Dataset Information

Breast Cancer in a Caribbean Population in Transition: Design and Implementation of the Atabey Population-Based Case-Control Study of Women in the San Juan Metropolitan Area in Puerto Rico.

ABSTRACT: Global breast cancer incidence varies considerably, particularly in comparisons of low- and high-income countries; rates may vary even within regions. Breast cancer rates for Caribbean countries are generally lower than for North America and Europe. Rates in Puerto Rico are in the middle of the range between the highest and the lowest Caribbean countries. Populations in transition, with greater variability in risk factor exposures, provide an important opportunity to better understand breast cancer etiology and as potential sources of variation in rates. Understanding of exposures across the life span can potentially contribute to understanding regional differences in rates. We describe here the design and implementation of a population-based, case-control study in the San Juan Metropolitan Area (SJMA) of Puerto Rico, the Atabey Epidemiology of Breast Cancer Study. We describe steps taken to ensure that the study was culturally appropriate, leveraging the Atabey researchers' understanding of the culture, local health system, and other required resources to effectively recruit participants. A standardized, in-person interview was developed, with attention to life course events customized to the study population. In order to understand variation in global breast cancer rates, studies customized to the populations outside of North America and Europe are required.

SUBMITTER: Rosario-Rosado RV

PROVIDER: S-EPMC7068544 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Breast Cancer in a Caribbean Population in Transition: Design and Implementation of the Atabey Population-Based Case-Control Study of Women in the San Juan Metropolitan Area in Puerto Rico.

Rosario-Rosado Rosa V RV Nazario Cruz M CM Hernández-Santiago Johan J Schelske-Santos Michelle M Mansilla-Rivera Imar I Ramírez-Marrero Farah A FA Ramos-Valencia Gilberto G Climent Consuelo C Nie Jing J Freudenheim Jo L JL

International journal of environmental research and public health 20200219 4

Global breast cancer incidence varies considerably, particularly in comparisons of low- and high-income countries; rates may vary even within regions. Breast cancer rates for Caribbean countries are generally lower than for North America and Europe. Rates in Puerto Rico are in the middle of the range between the highest and the lowest Caribbean countries. Populations in transition, with greater variability in risk factor exposures, provide an important opportunity to better understand breast can ...[more]

PMID: 32092890

Similar Datasets

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Project description:Tropical mangrove forests have been described as "coastal kidneys," promoting sediment deposition and filtering contaminants, including excess nutrients. Coastal areas throughout the world are experiencing increased human activities, resulting in altered geomorphology, hydrology, and nutrient inputs. To effectively manage and sustain coastal mangroves, it is important to understand nitrogen (N) storage and accumulation in systems where human activities are causing rapid changes in N inputs and cycling. We examined N storage and accumulation rates in recent (1970 - 2016) and historic (1930 - 1970) decades in the context of urbanization in the San Juan Bay Estuary (SJBE, Puerto Rico), using mangrove soil cores that were radiometrically dated. Local anthropogenic stressors can alter N storage rates in peri-urban mangrove systems either directly by increasing N soil fertility or indirectly by altering hydrology (e.g., dredging, filling, and canalization). Nitrogen accumulation rates were greater in recent decades than historic decades at Piñones Forest and Martin Peña East. Martin Peña East was characterized by high urbanization, and Piñones, by the least urbanization in the SJBE. The mangrove forest at Martin Peña East fringed a poorly drained canal and often received raw sewage inputs, with N accumulation rates ranging from 17.7 to 37.9 g -2 y-1 in recent decades. The Piñones Forest was isolated and had low flushing, possibly exacerbated by river damming, with N accumulation rates ranging from 18.6 to 24.2 g -2 y-1 in recent decades. Nearly all (96.3%) of the estuary-wide mangrove N (9.4 Mg ha-1) was stored in the soils with 7.1 Mg ha-1 sequestered during 1970-2017 (0-18 cm) and 2.3 Mg ha-1 during 1930-1970 (19-28 cm). Estuary-wide mangrove soil N accumulation rates were over twice as great in recent decades (0.18 ± 0.002 Mg ha-1y-1) than historically (0.08 ± 0.001 Mg ha-1y-1). Nitrogen accumulation rates in SJBE mangrove soils in recent times were twofold larger than the rate of human-consumed food N that is exported as wastewater (0.08 Mg ha-1 y-1), suggesting the potential for mangroves to sequester human-derived N. Conservation and effective management of mangrove forests and their surrounding watersheds in the Anthropocene are important for maintaining water quality in coastal communities throughout tropical regions.

Dataset Information

Breast Cancer in a Caribbean Population in Transition: Design and Implementation of the Atabey Population-Based Case-Control Study of Women in the San Juan Metropolitan Area in Puerto Rico.

Publications

Breast Cancer in a Caribbean Population in Transition: Design and Implementation of the Atabey Population-Based Case-Control Study of Women in the San Juan Metropolitan Area in Puerto Rico.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets