This study aims to look at the trends in our head and neck cancer patient population over the past 5 years with an emphasis on the past 2 years to evaluate how the coronavirus disease 2019 (COVID-19) pandemic has impacted our disparities and availability of care for patients, especially those living in rural areas. An additional aim is to identify existing disparities at our institution in the treatment of head and neck patients and determine solutions to improve patient care.

A retrospective chart review was performed to identify patients who were consulted and subsequently treated with at least one fraction of radiation therapy at our institution with palliative or curative intent. Patient demographic information was collected including hometown, distance from the cancer centre based on zip-codes and insurance information and type of appointment (in-person or telehealth). Rural–urban continuum codes were used to determine rurality.

A total of 490 head and neck cancer patients (n = 490) were treated from 2017 to 2021. When broken down by year, there were no significant trends in patient population regarding travel distance or rurality. Roughly 20–30% of our patients live in rural areas and about 30% have a commute > 50 miles for radiation treatment. A majority of our patients rely on public insurance (68%) with a small percentage of those uninsured (4%). Telehealth visits were rare prior to 2019 and rose to 5 and 2 visits in 2020 and 2021, respectively.

Head and neck cancer patients, despite rurality or distance from a cancer centre, may present with alarmingly enough symptoms despite limitations and difficulties with seeking medical attention even during the COVID-19 pandemic in 2020. However, providers must be aware of these potential disparities that exist in the rural population and seek to address these.

The present study aimed to examine the availability and price of healthier compared with less healthy foods by geography, store category and store type for convenience stores, and by store size for grocery stores in Nova Scotia.

A cross-sectional study that examined differences in the overall availability and price of healthier compared to less healthy foods in grocery and convenience stores in Nova Scotia. The Nova Scotia Consumer Food Environment project was part of a larger initiative of the Nova Scotia government (Department of Health and Wellness) to assess the food and beverage environment in Nova Scotia in 2015/16.

Four geographic zones (Nova Scotia Health Authority Management Zones) in Nova Scotia, Canada.

A sample of forty-seven grocery stores and fifty-nine convenience stores were selected from a list of 210 grocery stores and 758 convenience stores in Nova Scotia to ensure geographic and store type representation in our sample.

Findings indicate that rurality had a significant effect on food availability as measured by the Nutrition Environment Measures Surveys (NEMS) score (P < 0·01); there was a higher availability of healthy foods in rural compared to urban areas for convenience stores but not grocery stores. Healthier foods were also more available in chain stores compared to independent stores (P < 0·01) and in large stores compared to small and medium stores (P < 0·001 and P < 0·01, respectively).

The availability of and accessibility to less healthy foods in Nova Scotia food environment suggests that there is a need for government policy action to support a food environment that contributes to healthier diets.

Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across regions of variable rurality. The objective of the current study was to assess the validity of a CAB dataset using a government dataset at the provincial scale.

A ground-truthed dataset provided by the government of Newfoundland and Labrador (NL) was used to assess a popular commercial dataset. Concordance, sensitivity, positive-predictive value (PPV) and geocoding errors were calculated. Measures were stratified by store types and rurality to investigate any association between these variables and database accuracy.

NL, Canada.

The current analysis used store-level (ecological) data.

Of 1125 stores, there were 380 stores that existed in both datasets and were considered true-positive stores. The mean positional error between a ground-truthed and test point was 17·72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0·64, PPV = 0·60 and concordance = 0·45. Gas stations had the least agreement, sensitivity = 0·26, PPV = 0·32 and concordance = 0·17. Only 4 % of commercial data points in rural areas matched every criterion examined.

The commercial dataset exhibits a low level of agreement with the ground-truthed provincial data. Particularly retailers in rural areas or belonging to the gas station category suffered from misclassification and/or geocoding errors. Taken together, the commercial dataset is differentially representative of the ground-truthed reality based on store-type and rurality/urbanity.

Studies suggest that addressing the needs of the older population in rural areas may substantially reduce their low-urgency use of emergency medical services (LUEMS). It may ultimately also help improve the efficiency in our health system. There is, however, a dearth of evidence substantiating geographic patterns in LUEMS by different age cohorts. This exploratory study was aimed to clarify the understanding of emergency medical services (EMS) use in Nova Scotia through a geographic analysis.

Records with Canadian Triage and Acuity Scale of 4 and 5 were considered as LUEMS. We assessed the distribution of LUEMS incidence rates (proportion of LUEMS out of all EMS uses) by age and rurality, using descriptive statistics and Geographic Information Systems mapping.

Nearly half of all EMS transports were individuals of 65+ years of age; 35% of those were LUEMS. The rates increased along with the level of rurality, and the older cohort had the highest incidence rates in non-metro communities. High rates were seen primarily in some rural communities farthest away from the capital/tertiary care centre.

High LUEMS incidence rates are rural phenomena but not specific to the older population. However, the absolute number of LUEMS by the older cohort is significant, and elder-specific interventions in rural regions could still lead to effective cost savings. Further investigation of other factors, such as distance to the emergency department, availability of public transportation, and socioeconomic conditions of EMS users, is needed.

Despite differences in obesity and ill health between urban and rural areas in the UK being well documented, very little is known about differences in dietary patterns across these areas. The present study aimed to examine whether urban/rural status is associated with dietary patterns in a population-based UK cohort study of children.

Dietary patterns were obtained using principal components analysis and cluster analysis of 3 d diet records collected from children at 10 years of age. Rurality was obtained from the 2001 UK Census urban/rural indicator at the time of dietary assessment. General linear models were used to examine the relationship between rurality and dietary pattern scores from principal components analysis; multinomial logistic regression was used to assess the association between rurality and dietary clusters.

The Avon Longitudinal Study of Parents and Children (ALSPAC), South West England.

Children (n 5677) aged 10 years (2817 boys and 2860 girls).

After adjustment, increases in rurality were associated with increased scores on the ‘health awareness’ dietary pattern (β=0·35; 95 % CI 0·14, 0·56; P<0·001 for the most rural compared with the most urban group) and lower scores on the ‘packed lunch/snack’ dietary pattern (β=−0·39; 95 % CI −0·59, −0·19; P<0·001 for the most rural compared with the most urban group). The odds ratio for participants being in the ‘healthy’ compared with the ‘processed’ dietary cluster for the most rural areas was 1·61 (95 % CI 1·05, 2·49; P=0·02) compared with those in the most urban areas.

There is evidence to suggest that differences exist in dietary patterns between rural and urban areas. Similar results were found using two different methods of dietary pattern analysis, showing that children residing in rural households were more likely to consume healthier diets than those in urban households.