Selective screening for cervical neoplasia: an approach for resource-poor settings

Cathrine Hoyoa,b, William C Millera,b, Beth M Newmana and Judith A Fortneya,c

a Departments of Epidemiology, and
b Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
c Family Health International, Research Triangle Park, NC, USA.

Reprint requests to: William C Miller, Department of Epidemiology, 2105F McGavran-Greenberg Building, CB # 7400, UNC—Chapel Hill, Chapel Hill, NC 27599–7400, USA.


    Abstract
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background Cervical malignancies are the leading cause of cancer-related morbidity and mortality among women in developing countries. Although early detection programmes using cytological methods, followed by aggressive treatment of precursor lesions are accepted as the main disease control strategy, fiscal limitations make this strategy unfeasible in many countries.

Methods To screen selectively, we developed two risk scores using data from a population-based case-control study in Jamaica with 202 cases and 363 controls. Independent risk factors for cervical neoplasia were determined using logistic regression. An unweighted risk score for each subject was developed by a simple count of risk factors present and a weighted risk score was calculated by summing regression coefficients for each risk factor.

Results Four patient characteristics were independently predictive of cervical neoplasia, older age (OR = 3.4, 95% CI : 1.8–6.7), >=4 pregnancies (OR = 5.6, 95% CI : 1.2– 18.7), poverty (OR = 2.1, 95% CI : 1.3–3.3) and cigarette smoking (OR = 1.9, 95% CI : 1.2–3.2). Using cut-off points of >=20 for the weighted scores and >3 for unweighted scores, the sensitivity and specificity were 65% and 69% for the unweighted score and 75% and 61%, respectively, for the weighted score. Areas under the receiver operating characteristic (ROC) curves for the weighted versus the unweighted scores were similar, suggesting similar overall accuracy.

Conclusion Selective screening using risk assessment strategies is potentially useful, particularly in resource-poor settings. However, whether weighting factors is essential is dependent on prevalence of factors in a given setting. Although this approach needs validation in other populations, women at highest risk for cervical neoplasia can be identified using demographic factors available during a regular clinic visit.

Keywords Cervical neoplasia, screening, risk scores, receiver operating characteristic (ROC) curves

Accepted 7 February 2000


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Cervical cancer is the second most common malignancy among women worldwide behind breast cancer and the most common cause of cancer-related morbidity and mortality in the developing world.1 However, cervical cancer incidence varies widely in both the developed and developing world, with up to 15-fold higher rates reported for women living in low-resource settings.2 Factors contributing to this disparity have been hypothesized to include differences in: (1) the distribution of underlying, sexually transmitted infections, including human papillomavirus (HPV);3 (2) provision of health education information with respect to risk factors;4,5 and (3) cytology-based screening practices including the frequency and the target population.6

Successful control of cervical cancer has been reported in settings with the highest screening coverage of the population at risk and/or in populations with high screening frequency.6 Cytology-based screening requires highly trained personnel for slide reading and verification. Hence, a comprehensive screening programme is expensive, a major limiting factor for programmes in low-resource settings.7–9

In most settings, women undergoing cytology-based screening have varying risk for cervical cancer. When the proportion of women undergoing screening is low, estimated at 5% in some settings,10 targeting women at highest risk and selectively screening them regularly optimizes the use of limited resources and increases the case yield among those screened.11 However, selective screening requires a formalized process for identifying women at relatively increased risk based on the prevalence of selected risk factors. In this study, we used data collected in a population-based case-control study of cervical neoplasia in Jamaica to develop a risk scoring system with applicability in low resource settings.


    Methods
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Study subjects
Participants for this study were drawn from women using Papanicolaou (Pap) smear services in the Kingston-St Andrews Corporate Area, Jamaica. Cervical cancer cases were <=50 years old at the time of the Pap smear that led to the diagnosis and residents of this defined geographical region at the time. The average time between diagnosis and interview was 3 months. Diagnoses and histological confirmation for either carcinoma in situ (CIS) or invasive cancer were made between November 1982 and December 1987. Case status was further verified retrospectively with biopsy slides and specimens used to make the original diagnosis. Only women with asymptomatic disease, detected by Pap smear, were included in the analyses. For each case, two controls were randomly selected from the medical facility where the case obtained the Pap smear leading to her diagnosis. In order to control for the temporal difference between cases and controls, all controls had a normal Pap (Class I) within 3 months of the diagnosis of the case. Women with prior cervical cancer or hysterectomy in which the cervix was removed, were excluded from the control group.

Of the women contacted, 83% of the cases (83% of CIS and 82% of invasive cancer) and 60% of controls were able to participate. Reasons for failure of cases to participate were patient or attending physician refusal (10%) or study logistical problems (7%). Among controls, study logistical problems accounted for 2% of losses while 38% either refused or failed to respond. Thus, there were 147 cases of carcinoma in situ, 55 cases of invasive cancer and 363 controls available for these analyses.

Trained female interviewers conducted the interviews using a standardized questionnaire. Information was collected on demographics, socioeconomic status, reproductive and sexual histories, contraceptive use, Pap smear screening histories, smoking and personal hygiene.

Statistical analysis
The objective of these analyses was to determine factors that would, singly or in combination, maximize the likelihood of cervical neoplasia status, in order to identify for screening, women at highest risk. Potential associations between possible predictors and cervical neoplasia were determined using odds ratios (OR).12 Cut-off points for count variables, such as number of pregnancies, were based on the similarity of OR in adjacent categories. Risk factors for cervical neoplasia with an unadjusted P-value of <=0.2 in bivariate analyses were evaluated in a logistic model using SAS PROC LOGISTIC.13 The modelling procedure entailed: (1) assessment of collinearity among risk indicators; (2) removal of factors from the model by backward elimination;14 and (3) construction of first-order interaction terms among selected covariates. Demographic, sexual health and reproductive factors were assessed. Demographic factors were age and socioeconomic status, including educational achievement, number of persons per room and the ability to employ a gardener. Reproductive factors were use of tampons or pads, tubal ligation status, number of pregnancies, intervals between pregnancies, hormonal contraceptive use, use of intrauterine devices and age at first pregnancy. Sexual health factors included reported histories of sexually transmitted diseases including trichomoniasis, pelvic inflammatory disease, gonorrhoea, yeast infection, and vaginal discharge of unknown aetiology as well as age at first coitus. Other health aspects assessed were cigarette smoking and douching.

Risk score development
Each independent predictor of cervical neoplasia was assigned a weight, equivalent to the logistic regression coefficient multiplied by 10.15,16 For the ith woman, a weighted risk score was estimated by summing the weighted risk for each variable (risk score = {Sigma}10ßixi, where ß was the regression coefficient associated with the factor, and x was equal to one when the factor was present and zero otherwise). For ease of use in clinical settings, unweighted scores were also estimated by summing up all risk factors present, when each factor present was assigned a value of 1, regardless of the strength of the association with cervical cancer.

To determine the overall accuracy of the weighted and unweighted risk scores in predicting cervical neoplasia status, and compare their accuracy, receiver operating characteristic (ROC) curves were plotted. The areas under these curves were calculated using the trapezoidal method proposed by Hanley.17 Sensitivity and specificity were calculated at all possible cut-off points for each risk score using standard methods.12 Selection of the optimal cut-off point was based on the supposition that the number of tests available to a screening programme was limited. Thus, to reduce the number of Pap smear tests performed by 50–60%, a cut-off point was chosen that maintained a specificity of at least 60%.

Risk scores constructed in this manner are typically validated using a population other than that used to develop them.15 In the absence of a new population data set, the bootstrap technique, using 200 data sets derived from the original data and sampled with replacement, was employed. Sensitivity, specificity and areas under the ROC curves were re-calculated for each data set.


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 Methods
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 Discussion
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Characteristics of study population
The relationship between demographic, reproductive and sexual health factors and cervical neoplasia are shown in Table 1Go. Cases were older (mean 38.6 years ± 6.4 years) than controls (34.7 ± 7.7 years). Cases also tended to be of lower socioeconomic status, as measured by educational achievement, number of people per room in the household, or ability to hire a gardener. In addition, among parous women, cases had more pregnancies (4 ± 2.4) than controls (2.5 ± 2.2) and a shorter interval between two pregnancies (21 ± 14 months for cases versus 18 ± 13 months for controls). They also had their first pregnancy earlier than controls (18.9 ± 3.1 versus 20 ± 3.7 years respectively).


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Table 1 Potential risk factors/risk markers for cervical neoplasia
 
Cases also had more lifetime sexual partners than controls (4.3 ± 2.9 among cases versus 3.7 ± 2.8 among controls) and tended to have first coitus at an earlier age (16.3 ± 2.5 years) than controls (17.5 ± 3.3 years). Self-reported history of trichomoniasis and yeast infection were more common among cases, but other sexually transmitted infections, including pelvic inflammatory disease, did not differ among cases and controls. Pap smear regularity was similar among cases and controls, as was the use of hormonal and barrier contraceptives. However, 32% of cases had tubal ligation compared to 20% of controls. Personal hygiene markers including douching, use of tampons as well as pads, and the use of spermicides did not differ between cases and controls. Cases were more likely to have smoked >100 cigarettes in their lifetime.

Independent risk markers for cervical neoplasia
The independent association of several risk factors with cervical neoplasia was assessed using multiple logistic regression. Consistent with previous studies, a history of >=2 pregnancies, age 30–50 years, smoking >100 cigarettes in a lifetime, and lower socioeconomic status, had independent associations with risk of cervical neoplasia (Table 2Go).


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Table 2 Adjusted odds ratios (OR) for the association of independent risk markers and cervical neoplasia
 
Risk scores
Weighted risk scores for individual risk markers were based on the logistic regression coefficients and were summed to create an overall risk score. Weighted scores ranged from 0 in women with no risk factor, to 35 in women with all four risk factors present. Unweighted scores were developed by assigning each risk marker a value of 1 if present, and 0 otherwise. Unweighted scores ranged from 0 in women with no risk factor present to 4 in women with all factors present. While the mean weighted score was higher among cases (24.5 ± 5.1) than controls (17.0 ± 11.4), this difference changed little when the unweighted risk score was used (1.9 ± 1.1 for cases versus 1.2 ± 1.1 for controls). When the overall diagnostic accuracy of the risk scores was assessed by calculating the area under ROC curves, weighted scores did not significantly out-perform unweighted scores (0.72 ± 0.02 and 0.71 ± 0.02, P > 0.05; Figure 1Go).



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Figure 1 Receiver operator characteristic (ROC) curves for weighted and unweighted selective screening criteria

Receiver operator characteristic curves for the weighted and unweighted selective screening criteria are plotted. ROC curves plot sensitivity versus 1 – specificity (false positive rate) for all possible cut-offs and provide insight into the overall performance for the criteria. Areas under the curve were 0.72 and 0.71 for the weighted and unweighted criteria, respectively.

 
Risk score application
In settings with limited resources, the absolute number of tests (a function of the funds available) and the prevalence of cervical neoplasia in the population will dictate the selection of an appropriate cut-off for implementing selective screening. With a low prevalence condition, such as cervical neoplasia, the false positive rate (1 – specificity) approximates the number of tests that will be used. We applied a decision rule requiring maintenance of specificity of >=60%. Use of this cut-off would ensure that the number of women referred for testing would be approximately 40%. With this criterion for cut-off selection, the weighted algorithm (cut-off >=20) had a sensitivity of 75% (95% CI : 70–82%) and a specificity of 61% (95% CI : 53–64%; Table 3Go). For the unweighted algorithm (cut-off >=3), the sensitivity was 65% (95% CI : 58–71%) and the specificity was 69% (95% CI : 64–74%). Confidence intervals derived from bootstrap validation were consistent with these findings.


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Table 3 Performance of weighted and unweighted selective screening criteria—sensitivity and specificity
 
Sensitivity and specificity for other possible cut-offs are also presented in Table 3Go. For example, screening women with weighted scores of >=13 would reduce the population screened by approximately one-third, while detecting 90% of cases.


    Discussion
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 Methods
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Successful selective screening presumes there is a high-risk population or subgroup that is identifiable by high prevalence of a single or a combination of risk factors. Furthermore, these factors should be easily identifiable during a regular clinic visit, preferably with little laboratory support and retain sufficient specificity to reduce the number of people screened. Our results suggest that the combination of four risk factors, age, socioeconomic status, smoking history and number of pregnancies, provide a potentially useful selective screening tool. Using a cut-off that would limit the number of Pap smears performed to ~40% of women, our weighted algorithm provided a sensitivity of 75% and a specificity of 61%.

The four risk markers identified in this study are known to be associated with cervical neoplasia. Up to fourfold increases in risk of invasive cervical cancer have been reported in women with >12 live births, compared to those with one or no births.18 Studies using carcinoma in situ as an endpoint have also reported up to threefold increases in risk among women with >6 live births, compared to those with one or no births.19,20 While some have attributed this association with surrogacy for sexual behaviour and possible infection with HPV,6 recent and more carefully controlled studies have reported a persistence of this association after adjustment for HPV infection.21 Older age is a strong determinant of cervical cancer, and the risk has been previously demonstrated to increase with increasing age.2 The association between cervical cancer and cigarette smoking was first reported in the late 1970s22 and has been observed repeatedly.23–27 The association of cervical cancer and lower socioeconomic status has also been reported,18,19,28 although its aetiological significance remains unclear.

The high case-fatality rate of untreated cervical cancer, the prolonged asymptomatic phase, and potential cure of early lesions support population-based screening for cervical cancer. In addition, the Pap smear test has an acceptable level of discomfort, and with functional laboratories, has a sensitivity of 75–80%, and a specificity of 85%.29 The efficacy of screening is supported by results of programmes in Nordic countries, where centralized cytology laboratories and a modern tracking system have resulted in nearly complete coverage and a reduction in the incidence of invasive cervical cancer by 90% in less than two decades.

Despite evidence of the efficacy and appropriateness of Pap smear screening in reducing cervical cancer morbidity and mortality, a large percentage of the world's population present with invasive cancer at advanced and incurable stages. In resource-poor settings, screening is frequently not available to the entire population and the selection of women to screen is not systematic. Indiscriminate screening often leads to screening of women at relatively low risk.8,30 Focusing screening on high-risk women will free resources that could be used to design and implement programmes to increase coverage.

The number of studies investigating methods to reduce the cost of cervical cancer screening attests to the need for alternatives to current screening practices. Attempts to reduce screening costs have included use of gross visualization of an aceto-white cervix in the US,9 Zimbabwe31 and India,32 as well as the use of the gynaescope in the US.33 While laudable, these alternatives still require a large capital outlay for training of personnel and preliminary results suggest that their specificity is low.10,31

An alternative approach is to use selective screening criteria (risk assessment) to identify women at relatively increased risk for cervical cancer. By selectively screening women at highest risk using this risk assessment approach, limited resources can be targeted to those women at greatest risk of cervical neoplasia.

The performance of the algorithms presented here are comparable to selective screening algorithms for other reproductive tract diseases, such as chlamydial infection.34 Using our weighted algorithm with a cut-off of >=20 in a low-prevalence setting would result in referral of 75% of women with cervical neoplasia for testing, while testing only ~40% of women overall. Thus, compared to universal testing by Pap smear, the number of tests performed is reduced by ~60%. The penalty for this reduction in costs is that, of women with cervical neoplasia, 25% would not be referred for Pap smear. If this penalty is too great, then alternative cut-offs can be used, such as a cut-off of >=13, which would allow detection of 90% of cases with testing of ~67% of women.

The proposed selective screening criteria may be relevant to a large segment of the world's population, but such generalizations must be made cautiously. These weights are a result of modelling available data and thus specific weights may not apply to all settings. In addition, this study was conducted in a country with one of the highest rates of cervical neoplasia in the world. However, similar risk factors have been found in aetiological studies elsewhere,35,18–20 supporting the potential applicability of these criteria elsewhere. Although validation using bootstrap techniques supported the consistency of the criteria, we were unable to validate the criteria on an independent data set from another population. Thus, validation prior to implementation is recommended.

Response rates, particularly for controls, were low. The main reason for failure to participate was non-response. However, like most Caribbean islands, this island is characterized by high population mobility, and it is likely that failure to respond was due to change of address. It is unlikely that women who failed to respond, particularly those who may have moved (the majority), were different with respect to disease status or predictors used. On the contrary, there is evidence from population mobility studies that typical migrants have smaller family sizes, are of higher socioeconomic status and are generally younger.36 Thirty-eight deaths occurred between case identification and the interview. In addition, exclusion of cytology classes 2–5 removes borderline cases, thereby increasing the specificity of the risk scores. While these limitations could have led to biased estimates, the predictors used to generate risk scores have been reported in similar magnitudes in previous studies.

In summary, this study found that in women under 50, risk factors for cervical neoplasia are similar to those found in other aetiological studies. We also showed that it is possible to determine a risk profile to target women at highest risk using socio-demographic factors readily available in a regular clinic visit. Therefore, while validation of this approach is necessary in other populations, selective screening may be an option worth consideration, especially in low-resource settings.


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 Discussion
 References
 
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