Primary Care Issues in Rural Populations

Addressing food insecurity in rural primary care: a mixed-methods evaluation of barriers and facilitators

BACKGROUND

Food insecurity (FI) is associated with negative health outcomes and increased healthcare utilization. Rural populations face increased rates of FI and encounter additional barriers to achieving food security. We sought to identify barriers and facilitators to screening and interventions for FI in rural primary care practices.

METHODS

We conducted a mixed-methods study using surveys and semi-structured interviews of providers and staff members from rural primary care practices in northern New England. Survey data were analyzed descriptively, and thematic analysis was used to identify salient interview themes.

RESULTS

Participants from 24 rural practices completed the survey, and 13 subsequently completed an interview. Most survey respondents (54%) reported their practices systematically screen for FI and 71% reported food needs were "very important" for their patients and communities. Time and resource constraints were the most frequently cited barriers to screening for and addressing FI in practices based on survey results. Interview themes were categorized by screening and intervention procedures, community factors, patient factors, external factors, practice factors, process and implementation factors, and impact of FI screening and interventions. Time and resource constraints were a major theme in interviews, and factors attributed to rural practice settings included geographically large service areas, stigma from loss of privacy in small communities, and availability of food resources through farming.

CONCLUSIONS

Rural primary care practices placed a high value on addressing food needs but faced a variety of barriers to implementing and sustaining FI screening and interventions. Strategies that utilize practice strengths and address time and resource constraints, stigma, and large service areas could promote the adoption of novel interventions to address FI.

Care coordination between rural primary care and telemedicine to expand medication treatment for opioid use disorder: Results from a single-arm, multisite feasibility study

PURPOSE

The use of telemedicine (TM) has accelerated in recent years, yet research on the implementation and effectiveness of TM-delivered medication treatment for opioid use disorder (MOUD) has been limited. This study investigated the feasibility of implementing a care coordination model involving MOUD delivered via an external TM provider for the purpose of expanding access to MOUD for patients in rural settings.

METHODS

The study tested a care coordination model in 6 rural primary care sites by establishing referral and coordination between the clinic and a TM company for MOUD. The intervention spanned approximately 6 months from July/August 2020 to January 2021, coinciding with the peak of the COVID-19 pandemic. Each clinic tracked patients with OUD in a registry during the intervention period. A pre-/post-intervention design (N = 6) was used to assess the clinic-level outcome as patient-days on MOUD based on patient electronic health records.

FINDINGS

All clinics implemented critical components of the intervention, with an overall TM referral rate of 11.7% among patients in the registry. Five of the 6 sites showed an increase in patient-days on MOUD during the intervention period compared to the 6-month period before the intervention (mean increase per 1,000 patients: 132 days, P = .08, Cohen's d = 0.55). The largest increases occurred in clinics that lacked MOUD capacity or had a greater number of patients initiating MOUD during the intervention period.

CONCLUSIONS

To expand access to MOUD in rural settings, the care coordination model is most effective when implemented in clinics that have negligible or limited MOUD capacity.

Adult asthma prevalence and trend analysis by urban-rural status across sociodemographic characteristics-United States, 2012-20

Background

Asthma prevalence estimates among adults are limited for urban-rural classification across sociodemographic characteristics.

Objectives

This study examined current asthma prevalence and annual trends by 6-level urban-rural categories across sociodemographic characteristics among US adults.

Methods

Asthma prevalence for 2020 and annual trends for 2012-20 were estimated using Behavioral Risk Factor Surveillance System data. The 2013 National Center for Health Statistics urban and rural categories were used to define urban-rural status.

Results

Current asthma prevalence was higher in medium (9.7%; prevalence ratio 1.103 [95% CI 1.037, 1.174]) and small (9.9%; 1.111 [1.031, 1.197]) metro than in large fringe metropolitan (8.6%), was higher in micropolitan (10.2%) than in both large fringe (8.6%; 1.115 [1.042, 1.194]) and large central metropolitan (8.8%; 1.080 [1.001, 1.066]) areas. Prevalence by sociodemographic characteristics varied between urban-rural scheme; the prevalence was significantly higher among adults aged 55-64 years in micropolitan (11.9%), women in small metro (12.8%), and other race non-Hispanic in noncore (most rural) (13.6%) areas, adults without a high school diploma in micropolitan areas (13.8%), household income <100% of federal poverty level in micropolitan areas (15.7%), and adults with insurance coverage in micropolitan areas (10.3%) compared to the corresponding populations in other urban-rural categories. During 2012-20, an increasing trend in prevalence was observed only in medium metro areas, with an annual percentage change of 0.81.

Conclusions

Asthma prevalence differed by 6-level urban-rural categories. These findings might be helpful in establishing effective asthma control programs and targeting resource allocation at the local level.

A nursing socio-environmental approach for acute pesticide poisoning: A qualitative focus group study

AIM

To explore the experiences and perceptions of final-year nursing students in assessing and managing acute pesticide poisoning using a socio-environmental care approach in a primary health care simulation-based scenario.

DESIGN

A qualitative descriptive study was conducted using focus groups.

METHODS

Twelve focus groups were held, with 114 fourth-year nursing students. The study was carried out from September to November 2021. To grant an in-depth analysis of the collected data, a thematic analysis was used.

RESULTS

After our data analysis, three main themes emerged: (i) experiences of an acute pesticide poisoning case in a simulation-based scenario; (ii) environmental health nursing competencies for a culturally competent practice and (iii) social factors in prevention strategies.

CONCLUSIONS

Using a socio-environmental care approach with a primary care simulation-based poisoning case can provide a realistic public health clinical experience for local problems and prepare the context for the development of global citizenship values, as well as offer numerous opportunities to facilitate transformative learning, including Sustainable Development Goals (SDGs) into local nursing clinical practice.

IMPACT

Nurse managers, policymakers and educators must collaborate together to broaden their reach in teaching the role of the environment and social determinants of health through evidence-based care models, as well as actively contributing to the achievement of the SDGs to promote "glocal" competencies among nursing professionals.

A Systematic Literature Review of Blockchain Technology for Smart Villages

According to the United Nations, Sustainable Development Goals are framed for improving rural health, hunger, poverty issues, environmental conditions, and illiteracy globally. With the upcoming technology, there have been many advances in the lifestyle of people all around the world. Comparatively, more emphasis has been given to the development of urban areas than rural. The sustainable development of a country depends on the growth of its rural areas. Countless technological and theoretical models, projects, and frameworks have been proposed and implemented to help overcome sundry issues and challenges faced by rural people in quotidian life. New technological methods are deemed to be the future of livability, therefore; a technologically advanced solution for sustainable rural development is called for. Blockchain Technology is the next step for innovation and development and it has far many applications in sustainable rural development that are yet to be discovered. The objective of this paper is to explicitly review research conducted in rural development to fill the undone work in the future with better research ideas, to make rural areas a livable and advanced place while also maintaining their integrity leading to sustainable development. To conduct such a review, a systematic research methodology is applied following regulations in the conduction of standardized but explorative analysis. Within the timeline of 2010-2021, 112 papers are carefully selected to perform the systematic review. This review will provide a comprehensible as well as concise research compendium for all applications proposed, implemented, and possible in the future to realize the concept of smart villages for the development of rural areas using blockchain technology.

Is telemedicine the answer to rural expansion of medication treatment for opioid use disorder? Early experiences in the feasibility study phase of a National Drug Abuse Treatment Clinical Trials Network Trial

Telemedicine (TM) enabled by digital health technologies to provide medical services has been considered a key solution to increasing health care access in rural communities. With the immediate need for remote care due to the COVID-19 pandemic, many health care systems have rapidly incorporated digital technologies to support the delivery of remote care options, including medication treatment for individuals with opioid use disorder (OUD). In responding to the opioid crisis and the COVID-19 pandemic, public health officials and scientific communities strongly support and advocate for greater use of TM-based medication treatment for opioid use disorder (MOUD) to improve access to care and have suggested that broad use of TM during the pandemic should be sustained. Nevertheless, research on the implementation and effectiveness of TM-based MOUD has been limited. To address this knowledge gap, the National Drug Abuse Treatment Clinical Trials Network (CTN) funded (via the NIH HEAL Initiative) a study on Rural Expansion of Medication Treatment for Opioid Use Disorder (Rural MOUD; CTN-0102) to investigate the implementation and effectiveness of adding TM-based MOUD to rural primary care for expanding access to MOUD. In preparation for this large-scale, randomized controlled trial incorporating TM in rural primary care, a feasibility study is being conducted to develop and pilot test implementation procedures. In this commentary, we share some of our experiences, which include several challenges, during the initial two-month period of the feasibility study phase. While these challenges could be due, at least in part, to adjusting to the COVID-19 pandemic and new workflows to accommodate the study, they are notable and could have a substantial impact on the larger, planned pragmatic trial and on TM-based MOUD more broadly. Challenges include low rates of identification of risk for OUD from screening, low rates of referral to TM, digital device and internet access issues, workflow and capacity barriers, and insurance coverage. These challenges also highlight the lack of empirical guidance for best TM practice and quality remote care models. With TM expanding rapidly, understanding implementation and demonstrating what TM approaches are effective are critical for ensuring the best care for persons with OUD.

Low Burden Strategies Are Needed to Reduce Smoking in Rural Healthcare Settings: A Lesson from Cancer Clinics

Rural populations face significant smoking-related health disparities, such as a higher prevalence of lung cancer and cancer mortality, higher prevalence of smoking, and lower likelihood of receiving cessation treatment than urban counterparts. A significant proportion of health disparities in rural populations could be eliminated with low-barrier, easy-access treatment delivery methods for smoking cessation. In this study, we assessed treatment engagement among patients in rural and urban settings. Then, we examined the effect of an electronic health record-based smoking cessation module on patient receipt of evidence-based cessation care. As part of a quality improvement project, we retrospectively observed 479,798 unique patients accounting for 1,426,089 outpatient clinical encounters from June 2018-March 2019 across 766 clinics in the greater St. Louis, southern Illinois, and mid-Missouri regions. Smoking prevalence was higher in rural versus urban clinics (20.7% vs. 13.9%, 6.7% [6.3, 7.1], odds ratio = 1.6 [1.6, 1.6], p < 0.0001), and yet rural smokers were nearly three times less likely than their urban counterparts to receive any smoking cessation treatment after adjusting for patients clustering within clinics (9.6% vs. 25.8%, -16.2% [-16.9, -15.5], odds ratio = 0.304 [0.28, 0.33], p < 0.0001). Although not yet scaled up in the rural setting, we examined the effects of a low-burden, point-of-care smoking module currently implemented in cancer clinics. After adjusting for patient clustering within clinics, patients were more likely to receive smoking treatment in clinics that implemented the module versus clinics that did not implement the module (31.2% vs. 17.5%, 13.7% [10.8, 16.6], odds ratio = 2.1 [1.8, 2.6], p < 0.0001). The point-of-care treatment approach offers a promising solution for rural settings, both in and outside the context of cancer care.

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such a system in the future.