COVID-19, mental health, psychology, social determinants of health, utilization of health services. Individuals in rural communities are at increased risk for suicide. 1,2 While the impact of Coronavirus Disease 2019 (COVID-19) continues to unfold, 3 it is likely that suicide risk factors among individuals residing in rural areas will be exacerbated and suicide rates may subsequently increase. 4 Awareness of these factors is essential to ensure that appropriate steps are taken to prevent suicide in rural communities, both during and in the aftermath of this pandemic. In this commentary, we delineate key considerations for doing so, with potential solutions summarized in Table 1.

BackgroundWomen with type 1 diabetes (T1D) have a four-fold increased risk for cardiovascular disease (CVD) compared to non-diabetic (non-DM) women, as opposed to double the risk in T1D men compared to non-DM men. It is unclear how early in life CVD risk differences begin in T1D females. Therefore, our objective was to compare CVD risk factors in adolescents with and without T1D to determine the effects of gender on CVD risk factors.MethodsThe study included 300 subjects with T1D (age 15.4±2.1 years, 50 % male, 80 % non-Hispanic White (NHW), glycated hemoglobin (A1c) 8.9±1.6 %, diabetes duration 8.8±3.0 years, BMI Z-score 0.62±0.77) and 100non-DM controls (age 15.4±2.1 years, 47 % male, 69 % NHW, BMI Z-score 0.29±1.04). CVD risk factors were compared by diabetes status and gender. Multivariate linear regression analyses were used to determine if relationships between diabetes status and CVD risk factors differed by gender independent of differences in A1c and BMI.ResultsDifferences in CVD risk factors between T1D subjects and non-DM controls were more pronounced in girls. Compared to boys with T1D and non-DM girls, T1D girls had higher A1c (9.0 % vs. 8.6 % and 5.1 %, respectively), BMI Z-score (0.70 vs. 0.47 and 0.27), LDL-c (95 vs. 82 and 81 mg/dL), total cholesterol (171 vs. 153 and 150 mg/dL), DBP (68 vs. 67 and 63 mmHg), and hs-CRP (1.15 vs. 0.57 and 0.54 mg/dL) after adjusting for Tanner stage, smoking status, and race/ethnicity (p <0.05 for all). In T1D girls, differences in lipids, DBP, and hs-CRP persisted even after adjusting for centered A1c and BMI Z-score.Testing interactions between gender and T1D with CVD risk factors indicated that differences were greater between girls with T1D and non-DM compared to differences between boys with T1D and non-DM. Overall, observed increases in CVD risk factors in T1D girls remained after further adjustment for centered A1c or BMI Z-score.ConclusionsInterventions targeting CVD risk factors in addition to lowering HbA1c and maintaining healthy BMI are needed for youth with T1D. The increased CVD risk factors seen in adolescent girls with T1D in particular argues for earlier intervention to prevent later increased risk of CVD in women with T1D.

Individuals with type 1 diabetes engage in less MVPA than those without diabetes despite similar self-reported levels, with the main barrier being perceived risk of hypoglycemia. Adults with type 1 diabetes require guidance to meet current PA guidelines and reduce cardiovascular risk.

These research studies published since 2009 support an association between high altitude and suicide rates at the state or county level, but do not provide sufficient data to estimate the effect of high altitude on an individuals' suicide risk. Although the impact of hypoxia on mood and depression has been hypothesized to be a contributing cause, many other individual factors likely play more important roles.

No abstract

A483(75 patients each). The 75 patients on our intervention group were educated by the Pharmacist on diabetes and hypertension, their complications, risks, preventive measures and management. This was done at least six times during the study period unlike the control group who received no such education. In particular, they were counseled on the need for medication and treatment adherence such as clinic visits, and life style modifications including diet and exercise. Outcome measure included changes in fasting blood sugar (FBS), blood pressure (BP), body mass index (BMI) and adherence to instructions. Results: There were no statistical differences between the baseline and 6 months data of the control group as mean fasting blood sugar were 162.2 ± 69.1 and 159.9 ±57.2 (P= 0.825) and mean systolic blood pressure of 144.7 ± 23.8 and 145.5±18.6 (P= 0.819) respectively. The intervention group had mean fasting blood sugar of 156.7 ± 30.5 and 131.8± 40.4 (P< 0.001) and mean systolic blood pressure of 146.4 ± 13.9 and 133.8 ± 18.5 (P< 0.001) respectively. Adherence levels to medication taking in the groups were 42.7% : 94.7% respectively (P= 0.001). ConClusions: In diabetes management, patient education and counseling have become key tools in achieving both glycaemic and blood pressure control.

We appreciate the help of Marian Betz in reviewing and commenting on a draft of the manuscript.

ObjectiveIn order to meet local mental health surveillance needs, we created multiple mental health-related indicators using emergency department data from the Colorado North Central Region (CO-NCR) Early Notification of Community Based Epidemics (ESSENCE), a Syndromic Surveillance (SyS) platform.IntroductionMental health is a common and costly concern; it is estimated that nearly 20 percent of adults in the United States live with a mental illness[1] and that more money is spent on mental illness than any other medical condition.[2] One spillover effect of unmet mental health needs may be increasing emergency department utilization. National analysis by Healthcare Cost and Utilization Project (H-CUP) found a 55% increase in emergency department visits for depression, anxiety, and stress reactions between 2006-2013.[3] Local public health agencies (LPHAs) can play an important role in reducing costs and burden associated with mental illness. There is opportunity to use emergency department data at a local level to monitor trends and evaluate the effectiveness of local strategies. ESSENCE, available in 31 states, provides near-real time observation-level emergency department data, which can be analyzed and disseminated according to local needs. Using ESSENCE data from 6 local counties in Colorado, we developed methods to estimate the overall burden of mental health and specific mental health disorders seen in the emergency department.MethodsBoulder County Public Health expanded on existing methods to develop multiple mental health queries in ESSENCE using data from the six Colorado counties that currently participate in the Colorado North Central Region (CO-NCR) SyS (i.e., Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties). Our query was based solely off relevant International Classification of Disease version 10 Clinical Modification (ICD-10-CM) mental health codes: F20-F48, F99, R45.851, X71–X83, T14.91, and R45.851. We also included T36-T65 and T71 where intentional self-harm was specified. In addition to an overall mental health query we created 11 sub-queries for: anxiety disorder, conversion disorder, intentional self-harm/suicide attempt, mood disorder, obsessive compulsive disorder (OCD), dissociative disorder, schizophrenia, somatoform disorders, stress adjustment disorder, suicide ideation, and other mental health disorder). One observation could fall into multiple subcategories through inclusion of multiple discharge diagnosis (DD).One challenge of using the DD field in ESSENCE is that in Colorado, similar to other states, there can be excess of 40 unique ICD-10-CM codes listed in the DD field, and queries identify cases by searching all listed codes. For this project, that is problematic as codes may refer to historic and underlying health conditions, rather than acute cause of the ED visit. To handle this, we performed a secondary analysis to determine whether observations were “true mental health cases” based on order of codes listed in DD field, triage notes and chief complaint. We then calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) of including observations where mental health was listed as the first (or primary) code, first or second, or first second or third code. Our analysis revealed that observations where mental health codes are listed later were less likely to be identifiable as true mental health cases, and led to our decision to only include observations with qualifying codes listed first or second.To assess the mental health burden, we developed code in SAS 9.4 that parsed ESSENCE output by discharge diagnosis, create aforementioned sub-queries, and calculated counts and age-adjusted rates (based on 2000 US Population) to summarize demographic and geographic trends.ResultsThere were 22,451 observations with mental health discharge diagnosis codes for the six Colorado counties between January and June 2018. Of these codes, 13,331 had a mental health code as the first and/or second listed DD and were counted as true mental health visits. The age-adjusted rates of any mental health visit ranged from approximately 425 per 100,000 in Douglas County to 1,026 per 100,000 in Denver County. The most common reasons for mental health visits across the region were anxiety, mood disorder, and suicide ideation (Figure 1). There was a significant spike in mental health ED visits among the 15-24 age group, followed by decreasing rates in older age groups (Figure 2). Younger age groups most commonly had ED visits for mood disorder (all age groups under 24), while in the age groups 25-34, 35-44, 65-74 and 75+ the most common reason for ED visit was anxiety. Also of note, ED visits for suicide ideation and self- harm were highest for the 15-24 age group. Males and females had similar rates of ED visits for most diagnoses, which is notable given males generally utilize healthcare services at lower rates than females.ConclusionsSyndromic surveillance is a valuable addition to available mental health surveillance. Our methods and results demonstrate the feasibility of tracking overall and specific mental health trends using the ESSENCE platform. Unlike other available mental health data, ESSENCE provides data that is local, observation level, and near-real time. Through continued collaboration with public health, medical and other stakeholders we hope this data can be pivotal in gauging disparities in mental health burden, monitoring trends, and prioritizing solutions.References[1] Mental Illness. National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/mental-illness.shtml[2] Roehrig C. Mental Disorders Top The List Of The Most Costly Conditions In The United States: $201 Billion. Health Aff (Millwood). 2016 Jun 1;35(6):1130-5. https://www-healthaffairs-org.ezp.welch.jhmi.edu/doi/pdf/10.1377/hlthaff.2015.1659[3]Weiss AJ, Barrett ML, Heslin KC. , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006-2013. HCUP Statistical Brief #216. Agency for Healthcare Research and Quality. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf. December 2016.