Sage Journals: Discover world-class research

Abstract

Objective

To analyse the positive rates of low-grade (LSIL) and high-grade (HSIL) squamous intraepithelial lesions, and cervical cancer (CC), and identify groups at high risk for CC in Guangxi.

Setting

CC screening options in Guangxi, which is the only minority autonomous area in South China, include the National Cervical Cancer Screening Project (NCCSP) and physical examination (PE).

Methods

This study was based on PE and NCCSP sample data obtained from 2012 to 2019. We calculated the positive rates of LSIL, HSIL, and CC; analysed the adjusted odds ratio (aOR) and 95% confidence intervals (CI) of the variables in multivariate logistic regression; and subsequently identified groups at high risk for CC.

Results

The positive rates of LSIL, HSIL, and CC for the total of 873,880 samples were 1.89%, 0.60%, and 0.03%, respectively. Females over 64 years of age (vs. 50–64; aOR = 2.05; 95% CI, 1.71–2.46; P < 0.001) and those from urban (vs. rural; aOR = 1.66; 95% CI, 1.57–1.76; P < 0.001), minority (vs. non-minority; aOR = 1.24; 95% CI, 1.13–1.35; P < 0.001), and coastal (vs. inland; aOR = 1.15; 95% CI, 1.06–1.25; P = 0.001) areas were associated with a high risk of HSIL. Females over 64 (vs. 50–64; aOR = 4.37; 95% CI, 2.88–6.63; P < 0.001) and those from urban (vs. rural; aOR = 3.05; 95% CI, 2.36–3.95; P < 0.001) areas were significantly associated with a high risk of CC.

Conclusion

Females from urban areas in Guangxi are at high risk for CC. Public health strategies should focus on high-risk populations.

Keywords

High-risk population cervical cancer screening urban

Abbreviations

cervical cancer.

NCCSP

national cervical cancer screening project.

physical examination.

aOR

adjusted odds ratios.

CIs

confidence intervals.

LSIL

low-grade squamous intraepithelial lesion.

HSIL

high-grade squamous intraepithelial lesion.

CIN1

cervical intraepithelial neoplasia 1.

CIN2

cervical intraepithelial neoplasia 2.

CIN3

cervical intraepithelial neoplasia 3.

Introduction

Cervical cancer (CC) is the fourth leading cause of cancer-related death in females worldwide.¹ In 2018, approximately 570,000 new cases were reported worldwide and more than 31,100 patients died directly from CC.² CC puts a heavy social and economic burden on individuals, families, and countries.^3,4 It is also the second most common cancer that endangers females’ physical and mental health in China,⁵ where its incidence and mortality are increasing.^6,7 Human papillomavirus (HPV) vaccines and population-based screening are considered to be effective methods to address CC.⁸

To improve the public health status of females, the health department of China implemented the National Cervical Cancer Screening Project (NCCSP) in 2009. Rural females between 35 and 64 years of age are the main target of NCCSP, and this service is free. The NCCSP also allows urban females to pay for this service. Physical examination (PE) is a program run by the Department of Health Examination in public and private hospitals. PE for females includes a CC test, and the primary PE customers are urban females. PE is not a free service, and CC test charges range between $55 and $90, depending on which reagents the customers choose. Although hundreds of hospitals in Guangxi have implemented PE to date, there are no exact data on its coverage. The NCCSP and PE programmes are voluntary and not mutually exclusive, and females are free to select either programme. It is estimated that approximately 0.26% of women have undergone both the NCCSP and PE programmes. Although health departments recommend that women undergo CC screening every three years, the NCCSP only covered 21.9% of target people in rural areas due to funding limitations, which is far below the expected rate of 80% noted in a recent report from the National People's Congress of Guangxi. It is estimated that approximately 3.9% of women have undergone CC screening multiple times. The exact data for PE coverage have not been reported.

Liquid-based cytological testing for CC screening is a conventional examination for adult females. This method has been widely used in NCCSP and PE programmes for several years. According to local health authorities, approximately 2.2 million samples were collected during the CC screening project in Guangxi from 2011 to 2020. After taking some factors into account, such as testing cost, efficiency, and capacity,⁹ approximately 1 million cytology samples from local health departments and hospitals have been delivered to and tested by an independent clinical laboratory (ICL) since 2012, based on cooperation contracts. Two other independent gene laboratories participated in the Guangxi CC screening project. However, they mainly performed HPV diagnosis. This study is based on the data from the ICL. The sources of cytology samples included the NCCSP and PE programmes of 105 counties, which represent approximately 94.5% (105/110) of Guangxi. Samples from the remaining five counties were tested by local hospitals but were excluded as the data were not shared. Although not all samples were included, the study still covered a statistically representative sample of CC screening.

Many studies have reported the incidence of CC based on data from the NCCSP in China, and this condition primarily affects females aged 35 to 64 years. However, only a limited number of studies have focused on populations at a high risk of CC in minority areas, such as Guangxi, which is the only ethnic minority autonomous region in South China. In addition to NCCSP samples, PE samples were included in this study. Compared with NCCSP, the main target population of PE is urban females. This paper aims to analyse the positive rates of low-grade (LSIL) and high-grade (HSIL) squamous intraepithelial lesions, and CC, and identify groups at high risk for CC in Guangxi.

Materials and methods

Study population

This study was based on data from the laboratory database system (LDS) of an ICL in Guangxi. LDS is a data management platform that is used to collect and manage information on samples, including test methods, cytology types, diagnosticians, and diagnoses.

The NCCSP samples included in the study met the inclusion and exclusion criteria. The following inclusion criteria were employed: 1) The type of cytology sample was a liquid-based cytology cervical smear, and the date of diagnosis ranged from January 1, 2012 to October 31, 2019. 2) The origin of cytology samples was NCCSP in Guangxi. 3) Cytology samples had a defined cytopathology diagnosis. The exclusion criteria were as follows: 1) cytology samples obtained from other projects, 2) cytology samples from other provinces, and 3) unqualified cytology samples or without defined diagnosis.

PE samples included in the study met the inclusion and exclusion criteria. The inclusion criteria were as follows: 1) the type of cytology sample was liquid-based cytology cervical smear, and the date of diagnosis was from January 1, 2012 to October 31, 2019. 2) The origin of cytology samples was PE in Guangxi. 3) Cytology samples should qualify and have defined cytopathology diagnosis. The exclusion criteria were as follows: 1) cytology samples obtained from projects other than PE, 2) cytology samples from other provinces, and 3) unqualified cytology samples or samples without a defined diagnosis (Figure 1).

Figure 1.

Flowchart of sample inclusion and exclusion criteria. PE: Physical examination; NCCSP: National cervical cancer screening project.

Study variables

Diagnoses of cytological testing of cervical cells were classified into the following types based on the Bethesda System for reporting cervical cytology¹⁰: 1) atypical squamous cells encompassing cells of undetermined significance (ASC-US) and not excluding HSIL (ASC-H); 2) LSILs encompassing HPV/mild dysplasia / cervical intraepithelial neoplasia (CIN) 1; 3) HSIL encompassing moderate and severe dysplasia, carcinoma in situ, CIN 2 and CIN 3; and 4) squamous cell carcinoma.

The characteristics of the samples were defined as based on the following subgroups for rate calculations: 1) districts (urban or rural); 2) GDP¹¹ (< 6228.0 USD$ per year or ≥ 6228.0 USD$ per year based on the average exchange rate of renminbi against dollars (6.8985 to 1) in 2019);¹² 3) minority status (minority or non-minority areas); 4) geography (coastal or inland areas); 5) sample source (NCCSP or PE); and 6) age (< 35, 35–49, 50–64, or > 64 years).

Statistical analysis

Sample structures (encompassing detection time and other variables—see Table 1) between PE and NCCSP were compared by chi-square testing, and the positive rates of LSIL, HSIL, and invasive cancer were calculated according to age stratification. Adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) of variables were calculated using the multivariate logistic regression model. Considering the structural difference between PE and NCCSP (Table 1), sample origin (PE or NCCSP) was controlled for covariables in the multivariate logistic regression model. All statistical procedures of this paper were performed using IBM SPSS statistic version 22; statistical approaches and procedures were recorded as syntax of SPSS for review. Graphs were generated using draw.io version 14.4.3. The level of significance was set at α = 0.05.

Table 1.

Sample characteristics of CC screening tested by ICLs in Guangxi, China, 2012–2019.^a

Variables	Sample source, No. (%)		Total	P value
Variables	PE (n = 309,643)	NCCSP (n = 564,237)	Total	P value
Years				< 0.001
Before 2015	44,051 (14.23)	8492 (1.51)	52,543
2015	36,187 (11.69)	1014 (0.18)	37,201
2016	45,907 (14.83)	65,032 (11.53)	110,939
2017	54,822 (17.70)	170,944 (30.30)	225,766
2018	64,054 (20.69)	123,039 (21.81)	187,093
2019	64,622 (20.87)	195,716 (34.69)	260,338
District				< 0.001
Urban	273,376 (88.29)	53,268 (9.44)	326,644
Rural	36,267 (11.71)	510,969 (90.56)	547,236
Minority status				< 0.001
Minority areas	26,146 (8.44)	52,191 (9.25)	78,337
Non-minority areas	283,497 (91.56)	512,046 (90.75)	795,543
Areas with GDP, $USD per year				< 0.001
< 6228.0	181,346 (58.57)	390,719 (69.25)	572,065
≥ 6228.0	128,297 (41.43)	173,518 (30.75)	301,815
Geographic setting				< 0.001
Coastal areas	35,125 (11.34)	71,450 (12.66)	106,575
Inland areas	274,518 (88.66)	492,787 (87.34)	767,305
Region setting				< 0.001
in southeast Guangxi	80,984 (26.15)	139,154 (24.66)	220,138
in central Guangxi	44,955 (14.52)	44,891 (7.96)	89,846
in northwest Guangxi	36,535 (11.80)	73,860 (13.09)	110,395
in southwest Guangxi	84,178 (27.19)	99,464 (17.63)	183,642
in north Guangxi	62,991 (20.34)	206,868 (36.66)	269,859
Age				< 0.001
< 35 years	112,203 (36.24)	52,075 (9.23)	164,278
35–49 years	150,432 (48.58)	326,673 (57.90)	477,105
50–64 years	42,639 (13.77)	181,312 (32.13)	223,951
> 64 years	4369 (1.41)	4177 (0.74)	8546

P value < 0.05 was considered statistically significant.

CC: cervical cancer; ICL: independent clinical laboratory; PE: physical examination; NCCSP: national cervical cancer screening project.

Results

Characteristics

A final total of 873,880 samples were included in the study, and the PE and NCCSP sample groups included 309,643 and 564,237 individuals, respectively (Table 1). The average age of women receiving PE services was 39.00 ± 10.29 years, which was younger than that of women involved in the NCCSP (45.26 ± 8.83 years, P < 0.001). The sample structures of PE and NCCSP were different, and the proportions of patients aged 35 to 64 in the two groups were 62.35% and 90.03%, respectively. A total of 88.29% of the PE samples were collected from urban areas, and 90.56% of the NCCSP samples were obtained from rural areas (P < 0.001). The proportions of samples from non-minority areas, inland areas, and areas with GDP ≥ 6228.0 (USD$ per year) in the PE group were 91.56%, 88.66%, and 41.43%, respectively. These values were greater than those noted in the NCCSP group (90.75%, P < 0.001; 87.34%, P < 0.001; and 30.75%, P < 0.001, respectively) (Table 1). The PE and NCCSP sample sizes from 2012 to 2019 are presented in Figure 2.

Figure 2.

Annual sample size for (a) PE and (b) NCCSP in guangxi zhuang autonomous region from 2012 to 2019. The total sample size for PE was 309,643, and for NCCSP was 564,237. PE: Physical examination; NCCSP: National cervical cancer screening project.

Positive rates

The positive rates of LSIL, HSIL, and invasive cancer in all 873,880 participants were 1.89% (16,500/873,880), 0.60% (5262/873,880), and 0.03% (247/873,880, 28.26/100,000), respectively. The positive rates in the PE samples were 2.32% for LSIL (7184/309,643), 0.78% for HSIL (2417/309,643), and 0.05% for invasive cancer (142/309,643). These values were greater than those noted in NCCSP samples (1.65% for LSIL [9316/564,237], 0.50% for HSIL [2845/564,237], and 0.02% [105/564,237] for invasive cancer). The results were stratified by age (Table 2).

Table 2.

Comparison of the positive rates of LSIL, HSIL, and invasive cancer identified by PE or the NCCSP.^a

Age group	Cytopathology diagnosis	PE (n = 309,643)		NCCSP (n = 564,237)		OR (95% CI)^b	P Value
Age group	Cytopathology diagnosis	n	%	n	%	OR (95% CI)^b	P Value
< 35	Positive	2937	2.62	963	1.85	1.11(1.09–1.13)	< 0.001
	Negative	109,266	97.38	51,112	98.15
35–49	Positive	3327	2.21	5891	1.80	1.15(1.12–1.18)	< 0.001
	Negative	147,105	97.79	320,782	98.20
50–64	Positive	819	1.92	2395	1.32	1.35(1.27–1.43)	< 0.001
	Negative	41,820	98.08	178,917	98.68
> 64	Positive	101	2.31	67	1.60	1.18(1.04–1.34)	0.018
	Negative	4268	97.69	4110	98.40
total	Positive	7184	2.32	9316	1.65	1.23(1.21–1.26)	< 0.001
	Negative	302,459	97.68	554,921	98.35
< 35	Positive	546	0.49	168	0.32	1.12(1.08–1.17)	< 0.001
	Negative	111,657	99.51	51,907	99.68
35–49	Positive	1339	0.89	1652	0.51	1.42(1.37–1.48)	< 0.001
	Negative	149,093	99.11	325,021	99.49
50–64	Positive	443	1.04	986	0.54	1.64(1.51–1.77)	< 0.001
	Negative	42,196	98.96	180,326	99.46
> 64	Positive	89	2.04	39	0.93	1.37(1.22–1.54)	< 0.001
	Negative	4280	97.96	4138	99.07
total	Positive	2417	0.78	2845	0.50	1.30(1.26–1.34)	< 0.001
	Negative	307,226	99.22	561,392	99.50
< 35	Positive	9	0.01	0	0.00	1.46(1.46–1.47)	0.041
	Negative	112,194	99.99	52,075	100.00
35–49	Positive	57	0.04	31	0.01	2.06(1.76–2.40)	< 0.001
	Negative	150,375	99.96	326,642	99.99
50–64	Positive	57	0.13	65	0.04	2.46(2.03–2.97)	< 0.001
	Negative	42,582	99.87	181,247	99.96
> 64	Positive	19	0.43	9	0.22	1.33(1.03–1.72)	0.076
	Negative	4350	99.57	4168	99.78
total	Positive	142	0.05	105	0.02	1.62(1.46–1.81)	< 0.001
	Negative	309,501	99.95	564,132	99.98

P value < 0.05 was considered statistically significant.

The reference was the positive rate of NCCSP.

LSIL: low-grade squamous intraepithelial lesion; HSIL: high-grade squamous intraepithelial lesion; PE: physical examination; NCCSP: national cervical cancer screening project; OR: odds ratio.

High-risk population

The variable “sample source” (samples from NCCSP or PE) was controlled for covariables, and other significant variables in single factor analysis were entered into the multivariate logistic regression model. The results showed that females from coastal areas (vs. inland areas; aOR = 0.90; 95% CI, 0.86–0.95; P < 0.001) were significantly associated with a low risk of LSIL. Urban areas (vs. rural; aOR = 1.23; 95% CI, 1.19–1.27; P < 0.001), age < 35 years (vs. 50–64; aOR = 1.54; 95% CI, 1.50–1.62; P < 0.001), 35–49 years (vs. 50–64; aOR = 1.33; 95% CI, 1.27–1.38; P < 0.001), and > 64 years (vs. 50–64; aOR = 1.30; 95% CI, 1.12–1.53; P = 0.001) were significantly associated with a high risk of LSIL (Table 3).

Table 3.

Positive rates and multivariate logistic regression for LSIL.^a

Variables	Samples (n = 873,880)	LSIL		Single factor analysis		Multivariate logistic regression
Variables	Samples (n = 873,880)	Positive (n = 16,500)	Positive rate (%)	OR (95% CI)^b	P value	aOR (95% CI)^c	P value
District
Rural	547,236	9269	1.69	1.00		1.00
Urban	326,644	7231	2.21	1.31(1.27–1.36)	< 0.001	1.23(1.19–1.27)	< 0.001
Minority areas
No	795,543	15,066	1.89	1.00
Yes	78,337	1434	1.83	0.97(0.92–1.02)	0.215
Areas with GDP
≥ 6228.0 USD	301,815	5868	1.94	1.00
< 6228.0 USD	572,065	10,632	1.86	0.96(0.93–0.99)	0.005
Geographic setting
Inland areas	767,305	14,686	1.91	1.00		1.00
Coastal areas	106,575	1814	1.70	0.89(0.85–0.93)	< 0.001	0.90(0.86–0.95)	< 0.001
Age
< 35	164,278	3900	2.37	1.67(1.59–1.75)	< 0.001	1.54(1.50–1.62)	< 0.001
35–49	477,105	9218	1.93	1.35(1.30–1.41)	< 0.001	1.33(1.27–1.38)	< 0.001
50–64	223,951	3214	1.44	1.00		1.00
> 64	8546	168	1.97	1.38(1.18–1.61)	< 0.001	1.30(1.12–1.53)	0.001

P value < 0.05 was considered statistically significant.

The value 1.00 represents the reference in single-factor analysis.

The value 1.00 represents the reference in multivariate logistic regression.

LSIL: low-grade squamous intraepithelial lesion; OR: odds ratio; aOR: adjusted odds ratio.

Females aged over 64 (vs. 50–64; aOR = 2.05; 95% CI, 1.71–2.46; P < 0.001) and females from urban (vs. rural; aOR = 1.66; 95% CI, 1.57–1.76; P < 0.001), minority (vs. non-minority; aOR = 1.24; 95% CI, 1.13–1.35; P < 0.001), and coastal (vs. inland; aOR = 1.15; 95% CI, 1.06–1.25; P = 0.001) areas were associated with a high risk of HSIL. Females under 35 (vs. 50–64; aOR = 0.57; 95% CI, 0.52–0.62; P < 0.001) were associated with a low risk of HSIL (Table 4).

Table 4.

Positive rates and multivariate logistic regression for HSIL.^a

Variables	Samples (n = 873,880)	HSIL		Single factor analysis		Multivariate logistic regression
Variables	Samples (n = 873,880)	Positive (n = 5262)	Positive Rate (%)	OR (95% CI)^b	P value	aOR (95% CI)^c	P value
District
Rural	547,236	2767	0.51	1.00		1.00
Urban	326,644	2495	0.76	1.52(1.44–1.60)	< 0.001	1.66(1.57–1.76)	< 0.001
Minority areas
No	795,543	4708	0.59	1.00		1.00
Yes	78,337	554	0.71	1.20(1.10–1.31)	< 0.001	1.24(1.13–1.35)	< 0.001
Areas with GDP
≥ 6228.0 USD	301,815	1833	0.61	1.00
< 6228.0 USD	572,065	3429	0.60	0.99(0.93–1.05)	0.649
Geographic setting
Inland areas	767,305	4548	0.59	1.00		1.00
Coastal areas	106,575	714	0.67	1.13(1.05–1.22)	0.002	1.15(1.06–1.25)	0.001
Age
< 35	164,278	714	0.43	0.68(0.62–0.74)	< 0.001	0.57(0.52–0.62)	< 0.001
35–49	477,105	2991	0.63	0.98(0.92–1.05)	0.581	0.94(0.88–1.00)	0.051
50–64	223,951	1429	0.64	1.00		1.00
> 64	8546	128	1.50	2.37(1.97–2.84)	< 0.001	2.05(1.71–2.46)	< 0.001

P value < 0.05 was considered statistically significant.

The value 1.00 represents the reference in single-factor analysis.

The value 1.00 represents the reference in multivariate logistic regression.

HSIL: high-grade squamous intraepithelial lesion; OR: odds ratio; aOR: adjusted odds ratio.

Females over 64 (vs. 50–64; aOR = 4.37; 95% CI, 2.88–6.63; P < 0.001) and females from urban areas (vs. rural; aOR = 3.05; 95% CI, 2.36–3.95; P < 0.001) were significantly associated with a high risk of invasive cancer. Females under 35 (vs. 50–64; aOR = 0.07; 95% CI, 0.03–0.13; P < 0.001) and 35–49 (vs. 50–64; aOR = 0.30; 95% CI, 0.23–0.40; P < 0.001) were significantly associated with a low risk of invasive cancer (Table 5).

Table 5.

Positive rates and multivariate logistic regression for invasive cervical cancer.^a

Variables	Samples	Invasive cancer		Single factor analysis^b		Multivariate logistic regression^c
Variables	(n = 873,880)	Positive (n = 247)	Positive Rate (%)	OR (95% CI)	P value	aOR (95% CI)	P value
District
Rural	547,236	102	0.02	1.00		1.00
Urban	326,644	145	0.04	2.38(1.85–3.07)	< 0.001	3.05(2.36–3.95)	< 0.001
Minority areas
No	795,543	229	0.03	1.00
Yes	78,337	18	0.02	0.80(0.49–1.29)	0.356
Areas with GDP
≥ 6228.0 USD	301,815	82	0.03	1.00
< 6228.0 USD	572,065	165	0.03	1.06(0.82–1.38)	0.685
Coastal or inland
Inland areas	767,305	205	0.03	1.00
Coastal areas	106,575	42	0.04	1.48(1.06–2.06)	0.021
Age
< 35	164,278	9	0.01	0.10(0.05–0.19)	< 0.001	0.07(0.03–0.13)	< 0.001
35–49	477,105	88	0.02	0.34(0.26–0.45)	< 0.001	0.30(0.23–0.40)	< 0.001
50–64	223,951	122	0.05	1.00		1.00
> 64	8546	28	0.33	6.03(3.99–9.10)	< 0.001	4.37(2.88–6.63)	< 0.001

P value < 0.05 was considered statistically significant.

The value 1.00 represents the reference in single-factor analysis.

The value 1.00 represents the reference in multivariate logistic regression.

OR: odds ratio; aOR: adjusted odds ratio.

Discussion

Guangxi is the only ethnic minority coastal province in South China; it has a relatively weak economy and borders northern Vietnam. The ethnic structure of Guangxi is relatively complex, with Han, Zhuang and Yao accounting for 62%, 31% and 3%, respectively. The remaining ethnic minorities account for approximately 4% of the population. Guangxi is home to the largest Zhuang and Yao populations. Similar to other developing areas,^13,14 the trend of increasing mortality from CC puts an increasingly heavy burden on individuals, families, and the country.^7,15 This study found that the positive rate of CC in Guangxi was 28.26/100,000, which was higher than that reported in previous studies in China (13.30/100,000)¹⁶ but lower than that reported in Guangdong (45.03/100,000), a developed province located in southeastern Guangxi.¹⁷

Females aged 35–64 were the main target population of the NCCSP, and this population was associated with a higher risk of CC than younger females in this study. Similar results were noted in a previous study in China.¹⁸ We found that females over 64 were associated with a significantly higher risk of CC than females aged 50–64 (the main target population of the NCCSP). The Chinese National Cancer Center also estimated a high incidence rate in elderly females in China¹⁹ and suggested that CC screening should include those aged 65 and older.²⁰

We found that urban females in Guangxi were associated with a high risk of CC, similar to previous studies describing the incidence of CC in urban and rural areas.^21–23 Previous studies suggested that urban females have more sex partners and first intercourse at a younger age than rural females.²⁴ These sexual behaviours are risk factors for CC.²⁵ Some studies conducted in developed countries suggested a high incidence of CC in rural females and ethnic minority populations.²⁶ In this paper, ethnic disparities in HSIL were observed. In contrast, spatial and economic condition disparities of CC have been reported worldwide.^27,28 We found that low economic conditions were not a risk factor in this study. We hypothesize that although Guangxi is a developing region with low economic conditions, the regional economic differences among counties in Guangxi are not as large as those reported in previous studies between developed and developing countries.

The positive rate of HSIL (0.60%) in Guangxi was higher than that reported in recent studies (0.48%–0.56%) in China^29–31 but lower than that reported in other developing counties or areas.^31,32 Notably, this study was based on cervical cytological results rather than histological results, which is similar to some population-based studies on CC screening. In this study, we found that being female over 64 and being from an urban area were common risk factors for CC and HSIL. We also discovered that the 35–49 and 50–64 age groups had similar HSIL risks. CC screening showed high value in the early detection of HSIL in the target population. Previous studies have reported a strong relationship between HSIL and cancers,^33–35 particularly CC and anal cancer.^36–38 We should focus on these populations at high risk for CC and HSIL in Guangxi.

Notably, the urban female population is not yet covered by the NCCSP in Guangxi, but the NCCSP allows these females to self-pay for CC testing. However, compared with the periodic check-up for rural females in the NCCSP, PE appears to be an ideal screening method for most urban females. In addition, some urban female workers can have regular check-ups paid for by employers. Regular check-ups may help with the early detection of HSIL, and potentially give more opportunities to control or interrupt its progression through appropriate treatments.^39,40 The main purpose of this screening is to identify CC patients early, to save lives, and to improve quality of life.^41,42 This paper identified populations in Guangxi at high risk for CC. This finding suggests that if we pay more attention to the high-risk population, then clinicians have more opportunities to identify potential patients and treat them at early stages.^43,44

In minority areas, the natural environment may be harsh and the economic level can be low because of traditional customs, lifestyle, and culture. The low coverage rate of CC screening, high prevalence of STIs and tuberculosis,and late detection of HIV/AIDS in Guangxi may indicate certain obstacles to the implementation of public health services, especially in undeveloped minority areas, despite the similar expected standards of medical care and services at the county level.^45–47 Our results suggest that health departments should actively mobilize urban women to participate in CC screening in their early 60 s to identify HSIL and cancer patients at an early stage. Surveys of target populations in low coverage areas are conducive to exploring possible barriers to CC screening.^48,49 Targeted campaigns in the local language can promote awareness of women's health, which has a positive effect on CC screening.⁵⁰

This paper has the following limitations. 1) Given differences in the serial numbers of HPV and liquid-based cytological samples, we did not incorporate the HPV results into this study. 2) Given the significantly different features between PE and NCCSP, the sample source was included in the multivariate regression model as a covariate. Therefore, the source could not be considered a potential variable in risk analysis. 3) Both NCCSP and PE samples may contain a small number of patients with HSIL who require regular follow-up examination. Approximately 1.7% of HSIL patients are estimated to undergo repeat CC screening in follow-up visits, and the positive rate of HSIL may be overestimated.

Despite these limitations, there are also some strengths. Many studies have focused on CC screening in China. However, the majority were based on the NCCSP, and our study is based on data from a large sample of the NCCSP and PE. This paper expounds on the positive rates of LSIL, HSIL and CC in Guangxi, thereby identifying high-risk populations and providing evidence for public health strategy developers.

Conclusion

Our study revealed that females from urban areas are at high risk for CC and HSIL in Guangxi. We suggest that public health strategies must focus on these high-risk populations.

Footnotes

Acknowledgments

We thank Dr Fengting Huang, Liying Ou, Jianyun Lan, Xiaoxia Li and colleagues at cytopathology diagnosis department of Guangxi Kingmed diagnostics group for supporting our study. All authors in the study provided important contribution and revision of the manuscript.

Ethics approval

This study protocol was approved by the Ethics and Human Subject Committee of Guangxi Medical University and Guilin Medical University (No.20180702-3).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported by the National Natural Science Foundation of China [grant No.81560523 and 81760577], the Guangxi Science and Technology Development Project [grant No.AD17129003, No.14124005-2-11], the Fundamental Research Funds for Young and Middle-aged Teachers by the Education Department of Guangxi government [grant No.KY2016YB076], the Innovation Project of Guangxi Graduate Education (grant No. YCBZ2020052), Guangxi Scholarship Fund of Guangxi Education Department of China. Young Researchers Science Foundation Program of Guangxi Medical University (grant No. GXMUYSF201814).

ORCID iD

Huaxiang Lu

References

Bray

Ferlay

Soerjomataram

, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68: 349–424.

Arbyn

Weiderpass

Bruni

, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 2020; 8: e191–e203.

Pilleron

Cabasag

Ferlay

, et al.

Cervical cancer burden in Latin America and the Caribbean: where are we?

Int J Cancer 2020; 147: 1638–1648.

Ginsburg

Bray

Coleman

, et al. The global burden of women's Cancers: a grand challenge in global health. Lancet (London, England) 2017; 389: 847–860.

Pan

Zhu

, et al. Cancer incidence and mortality: a cohort study in China, 2008–2013. Int J Cancer 2017; 141: 1315–1323.

Chen

Wei

Liu

, et al. Rising mortality rate of cervical cancer in younger women in urban China. J Gen Intern Med 2020; 35: 593.

Wei

Zhou

, et al. Rising mortality rate of cervical cancer in younger women in urban China. J Gen Intern Med 2019; 34: 281–284.

Zhao

Qiao

. Cervical cancer prevention in China: a key to cancer control. Lancet (London, England) 2019; 393: 969–970.

Yang

, et al. A study on service capacity of primary medical and health institutions for cervical cancer screening in urban and rural areas in China. Chin J Cancer Res = Chung-kuo yen cheng yen chiu 2019; 31: 838–848.

10.

Nayar

Wilbur

. The Bethesda System for Reporting Cervical Cytology: Springer-Verlag, 2018.

11.

Statistics GZARBo. Statistical Communique of the Guangxi zhuang autonomous region in 2019: National Economic and Social Development. Available at: http://tjj.gxzf.gov.cn/tjsj/tjgb/qqgb/t2381676.shtml

12.

China NBoSo. Statistical Communique of the People's Republic of China in 2019: National Economic and Social Development. Available at: http://www.stats.gov.cn/tjsj/zxfb/202002/t20200228_1728913.html

13.

Nguyen DN

Simms

Vu Nguyen

, et al. The burden of cervical cancer in Vietnam: synthesis of the evidence. Cancer Epidemiol 2019; 59: 83–103.

14.

Torre

Siegel

Ward

, et al. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiology, Biomarkers & Prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2016; 25: 16–27.

15.

Zhou

Wang

Zeng

, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet (London, England) 2019; 394: 1145–1158.

16.

Zheng

Sun

, et al. Incidence and mortality of cervical cancer in China, 2014. Chin J Oncol 2018; 40: 241–246.

17.

Qian-feng

HUANG

C-w

. Evaluation on detection rate and follow-up quality of different cervical cancer screening methods. Chin J Pub Health 2018; 34: 1380–1382.

18.

Zheng

, et al. Trends of incidence rate and age at diagnosis for cervical cancer in China, from 2000 to 2014. Chin J Cancer Res = Chung-kuo yen cheng yen chiu 2017; 29: 477–486.

19.

Ping

Zhijian

Kai

, et al. Opportunistic screening for cervical cancer in elderly women aged ≥65 years(in Chinese). Chin J Health Manage 2019; 13: 411–415.

20.

Pankakoski

Anttila

Sarkeala

, et al. Effectiveness of cervical cancer screening at age 65 - A register-based cohort study. PloS one 2019; 14: e0214486.

21.

Zahnd

James

Jenkins

, et al. Rural-Urban differences in cancer incidence and trends in the United States. Cancer Epidemiology, Biomarkers & Prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2018; 27: 1265–1274.

22.

Ojamaa

Innos

Baburin

, et al. Trends in cervical cancer incidence and survival in Estonia from 1995 to 2014. BMC cancer 2018; 18: 1075.

23.

Mezei

Armstrong

Pedersen

, et al. Cost-effectiveness of cervical cancer screening methods in low- and middle-income countries: a systematic review. Int J Cancer 2017; 141: 437–446.

24.

Zhao

Tiggelaar

, et al. A multi-center survey of age of sexual debut and sexual behavior in Chinese women: suggestions for optimal age of human papillomavirus vaccination in China. Cancer Epidemiol 2012; 36: 384–390.

25.

Stone

Zaidi

Rosero-Bixby

, et al. Sexual behavior, sexually transmitted diseases, and risk of cervical cancer. Epidemiology 1995; 6: 409–414.

26.

Sabatino

. White MC. Rural-urban and racial/ethnic disparities in invasive cervical cancer incidence in the United States, 2010–2014. Prev Chronic Dis 2019; 16: E70. https://www.cdc.gov/pcd/issues/2019/18_0447.htm

27.

Cohen

Jhingran

Oaknin

, et al. Cervical cancer. Lancet (London, England) 2019; 393: 169–182.

28.

Awolude

, et al. Cervical cancer worldwide. Curr Probl Cancer 2018; 42: 457–465.

29.

Zhao

Dai

Dang

, et al. Real-world research on cervical cancer screening program and effect evaluation for Chinese population. Chin J Oncol 2018; 40: 764–771.

30.

Tao

Austin

Zhang

, et al. Histopathologic follow-up and HPV test results with HSIL papanicolaou test results in China's largest academic women's hospital. Cancer Cytopathol 2017; 125: 947–953.

31.

Wang

Yang

, et al. Diagnostic yield and performance of a large population-based cervical cancer screening program in high-risk rural China. J Cancer 2020; 11: 4000–4006.

32.

Baldur-Felskov

Mwaiselage

Faber

, et al. Factors associated with a cervical high-grade lesion on cytology or a positive visual inspection with acetic acid among more than 3300 Tanzanian women. Trop Med Int Health 2019; 24: 229–237.

33.

Dalstein

Riethmuller

Prétet

, et al. Persistence and load of high-risk HPV are predictors for development of high-grade cervical lesions: a longitudinal French cohort study. Int J Cancer 2003; 106: 396–403.

34.

Schiffman

Castle

Jeronimo

, et al. Human papillomavirus and cervical cancer. Lancet (London, England) 2007; 370: 890–907.

35.

Schiffman

Brinton

. The epidemiology of cervical carcinogenesis. Cancer 1995; 76: 1888–1901.

36.

Bagcchi

. HSIL Linked to anal cancer in MSM with HIV. Lancet Oncol 2013; 14: e446.

37.

Chin-Hong

Vittinghoff

Cranston

, et al. Age-related prevalence of anal cancer precursors in homosexual men: the EXPLORE study. J Natl Cancer Inst 2005; 97: 896–905.

38.

Berti

FCB

Salviano-Silva

Beckert

, et al. From squamous intraepithelial lesions to cervical cancer: circulating microRNAs as potential biomarkers in cervical carcinogenesis. Biochim Biophys Acta Rev Cancer 2019; 1872: 188306.

39.

Meng

Shi

, et al. LEEP/Cone combined with photodynamic therapy for successful treatment of high-grade squamous intraepithelial lesion. Photodiagn Photodyn Ther 2019; 25: 237–238.

40.

Lee

Finlayson

Gukova

, et al. Outcomes of conservative management of high grade squamous intraepithelial lesions in young women. J Low Genit Tract Dis 2018; 22: 212–218.

41.

Zhou

Yang

Dai

, et al. Survey of cervical cancer survivors regarding quality of life and sexual function. J Cancer Res Ther 2016; 12: 938–944.

42.

Sankaranarayanan

Nene

Shastri

, et al. HPV Screening for cervical cancer in rural India. N Engl J Med 2009; 360: 1385–1394.

43.

Cheng

. Advances in diagnosis and treatment of metastatic cervical cancer. J Gynecol Oncol 2016; 27: e43. https://ejgo.org/DOIx.php?id=10.3802/jgo.2016.27.e43

44.

Holschneider

Petereit

Chu

, et al. Brachytherapy: a critical component of primary radiation therapy for cervical cancer: from the society of gynecologic oncology (SGO) and the American brachytherapy society (ABS). Gynecol Oncol 2019; 152: 540–547.

45.

Liang

Zhou

, et al. HIV Late presentation and advanced HIV disease among patients with newly diagnosed HIV/AIDS in southwestern China: a large-scale cross-sectional study. AIDS Res Ther 2019; 16: 6.

46.

Wang

Liu

Wang

, et al. HIV Prevalence among pulmonary tuberculosis patients in Guangxi, China. J Acquir Immune Defic Syndr 2010; 53 Suppl 1(Suppl 1): S61–S65.

47.

Wang

Lan

Shen

, et al. HIV And syphilis prevalence trends among men who have sex with men in Guangxi, China: yearly cross-sectional surveys, 2008–2012. BMC Infect Dis 2014; 14: 367.

48.

Lin

Chen

, et al. Cervical cancer screening rate and willingness among female migrants in Shenzhen, China: three-year changes in citywide surveys. Cancer Res Treat 2021; 53: 212–222.

49.

Yang

Chen

, et al. Barriers to cervical cancer screening among rural women in eastern China: a qualitative study. BMJ open 2019; 9: e026413.

50.

Maree

Kampinda-Banda

. Knowledge and practices of cervical cancer and its prevention among Malawian women. J Cancer Educ 2020; 35: 86–92.

Populations at high risk of cervical cancer in Guangxi Province: Findings from two screening projects in a minority area of South China

Abstract

Objective

Setting

Methods

Results

Conclusion

Keywords

Abbreviations

Introduction

Materials and methods

Study population

Study variables

Statistical analysis

Results

Characteristics

Positive rates

High-risk population

Discussion

Conclusion

Footnotes

Acknowledgments

Ethics approval

Declaration of conflicting interests

Funding

ORCID iD

References