Urban-rural disparities in cervical cancer screening among Indian women between 30–49 years: a geospatial and decomposition analysis using a nationally representative survey

Abstract

Introduction

Existing evidence suggests a lower uptake of cervical cancer screening among Indian women. Coverage is lower in rural than urban women, but such disparities are less explored. So, the present study was conducted to explore the self-reported coverage of cervical cancer screening in urban and rural areas stratified by socio-demographic characteristics, determine the spatial patterns and identify any regional variations, ascertain the factors contributing to urban-rural disparities and those influencing the likelihood of screening among women aged 30–49 years factors residing in urban, rural, and overall Indian settings.

Methods

We did a secondary analysis of the fifth round of the National Family Health Survey in India (2019-21) data with a sample size of 3,48,882 women. The coverage of cervical cancer screening was estimated using sampling weights. Urban-rural differences were compared using the chi-square test. Spatial patterns were analysed using aggregated district-level data, and the contribution of different independent variables to the urban-rural disparities was estimated using multivariate decomposition analysis. Multivariable logistic regression was conducted using STATA 17 to obtain the significant factors of reported screening in urban and rural areas.

Results

The nationwide coverage of cervical cancer screening was 2.0% (95% CI: 1.9-2.0). The urban (2.4%; 2.3–2.5) participants had higher screening coverage than their rural (1.8%; 1.7–1.8) counterparts. Moran’s I statistic confirmed the presence of spatial dependence and geographical gradient. Decomposition analysis depicted small urban-rural differences in the screening coverage of 0.60% (0.4–0.8). Endowment and coefficient contributed to 88.15% and 11.85% of the disparities. Compositional changes were contributed majorly by regional differences, low education, scheduled tribes, and having living children > 2. Higher odds of having screening were associated with older age (AOR 1.45, 95% CI: 1.03–1.28), higher education (1.32; 1.13–1.55), higher age of first intercourse (1.60; 1.43–1.79), married (1.25; 1.08–1.45) and diabetic (1.39; 1.17–1.65) women, and those from South India (6.76; 5.90–7.75). The odds were lower among Muslims, scheduled tribes and participants using hormonal contraceptives.

Conclusion

There are significant urban-rural disparities in cervical cancer screening uptake that can be attributed to regional variation, educational inequalities, tribal groups, socio-economic inequalities and parity, necessitating the need to comprehensively design tailor-made advocacy initiatives and simultaneously address the broader determinants of health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-025-13446-z.

Introduction

With an estimated 9.6 million fatalities, or 1 in 6 deaths, worldwide, cancer is the second most significant cause of mortality [1]. GLOBOCAN 2022 reports three times greater cancer incidence in high-income countries (HICs) than in low- and middle-income countries (LMICs) [2]. Due to widespread disparities, while 50% of diagnosed cancer cases are from Asia, the continent witnesses a much higher proportion of global cancer-related deaths (58%) [3]. Likewise, the Medical Certification of Cause of Death (2020) estimates cancer as the fifth leading cause of death in India [4]. The age-standardized incidence rate (ASIR) of cancer in India is approximately 98.5 per 100,000 population, higher in females (100.8/100,000) than males (97.1/100,000), and the age-standardized mortality rate (ASMR) is approximately 64.4 /100,000 individuals, higher in males (66.5/100,000) than females (62.6/100,000) [5, 6]. Further, the Indian National Cancer Registry Programme (NCRP, 2020) estimates suggest that around 3.5% of Indian women develop cancer at some point in their lifespan [7]. Among all cancers in Indian women, cervical cancer is the second most frequent [8]. A greater proportion of cervical cancer-related deaths can be attributed to inadequate screening and delayed management. [5, 6]. Previous studies report that only 44% of cervical cancer patients are diagnosed in the earlier stages, leading to a decreased 5-year survival rate that varies between 17–59%, compared to survival rates over 90% with timely diagnosis [9, 10].

Early diagnosis can be ascertained by emphasising continued population-based screening of the vulnerable population. The World Health Organisation (WHO) also advocates for early identification of cervical cancer, screening for the disease every 5–10 years, starting from 30 years, and ensuring that established referral channels and suitable care are available [11, 12]. In compliance, the Indian National Programme for Prevention & Control of Non-Communicable Diseases (NP-NCD), previously known as the National Programme for Prevention & Control of Cancer, diabetes, cardiovascular diseases & stroke, also recommends visual inspections with acetic acid (VIA) based cervical cancer screening of women between the ages of 30 and 65 years every five years [13, 14]. The program mandates healthcare workers to regularly evaluate the target population in the community for risk factors of common cancers, e.g. oral, breast, and cervical, during their household visits to promote health education and accessibility. Despite the efforts, the fourth round of the Indian National Family Health Survey (NFHS-4) reported abysmally lower coverage (29.8%) for cervical examination, and the fifth National Family Health Survey (NFHS-5; 2019-21) documented even lesser screening (1.9%) in women aged 30–49 years [15, 16].

Previous studies have depicted urban-rural disparities affecting other non-communicable disease burdens and their epidemiology. However, the impact of such societal and spatial disparities on cervical cancer screening. coverage remains unexplored with respect to cervical cancer screening [17, 18]. The low coverage is attributed to a gap in our understanding of implementing screening programs and the population's acceptance of these measures. While invitations to the eligible population are utilised in HICs to conduct screening, in India, we largely rely on individuals’ willingness to disclose risk factors to healthcare workers and avail screening facilities in nearby health clinics [19]. The most commonly reported barriers to screening are "embarrassment or shyness" (psychological), "lack of comprehension and understanding of screening, diagnosis and treatment" (knowledge and awareness), "shortage of time" (structural), and "shortage of household involvement" (sociocultural and religious) [20]. The Link and Phelan’s Fundamental Cause Theory (FCT) link these barriers to socioeconomic disparities, which are crucial in determining sickness and healthcare demand. This suggests that having access to resources like wealth, knowledge, and social networks helps people live healthier lifestyles, make wiser decisions when seeking medical attention, and put themselves in the best possible position to be less influenced by ailments [21]. While NFHS-5 documented higher coverage of cervical cancer screening in urban (2.36%) than in rural (1.76%) residents, we still need a better understanding of the factors contributing to such disparities. Within this context, the NFHS allows us to study the socio-demographic disparities in cervical cancer screening coverage in detail. So, the present study was conducted to explore the coverage of cervical cancer screening in urban and rural areas stratified by socio-demographic characteristics, determine the spatial patterns and identify any regional variations, ascertain the factors contributing to urban-rural disparities and those influencing the likelihood of cervical cancer screening separately among women aged 30–49 years, residing in urban, rural, and overall Indian settings.

Methods

Study design

The present study is a secondary data analysis of a national survey which is designed to be nationally representative [22, 23].

Data sources

We used data from the fifth round of the National Family Health Survey, NFHS-2019–21 in the present research [15]. This extensive survey conducted by the International Institute for Population Sciences, Mumbai, under the direction of the Indian Ministry of Health and Family Welfare, uses a multi-phase, stratified cluster sampling methodology. The primary sampling units (PSU) were census enumeration blocks (CEB) in urban and villages in rural areas. Probability proportion to size (PPS) was used to select the PSU [15]. Data on emerging family and health-related issues are gathered for the survey conducted by the NFHS from time to time. It provides solid evidence to support, monitor and evaluate ongoing national programmes and opens new avenues for finding unmet needs in the population.

Study population

The original survey included women in the reproductive age group, i.e. between 15–49 years and men between 15–54 years. A total of 636,699 households were included in NFHS-5, comprising 724,115 women and 101,839 men were interviewed. Because the NFHS survey primarily focuses on women of reproductive age and children under five, it has an uneven proportion of women and men.

Sample Size and sampling procedure

The data from the urban and rural areas of all Indian districts (a territorial division of Indian state/province for administrative, judicial, electoral, or other purposes.) were collected using a two-stage sampling. Census Enumeration Blocks (CEB) were selected in the first stage, after which 22 households in each CEB were randomly selected. In rural areas, the villages were considered as the Primary Sampling Units (PSU) in the first stage. In the second stage, 22 households were randomly chosen from each PSU. A total of 636,699 households were included in NFHS-5, comprising 724,115 women (15 to 49 years) and 101,839 men (15 to 54 years). As per population-based screening (PBS) protocols of the NP-NCD, women aged≥30 years are recommended to undergo regular cervical cancer screening [17]. Thus, we have only included a subset of women in the reproductive age group (30–49 years) for the present study. After excluding cases with missing variables and outliers by complete case analysis (row-wise complete deletion), the final sample of 348882 women was included in the analysis (Fig. 1).

Fig. 1.

Flowchart depicting the sample selection process of eligible female participants from the fifth round of the Indian National Family Health Survey (2019-21)

Data collection

The NFHS uses four types of tools to collect the data through computer-assisted personal interviewing (CAPI) after translating them into the local languages and include the household questionnaires, women’s questionnaires, men’s questionnaires, and biomarker assessments. All regular household members and guests who spent the night before were asked to complete a Household Schedule. Specifically, the woman’s questionnaire collected information from all eligible women aged 15–49, who were asked questions on a large variety of topics related to background characteristics, reproduction, prevalence of hysterectomy, menstrual hygiene (for women aged 15–24 years), family planning, contacts with community health workers; maternal and child health, breastfeeding, and nutrition (antenatal care; delivery care; postnatal care, postpartum amenorrhoea, breastfeeding and child feeding practices, vaccination coverage, prevalence and treatment of diarrhea, symptoms of acute respiratory infection, and fever, use of oral rehydration therapy (ORT), utilization of ICDS services), Marriage and sexual activity, fertility preferences, husband’s background and woman’s work (husband’s age, schooling, and occupation, and the woman’s employment and type of earnings), Women’s empowerment (household decision making, mobility, use of a bank account and a mobile phone, ownership of a house or land, barriers to medical treatment), HIV/AIDS; other health issues and domestic violence [15]. In addition, the Biomarker questionnaire included measures of height, weight, waist, hip circumference, haemoglobin, blood pressure, and random blood glucose levels.

Study variables

Dependent variable

Self-reported cervical cancer screening (only mentioned as screening thereafter) was our primary dependent variable. This was derived using the question “Have you ever undergone a screening test for cervical cancer?” Answers were recorded in dichotomous format- “no, yes.” “No” was treated as a reference group.

Explanatory variable

Residence in urban or rural areas was our primary explanatory variable. The current urban-rural area classification adopted in the survey adopts the census definitions of urban and rural, which is a combination of administrative definitions and census criteria. So, any settlement that is not considered ‘Urban’ is automatically considered ‘Rural’. There are two types of urban settlements: administratively urban and census urban. Administratively, urban settlements are those that are governed by an Urban Local Body (ULB), which are either Municipal Corporations, Municipal Councils, or Nagar Panchayats. Rural settlements are, on the other hand, governed by a Gram Sabha [24].

Covariates

We classified explanatory variables as demographic and socio-economic factors and health-related factors. Under demographic and socio-economic factors, we have included age groups in years (30–34, 35–39, 40–44 and 45–49), religion (Hindu, Muslim, Christian and Others), Caste (scheduled caste, scheduled tribe, Other Backward Class (OBC) and others), education (illiterate, primary secondary and higher secondary), gender of head of household (Male, female), marital status (married, others), age of first intercourse (< 18, 18–29 and ≥30 years), living children (up to 2, > 2), health insurance (no, yes) and region (north, central, east, northeast, west and south). Socio-economic status was estimated using the wealth quintiles (poorest -lowest 20% quintile ), poorer, middle, richer, richest (highest 20%-quintile 5) that was derived from the wealth index, which is a composite measure of a household's cumulative living standard, calculated using data on a household's ownership of selected assets, like televisions and bicycles; housing materials; access to drinking water and presence of sanitation facilities and calculated using principal component analysis (PCA) [25]. Under health-related and behavioural factors, we have included- Body Mass Index/BMI (Underweight (< 18.5), Normal (18.5–22.9), Overweight (23.0–24.9) and Obesity (≥25.0)), eating fruits (never, daily, weekly and occasionally), eating fried food (never, daily, weekly and occasionally), smoking/ tobacco usage (no, yes), alcohol consumption (no, yes), diabetes (no, yes and don’t know), hypertension (no, yes and don’t know), exposure to media i.e. television or radio or phone (no, yes), usage of hormonal contraceptive pills (no, yes) and high-risk fertility behaviour/ HRFB (maternal age less than 18 years or more than 34 years/ the birth of order four or higher/ birth interval less than 24 months: no, yes) [15].

Statistical analysis

Data were analysed using STATA v17 (StataCorp LLC, College Station, TX). The proportions of cervical cancer screening coverage are presented along with their 95% confidence intervals. A bivariate analysis using the Chi-square test with appropriate survey weights was then conducted to compare coverage between urban and rural groups across different demographic, socioeconomic, and health-related factors [15, 26]. The spatial pattern of cervical cancer screening coverage across India was assessed using the Global Spatial Autocorrelation to identify disparities (clustered, dispersed, or random). Cluster level GIS, i.e. geographical information system data from NFHS-5, including 707 districts, were analysed using “GeoDa software version 1.14.” Detailed methodology has been documented elsewhere [27]. A Queen’s first-order matrix generated weights, and Moran’s I statistics quantified the pattern. Autocorrelation was confirmed with a pseudo-p-value < 0.05 after 999 random permutations. A positive Moran’s I indicate clustering (similar neighbouring values), while a negative value signals spatial outliers (dissimilar neighbouring values) [28]. Further, Local spatial autocorrelation identifies specific clusters or anomalies, like hotspots (high coverage) and cold spots (low coverage), to provide a more granular view, pinpointing areas where cervical cancer screening significantly differs from their surroundings, and is essential for targeting localised interventions. "Local Indicators of Spatial Association (LISA)" were used to locate these clusters and outliers. Univariate local Moran’s scatter plots, LISA significance, and cluster maps depicted the spatial pattern. Additional geographical indicators of local autocorrelation were generated, including Getis Ord and Local Geary statistics [28].

Further, to determine the contribution of different explanatory variables to urban-rural differences in cervical cancer screening coverage, we used the multivariate decomposition analysis that was done using the STATA command, mvdcmp. The logit regression was used for a non-linear model to determine the changeover across urban and rural coverage into two components. The Mean difference between urban (A) and rural (B) disparities in coverage of cervical cancer screening was decomposed with the following formula [29].

$$\begin{array}{cc}\hfill {\overline{\mathrm{Y}}}_{\mathrm{A}}-{\overline{\mathrm{Y}}}_{\mathrm{B}}& =\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{A}}{\beta }_{\mathrm{A}})}-\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{B}}{\beta }_{\mathrm{B}})}\hfill \\ \hfill & =\underset{\mathrm{E}}{\underbrace{\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{A}}{\beta }_{\mathrm{A}})}-\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{B}}{\beta }_{\mathrm{A}})}}}+\underset{\mathrm{C}}{\underbrace{\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{B}}{\beta }_{\mathrm{A}})}-\overline{\mathrm{F}({\mathrm{X}}_{\mathrm{B}}{\beta }_{\mathrm{B}})}}}\hfill \end{array}$$

Y = N ×1 Outcome variable, X = N ×K matrix of explanatory variables, β = K ×1 vector of coefficients, F = function, E = differential attributable to differences in (endowments/ characteristics), known as the explained component or characteristics effects. It shows the expected difference in reported cervical cancer screening coverage if urban participants were given rural participants’ distribution of covariates (urban perspective). C = unexplained component or coefficient effects. It shows the expected difference if rural participants experienced urban participants’ behavioural responses to X (rural perspective). Lastly, we explored the factors associated with cervical cancer screening in an overall sample using multivariable binary logistic regression, specifically for urban and rural areas, to compare the odd ratio for screening. Multicollinearity was assessed between independent variables, and highly correlated variables with Variance Inflation factor > 4 were removed from the final model. Output was depicted using crude and adjusted odds ratio (COR, AOR) with 95% Confidence Intervals. A P-value < 0.05 was considered statistically significant.

Data availability statement

This study analyses a nationally representative survey database that is available freely in the public domain and can be accessed using standard protocols from the Demographic Health Surveillance (DHS) website at https://dhsprogram.com/data/available-datasets.cfm [30].

Results

Of the 348882 women included in the analysis, 89228 were from urban areas, and 259654 were from rural areas. Supplementary material 1 segregates the women as per their socio-demographic characteristics. Overall, 2.0% (95% CI: 1.9–2.0) of the study participants reported having ever undergone screening. Table 1 depicts the reported cervical cancer screening coverage among the total sample and separately as per their urban-rural residential status and stratified by their socio-demographic characteristic. Screening was higher among the participants from the urban areas (2.4; 2.3–2.5) than the rural (1.8; 1.7–1.8). With increasing age, screening coverage increased. The proportion of screening coverage was highest among Christians (3.8; 3.7–3.8) and OBCs (2.3; 2.3–2.4). Lowest coverage was documented by Muslims (1.1; 1.1–1.2), Scheduled tribes (0.9; 0.9–0.9) and participants having more than two living children (1.9; 1.8–1.9). With an increase in the wealth quintile, education status, and age of first intercourse, coverage increased. The highest coverage was reported from the South (5.0; 4.9–5.1); the Northeast (0.6; 0.5–0.6), followed by the East (0.6; 0.5–0.7) region reported the lowest coverage. Participants with obesity (2.7; 2.6–2.7) had the highest coverage, while participants with normal BMI (1.56; 1.5–1.6) had the lowest coverage. The coverage of cervical cancer was higher in a group with a higher frequency of consuming fruits and a lower frequency of consuming fried food. Lower coverage was reported among participants with smoking/ tobacco usage (1.1; 0.9–1.2) and alcohol consumption (1.3; 1.2–1.4). Participants who did not know their diabetes (0.8; 0.7–0.9) and hypertension (1.1; 1.1–2.2) status had the lowest coverage. On the contrary, the highest coverage was documented by participants having diabetes (3.6; 3.5–3.6) and hypertension (2.3; 2.2–2.3). Participants not exposed to media (1.2; 1.0–1.3) and usage of hormonal contraceptives (0.6; 0.6–0.7) had lower coverage. Specifically, the coverage was higher in urban areas across all the socio-demographic variables. However, no significant urban-rural differences (p-value > 0.05) were observed among certain groups: women with no education, younger age of intercourse, those residing in central, east and western regions of India, those who never eat fruits, and those who were unaware of their diabetes or hypertension status. Also, coverage was the lowest in both urban and rural areas for these categories.

Table 1.

Socio-demographic characteristics of the women participants from the fifth round of the National Family Health Survey, India (2019-21) between 30–49 years, who had ever undergone a cervical examination

Variable	Total Sample		Urban		Rural		p-value
	Unweighted frequency	Weighted % (95% CI)	Unweighted frequency	Weighted % (95% CI)	Unweighted frequency	Weighted % (95% CI)
Total sample analysed	348882		89228		259654
Total women screened	5430	2.0 (1.9–2.0)	1870	2.4 (2.3–2.5)	3560	1.8 (1.7–1.8)	< 0.001
Demographic and socio-economic factors
Age (completed years)
30–34	1185	1.6 (1.5–1.7)	408	1.9 (1.8–2.0)	777	1.4 (1.3–1.5)	< 0.001
35–39	1353	1.9 (1.8–2.0)	441	2.3 (2.2–2.3)	914	1.7 (1.6–1.8)	< 0.001
40–44	1358	2.1 (2.1–2.2)	505	2.5 (2.5–2.6)	850	2.0 (1.9–2.1)	< 0.001
45–49	1535	2.4 (2.3–2.4)	516	2.8 (2.7–2.9)	1019	2.1 (2.0–2.2)	< 0.001
Religion
Hindu	4202	2.0 (1.9–2.1)	1350	2.4 (2.4–2.5)	2852	1.8 (1.7-1.)	< 0.001
Muslim	393	1.1 (1.1–1.2)	182	1.4 (1.3–1.5)	211	1.0 (0.9–1.1)	< 0.001
Christian	554	3.7 (3.7–3.8)	247	4.9 (4.8–5.0)	307	3.1 (3.0–3.2)	< 0.001
Others	281	2.6 (2.5–2.7)	91	3.6 (3.6–3.7)	190	2.1 (2.0–2.)	< 0.001
Social Caste
Schedule caste	1225	2.30 (2.3–2.4)	334	2.7 (2.6–2.8)	891	2.1 (2.0–2.3)	0.005
Schedule tribe	673	0.9 (0.9–1.0)	223	1.6 (1.5–1.6)	450	0.8 (0.8–0.9)	< 0.001
Other backward castes	2520	2.3 (2.3–2.4)	920	3.0 (3.0–3.1)	1600	2.1 (2.0–2.2)	< 0.001
Others	1012	1.4 (1.3–1.5)	393	1.5 (1.4–1.6)	619	1.3 (1.3–1.4)	0.001
Wealth quintile
Poorest	580	1.0 (0.9–1.1)	30	1.2 (1.2–1.3)	550	1.0 (0.9–1.0)	0.028
Poorer	940	1.6 (1.5–1.7)	100	1.8 (1.7–1.9)	840	1.6 (1.5–1.7)	0.029
Middle	1343	2.2 (2.2–2.3)	336	2.6 (2.5–2.6)	1007	2.1 (2.0–2.1)	< 0.001
Richer	1317	2.4 (2.3–2.4)	555	2.3 (2.2–2.4)	762	2.5 (2.4–2.5)	0.023
Richest	1250	2.5 (2.4–2.5)	849	2.5 (2.4–2.6)	401	2.4 (2.3–2.4)	0.001
Education
Illiterate	1516	1.4 (1.4–1.5)	232	1.6 (1.5–1.7)	1284	1.4 (1.3–1.5)	0.061
Primary	815	2.1 (2.0–2.2)	238	2.7 (2.6–2.8)	577	1.8 (1.8–1.9)	< 0.001
Secondary	2454	2.3 (2.2–2.4)	967	2.5 (2.4–2.6)	1487	2.1 (2.1–2.2)	< 0.001
Higher Secondary	645	2.5 (2.4–2.5)	433	2.6 (2.5–2.6)	212	2.3 (2.2–2.4)	< 0.001
Gender of Head of Household
Male	4505	2.0 (1.9–2.0)	1540	2.3 (2.3–2.3)	2965	1.8 (1.7–1.8)	< 0.001
Female	925	2.0 (2.0–2.1)	330	2.5 (2.4–2.6)	595	1.8 (1.7–1.9)	< 0.001
Marital status
Married	4931	2.0 (1.9–2.0)	1685	2.4 (2.3–2.5)	3246	1.8 (1.7–1.8)	< 0.001
Others	499	1.9 (1.8–2.0)	185	2.0 (1.9–2.1)	314	1.9 (1.8–1.9)	< 0.001
Age of first intercourse
< 18 years	811	1.2 (1.1–1.3)	178	1.4 (1.3–1.5)	633	1.2 (1.1–1.3)	0.051
18–29 years	1216	1.5 (1.4–1.6)	384	1.7 (1.6–1.8)	832	1.4 (1.3–1.5)	< 0.001
≥30 years	3403	2.4 (1.3–1.5)	1308	2.9 (2.8–3.0)	2095	2.2 (2.1–1.2)	< 0.001
Living children
Up to 2	3388	2.3 (2.2–2.4)	1359	2.7 (2.6–2.8)	2029	2.1 (2.0–2.2)	< 0.001
> 2	2042	1.5 (1.4–1.6)	511	1.7 (1.6–1.8)	1531	1.5 (1.4–1.5)	< 0.001
Health Insurance
No	3401	1.9 (1.8–1.9)	1242	2.3 (2.2–2.4)	2159	1.7 (1.6–1.7)	< 0.001
Yes	2029	2.1 (2.1–2.2)	1401	2.6 (2.5–2.7)	1401	1.9 (1.9–2.0)	< 0.001
Region
North	599	0.9 (0.81.0)	235	1.1 (1.0–1.2)	364	0.8 (0.7–0.9)	< 0.001
Central	836	1.3 (1.2–1.4)	160	1.1 (1.1–1.2)	676	1.3 (1.3–1.4)	0.297
East	323	0.6 (0.5–0.7)	50	0.4 (0.3–0.5)	273	0.6 (0.5–0.7)	0.209
Northeast	501	0.6 (0.5–0.7)	249	1.5 (1.4–1.5)	252	0.3 (0.3–0.4)	< 0.001
West	402	1.7 (1.7–1.8)	147	2.0 (1.9–2.1)	255	1.5 (1.4–1.6)	0.193
South	2769	5.0 (4.9–5.1)	1029	5.3 (5.2–5.4)	1740	4.8 (4.7–4.9)	0.004
Health related and behavioural factors
Body Mass Index
Underweight	420	1.6 (1.5–1.7)	69	2.5 (2.4–2.6)	351	1.4 (1.3–1.4)	0.021
Normal	1,740	1.6 (1.5–1.6)	481	1.9 (1.8–2.0)	1259	1.4 (1.4–1.5)	< 0.001
Overweight	929	1.7 (1.7–1.8)	300	2.0 (1.9–2.1)	629	1.6 (1.5-1.)	< 0.001
Obesity	2,341	2.7 (2.6–2.7)	1020	2.8 (2.8–2.9)	1321	2.5 (2.4–2.6)	< 0.001
Eat Fruits
Never	61	1.0 (1.0–1.1)	15	1.0 (1.9–2.0)	46	0.8 (0.7–0.9)	0.150
Daily	800	2.4 (2.3–2.5)	432	2.7 (2.6–2.7)	368	2.0 (2.0–2.1)	< 0.001
Weekly	2121	2.2 (2.0–2.3)	869	2.5 (2.4–2.6)	1252	2.0 (1.9–2.1)	< 0.001
Occasionally	2448	1.7 (1.6–1.8)	554	2.0 (1.9–2.2)	1894	1.6 (1.5–1.6)	< 0.001
Eat fried food
Never	351	2.5 (2.4–2.6)	118	3.3 (3.2–3.3)	233	2.2 (2.1–2.2)	< 0.001
Daily	511	1.3 (1.3–1.4)	245	1.8 (1.7–1.9)	266	1.1 (1.0–1.2)	< 0.001
Weekly	1716	2.0 (2.0–2.1)	598	2.4 (1.3–2.4)	1118	1.9 (1.8–1.9)	< 0.001
Occasionally	2852	2.0 (1.9–2.0)	909	2.4 (2.3–2.5)	1943	1.8 (1.7–1.8)	< 0.001
Smoking/ tobacco usage
No	4976	2.0 (1.9–2.2)	1723	2.4 (2.3–2.5)	3253	1.8 (1.7–1.9)	< 0.001
Yes	454	1.0 (1.0–1.2)	307	1.3 (1.2–1.4)	307	1.0 (0.9–1.1)	< 0.001
Alcohol consumption
No	5323	2.0 (1.9–2.1)	1850	2.4 (2.3–2.5)	3473	1.8 (1.7–1.8)	< 0.001
Yes	107	1.3 (1.2–1.4)	20	1.5 (1.4–1.6)	87	1.5 (1.4–1.6)	0.003
History of Diabetes
No	5093	1.9 (1.9–2.0)	1726	2.3 (2.2–2.4)	3,367	1.7 (1.6–2.0)	< 0.001
Yes	290	3.6 (3.5–3.6)	132	3.8 (3.7–3.9)	158	3.4 (3.3–3.4)	0.004
Don’t know	47	0.8 (0.7–0.9)	12	0.8 (0.7–0.9)	35	0.8 (0.8–0.9)	0.230
History of Hypertension
No	4884	1.9 (1.9–2.0)	1669	2.4 (2.3–2.4)	3215	1.7 (1.7–1.8)	< 0.001
Yes	505	2.3 (2.2–2.3)	189	2.4 (2.4–2.5)	316	2.2 (2.1–1.3)	< 0.001
Don’t know	41	1.1 (1.1–2.2)	12	1.9 (1.8–2.0)	29	0.9 (0.8–1.0)	0.037
Exposure of Media
No	927	1.2 (1.0–1.3)	133	1.5 (1.4–1.6)	794	1.1 (1.0–1.2)	< 0.001
Yes	4503	2.3 (2.2–2.4)	1737	2.5 (2.4–2.6)	2766	2.1 (2.0–2.2)	< 0.001
Hormonal contraceptives use
No	5303	2.0 (1.9–2.1)	1828	2.4 (2.3–2.5)	3475	1.8 (1.7–1.9)	< 0.001
Yes	127	0.6 (0.6–0.7)	42	0.8 (0.7–0.8)	85	0.6 (0.5–0.7)	< 0.001

We observed significant spatial patterns of screening coverage. Moran’s I statistic (0.620) confirmed the presence of spatial dependence and geographical gradient of coverage in India (Fig. 2). High coverage of cervical cancer reported screening is spatially clustered (hotspots) in Southern states like Kerala and Western states like Maharashtra; Low coverage (cold spots) of cervical cancer screening is spatially clustered in Eastern and North-eastern states (Fig. 3). Overall, Tamil Nadu (10.10%) had the highest coverage, while both West Bengal and Assam (0.20%) had the lowest coverage. In Urban areas, the highest and lowest coverage was revealed by Tamil Nadu (10.53%) and West Bengal (0.13%), respectively. The highest and lowest coverage in rural areas was seen in Puducherry (12.32%) and Chandigarh (0%). In view of urban-rural disparity, the highest difference in coverage was seen in Madhya Pradesh (6.41%), where urban areas had higher coverage than rural areas. In the other extreme, rural areas of Puducherry had the highest difference (7.28%) from urban areas. (Fig. 4, Supplementary Material 2)

Fig. 2.

Coverage of cervical cancer screening among Indian women aged 30–49 years A) Moran’s Scatter plot, B) Randomization with 999 permutations

Fig. 3.

Choropleth maps depicting the spatial clustering of the cervical cancer screening coverage (4a-c) and geographical clustering of hotspots and cold spots (4d-f)

Fig. 4.

Urban-rural coverage of cervical cancer screening among the eligible Indian women participants of the fifth round of the National Family Health Survey (2019-21) per states and union territories

The multivariate decomposition analysis depicted small urban-rural differences in the cervical cancer screening coverage of 0.60% (95% CI: 0.48–0.71) (Table 2). The endowment (E) accounted for the change in the variable composition, and the effect change of the variable accounted for coefficient (C). Endowment (E)/compositional and coefficient (C)/ effect disparities contributed to 88.63% and 11.37% of the urban-rural coverage disparity, respectively. This disparity due to compositional changes was positively contributed majorly by residing in South (36.92%), East (18.66%), West (15.87%) region; education (higher secondary: 13.65%, secondary: 10.06%); Scheduled tribe (8.90%) and having living children > 2 (8.25%). These positive contributing factors made a significant contribution. The major negative contributions were due to differences in other castes (−9.04%), Muslim religion(−6.47%) and the richest wealth quintile (−6.37%), but were statistically non-significant.

Table 2.

Multivariate decomposition analysis depicting the urban-rural disparities affecting the coverage of cervical screening among the eligible Indian women participants of the fifth round of the National Family Health Survey (2019-21)

Factors	Urban (A) - Rural (B)
	Due to the difference in the composition of the respondent (endowment) (E)		Due to differences in coefficient (difference in the effect of characteristics) (C)
	Coefficient (%) (95% CI)	%	Coefficient (%) (95% CI)	%
Age-group in years
30–34	Reference	-	Reference	-
35–39	0 (0 to 0.001)	0.08	0.010 (−0.079 to 0.099)	1.71
40–44	0.003 (0 to 0.005)*	0.45	0.018 (−0.096 to 0.131)	2.97
45–49	−0.004 (−0.006 to −0.002)*	−0.61	0.002 (−0.06 to 0.064)	0.29
Religion
Hindu	Reference	-	Reference	-
Muslim	−0.038 (−0.057 to −0.019)*	−6.42	0.015 (−0.072 to 0.101)	2.46
Christian	0.001 (−0.001 to 0.003)	0.17	−0.004 (−0.026 to 0.018)	−0.63
Others	0 (0 to 0)*	0.03	−0.002 (−0.023 to 0.018)	−0.41
Caste
Schedule caste	Reference	-	Reference	-
Schedule tribe	0.052 (−0.001 to 0.106)	8.8	−0.023 (−0.168 to 0.122)	−3.91
OBC	0 (0 to 0.001)	0.05	−0.026 (−0.202 to 0.151)	−4.32
Others	−0.054 (−0.099 to −0.008)	−9.01	0.007 (−0.069 to 0.082)	1.11
Wealth index
Poorest	Reference	-	Reference	-
Poorer	0.001 (−0.169 to 0.172)	0.24	0.004 (−0.162 to 0.17)	0.69
Middle	−0.013 (−0.075 to 0.048)	−2.27	−0.046 (−0.275 to 0.183)	−7.72
Richer	−0.036 (−0.15 to 0.079)	−6.02	0.03 (−0.192 to 0.252)	5.02
Richest	−0.023 (−0.349 to 0.302)	−3.92	0.014 (−0.095 to 0.123)	2.36
Education
Illiterate	Reference	-	Reference	-
Primary	−0.035 (−0.055 to −0.014)*	−5.81	−0.045 (−0.291 to 0.201)	−7.6
Secondary	0.06 (0.01 to 0.109)*	10.02	0.005 (−0.095 to 0.105)	0.91
Higher Secondary	0.08 (0.011 to 0.149)*	13.38	−0.005 (−0.036 to 0.026)	−0.81
Gender of Head of Household
Male	Reference	-	Reference		-
Female	0 (−0.001 to 0.001)	−0.05	−0.014 (−0.099 to 0.072)		−2.32
Marital status
Others	Reference	-	Reference		-
Married	−0.011 (−0.02 to −0.002)*	−1.86	−0.211 (−1.403 to 0.981)		−35.48
Age of first intercourse
< 18 years	Reference	-	Reference		-
18–29 years	−0.002 (−0.011 to 0.007)	−0.30	0.036 (−0.175 to 0.246)		6.01
≥30 years	0.048 (0.021 to 0.076)*	8.15	−0.003 (−0.174 to 0.167)		−0.56
Living children
Upto 2	Reference	-	Reference		-
> 2	0.048 (0.001 to 0.096)*	8.10	0.096 (−0.43 to 0.622)		16.18
Health Insurance
No	Reference	-	Reference		-
Yes	0.016 (0.002 to 0.031)*	2.77	0.003 (−0.066 to 0.073)		0.57
Region
North	Reference	-	Reference		-
Central	−0.028 (−0.06 to 0.004)	−4.71	0.164 (−0.708 to 1.035)		27.53
East	0.111 (0.062 to 0.159)*	18.59	0.266 (−1.133 to 1.665)		44.76
Northeast	−0.017 (−0.029 to −0.006)*	−2.93	−0.049 (−0.305 to 0.208)		−8.16
West	0.093 (0.05 to 0.135)*	15.57	0.028 (−0.124 to 0.179)		4.66
South	0.218 (0.163 to 0.273)*	36.66	0.086 (−0.381 to 0.554)		14.52
BMI
Underweight (< 18.5)	−0.041 (−0.089 to 0.007)	−6.83	−0.044 (−0.284 to 0.197)		−7.34
Normal (18.5–22.9)	Reference	-	Reference		-
Overweight/ Obesity (≥23.0)	0.037 (−0.011 to 0.084)	6.16	0.008 (−0.096 to 0.112)		1.32
Smoking/ tobacco usage
No	Reference	-	Reference		-
Yes	0.008 (−0.019 to 0.036)	1.37	−0.004 (−0.048 to 0.039)		−0.72
Alcohol consumption
No	Reference	-	Reference		-
Yes	0.006 (−0.008 to 0.019)	0.99	0.006 (−0.028 to 0.04)		0.98
Diabetes
No	Reference	-	Reference		-
Yes	0.011 (0.002 to 0.02)*	1.84	−0.001 (−0.011 to 0.01)		−0.1
Don’t know	0.01 (−0.004 to 0.024)	1.75	0.013 (−0.058 to 0.084)		2.15
Hypertension
No	Reference	-	Reference		-
Yes	0.001 (−0.004 to 0.006)	0.09	0.015 (−0.068 to 0.099)		2.57
Don’t know	−0.005 (−0.014 to 0.003)	−0.95	−0.008 (−0.053 to 0.037)		−1.39
Exposure to Media
No	Reference	-	Reference		-
Yes	0.023 (−0.082 to 0.129)	3.94	−0.029 (−0.282 to 0.224)		−4.82
Usage of Hormonal contraceptives
No	Reference	-	Reference		-
Yes	0.004 (−0.002 to 0.01)	0.65	−0.001 (−0.029 to 0.028)		−0.1
Intercept	-	-	−0.296 (2.322 to 1.730)		−49.79
Overall	0.524 (0.365 to 0.684)*	88.15	0.070 (−0.127 to 0.268)		11.85
Overall difference (A-B)= (E + C)	0.6 (0.4 to 0.8)*	-	-		-

*p-value < 0.05; Mean (A or Urban) = 2.36% > Mean (B or Rural) = 1.76%

While the crude odds ratio depicted lower odds of screening in rural areas (cOR: 0.7; 95% CI: 0.7–0.8) compared to urban areas (Supplementary material 3), the adjusted multivariable binary logistic regression analysis depicted the odd ratio (1.0; 0.9–1.1) to be statistically non-significant (Table 3). The adjusted analysis further depicted the significant odds of having higher reported cervical cancer screening coverage were associated with an increase in the age group (45–49 years: AOR 1.45, 95% CI: 1.03–1.28), education (higher secondary-1.32; 1.13–1.55) and age of first intercourse (after 30 years − 1.60; 1.43–1.79). Married (1.25; 1.08–1.45), diabetic (1.39; 1.17–1.65) women, and participants from the South region (6.76, 5.90–7.75) were associated with higher coverage. The odds were lower among Muslims (0.65; 0.57–0.75), Scheduled tribes (0.57; 0.48–0.68) and participants with usage of hormonal contraceptives (0.74; 0.58–0.94). Similar comparable regression results were documented in both urban and rural areas with variation in the strength of associations except for BMI (obesity: 1.24; 1.06–1.44), frequency of eating fruits (weekly: 1.71; 1.17–2.50), hypertension (1.23; 1.05–1.45) and usage of contraceptive (0.75; 0.36–1.55), which have been only significant in rural areas. On the other hand, marital status (married: 1.42; 1.09–1.84) is only significant in urban areas.

Table 3.

Determinants of coverage of cervical cancer screening among women in the reproductive age group(15–49 years) and those residing in Urban and Rural areas as per the fifth round of the NFHS (2019-21), India

Variable	Crude OR	Overall	Urban	Rural
	(95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
Residence
Urban	Reference	Reference	-	-
Rural	0.7 (0.6–0.7)*	1.0 (0.9–1.1)	-	-
Age (completed years)
30–34		Reference	Reference	Reference
35–39		1.2 (1.0–1.3)*	1.1 (0.9–1.4)	1.2 (1.0–1.3)*
40–44		1.3 (1.2–1.5)*	1.3 (1.1–1.6)*	1.4 (1.2–1.6)*
45–49		1.5 (1.3–1.6)*	1.4 (1.2–1.8)*	1.5 (1.3–1.7)*
Religion
Hindu		Reference	Reference	Reference
Muslim		0.7 (0.6–0.8)*	0.6 (0.5–0.8)*	0.7 (0.6–0.9)
Christian		1.1 (0.9–1.3)	1.1 (0.9–1.5)	1.0 (0.8–1.2)
Others		2.2 (1.7–2.8)*	2.3 (1.5–3.6)*	2.1 (1.7–2.7)
Social Caste
Schedule caste		Reference	Reference	Reference
Schedule tribe		0.6 (0.5–0.7)*	0.7 (0.4–1.0)	0.6 (0.5–0.7)*
Other backward castes		0.9 (0.8–1.0)*	0.9 (0.8–1.1)	1.0 (0.8–1.2)
Others		0.8 (0.7–0.9)*	0.8 (0.6–1.0)*	0.8 (0.8–0.9)*
Wealth quintile
Poorest		Reference	Reference	Reference
Poorer		1.0 (0.9–1.1)	1.0 (0.6–1.7)	1.0 (0.9–1.2)
Middle		1.0 (0.8–1.1)	1.1 (0.7–1.8)	0.9 (0.8–1.1)
Richer		0.9 (0.8–1.1)	0.9 (0.5–1.4)	1.0 (0.8–1.2)
Richest		1.0 (0.8–1.2)	1.0 (0.6–1.6)	1.1 (0.9–1.4)
Education
Illiterate		Reference	Reference	Reference
Primary		1.3 (1.2–1.5)*	1.6 (1.2–2.0)*	1.2 (1.1–1.4)*
Secondary		1.3 (1.2–1.4)*	1.3 (1.1–1.6)*	1.3 (1.2–1.5)*
Higher Secondary		1.3 (1.1–1.5)*	1.4 (1.1–1.8)*	1.3 (1.0–1.6)*
Gender of Head of Household
Male		Reference	Reference	Reference
Female		1.0 (0.9–1.2)	1.1 (0.9–1.3)	1.0 (0.9–1.1)
Marital status
Married		1.3 (1.1–1.5)*	1.4 (1.1–1.9)*	1.2 (1.0–1.4)
Others		Reference	Reference	Reference
Age of first intercourse
< 18 years		Reference	Reference	Reference
18–29 years		1.0 (0.9–1.2)	0.9 (0.7–1.3)	1.1 (0.9–1.3)
≥30 years		1.6 (1.5–1.8)*	1.6 (1.3–2.1)*	1.6 (1.4–1.8)*
Living children
Upto 2		Reference	Reference	Reference
> 2		1.0 (0.9–1.0)	0.8 (0.7–1.0)*	1.0 (0.9–1.1)
Health Insurance
No		Reference	Reference	Reference
Yes		0.8 (0.7–1.0)	0.9 (0.7–1.0)	0.9 (0.8–0.9)*
Region
North		Reference	Reference	Reference
Central		1.8 (1.6–2.1)*	1.3 (1.0–1.7)	2.3 (2.0–2.7)*
East		0.9 (0.7–1.1)	0.5 (0.3–0.7)*	1.2 (1.0–1.5)
Northeast		0.9 (0.8–1.1)	1.6 (1.2–2.2)*	0.6 (0.5–0.8)*
West		2.3 (1.9–2.8)*	2.1 (1.5–2.7)*	2.5 (2.0–3.1)*
South		6.8 (5.9–7.8)*	5.5 (4.4–6.8)*	8.0 (6.8–9.3)*
Body Mass Index
Underweight		1.1 (0.9–1.3)	1.4 (0.9–2.0)	1.0 (0.9–1.2)
Normal		Reference	Reference	Reference
Overweight/ Obesity		1.1 (1.1–1.2)*	1.1 (1.0–1.3)	1.2 (1.0–1.3)*
Smoking/ tobacco usage
No		Reference	Reference	Reference
Yes		0.8 (0.7–1.0)	0.9 (0.6–1.4)	0.8 (0.7–1.0)
Alcohol consumption
No		Reference	Reference	Reference
Yes		0.9 (0.7–1.3)	0.7 (0.3–1.6)	0.8 (0.4–1.6)
History of Diabetes
No		Reference	Reference	Reference
Yes		1.4 (1.2–1.7)*	1.4 (1.1–1.8)*	1.4 (1.1–1.7)*
Don’t know		0.6 (0.3–1.2)	0.3 (0.1–1.5)	0.8 (0.4–1.6)
History of Hypertension
No		Reference	Reference	Reference
Yes		1.1 (1.0–1.3)*	1.0 (0.8–1.3)	1.2 (1.1–1.5)*
Don’t know		1.3 (0.6–2.8)	2.5 (0.6–9.8)	1.0 (0.5–2.3)
Exposure of Media
No		Reference	Reference	Reference
Yes		1.0 (0.9–1.1)	1.1 (0.8–1.4)	1.0 (0.9–1.2)
Hormonal contraceptives use
No		Reference	Reference	Reference
Yes		0.7 (0.6–0.9)*	0.8 (0.5–1.2)	0.8 (0.5–1.0)
High risk fertility behaviours
No		Reference	Reference	Reference
Yes		1.1 (0.9–1.2)	1.0 (0.9–1.2)	1.1 (0.9–1.2)

Variables like ‘frequency of eating food’ and ‘fried food’ were removed due to multi-collinearity

AOR Adjusted odds ratio, 95% CI 95% confidence interval

*Statistically significant (p-value < 0.05)

Discussion

India was an active participant in the recent Quad summit that hosted the launch of the Cancer Moonshot Initiative to further their efforts towards the control of cervical cancer and attain a leadership position in curbing the rising burden of cervical cancer that continues to be a major health crisis in the region and laying the groundwork to address other forms of cancer as well [31]. However, every initiative should be backed up by robust data that timely support the policy, but there are relatively few nationally representative studies that look at the distribution, determinants, spatial patterns, and urban-rural disparities in a comprehensive manner at the national and sub-national levels. Our study is one of the first from India that comprehensively delves into the urban-rural disparities in a subset of women in the reproductive age group (30–49 years). We report certain interesting findings. First, the coverage of cervical cancer screening is low overall and even lower in rural areas compared to urban areas, with significant geospatial variations. Second, the decomposition analysis depicted the role of critical socio-demographic factors in increasing disparities. Lastly, while the crude odds ratio depicted lower odds of screening in rural areas, adjusted analysis depicted no specific association.

We report abysmally low cervical cancer screening among Indian women between 30–49 years, who constitute most of the population's economically productive age group [32]. An abrupt fall in coverage in NFHS-5 compared to NFHS-4 can be attributed to the COVID-19 pandemic that disrupted the preventive care protocols, including screening for common cancers [33]. Even the United States observed a decline in cervical cancer screening during the pandemic, i.e. from 81.89% in 2018 to 47.71% in 2022 [34]. Otherwise, by 2021, India was among the 133 countries that had a robust cervical cancer screening program in place for quite some time. Still, it is placed at a concerningly lower rank in screening coverage among women 30–49 years of age from 196 countries [35]. India is among 22 countries primarily relying on VIA as the preferred screening method. Globally, two in three women have never been screened for cervical cancer, and disparities are higher in low, middle-income countries [36, 37]. Worldwide screening coverage depicts disparities and ranges from 100% (97–102) in Sweden to 0% in Benin [38]. Low screening coverage among Indian women in rural areas is another cause of concern. Our results are in concurrence with other global and regional studies that report lower screening coverage in rural areas [39]. In India, low coverage can be attributed to lower health awareness of the participants, necessitating the need for deeper penetration of population-based screening (PBS) under NP-NCD [14]. NFHS-4 provided much higher levels of screening coverage (29.8%). This might be due to the fact that cervical examination was taken as a proxy for cervical cancer screening [19]. Lack of knowledge, fear of unfavourable test results, the stigma associated with cancer in society, the perceived inevitability of death after the cancer diagnosis, restricted access to healthcare facilities, lack of health insurance, and embarrassment of displaying body parts to male physicians are key barriers of cervical cancer screening coverage in India [40]. The health of rural women and their access to health facilities is compromised due to socio-cultural, economic and environmental factors [41]. Previous studies found that awareness about symptoms, the possibility of early detection, available tests and the possibility of the cure of the disease among rural women was low, and the main barriers faced by women were cognitive barriers [40].

Multivariable logistic regression revealed that the odds of having higher cervical cancer screening coverage were associated with an increase in the age education and age of first intercourse. Those with higher levels of education typically possess more health literacy and an understanding of preventative actions. They are more willing to seek out screenings because they see their value. Access to healthcare services and cost are positively correlated with higher education [42]. The chance of Human Papillomavirus (HPV) infection can be reduced by delaying first sexual interaction [43]. The odds were lower among Muslims, Scheduled tribes and participants with usage of hormonal contraceptives. Cultural norms and beliefs may influence healthcare-seeking behaviour. Cervical cancer screening may be less common or stigmatised in certain Muslim communities. Socioeconomic inequalities may cause lower screening rates among scheduled tribes, restricted access to healthcare, and cultural considerations [44]. Women in the richest wealth quintile are 1.5 times more likely to get CCS done than those in the poorest wealth quintile. The wealth index findings align with the reported studies, making it a long-term modifiable factor that policymakers should not ignore [45]. However, long-term use of hormonal contraceptives is associated with cervical cancer. Still, people using contraceptives may believe that they are at a decreased risk, which would decrease their willingness to get screened. Proper health education and awareness campaigns should be implemented [46]. Married and diabetic women were associated with higher coverage. Shared responsibilities, consistent peer networks, and encouragement from spouses can all promote health-seeking behaviour in married women. Healthcare centres might provide awareness and access to early screening during family planning sessions [47]. As a part of PBS, diabetes is also screened along with cervical cancer among women aged 30 years and above. Due to various shared risk factors like sedentary lifestyle, obesity, etc., these might coexist. Due to higher health consciousness in diabetic participants, continuing regular health monitoring might lead to higher screening [48]. Participants from the south region were associated with higher coverage. High cervical cancer screening coverage is spatially clustered (hotspots) in Southern and Western states. Tamil Nadu documented the highest coverage in overall and urban areas. Among rural areas, Puducherry had the highest coverage. These states have worked hard to raise public awareness of cervical cancer and the value of screening. Improved engagement and understanding have resulted from educational campaigns and community outreach initiatives. These areas have reputable healthcare systems and easily accessible screening options. Greater coverage is a result of screening facilities and qualified personnel being available. Funding and policies unique to each state are important. The local government implemented proactive measures to increase screening rates. Because they have better access to healthcare services, urban communities in these states frequently have higher screening rates [32, 35].

Low coverage (cold spots) of cervical cancer screening is spatially clustered in Eastern and Northeastern states. Both West Bengal and Assam had the lowest overall coverage. West Bengal and Chandigarh (0%) revealed the lowest coverage among urban and rural areas. These areas confront difficulties with awareness, accessibility, and infrastructure for healthcare. Remote and rural locations might not have access to screening resources or qualified staff. Certain states in the Northeast and East have greater rates of poverty [49]. Preventive care is frequently inaccessible to low-income communities. Screening may be discouraged by cultural norms and false beliefs about cervical cancer. Awareness campaigns must address these obstacles. Coverage is impacted by uneven screening program implementation throughout states. Improving access in cold places requires focused initiatives [50]. The coverage of cervical cancer was 0.60% (95% CI: 0.48–0.71) higher in urban areas than in rural areas. This disparity due to compositional changes was contributed significantly by residing in the South, East, and West regions, having education, being a Scheduled tribe, and having living children > 2. The negative contribution was documented majorly by the richest (−6.37%). Regional variation, educational inequalities, tribal groups and having multiple children contributed to widening the gap of this disparity. Being wealthy leads to higher accessibility and affordability towards healthcare needs [32]. Improving cervical cancer prevention and screening rates still depends on raising awareness and encouraging education and safe sexual behaviours [51]. In West Bengal's rural communities, there has been a high frequency of key risk factors for carcinoma cervix, including age, age at marriage, age of first childbirth, parity, family planning techniques, and reproductive tract infections [52]. One suggestion was to focus screening and early detection efforts on the at-risk population [53, 54].

This analysis has certain strengths and limitations. Being a subset analysis of a nationally representative survey of women in the reproductive age group, the results are generalisable to the studied age group, i.e. 30–49 years. However, the non-availability of data beyond this age group is also the biggest limitation of this study, when global literature suggests that screening rates increase with age as women become more concerned about their health in the post-menopausal period. Due to the limitations of having a cross-sectional study design, the temporality of screening cannot be established with other explanatory variables. Due to the self-reporting patterns of questionnaires, there are high chances of recall bias and social desirability bias in the responses. A proportion of participants with missing data were excluded during the analysis as per the complete case analysis protocols. The missingness can be seen as a proxy indicator for overall disparities in receiving an adequate education, awareness, etc., and it may lead to a certain degree of bias, which was, however, not the case in our study. The Ministry launched screening programmes simultaneously in every state, although differentiating rollouts and participating in some large-scale, state-specific programmes could have increased screening adoption in some states. These impacts were too great for this study to measure. Some of the factors were not included or available, like screening (privacy/equipment), diagnostic services, trained manpower per catchment area, knowledge about cervical cancer risk factors, importance of screening, myths, etc, and lack of transport facilities to the hospital/proximity to the hospital.

This study has important clinical and health policy implications. Our findings highlight key determinants of urban-rural disparities in cervical cancer screening, emphasising the urgent need for policy measures that address inequities in healthcare access and outcomes. While we have not looked into the specific causes of disparities like lack of awareness in the population, scarcity of resources needed for screening, and health system’s capabilities concerning counselling, advocacy and established standard of care protocols, this study underscores the importance of region-specific, culturally sensitive interventions, such as tailored awareness campaigns and community engagement programs that resonate with rural populations. This research can guide policymakers in designing targeted interventions that reduce these disparities by pinpointing specific factors contributing to low screening rates in rural areas. Adequate resources should be strategically allocated to the identified "cold spots," focusing on scaling up screening programs where they are most needed. Policymakers might consider implementing mobile screening units or incentivising healthcare providers to work in underserved areas to improve access. Drawing on best practices from high-performing "hot spots," policymakers can adopt evidence-based strategies, such as patient navigation programs, subsidies for screening costs, and partnerships with local organisations to foster trust and encourage screening participation. By integrating these findings into broader health policy frameworks, such as universal health coverage and preventive care initiatives, meaningful steps in this direction are expected to reduce the burden of cervical cancer and ensure equitable healthcare access across urban and rural communities.

To conclude, despite the global acknowledgement, cervical cancer is emerging as one of the significant public health problems among Indian women. While timely screening is known to be effective in curbing the rise of incidence, its coverage is abysmally low, even with operational guidelines and public health institutions offering screening. We did an exhaustive secondary data analysis of women in the reproductive age group who participated in a national survey. Overall, we noticed low coverage of cervical cancer screening. The coverage was even lower in rural areas compared to urban across different socio-demographic characteristics. We observed significant variations in geospatial coverage depicting regional imbalances. Our decomposition analysis further depicted the major contribution of regional disparities and pertinent sociodemographic characteristics like education, wealth quintile, social caste, preferred religion, and exposure to media towards these differences, necessitating the need to comprehensively design tailor-made advocacy initiatives. To address the existing situation, a high-quality nationwide screening program with high coverage and participation and an efficient referral system is urged to include female healthcare providers. A high focus should be on the areas of cold spots and higher urban-rural disparities. Self-awareness can be a crucial tactic in addition to infrastructure advancements. The stigma and taboos surrounding cervical malignancies may be dispelled with the aid of qualified community health workers. We must simultaneously address the broader determinants of health, including urban-rural disparities, to achieve our international commitments and empower our women to lead lives grounded in health, dignity, and equal opportunity.

Authors’ contributions

PG, YK, SK, and MV contributed to the conception and design of the study. RK and AG contributed to the analysis plan. YK, SR, PH, and MV contributed to the analysis and interpretation of data. PG, PH, MV, and AK drafted the manuscript. All authors reviewed the manuscript. JL supervised the study. All authors have approved the final version of the manuscript for submission.

Funding

No funding or financial grant was received to conduct the present study.

Data availability

This study analyses a nationally representative survey database that is available freely in the public domain and can be accessed using standard protocols from the Demographic Health Surveillance (DHS) website at https://dhsprogram.com/data/available-datasets.cfm

Declarations

Ethics approval and consent to participate

The NFHS-5 received ethical approval from the International Institute for Population Sciences (IIPS), Mumbai (2019–21). It was also reviewed by the ICF International Review Board (IRB), which approved it ethically. After receiving complete information about the goal and methodology of the survey, the respondents signed to confirm their agreement. Interviews were conducted only after receiving each participant's informed consent. Being an anonymous dataset, it is publicly available on the Demographic and Health Surveys (DHS) Programme website that cannot be used to identify survey respondents- (https://dhsprogram.com/Countries/CountryMain.cfm?ctry_id=57&c=India).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.