Risk-based Monitoring Research Articles

Prioritization of chemical food safety hazards in the European feed supply chain.

Extensive monitoring programs of chemical hazards in the animal feed chain are in place, both organized by public and private organizations. The objective of this review was to prioritize chemical hazards for monitoring in the European animal feed supply chain. A step-wise approach was designed for the prioritization, based on: historical occurrence of the chemicals in animal feed ingredients and animal feeds (in relation to European guidance values or maximum limits in feed); information on transfer of the chemical to edible animal products, and; the extent of human dietary intake of the products and possible adverse human health effects of the chemical. Possible prioritization outcomes were: high (H), medium (M), or low (L) priority for monitoring, or classification not possible (NC) because of limited available data on the transfer of the chemical to edible animal tissues. The selection of chemicals included (with results in parentheses): dioxins and polychlorinated biphenyls (H); brominated flame retardants (H); per- and polyfluorinated alkyl substances (H); the heavy metals arsenic (H) and cadmium (H) as well as lead (M) and mercury (M); aflatoxins (H), ochratoxin A (NC), and other mycotoxins (L); pyrrolizidine alkaloids (H) and other plant toxins (NC); organochlorine pesticides (H) and other pesticides (L); pharmaceutically active substances (M); hormones (NC); polycyclic aromatic hydrocarbons (L), heat-induced processing contaminants (NC), and mineral oils (NC). Results of this study can be used to support risk-based monitoring by food safety authorities and feed-producing companies in Europe.

Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring.

In alignment with the ICH guideline for Good Clinical Practice [ICH E6(R2)], quality tolerance limit (QTL) monitoring has become a standard component of risk-based monitoring of clinical trials by sponsor companies. Parameters that are candidates for QTL monitoring are critical to participant safety and quality of trial results. Breaching the QTL of a given parameter could indicate systematic issues with the trial that could impact participant safety or compromise the reliability of trial results. Methods for QTL monitoring should detect potential QTL breaches as early as possible while limiting the rate of false alarms. Early detection allows for the implementation of remedial actions that can prevent a QTL breach at the end of the trial. We demonstrate that statistically based methods that account for the expected value and variability of the data generating process outperform simple methods based on fixed thresholds with respect to important operating characteristics. We also propose a Bayesian method for QTL monitoring and an extension that allows for the incorporation of partial information, demonstrating its potential to outperform frequentist methods originating from the statistical process control literature.

Temperature-driven and discharge-driven variability of organic micropollutants in a large urban river and its implications for risk-based monitoring

Urban rivers are exposed to an increasing load of organic micropollutants from wastewater effluent posing an ecological as well as public health hazard. One-off surveys can capture a snapshot of the pollution profile but fail to reveal the full scale of spatial and temporal heterogeneity. In the present study, 41 micropollutants (non-steroid anti-inflammatory drugs (NSAID), antihypertensives, antiepileptic, antidiabetic, antibiotics, iodinated contrast media (ICM), corrosion inhibitors, pesticides) were monitored every two weeks for one-year upstream and downstream of the Budapest metropolitan area in Danube River (336 samples total). ICMs, benzotriazoles and metamizole degradation products were detected in highest concentration regularly exceeding 100 ng/L. Median concentration of other pharmaceuticals ranged from <1 to 26 ng/L, while pesticides were typically below 10 ng/L. Variability of micropollutant concentration was primarily temporal, exhibiting two different patterns: (1) inverse correlation to river discharge, observed for corrosion inhibitors and carbamazepine (r = −0.505 to −0.665) or (2) inverse correlation to water temperature, observed primarily for ICMs, antihypertensives and antibiotics, r = −0.654 to −0.904). Temperature dependence was also significant after correcting for river discharge. Relative increase of pharmaceuticals was 2–134% after the metropolitan area, partially explained by emission estimates calculated from retail data and metabolization rates.The concentration of five ICMs (iopamidol in 100, iodixanol in 96, diatrizoate in 22, iomeprol in 21 and iohexol 13% of the samples) and two NSAIDs (ibuprofen and diclofenac (in 31.5 and 23% of the samples) exceeded the predicted no environmental effect concentration, posing a risk to algae (HQ = 1.2–6) and fish (HQ = 1.4–1.9), respectively. Results suggest that risk-based monitoring and risk management efforts should focus on ICMs, NSAIDs and industrial chemicals, taking into account that sampling in cold periods and during low flow provides the worst-case estimates.

Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach

BackgroundOn-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate’s method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking.MethodsWe propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints.ResultsWe evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively.ConclusionGiven the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture

BackgroundIn recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC.MethodsR2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection.ResultsOf the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8–22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1–7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2–47.9) of uploaded photographs for R2BM were reduced.ConclusionsR2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.

Assessing the performance of methods for central statistical monitoring of a binary or continuous outcome in multi-center trials: A simulation study

BackgroundQuality study monitoring is fundamental to patient safety and data integrity. Regulators and industry consortia have increasingly advocated for risk-based monitoring (RBM) and central statistical monitoring (CSM) for more effective and efficient monitoring. Assessing which statistical methods underpin these approaches can best identify unusual data patterns in multi-center clinical trials that may be driven by potential systematic errors is important. MethodsWe assessed various CSM techniques, including cross-tests, fixed-effects, mixed-effects, and finite mixture models, across scenarios with different sample sizes, contamination rates, and overdispersion via simulation. Our evaluation utilized threshold-independent metrics such as the area under the curve (AUC) and average precision (AP), offering a fuller picture of CSM performance. ResultsAll CSM methods showed consistent characteristics across center sizes or overdispersion. The adaptive finite mixture model outperformed others in AUC and AP, especially at 30% contamination, upholding high specificity unless converging to a single-component model due to low contamination or deviation. The mixed-effects model performed well at lower contamination rates. However, it became conservative in specificity and exhibited declined performance for binary outcomes under high deviation. Cross-tests and fixed-effects methods underperformed, especially when deviation increased. ConclusionOur evaluation explored the merits and drawbacks of multiple CSM methods, and found that relying on sensitivity and specificity alone is likely insufficient to fully measure predictive performance. The finite mixture method demonstrated more consistent performance across scenarios by mitigating the influence of outliers. In practice, considering the study-specific costs of false positives/negatives with available resources for monitoring is important.

Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials

BackgroundRisk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need.MethodsA team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports.ResultsThe Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality.ConclusionsGood Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

Making a Case for Environmental Risk-Based Monitoring of Floating Solar Systems.

Making a Case for Environmental Risk-Based Monitoring of Floating Solar Systems.

A fast energized dispersive guided extraction of polycyclic aromatic hydrocarbons for risk-based seafood monitoring after an oil spill

After the 2019 oil spill disaster on the Brazilian coast, the demand for polycyclic aromatic hydrocarbons (PAH) analysis in seafood samples increased. The aim of this study was to develop an advanced method of energized dispersive guided extraction (EDGE) and quantification of PAH by liquid chromatography coupled to tandem mass spectrometry with atmospheric-pressure chemical ionization source (LC-APCI-MS/MS). The developed method was validated in terms of selectivity, working range (1.25 – 20.0 µg kg−1), matrix effect (relative errors > 20%, precision (CV < 30%), accuracy (77 – 128%), limits of detection (0.9 – 1.25 µg kg−1) and quantification (1.25 – 5.00 µg kg−1), and decision limit (2.9 – 7.8 µg kg−1). This method was applied to 238 samples to consolidate the first official Brazilian monitoring of PAH in seafood. PAH were quantified in 15% of samples, with concentrations up to 69.3 µg kg−1. The carcinogenic compound B[a]P was present in 4% of samples, with concentrations up to 42.7 µg kg−1. The health risk of seafood consumers was assessed using the margin of exposure (MOE) approach. Despite this, the risk assessment indicated that the disaster was of low concern to human health (MOE > 10.000). This new method increased laboratory throughput and was successfully applied to different types of samples, supporting monitoring of seafood after the disaster.

A code for clinical trials centralized monitoring, sharing open-science solutions to high-quality data.

Monitoring of clinical trials is critical to the protection of human subjects and the conduct of high-quality research. Even though the adoption of risk-based monitoring (RBM) has been suggested for many years, the RBM approach has been less widespread than expected. Centralized monitoring is one of the RMB pillars, together with remote-site monitoring visits, reduced Source Data Verification (SDV) and Source Document Reviews (SDR). The COVID-19 pandemic promoted disruptions in the conduction of clinical trials, as on-site monitoring visits were adjourned. In this context, the transition to RBM by all actors involved in clinical trials has been encouraged. In order to ensure the highest quality of data within a COVID-19 clinical trial, a centralized monitoring tool alongside Case Report Forms (CRFs) and synchronous automated routines were developed at the clinical research platform, Fiocruz, Brazilian Ministry of Health. This paper describes how these tools were developed, their features, advantages, and limitations. The software codes, and the CRFs are available at the Fiocruz Data Repository for Research-Arca Dados, reaffirming Fiocruz's commitment to Open Science practices.

Risk ranking of chemical hazards in food and feed – A case study in cereals in the Netherlands

Monitoring programs for food safety hazards in feed and food are increasingly performed on a risk-basis. Various methods are available to rank hazard-product combinations to be included in risk-based monitoring programs. Such methods have been developed for either feed or food, but until now these do not consider hazards in both feed and food simultaneously. Based on available time, budget and data, two methods, i.e. a scoring method and a risk ratio method, were selected that are capable of incorporating both food and feed products for ranking chemical hazards. These methods were compared by applying a case study on chemical hazards in cereals in the Netherlands for various animals and human age groups, using available data on concentrations, consumption available in national databases and toxicity. Results showed that both methods resulted in the highest ranking for the mycotoxins deoxynivalenol, aflatoxin B1 and zearalenone. Maize and wheat products were most frequently included in the top 50 ranked hazard-product combinations. Both methods showed to be capable of ranking hazard-product combinations for various animal and human groups. The risk ratio method provided a more objective and accurate outcome since it is based on actual data. Nevertheless, the risk scoring method may be preferred as it allows more flexibility in type of input data used. Also, it allows for the inclusion of country of origin of the materials used, which is relevant for imported products. This study showed that the risk ratio and the scoring method can successfully be used to rank both food and feed products simultaneously.

Complex diagnostic search in a patient with visceral leishmaniasis

Visceral leishmaniasis (VL) is one of the parasitic infections with the highest epidemic potential and mortality. The paper describes a clinical case of a 41-year old patient with VL with a long incubation period. The complexity of its diagnosis was the need for differential diagnosis with diseases accompanied by fever, hepatosplenomegaly, and pantocytopenia. Only the detection of the pathogen in the bone marrow punctate could make a diagnosis. Lack of systematic risk-based monitoring of exotic diseases, the low awareness of physicians and the population frequently complicate the timely diagnosis of leishmaniasis.

Adenosine triphosphate (ATP) sampling algorithm for monitoring the cleanliness of surgical instruments.

Timely detection of cleaning failure is critical for quality assurance within Sterilising Service Units (SSUs). Rapid Adenosine Triphosphate (ATP) testing provides a real time and quantitative indication of cellular contaminants, when used to measure surface or device cleanliness. The aim of this study was to investigate the use of an ATP algorithm and to whether it could be used as a routine quality assurance step, to monitor surgical instruments cleanliness in SSUs prior to sterilisation. Cleanliness monitoring using rapid ATP testing was undertaken in the SSUs of four hospitals located in the western (Amazonia) region of Brazil. ATP testing was conducted (Clean Trace, 3M) on 163 surgical instruments, following manual cleaning. A sampling algorithm using a duplicate swab approach was applied to indicate surgical instruments as (i) very clean, (ii) clean, (iii) equivocal or (iv) fail, based around a 'clean' cut-off of 250 Relative Light Units (RLU) and a 'very clean' <100 RLU. The four cleanliness categories were significantly differentiated (P≤0.001). The worst performing locations (hospitals A & C) had failure rates of 39.2% and 32.4%, respectively, and were distinctly different from hospitals B & D (P≤0.001). The best performing hospitals (B & D) had failure rates of 7.7% and 2.8%, respectively. The ATP testing algorithm provides a simple to use method within SSUs. The measurements are in real time, quantitative and useful for risk-based quality assurance monitoring, and the tool can be used for staff training. The four-tiered approach to the grading of surgical instrument cleanliness provides a nuanced approach for continuous quality improvement within SSU than does a simple pass/fail methodology.

A quality management system aiming to ensure regulatory-grade data quality in a glaucoma registry.

Disease/patient registries are underutilized despite their multiple advantages over clinical trials in the clinical evaluation of drugs, such as the capacity for long-term curation, provision of patient outcome data in routine clinical practice, and provision of benchmark data for comparison. Ensuring the fit-for-purpose quality of data generated from such registries is important to informing regulatory decision making. Here, we report the construction of a quality management system aiming to ensure regulatory-grade data quality for a registry of Japanese patients with glaucoma to evaluate long-term patient outcomes. The quality management system was established by reference to the risk-based approach in the ICH-E6 (R2) recommendations. The following three-component approach was taken: establishment of governance, computerized system validation (CSV), and implementation of risk assessment and control. Compliance of the system with the recommendations of regulatory guidelines relevant to use of the registry was assessed. Governance by academic collaboration was established. This was followed by the development of a total of 15 standard operating procedures, including CSV, data management, monitoring, audit, and management of imaging data. The data management system was constructed based on a data management plan, which specified data/paper flow and data management procedures. The electronic data capture (EDC) system was audited by an external vendor, and configured and validated using the V-model framework as recommended in the GAMP5 guideline. Informed consent, eligibility assessment and major ophthalmology measurements were determined as Critical to Quality (CTQ) factors. A total of 22 risk items were identified and classified into three categories, and operationalized in the form of a risk control plan, which included training sessions and risk-based monitoring. The glaucoma registry addressed most quality recommendations in official guidelines issued by multiple health authorities, although two recommendations were not met. We established and configured a quality management system for a glaucoma registry to ensure fit-for-purpose data quality for regulatory use, and to curate long-term follow-up data of glaucoma patients in a prospective manner.

Prioritizing veterinary drug residues in animal products for risk-based monitoring

The EU Official Controls Regulation (EU) 2017/625 (OCR) requires a risk-based monitoring program for veterinary drug residues in animal products. The aim of this research was to rank various substances in animal products as input for such a Multi-Annual National Control Plan (MANCP). Previously derived decision trees were used to prioritize a total of 438 substances and 5228 substance-product combinations. The prioritization incorporated information on non-compliances, use of veterinary drugs and potential human health effects. Overall, the majority of the unauthorised substances (63%) were classified as high priority, although there are distinct differences between substance groups. For the authorised substances, around 27% were classified as low priority, 17% as medium priority and 12% as high priority. For the remaining substances, there was a lack of data resulting in the recommendation to start a survey. The evaluation revealed that not all relevant substance-product combinations are currently included in the MANCP and data or information on (potential) use is often difficult to retrieve. Overall, the decision trees provided a successful tool to classify substances in low, medium and high priority to include in the MANCP and the approach could be applied by other EU MS as input to their risk-based monitoring programs.

Methodology for risk-based monitoring of contaminants in food – A case study in cereals and fish

In this study, a methodology was developed that can be used as input for risk-based monitoring plans for chemical contaminants in food products. The novel methodology was applied to a case study in which cereals and fish were evaluated simultaneously for the possible presence of mycotoxins and heavy metals. The methodology was based on hazard quotients that were estimated by dividing the daily intake - using concentrations of the contaminants in the different food products and consumption of the respective products combined per product group - by the health based guidance value (HBGV) or reference points used for assessing potential health concerns (RPHC). The most relevant hazard-product combinations were further ranked based on the volume of import of the ingredients, per import country and a defined contaminant prevalence level per country. For fish, the hazard quotients were around ten times lower compared to the highest hazard quotients in cereals. Consumption of molluscs, mackerel-type fish and herring-type fish contaminated with mercury contributed most to the HBGV or RPHC. The top 25 hazard-product combinations for various age groups included: aflatoxin B1 in combination with wheat, rice (products), maize (products), and pasta, zearalenone in combination with wheat (products), T2/HT2-toxin in combination with rice (products), and DON in combination with wheat (products). The methodology presented showed to be useful in identifying the most relevant hazard-food-age group combinations and the most relevant import countries linked to these that should be included in the monitoring. As such, the method can help risk managers in establishing risk-based monitoring programs.

Active Surveillance for Prostate Cancer: Past, Current, and Future Trends.

In response to the rising incidence of indolent, low-risk prostate cancer (PCa) due to increased prostate-specific antigen (PSA) screening in the 1990s, active surveillance (AS) emerged as a treatment modality to combat overtreatment by delaying or avoiding unnecessary definitive treatment and its associated morbidity. AS consists of regular monitoring of PSA levels, digital rectal exams, medical imaging, and prostate biopsies, so that definitive treatment is only offered when deemed necessary. This paper provides a narrative review of the evolution of AS since its inception and an overview of its current landscape and challenges. Although AS was initially only performed in a study setting, numerous studies have provided evidence for the safety and efficacy of AS which has led guidelines to recommend it as a treatment option for patients with low-risk PCa. For intermediate-risk disease, AS appears to be a viable option for those with favourable clinical characteristics. Over the years, the inclusion criteria, follow-up schedule and triggers for definitive treatment have evolved based on the results of various large AS cohorts. Given the burdensome nature of repeat biopsies, risk-based dynamic monitoring may further reduce overtreatment by avoiding repeat biopsies in selected patients.

Methodological aspects of risk-based monitoring of economic security of apartment complex management companies

Subject. The article considers the monitoring of economic security of apartment management companies. Objectives. The aim is to underpin methodological approaches to identifying risks and threats associated with the activities of management companies of apartment buildings that are significant for socio-economic development of municipalities and regions. Methods. The study employs general scientific methods of cognition. Results. We established that management companies do not use methods for assessing threats and risks to ensure economic security. The study of the economic security of management companies demonstrated their clear impact on the economy of the region and the well-being of some industries related to water, electricity and heat supply of apartment buildings, as well as on socio-economic indicators of regional (municipal) development. In connection with these circumstances, in the development of the methodology, we considered areas of risk identification and then, on their basis, threats. The developed methodology can be used for external monitoring by industry regulatory authorities to minimize threats to economic security at the regional level, and to support the management companies themselves. Conclusions. To assess the economic stability of the housing and communal services industry, it is important to investigate the activities of management companies. The offered methodology for identifying threats to management companies to monitor their economic security enables to identify risks and threats to the economic condition of management companies, which is necessary to ensure economic security.