skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Using the network scale-up method to characterise kidney trafficking in Kalai Upazila, Bangladesh
This study aimed to estimate the prevalence of illegal kidney sales in Kalai Upazila, Bangladesh, using the Network Scale-Up Method (NSUM), an ego-centric network survey-based technique used to estimate the size of hidden populations. The study estimated the size of the kidney seller population, analysed the profiles of kidney sellers and kidney brokers and investigated the characteristics of villagers who are more likely to be connected to kidney sellers to identify possible biases of the NSUM estimate. The study found that the prevalence of kidney trafficking in Kalai Upazila was between 1.98% and 2.84%, which is consistent with the estimates provided by a local leader and reporters, but with much narrower bounds. The study also found that a large proportion of kidney sellers and brokers were men (over 70% and 90%, respectively) and relatively young (mean age of 33 and 39, respectively). Specific reasons for kidney sales included poverty (83%), loan payment (4%), drug addiction (2%) and gambling (2%). While most reported male sellers were farmers (56%) and female sellers were housewives (78%) in need of money, most reported brokers were characterised as rich, well-known individuals.  more » « less
Award ID(s):
2146067
PAR ID:
10529230
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
BMJ
Date Published:
Journal Name:
BMJ Global Health
Volume:
8
Issue:
11
ISSN:
2059-7908
Page Range / eLocation ID:
e012774
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, BMJ Global Health)
    Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts of the world including South Asia, Central America, the Middle East and East Asia. Kidney trade networks reported in these hot spots are often complex systems involving several players such as buyers, sellers and surgery countries operating across international borders so that they can bypass domestic laws in sellers and buyers’ countries. The exact patterns of the country networks are, however, largely unknown due to the lack of a systematic approach to collect the data. Most of the kidney trade information is currently available in the form of case studies, court materials and news articles or reports, and no comprehensive database exists at this time. The present study thus explored online newspaper scraping to systematically collect 10 419 news articles from 24 major English newspapers in South Asia (January 2016 to May 2019) and build transnational kidney trade networks at the country level. Additionally, this study applied text mining techniques to extract words from each news article and developed machine learning algorithms to identify kidney trade and non-kidney trade news articles. Our findings suggest that online newspaper scraping coupled with the machine learning method is a promising approach to compile such data, especially in the dire shortage of empirical data. 
    more » « less
  2. ; ; ; ; ; ; (, BMJ Public Health)
    IntroductionInappropriate antibiotic use is a major driver of antimicrobial resistance. However, the scope of literature and its prevalence across world regions remain largely unknown, as do the most common indicators and study designs used. In this study, we summarised the current literature on inappropriate use of antibiotics by world regions. We also provided the first global estimates of the overall amount of antibiotics that are potentially used inappropriately each year. MethodsWe considered both patient and provider-mediated inappropriate antibiotic use. We reviewed 412 studies published between 2000 and 2021 and used beta regression and marginal contrasts to compare prevalence of inappropriate use by study design, indicator, world region, and national income level. Country-level sales of antibiotics from 2022 were combined with inappropriate antibiotic use estimates derived from two study designs (clinical audits and patient interviews) and one indicator (lack of indication) to estimate the amount of antibiotics inappropriately used globally. ResultsClinical audits (50.1%, 208/412) and ‘non-prescription’ use (37.1%, 153/412) were the most common study design and indicator, respectively, used to estimate inappropriate antibiotic use. Inappropriate antibiotic use prevalence was ~6% higher in low-income and middle-income than in high-income countries. However, this difference disappeared after accounting for a proxy of access to care: physicians per capita. Globally, based on clinical audits, patient interviews and lack of indication, the estimated proportion of inappropriate antibiotic use was 29.5%, 36.5% and 30.8%, respectively, with an average of ~30% (~13 000 000 kg) the equivalent of the annual antibiotic consumption in China. ConclusionsInappropriate antibiotic use is highly prevalent across all countries regardless of national income level, with a third of global antibiotic consumption potentially due to unnecessary prescription (‘lack of indication’). Antibiotic stewardship efforts and defining internationally standardised indicators are needed to track progress in reducing the occurrence of inappropriate antibiotic use where necessary, as well as identifying gaps in access to care. 
    more » « less
  3. ; ; (, International Journal of Infectious Diseases)
    Background Removal of zero-COVID restrictions in China led to a surge in COVID-19 cases. In response, countries imposed restrictions on Chinese travelers. However, border policies imposed may not have been informed by accurate data and may not have provided substantial benefits. Methods We analyzed quarantines sufficient to prevent additional in-country transmission for February 13–19, 2023 based on World Health Organization (WHO) and self-reported infections to estimate prevalence. Results We have shown that self-reported prevalence data indicated more stringent border restrictions compared to WHO-published prevalence statistics. No travel restrictions were required for Singapore, South Korea, and Japan so that infections would not be greater than with complete border closure. However, a 1-, 2-, and 3-day quarantine were indicated for England, Germany, and Scotland respectively. A 10-, 13-, and 14-day quarantine were required for Italy, France, and the Philippines, respectively, to prevent an increase in within-country infections due to travel. Vietnam and Thailand required a complete border shutdown. Conclusion Our results demonstrated the necessity for accurate and timely reporting of pandemic statistics to prevent an increase in viral spread. Through the minimum quarantine analysis, countries can use science to determine policy, minimize international friction, and improve the cost-efficiency of interventions. 
    more » « less
  4. ; ; ; ; ; ; ; (, 2021 AAAR 39th Annual Conference)
    Organosulfates (OSs) are the most abundant class of organosulfur compounds (OrgS) in atmospheric fine particulate matter (PM2.5). Globally, isoprene‐derived OSs (iOSs) are the most abundantly reported OSs. The methyltetrol sulfates (MTSs), formed from multiphase chemical reactions of isoprene‐derived epoxidiols (IEPOX) with acidic sulfate aerosols, are the predominant iOSs. A recent study revealed that the heterogeneous hydroxyl radical (•OH) oxidation of fine particulate MTSs yields several highly oxygenated and functionalized OSs previously attributed to non‐IEPOX pathways. By using hydrophilic interaction liquid chromatography interfaced to electrospray ionization high‐resolution quadrupole time‐of‐flight mass spectrometry (HILIC/ESI‐HRQTOFMS), iOSs were quantitatively characterized in PM2.5 collected from 20 ground sites within the Interagency Monitoring of Protected Visual Environments (IMPROVE) network during the 2016 summer and winter seasons. Total water‐soluble sulfur (TWS‐S) and sulfur in the form of inorganic sulfate (Sinorg) were determined by inductively coupled plasmaoptical emission spectroscopy (ICP‐OES) and ion chromatography (IC), respectively. The difference between TWS‐S and Sinorg was used as an upper bound estimate of water‐soluble OrgS concentration. Significantly higher OrgS concentrations, coincident with elevated iOS concentrations, were observed only in summer. On average, iOSs (130 ± 60, up to 240 ng m‐3) explained 29% (± 7%) of OrgS and 5% (± 2%) of organic matter (OM = 1.8*OC) in summertime PM2.5 collected from the eastern U.S. For the western U.S., iOSs (11 ± 6 ng m‐3) account for 6% (± 5%) of OrgS and 0.7% (± 0.4%) of OM. This study provides critical insights into the abundance, prevalence, spatial variability of iOSs across the U.S. 
    more » « less
  5. ; (, PLOS Medicine)
    Turner, Richard (Ed.)
    Background With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Methods and findings County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R 2 ); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R 2 . Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R 2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. Conclusions Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email