This paper reports on the enrollment phase of a population-based natural history study of cervical neoplasia in Guanacaste, a rural province of Costa Rica with consistently high rates of invasive cervical cancer. The main goals of the study are to investigate the role of human papillomavirus (HPV) infection and its co-factors in the etiology of high-grade cervical neoplasia, and to evaluate new cervical cancer screening technologies. To begin, a random sample of censal segments was selected and enumeration of all resident women 18 years of age and over was conducted with the aid of outreach workers of the Costa Rican Ministry of Health. Of the 10 738 women who were eligible to participate, 10 049 (93.6%) were interviewed after giving written informed consent. After the interview on cervical cancer risk factors was administered, a pelvic examination was performed on those women who reported previous sexual activity. The pelvic examination included a vaginal pH determination and collection of cervical cells for cytologic diagnosis using three different techniques. Additional cervical cells were collected for determination of the presence and amount of DNA from 16 different types of HPV, and two photographic images of the cervix were taken and interpreted offsite by an expert colposcopist. Finally, blood samples were collected for immunologic and micronutrient assays. Women with any abnormal cytologic diagnosis or a positive Cervigram, as well as a sample of the whole group, were referred for colposcopy, and biopsies were taken when lesions were observed. The enrollment screening will serve as the basis for a prevalent case-control study, and the members of the cohort free from serious disease will be followed actively, at intervals of no more than a year, to study the natural history of HPV infection and the origins of high-grade squamous intraepithelial lesions (HSIL). Details of the field operation are outlined, with particular reference to the realization of this kind of study in developing countries. Descriptive data on the prevalence of disease and exposure to various risk factors are also presented.

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The problem of estimating any sequence of missing observations in series with a nonstationary ARIMA model representation was solved by Kohn and Ansley (1986). In their approach, the likelihood is defined first by means of a transformation of the data; then, in order to obtain an efficient estimation procedure, a modified Kalman filter and a modified fixed point smoothing algorithm are used. In this paper we show how an alternative definition of the likelihood, based on the usual assumptions made in estimation of and forecasting with ARIMA models, permits a direct and standard state space representation of the nonstationary (original) data, so that the ordinary Kalman filter and fixed point smoother can be efficiently used for estimation, forecasting and interpolation. Our approach, like that of Kohn and Ansley (1986), can handle any arbitrary pattern of missing data and we show that the same results are obtained with both approaches. In this way, the problem of estimating missing values in nonstationary series is considerably simplified. When the available observations do not permit estimation of some of the missing values, the method indicates which are these values, and the forecasts that might be affected. Moreover, if linear combinations of the unestimable missing observations are estimable, the estimates are readily obtained. The method is illustrated using the same examples of Kohn and Ansley (1986), and an additional one for the case of unestimable missing values with estimable linear combinations thereof. It is shown that our likelihood is equal to that of Kohn and Ansley (1986); it also coincides with that of Harvey and Pierse (1984) when applicable, and to that of Box and Jenkins (1976) when no observation is missing. The results are extended to regression models with ARIMA errors, and a computer program, written in Fortran for MSDOS computers, is available from the authors.

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