. The exclusive use of hygienic absorbents among educated adolescent women was five times higher than those who never attended school (6.0%). While 43% of women in non-poor households used hygienic absorbents, only 16.0% of women in poor households did so. The exclusive use of hygienic absorbents was significantly low among SC/ST women (24.8%). Exclusive hygienic absorbent use was higher among the adolescents exposed to mass media (32.1%) than those who reported no exposure to mass media (8.6%).
Table 3Percentage of rural adolescent women aged 15–19 who exclusively used hygienic absorbents for menstrual bloodstain prevention by selected background characteristics, India, NFHS-4, 2015-16.
| Background characteristics | Percent of women using hygienic absorbents (weighted percentage) N = 91,655 | 95% CI | |
|---|---|---|---|
| Lower | Upper | ||
| Religion | |||
| Non-Muslim | 28.49 | 27.85 | 29.14 |
| Muslim | 19.85 | 18.57 | 21.20 |
| Household wealth | |||
| Poor | 15.81 | 15.24 | 16.40 |
| Non-poor | 42.69 | 41.81 | 43.57 |
| Education level of respondent | |||
| No education | 6.444 | 5.71 | 7.26 |
| Educated | 29.17 | 28.55 | 29.81 |
| Social group | |||
| Non-SC/ST | 28.68 | 27.96 | 29.41 |
| SC/ST | 24.75 | 23.89 | 25.64 |
| Mass media exposure | |||
| No | 8.66 | 8.07 | 9.28 |
| Yes | 32.14 | 31.46 | 32.83 |
| Interaction with healthcare workers | |||
| No | 27.17 | 26.54 | 27.81 |
| Yes | 28.09 | 26.89 | 29.33 |
Note: N = number of rural adolescent women, CI = confidence interval.
a Interaction with healthcare workers in last three months.
3.3 Spatial patterns of exclusive use of hygienic absorbents among rural adolescent women across states and 640 districts of India
Although the state-level analysis may provide a broad idea about spatial variation in the exclusive use of hygienic absorbents; however, it masks spatial heterogeneity at the district level. Therefore, we mapped the exclusive use of hygienic absorbents for India's districts.
Fig. 2 depicts the spatial pattern of exclusive use of hygienic absorbents among rural adolescent women across 640 districts. Out of these 640 districts, the exclusive use of hygienic absorbents was more than 60% in 89 districts, between 20 and 60% in 288 districts, and less than 20% in the remaining 246 districts. Many districts in Uttar Pradesh, Bihar, Chhattisgarh, Odisha, Rajasthan, and some North-eastern states have lower exclusive use of hygienic absorbents. In contrast, districts in Tamil Nadu, Kerala, Andhra Pradesh, Sikkim, Arunachal Pradesh, and Mizoram have higher exclusive use of hygienic absorbents. The two most laggard districts in terms of exclusive use of hygienic absorbents (Mandla and Annupur) were from Madhya Pradesh. In contrast, two leading districts (Aizawl and Madurai) were from the state of Mizoram and Tamil Nadu.
Fig. 2District wise distribution of exclusive use of hygienic absorbents during menstruation among the rural adolescent women in India, 2015-16.
3.4 Global Moran's I
To examine the spatial autocorrelation, we computed Global Moran's I value for the exclusive use of hygienic absorbents at the district level in India. The Global Moran's I index was 0.51, with the Z score of 63.72 and a p-value of <0.01, so there was less than a 1% likelihood that this clustering resulted from random chance, which signified high spatial autocorrelation. This result necessitated that we looked for spatial clusters of exclusive use of hygienic absorbents at the district level. To locate where the clusters were, we employed the Cluster and Outlier Analysis, a type of Local Indicator of Spatial Association (LISA).
3.5 Cluster and outlier analysis (Local Moran's I)
Fig. 3 shows the statistically significant spatial outliers, hot and cold spots. Hot spots were identified in the southern states of India. On the other hand, some of the districts of Uttar Pradesh, Bihar, Madhya Pradesh, and Chhattisgarh formed statistically significant cold spots. Two districts of Karnataka and Punjab showed Low-High spatial outliers. In contrast, two districts of Uttar Pradesh and two from the eastern and western parts of Madhya Pradesh showed significant High-Low spatial outliers.
Fig. 3Cluster and Outlier analysis map (Anselin Local Moran's I) using polygon contiguity edges and corners weight in ArcGIS 10.5, showing the statistically significant (p value < 0.05) spatial clusters and outliers in the exclusive use of hygienic absorbents among rural adolescent women accross the districts of India, 2015-16.
3.6 OLS and spatial regression models
Global and Local Moran's I index indicated strong significant geospatial clustering in the outcome variable, so we used OLS, SLM, and SEM to detect relevant and significant determinants of exclusive use of hygienic absorbents.
Table 4 presents the OLS, SLM, and SEM results. OLS estimation was a preliminary check of the association between the exclusive use of hygienic absorbents and their correlates without considering the spatial structure of data. During OLS estimation, two sets of LM and Robust LM tests were used to decide the suitability of the model to predict spatial dependence in our spatial dataset. Both LM and Robust LM values for SLM and SEM were statistically significant, indicating spatial dependence in our dataset, so we decided to run both SLM and SEM in our analysis. We found that the Robust LM value of error was greater than that of lag during the comparison. Also, the values of AIC and SC (explaining better suitability of the model) were least for SEM, which guided us to apply SEM to analyze the spatial dependence of exclusive use of hygienic absorbents among rural adolescent women with various predictors.
Table 4Spatial regression model for estimating spatial association between percentage of exclusive use of hygienic absorbents among rural adolescent women (15–19 years) and background.
| Variables | OLS (p value) | SLM (p value) | SEM (p value) |
|---|---|---|---|
| Muslim (%) | −0.06 (0.10) | −0.04 (0.22) | −0.13 (0.00) |
| Non-poor (%) | 0.41 (0.00) | 0.21 (0.00) | 0.47 (0.00) |
| Educated (%) | 1.57 (0,00) | 0.86 (0.00) | 0.66 (0.00) |
| SC/ST (%) | 0.23 (0.00) | 0.14 (0.00) | 0.08 (0.01) |
| Have mass media exposure (%) | 0.89 (0.00) | 0.49 (0.00) | 0.67 (0.00) |
| Met with healthcare workers (%) | −0.01 (0.97) | −0.10 (0.48) | −0.18 (0.25) |
| Rho | 0.49 (0.00) | ||
| Lambda | 0.73 (0.00) | ||
| AIC | 5269.98 | 5081.72 | 4970.69 |
| Log Likelihood | −2627.99 | −2532.86 | −2478.35 |
| SC | 5301.04 | 5117.22 | 5001.76 |
Note: OLS: Ordinary Least Squares, SEM: Spatial Error Model, SLM: Spatial Lag Model, AIC: Akaike Information Criterion, SC: Schwarz Criterion.
Results of the SEM are also presented in Table 4. The lambda value was 0.73 (p < 0.01), which is highly significant and implies that the error terms are spatially dependent. SEM confirmed that women's religion (β = −0.13, p < 0.01), household wealth (β = 0.47, p < 0.01), education (β = 0.66, p < 0.01), social group (β = 0.08, p < 0.01), exposure to mass media (β = 0.67, p < 0.01) were statistically significant indicators of the exclusive use of hygienic absorbents among rural adolescent women. The coefficient of mass media exposure was found to be highest, followed by education and household wealth status. SEM model results suggested that if the proportion of Muslim adolescent women in a district increased by 10%, then the exclusive use of hygienic absorbents declined by 1.27%. On the other hand, if women from the non-poor household in a district increased by 10%, the use of hygienic absorbents also significantly increased by 4.69%. The respondent's level of education was positively associated with the use of hygienic absorbents. Similarly, a 10% increase in SC/ST women was associated with a slight increase in the outcome's prevalence. With the exposure to mass media, the exclusive use of hygienic absorbents also significantly increased at the district level among rural adolescent women in India.
4. Discussion
We employed multiple methods to identify spatial clustering, hot spots, and cold spots across Indian districts. The Global Moran's I indicated a high spatial autocorrelation.
The regional disparity in the exclusive use of hygienic absorbents across districts of India was clearly visible. Spatial clustering was more common in districts in Uttar Pradesh, Bihar, and Madhya Pradesh, where the exclusive adolescents are very low. Menstruation carries a greater social stigma in the rural districts of Madhya Pradesh and Uttar Pradesh, Bihar, and Chhattisgarh, particularly in India's central and northern states. These states have relatively low socioeconomic development, educational attainment, and healthcare access. As a result, adolescent girls are less likely to utilize sanitary absorbents in these states.
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The increased use of absorbents in Tamil Nadu, Andhra Pradesh, and Telangana may be due to several subsidized and free sanitary napkin programs. For example, Tamil Nadu has been delivering free sanitary napkins to rural adolescent women since 2011 through the Pudhu Yugam (New Era) program.
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In collaboration with local NGOs, some states have installed soap vending machines in schools.28
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The region's poorer socioeconomic development may explain the low use of hygienic absorbents in a cluster of districts in north-eastern Karnataka. In central UP and eastern Madhya Pradesh districts, there were pockets of ultra-low (less than 10% and 5%) prevalence in the use of hygienic absorbents. Because over 250 districts in the central, east, north-eastern, and western regions had very low exclusive use of hygienic absorbents, it is critical to focus on these districts if the overall exclusive use of hygienic absorbents among rural adolescent women is to be increased.In order of importance, exposure to mass media, respondent education, household wealth, religion, social group, and religion emerged as critical correlates of the exclusive use of hygienic absorbents.
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As mass media is vital in conveying critical societal issues, in rural places, media's power should be utilized to spread knowledge about menstrual hygiene practices, particularly in the country's central and western districts.The hygienic absorbent use increased with the increasing education level of respondents. The exclusive use of hygienic absorbents was higher among educated rural adolescent women than the women with no formal education. Evidence suggests that educated women are well-versed in using hygienic absorbents
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and are aware of the dangers of unsanitary menstrual traditions.6
With the increase in household wealth status, the exclusive use of hygienic absorbents increased significantly.
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Poverty has been identified as a major barriers to use of hygienic absorbents.3
A pack of 10 sanitary napkins costs around 30–40 INR (0.39–0.52 USD), which is unaffordable for most rural households in India.1
Most studies on menstrual hygiene have found that Muslim women are less likely to use disposable hygienic absorbents.
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Our study confirms the same, and it is observed that rural Muslim adolescent women prefer clothes to prevent blood stains from being visible. The underlying cause of this behaviour could be the low autonomy of Muslim adolescent girls and low level of awareness of the availability of hygienic absorbents in the market. Also, some social and religious taboos also probably restrict the exclusive use of hygienic absorbents among rural adolescent Muslim girls.19
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Most past studies have documented that the prevalence of exclusive hygienic absorbents is relatively low among SC/ST women.
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Our results, confirming the same, clearly indicate that rural adolescent SC/ST women are less inclined to choose exclusive hygienic absorbents during their menstruation. Most tribal communities still live in inaccessible far-off places with almost no access to markets and healthcare facilities. Because of this isolation, disposable sanitary products are scarce.3
Despite providing detailed information on the geospatial pattern, spatial association, and correlates of exclusive use of hygienic absorbents by rural adolescent women across districts of India, this study has some limitations. First of all, due to the lack of supply-side factors in the NFHS-4 dataset, variables on the supply of subsidized absorbents to rural health workers, the availability and cost of hygienic absorbents at rural pharmacies and provision stores, and the supply situation of sanitary napkins in schools could not be considered in the study. These variables are essential for explaining the level of service or product used according to the demand-supply framework of healthcare utilization. Furthermore, as the analysis uses cross-sectional data, it prevents us from establishing a causal relationship between the predictors and the outcome variable. In addition, the present study could not assess whether women used sanitary napkins hygienically. Another limitation is that the NFHS-4 does not provide data on some potential determinants of absorbent use, such as access to hygienic absorbents, sanitary situations at schools, disposability issues, social-cultural norms, myths, attitudes, and beliefs regarding menstrual practices. Hence, we could not include these variables in the analysis. We could not include variables, such aas respondent's autonomy, working status of respondents, ownership of bank account and mobile phone in our model as these were only available in the state module of NFHS-4. Finally, the NFHS-4 data on menstrual absorbent use could be affected by some biases like social desirability bias, where respondents under-report unhygienic menstrual practices and over-report the use of hygienic absorbents.
5. Conclusion
The exclusive use of hygienic absorbents by rural adolescent women in India varies across the districts of India. We discovered substantial north-south clustering in the exclusive use of hygienic absorbents, where the hygienic absorbents use is considerably low and clustered in the central states of India. We identified considerable spatial clustering in areas of high poverty, low female education, low media exposure, and low SC/ST social group membership. Policymakers may focus on rural adolescent women in these regions and educate them on menstrual hygiene to promote exclusive use of hygienic absorbents.
Author contributions
Aditya Singh Conceptualization; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing. Mahashweta Chakrabarty Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Visualization; Writing – original draft; Writing – review & editing. Sourav Chowdhury Writing – original draft; Writing – review & editing. Shivani Singh Supervision; Writing – original draft; Writing – review & editing.
Funding statement
This research did not receive any specific grant from public, commercial, or not-for-profit funding agencies.
Declaration of competing interest
The authors have no conflicts of interest to declare for this study.
APPENDIX.
Spatial autocorrelation
Spatial autocorrelation generally measures the degree of correlation between the value of a variable in a specific location and the values of the same variable at neighbouring locations.
Global Moran's I
Global Moran's I estimate spatial autocorrelation by the single value for the entire study area, known as global spatial autocorrelation measures.
Where is Moran's I value, n is the number of the spatial features, is the attribute value of feature I, is the attribute value of feature j, is the mean of this attribute, is the spatial weight between feature i and j, is the aggregation of all spatial weights. The tool calculated the mean , the deviation from the mean and the data variance (denominator). Deviations from all neighbouring features are multiplied to create cross-products (the covariance term). Then, the covariance term is multiplied by the spatial weight. All other parameters are used to normalize the value of the index. Moran's-I values vary from −1 to +1. A value of zero denotes a random spatial pattern. Positive Moran's I indicates the clustering in the geographical distribution and positive spatial autocorrelation (i.e., nearby locations have similar values) and vice versa.
Where is Moran's I value, n is the number of the spatial features, is the attribute value of feature I, is the attribute value of feature j, is the mean of this attribute, is the spatial weight between feature i and j, is the aggregation of all spatial weights. The tool calculated the mean , the deviation from the mean and the data variance (denominator). Deviations from all neighbouring features are multiplied to create cross-products (the covariance term). Then, the covariance term is multiplied by the spatial weight. All other parameters are used to normalize the value of the index. Moran's-I values vary from −1 to +1. A value of zero denotes a random spatial pattern. Positive Moran's I indicates the clustering in the geographical distribution and positive spatial autocorrelation (i.e., nearby locations have similar values) and vice versa.
Local Moran's I
Local Moran's I index helps to estimate spatial autocorrelation at the local level and identifies clustering of high or low values and also trace spatial outliers. The formula to calculate the Local Moran's I statistic is as follows-
Where n is the total number of observations (spatial objects), is the attribute value feature j, is the attribute value of feature j, is the mean of this attribute, is the spatial weight between feature i and j, is a constant for all locations. It is a consistent but not unbiased estimate of the variance.
Where n is the total number of observations (spatial objects), is the attribute value feature j, is the attribute value of feature j, is the mean of this attribute, is the spatial weight between feature i and j, is a constant for all locations. It is a consistent but not unbiased estimate of the variance.
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Article Info
Publication History
Published online: August 01, 2022
Accepted:
July 21,
2022
Received in revised form:
July 10,
2022
Received:
May 19,
2022
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