A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach.

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017-2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago.

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area.

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria.

BACKGROUND: Women in low- and middle-income countries (LMIC) remain at high risk of developing cervical cancer and have limited access to screening programs. The limits include geographical barriers related to road network characteristics and travel behaviors but these have neither been well studied in LMIC nor have methods to overcome them been incorporated into cervical cancer screening delivery programs. METHODS: To identify and evaluate spatial barriers to cervical cancer prevention services in Ondo State, Nigeria, we applied a Multi-Mode Enhanced Two-Step Floating Catchment Area model to create a spatial access index for cervical cancer screening services in Ondo City and the surrounding region. The model used inputs that included the distance between service locations and population centers, local population density, quantity of healthcare infrastructures, modes of transportation, and the travel time budgets of clients. Two different travel modes, taxi and mini bus, represented common modes of transit. Geocoded client residential locations were compared to spatial access results to identify patterns of spatial access and estimate where gaps in access existed. RESULTS: Ondo City was estimated to have the highest access in the region, while the largest city, Akure, was estimated to be in only the middle tier of access. While 73.5% of clients of the hospital in Ondo City resided in the two highest access zones, 21.5% of clients were from locations estimated to be in the lowest access catchment, and a further 2.25% resided outside these limits. Some areas that were relatively close to cervical cancer screening centers had lower access values due to poor road network coverage and fewer options for public transportation. CONCLUSIONS: Variations in spatial access were revealed based on client residential patterns, travel time differences, distance decay assumptions, and travel mode choices. Assessing access to cervical cancer screening better identifies potentially underserved locations in rural Nigeria that can inform plans for cervical cancer screening including new or improved infrastructure, effective resource allocation, introduction of service options for areas with lower access, and design of public transportation networks.

Component analyses of urinary nanocrystallites of uric acid stone formers by combination of high-resolution transmission electron microscopy, fast Fourier transformation, energy dispersive X-ray spectroscopy, X-ray diffraction and Fourier transform infrared spectroscopy.

This study aimed to analyse the components of nanocrystallites in urines of patients with uric acid (UA) stones. X-ray diffraction (XRD), Fourier transform infrared spectroscopy, high-resolution transmission electron microscopy (HRTEM), fast Fourier transformation (FFT) of HRTEM, and energy dispersive X-ray spectroscopy (EDS) were performed to analyse the components of these nanocrystallites. XRD and FFT showed that the main component of urinary nanocrystallites was UA, which contains a small amount of calcium oxalate monohydrate and phosphates. EDS showed the characteristic absorption peaks of C, O, Ca and P. The formation of UA stones was closely related to a large number of UA nanocrystallites in urine. A combination of HRTEM, FFT, EDS and XRD analyses could be performed accurately to analyse the components of urinary nanocrystallites.

Changes in urinary nanocrystallites in calcium oxalate stone formers before and after potassium citrate intake.

The property changes of urinary nanocrystallites in 13 patients with calcium oxalate (CaOx) stones were studied before and after ingestion of potassium citrate (K3cit), a therapeutic drug for stones. The analytical techniques included nanoparticle size analysis, transmission electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The studied properties included the components, morphologies, zeta potentials, particle size distributions, light intensity autocorrelation curves, and polydispersity indices (PDIs) of the nanocrystallites. The main components of the urinary nanocrystallites before K3cit intake included uric acid, ß-calcium phosphate, and calcium oxalate monohydrate. After K3cit intake, the quantities, species, and percentages of aggregated crystals decreased, whereas the percentages of monosodium urate and calcium oxalate dehydrate increased, and some crystallites became blunt. Moreover, the urinary pH increased from 5.96 ± 0.43 to 6.46 ± 0.50, the crystallite size decreased from 524 ± 320 nm to 354 ± 173 nm, and the zeta potential decreased from -4.85 ± 2.87 mV to -8.77 ± 3.03 mV. The autocorrelation curves became smooth, the decay time decreased from 11.4 ± 3.2 ms to 4.3 ± 1.7 ms, and the PDI decreased from 0.67 ± 0.14 to 0.53 ± 0.19. These changes helped inhibit CaOx calculus formation.

[The property changes of urine crystallites of calcium oxalate stone formers before and after taking medicine].

The property changes of urine crystallites of six cases of calcium oxalate stone formers before and after taking medicine were comparatively studied using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, nanoparticle size analyzer, and transmission electron microscopy (TEM). The results showed that the urinary pH increases after taking medicine (before taking 5.87 +/- 0.51, after taking 6.23 +/- 0.74.) Before taking medicine the main components of urine crystallites were uric acid, calcium oxalate monohydrate (COM) and biphosphate. The types and quantities of urine crystallites after taking medicine were less than that of before. The average size of urine crystallite before taking medicine was (579 +/- 326) nm, and it reduced to (404 +/- 338) nm after taking medicine. After taking medicine the zeta potential was also decreased to (-7.29 +/- 4.16) mV from (-4.28 +/- 2.55) mV. The decrease in zeta potential is beneficial to preventing urinary crystallites deposition. The edges and corners of urine crystallite were sharp with significant aggregation before taking medicine, while they became blunt and less aggregation after taking medicine. The analysis of the property changes of urine crystallites of calcium oxalate stone patients before and after taking medicine by using modern equipments has important clinical significance to the clinical prevention and treatment of urinary stones.