Jaione Etxeberria

Age-Specific Spatio-Temporal Patterns of Female Breast Cancer Mortality in Spain (1975–2005)

PURPOSE In recent decades, a decline in breast cancer mortality has been observed across Europe, and also in Spain. Our objective is to assess the spatio-temporal pattern during the period 1975-2005 by specific age groups (<45, 45-64, ≥65) in the Spanish provinces. METHODS For each age group, a spatio-temporal P-spline model with a B-spline basis is used to smooth the mortality risks. Smoothing is carried out in three dimensions: longitude, latitude, and time, allowing for a different time evolution of both spatial components. The age-specific decline is calculated as the maximum of the estimated curve in each province. A confidence band for each curve is also provided. RESULTS For the first age group (<45), the decline in the different provinces is observed between 1986 and 1991. For women aged between 45 to 64 years, the change occurs between 1990 and 1993. For the third age group (≥65), change points range from 1992 to 2000, unlike Malaga and Cadiz where the change has not been observed in the studied period. Northern and some Mediterranean provinces are the areas with higher mortality risks for all the age groups. CONCLUSIONS A different behavior for breast cancer mortality risks is observed for different provinces among the age specific groups. The decline of mortality is delayed for the oldest age group. Province differences in the implementation of screening programs could explain some of the observed differences.

Effectiveness of Jeryl Lynn-containing vaccine in Spanish children

We evaluated the effectiveness of the Jeryl Lynn strain vaccine in a large outbreak of mumps in Navarre, Spain, 2006-2008. Each of the 241 cases of mumps occurring in children over 15 months of age born between 1998 and 2005 was compared with 5 controls individually matched by sex, birth date, district of residence and paediatrician. Vaccination history was obtained blindly from clinical records. Conditional logistic regression was used to obtain the matched odds ratios (ORs), and effectiveness was calculated as 1-OR. Some 70% of cases had received one dose of measles-mumps-rubella vaccine, and 24% had received two doses. Overall vaccine effectiveness was 72% (95% CI, 39-87%). Two doses were more effective (83%; 54-94%) than a single dose (66%; 25-85%). Among vaccinated children, risk was higher in those who had received the first dose after 36 months of age (OR=3.1; 1.2-8.4) and those who had received the second dose 3 or more years before study enrolment (OR=10.2; 1.5-70.7). Early waning of immunity in children after the second dose may contribute to reduced vaccine effectiveness for mumps prevention.

A P-spline ANOVA type model in space-time disease mapping

One of the main objectives in disease mapping is the identification of temporal trends and the production of a series of smoothed maps from which spatial patterns of mortality risks can be monitored over time. When studying rare diseases, conditional autoregressive models have been commonly used for smoothing risks. In this work, a P-spline ANOVA type model is used instead. The model is anisotropic and explicitly considers different smooth terms for space, time, and space-time interaction avoiding, in addition, model identifiability problems. The mean squared error of the log-risk predictor is derived accounting for the variability associated to the estimation of the smoothing parameters. The procedure is illustrated analyzing Spanish prostate cancer mortality data in the period 1975–2008.

Comparing CAR and P-spline models in spatial disease mapping

Smoothing risks is one of the main goals in disease mapping as classical measures, such as standardized mortality ratios, can be extremely variable. However, smoothing risks might hinder the detection of high risk areas, since these two objectives are somewhat contradictory. Most of the work on smoothing risks and detection of high risk areas has been derived using conditional autoregressive (CAR) models. In this work, penalized splines (P-splines) models are also investigated. Confidence intervals for the log-relative risk predictor will be derived as a tool to detect high-risk areas. The performance of P-spline and CAR models will be compared in terms of smoothing (relative bias), sensitivity (ability to detect high risk areas), and specificity (ability to discard false patterns created by noise) through a simulation study based on the well-known Scottish lip cancer data.

Age-space-time CAR models in Bayesian disease mapping.

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright

Evaluating space-time models for short-term cancer mortality risk predictions in small areas

Current cancer mortality data are available with a delay of roughly three years due to the administrative procedure necessary to create the registries. Therefore, health agencies rely on forecast cancer deaths. In this context, statistical procedures providing mortality/incidence risk predictions for different regions or health areas are very useful. These predictions are essential for defining priorities for cancer prevention and treatment. The main objective of this work is to evaluate the predictive performance of alternative spatio-temporal models for short-term cancer risk/counts prediction in small areas. All the models analyzed here are presented under a general-mixed model framework, providing a unified structure of presentation and facilitating the use of similar tools for computing the prediction mean squared error. Prostate cancer mortality data are used to illustrate the behavior of the different models in Spanish provinces.

Projections of cancer mortality risks using spatio-temporal P-spline models

Cancer mortality risk estimates are essential for planning resource allocation and designing and evaluating cancer prevention and management strategies. However, mortality figures generally become available after a few years, making necessary to develop reliable procedures to provide current and near future mortality risks. In this work, a spatio-temporal P-spline model is used to provide predictions of mortality/incidence counts. The model is appropriate to capture smooth temporal trends and to predict cancer mortality/incidence counts in different regions for future years. The prediction mean squared error of the forecast values as well as an appropriate estimator are derived. Spanish prostate cancer mortality data in the period 1975–2008 will be used to illustrate results with a focus on cancer mortality forecasting in 2009–2011.

Spatial gender-age-period-cohort analysis of pancreatic cancer mortality in Spain (1990-2013)

Recently, the interest in studying pancreatic cancer mortality has increased due to its high lethality. In this work a detailed analysis of pancreatic cancer mortality in Spanish provinces was performed using recent data. A set of multivariate spatial gender-age-period-cohort models was considered to look for potential candidates to analyze pancreatic cancer mortality rates. The selected model combines features of APC (age-period-cohort) models with disease mapping approaches. To ensure model identifiability sum-to-zero constraints were applied. A fully Bayesian approach based on integrated nested Laplace approximations (INLA) was considered for model fitting and inference. Sensitivity analyses were also conducted. In general, estimated average rates by age, cohort, and period are higher in males than in females. The higher differences according to age between males and females correspond to the age groups [65, 70), [70, 75), and [75, 80). Regarding the cohort, the greatest difference between men and women is observed for those born between the forties and the sixties. From there on, the younger the birth cohort is, the smaller the difference becomes. Some cohort differences are also identified by regions and age-groups. The spatial pattern indicates a North-South gradient of pancreatic cancer mortality in Spain, the provinces in the North being the ones with the highest effects on mortality during the studied period. Finally, the space-time evolution shows that the space pattern has changed little over time.

Brain and Central Nervous System Cancer Incidence in Navarre (Spain), 1973-2008 and Projections for 2014

Different studies have pointed out Navarre as one of the regions of Spain with the highest incidence rates of brain and other central nervous system (CNS) cancer. Trend analysis for cancer incidence rates for long periods of time, might help determining risk factors as well as, assessing prevention actions involved in this disease. The objective of this study was to describe the incidence of brain and CNS cancer using data from the population-based cancer registry of Navarre, (Spain) during the period 1973-2008 and provide forecast figures up to-2014. Crude and age-standardized (world population) incidence rates of brain cancer per 100,000 person-years were calculated by the direct method separately by gender, area (Pamplona and others), and age-groups. Penalized splines for smoothing rates in the temporal dimensions were applied in order to estimate and forecast cancer incidence rates. Age-adjusted incidence rates showed an increase over the study and forecast periods in both sexes more marked in women than in men. Higher incidence rates were observed in men compared with women but the differences became smaller with time. The increase was due to the rise of rates in the oldest age groups since the rates for younger age groups remained stable or decreased over time. As the entire aetiology of brain and other CNS cancer is not still clear, keep promoting healthful lifestyles for cancer primary prevention among the whole population is necessary.

Age- and sex-specific spatio-temporal patterns of colorectal cancer mortality in Spain (1975-2008)

In this paper, space-time patterns of colorectal cancer (CRC) mortality risks are studied by sex and age group (50-69, ≥70) in Spanish provinces during the period 1975-2008. Space-time conditional autoregressive models are used to perform the statistical analyses. A pronounced increase in mortality risk has been observed in males for both age-groups. For males between 50 and 69 years of age, trends seem to stabilize from 2001 onward. In females, trends reflect a more stable pattern during the period in both age groups. However, for the 50-69 years group, risks take an upward trend in the period 2006-2008 after the slight decline observed in the second half of the period. This study offers interesting information regarding CRC mortality distribution among different Spanish provinces that could be used to improve prevention policies and resource allocation in different regions.