Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County.

Research on retail food environment (RFE) relies on data availability and accuracy. However, the discrepancies in RFE datasets may lead to imprecision when measuring association with health outcomes. In this research, we present a two-tier hierarchical point of interest (POI) matching framework to compare and triangulate food outlets across multiple geospatial data sources. Two matching parameters were used including the geodesic distance between businesses and the similarity of business names according to Levenshtein distance (LD) and Double Metaphone (DM). Sensitivity analysis was conducted to determine thresholds of matching parameters. Our Tier 1 matching used more restricted parameters to generate high confidence-matched POIs, whereas in Tier 2 we opted for relaxed matching parameters and applied a weighted multi-attribute model on the previously unmatched records. Our case study in San Diego County, California used government, commercial, and crowdsourced data and returned 20.2% matched records from Tier 1 and 18.6% matched from Tier 2. Our manual validation shows a 100% matching rate for Tier 1 and up to 30.6% for Tier 2. Matched and unmatched records from Tier 1 were further analyzed for spatial patterns and categorical differences. Our hierarchical POI matching framework generated highly confident food POIs by conflating datasets and identified some food POIs that are unique to specific data sources. Triangulating RFE data can reduce uncertain and invalid POI listings when representing food environment using multiple data sources. Studies investigating associations between food environment and health outcomes may benefit from improved quality of RFE.

Service readiness for the management of non-communicable diseases in publicly financed facilities in Malawi: findings from the 2019 Harmonised Health Facility Assessment census survey.

INTRODUCTION: Non-communicable diseases (NCDs) are rising in low-income and middle-income countries, including Malawi. To inform policy-makers and planners on the preparedness of the Malawian healthcare system to respond to NCDs, we estimated NCD service readiness in publicly financed healthcare facilities in Malawi. METHODS: We analysed data from 564 facilities surveyed in the 2019 Harmonised Health Facility Assessment, including 512 primary healthcare (PHC) and 52 secondary and tertiary care (STC) facilities. To characterise service readiness, applying the law of minimum, we estimated the percentage of facilities with functional equipment and unexpired medicines required to provide NCD services. Further, we estimated permanently unavailable items to identify service readiness bottlenecks. RESULTS: Fewer than 40% of PHC facilities were ready to deliver services for each of the 14 NCDs analysed. Insulin and beclomethasone inhalers had the lowest stock levels at PHC facilities (6% and 8%, respectively). Only 17% of rural and community hospitals (RCHs) have liver and kidney diagnostics. STC facilities had varying service readiness, ranging from 27% for managing acute diabetes complications to 94% for chronic type 2 diabetes management. Only 38% of STC facilities were ready to manage chronic heart failure. Oral pain medicines were widely available at all levels of health facilities; however, only 22% of RCHs and 29% of STCs had injectable morphine or pethidine. Beclomethasone was never available at 74% of PHC and 29% of STC facilities. CONCLUSION: Publicly financed facilities in Malawi are generally unprepared to provide NCD services, especially at the PHC level. Targeted investments in PHC can substantially improve service readiness for chronic NCD conditions in local communities and enable STC to respond to acute NCD complications and more complex NCD cases.

Population structure and antimicrobial resistance patterns of Salmonella Typhi and Paratyphi A amid a phased municipal vaccination campaign in Navi Mumbai, India.

We performed whole-genome sequencing of 174 Salmonella Typhi and 54 Salmonella Paratyphi A isolates collected through prospective surveillance in the context of a phased typhoid conjugate vaccine introduction in Navi Mumbai, India. We investigate the temporal and geographical patterns of emergence and spread of antimicrobial resistance. We evaluated the relationship between the spatial distance between households and genetic clustering of isolates. Most isolates were non-susceptible to fluoroquinolones, with nearly 20% containing ≥3 quinolone resistance-determining region mutations. Two H58 isolates carried an IncX3 plasmid containing blaSHV-12, associated with ceftriaxone resistance, suggesting that the ceftriaxone-resistant isolates from India independently evolved on multiple occasions. Among S. Typhi, we identified two main clades circulating (2.2 and 4.3.1 [H58]); 2.2 isolates were closely related following a single introduction around 2007, whereas H58 isolates had been introduced multiple times to the city. Increasing geographic distance between isolates was strongly associated with genetic clustering (odds ratio [OR] = 0.72 per km; 95% credible interval [CrI]: 0.66-0.79). This effect was seen for distances up to 5 km (OR = 0.65 per km; 95% CrI: 0.59-0.73) but not seen for distances beyond 5 km (OR = 1.02 per km; 95% CrI: 0.83-1.26). There was a non-significant reduction in odds of clustering for pairs of isolates in vaccination communities compared with non-vaccination communities or mixed pairs compared with non-vaccination communities. Our findings indicate that S. Typhi was repeatedly introduced into Navi Mumbai and then spread locally, with strong evidence of spatial genetic clustering. In addition to vaccination, local interventions to improve water and sanitation will be critical to interrupt transmission. IMPORTANCE Enteric fever remains a major public health concern in many low- and middle-income countries, as antimicrobial resistance (AMR) continues to emerge. Geographical patterns of typhoidal Salmonella spread, critical to monitoring AMR and planning interventions, are poorly understood. We performed whole-genome sequencing of S. Typhi and S. Paratyphi A isolates collected in Navi Mumbai, India before and after a typhoid conjugate vaccine introduction. From timed phylogenies, we found two dominant circulating lineages of S. Typhi in Navi Mumbai-lineage 2.2, which expanded following a single introduction a decade prior, and 4.3.1 (H58), which had been introduced repeatedly from other parts of India, frequently containing "triple mutations" conferring high-level ciprofloxacin resistance. Using Bayesian hierarchical statistical models, we found that spatial distance between cases was strongly associated with genetic clustering at a fine scale (<5 km). Together, these findings suggest that antimicrobial-resistant S. Typhi frequently flows between cities and then spreads highly locally, which may inform surveillance and prevention strategies.

Understanding the spatial heterogeneity of COVID-19 vaccination uptake in England.

BACKGROUND: Mass vaccination has been a key strategy in effectively containing global COVID-19 pandemic that posed unprecedented social and economic challenges to many countries. However, vaccination rates vary across space and socio-economic factors, and are likely to depend on the accessibility to vaccination services, which is under-researched in literature. This study aims to empirically identify the spatially heterogeneous relationship between COVID-19 vaccination rates and socio-economic factors in England. METHODS: We investigated the percentage of over-18 fully vaccinated people at the small-area level across England up to 18 November 2021. We used multiscale geographically weighted regression (MGWR) to model the spatially heterogeneous relationship between vaccination rates and socio-economic determinants, including ethnic, age, economic, and accessibility factors. RESULTS: This study indicates that the selected MGWR model can explain 83.2% of the total variance of vaccination rates. The variables exhibiting a positive association with vaccination rates in most areas include proportion of population over 40, car ownership, average household income, and spatial accessibility to vaccination. In contrast, population under 40, less deprived population, and black or mixed ethnicity are negatively associated with the vaccination rates. CONCLUSIONS: Our findings indicate the importance of improving the spatial accessibility to vaccinations in developing regions and among specific population groups in order to promote COVID-19 vaccination.

Spatial and temporal pattern of cannabis use disorder in California 2010-2019.

As cannabis use is being legalized in an increasing number of states, it is important to understand the changing dynamic of the risk in cannabis use disorder (CUD). Shape-based time-series clustering was used to identify ZIP Code Tabulation Areas (ZCTAs) with similar changing pattern in CUD over time. We conducted a cross-sectional logistic regression analysis to investigate the most recent ZCTA socio-demographic characteristics in relation to the changing CUD rates. The emergency discharge rates generally increased during 2010-2016. Increase during 2017-2019 was found in Sacramento and Santa Barbara County. Approximately 13% of ZCTAs showed an increasing trend of hospitalization discharge during 2017-2019. Males and non-Hispanic Black had larger increase than other groups during 2017-2019. The recent growing trend was found associated with greater racial diversity and rural ZCTAs. The findings from this study hold promise for local public health officials to adjust the cannabis intervention strategies in target districts and improve overall health outcomes.

The international and intercontinental spread and expansion of antimicrobial-resistant Salmonella Typhi: a genomic epidemiology study.

BACKGROUND: The emergence of increasingly antimicrobial-resistant Salmonella enterica serovar Typhi (S Typhi) threatens to undermine effective treatment and control. Understanding where antimicrobial resistance in S Typhi is emerging and spreading is crucial towards formulating effective control strategies. METHODS: In this genomic epidemiology study, we sequenced the genomes of 3489 S Typhi strains isolated from prospective enteric fever surveillance studies in Nepal, Bangladesh, Pakistan, and India (between 2014 and 2019), and combined these with a global collection of 4169 S Typhi genome sequences isolated between 1905 and 2018 to investigate the temporal and geographical patterns of emergence and spread of antimicrobial-resistant S Typhi. We performed non-parametric phylodynamic analyses to characterise changes in the effective population size of fluoroquinolone-resistant, extensively drug-resistant (XDR), and azithromycin-resistant S Typhi over time. We inferred timed phylogenies for the major S Typhi sublineages and used ancestral state reconstruction methods to estimate the frequency and timing of international and intercontinental transfers. FINDINGS: Our analysis revealed a declining trend of multidrug resistant typhoid in south Asia, except for Pakistan, where XDR S Typhi emerged in 2016 and rapidly replaced less-resistant strains. Mutations in the quinolone-resistance determining region (QRDR) of S Typhi have independently arisen and propagated on at least 94 occasions, nearly all occurring in south Asia. Strains with multiple QRDR mutations, including triple mutants with high-level fluoroquinolone resistance, have been increasing in frequency and displacing strains with fewer mutations. Strains containing acrB mutations, conferring azithromycin resistance, emerged in Bangladesh around 2013 and effective population size of these strains has been steadily increasing. We found evidence of frequent international (n=138) and intercontinental transfers (n=59) of antimicrobial-resistant S Typhi, followed by local expansion and replacement of drug-susceptible clades. INTERPRETATION: Independent acquisition of plasmids and homoplastic mutations conferring antimicrobial resistance have occurred repeatedly in multiple lineages of S Typhi, predominantly arising in south Asia before spreading to other regions. FUNDING: Bill & Melinda Gates Foundation.

Geographic Pattern of Typhoid Fever in India: A Model-Based Estimate of Cohort and Surveillance Data.

BACKGROUND: Typhoid fever remains a major public health problem in India. Recently, the Surveillance for Enteric Fever in India program completed a multisite surveillance study. However, data on subnational variation in typhoid fever are needed to guide the introduction of the new typhoid conjugate vaccine in India. METHODS: We applied a geospatial statistical model to estimate typhoid fever incidence across India, using data from 4 cohort studies and 6 hybrid surveillance sites from October 2017 to March 2020. We collected geocoded data from the Demographic and Health Survey in India as predictors of typhoid fever incidence. We used a log linear regression model to predict a primary outcome of typhoid incidence. RESULTS: We estimated a national incidence of typhoid fever in India of 360 cases (95% confidence interval [CI], 297-494) per 100 000 person-years, with an annual estimate of 4.5 million cases (95% CI, 3.7-6.1 million) and 8930 deaths (95% CI, 7360-12 260), assuming a 0.2% case-fatality rate. We found substantial geographic variation of typhoid incidence across the country, with higher incidence in southwestern states and urban centers in the north. CONCLUSIONS: There is a large burden of typhoid fever in India with substantial heterogeneity across the country, with higher burden in urban centers.

Comparison of Strategies for Typhoid Conjugate Vaccine Introduction in India: A Cost-Effectiveness Modeling Study.

BACKGROUND: Typhoid fever causes substantial global mortality, with almost half occurring in India. New typhoid vaccines are highly effective and recommended by the World Health Organization for high-burden settings. There is a need to determine whether and which typhoid vaccine strategies should be implemented in India. METHODS: We assessed typhoid vaccination using a dynamic compartmental model, parameterized by and calibrated to disease and costing data from a recent multisite surveillance study in India. We modeled routine and 1-time campaign strategies that target different ages and settings. The primary outcome was cost-effectiveness, measured by incremental cost-effectiveness ratios (ICERs) benchmarked against India's gross national income per capita (US$2130). RESULTS: Both routine and campaign vaccination strategies were cost-saving compared to the status quo, due to averted costs of illness. The preferred strategy was a nationwide community-based catchup campaign targeting children aged 1-15 years alongside routine vaccination, with an ICER of $929 per disability-adjusted life-year averted. Over the first 10 years of implementation, vaccination could avert 21-39 million cases and save $1.6-$2.2 billion. These findings were broadly consistent across willingness-to-pay thresholds, epidemiologic settings, and model input distributions. CONCLUSIONS: Despite high initial costs, routine and campaign typhoid vaccination in India could substantially reduce mortality and was highly cost-effective.

Spatial Heterogeneity of Enteric Fever in 2 Diverse Communities in Nepal.

BACKGROUND: Typhoid fever is endemic in the urban Kathmandu Valley of Nepal; however, there have been no population-based studies of typhoid outside of this community in the past 3 decades. Whether typhoid immunization should be prioritized in periurban and rural communities has been unclear. METHODS: We performed population-based surveillance for enteric fever in 1 urban catchment (Kathmandu) and 1 periurban and rural catchment (Kavrepalanchok) as part of the Surveillance for Enteric Fever in Asia Project (SEAP). We recruited individuals presenting to outpatient and emergency departments at 2 study hospitals with suspected enteric fever and performed blood cultures. Additionally, we conducted a household survey in each catchment area to characterize care seeking for febrile illness. We evaluated spatial heterogeneity in febrile illness, care seeking, and enteric fever incidence. RESULTS: Between September 2016 and September 2019, we enrolled 5736 participants with suspected enteric fever at 2 study hospitals. Among these, 304 (5.3%) were culture positive for Salmonella Typhi (249 [81.9%]) or Paratyphi A (55 [18.1%]). Adjusted typhoid incidence in Kathmandu was 484 per 100 000 person-years and in Kavrepalanchok was 615 per 100 000 person-years. While all geographic areas for which estimates could be made had incidence >200 per 100 000 person-years, we observed spatial heterogeneity with up to 10-fold variation in incidence between communities. CONCLUSIONS: In urban, periurban, and rural communities in and around Kathmandu, we measured a high but heterogenous incidence of typhoid. These findings provide some support for the introduction of conjugate vaccines in Nepal, including outside urban areas, alongside other measures to prevent enteric fever.

Using socially-sensed data to infer ZIP level characteristics for the spatiotemporal analysis of drug-related health problems in Maryland.

This research investigated how socially sensed data can be used to detect ZIP level characteristics that are associated with spatial and temporal patterns of Emergency Department patients with a chief complaint and/or diagnosis of overdose or drug-related health problems for four hospitals in Baltimore and Anne Arundel County, MD during 2016-2018. Dynamic characteristics were identified using socially-sensed data (i.e., geo-tagged Twitter data) at ZIP code level over varying temporal resolutions. Data about three place-based variables including comments and concerns about crime, drug use, and negative or depressed sentiments, were extracted from tweets, along with data from four socio-environmental variables from the American Community Survey were collected to explore socio-environmental characteristics during the same period. Our study showed a statistically significant increase in adjusted rates of Emergency Department (ED) visits occurred between June and November 2017 for patients residing in ZIP codes in western Baltimore and northeastern Anne Arundel County. During this period, the three topics extracted from Twitter data were highly correlated with the ZIP codes where the patients were residing. Exploring the dynamic spatial associations between socio-environmental variables and ED visits for acute overdose assists local health officials in optimizing interventions for vulnerable locations.

Determining spatial access to opioid use disorder treatment and emergency medical services in New Hampshire.

This research presents an analysis of spatial access to both opioid use disorder treatment facilities and emergency medical services in New Hampshire during 2015-2016, a period during which there was a steep increase in unintentional overdoses involving fentanyl. For this research, spatial access was computed using the enhanced two-step floating catchment area model combined with the Huff model to assess access across New Hampshire and gives attention to supply-side parameters that can impact spatial access. The model is designed to measure access to healthcare services for opioid use disorder patients offered at treatment centers or from buprenorphine treatment practitioners, as well as from emergency medical services across New Hampshire. A composite index of accessibility is proposed to represent overall access to these different treatment services for opioid use disorder patients. Geospatial determinants of spatial access included street network distances, driving times and distance decay relationships, while other key factors were services availability and population demand. Among the towns with the highest composite access scores, approximately 40% were metropolitan locations while 16% were rural towns. The insights from this research showed that for this period, while the opioid crisis was impacting many towns in New Hampshire, high levels of access to treatment services were not uniform across the state. When comparing the access results with data on the towns of residence for individuals who died from unintentional overdoses involving fentanyl during 2015 and 2016, estimates found that approximately 40% of the towns were not estimated to be in the highest class of access to treatment services at the time. This research provides information for local public health officials to support planning strategies to address opioid use disorder treatment access in high-risk regions.

Geospatial Analysis of Drug Poisoning Deaths Involving Heroin in the USA, 2000-2014.

We investigate the geographic patterns of drug poisoning deaths involving heroin by county for the USA from 2000 to 2014. The county-level patterns of mortality are examined with respect to age-adjusted rates of death for different classes of urbanization and racial and ethnic groups, while rates based on raw counts of drug poisoning deaths involving heroin are estimated for different age groups and by gender. To account for possible underestimations in these rates due to small areas or small numbers, spatial empirical Baye's estimation techniques have been used to smooth the rates of death and alleviate underestimation when analyzing spatial patterns for these different groups. The geographic pattern of poisoning deaths involving heroin has shifted from the west coast of the USA in the year 2000 to New England, the Mid-Atlantic region, and the Great Lakes and central Ohio Valley by 2014. The evolution over space and time of clusters of drug poisoning deaths involving heroin is confirmed through the SaTScan analysis. For this period, White males were found to be the most impacted population group overall; however, Blacks and Hispanics are highly impacted in counties where significant populations of these two groups reside. Our results show that while 35-54-year-olds were the most highly impacted age group by county from 2000 to 2010, by 2014, the trend had changed with an increasing number of counties experiencing higher death rates for individuals 25-34 years. The percentage of counties across the USA classified as large metro with deaths involving heroin is estimated to have decreased from approximately 73% in 2010 to just fewer than 56% in 2014, with a shift to small metro and non-metro counties. Understanding the geographic variations in impact on different population groups in the USA has become particularly necessary in light of the extreme increase in the use and misuse of street drugs including heroin and the subsequent rise in opioid-related deaths in the USA.