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Medicines use before and after comprehensive medicines review among residents of long-term care facilities: a retrospective cohort study
Janet K. Sluggett1,2,3,
Gillian E. Caughey1,2,
Tracy Air2,
Max Moldovan2,4,
Catherine Lang2,
Grant Martin5,
Stephen R. Carter6,
Shane Jackson7,
Andrew C. Stafford8,
Steve L. Wesselingh2 &
Maria C. Inacio1,2
Residential Medication Management Review (RMMR) is a subsidized comprehensive medicines review program for individuals in Australian residential aged care facilities (RACFs). This study examined weekly trends in medicines use in the four months before and after an RMMR and among a comparison group of residents who did not receive an RMMR.
This retrospective cohort study included individuals aged 65 to 105 years who first entered permanent care between 1/1/2012 and 31/12/2016 in South Australia, Victoria, or New South Wales, and were taking at least one medicine. Individuals with an RMMR within 12 months of RACF entry were classified into one of three groups: (i) RMMR within 0 to 3 months, (ii) 3 to 6 months, or (iii) within 6 to 12 months of RACF entry. Individuals without RMMRs were included in the comparison group. Weekly trends in the number of defined daily doses per 1000 days were determined in the four months before and after the RMMR (or assigned index date in the comparison group) for 14 medicine classes.
113909 individuals from 1979 RACFs were included, of whom 55021 received an RMMR. Across all three periods examined, decreased use of statins and proton pump inhibitors was observed post-RMMR in comparison to those without RMMRs. Decreases in calcium channel blockers, benzodiazepines/zopiclone, and antidepressants were observed following RMMR provision in the 3–6 and 6–12 months after RACF entry. Negligible changes in antipsychotic use were also observed following an RMMR in the 6–12 months after RACF entry by comparison to those without RMMRs. No changes in use of opioids, ACE inhibitors/sartans, beta blockers, loop diuretics, oral anticoagulants, or medicines for osteoporosis, diabetes or the cognitive symptoms of dementia were observed post-RMMR.
For six of the 14 medicine classes investigated, modest changes in weekly trends in use were observed after the provision of an RMMR in the 6–12 months after RACF entry compared to those without RMMRs. Findings suggest that activities such as medicines reconciliation may be prioritized when an RMMR is provided on RACF entry, with deprescribing more likely after an RMMR the longer a resident has been in the RACF.
Older people living in residential aged care facilities (RACFs) are frequently exposed to high-risk and potentially inappropriate medicines. Between 19 and 22% of residents are exposed to an antipsychotic, 22–52% receive an opioid, 3–58% of residents with diabetes receive insulin and 36–58% of residents with atrial fibrillation receive an anticoagulant, with half of all residents of RACFs exposed to at least one potentially inappropriate medicine [1,2,3,4,5]. While appropriate for some individuals, these medicines are often associated with harms such as drowsiness, confusion, falls and hospitalization, and may necessitate strategies to optimize care such as close monitoring, deprescribing and/or substitution with non-pharmacological therapies [6].
Initiatives to support safe and efficacious medicines use in RACFs include clinical governance structures, standardized medicines administration charts, clinical guidelines, decision support systems, quality indicators, education, case conferencing, and comprehensive medicines reviews [1, 7]. In Australia, the government-subsidized Residential Medication Management Review (RMMR) comprehensive medicines review program has operated since 1997 [8, 9]. In 2019, there were 95491 RMMR claims submitted by pharmacists and the Australian Government spent AUD 18.75 million on pharmacist and general medical practitioner (GP) remuneration for RMMR services [10, 11]. RMMRs involve collaboration between a resident's usual GP and a pharmacist who has successfully completed a two-stage accreditation process to provide RMMR services. National guidance is available regarding the types of activities that can be provided during an RMMR [9], although accredited pharmacists are encouraged to tailor RMMR activities to the reason for referral and the resident's care goals. In general, after receiving a GP referral, the pharmacist visits the RACF to review clinical documentation and participate in discussions with the resident, family and/or RACF staff to obtain a best possible medicines history, identify medicines related problems and provide education. RMMR guidelines and medication management guidelines for Australian RACFs both note the importance of medicines reconciliation and review following care transitions such as RACF entry or on return from hospital [9, 12]. An average of three to four medicines related problems (e.g., undertreated conditions, problems with medicine selection such as duplications or drug interactions, adverse drug reactions, need for additional monitoring) are identified per RMMR [13]. The pharmacist provides a report to the GP outlining suggestions to address the medicines-related problems, which the GP reviews and uses to develop and implement a medicines management plan for the resident.
RMMRs are generally recommended when a resident first enters an RACF and when an individual's clinical circumstances change. The need for broad and consistent implementation of RMMRs was identified during the recent Royal Commission into Aged Care Quality and Safety and as part of Australia's response to the World Health Organization's (WHO) Third Global Patient Safety Challenge, Medication without harm, which aims to reduce severe, avoidable medicines-related harm by 50% globally over a five-year period [14, 15]. International consensus guidelines also recommend regular medicines reconciliation and review for frail older people to optimize medicines management [16].
Provision of comprehensive medicines reviews in RACFs has been shown to improve appropriateness of medicines use [13, 17]. However, few studies have examined changes in the use of specific medicines following an RMMR. A cluster randomized controlled trial published in 2001 that involved 52 Australian RACFs and tested a multifaceted intervention comprising stakeholder engagement, education and medicines reviews reported reductions in the use of laxatives, medicines for reflux, non-steroidal anti-inflammatory drugs, and benzodiazepines in the intervention arm during follow-up [18]. However, this trial was undertaken prior to the implementation to the current RMMR program and utilized a different model than what is currently in practice. More recently, two small studies reported modest reductions in exposure to anticholinergic and/or sedative medicines following an RMMR [19, 20]. A recent Australian study reported no change in benzodiazepine use in the six months following provision of an RMMR or Home Medicines Review (HMR), a related service for community-dwelling people, but lacked data on access to aged care service use and did not stratify findings by type of medicines review [21]. Despite the 25-year history of the RMMR program in Australia, population-based changes in medicines use following an RMMR have not been examined. Hence, this study examined weekly trends in medicines use in the four months before and after an RMMR in RACFs and among a comparison group of individuals in the RACF who never received an RMMR.
Study design and data sources
A retrospective cohort study was conducted using data from the National Historical cohort of the Registry of Senior Australians (ROSA) [22, 23]. ROSA contains deidentified demographic, clinical, aged care, health service and medicines utilization information for all people aged ≥ 65 years who access government-subsidized residential aged care services. ROSA includes linked aged care data, including data collected during aged care eligibility assessments [24], entry into residential care assessments [25] and aged care service records, together with data from the Australian Institute of Health and Welfare National Death Index. ROSA also includes administrative records providing information about medicines dispensed via the Pharmaceutical Benefits Scheme (PBS) (Australia's national medicines subsidy scheme) which subsidizes the cost of many approved medicines in Australia, with 906 different medicines (5,380 brand names) listed on the PBS as of 30th June 2021 [26]. ROSA also includes information about GP and other health services accessed through Australia's Medicare Benefits Schedule (MBS), and hospitalization records from four Australian states' health authorities. Claims for medicines are coded according to the WHO Anatomical Therapeutic Chemical classification system [27] and PBS item codes [26]. MBS claims are coded according to MBS item codes [28]. The Australian Institute of Health and Welfare and the University of South Australia Human Research Ethics Committees approved a waiver of written informed consent for individuals whose data is included in the ROSA National Historical Cohort and utilized in this study as described in the 'Ethics approval and consent to participate' section below.
Study cohort
Individuals aged between 65 and 105 years with an aged care eligibility assessment who entered an RACF for the first time between 1st January 2012 and 31 December 2016 in three Australian states (South Australia (SA), Victoria (VIC) or New South Wales (NSW)) and were dispensed at least one government-subsidized (i.e., PBS) medicine in the six months before entry were eligible for inclusion (n = 185101, Fig. 1). The date of RACF entry was the date of entry into permanent residential aged care and did not include time spent in temporary respite care because RMMRs were not subsidized for respite care during the period of interest.
Flow chart describing the study cohort selection. DVA Department of Veterans' Affairs, PRAC permanent residential aged care, RACF residential aged care facility, RMMR residential medication management review
The following were excluded: individuals identifying as Aboriginal and/or Torres Strait Islander (due to lack of current ethical approvals), individuals receiving healthcare services subsidized by the Australian Government Department of Veterans' Affairs, individuals who did not receive an entry into care assessment within 100 days of RACF entry, individuals missing data on RACF remoteness or dementia status (because this was utilized for index date assignment as described below), and those with an RMMR in the year prior to RACF entry (as RMMRs can be provided in certain circumstances such as during transition care) (n = 38528, Fig. 1). This left n = 146573 individuals eligible for index date assignment, of whom n = 113090 met the final inclusion criteria.
Exposure of interest
The exposure of interest was the first GP MBS claim for a comprehensive medicines review (RMMR MBS item code 903, HMR MBS item code 900) within 12 months of RACF entry. Individuals who received their first RMMR more than 12 months after entry to RACF were not eligible for inclusion. Previous research has shown that the majority of residents who receive an RMMR are provided the service within the first 12 months of RACF entry [28]. Claims for HMRs after RACF entry were also considered to represent RMMRs due to similarities in names and MBS item codes, comprising 3.5% of all comprehensive medicines review claims after RACF entry in previous studies [23, 29].
Index date assignment for exposed (RMMR) and unexposed (no RMMR)
For residents with an RMMR (exposed group), the index date for the analysis was the date of RMMR provision. These individuals were then classified into three groups: (i) RMMR within 0 to 3 months (1–90 days), (ii) RMMR within 3 to 6 months (91–182 days), or (iii) RMMR within 6 to 12 months (183–365 days) of RACF entry (Supplementary Fig. 1). While RMMRs are recommended at RACF entry, few residents receive an RMMR at this time and considerable changes in medicines use occur around the time of RACF entry [3, 23, 29, 30]. Therefore, examining a wider time-period enabled the identification of individuals receiving an RMMR after the initial transition to an RACF who may be experiencing less changes in medicines and healthcare use.
The index date for individuals without RMMRs was determined by matching to individuals with RMMRs based on five criteria: age at RACF entry (categorized as 65–74, 75–84, 85–94, ≥ 95 years), sex (male, female), diagnosis of dementia (yes, no), remoteness of residence (major city, other), and number of unique prescriptions dispensed in the year prior to RACF entry (categorized as 1–5, 6–10, 11–15, 16–20, ≥ 21 prescriptions dispensed) (i.e., matching into one of 160 unique subgroups). The median time from RACF entry to the first RMMR for each subgroup was assigned as the corresponding index date for unexposed individuals in the same subgroups. The matching described above was only used to assign the index date in the unexposed group and was not used for initial cohort selection or any additional analyses.
Individuals who did not receive an RMMR during their RACF stay (unexposed group) were included in the comparison group provided they were alive at the index date. Unexposed individuals were eligible for inclusion in more than one analysis period, with a different index date for each period if they remained eligible for inclusion.
Follow-up period
The index date was day 1 of follow-up and individuals were followed for up to 119 days (17 weeks) (i.e., 4 months). Individuals in the RMMR group who received a subsequent RMMR during follow-up were excluded (Fig. 1). All individuals needed to complete a minimum of 4 weeks follow-up. Thereafter, residents that died or left the RACF for another reason were censored (i.e., removed from the analysis) and thus contributed data up until the date of RACF departure.
Outcomes of interest
The outcome of interest was medicines use in the four months (17 weeks) before and four months after the index date, for 14 medicine classes (Supplementary Table 1). The medicines of interest were selected a priori based on the most common utilized medicine classes and health conditions among residents included in previous Australian RACF studies [22, 31, 32] and the availability of PBS subsidies. Psychotropic medicines were included as per concerns raised by the recent Royal Commission into Aged Care Quality and Safety [15]. The outcome was quantified by the number of defined daily doses (DDDs) of the 14 specific medicine classes per 1000 resident-days per week. The DDD for a medicine reflects the average maintenance dose when the medicine is used for its main indication in an adult [27].
To determine weekly medicines exposure, estimated prescription durations were applied to medicines claims data in the year prior to the index date and for 17 weeks (119 days) after the index date (Supplementary Fig. 2). The waiting time distribution [33] for each medicine was used to estimate the duration of use for each prescription dispensed as dosing information is not recorded in PBS claims data. Individuals were considered as taking the medicine from the date of dispensing plus the length of the prescription duration estimate. Each time a medicine of interest was dispensed, the number of DDDs available for use each day was calculated by dividing the total number of DDDs dispensed by the prescription duration. The 17-week period was selected to reflect changes around the time of RMMR provision and was informed by the usual prescription duration for the medicines examined. Many prescription durations were < 17 weeks, meaning the majority of medicines dispensed immediately prior to the RMMR would be considered depleted prior to the end of the 17-week follow-up period.
Descriptive statistics were used to describe the study cohort stratified by the index date relative to RACF entry (i.e., 0–3 months, 3–6 months, and 6–12 months after RACF entry).
Medicines use in the 17 weeks before and after the index date was plotted graphically for exposed and unexposed individuals for each of the index date groups. Significant variability in medicines use during the transition into an RACF was observed for individuals with an index date in the 0–3 months or 3–6 months after RACF entry; consequently, only individuals with an index date in the 6–12 months after RACF entry were modelled further.
For those with an index date during the 6–12 months after RACF entry, controlled interrupted time series (CITS) analyses were performed using segmented regression modelling. The segmented regression examined three periods: i) the 17-week pre-intervention period (17 time-points before the index date), ii) the washout period (a 5-week period commencing at the index date; 5 time-points), and iii) the follow-up period (from weeks 6 to 17 after the index date; 12 time-points) (Supplementary Fig. 3). The 5-week washout period was selected based on the prescription duration for a medicine with a standard 28- or 30-day pack size, to allow time for a supply of a medicine dispensed immediately before the index date to end prior to follow-up. The segmented regression models compared the baseline level of medicine use at the start of the pre-intervention period, step changes (i.e., changes in level of medicine use at the start of the washout and follow-up periods) and ramp changes (i.e., gradual slope changes during the pre-intervention, washout, and follow-up periods) among those who did and did not receive an RMMR to estimate both immediate and sustained changes in medicines use over time. Complete case analysis was conducted and the statistical significance level was set at 5%.
Segmented regression models were undertaken using Stata itsa module [34, 35] that estimated coefficients using ordinary least squares regression with Newey-West standard errors (Stata v16.0; StataCorp, College Station, Texas). Cumby-Huizinga and Arellano-Bond tests were applied to identify autocorrelation and/or seasonality, with lag terms applied to regression models where necessary. All other analyses were undertaken using R statistical package v3.6.0 (R Foundation for Statistical Computing, Vienna, Austria).
Supplementary analyses
Segmented regression modelling was also used to examine weekly trends in prevalence of use (i.e., the percentage of individuals receiving at least one day supply of the medicine in a week) before and after the index date among residents with an index date in the 6–12 months post-RACF entry.
There were 113909 residents from 1979 RACFs included in this study, of whom 23979 (21.1%) had an RMMR within 0–3 months after RACF entry, 19080 (16.8%) received an RMMR within 3–6 months and 11962 (10.5%) received an RMMR within 6–12 months of RACF entry (Fig. 1). A total of 58888 unexposed individuals were included (51.7% of the total cohort), of whom 55609, 49468 and 42573 individuals were assigned index dates within 0–3, 3–6 and 6–12 months after RACF entry, respectively, as eligible unexposed individuals could be assigned different index dates for each analysis period.
Overall, at RACF entry the cohort median age was 85 (interquartile range (IQR) 80–89) years, 64.1% were women, and 49.9% were living with dementia. Residents were dispensed a median of 11 (IQR 7–15) unique prescriptions for PBS-listed medicines in the year prior to RACF entry and the median comorbidity score was 5 (IQR 3–7). Resident characteristics stratified by the timing of the index date relative to RACF entry are presented in Table 1. Individuals who received an RMMR in the 0–3 months after RACF entry were more likely to reside in a major city than those who did not receive an RMMR, but geographical differences between those with and without RMMRs in the 3–6 and 6–12 months after RACF entry were minimal. Minor differences in care needs with regards to activities of daily living, behavioral daily living and complex health care were observed. Characteristics of exposed and unexposed individuals were otherwise similar regardless of the timing of the index date relative to RACF entry.
Table 1 Characteristics of the study cohort, stratified by the index date relative to RACF entry
Trends in medicines use among individuals with index dates in the 0–3 or 3–6 months after RACF entry
Considerable weekly variation in DDDs/1000 resident-days prior to the index date was observed for those with and without an RMMR for most medicines investigated (examples shown in Supplementary Figs. 4 and 5). After the index date, small decreases in statin and proton pump inhibitor (PPI) DDDs/1000 resident-days were observed among RMMR recipients compared to individuals without an RMMR in the 0–3 months after RACF entry, with fewer DDDs/1000 resident-days in the RMMR group in the last week of follow-up (statins: 457 versus 482 DDDs/1000 resident-days; PPIs: 449 versus 467 DDDs/1000 resident-days) (Supplementary Table 2, Supplementary Fig. 4). Small decreases in DDDs/1000 resident-days were also observed after an RMMR compared to those without an RMMR in the 3–6 months after RACF entry for the following medicines: stains, PPIs, benzodiazepines/zopiclone, calcium channel blockers (CCBs), and antidepressants (Supplementary Table 2, Supplementary Fig. 5).
Trends in medicines use among individuals with an index date in the 6–12 months after RACF entry
Table 2 summarizes the number of DDDs of each medicine examined per 1000 resident-days during the observation period for individuals with an index date in the 6–12 months after RACF entry and the output from the segmented regression models. Table 3 shows the example of statin use, where significant reductions in use were observed in the RMMR group after the index date. Compared to those without an RMMR, statin use declined at a faster rate in the RMMR group during the washout period (p < 0.001) and at the start of the follow-up period (p < 0.001) (Table 3, Fig. 2). Similar decreases in DDDs/1000 resident-days for CCBs and PPIs were also observed (Table 2, Supplementary Fig. 6). In addition, antidepressant use plateaued in the RMMR group after the index date but continued to slowly increase in those without an RMMR (p < 0.001). Small changes in benzodiazepine and antipsychotic use also occurred post-RMMR (0.4 DDDs/1000 resident-days/week). For all other medicines, there were either no significant differences in trends for those who did and did not receive an RMMR, or outlier values at the start or end of follow-up impacted the trends observed (Table 2).
Table 2 Weekly number of defined daily doses of medicines per 1000 resident-days during the study period and summary of trends in individuals who did and did not receive an RMMR in the 6–12 months after RACF entry
Table 3 Segmented regression output showing impact of RMMR on weekly statin DDDs per 1000 resident-days during pre-intervention, washout, and follow-up periods for individuals with an index date in the 6–12 months after RACF entry
Trends in weekly number of defined daily doses of per 1000 resident-days for statins among individuals with and without an RMMR in the 6–12 months after RACF entry
Supplementary analysis
Supplementary Table 3 summarizes prevalence of use of each medicine class for individuals with an index date in the 6–12 months after RACF entry. Decreases in prevalence of use of statins, benzodiazepines/zopiclone, CCBs, PPIs and antipsychotics were observed post-RMMR in comparison to those without an RMMR (Supplementary Fig. 7). The prevalence of oral anticoagulant use decreased at a slower rate in the washout and follow-up periods among those with an RMMR compared to those who did not receive an RMMR.
This study of 113909 residents from 1979 facilities, capturing 67% of all older Australians with a new RACF admission nationally over a four-year period [38], is the first examination of population-based trends in medicines use following provision of an RMMR in the 25-year history of Australia's RMMR program. Decreased use of statins and PPIs were observed across all three time periods examined, and small decreases in CCBs, benzodiazepines/zopiclone, and antidepressants post-RMMR were observed following provision of an RMMR in the 3–6 and 6–12 months after RACF entry by comparison to those without RMMRs. These changes, although modest, may translate to meaningful differences at the population level due to high exposure to these medicines in RACFs. For example, extrapolation of the prevalence estimates for statins among individuals with an index date in the 6–12 months after RACF entry to all 60,723 new entrants to Australian RACFs between July 2019 and June 2020 [38] suggests if all individuals received an RMMR, an estimated 486 fewer residents would be taking a statin four months later, provided all were alive at follow-up. This finding likely reflects an increasing focus on statin discontinuation among individuals with limited life expectancy or severe cognitive or functional decline during the analysis period [39].
Although RMMRs are generally recommended for all individuals on RACF entry and when clinical circumstances change [9, 23], changes in use of a greater number of medicines were observed among individuals receiving an RMMR in the 6 to 12 months after entering an RACF. This is despite similarities in both resident characteristics and DDDs/1000 resident-days for individual medicine classes across all three periods. It may be that RMMRs conducted on RACF entry have a different focus to those conducted when an individual is more settled in the RACF. Numerous activities undertaken during RMMRs could enhance medicines safety and effectiveness or resident quality of life without directly impacting trends in medicines use, including: medicines reconciliation, assessment of an individual's ability to self-administer medicines, medicines simplification, identifying non-pharmacological approaches, screening for adverse drug events, recommending pathology tests and health professional referrals, and providing education [9, 13]. Pharmacists likely spend more time on activities such as medicines reconciliation for new residents due to known problems with information transfer during care transitions and because there are often changes to resident's usual GP on RACF entry [40,41,42]. For long-term residents with an established history of medicines use, there may be greater opportunity to focus on other areas such as deprescribing. Additionally, GPs may be hesitant to deprescribe psychotropics immediately on RACF entry for residents whose admissions are preceded by worsening behavioral and psychological symptoms of dementia and may prefer to delay deprescribing attempts until a resident is settled in the RACF. Our findings suggest further research into optimal timepoints for delivering services to enhance medicines use and resident health outcomes could be beneficial, particularly given recent changes to the RMMR program that enable pharmacists to provide up to two follow-up visits and the increasing interest in integrating pharmacists within RACFs to deliver clinical services [43]. Future directions also include the need to characterize pharmacist activities and associated quality and safety measures across a resident's entire RACF journey to ensure medicines management needs are met and identify areas for improvement.
Another factor impacting the opportunity to observe changes in medicines use post-RMMR was the considerable variability in use before the index date for residents with index dates 0–3 and 3–6 months post-RACF entry. Increases in both prevalence of use and frequency of dispensing due to supply arrangements at hospital discharge and RACF entry likely contributed to this variability. Recent studies report increased use of psychotropics, laxatives and medicines for dementia in the year before and on entry to an RACF [3, 30, 44]. Medicines are often re-dispensed at RACF entry and packed into dose administration aids, with any existing supplies discarded, possibly resulting in an increased number of DDDs available for use. However, trends in medicines use over the four months before the index date had generally stabilized for individuals with an index date 6–12 months post-RACF entry, which may have made it easier to observe medicines changes in this cohort. Hence, careful selection of study cohorts and methodological approaches for studies exploring the impact of interventions delivered in RACFs to optimize medicines use after RACF entry are needed.
The modest reductions in weekly trends in statin, PPI, and CCB use in the four months after an RMMR suggest dose reductions, switches to pro re nata (when required) use (for PPIs) and/or deprescribing occurs more frequently after an RMMR than initiation or dose increases. This is in line with existing guidance that suggests statins, PPIs and some antihypertensives could be targeted for deprescribing in RACFs in certain circumstances [45,46,47,48]. Although we were unable to ascertain the appropriateness of medicines use in this study, population-based reductions in the use of these medicines could have important implications for both patients and healthcare funders. This includes potential reductions in the cost of medicines and unnecessary harm due to adverse drug events (e.g., Clostridioides difficile infection associated with PPI use).
However, during the period of this analysis, RMMRs had minimal impact on psychotropic medicines use at the population-level. This is despite long-standing concerns regarding an overreliance on antipsychotics and benzodiazepines in RACFs in Australia and internationally [1]. This concern has heightened since the analysis period, particularly during the recent Royal Commission into Aged Care Quality and Safety in Australia, and future research should analyze the effect of RMMRs on psychotropic medicines in the period after the Commissioner's interim recommendations [15]. Several factors may have contributed to an absence of population-based changes in other medicines post-RMMR compared to those without RMMRs, including known barriers to deprescribing [49] and RMMR delivery [23, 50, 51] in RACFs. Our study examined trends medicines use in the four months post-RMMR however it is possible that the deprescribing may be implemented over a longer period as some medicines may need to be discontinued slowly, and generally only one to three medicines can be deprescribed concurrently [48]. The impact of an RMMR and any resulting population-level changes in medicines use may also be impacted by the quality and type of recommendations made by pharmacists during an RMMR, GP attitude, beliefs, and experiences with deprescribing, opportunities for shared decision-making, interprofessional relationships and communication, and varying rates of GP implementation of recommendations. The top three recommendations made by pharmacists during RMMRs comprise identifying the need for clinical or laboratory monitoring (27% of recommendations), dose/schedule changes (21.4%) and ceasing or withdrawing a medicine (16%) [13]. Although 45–84% of pharmacists' RMMR recommendations are accepted by GPs [13], uptake varies by recommendation type. GPs accept two thirds (64%) of recommendations focusing on medicines cessation, while 85–98% of recommendations not directly involving changes to medicines (i.e., referrals to other health professionals, pathology testing or education) are accepted by GPs [13]. Together, these findings indicate RMMR program changes and other system changes are needed to address known barriers, enhance service delivery, support interprofessional collaboration and incorporate new models of care [52] to support deprescribing in RACFs.
Strengths and limitations
This study utilized a population-based approach to examine short-term trends in medicines use after an RMMR. Study strengths include the use of Australia's largest and most comprehensive linked health dataset of older people accessing RACF care to examine trends in medicines that are commonly utilized in RACFs. Use of CITS rather than single group interrupted time series enabled comparison of trends among those with and without an RMMR in the 6–12 months after RACF entry [53, 54]. Examination of medicines use over four-months either side of the index date limited the risk of time-varying confounding due to other interventions during the study period or changes in an individual's clinical circumstances [54]. DDDs/1000 days is a standardized, widely used measure of drug utilization although doses prescribed to older residents can be different to the DDD for medicines such as opioids [55] and antipsychotics. Importantly, supplementary analyses examining trends in prevalence of use showed patterns consistent with the main analysis.
Our analysis used GP MBS claims to determine RMMR provision. Pharmacists claim for RMMRs via a different mechanism that is not captured in our dataset. Hence, study limitations include the possible misclassification of RMMR exposure as fewer claims for RMMRs are submitted by GPs compared to pharmacists [23, 29]. However, the impact of any misclassification would be towards the null (i.e., no difference between trends, thereby underestimating the impact of an RMMR on medicines use). This may in part explain why changes in medicines use were sometimes observed after the index date for those classified as not receiving an RMMR. Other limitations are that we could not examine specific recommendations made during the RMMR or appropriateness of medicines use. Linkage between ROSA and the pharmacist RMMR claims dataset could help to address some of these knowledge gaps in the future.
Other limitations were that we could not ascertain use of non-government subsidized medicines, although any resulting impact is likely to be minor as most medicines are PBS-listed and 97% of residents of RACFs hold a government-issued concession card that entitles them to subsidized medicines at the lowest copayment level (AUD $6.80 in 2022) [56]. Overall, the medicines examined in this study comprised 502901 of the 856492 (58.7%) prescriptions dispensed over a 12-month period for all 11792 individuals with an index date in the final year of our study (2016). Hence, while we examined the most utilized medicines, including four classes of high-risk medicines prioritized for national action to reduce severe avoidable medicines-related harm [14], we did not examine use of medicines such as urinary anticholinergics, digoxin, or medicines for Parkinson's disease. Medicines for residents are often provided in dose administration aids which may impact dispensing patterns. Additionally, medicines use may have been overestimated as not all medicines dispensed are necessarily administered, with impact greatest for medicines with long prescription durations. For example, insulin glargine has a prescription duration of 210 days (30 weeks), which means individuals with a dispensing in the 13 weeks prior to the index date would be considered as taking the medicine for the remainder of the observation period. We examined changes in specific medicines at the population level but did not examine switching between classes or changes in overall exposure to potentially inappropriate medicines or those contributing to anticholinergic and/or sedative burden. Recent changes to RMMR program rules in early 2020 that enable up to two pharmacist follow-up visits and temporary delivery via telehealth [51] require further evaluation.
This population-based examination of medicines use found modest changes in use of statins, PPIs, CCBs, antidepressants, and small changes in use of benzodiazepines/zopiclone and antipsychotics following provision of an RMMR in the 6–12 months after RACF entry compared to those who did not receive an RMMR. No population-based changes were observed after an RMMR for the eight other medicine classes examined despite high rates of exposure to these medicines in RACFs. Our findings have important implications for care recipients, providers, and policy makers. Although RMMRs are recommended for all residents on entry to an RACF, medicines use is highly variable during this period. Our findings suggest that activities such as medicines reconciliation may be prioritized when an RMMR is provided on RACF entry, with deprescribing more likely to occur after an RMMR the longer a resident has been in the RACF. Although deprescribing is not the primary goal of an RMMR, the modest changes in psychotropic medicines use when an RMMR is provided in the 6–12 months after RACF entry suggest enhancements to the Australian medicines review program in RACFs and other systems changes are required to effectively address the high rates of psychotropic medicines use in RACFs. Determining associations between RMMR provision and health outcomes such as hospitalization remains an important future research direction.
The data for this study were obtained from the Australian Institute of Health and Welfare and Australian Government Department of Health. These data were made available to the researchers under ethical, governance, and confidentiality agreements that do not allow public sharing. JKS can be contacted to discuss data and materials utilized for this study.
AACP:
Australian Association of Consultant Pharmacy
ATC:
Anatomical Therapeutic Chemical
CITS:
Controlled interrupted time series
DDD:
Defined daily dose
General medical practitioner
HMR:
Home Medicines Review
IQR:
MBS:
Medicare Benefits Schedule
NHMRC:
National Health and Medical Research Council
NSW:
PBS:
Pharmaceutical Benefits Scheme
PPI:
Proton pump inhibitor
RACF:
Residential aged care facility
ROSA:
Registry of Senior Australians
RMMR:
Residential Medication Management Review
VIC:
Sluggett JK, Harrison SL, Ritchie L, Clough AJ, Rigby D, Caughey GE, Tan EC. High-risk medication use in older residents of long-term care facilities: prevalence, harms and strategies to mitigate risks and enhance use. Sr Care Pharm. 2020;35(10):419–33. https://doi.org/10.4140/TCP.n.2020.419.
Van Brunt K, Curtis B, Brooks K, Heinloth A, de Cassia CR. Insulin use in long term care settings for patients with type 2 diabetes mellitus: a systematic review of the literature. J Am Med Dir Assoc. 2013;14(11):809–16. https://doi.org/10.1016/j.jamda.2013.08.018.
Harrison SL, Sluggett JK, Lang C, Whitehead C, Crotty M, Corlis M, Wesslingh S, Inacio MC. The dispensing of psychotropic medicines to older people before and after they enter residential aged care. Med J Aust. 2020;212(7):309–13. https://doi.org/10.5694/mja2.50501.
Campitelli MA, Bronskill SE, Huang A, Maclagan LC, Atzema CL, Hogan DB, Lapane KL, Harris DA, Maxwell CJ. Trends in anticoagulant use at nursing home admission and variation by frailty and chronic kidney disease among older adults with atrial fibrillation. Drugs Aging. 2021;38(7):611–23. https://doi.org/10.1007/s40266-021-00859-1.
Morin L, Laroche ML, Texier G, Johnell K. Prevalence of potentially inappropriate medication use in older adults living in nursing homes: a systematic review. J Am Med Dir Assoc. 2016;17(9):862.e861–869. https://doi.org/10.1016/j.jamda.2016.06.011.
Steinman MA, Fick DM. Using Wisely: A reminder on the proper use of the American Geriatrics Society Beers Criteria®. J Am Geriatr Soc. 2019;67(4):644–6. https://doi.org/10.1111/jgs.15766.
Alldred DP, Kennedy MC, Hughes C, Chen TF, Miller P. Interventions to optimise prescribing for older people in care homes. Cochrane Database Syst Rev. 2016;2(2):CD009095. https://doi.org/10.1002/14651858.CD009095.pub3.
Chen TF. Pharmacist-led Home Medicines Review and Residential Medication Management Review: the Australian model. Drugs Aging. 2016;33(3):199–204. https://doi.org/10.1007/s40266-016-0357-2.
Pharmaceutical Society of Australia. Guidelines for comprehensive medication management reviews. Canberra: PSA; 2020.
Medicare Item Reports [updated 18 December 2020]. Australian Government Services Australia. 2020. http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp. Accessed 20 Oct 2021.
Pharmacy Programs Data. Australian Government Department of Health. 2021. https://www1.health.gov.au/internet/main/publishing.nsf/Content/pharmacy-programs-data. Accessed 20 Oct 2021.
Australian Government Department of Health. Guiding principles for medication management in residential aged care facilities. Canberra: Commonwealth of Australia; 2012.
Chen EYH, Wang KN, Sluggett JK, Ilomäki J, Hilmer SN, Corlis M, Bell JS. Process, impact and outcomes of medication review in Australian residential aged care facilities: a systematic review. Australas J Ageing. 2019;38(Suppl. 2):9–25. https://doi.org/10.1111/ajag.12676.
Australian Commission on Safety and Quality in Health Care. Medication without harm - WHO Global Patient Safety Challenge. Australia's response. Sydney: ACSQHC; 2020.
Royal Commission into Aged Care Quality and Safety. Interim report: Neglect, vol. 1. Canberra: Commonwealth of Australia; 2019.
Liau SJ, Lalic S, Sluggett JK, Cesari M, Onder G, Vetrano DL, Morin L, Hartikainen S, Hamina A, Johnell K, et al. Medication management in frail older people: consensus principles for clinical practice, research, and education. J Am Med Dir Assoc. 2020;22(1):43–9. https://doi.org/10.1016/j.jamda.2020.05.004.
Thiruchelvam K, Hasan SS, Wong PS, Kairuz T. Residential aged care medication review to improve the quality of medication use: a systematic review. J Am Med Dir Assoc. 2017;18(1):87.e81–87.e14. https://doi.org/10.1016/j.jamda.2016.10.004.
Roberts MS, Stokes JA, King MA, Lynne TA, Purdie DM, Glasziou PP, Wilson DAJ, McCarthy ST, Brooks GE, De Looze FJ, et al. Outcomes of a randomized controlled trial of a clinical pharmacy intervention in 52 nursing homes. Br J Clin Pharmacol. 2001;51(3):257–65. https://doi.org/10.1046/j.1365-2125.2001.00347.x.
Nishtala PS, Hilmer SN, McLachlan AJ, Hannan PJ, Chen TF. Impact of Residential Medication Management Reviews on Drug Burden Index in aged-care homes. Drugs Aging. 2009;26(8):677–86. https://doi.org/10.2165/11316440-000000000-00000.
McLarin PE, Peterson GM, Curtain CM, Nishtala PS, Hannan PJ, Castelino RL. Impact of Residential Medication Management Reviews on anticholinergic burden in aged care residents. Curr Med Res Opin. 2016;32(1):123–31. https://doi.org/10.1185/03007995.2015.1105794.
Coleman L, Brett J, Daniels B, Pearson S, Falster M. Medicine reviews: do they reduce benzodiazepine use in older Australians? Pub Health Res Pract. 2021;31(3):30452015. https://doi.org/10.17061/phrp30452015.
Inacio MC, Lang C, Bray S, Visvanathan R, Whitehead C, Griffith E, Evans K, Corlis M, Wesselingh SL. Health status and health care trends of individuals accessing Australian aged care programs over a decade: the Registry of Senior Australians (ROSA) Historical Cohort. Intern Med J. 2021;51(5):712–24. https://doi.org/10.1111/imj.14871.
Sluggett JK, Bell JS, Lang C, Corlis M, Whitehead C, Wesselingh SL, Inacio MC. Variation in provision of collaborative medication reviews on entry to long-term care facilities. J Am Med Dir Assoc. 2021;22(1):148–155.e141. https://doi.org/10.1016/j.jamda.2020.10.027.
Aged Care Assessment Program Data Dictionary Version 1.0. 2002. Australian Institute of Health and Welfare. 2002. https://www.aihw.gov.au/reports/aged-care/aged-care-assessment-program-data-dictionary-versi/contents/table-of-contents. Accessed 20 Oct 2021.
Australian Government Department of Health and Ageing. Aged Care Funding Instrument (ACFI) User Guide. Canberra: Commonwealth of Australia; 2013.
PBS Expenditure and Prescriptions Report 1 July 2020 to 30 June 2021. Australian Government Department of Health and Ageing. 2021. https://www.pbs.gov.au/info/statistics/expenditure-prescriptions/pbs-expenditure-and-prescriptions-report-30-june-2021. Accessed 3 May 2022.
ATC/DDD index 2021. WHO Collaborating Centre for Drug Statistics Methodology. 2021. http://www.whocc.no/atc_ddd_index/. Accessed 20 Oct 2021.
MBS Online: Medicare Benefits Schedule. Australian Government Department of Health. 2021. http://www.mbsonline.gov.au/. Accessed 20 Oct 2021.
Sluggett JK, Bell JS, Lang C, Corlis M, Whitehead C, Wesselingh S, Inacio M. Residential Medication Management Reviews in Australian residential aged care facilities. Med J Aust. 2021;214(9):432–3. https://doi.org/10.5694/mja2.50921.
Lundby C, Jensen J, Larsen SP, Hoffmann H, Pottegard A, Reilev M. Use of medication among nursing home residents: a Danish drug utilisation study. Age Ageing. 2020;49(5):814–20. https://doi.org/10.1093/ageing/afaa029.
Liu E, Dyer SM, Whitehead C, O'Donnell LK, Gnanamanickam ES, Harrison SL, Milte R, Crotty M. Patterns of medication prescription by dementia diagnosis in Australian nursing home residents: a cross-sectional study. J Pharm Pract Res. 2019;49(1):33–40. https://doi.org/10.1002/jppr.1457.
Jokanovic N, Jamsen KM, Tan ECK, Dooley MJ, Kirkpatrick CM, Bell JS. Prevalence and variability in medications contributing to polypharmacy in long-term care facilities. Drugs Real World Outcomes. 2017;4(4):235–45. https://doi.org/10.1007/s40801-017-0121-x.
Pottegård A, Hallas J. Assigning exposure duration to single prescriptions by use of the waiting time distribution. Pharmacoepidemiol Drug Saf. 2013;22(8):803–9. https://doi.org/10.1002/pds.3459.
Linden A. Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata J. 2015;15(2):480–500. https://doi.org/10.1177/1536867x1501500208.
Linden A. A Comprehensive set of postestimation measures to enrich interrupted time-series analysis. Stata J. 2017;17(1):73–88. https://doi.org/10.1177/1536867x1701700105.
Australian Bureau of Statistics. Australian Standard Geographical Classification (ASHC), July 2011. Canberra: Commonwealth of Australia; 2011.
Pratt NL, Kerr M, Barratt JD, Kemp-Casey A, Kalisch Ellett LM, Ramsay E, Roughead EE. The validity of the Rx-Risk Comorbidity Index using medicines mapped to the Anatomical Therapeutic Chemical (ATC) Classification System. BMJ Open. 2018;8(4):e021122. https://doi.org/10.1136/bmjopen-2017-021122.
GEN Aged Care Data: Admissions into aged care. Australian Institute of Health and Welfare. 2021. https://www.gen-agedcaredata.gov.au/Topics/Admissions-into-aged-care. Accessed 20 Oct 2021.
Hilmer S, Gnjidic D. Statins in older adults. Aust Prescr. 2013;36(3):79–82. https://doi.org/10.18773/austprescr.2013.034.
Staykov E, Qureshi D, Scott M, Talarico R, Hsu AT, Howard M, Costa AP, Fung C, Ip M, Liddy C, et al. Do patients retain their family physicians after long-term care entry? a retrospective cohort study. J Am Med Dir Assoc. 2020. https://doi.org/10.1016/j.jamda.2020.04.016.
Welberry HJ, Jorm LR, Schaffer AL, Barbieri S, Hsu B, Harris MF, Hall J, Brodaty H. Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners. Med J Aust. 2021;215(3):130–6. https://doi.org/10.5694/mja2.51153.
Crotty M, Rowett D, Spurling L, Giles LC, Phillips PA. Does the addition of a pharmacist transition coordinator improve evidence-based medication management and health outcomes in older adults moving from the hospital to a long-term care facility? Results of a randomized, controlled trial. Am J Geriatr Pharmacother. 2004;2(4):257–64. https://doi.org/10.1016/j.amjopharm.2005.01.001.
Kosari S, Koerner J, Naunton M, Peterson GM, Haider I, Lancsar E, Wright D, Niyonsenga T, Davey R. Integrating pharmacists into aged care facilities to improve the quality use of medicine (PiRACF Study): protocol for a cluster randomised controlled trial. Trials. 2021;22(1):390. https://doi.org/10.1186/s13063-021-05335-0.
Pottegård A, Lundby C, Ejg Jarbøl D, Larsen SP, Hoppe BC, Hoffmann H, Thompson W. Use of sedating medications around nursing home admission in Denmark. Pharmacoepidemiol Drug Saf. https://doi.org/10.1002/pds.5341.
Deprescribing Reference Group. A guide to deprescribing: General information. Primary Health Tasmania. 2019. https://www.primaryhealthtas.com.au/resources/deprescribing-resources/. Accessed 20 Oct 2021.
Curtin D, Gallagher P, O'Mahony D. Deprescribing in older people approaching end-of-life: development and validation of STOPPFrail version 2. Age Ageing. 2020;50(2):465–71. https://doi.org/10.1093/ageing/afaa159.
Odden MC, Lee SJ, Steinman MA, Rubinsky AD, Graham L, Jing B, Fung K, Marcum ZA, Peralta CA. Deprescribing blood pressure treatment in long-term care residents. J Am Med Dir Assoc. https://doi.org/10.1016/j.jamda.2021.07.009.
Liacos M, Page AT, Etherton-Beer C. Deprescribing in older people. Aust Prescr. 2020;43(4):114–20. https://doi.org/10.18773/austprescr.2020.033.
Turner JP, Edwards S, Stanners M, Shakib S, Bell JS. What factors are important for deprescribing in Australian long-term care facilities? Perspectives of residents and health professionals. BMJ Open. 2016;6(3):e009781. https://doi.org/10.1136/bmjopen-2015-009781.
McDerby N, Bail K, Kosari S, Shield A, Peterson G, Dawda P, Naunton M. Canaries in the coalmine: stakeholder perspectives of medication management reviews for residents with dementia. Res Soc Adm Pharm. 2020;16(9):1220–7. https://doi.org/10.1016/j.sapharm.2019.12.002.
Hillen JB, Soulsby N, Clarke M. Too many pills, too many sick older Australians: working together is key. Aust J Gen Pract. 2021;50(3):171–4. https://doi.org/10.31128/AJGP-07-20-5548.
Sluggett JK, Collier L, Bartholomaeus JD, Inacio MC, Wesselingh SL, Caughey GE. National Trends and Policy Impacts on Provision of Home Medicines Reviews and Residential Medication Management Reviews in Older Australians, 2009–2019. Int J Environ Res Public Health. 2021;18(18):9898. https://doi.org/10.3390/ijerph18189898.
Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68(8):950–6. https://doi.org/10.1016/j.jclinepi.2014.12.018.
Lopez Bernal J, Cummins S, Gasparrini A. The use of controls in interrupted time series studies of public health interventions. Int J Epidemiol. 2018;47(6):2082–93. https://doi.org/10.1093/ije/dyy135.
Nielsen S, Gisev N, Bruno R, Hall W, Cohen M, Larance B, Campbell G, Shanahan M, Blyth F, Lintzeris N, et al. Defined daily doses (DDD) do not accurately reflect opioid doses used in contemporary chronic pain treatment. Pharmacoepidemiol Drug Saf. 2017;26(5):587–91. https://doi.org/10.1002/pds.4168.
Sluggett JK, Moldovan M, Lynn DJ, Papanicolas LE, Crotty M, Whitehead C, Wesselingh SL, Rogers GB, Inacio MC. National trends in antibiotic use in Australian residential aged care facilities, 2005–2016. Clin Infect Dis. 2021;72(12):2167–74. https://doi.org/10.1093/cid/ciaa436.
We acknowledge the Registry of Senior Australians' (ROSA) Steering Committee and the ROSA South Australian Health and Medical Research Institute (SAHMRI) Research Team for ensuring the success of the ROSA and support with this study. We also acknowledge the South Australian Government Department for Innovation and Skills (2017-2021) who provided us with support to establish ROSA, the Australian Government Medical Research Future Fund (2021-2024, PHRDI000009), and ROSA collaborating partners (SAHMRI, ECH Inc, Silver Chain, Life Care) for its ongoing support, and the Australian Institute of Health and Welfare for the linkage and construction of input data. We also acknowledge Marc Apolloni, chair of the Australian Association of Consultant Pharmacy (AACP) board, for comments on project findings.
This analysis was funded by the Australian Association of Consultant Pharmacy (AACP), an Australian organization that accredits pharmacists to provide residential medication management reviews. Members of the AACP Board and the Chief Executive Officer participated in study conceptualization, reviewed the study protocol prepared by the research team prior to research commencement, and participated in discussions about the results of the analysis. AACP had no role in data collection, data analysis, or presentation of results. AACP was provided a copy of the final draft of this manuscript prior to publication. All authors had final responsibility for the decision to submit the manuscript for publication. JKS is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (GNT1156439). MCI is supported by The Hospital Research Foundation Mid-Career Fellowship (MCF-27–2019) and an NHMRC Investigator Grant (GNT119378).
University of South Australia, UniSA Allied Health and Human Performance, GPO Box 2471, Adelaide, South Australia, Australia
Janet K. Sluggett, Gillian E. Caughey & Maria C. Inacio
Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
Janet K. Sluggett, Gillian E. Caughey, Tracy Air, Max Moldovan, Catherine Lang, Steve L. Wesselingh & Maria C. Inacio
Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
Janet K. Sluggett
Biometry Hub, Faculty of Sciences, Engineering and Technology, The University of Adelaide, Waite Campus, Urrbrae, South Australia, Australia
Max Moldovan
Australian Association of Consultant Pharmacy, Australian Capital Territory, Fyshwick, Australia
School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
Stephen R. Carter
School of Pharmacy and Pharmacology, University of Tasmania, Hobart, TAS, Australia
Shane Jackson
Curtin Medical School, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
Andrew C. Stafford
Gillian E. Caughey
Tracy Air
Catherine Lang
Steve L. Wesselingh
Maria C. Inacio
JKS, GM, SC, SJ, AS, MCI conceived the study. All authors participated in study design. JKS drafted the study protocol and oversaw study conduct. CL prepared the data for analysis. TA and MM analyzed the data. All authors contributed to interpretation of results. JKS wrote the manuscript. All authors critically reviewed the manuscript. All authors read and approved the final manuscript.
Correspondence to Janet K. Sluggett.
Ethical approval was obtained from the Australian Institute of Health and Welfare (AIHW) (reference, EO2018/1/418) and University of South Australia (reference, 200489) Human Research Ethics Committees. The AIHW and the University of South Australia Human Research Ethics Committees approved a waiver of written informed consent for individuals whose retrospective data is included in this study for reasons outlined in paragraph 2.3.10 of the National Statement on Ethical Conduct in Human Research 2007 (Updated 2018) (e.g., low risk nature of the research, benefits outweigh any risk of harm, it is impracticable to obtain consent for all individuals, there is no likely reasons that participants would not have consented if asked, data are de-identified and protected, results have significance for health and welfare). The waiver of consent for this study was applied in accordance with paragraph 2.3.10 of the National Statement on Ethical Conduct in Human Research 2007 (Updated 2018) and all conditions for the waiver of the requirement for consent under paragraph 2.3.10 have been strictly adhered to. Access to the de-identified, individual-level linked data used in this study was only permitted to research personnel with relevant approvals under Sect. 29 of the Australian Institute of Health and Welfare Act 1987, The Privacy Act 1988 (Part III), including the Australian Privacy Principles and project-specific data access and confidentiality agreements. The authors confirm the research was performed in accordance with relevant guidelines/regulations.
This analysis was funded by the Australian Association of Consultant Pharmacy (AACP), an Australian organization that accredits pharmacists to provide residential medication management reviews. JKS, SC, SJ, and AS are registered pharmacists and accredited by the AACP to perform residential medication management reviews (RMMRs). GM is the Chief Executive Officer of the AACP. SC and SJ are Non-Executive Directors of the AACP. AS is a member of the AACP National Advisory Group. There are no other competing interests to declare.
Additional file 1: Supplementary Table 1
. Codes used to identify medicine classes of interest, and corresponding prescription durations. Supplementary Table 2. Weekly number of defined daily doses of medicines per 1000 resident-days before and after the index date among individuals who did and did not receive an RMMR in the 0-3 months and 3-6 months after RACF entry. Supplementary Table 3. Weekly prevalence of medicines use during the study period among individuals with an index date in the 6-12 months after RACF entry. Supplementary Figure 1. Group assignment based on the date of an individual's first RMMR relative to first entry into permanent residential aged care. Supplementary Figure 2. Time frame for assessing medicines use pre- and post-index date. Supplementary Figure 3. Time periods analyzed in the segmented regression models. Supplementary Figure 4. Weekly number of DDDs available for use per 1000 resident-days for individuals with an index date in the 0-3 months after RACF entry for medicines with possible changes in use post-RMMR compared to individuals without an RMMR. Supplementary Figure 5. Weekly number of DDDs available for use per 1000 resident-days for individuals with an index date in the 3-6 months after RACF entry for medicines with possible changes in use post-RMMR compared to individuals without an RMMR. Supplementary Figure 6. Weekly number of DDDs per 1000 resident-days in the four months before and after the index date for individuals with an index date within 6-12 months of RACF entry. Supplementary Figure 7. Weekly prevalence of medicines use in the four months before and after the index date for individuals with an index date in the 6-12 months after RACF entry.
Sluggett, J.K., Caughey, G.E., Air, T. et al. Medicines use before and after comprehensive medicines review among residents of long-term care facilities: a retrospective cohort study. BMC Geriatr 22, 493 (2022). https://doi.org/10.1186/s12877-022-03187-0
Medication review
Drug utilization
Homes for the aged
Residential aged care


text:
Partly to mostly cloudy. Low 48F. Winds E at 5 to 10 mph..
Partly to mostly cloudy. Low 48F. Winds E at 5 to 10 mph.
Leesburg Mayor Jim Quinn
House District 152 candidates excited to be in Dec. 3 runoff
By Alan Mauldin alan.mauldin@albanyherald.com
Alan Mauldin
ALBANY – The two candidates in the Georgia House District 152 runoff are raring to knock on more doors and speak with potential voters in the four-week sprint to the Dec. 3 election.
In the four-candidate field of hopefuls who qualified for the special election to fill the unexpired term of Ed Rynders, Leesburg Mayor Jim Quinn finished with 3,861 votes, or 41.58% in the Tuesday election, while former Sylvester Mayor Bill Yearta had 3,182 votes – 34.27% of the 9,286 ballots cast in the race.
Tyler Johnson, the third Republican in the race received 835 votes, and Mary Egler, the sole Democrat, received 1,408 votes in the nonpartisan special election.
The winner in the runoff election will serve the remaining year of Rynders' term. Rynders announced plans earlier this year to step down from the seat he's held for the past 17 years after moving with his wife to St. Simons Island.
"We've got more work to do," said Yearta, who had to step down as mayor of Sylvester when he qualified to run for another office. "It's just about getting our message out and our vision for the district. It's just going to be an opportunity to see folks and try to let them know I'm the best person for the district and will be a strong voice for the people of District 152."
Yearta said that his experience from 17 years as mayor and serving on the board of the Municipal Electric Authority of Georgia will benefit residents of the district that includes all of Lee and Worth counties and southern Sumter County.
The district has challenges, but there are also opportunities, and the area is a good place to raise a family, Yearta said.
"There are a lot of issues, from health care to education, economic development, infrastructure," he said. "We need all the infrastructure help we can get. We need to support agriculture and grow agriculture. I just feel like I'm the strongest voice to get the message out.
"We're going to get out there and work hard and talk to all the folks we can and reach as many new people as we can. I want to thank the people who supported me."
Quinn said he also relishes the opportunity to go door-to-door and talk on the phone with anyone who wishes to speak with him.
"We get to do it all over again," Quinn said of the four weeks of campaigning ahead. "It's a chance to knock on more doors, see more people, make more phone calls. I like meeting people. I like talking to people."
Quinn said he has a long record of service in his community and church as well as the political experience as mayor. He said he relishes sitting down with others to talk things out, a skill that would benefit residents of the district if he were representing them in Atlanta.
He said he is grateful for those who turned out to vote for him and wants them to do the same in the Dec. 3 runoff.
"We just need the people who voted to come back in four weeks," he said. "Most of the stuff I sent out has my personal cellphone (number). I like for people, if you have a problem, I like them to call me. I want that to continue. I look forward to seeing you."
Jim Quinn
Bill Yearta
Ed Rynders
District 152 House Election
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Reports: Ex-Cowboys coach Jason Garrett joins Giants as OC

redpajama_set_name: RedPajamaCommonCrawl


text:
package tabletserver

import (
	"fmt"
	"time"

	log "github.com/golang/glog"
	"golang.org/x/net/context"

	"vitess.io/vitess/go/hack"
	"vitess.io/vitess/go/mysql"
	"vitess.io/vitess/go/sqlescape"
	"vitess.io/vitess/go/sqltypes"
	"vitess.io/vitess/go/stats"
	"vitess.io/vitess/go/vt/dbconfigs"
	"vitess.io/vitess/go/vt/dbconnpool"
	"vitess.io/vitess/go/vt/sqlparser"
	"vitess.io/vitess/go/vt/vterrors"
	"vitess.io/vitess/go/vt/vttablet/tabletserver/connpool"

	querypb "vitess.io/vitess/go/vt/proto/query"
	topodatapb "vitess.io/vitess/go/vt/proto/topodata"
	vtrpcpb "vitess.io/vitess/go/vt/proto/vtrpc"
)

const (
	sqlTurnoffBinlog   = "set @@session.sql_log_bin = 0"
	sqlCreateSidecarDB = "create database if not exists %s"

	sqlDropLegacy1 = "drop table if exists %s.redo_log_transaction"
	sqlDropLegacy2 = "drop table if exists %s.redo_log_statement"
	sqlDropLegacy3 = "drop table if exists %s.transaction"
	sqlDropLegacy4 = "drop table if exists %s.participant"

	// RedoStateFailed represents the Failed state for redo_state.
	RedoStateFailed = 0
	// RedoStatePrepared represents the Prepared state for redo_state.
	RedoStatePrepared       = 1
	sqlCreateTableRedoState = `create table if not exists %s.redo_state(
  dtid varbinary(512),
  state bigint,
  time_created bigint,
  primary key(dtid)
	) engine=InnoDB`

	sqlCreateTableRedoStatement = `create table if not exists %s.redo_statement(
  dtid varbinary(512),
  id bigint,
  statement mediumblob,
  primary key(dtid, id)
	) engine=InnoDB`

	// DTStatePrepare represents the PREPARE state for dt_state.
	DTStatePrepare = querypb.TransactionState_PREPARE
	// DTStateCommit represents the COMMIT state for dt_state.
	DTStateCommit = querypb.TransactionState_COMMIT
	// DTStateRollback represents the ROLLBACK state for dt_state.
	DTStateRollback       = querypb.TransactionState_ROLLBACK
	sqlCreateTableDTState = `create table if not exists %s.dt_state(
  dtid varbinary(512),
  state bigint,
  time_created bigint,
  primary key(dtid)
	) engine=InnoDB`

	sqlCreateTableDTParticipant = `create table if not exists %s.dt_participant(
  dtid varbinary(512),
	id bigint,
	keyspace varchar(256),
	shard varchar(256),
  primary key(dtid, id)
	) engine=InnoDB`

	sqlReadAllRedo = `select t.dtid, t.state, t.time_created, s.statement
	from %s.redo_state t
  join %s.redo_statement s on t.dtid = s.dtid
	order by t.dtid, s.id`

	sqlReadAllTransactions = `select t.dtid, t.state, t.time_created, p.keyspace, p.shard
	from %s.dt_state t
  join %s.dt_participant p on t.dtid = p.dtid
	order by t.dtid, p.id`
)

// TwoPC performs 2PC metadata management (MM) functions.
type TwoPC struct {
	readPool *connpool.Pool

	insertRedoTx        *sqlparser.ParsedQuery
	insertRedoStmt      *sqlparser.ParsedQuery
	updateRedoTx        *sqlparser.ParsedQuery
	deleteRedoTx        *sqlparser.ParsedQuery
	deleteRedoStmt      *sqlparser.ParsedQuery
	readAllRedo         string
	countUnresolvedRedo *sqlparser.ParsedQuery

	insertTransaction   *sqlparser.ParsedQuery
	insertParticipants  *sqlparser.ParsedQuery
	transition          *sqlparser.ParsedQuery
	deleteTransaction   *sqlparser.ParsedQuery
	deleteParticipants  *sqlparser.ParsedQuery
	readTransaction     *sqlparser.ParsedQuery
	readParticipants    *sqlparser.ParsedQuery
	readAbandoned       *sqlparser.ParsedQuery
	readAllTransactions string
}

// NewTwoPC creates a TwoPC variable.
func NewTwoPC(readPool *connpool.Pool) *TwoPC {
	return &TwoPC{readPool: readPool}
}

// Init initializes TwoPC. If the metadata database or tables
// are not present, they are created.
func (tpc *TwoPC) Init(sidecarDBName string, dbaparams *mysql.ConnParams) error {
	dbname := sqlescape.EscapeID(sidecarDBName)
	conn, err := dbconnpool.NewDBConnection(dbaparams, stats.NewTimings(""))
	if err != nil {
		return err
	}
	defer conn.Close()
	statements := []string{
		sqlTurnoffBinlog,
		fmt.Sprintf(sqlCreateSidecarDB, dbname),
		fmt.Sprintf(sqlDropLegacy1, dbname),
		fmt.Sprintf(sqlDropLegacy2, dbname),
		fmt.Sprintf(sqlDropLegacy3, dbname),
		fmt.Sprintf(sqlDropLegacy4, dbname),
		fmt.Sprintf(sqlCreateTableRedoState, dbname),
		fmt.Sprintf(sqlCreateTableRedoStatement, dbname),
		fmt.Sprintf(sqlCreateTableDTState, dbname),
		fmt.Sprintf(sqlCreateTableDTParticipant, dbname),
	}
	for _, s := range statements {
		if _, err := conn.ExecuteFetch(s, 0, false); err != nil {
			return err
		}
	}
	tpc.insertRedoTx = sqlparser.BuildParsedQuery(
		"insert into %s.redo_state(dtid, state, time_created) values (%a, %a, %a)",
		dbname, ":dtid", ":state", ":time_created")
	tpc.insertRedoStmt = sqlparser.BuildParsedQuery(
		"insert into %s.redo_statement(dtid, id, statement) values %a",
		dbname, ":vals")
	tpc.updateRedoTx = sqlparser.BuildParsedQuery(
		"update %s.redo_state set state = %a where dtid = %a",
		dbname, ":state", ":dtid")
	tpc.deleteRedoTx = sqlparser.BuildParsedQuery(
		"delete from %s.redo_state where dtid = %a",
		dbname, ":dtid")
	tpc.deleteRedoStmt = sqlparser.BuildParsedQuery(
		"delete from %s.redo_statement where dtid = %a",
		dbname, ":dtid")
	tpc.readAllRedo = fmt.Sprintf(sqlReadAllRedo, dbname, dbname)
	tpc.countUnresolvedRedo = sqlparser.BuildParsedQuery(
		"select count(*) from %s.redo_state where time_created < %a",
		dbname, ":time_created")

	tpc.insertTransaction = sqlparser.BuildParsedQuery(
		"insert into %s.dt_state(dtid, state, time_created) values (%a, %a, %a)",
		dbname, ":dtid", ":state", ":cur_time")
	tpc.insertParticipants = sqlparser.BuildParsedQuery(
		"insert into %s.dt_participant(dtid, id, keyspace, shard) values %a",
		dbname, ":vals")
	tpc.transition = sqlparser.BuildParsedQuery(
		"update %s.dt_state set state = %a where dtid = %a and state = %a",
		dbname, ":state", ":dtid", ":prepare")
	tpc.deleteTransaction = sqlparser.BuildParsedQuery(
		"delete from %s.dt_state where dtid = %a",
		dbname, ":dtid")
	tpc.deleteParticipants = sqlparser.BuildParsedQuery(
		"delete from %s.dt_participant where dtid = %a",
		dbname, ":dtid")
	tpc.readTransaction = sqlparser.BuildParsedQuery(
		"select dtid, state, time_created from %s.dt_state where dtid = %a",
		dbname, ":dtid")
	tpc.readParticipants = sqlparser.BuildParsedQuery(
		"select keyspace, shard from %s.dt_participant where dtid = %a",
		dbname, ":dtid")
	tpc.readAbandoned = sqlparser.BuildParsedQuery(
		"select dtid, time_created from %s.dt_state where time_created < %a",
		dbname, ":time_created")
	tpc.readAllTransactions = fmt.Sprintf(sqlReadAllTransactions, dbname, dbname)
	return nil
}

// Open starts the TwoPC service.
func (tpc *TwoPC) Open(dbconfigs dbconfigs.DBConfigs) {
	tpc.readPool.Open(&dbconfigs.App, &dbconfigs.Dba, &dbconfigs.AppDebug)
}

// Close closes the TwoPC service.
func (tpc *TwoPC) Close() {
	tpc.readPool.Close()
}

// SaveRedo saves the statements in the redo log using the supplied connection.
func (tpc *TwoPC) SaveRedo(ctx context.Context, conn *TxConnection, dtid string, queries []string) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid":         sqltypes.StringBindVariable(dtid),
		"state":        sqltypes.Int64BindVariable(RedoStatePrepared),
		"time_created": sqltypes.Int64BindVariable(time.Now().UnixNano()),
	}
	_, err := tpc.exec(ctx, conn, tpc.insertRedoTx, bindVars)
	if err != nil {
		return err
	}

	rows := make([][]sqltypes.Value, len(queries))
	for i, query := range queries {
		rows[i] = []sqltypes.Value{
			sqltypes.NewVarBinary(dtid),
			sqltypes.NewInt64(int64(i + 1)),
			sqltypes.NewVarBinary(query),
		}
	}
	extras := map[string]sqlparser.Encodable{
		"vals": sqlparser.InsertValues(rows),
	}
	b, err := tpc.insertRedoStmt.GenerateQuery(nil, extras)
	if err != nil {
		return err
	}
	_, err = conn.Exec(ctx, hack.String(b), 1, false)
	return err
}

// UpdateRedo changes the state of the redo log for the dtid.
func (tpc *TwoPC) UpdateRedo(ctx context.Context, conn *TxConnection, dtid string, state int) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid":  sqltypes.StringBindVariable(dtid),
		"state": sqltypes.Int64BindVariable(int64(state)),
	}
	_, err := tpc.exec(ctx, conn, tpc.updateRedoTx, bindVars)
	return err
}

// DeleteRedo deletes the redo log for the dtid.
func (tpc *TwoPC) DeleteRedo(ctx context.Context, conn *TxConnection, dtid string) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid": sqltypes.StringBindVariable(dtid),
	}
	_, err := tpc.exec(ctx, conn, tpc.deleteRedoTx, bindVars)
	if err != nil {
		return err
	}
	_, err = tpc.exec(ctx, conn, tpc.deleteRedoStmt, bindVars)
	return err
}

// PreparedTx represents a displayable version of a prepared transaction.
type PreparedTx struct {
	Dtid    string
	Queries []string
	Time    time.Time
}

// ReadAllRedo returns all the prepared transactions from the redo logs.
func (tpc *TwoPC) ReadAllRedo(ctx context.Context) (prepared, failed []*PreparedTx, err error) {
	conn, err := tpc.readPool.Get(ctx)
	if err != nil {
		return nil, nil, err
	}
	defer conn.Recycle()

	qr, err := conn.Exec(ctx, tpc.readAllRedo, 10000, false)
	if err != nil {
		return nil, nil, err
	}

	var curTx *PreparedTx
	for _, row := range qr.Rows {
		dtid := row[0].ToString()
		if curTx == nil || dtid != curTx.Dtid {
			// Initialize the new element.
			// A failure in time parsing will show up as a very old time,
			// which is harmless.
			tm, _ := sqltypes.ToInt64(row[2])
			curTx = &PreparedTx{
				Dtid: dtid,
				Time: time.Unix(0, tm),
			}
			st, err := sqltypes.ToInt64(row[1])
			if err != nil {
				log.Errorf("Error parsing state for dtid %s: %v.", dtid, err)
			}
			switch st {
			case RedoStatePrepared:
				prepared = append(prepared, curTx)
			default:
				if st != RedoStateFailed {
					log.Errorf("Unexpected state for dtid %s: %d. Treating it as a failure.", dtid, st)
				}
				failed = append(failed, curTx)
			}
		}
		curTx.Queries = append(curTx.Queries, row[3].ToString())
	}
	return prepared, failed, nil
}

// CountUnresolvedRedo returns the number of prepared transactions that are still unresolved.
func (tpc *TwoPC) CountUnresolvedRedo(ctx context.Context, unresolvedTime time.Time) (int64, error) {
	conn, err := tpc.readPool.Get(ctx)
	if err != nil {
		return 0, err
	}
	defer conn.Recycle()

	bindVars := map[string]*querypb.BindVariable{
		"time_created": sqltypes.Int64BindVariable(unresolvedTime.UnixNano()),
	}
	qr, err := tpc.read(ctx, conn, tpc.countUnresolvedRedo, bindVars)
	if err != nil {
		return 0, err
	}
	if len(qr.Rows) < 1 {
		return 0, nil
	}
	v, _ := sqltypes.ToInt64(qr.Rows[0][0])
	return v, nil
}

// CreateTransaction saves the metadata of a 2pc transaction as Prepared.
func (tpc *TwoPC) CreateTransaction(ctx context.Context, conn *TxConnection, dtid string, participants []*querypb.Target) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid":     sqltypes.StringBindVariable(dtid),
		"state":    sqltypes.Int64BindVariable(int64(DTStatePrepare)),
		"cur_time": sqltypes.Int64BindVariable(time.Now().UnixNano()),
	}
	_, err := tpc.exec(ctx, conn, tpc.insertTransaction, bindVars)
	if err != nil {
		return err
	}

	rows := make([][]sqltypes.Value, len(participants))
	for i, participant := range participants {
		rows[i] = []sqltypes.Value{
			sqltypes.NewVarBinary(dtid),
			sqltypes.NewInt64(int64(i + 1)),
			sqltypes.NewVarBinary(participant.Keyspace),
			sqltypes.NewVarBinary(participant.Shard),
		}
	}
	extras := map[string]sqlparser.Encodable{
		"vals": sqlparser.InsertValues(rows),
	}
	b, err := tpc.insertParticipants.GenerateQuery(nil, extras)
	if err != nil {
		return err
	}
	_, err = conn.Exec(ctx, hack.String(b), 1, false)
	return err
}

// Transition performs a transition from Prepare to the specified state.
// If the transaction is not a in the Prepare state, an error is returned.
func (tpc *TwoPC) Transition(ctx context.Context, conn *TxConnection, dtid string, state querypb.TransactionState) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid":    sqltypes.StringBindVariable(dtid),
		"state":   sqltypes.Int64BindVariable(int64(state)),
		"prepare": sqltypes.Int64BindVariable(int64(querypb.TransactionState_PREPARE)),
	}
	qr, err := tpc.exec(ctx, conn, tpc.transition, bindVars)
	if err != nil {
		return err
	}
	if qr.RowsAffected != 1 {
		return vterrors.Errorf(vtrpcpb.Code_NOT_FOUND, "could not transition to %v: %s", state, dtid)
	}
	return nil
}

// DeleteTransaction deletes the metadata for the specified transaction.
func (tpc *TwoPC) DeleteTransaction(ctx context.Context, conn *TxConnection, dtid string) error {
	bindVars := map[string]*querypb.BindVariable{
		"dtid": sqltypes.StringBindVariable(dtid),
	}
	_, err := tpc.exec(ctx, conn, tpc.deleteTransaction, bindVars)
	if err != nil {
		return err
	}
	_, err = tpc.exec(ctx, conn, tpc.deleteParticipants, bindVars)
	return err
}

// ReadTransaction returns the metadata for the transaction.
func (tpc *TwoPC) ReadTransaction(ctx context.Context, dtid string) (*querypb.TransactionMetadata, error) {
	conn, err := tpc.readPool.Get(ctx)
	if err != nil {
		return nil, err
	}
	defer conn.Recycle()

	result := &querypb.TransactionMetadata{}
	bindVars := map[string]*querypb.BindVariable{
		"dtid": sqltypes.StringBindVariable(dtid),
	}
	qr, err := tpc.read(ctx, conn, tpc.readTransaction, bindVars)
	if err != nil {
		return nil, err
	}
	if len(qr.Rows) == 0 {
		return result, nil
	}
	result.Dtid = qr.Rows[0][0].ToString()
	st, err := sqltypes.ToInt64(qr.Rows[0][1])
	if err != nil {
		return nil, fmt.Errorf("Error parsing state for dtid %s: %v", dtid, err)
	}
	result.State = querypb.TransactionState(st)
	if result.State < querypb.TransactionState_PREPARE || result.State > querypb.TransactionState_ROLLBACK {
		return nil, fmt.Errorf("Unexpected state for dtid %s: %v", dtid, result.State)
	}
	// A failure in time parsing will show up as a very old time,
	// which is harmless.
	tm, _ := sqltypes.ToInt64(qr.Rows[0][2])
	result.TimeCreated = tm

	qr, err = tpc.read(ctx, conn, tpc.readParticipants, bindVars)
	if err != nil {
		return nil, err
	}
	participants := make([]*querypb.Target, 0, len(qr.Rows))
	for _, row := range qr.Rows {
		participants = append(participants, &querypb.Target{
			Keyspace:   row[0].ToString(),
			Shard:      row[1].ToString(),
			TabletType: topodatapb.TabletType_MASTER,
		})
	}
	result.Participants = participants
	return result, nil
}

// ReadAbandoned returns the list of abandoned transactions
// and their associated start time.
func (tpc *TwoPC) ReadAbandoned(ctx context.Context, abandonTime time.Time) (map[string]time.Time, error) {
	conn, err := tpc.readPool.Get(ctx)
	if err != nil {
		return nil, err
	}
	defer conn.Recycle()

	bindVars := map[string]*querypb.BindVariable{
		"time_created": sqltypes.Int64BindVariable(abandonTime.UnixNano()),
	}
	qr, err := tpc.read(ctx, conn, tpc.readAbandoned, bindVars)
	if err != nil {
		return nil, err
	}
	txs := make(map[string]time.Time, len(qr.Rows))
	for _, row := range qr.Rows {
		t, err := sqltypes.ToInt64(row[1])
		if err != nil {
			return nil, err
		}
		txs[row[0].ToString()] = time.Unix(0, t)
	}
	return txs, nil
}

// DistributedTx is similar to querypb.TransactionMetadata, but
// is display friendly.
type DistributedTx struct {
	Dtid         string
	State        string
	Created      time.Time
	Participants []querypb.Target
}

// ReadAllTransactions returns info about all distributed transactions.
func (tpc *TwoPC) ReadAllTransactions(ctx context.Context) ([]*DistributedTx, error) {
	conn, err := tpc.readPool.Get(ctx)
	if err != nil {
		return nil, err
	}
	defer conn.Recycle()

	qr, err := conn.Exec(ctx, tpc.readAllTransactions, 10000, false)
	if err != nil {
		return nil, err
	}

	var curTx *DistributedTx
	var distributed []*DistributedTx
	for _, row := range qr.Rows {
		dtid := row[0].ToString()
		if curTx == nil || dtid != curTx.Dtid {
			// Initialize the new element.
			// A failure in time parsing will show up as a very old time,
			// which is harmless.
			tm, _ := sqltypes.ToInt64(row[2])
			st, err := sqltypes.ToInt64(row[1])
			// Just log on error and continue. The state will show up as UNKNOWN
			// on the display.
			if err != nil {
				log.Errorf("Error parsing state for dtid %s: %v.", dtid, err)
			}
			protostate := querypb.TransactionState(st)
			if protostate < querypb.TransactionState_UNKNOWN || protostate > querypb.TransactionState_ROLLBACK {
				log.Errorf("Unexpected state for dtid %s: %v.", dtid, protostate)
			}
			curTx = &DistributedTx{
				Dtid:    dtid,
				State:   querypb.TransactionState(st).String(),
				Created: time.Unix(0, tm),
			}
			distributed = append(distributed, curTx)
		}
		curTx.Participants = append(curTx.Participants, querypb.Target{
			Keyspace: row[3].ToString(),
			Shard:    row[4].ToString(),
		})
	}
	return distributed, nil
}

func (tpc *TwoPC) exec(ctx context.Context, conn *TxConnection, pq *sqlparser.ParsedQuery, bindVars map[string]*querypb.BindVariable) (*sqltypes.Result, error) {
	b, err := pq.GenerateQuery(bindVars, nil)
	if err != nil {
		return nil, err
	}
	return conn.Exec(ctx, hack.String(b), 1, false)
}

func (tpc *TwoPC) read(ctx context.Context, conn *connpool.DBConn, pq *sqlparser.ParsedQuery, bindVars map[string]*querypb.BindVariable) (*sqltypes.Result, error) {
	b, err := pq.GenerateQuery(bindVars, nil)
	if err != nil {
		return nil, err
	}
	return conn.Exec(ctx, hack.String(b), 10000, false)
}

redpajama_set_name: RedPajamaGithub


text:
Nuclear Factor Erythroid-2-related Factor 2 Gene Polymorphisms in Vitiligo
J Clin Aesthet Dermatol. 2021;14(6):14–17.
by Neveen Emad Sorour, MD; Heba Mohamed Abd El-Kareem, MD; Asmaa Em Ibrahim, MBBCh; and Rehab Mohammed Salem, MD
Drs. Sorour and Salem are with the Dermatology Department, Faculty of Medicine, Benha University in Benha, Egypt. Dr. Abd El-Kareem is with the Medical Biochemistry Department, Faculty of Medicine, Benha University in Benha, Egypt. Dr. Ibrahim is with the Ministry of Health Hospital in Cairo, Egypt.
FUNDING: No funding was provided for this article.
DISCLOSURES: The authors report no conflicts of interest relevant to the content of this article.
ABSTRACT: Background. Oxidative stress is now one of the accepted theories of vitiligo development. Nuclear factor erythroid-2-related factor 2 (Nrf2) regulates the expression of antioxidant proteins.
Objective. This work aimed to evaluate the association of Nrf2 gene polymorphisms with the susceptibility to vitiligo among a sample of Egyptian patients with vitiligo.
Methods. This case-control study included 100 patients with vitiligo and 50 healthy matched volunteers serving as a control group. Genotyping was carried out by real-time polymerase chain reaction.
Results. The frequencies of TT, CT, and combined (TT+CT) genotypes and the T allele of Nrf2 (rs35652124) were significantly increased in the studied patients with vitiligo relative to the healthy controls (p<0.001, p=0.012, p<0.001 and p<0.001, respectively). There was a nonsignificant difference between patients and controls regarding Nrf2 (rs6721961) genotypes. However, the T allele of Nrf2 (rs6721961) was significantly predominant in the studied patients compared to in the controls (p=0.029). Among the studied criteria, the T allele of Nrf2 (rs6721961) was predominant in patients with a marginal type of repigmentation (p=0.022), while the G allele of the same single-nucleotide polymorphism was associated with a higher body mass index value (p=0.034). One hundred percent of patients with vitiligo with the Nrf2 (rs6721961) GT genotype had a progressive disease course (p=0.015).
Conclusion. Nrf2 (-617 T/G) and (-653 T/C) polymorphism might play a role in patient susceptibility to vitiligo and modify the clinical presentation of the disease.
Keywords: Gene polymorphism, nuclear factor erythroid-2-related factor 2, vitiligo
Vitiligo is the most common acquired depigmenting disorder affecting genetically predisposed individuals.1 How vitiligo develops is not yet well established; however, autoimmune, oxidative stress, neural, and biochemical-based theories have been put forth.2 Reactive oxygen species (ROS) created via different pathways might contribute to the destruction of melanocytes either via direct damage or through increased melanocyte-specific autoantibody production.3
Nuclear factor erythroid-2-related factor 2 (Nrf2) is a basic leucine zipper protein that plays a role in regulating the expression of antioxidant proteins that protect cells against the damaging effects of oxidative stress.4,5
Nrf2 gene polymorphisms, especially those affecting the promoter region, can impair Nrf2 protein expression and activity. Two polymorphisms in the promoter region—a T to C substitution at position -653 (Nrf2 rs35652124) and a G to T substitution at -617 (Nrf2 rs6721961)—are described and known to be associated with different pathologic conditions.6
This study aimed to investigate the association between Nrf2 (rs35652124) and (rs6721961) gene polymorphisms and the susceptibility to vitiligo and to assess the effects of different genotypes on the clinical aspects of the disease.
The study included 100 patients suffering from different clinical presentations of vitiligo and 50 healthy, vitiligo-free individuals from the Dermatology Outpatient Clinic of Benha University Hospital. The study was approved by the ethical committee of human research of the Faculty of Medicine at Benha University. After discussing the study's risks, benefits, and other aspects with all participants, written informed consent was obtained from each participant before sample collection. The history of the present illness, related medical conditions, and associated diseases were discussed in details with all patients. Patients with other inflammatory or autoimmune cutaneous or systemic disorders were excluded from this study.
DNA extraction and genotyping. Genomic DNA was extracted from peripheral blood samples from all participants. Genomic DNA was isolated using the GeneJET genomic DNA purification kit (cat. no. K0721; Qiagen, Hilden, Germany). The purity and concentration of DNA were evaluated spectrophotometrically using Nanodrop (Thermo Fisher Scientific, Waltham, Massachusetts). The quality of DNA was assessed with the A260/280 ratio. Genotyping was performed using the Taqman single nucleotide polymorphism (SNP) ready-made assay (Qiagen), which includes the TaqMan Universal Master Mix II (cat. no. 4440043) and TaqMan SNP human genotyping assay (cat. no. 4351379). The TaqMan genotyping assay used forward primer, reverse primer, and two probes as follows: rs35652124, forward 5-CCT TGC CCTGCT TTT ATC TC-3 and reverse 5-CTT CTC CGT TTG CCT TTG AC-3; rs6721961, forward 5-GAA AGG CGT TGG TGT AGG AG-3 and reverse 5-GAA TGGAGA CAC GTG GGA GT-3; normal probe, (5′-3′) VIC-ACTCCTTTCACCTATTCCCAAGGCCT-MGB-NFQ; and mutant probe, (5′-3′) FAM-CAGCTACACCTGTATGTAGGCTAGA MGB-NFQ. The probes were designed with a minor groove binder and nonfluorescent quencher at the 3′ end, whereas the 5′ end contained the fluorescence reporter dyes 2′-chloro-7′-phenyl-1,4-dichloro-6-carboxyfluorescein (VIC) or 6-carboxyfluorescein (FAM). The wild-type probe was labeled with VIC dye, while the variant probe was labeled with FAM dye. Polymerase chain reaction was performed in a volume of 20uL using the Rotorgene real-time polymerase chain reaction system (Qiagens). Thermal cycling conditions were as follows: 60°C for 30 seconds, 95°C for 10 minutes, 40 cycles of denaturation, 95°C for 15 seconds, and 60°C for one minute.
Statistical analysis. Data were coded and entered using the Statistical Package for the Social Sciences version 25 software program (IBM Corporation, Armonk, New York). Data were summarized using mean, standard deviation, median, minimum, and maximum values if quantitative data and frequency (count) and relative frequency (percentage) of categorical data. Comparisons between quantitative variables were done using the nonparametric Kruskal–Wallis and Mann–Whitney U tests.
For comparing categorical data, the chi-squared test was performed. Fisher's exact test was used instead when the expected frequency was less than 5. Genotype and allele frequencies were compared between the disease and the control groups. Odds ratios with 95% confidence intervals were calculated using binary logistic regression, and p-values of less than 0.05 were considered to be statistically significant.
The patient and control groups were matched regarding age (23.79±15.87 vs. 24.46±9.12 years; p=0.147), sex (male:female ratios of 68:32 among patients and 30:20 among control participants; p=0.332), and body mass index (BMI) (24.41±5.57 kg/m2 vs. 25.49±4.40 kg/m2; p=0.094).
The mean disease duration was 6.68±6.39 years. A stressful condition was associated with disease onset in 51 percent of cases. Koebnerization was positive in 36 percent of the patients with vitiligo. Hearing and vision impairments were found in 10 percent and eight percent of the patients, respectively. Regarding the clinical type of vitiligo in the studied sample, 86 patients had nonsegmental vitiligo and 14 patients presented with segmental vitiligo. Leukotrichia was observed in 37 cases. The mean Vitiligo Area Scoring Index score was 13.16±24.16.
Applying the Hardy–Weinberg equation, it was revealed that Nrf2 genotypes and alleles frequency in the patient and control groups in both studied SNPs were in Hardy Weinberg equilibrium. The TT and CT genotypes and T allele of the Nrf2 gene (rs35652124) and T allele of Nrf2 (rs6721961) increase the risk of vitiligo significantly (Table 1).
Among the studied variables, there was a nonsignificant relationship between the genotypes and alleles of the studied SNPs, except for the significant association between the T allele of rs6721961 and the marginal type of repigmentation (Figure 1) and between the G allele of the same SNP and the BMI (Figure 2).
Nrf2 and its negative regulator protein, Keap1, play a crucial antioxidant role, protecting all body cells against the harmful effects exerted by the oxidative stress.7 Moreover, activating Nrf2 might be a promising therapeutic option in managing many inflammatory conditions in which oxidative stress might be involved in the pathogenesis.8
Many SNPs in Keap 1 and Nrf2 have been investigated and their association with many inflammatory diseases are now documented.9 Of particular importance, the two promotor regions of the gene SNP are the rs35652124 and the rs6721961 polymorphisms. The ancestral alleles at Nrf2 (rs35652124) and (rs6721961) are the C allele and the G allele, respectively. Both ancestral alleles are associated with greater Nrf2 activity.6,10
In the present work, a significant link between Nrf2 (rs35652124) polymorphism and vitiligo was observed, while there was a nonsignificant difference between patients and controls regarding Nrf2 (rs6721961) genotypes. However, the T allele of Nrf2 (rs6721961) increased the risk of vitiligo.
Song et al6 investigated the association of the same gene SNPs with vitiligo in a Chinese population (1,136 patients and 1,200 controls). In accordance with the present work, these authors reported an association between the Nrf2 (rs35652124) TT and CT genotypes and an increased risk of vitiligo. However, they didn not observe a significant difference between patients and controls regarding Nrf2 (rs6721961) genotypes or alleles. The discrepancy between the results of these two studies might be explained by the difference in sample size, ethnic variations, and different genetic and racial backgrounds.
In the present study, the T allele of Nrf2 (rs6721961) was significantly more predominant in vitiligo patients with the marginal type of repigmentation. Selvan et al11 reported that the marginal pattern was predominant in both narrowband ultraviolet B and excimer laser-treated patients. Moreover, Yang et al12 reported that patients with vitiligo treated with topical therapies mostly developed marginal repigmentation. However, Gan et al13 found that the most common pattern of repigmentation was the perifollicular pattern, followed by the diffuse, combined, and marginal repigmentation types, respectively. During six months of follow-up after cessation of the applied therapeutic lines, Gan et al13 also detected that marginal repigmentation was the most stable form. From these findings, we can predict that carriers of the T allele of Nrf2 (6721961) would have a good prognosis.
The G allele of Nrf2 (rs6721961), known to be associated with high Nrf2 activity, was associated with higher BMI in the studied patients. This finding was interesting because Shin et al14 demonstrated the effects of Nrf2 in preventing fat accumulation and body weight gain. However, this finding could be explained by the exhaustion of Nrf2 function due to its constant activation.15
The incidence of gastrointestinal diseases (e.g., ulcerative colitis, celiac disease) in vitiligo patients is higher than that in the normal population.16 Arisawa et al17 reported that the Nrf2 (rs35652124) C allele is protective against gastrointestinal diseases, and patients from a Japanese population heterozygous at locus Nrf2 (rs35652124) CT were more susceptible to ulcerative colitis. This could provide a genetic explanation for the association between these conditions.
Nrf2 (-617 T/G) and (-653 T/C) polymorphism might place a role in patients' susceptibility to vitiligo and modify the clinical presentation of the disease. Based on our results, we recommend further studies to investigate the Nrf2 expression in vitiligo lesions as well as the relationship between different Nrf2 (-617 T/G) and (-653 T/C) genotypes with the expression levels.
Saleem MD, Oussedik E, Schoch JJ, et al. Acquired disorders with depigmentation: a systematic approach to vitiliginoid conditions. J Am Acad Dermatol. 2019;80(5):1215–1231.e6.
Gianfaldoni S, Tchernev G, Wollina U, et al. Vitiligo in children: a better understanding of the disease. Maced J Med Sci. 2018;6(1): 181–184.
Wang Y, Li S, Li C. Perspectives of new advances in the pathogenesis of vitiligo: from oxidative stress to autoimmunity. Med Sci Monit. 2019;25:1017–1023.
Wang H, Liu X, Long M, et al. NRF2 activation by antioxidant antidiabetic agents accelerates tumor metastasis. Sci Transl Med. 2016;8:334ra51.
Suzuki T, Yamamoto M. Stress-sensing mechanisms and the physiological roles of the Keap1-Nrf2 system during cellular stress. J Biol Chem. 2017; 292(41): 16817–16824.
Song P, Li K, Liu L, et al. Genetic polymorphism of the Nrf2 promoter region is associated with vitiligo risk in Han Chinese populations. J Cell Mol Med. 2016;20(10):1840–1850.
Shimoyama Y, Mitsuda Y, Hamajima N, Niwa T. Polymorphisms of Nrf2, an antioxidative gene, are associated with blood pressure in Japanese. Nagoya J Med Sci. 2014;76(1–2):113–120.
Jiao Y, Niu T, Liu H, Tay FR. Protection against HEMA-induced mitochondrial injury in vitro by Nrf2 activation. Oxid Med Cell Longev. 2019;2019:3501059.
Dhamodharan U, Ponjayanthi B, Sireesh D, et al. Association of single-nucleotide polymorphisms of the KEAP1 gene with the risk of various human diseases and its functional impact using in silico analysis. Pharmacol Res. 2018;137:205–218.
Marzec JM, Christie JD, Reddy SP, et al. Functional polymorphisms in the transcription factor NRF2 in humans increase the risk of acute lung injury. FASEB J. 2007;21(9): 2237–2246.
Selvan BS, Thadeus J, Anandan H. Pattern of repigmentation in the treatment of vitiligo vulgaris with NBUVB therapy. Int J Contemp Med Res. 2018;5(5):E1–E3.
Yang K, Xiong X, Pallavi G, et al. The early repigmentation pattern of vitiligo is related to the source of melanocytes and by the choice of therapy: a retrospective cohort study. Int J Dermatol. 2018;57(3):324–331.
Gan EY, Eleftheriadou V, Esmat S, et al. Repigmentation in vitiligo: position paper of the Vitiligo Global Issues Consensus Conference. Pigment Cell Melanoma Res. 2017;30(1):28–40.
Shin S, Wakabayashi J, Yates MS, et al. Role of Nrf2 in prevention of high-fat diet-induced obesity by synthetic triterpenoid CDDO-imidazolide. Eur J Pharmacol. 2009;620(1–3):138–144.
Seo HA, Lee IK. The role of Nrf2: adipocyte differentiation, obesity, and insulin resistance. Oxid Med Cell Longev. 2013;2013:184598.
Pashankar D, Prendiville J , Israel DM . Vitiligo and Crohn's disease in children. J Pediatr Gastroenterol Nutr. 1999;28(2):227–229.
Arisawa T, Tahara T, Shibata T, et al. The influence of promoter polymorphism of nuclear factor-erythroid 2-related factor 2 gene on the aberrant DNA methylation in gastric epithelium. Oncol Rep. 2008;19(1):211–216.
Gene polymorphism, nuclear factor erythroid-2-related factor 2, vitiligo
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Does the nationality of the aircraft registration matter?
The Isle of Man do it differently!
Most aircraft are flying internationally and many non-EU operators are using the Temporary Admission procedure when they are flying in the EU. The basic simplified rules are that an aircraft owned/operated/registered/based outside the EU, can use the Temporary Admission procedure. There has been an on-going discussion for years in the aviation society about whether or not the 28 EU aircraft registrations can be used with the Temporary Admission procedure. Special focus has been on the Guernsey (2) and Isle of Man (M) registrations as both these jurisdictions are outside the EU but inside the EU customs territory. We have therefore commissioned another verification survey about this subject.
The nationality of the aircraft registration is extremely relevant to look at in this context. In our opinion there is no doubt that the 28 EU aircraft registrations, the Guernsey (2) and Isle of Man (M) registrations cannot be used combined with the Temporary Admission procedure. The important article 212 (a) in the EU customs code (Commission Delegated Regulation No 2015/2446) about the conditions for granting total relief from import duty for means of transport mentions the following "they (the aircraft) are registered outside the customs territory of the Union in the name of a person established outside that territory". This should leave no doubt.
The feed-back is unanimous and the result is that the mentioned EU aircraft registrations cannot be used in combination with the Temporary Admission procedure. This also means that an Isle of Man or Guernsey registered aircraft based e.g. in the Middle East or Hong Kong is basically not allowed a single entry into the EU if the Temporary Admission procedure is used. The risk is that local customs will asked for a full importation to be completed with a full payment of the VAT.
What kind of aircraft registration will local customs accept for use under the TA procedure?
1) Both jurisdictions are outside the EU but inside the EU customs area.
Even though the above result is unanimous, it is common knowledge at least within the aviation society that Isle of Man customs for years have accepted that their own M-registered aircraft could be used combined with the Temporary Admission procedure. It is unknown to us under which circumstances and legal references such a combination can be legal and aligned to the EU Customs Code and guidelines from the HMRC.