The relationship between obstructive sleep apnea (OSA) and chronic rhinosinusitis (CRS) still remains unclear. This retrospective cohort study aimed to investigate the relationship between OSA and subsequent CRS using a population-based dataset. The study used data from the Taiwan Longitudinal Health Insurance Database 2005. We selected 971 patients with OSA for the study cohort and 4855 patients without OSA for the comparison cohort. Each patient was tracked for 5 years to determine those who were subsequently diagnosed with CRS. Stratified Cox proportional hazard regression analyses were performed to examine the association of OSA with subsequent CRS. The results revealed that 161 (2.76%) of the total sampled patients were subsequently diagnosed with CRS. Subsequent incidences of CRS were found in 64 (6.59%) patients with OSA and 97 (2.00%) patients without OSA. The adjusted hazard ratio (HR) of subsequent CRS for patients with OSA was 3.18 (95% confidence interval: 2.27~4.45) compared to those without OSA. Furthermore, the HR for CRS was similar for subjects with OSA for both genders (with an adjusted HR of 3.44 for males and 2.63 for females). We concluded that patients with OSA had a higher risk of subsequent CRS compared to patients without OSA regardless of sex.

The Fire Effects Information System (FEIS) provides up-to-date information about fire effects on plants, lichens, and animals. It was developed at the United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory in Missoula, Montana. The FEIS database contains literature reviews, taken from current English-language literature of about 900 plant species, 7 lichen species, about 100 wildlife species, 17 Research Project Summaries, and 16 Kuchler plant communities of North America. The emphasis of each review and summary is fire and how it affects species. Background information on taxonomy, distribution, basic biology, and ecology of each species is also included. Reviews are thoroughly documented, and each contains a complete bibliography. Managers from several land management agencies (United States Department of Agriculture, Forest Service, and United States Department of Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, and National Park Service) choose the species included in the database. Those agencies funded the original work and continue to support maintenance and updating of the database. https://www.feis-crs.org/feis/

The Caltrans All Roads Linear Referencing Services (LRS) dataset provides the base geometry for federally required Highway Performance Monitoring System (HPMS) business data, functionally classified roads for the California Roads System (CRS) (a requirement for federal funding of local agency projects), and the State Highway Network (SHN), which supports a wide range of internal Caltrans business needs. Description The Federal Highway Administration (FHWA) requires all state DOT's to develop and submit a Linear Referencing System (LRS) network for all public roads in their respective states known as the All Roads Network of Linear Referenced Data (ARNOLD). This ARNOLD requirement is an integral part of each state’s federally mandated Highway Performance Monitoring System (HPMS) annual submittal. To meet the ARNOLD requirement, the Division of Research, Innovation and System Information (DRISI) has developed a representation of all roads in California using a combination of the Census Bureau’s Topologically Integrated Geographic Encoding and Reference (TIGER) files and previously developed line work representing the State Highway System. This data is published publicly.

Abstract

Feed the Future Nigeria Livelihoods Project (FNLP) is a multi-component development project based on the graduation model pioneered by Bangladesh Rural Advancement Committee (BRAC) that intends to help 42,000 very poor households across rural communities of northern Nigeria’s Sokoto and Kebbi states, and the Federal Capital Territory (FCT). FNLP is a 5-year program implemented by Catholic Relief Services (CRS). Both the program and the impact evaluation are funded by United States Agency for International Development (USAID).

This program approach is founded on an agriculture-led growth strategy that is expected to help vulnerable families diversify their income and grow assets while the community is strengthened by improving nutrition, water sanitation, and hygiene. The most vulnerable families receive cash transfers. A caseworker-led livelihood mentoring scheme also matches households with the resources they need to engage effectively in the local economy and break free from the cycle of poverty and malnutrition.

The impact evaluation, led by The World Bank’s Africa Gender Innovation Lab (GIL), is being conducted in Kebbi state in North-West Nigeria and will evaluate the impact of the overall program as well as two experiments that focus on the impact of the cash transfers and the caseworker mentoring scheme. Baseline data was collected for the FNLP starting in May 2015.

Geographic coverage

The impact evaluation was conducted in Kebbi state in two Local Government Authorities (LGAs) Birnin Kebbi and Danko Wasagu across eight wards: Ujariyo/Junju, Lagga/Randalli, Kardi, Makera/Maurida, Kanya, Ribah/Waje, Maga/Kyabu and Danko.

Analysis unit

• Households

Universe

Households in both FNLP villages and villages not receiving FNLP services but are part of the control group for the impact evaluation.

Kind of data

Sample survey data [ssd]

Sampling procedure

To determine which areas within Kebbi State would benefit from the FNLP program and to establish a sample of vulnerable households that will be part of the program and impact evaluation, CRS and GIL identified eligible communities and households in Kebbi using a number of steps. Detailed explanations of each stage in the process are provided in the baseline report (Attached in the Related Materials).

For the Impact Evaluation baseline survey, a sample of 2,400 EV households and 1,100 households equally divided between the VV and ML households was necessary based on power calculations. We sampled 2,074 of the ‘Class B’ households in FNLP treatment villages and 2,254 from FNLP control villages and sent this sample of 4,328 households to the survey firm to conduct a baseline survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

For the baseline survey, three instruments were used for data collection:

1. Household questionnaire: The household questionnaire was administered to all households in the sample and collected demographic characteristics for all household members, information on dwelling characteristics, household consumption expenditures, household asset holdings, aspirations, exposure to shocks, and level of participation in safety net programs. In addition, individual-level questions around food security, risk aversion, and time preferences were asked to both the male and female decision-makers in the households.

2. Women’ questionnaire: Women were also asked to respond to a separate Women’s Survey that had questions based on the Women’s Empowerment in Agriculture Index (WEAI).

3. Agricultural questionnaire: An agriculture questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The instrument asked questions on land holdings, agriculture production, sales, agricultural income and level of participation in extension services programs. Plot-level information was collected from the male and female decision-makers in the households who were the target respondents for this questionnaire.

4. Community questionnaire: A community questionnaire was administered to each village to collect information on the socio-economic indicators of the village communities where the sampled households reside. The community questionnaire collected information on basic characteristics of the community such as location, size, distance to larger towns and markets, and availability of and distance to sources of health services and schools. Data was collected from 5-10 community members during the Household Targeting Committee meetings.

Cleaning operations

Data quality was ensured at several levels. At the tablet level, the questionnaire was programmed so that questions or sections could not be skipped by interviewers. Numerous quality checks were also built into the programming that identified inconsistencies and prevented interviewers from moving forward with the survey until errors were corrected. Logic checks and range checks were also included in the programming so that implausible entries were flagged to the interviewer at the time of surveying.

Monitoring of data collection activities was also conducted by several people. Supervisors monitored interviewer performance by observing interviews and conducting spot checks that consisted of assessing whether questions were being asked appropriately and providing immediate feedback to interviewers. The World Bank’s Project Manager and Field Coordinator also provided another layer of quality control, visiting each interviewer team at least twice each week to observe interviews and review household listings.

A final level of data quality control involved the use of quality control reports that were automatically generated using a quality-check file created by the research team at the World Bank. The file would scan the data for possible errors or large outliers as soon as data was downloaded from the server. The types of checks the file would make included the following: whether the household identifiers were unique within the dataset, whether interviews were being completed in their entirety, reviewing observations with duplicate values of a variable for which duplicates are uncommon, checking that no variables have only missing values, checking important skip patterns, range checks and interviewer comments. This helped with data accuracy as the report was reviewed at least every week by the research team throughout the data collection period and any errors could be sent back to the field team and rectified in real time while the data collection was still taking place.

Response rate

The number of household interviews completed was 3,976 for a household response rate of 92 percent.

ObjectiveThe purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases.MethodsWe retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set.ResultsFor TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57–0.57) for CRS, 0.61 (0.60–0.61) for RSF in combination with CRS, and 0.70 (0.68–0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59–0.59) for CRS, 0.57 (0.56–0.57) for RSF in combination with CRS, and 0.60 (0.58–0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33–0.33) for CRS, 0.77 (0.75–0.78) for radiomics signature alone, and 0.58 (0.57–0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61–0.61) for CRS, 0.57 (0.56–0.57) for radiomics signature, and 0.75 (0.74–0.76) for DeepSurv score alone.ConclusionRadiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.