Rural Perspectives Challenging Pharmacotherapy

Assessing geographic disparities in mental health research participation

BACKGROUND

Large geographic health disparities are well-documented within the U.S. Although approximately 60 million Americans-or roughly 20% of the total U.S. population-live in rural areas, rural residents may be less likely to participate in health research, including mental health research, due to multiple barriers. This retrospective analysis evaluated the urbanity and rurality of inpatient research participants and potential research participants over a five-year period at the Experimental Therapeutics and Pathophysiology Branch (ETPB), NIMH-NIH (Bethesda, Maryland), which conducts experimental medicine and neurobiological research in mood disorders.

METHODS

Participant and potential participant zip codes were converted to Rural Urban Commuting Area (RUCA) codes (1-3 urban, 4-10 rural). These results were compared with each other and with U.S. population data.

RESULTS

The analysis included 182 research participants and 1864 potential research participants; the former were admitted to an in-person research unit and the latter were screened by phone or online. ETPB research participants had an urban residence rate of 93.4% and a rural residence rate of 6.6%. Potential ETPB research participants had an urban residence rate of 90.9% and a rural residence rate of 9.1%. In comparison, the U.S. urban residence rate is 80% and the rural rate is 20%.

CONCLUSION

At the ETPB, both research participants with mood disorders admitted to an in-person research unit and potential research participants screened online or by phone from rural areas were under-represented relative to participants from more urban areas. Further study of research recruitment barriers in rural areas of the U.S is needed.

Passive Auto Focusing of Pathological Microscope with Intelligent Field Image Collection Mechanism

The microscope is one of the widely used pathological equipment to analyze body fluids like blood, sputum, etc. in granular level. In order to reduce workload on pathologists and strengthen the telehealth services, an automatic self-focusing microscope with different field image collection mechanism is required. In this work, the conversion of a compound microscope into a complete digital self-focusing automatic microscope, with intelligent field image collection mechanism, is discussed. This method uses passive autofocusing technique. In this method, most informative regions are identified on the basis of texture information. Features from these identified regions are used for autofocusing the microscope. This system is capable of collecting multiple snaps from different regions of the smear sample slides. The problem with the smear slide is that it has un-uniform thickness upon the glass slide. So some region has a very thick layer and some region has a very thin layer. In general, both of these regions are not considered for pathological analysis. The proposed system is capable to detect the region of smear slide which is suitable for collection of snap images. A soft computing approach is used to detect the desired regions of the sample in the slide. The Raspberry pi is used to design the control section. Multi-threaded parallel programming is used to optimize I/O execution and waiting time. The performance of the proposed system is satisfactory. The average peak signal-to-noise ratio (PSNR) is about 33 in comparison with manual focusing by the domain expert. The performance of the system in terms of computation time, which is calculated on the benchmark microscopic image dataset, is better than other learning-based methods. Autofocusing of pathological microscope with an intelligent field image collection mechanism is highly useful in the remote healthcare domain. This work basically describes a mechanism to migrate the conventional compound microscope into a tale-health service compatible (IoT enabled) microscope. This system is highly suitable for developing countries where an overall change of existing infrastructure is difficult due to economic reasons.