Early detection and prediction of non-fatal drug-related incidents and fatal overdose outbreaks using the Farrington algorithm.

AIMS: The aim of this study was to assess the validity of undertaking time-series analyses on both fatal and non-fatal drug overdose outcomes for the surveillance of emerging drug threats, and to determine the validity of analyzing non-fatal indicators to support the early detection of fatal overdose outbreaks. DESIGN, SETTING AND PARTICIPANTS: Time-series analyses using county-level data containing fatal overdoses and non-fatal overdose counts were collected at monthly intervals between 2015 and 2021 in California and Florida, USA. To analyze these data, we used the Farrington algorithm (FA), a method used to detect aberrations in time-series data such that an abnormal increase in counts relative to previous observations would result in an alert. The FA's performance was compared with a bench-mark approach, using the standard deviation as an aberration detection threshold. We evaluated whether monthly alerts in non-fatal overdose can aid in identifying fatal drug overdose outbreaks, defined as a statistically significant increase in the 6-month overdose death rate. We also conducted analyses across regions, i.e. clusters of counties. MEASUREMENTS: Measurements were taken during emergency department and emergency medical service visits. FINDINGS: Both methods yielded a similar proportion of alerts across scenarios for non-fatal overdoses, while the bench-mark method yielded more alerts for fatal overdoses. For both methods, the correlations between surveillance evaluations were relatively poor in the detection of aberrations (typically < 35%) but were high between evaluations yielding no alerts (typically > 75%). For ongoing fatal overdose outbreaks, a strategy based on the detection of alerts at the county level from either method yielded a sensitivity of 66% for both California and Florida. At the regional level, the equivalent analyses had sensitivities of 81% for California and 77% for Florida. CONCLUSION: Aberration detection methods can support the early detection of fatal drug overdose outbreaks, particularly when methodologies are applied in combination rather than individual methods separately.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. OBJECTIVE: We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. METHODS: We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. RESULTS: In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. CONCLUSIONS: This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making.

PURPOSE: We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. METHODS: Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms. RESULTS: There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively. CONCLUSION: We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

Thus far, health-related quality of life estimates for patients with opioid use disorder in the United States are limited, and importantly, they were not generated from studies among people living with the condition. This study extracted data from six clinical trials providing data among 1,777 people with opioid use disorder, made publicly available by the National Institutes of Health, to produce estimates of health-related quality of life. Our study found higher health-related quality of life estimates as compared to previous studies, modest impact of medications for opioid use disorder and strong impact of withdrawal symptoms on this outcome. These higher values among people with opioid use disorder might reflect the very negative perception of this condition among members of the general population (among whom these estimates have been generated previously). However, these relatively high estimates could also reflect an adaptation to the condition or a lack of awareness of associated-health damage in the context of dependence. The low number of observations providing data on medications for opioid use disorder led to high uncertainty around related estimates of health-related quality of life, but our findings could also reflect real experiences by patients in the absence of the positive effects of non-medication opioids, which deserve more attention in clinical practice. Our study suggests that systematically measuring withdrawal symptoms and representing these in health economic models might provide a more accurate representation of health-related quality of life among people with opioid use disorder and therefore of the impact and cost-effectiveness of interventions.

The health equity implications of the Health Resources and Services Administration's Ryan White HIV/AIDS Program.

OBJECTIVE: Investigate the role of the Ryan White HIV/AIDS Program (RWHAP) - which funds services for vulnerable and historically disadvantaged populations with HIV - in reducing health inequities among people with HIV over a 10-year horizon. DESIGN: We use an agent-based microsimulation model to incorporate the complexity of the program and long-time horizon. METHODS: We use a composite measure (the Theil index) to evaluate the health equity implications of the RWHAP for each of four subgroups (based on race and ethnicity, age, gender, and HIV transmission category) and two outcomes (probability of being in care and treatment and probability of being virally suppressed). We compare results with the RWHAP fully funded versus a counterfactual scenario, in which the medical and support services funded by the RWHAP are not available. RESULTS: The model indicates the RWHAP will improve health equity across all demographic subgroups and outcomes over a 10-year horizon. In Year 10, the Theil index for race and ethnicity is 99% lower for both outcomes under the RWHAP compared to the non-RWHAP scenario; 71-93% lower across HIV transmission categories; 31-44% lower for age; and 73-75% lower for gender. CONCLUSION: Given the large number of people served by the RWHAP and our findings on its impact on equity, the RWHAP represents an important vehicle for achieving the health equity goals of the National HIV/AIDS Strategy (2022-2025) and the Ending the HIV Epidemic Initiative goal of reducing new infections by 90% by 2030.

Estimating the impact of a police education program on hepatitis C virus transmission and disease burden among people who inject drugs in Tijuana, Mexico: A dynamic modeling analysis.

BACKGROUND AND AIMS: Criminalization of drug use and punitive policing are key structural drivers of hepatitis C virus (HCV) risk among people who inject drugs (PWID). A police education program (Proyecto Escudo) delivering training on occupational safety together with drug law content was implemented between 2015 and 2016 in Tijuana, Mexico, to underpin drug law reform implementation. We used data from a longitudinal cohort of PWID in Tijuana to inform epidemic modeling and assess the long-term impact of Escudo on HCV transmission and burden among PWID in Tijuana. METHODS: We developed a dynamic, compartmental model of HCV transmission and incarceration among PWID and tracked liver disease progression among current and former PWID. The model was calibrated to data from Tijuana, Mexico, with 90% HCV seroprevalence. We used segmented regression analysis to estimate impact of Escudo on recent incarceration among an observational cohort of PWID. By simulating the observed incarceration trends, we estimated the potential impact of the implemented (2-year reduction in incarceration) and an extended (10-year reduction in incarceration) police education program over a 50-year follow-up (2016-2066) on HCV outcomes (incidence, cirrhosis, HCV-related deaths and disability adjusted life-years averted) compared with no intervention. RESULTS: Over the 2-year follow-up, Proyecto Escudo reduced HCV incidence among PWID from 21.5 per 100 person years (/100py) (95% uncertainty interval [UI] = 15.3-29.7/100py) in 2016 to 21.1/100py (UI = 15.0-29.1/100py) in 2018. If continued for 10 years, Escudo could reduce HCV incidence to 20.0/100py (14.0-27.8/100py) by 2026 and avert 186 (32-389) new infections, 76 (UI = 12-160) cases of cirrhosis and 32 (5-73) deaths per 10 000 PWID compared with no intervention over a 50-year time horizon. CONCLUSIONS: In Tijuana, Mexico, implementation of a police education program delivering training on occupational safety and drug law content appears to have reduced hepatitis C virus incidence among people who inject drugs.

Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis.

BACKGROUND: There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information. OBJECTIVE: This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change. METHODS: We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database. RESULTS: Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P≤.05). CONCLUSIONS: This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders.

Illicit Substance Use and the COVID-19 Pandemic in the United States: A Scoping Review and Characterization of Research Evidence in Unprecedented Times.

We carried out a scoping review to characterize the primary quantitative evidence addressing changes in key individual/structural determinants of substance use risks and health outcomes over the first two waves of the COVID-19 pandemic in the United States (US). We systematically queried the LitCovid database for US-only studies without date restrictions (up to 6 August 2021). We extracted quantitative data from articles addressing changes in: (a) illicit substance use frequency/contexts/behaviors, (b) illicit drug market dynamics, (c) access to treatment and harm reduction services, and (d) illicit substance use-related health outcomes/harms. The majority of 37 selected articles were conducted within metropolitan locations and leveraged historical timeseries medical records data. Limited available evidence supported changes in frequency/behaviors/contexts of substance use. Few studies point to increases in fentanyl and reductions in heroin availability. Policy-driven interventions to lower drug use treatment thresholds conferred increased access within localized settings but did not seem to significantly prevent broader disruptions nationwide. Substance use-related emergency medical services' presentations and fatal overdose data showed a worsening situation. Improved study designs/data sources, backed by enhanced routine monitoring of illicit substance use trends, are needed to characterize substance use-related risks and inform effective responses during public health emergencies.

Using Economic Evaluation to Inform Responses to the Opioid Epidemic in the United States: Challenges and Suggestions for Future Research.

Background: Several aspects of the opioid epidemic and of public health care organization in the United States (US) make the conduct of economic evaluation and the design of policies to respond to this crisis particularly challenging. Objectives: This commentary offers suggestions for how economic evaluation may address and overcome four key features of the opioid epidemic: 1) its magnitude and geographical distribution, 2) its intersection with multiple epidemics, 3) its rapidly changing dynamics, 4) its multi-sectoral causes and consequences. Results: We first offer pragmatic suggestions to address the difficulties in delivering a coordinated response given the fragmented nature of health care in the US. In view of the broad suite of responses required to address opioid use disorder and its associated comorbidities, we highlight the need for economic evaluations which consider interventions throughout the continuum of care (i.e. primary, secondary and tertiary levels of prevention). We examine how the use of predictive modelling alongside economic evaluation might be adopted to address the rapidly evolving situation affecting distinct populations and geographic areas and encourage investments in epidemic preparedness. Finally, we propose methods to capture the interdependence of various sectors of government affected by the opioid crisis in economic evaluations to ensure optimal levels of investment towards a comprehensive response. Conclusions: The opioid epidemic in the US represents an unprecedented public health challenge, but sound epidemiological modelling and economic analysis can help to guide use of limited resources committed to addressing it in ways that can have greatest impact in limiting its adverse consequences.

Using EQ-5D Data to Measure Hospital Performance: Are General Population Values Distorting Patients' Choices?

Background. The English National Health Service publishes hospital performance indicators based on average postoperative EQ-5D index scores after hip replacement surgery to inform prospective patients' choices of hospital. Unidimensional index scores are derived from multidimensional health-related quality-of-life data using preference weights estimated from a sample of the UK general population. This raises normative concerns if general population preferences differ from those of the patients who are to be informed. This study explores how the source of valuation affects hospital performance estimates. Methods. Four different value sets reflecting source of valuation (general population v. patients), valuation technique (visual analog scale [VAS] v. time tradeoff [TTO]), and experience with health states (currently experienced vs. experimentally estimated) were used to derive and compare performance estimates for 243 hospitals. Two value sets were newly estimated from EQ-5D-3L data on 122,921 hip replacement patients and 3381 members of the UK general public. Changes in hospital ranking (nationally) and performance outlier status (nationally; among patients' 5 closest hospitals) were compared across valuations. Results. National rankings were stable under different valuations (rank correlations >0.92). Twenty-three (9.5%) hospitals changed outlier status when using patient VAS valuations instead of general population TTO valuations, the current approach. Outlier status also changed substantially at the local level. This was explained mostly by the valuation technique, not the source of valuations or experience with the health states. Limitations. No patient TTO valuations were available. The effect of value set characteristics could be established only through indirect comparisons. Conclusion. Different value sets may lead to prospective patients choosing different hospitals. Normative concerns about the use of general population valuations are not supported by empirical evidence based on VAS valuations.

Aggregate Distributional Cost-Effectiveness Analysis of Health Technologies.

BACKGROUND: Health inequalities can be partially addressed through the range of treatments funded by health systems. Nevertheless, although health technology assessment agencies assess the overall balance of health benefits and costs, no quantitative assessment of health inequality impact is consistently undertaken. OBJECTIVES: To assess the inequality impact of technologies recommended under the NICE single technology appraisal process from 2012 to 2014 using an aggregate distributional cost-effectiveness framework. METHODS: Data on health benefits, costs, and patient populations were extracted from the NICE website. Benefits for each technology were distributed to social groups using the observed socioeconomic distribution of hospital utilization for the targeted disease. Inequality measures and estimates of cost-effectiveness were compared using the health inequality impact plane and combined using social welfare indices. RESULTS: Twenty-seven interventions were evaluated. Fourteen interventions were estimated to increase population health and reduce health inequality, 8 to reduce population health and increase health inequality, and 5 to increase health and increase health inequality. Among the latter 5, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact. CONCLUSIONS: The methods proposed offer a way of estimating the health inequality impacts of new health technologies. The methods do not allow for differences in technology-specific utilization and health benefits, but require less resources and data than conducting full distributional cost-effectiveness analysis. They can provide useful quantitative information to help policy makers consider how far new technologies are likely to reduce or increase health inequalities.

Novel systemic therapies in atopic dermatitis: what do we need to fulfil the promise of a treatment revolution?

Patients with atopic dermatitis (AD) who do not adequately respond to topical therapy and phototherapy often need systemic immunomodulatory treatment to control their symptoms. Conventional systemic agents, such as ciclosporin, azathioprine, and methotrexate, have been used for decades, but there are concerns about their safety profile. There are now many novel systemic agents emerging through clinical trials, which may have great potential in the treatment of AD. Despite this, there are very few data comparing the performance of these drugs against each other. The purpose of this article is to review the current systemic therapies in AD and present an indirect comparison of systemic AD treatments using effectiveness and safety data from published randomised controlled trials, highlighting important remaining gaps in knowledge. Although the latest developments in systemic AD treatments are exciting and dearly needed, further work is required before the promise of a therapeutic revolution becomes reality.

Mapping between HAQ-DI and EQ-5D-5L in a Chinese patient population.

OBJECTIVES: In order to address the current deficiency of health utility evidence relevant for economic evaluations involving treatments for rheumatoid arthritis (RA) in the Chinese setting, this study aims to develop a mapping algorithm linking the Health Assessment Questionnaire (HAQ) and EQ-5D-5L in a Chinese population of patients with RA. METHODS: An estimation sample was obtained from a cross-sectional study that collected HAQ, the pain Visual Analogue Scale, and EQ-5D-5L in RA patients in two tertiary referral hospitals in China. Mapping algorithms were derived in this study using two alternative regression methods: the beta regression and a multivariate ordered probit regression. The internal validity of the mapping algorithms was assessed in each case by calculating predictive performance using a bootstrapping procedure. RESULTS: Of the several algorithms developed using these data, predictive performance was shown to be better when VAS pain was included as a predictor and when the multivariate ordered probit regression method was used, rather than the beta regression method. The algorithms developed were shown to be comparable, in terms of predictive performance, to existing mapping studies despite the small sample size of the estimation data. CONCLUSION: It is hoped that the availability of these algorithms will facilitate the development of cost-effectiveness studies evaluating RA treatments in the Chinese health care setting.

Evaluating the cost-effectiveness of biologic treatments for psoriatic arthritis: can we make better use of patient data registries?

The primary aim of this study is to explore the extent to which registry data may fulfill the evidence requirements of cost-effectiveness analysis (CEA) studies evaluating biologic therapies for the treatment of psoriatic arthritis (PsA), where trial data are lacking or insufficient. In addition, the paper aims to identify how future data collection in PsA registries might be better tailored to inform CEA research. A review of the literature was performed to identify existing registries containing PsA patients. Where possible, information was extracted on the design and characteristics of the registries. The registries were then appraised according to a set of criteria that was formulated based on the methods currently used to model PsA in the CEA literature. A review of the literature identified 21 potentially relevant registries from around the world containing patients with PsA. There was substantial variation regarding the extent to which the registries, as a whole, were useful for the purposes of CEA studies. There were also notable disparities found in terms of the accessibility of the registries to researchers. The critical review conducted in this study showed that all of the registries identified are potentially useful, at least in some degree, for the purposes of informing CEA studies in PsA. However, no individual registry on its own was found to meet all of the evidence requirements when considering how the disease has been modeled previously.

Reactive oxygen species (ROS) and wound healing: the functional role of ROS and emerging ROS-modulating technologies for augmentation of the healing process.

Reactive oxygen species (ROS) play a pivotal role in the orchestration of the normal wound-healing response. They act as secondary messengers to many immunocytes and non-lymphoid cells, which are involved in the repair process, and appear to be important in coordinating the recruitment of lymphoid cells to the wound site and effective tissue repair. ROS also possess the ability to regulate the formation of blood vessels (angiogenesis) at the wound site and the optimal perfusion of blood into the wound-healing area. ROS act in the host's defence through phagocytes that induce an ROS burst onto the pathogens present in wounds, leading to their destruction, and during this period, excess ROS leakage into the surrounding environment has further bacteriostatic effects. In light of these important roles of ROS in wound healing and the continued quest for therapeutic strategies to treat wounds in general and chronic wounds, such as diabetic foot ulcers, venous and arterial leg ulcers and pressure ulcers in particular, the manipulation of ROS represents a promising avenue for improving wound-healing responses when they are stalled. This article presents a review of the evidence supporting the critical role of ROS in wound healing and infection control at the wound site, and some of the new emerging concepts associated with ROS modulation and its potential in improving wound healing are discussed.

The antimicrobial activity of prototype modified honeys that generate reactive oxygen species (ROS) hydrogen peroxide.

BACKGROUND: Antimicrobial resistance continues to be a global issue in healthcare organisations. Honey has long been shown to possess wound healing and antimicrobial properties that are dependent on a number of physical and chemical properties of the honey. We tested the antimicrobial activity of a medicinal honey, Surgihoney® (SH) and two prototype modified honeys made by Apis mellifera (honeybee) against Staphylococcus aureus (NCIMB 9518). We also examined the modified honey prototypes for the ability to generate reactive oxygen species (ROS) by changing the level of production of hydrogen peroxide from the samples. METHODS: Surgihoney® (SH) was compared with two modified honeys, Prototype 1 (PT1) and Prototype 2 (PT2) using a bioassay method against a standard strain of Staphylococcus aureus. Further work studied the rate of generation of ROS hydrogen peroxide from these preparations. RESULTS: Surgihoney® antimicrobial activity was shown to be largely due to ROS hydrogen peroxide production. By modification of Surgihoney®, two more potent honey prototypes were shown to generate between a two- and three-fold greater antibacterial activity and up to ten times greater ROS peroxide activity. CONCLUSIONS: Surgihoney® is a clinically available wound antiseptic dressing that shows good antimicrobial activity. Two further honey prototypes have been shown to have antimicrobial activity that is possible to be enhanced due to demonstrated increases in ROS peroxide activity.

Propofol withdrawal seizures: non-epileptic nature of seizures in a patient with recently controlled status epilepticus.

Seizures or seizure-like phenomena which are mostly convulsive have been observed during the induction, maintenance and withdrawal phases of propofol administration. The nature and mechanism of this phenomenon are not well understood and several case reports on these phenomena have presented only indirect evidence. We report on a patient who was administered propofol in order to control status epilepticus with success. However, every attempt at propofol withdrawal was followed by convulsive seizure-like activity. Continuous EEG monitoring showed muscle artefacts without any ictal discharges. Based on this finding, the propofol treatment was withdrawn and the seizure-like activity eventually attenuated and resolved. We propose that seizure-like phenomena associated with propofol withdrawal may not be ictal in nature and should not lead to unnecessary resumption of propofol infusion without documentation of an epileptic origin by EEG.

Use of a spectrophotometric bioassay for determination of microbial sensitivity to manuka honey.

The antimicrobial activity of manuka honey has been well documented (Molan, 1992a,b,c, 1997) [Molan, P.C., 1992. The antibacterial activity of honey. 1: the nature of the antibacterial activity. Bee World 73 (1) 5-28; Molan, P.C., 1992. The antibacterial activity of honey. 2: variation in the potency of the antibacterial activity. Bee World 73 (2) 59-76; Molan, P.C., 1992. Medicinal uses for honey. Beekeepers Quarterly 26; Molan, P.C., 1997. Finding New Zealand honeys with outstanding antibacterial and antifungal activity. New Zealand Beekeeper 4 (10) 20-26]. The current bioassays for determining this antimicrobial effect employ a well diffusion (Ahn and Stiles, 1990) [Ahn, C., Stiles, M.E., 1990. Antibacterial activity of lactic acid bacteria isolated from vacuum-packed meats. Journal of Applied Bacteriology 69, 302-310], (Weston et al., 1999) [Weston, R.J., Mitchell, K.R., Allen, K.L., 1999. Antibacterial phenolic components of New Zealand manuka honey. J. Food Chem. 64, 295-301] or disc diffusion (Taormina et al., 2001) [Taormina, Peter J., Niemira, Brendan A., Beuchat, Larry R., 2001. Inhibitory activity of honey against food borne pathogens as influenced by the presence of hydrogen peroxide and level of antioxidant power. Int. J. Food Microbiol. 69, 217-225] assay using zones of inhibition as indicators of bacterial susceptibility. The development of a 24-h spectrophotometric assay employing 96-well microtiter plates, that is more sensitive and more amenable to statistical analysis than the assays currently employed, was undertaken. This simple and rapid assay permits extensive kinetic studies even in the presence of low honey concentrations, and is capable of detecting inhibitory levels below those recorded for well or disc diffusion assays. In this paper, we compare the assay to both well and disc diffusion assays. The results we obtained for the spectrophotometric method MIC values show that this method has greater sensitivity than the standard well and disc diffusion assays. In addition, inter- and intra-assay variance for this method was investigated, demonstrating the methods reproducibility and repeatability.