Explore the vast range of titles available.

Monitoring and improving rural health is challenging because of varied and conflicting concepts of just what rural means. Federal, state, and local agencies and data resources use different definitions, which may lead to confusion and inequity in the distribution of resources depending on the definition used. This article highlights how inconsistent definitions of rural may lead to measurement bias in research, the interpretation of research outcomes, and differential eligibility for rural-focused grants and other funding. We conclude by making specific recommendations on how policy makers and researchers could use these definitions more appropriately, along with definitions we propose, to better serve rural residents. We also describe concepts that may improve the definition of and frame the concept of rurality.

Many patients receiving dialysis in the USA share the socioeconomic characteristics of underserved communities, and undergo routine monthly laboratory testing, facilitating a practical, unbiased, and repeatable assessment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence. For this cross-sectional study, in partnership with a central laboratory that receives samples from approximately 1300 dialysis facilities across the USA, we tested the remainder plasma of 28 503 randomly selected adult patients receiving dialysis in July, 2020, using a spike protein receptor binding domain total antibody chemiluminescence assay (100% sensitivity, 99·8% specificity). We extracted data on age, sex, race and ethnicity, and residence and facility ZIP codes from the anonymised electronic health records, linking patient-level residence data with cumulative and daily cases and deaths per 100 000 population and with nasal swab test positivity rates. We standardised prevalence estimates according to the overall US dialysis and adult population, and present estimates for four prespecified strata (age, sex, region, and race and ethnicity). The sampled population had similar age, sex, and race and ethnicity distribution to the US dialysis population, with a higher proportion of older people, men, and people living in majority Black and Hispanic neighbourhoods than in the US adult population. Seroprevalence of SARS-CoV-2 was 8·0% (95% CI 7·7–8·4) in the sample, 8·3% (8·0–8·6) when standardised to the US dialysis population, and 9·3% (8·8–9·9) when standardised to the US adult population. When standardised to the US dialysis population, seroprevalence ranged from 3·5% (3·1–3·9) in the west to 27·2% (25·9–28·5) in the northeast. Comparing seroprevalent and case counts per 100 000 population, we found that 9·2% (8·7–9·8) of seropositive patients were diagnosed. When compared with other measures of SARS-CoV-2 spread, seroprevalence correlated best with deaths per 100 000 population (Spearman's ρ=0·77). Residents of non-Hispanic Black and Hispanic neighbourhoods experienced higher odds of seropositivity (odds ratio 3·9 [95% CI 3·4–4·6] and 2·3 [1·9–2·6], respectively) compared with residents of predominantly non-Hispanic white neighbourhoods. Residents of neighbourhoods in the highest population density quintile experienced increased odds of seropositivity (10·3 [8·7–12·2]) compared with residents of the lowest density quintile. County mobility restrictions that reduced workplace visits by at least 5% in early March, 2020, were associated with lower odds of seropositivity in July, 2020 (0·4 [0·3–0·5]) when compared with a reduction of less than 5%. During the first wave of the COVID-19 pandemic, fewer than 10% of the US adult population formed antibodies against SARS-CoV-2, and fewer than 10% of those with antibodies were diagnosed. Public health efforts to limit SARS-CoV-2 spread need to especially target racial and ethnic minority and densely populated communities. Ascend Clinical Laboratories.

ObjectivesWe aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status.DesignA retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted.SettingWe use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties.ParticipantsA total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers.Primary outcome measuresRegression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).ResultsStatistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the USA.ConclusionsHigher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (−0.0395 to −0.0125)) in China and by 0.020 (95% CI (−0.0311 to −0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (−0.0108 to −0.0045)) in China and by 0.0080 (95% CI (−0.0150 to −0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.

Objectives. To examine whether growing use of telemental health (TMH) has reduced the rural–urban gap in specialty mental health care use in the United States. Methods. Using 2010–2017 Medicare data, we analyzed trends in the rural–urban difference in rates of specialty visits (in-person and TMH). Results. Among rural beneficiaries diagnosed with schizophrenia or bipolar disorder, TMH use grew by 425% over the 8 years and, in higher-use rural areas, accounted for one quarter of all specialty mental health visits in 2017. Among patients with schizophrenia or bipolar disorder, TMH visits differentially grew in rural areas by 0.14 visits from 2010 to 2017. This growth partially offset the 0.42-visit differential decline in in-person visits in rural areas. In net, the gap between rural and urban patients in specialty visits was larger by 2017. Conclusions. TMH has improved access to specialty care in rural areas, particularly for individuals diagnosed with schizophrenia or bipolar disorder. While growth in TMH use has been insufficient to eliminate the overall rural–urban difference in specialty care use, this difference may have been larger if not for TMH. Public Health Implications. Targeted policy to extend TMH to underserved areas may help offset declines in in-person specialty care.

There is growing concern about the health of older US adults who live in rural areas, but little is known about how mortality has changed over time for low-income Medicare beneficiaries residing in rural areas compared with their urban counterparts. We evaluated whether all-cause mortality rates changed for rural and urban low-income Medicare beneficiaries dually enrolled in Medicaid, and we studied disparities between these groups. The study cohort included 11,737,006 unique dually enrolled Medicare beneficiaries. Between 2004 and 2017 all-cause mortality declined from 96.6 to 92.7 per 1,000 rural beneficiaries (relative percentage change: -4.0 percent). Among urban beneficiaries, declines in mortality were more pronounced (from 86.9 to 72.8 per 1,000 beneficiaries, a relative percentage change of -16.2 percent). The gap in mortality between rural and urban beneficiaries increased over time. Rural mortality rates were highest in East North central states and increased modestly in West North central states during the study period. Public health and policy efforts are urgently needed to improve the health of low-income older adults living in rural areas.

Background In recent years, reduced participation has been encountered across all epidemiological study designs, both in terms of non-response as well as refusal. A low response rate may reduce the statistical power but, more importantly, results may not be generalizable to the wider community. Methods In a telephone survey of 1413 randomly selected members of the Australian general population and of 690 participants sourced from previous studies, we examined factors affecting people’s stated willingness to participate in health research. Results The majority of participants (61 %) expressed willingness to participate in health research in general but the percentage increased when provided with more specific information about the research. People were more willing if they have personal experience of the disease under study, and if the study was funded by government or charity rather than pharmaceutical companies. Participants from previous studies, older people and women were the groups most willing to participate. Younger men preferred online surveys, older people a written questionnaire, and few participants in any age and sex groups preferred a telephone questionnaire. Conclusion Despite a trend toward reduced participation rates, most participants expressed their willingness to participate in health research. However, when seeking participants, researchers should be concrete and specific about the nature of the research they want to carry out. The preferred method of recommended contact varies with the demographic characteristics.

In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company. Baseline SVM models were initially built to find out the most suitable kernel types and will be used to make comparison with other approaches. Dimension reduction strategies such as Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) were applied to the dataset to find out the most important features. Furthermore, resampling techniques to deal with imbalanced data such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE Tomek) and Synthetic Minority Oversampling Technique ENN (SMOTE ENN) were used on the dataset. Using the above-mentioned techniques, we have obtained better results compared to those obtained from previous works, we achieved an F1-score and accuracy of 99% and 98.9% respectively.

ObjectiveInfant mortality is increased in isolated rural areas. This study compares prenatal factors, access to care, and health outcomes for very-low birthweight (VLBW) infants by degree of maternal residential rurality.MethodsThis descriptive population-based retrospective cohort study used the California Perinatal Quality Care Collaborative registry to study VLBW infants. Rurality was assigned as urban, large rural, and small rural/isolated using the Rural Urban Commuting Area codes. We used hierarchical random effect models to test the association of rurality with survival without major morbidity.ResultsThe study included 38 614 dyads. VLBW survival without major morbidity decreased with increasing rurality and the relationship remained significant for small rural/isolated areas (OR 0.79, p = 0.03) after adjustment. Birth weight, gestational age, and infant sex were similar across geographic groups.ConclusionA rural urban disparity exists for VLBW survival without major morbidity. Our findings generate hypotheses about factors that may be driving these disparities.

Objectives. To investigate differences in funding and service delivery between rural and urban local health departments (LHDs) in the United States. Methods. In this repeated cross-sectional study, we examined rural–urban differences in funding and service provision among LHDs over time using 2010 and 2016 National Association of County and City Health Officials data. Results. Local revenue among urban LHDs (41.2%) was higher than that in large rural (31.3%) and small rural LHDs (31.2%; P < .05). Small (20.9%) and large rural LHDs (19.8%) reported greater reliance on revenue from Center for Medicare and Medicaid Services than urban LHDs (11.5%; P < .05). All experienced decreases in clinical revenue between 2010 and 2016. Urban LHDs provided less primary care services in 2016; rural LHDs provided more mental health and substance abuse services (P < .05). Conclusions. Urban LHDs generated more revenues from local sources, and rural LHDs generated more from the Center for Medicare and Medicaid Services and clinical services. Rural LHDs tended to provide more clinical services. Given rural LHDs’ reliance on clinical revenue, decreases in clinical services could have disproportionate effects on them. Public Health Implications. Differences in financing and service delivery by rurality have an impact on the communities. Rural LHDs rely more heavily on state and federal dollars, which are vulnerable to changes in state and national health policy.