Counting the lifetime cost of obesity: Analysis based on national England data.

AIM: Obesity has a significant impact on all-cause mortality rate and overall health care resource use (HCRU). These outcomes are also strongly linked to age, sex and local deprivation of the population. We aimed to establish the lifetime costs of obesity by demographic group/geographic area using published mortality rates and HCRU use for integrated care boards (ICB) in England in the context of costs of therapeutic intervention. METHODS: Population and expected mortality rates by age, sex and deprivation were obtained from national data. Obesity class prevalence was taken from the health of the nation study. The published impact of obesity by age, group, sex and deprivation on mortality and HCRU were applied to estimate life years lost and lifetime HCRU [by sex, age band and body mass index (BMI) class for each ICB]. The year 2019 was chosen as the study basis data to avoid influences of COVID-19 pandemic on obesity rates with application of 2022/23 HCRU values. Outcomes including prevalence, deaths, life years lost, HCRU and lifetime HCRU were compared by age and sex groups across four BMI classes normal/underweight (BMI <25 kg/m2 ), overweight (25-29.9 kg/m2 ), obese class I and II (30-39.9 kg/m2 ), and obese class III (≥40), with benchmarking being set against all population being BMI <25 kg/m2 overall and by each of the 42 ICBs. We also associated future life with deaths to provide an estimate of 'future life years lost' occurring each year. RESULTS: Total population aged >16 years was 45.4 million (51% female). PREVALENCE: 13.7 million (28% of the total adult population) had a BMI ≥30 mg/m2 and BMI ≥40 kg/m2 were 1.50 million (12%) of these 1.0 million (68%) were female and of these 0.6 million 40% were women aged 16-49 years. In addition, 35% of those with a BMI ≥40 kg/m2 were in the top deprivation quintile (i.e. overall 20%). Mortality was based on expected deaths of 518K/year, and modelling suggested that if a BMI <25 kg/m2 was achieved in all individuals, the death rate would fall by 63K to 455K/year for the English population (12% reduction). For those with a BMI ≥40 kg/m2 the predicted reduction was 12K deaths (54% lower); while in those aged 16-49 years with a BMI ≥40 kg/m2 72% of deaths were linked to obesity. For future life years lost, we estimated 2.5 years were lost in people with BMI 30-39.9 kg/m2 6.7 years when BMI ≥40 kg/m2 . However, for those aged 16-49 years with a BMI ≥40 kg/m2 , 8.3 years were lost. HCRU, for weight reduction, the annual HCRU decrease from BMI ≥40 kg/m2 to BMI 30-39.9 kg/m2 was £342 per person and from BMI 30-39.9 to 25-29.9 kg/m2 the reduction was £316/person. However, lifetime costs were similar because of reduced life expectancy for obese individuals. In quality adjusted life years (QALY), overall, 791 689 future life years were lost (13.1% of all) in people with BMI ≥25 kg/m2 and were related to excess weight. When the NICE £30 000 per QALY value was applied to the estimated total 791 689 future life years lost then the potential QALY value reduction lost was equivalent to £24 billion/year or £522/person in the obese population. For morbidly obese men and women the potential QALY value lost was £2864/person/year. Regarding geography, across the 42 ICBs, we observed significant variation in the prevalence of BMI ≥40 (1.8%-4.3%), excess mortality (11.6%-15.4%) and HCRU linked to higher BMI (7.2%-8.8%). The areas with the greatest impact on HCRU were in the north-west, north-east and Midlands of England, while the south shows less impact. CONCLUSION: The expected increases in annual HCRU because of obesity, when considered over a lifetime, are being mitigated by the increased mortality of obese individuals. Our data suggest that simple short-term HCRU reduction brought about through BMI reduction will be insufficient to fund additional specialist weight reduction interventions. The HRCUs associated with BMI are not in most cases related to short-term health conditions. They are a cumulative result over a number of years, so for age 16-49 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £325/year for women and £80/year for men but this might not have immediately occurred within that year. For those aged >70 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £777/year for women and £796/year for men but also may not be manifest within that year. However, for the morbidly obese men and women, the potential QALY value lost was £2864 per person per year with the potential for these funds to be applied to intensive weight management programmes, including pharmacotherapy.

Failure to control conventional cardiovascular risk factors in women with type 2 diabetes might explain worse mortality.

INTRODUCTION: The standardised mortality rate (SMR) for people with diabetes in England is 1.5-1.7, with differences in outcomes between sexes. There has been little work examining the factors that could have an impact on this or on what may determine sex differences in outcome. METHODS: Data were extracted for patients with type 2 diabetes (T2D) in Salford (England) in 2010 for the years up to 2020, including any deaths recorded. Expected deaths were calculated from annual Office of National Statistics mortality rate and life expectancy by age and gender, adjusted for the local Index of Multiple Deprivation (IMD). This provided the SMR deprivation (SMRd), and life expectancy years lost per death (LEYLD). The effects of treatment type, and clinical features on SMRd relative to sex were examined by univariable and multivariable analysis. RESULTS: Data from n = 11,806 (F = 5184; M = 6622) patients were included. Of these, n = 5540 were newly diagnosed and n = 3921 died (F = 1841; M = 2080). In total, n = 78,930 patient years. The expected deaths numbered n = 2596 (adjusted for age, sex, and IMD). Excess deaths were n = 1325 (F = 689; M = 636). Life expectancy years lost (LEYL) 18,989 (F = 9714; M = 9275). SMRd 1.51 (F = 1.60; M = 1.44) and LEYLD 4.84 years (F = 5.28; M = 4.46). The impact of risk factors was not different by sex. However, women had higher prevalence of % diagnosed >65 years of age; % last eGFR <60 mLs/min/1.73 m2 , and lower prevalence of % prescribed ACE-inhibitor/ARB, DPP4-inhibitor and SGLT2-inhibitor. Applying the male prevalence rate to the female population and expected mortality suggested n = 437 (55%) of excess T2D female deaths were attributed to sex difference in the prevalence of these risk and protective factors. CONCLUSIONS: Outcomes in women with T2DM are worse than in men, contributed to by greater prevalence of adverse factors and less prescribing of cardioprotective medication.

The change in glycaemic control immediately after COVID-19 vaccination in people with type 1 diabetes.

AIMS: Evidence suggests that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels after COVID-19 vaccination. We aimed to assess this objectively. METHODS: We examined the interstitial glucose profile of 97 consecutive adults (age ≥ 18 years) with T1DM using the FreeStyle Libre® flash glucose monitor in the periods immediately before and after their first COVID-19 vaccination. The primary outcome measure was percentage (%) interstitial glucose readings within the target range 3.9-10 mmol/L for 7 days prior to the vaccination and the 7 days after the vaccination. Data are mean ± standard error. RESULTS: There was a significant decrease in the % interstitial glucose on target (3.9-10.0) for the 7 days following vaccination (mean 52.2% ± 2.0%) versus pre-COVID-19 vaccination (mean 55.0% ± 2.0%) (p = 0.030). 58% of individuals with T1DM showed a reduction in the 'time in target range' in the week after vaccination. 30% showed a decrease of time within the target range of over 10%, and 10% showed a decrease in time within target range of over 20%. The change in interstitial glucose proportion on target in the week following vaccination was most pronounced for people taking metformin/dapagliflozin + basal bolus insulin (change -7.6%) and for people with HbA1c below the median (change -5.7%). CONCLUSION: In T1DM, we have shown that initial COVID-19 vaccination can cause temporary perturbation of interstitial glucose, with this effect more pronounced in people talking oral hypoglycaemic medication plus insulin, and when HbA1c is lower.

Trends in costs and prescribing for liothyronine and levothyroxine in England and wales 2011-2020.

INTRODUCTION: Recent prescribing policies in England and Wales have imposed significant restrictions on liothyronine prescribing in general practice driven by the prohibitive costs and uncertain benefits of liothyronine in the management of hypothyroidism. However, the impact of these policies on liothyronine usage and costs is still unclear. METHODS: Data were downloaded from the NHS monthly General Practice Prescribing Data in England and from the Comparative Analysis System for Prescribing Audit (CASPA) in Wales for 2011-2020. Trends over the period in amount and costs of levothyroxine and liothyronine prescribing were analysed. RESULTS: The total medication costs per year for England Wales for hypothyroidism rose from £60.8 million to £129.8 million in 2015-16 and have since reduced to £88.4 million. Levothyroxine prescriptions have been growing above the population growth rate at 0.7%/annum in England and 1.1% in Wales. The costs/patient/year for liothyronine rose from £550 to £3000 in 2015-16 and has since fallen to £2500. Use of liothyronine as a percentage of levothyroxine started to fall in 2015-16 at 7%/annum in England and 3% in Wales. Nevertheless, 0.5% of levothyroxine-treated patients continue to receive liothyronine. All Clinical Commission Groups (CCGs) in England continue to have at least one liothyronine prescribing practice and 48.5% of English general practices prescribed liothyronine in 2019-20. CONCLUSION: In spite of strenuous attempts to limit prescribing of liothyronine in general practice, a significant number of patients continue to receive this therapy. The price differential of liothyronine vs levothyroxine should be examined again in light of the continuing use of liothyronine.

Lessons learnt from the variation across 6741 family/general practices in England in the use of treatments for hypogonadism.

INTRODUCTION: We have previously reported rates of diagnosis of male hypogonadism in United Kingdom (UK) general practices. We aimed to identify factors associated with testosterone prescribing in UK general practice. METHODS: We determined for 6741 general practices in England, the level of testosterone prescribing in men and the relation between volume of testosterone prescribing and (1) demographic characteristics of the practice, (2) % patients with specific comorbidities and (3) national GP patient survey results. RESULTS: The largest volume (by prescribing volume) agents were injectable preparations at a total cost in the 12-month period 2018/19 of £8,172,519 with gel preparations in second place: total cost £4,795,057. Transdermal patches, once the only alternative to testosterone injections or implants, were little prescribed: total cost £222,022. The analysis accounted for 0.27 of the variance in testosterone prescribing between general practices. Thus, most of this variance was not accounted for by the analysis. There was a strong univariant relation (r = .95, P < .001) between PDE5-I prescribing and testosterone prescribing. Other multivariant factors independently linked with more testosterone prescribing were as follows: HIGHER proportion of men with type 2 diabetes(T2DM) on target control (HbA1c ≤ 58 mmol/mol) and HIGHER overall practice rating on the National Patient Survey for good experience, while non-white ethnicity and socio-economic deprivation were associated with less testosterone prescribing. There were a number of comorbidity factors associated with less prescribing of testosterone (such as T2DM, hypertension and stroke/TIA). CONCLUSION: Our analysis has indicated that variation between general practices in testosterone prescribing in a well developed health economy is only related to small degree (r2  = 0.27) to factors that we can define. This suggests that variation in amount of testosterone prescribed is largely related to general practitioner choice/other factors not studied and may be amenable to measures to increase knowledge/awareness of male hypogonadism, with implications for men's health.

How fast should social restrictions be eased in England as COVID-19 vaccinations are rolled out?

INTRODUCTION: Vaccination against the COVID-19 virus began in December 2020 in the UK and into Spring 2021 has been running at 5% population/week. High levels of social restrictions were implemented for the third time in January 2021 to control the second wave and resulting increases in hospitalisations and deaths. Easing those restrictions must balance multiple challenging priorities, weighing the risk of more deaths and hospitalisations against damage done to mental health, incomes and standards of living, education and provision of non-Covid-19 healthcare. METHODS: Weekly and monthly officially published data for 2020/21 were used to estimate the influence of seasonality and social restrictions on the spread of COVID-19 by age group, on the economy and on healthcare services. These factors were combined with the estimated impact of vaccinations and immunity from past infections into a model that retrospectively reflected the actual numbers of reported deaths closely both in 2020 and early 2021. The model was applied prospectively to the next 6 months to evaluate the impact of different speeds of easing social restrictions. RESULTS: The results show vaccinations as significantly reducing the number of hospitalisations and deaths. The central estimate is that relative to rapid easing, the avoided loss of 57 000 life-years from a strategy of relatively slow easing over the next several  months comes at a cost in terms of GDP reduction of around £0.4 million/life-year loss avoided. This is over 10 times higher than the usual limit the NHS uses for spending against Quality Adjusted Life Years (QALYs) saved. Alternative assumptions for key factors affecting the spread of the virus give significantly different trade-offs between costs and benefits of different speeds of easing. Disruption of non-Covid-19 Healthcare provision also increases in times of higher levels of social restrictions. CONCLUSION: In most cases, the results favour a somewhat faster easing of restrictions in England than current policy implies.

"Stay at Home, Protect the National Health Service, Save Lives": A cost benefit analysis of the lockdown in the United Kingdom.

INTRODUCTION: The COVID-19 pandemic has transformed lives across the world. In the UK, a public health driven policy of population "lockdown" has had enormous personal and economic impact. METHODS: We compare UK response and outcomes with European countries of similar income and healthcare resources. We calibrate estimates of the economic costs as different % loss in Gross Domestic Product (GDP) against possible benefits of avoiding life years lost, for different scenarios where current COVID-19 mortality and comorbidity rates were used to calculate the loss in life expectancy and adjusted for their levels of poor health and quality of life. We then apply a quality-adjusted life years (QALY) value of £30,000 (maximum under national guidelines). RESULTS: There was a rapid spread of cases and significant variation both in severity and timing of both implementation and subsequent reductions in social restrictions. There was less variation in the trajectory of mortality rates and excess deaths, which have fallen across all countries during May/June 2020. The average age at death and life expectancy loss for non-COVID-19 was 79.1 and 11.4 years, respectively, while COVID-19 were 80.4 and 10.1 years; including adjustments for life-shortening comorbidities and quality of life plausibly reduces this to around 5 QALY lost for each COVID-19 death. The lowest estimate for lockdown costs incurred was 40% higher than highest benefits from avoiding the worst mortality case scenario at full life expectancy tariff and in more realistic estimations they were over 5 times higher. Future scenarios showed in the best case a QALY value of £220k (7xNICE guideline) and in the worst-case £3.7m (125xNICE guideline) was needed to justify the continuation of lockdown. CONCLUSION: This suggests that the costs of continuing severe restrictions are so great relative to likely benefits in lives saved that a rapid easing in restrictions is now warranted.

Modelling the impact of the mandatory use of face coverings on public transport and in retail outlets in the UK on COVID-19-related infections, hospital admissions and mortality.

INTRODUCTION: The rapid spread of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2/)(COVID-19) virus resulted in governments around the world instigating a range of measures, including mandating the wearing of face coverings on public transport/in retail outlets. METHODS: We developed a sequential assessment of the risk reduction provided by face coverings using a step-by-step approach. The UK Office of National Statistics (ONS) Population Survey data were utilised to determine the baseline total number of community-derived infections. These were linked to reported hospital admissions/hospital deaths to create case admission risk ratio and admission-related fatality rate. We evaluated published evidence to establish an infection risk reduction for face coverings. We calculated an Infection Risk Score (IRS) for a number of common activities and related it to the effectiveness of reducing infection and its consequences, with a face covering, and evaluated their effect when applied to different infection rates over 3 months from July 24, 2020, when face coverings were made compulsory in England on public transport/retail outlets. RESULTS: We show that only 7.3% of all community-based infection risk is associated with public transport/retail outlets. In the week of July 24, The reported weekly community infection rate was 29 400 new cases at the start (July 24). The rate of growth in hospital admissions and deaths for England was around -15%/week, suggesting the infection rate, R, in the most vulnerable populations was just above 0.8. In this situation, average infections over the evaluated 13 week follow-up period, would be 9517/week with face covering of 40% effectiveness, thus, reducing average infections by 844/week, hospital admissions by 8/week and deaths by 0.6/week; a fall of 9% over the period total. If, however, the R-value rises to 1.0, then, average community infections would stay at 29 400/week and mandatory face coverings could reduce average weekly infections by 3930, hospital admissions by 36 and deaths by 2.9/week; a 13% reduction. If the R-value rose and stayed at 1.2, then, expected average community-derived hospital admissions would be 975/week and 40% effective face coverings would reduce this by 167/week and reduce possible expected hospital deaths from 80/week to 66/week. These reductions should be seen in the context that there was an average of 102 000/week all-cause hospital emergency admissions in England in June and 8900 total reported deaths in the week ending August 7, 2020. CONCLUSION: We have illustrated that the policy on mandatory use of face coverings in retail outlets/on public transport may have been very well followed, but may be of limited value in reducing hospital admissions and deaths, at least at the time that it was introduced, unless infections begin to rise faster than currently seen. The impact appears small compared with all other sources of risk, thereby raising questions regarding the effectiveness of the policy.

Personal protective equipment (PPE) and infection among healthcare workers - What is the evidence?

BACKGROUND: The worldwide outbreak of coronavirus disease-19 (COVID-19) has already put healthcare workers (HCWs) at a high risk of infection. The question of how to give HCWs the best protection against infection is a priority. METHODS: We searched systematic reviews and original studies in Medline (via Ovid) and Chinese Wan Fang digital database from inception to May, 2020, using terms 'coronavirus', 'health personnel', and 'personal protective equipment' to find evidence about the use of full-body PPEs and other PPEs by HCW exposed highly infectious diseases. RESULTS: Covering more of the body could provide better protection for HCWs. Of importance, it is not just the provision of PPE but the skills in donning and doffing of PPE that are important, this being a key time for potential transmission of pathogen to the HCW and in due time from them to others. In relation to face masks, the evidence indicates that a higher-level specification of face masks and respirators (such as N95) seems to be essential to protect HCWs from coronavirus infection. In community setting, the use of masks in the case of well individuals could be beneficial. Evidence specifically around PPE and protection from the COVID-19 virus is limited. CONCLUSION: Covering more of the body, and a higher-level specification of masks and respirators could provide better protection for HCWs. Community mask usecould be beneficial. High quality studies still need to examine the protection of PPE against COVID-19.

Controlling antibiotic usage-A national analysis of General Practitioner/Family Doctor practices links overall antibiotic levels to demography, geography, comorbidity factors with local discretionary prescribing choices.

INTRODUCTION: Ecological studies show association between antimicrobial resistance (AMR), and inappropriate oral antibiotics use. Moderating antibiotic prescribing requires an understanding of all drivers of local prescribing. The aim was to quantify how much is determined by external factors compared with discretionary clinical choices. METHODS: Oral antibiotic usage taken from England General Practitioner/Family Doctor practice prescribing data was aggregated using WHO/ATC defined daily doses (DDDs). The average annual antibiotic daily prescribing rate (AAADPR) in each practice was the total DDD of oral antibiotics divided by registered population and 365. The AAADPR of English practices in 2017_18 was linked by regression to factors including demographics, geography, medical comorbidities, clinical performance, patient satisfaction, medical workforce characteristics and prescribing selection. The regression coefficients for modifiable prescribing selection factors were applied to the difference between the median and top decile practice values to establish overall reduction opportunities through changing prescribing behaviour. RESULTS: Twenty five factors accounted for 58% of the AAADPR variation in 5889 practices supporting 49.8 million patients. Non-modifiable factors linked increased AAADPR to more northerly location, higher prevalence of diabetes, COPD, CHD, and asthma; higher white ethnicity; higher patient satisfaction and lower population density. Modifiable behaviour accounted for 11% of the variation in AAADPR, with increases associated with a wider range of antibiotics, higher proportion taken as liquids, higher doses in each prescription, lower guideline compliance, lower targeted antibiotics, lower spend/dose, and less seasonal variation. If all practices achieved the level of modifiable factors of the top decile, this model suggests that overall AAADPR could reduce by 31%. CONCLUSION: Such analysis is associative and does not infer causation. However, demographics, location, medical condition of the population, and prescribing selection are drivers of overall antibiotic prescribing. This analysis provides benchmarks for both non-modifiable and modifiable factors against which practices could evaluate their opportunities to reduce antibiotic prescribing.

Variability in Test Interval Is Linked to Glycated Haemoglobin (HbA1c) Trajectory over Time.

Aims: We previously showed that the glycated haemoglobin (HbA1c) testing frequency links to diabetes control. Here, we examine the effect of variability in test interval, adjusted for the frequency, on change in HbA1c (ΔHbA1c). Materials & Methods. HbA1c results were collected on 83,872 people with HbA1c results at baseline and 5 years (±3 months) later and ≥6 tests during this period. We calculated the standard deviation (SD) of test interval for each individual and examined the link between deciles of SD of the test interval and ΔHbA1c level, stratified by baseline HbA1c. Results: In general, less variability in testing frequency (more consistent monitoring) was associated with better diabetes control. This was most evident with moderately raised baseline HbA1c levels (7.0-9.0% (54-75 mmol/mol)). For example, in those with a starting HbA1c of 7.0-7.5% (54-58 mmol/mol), the lowest SD decile was associated with little change in HbA1c over 5 years, while for those with the highest decile, HbA1c rose by 0.4-0.6% (4-6 mmol/mol; p < 0.0001). Multivariate analysis showed that the association was independent of the age/sex/hospital site. Subanalysis suggested that the effect was most pronounced in those aged <65 years with baseline HbA1c of 7.0-7.5% (54-58 mmol/mol). We observed a 6.7-fold variation in the proportion of people in the top-three SD deciles across general practices. Conclusions: These findings indicate that the consistency of testing interval, not the just number of tests/year, is important in maintaining diabetes control, especially in those with moderately raised HbA1c levels. Systems to improve regularity of HbA1c testing are therefore needed, especially given the impact of COVID-19 on diabetes monitoring.

Applying Parkes Grid Method to Evaluate Impact of Variation in Blood Glucose Monitoring (BGM) Strip Accuracy Performance in Type 1 Diabetes Highlights the Potential for Amplification of Imprecision With Less Accurate BGM Strips.

BACKGROUND: The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being used to increase test frequency with less focus on accuracy.Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact. METHOD: BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).The Parkes error grid model with its associated category of risk including "alter clinical decision" and "affect clinical outcomes" was used, with the profile of frequency of expected results fitted into each BGM accuracy category. RESULTS: Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the "alter clinical action" category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%. CONCLUSION: There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.

Can we check serum lithium levels less often without compromising patient safety?

BACKGROUND: Lithium is viewed as the first-line long-term treatment for prevention of relapse in people with bipolar disorder. AIMS: This study examined factors associated with the likelihood of maintaining serum lithium levels within the recommended range and explored whether the monitoring interval could be extended in some cases. METHOD: We included 46 555 lithium rest requests in 3371 individuals over 7 years from three UK centres. Using lithium results in four categories (<0.4 mmol/L; 0.40-0.79 mmol/L; 0.80-0.99 mmol/L; ≥1.0 mmol/L), we determined the proportion of instances where lithium results remained stable or switched category on subsequent testing, considering the effects of age, duration of lithium therapy and testing history. RESULTS: For tests within the recommended range (0.40-0.99 mmol/L categories), 84.5% of subsequent tests remained within this range. Overall, 3 monthly testing was associated with 90% of lithium results remaining within range, compared with 85% at 6 monthly intervals. In cases where the lithium level in the previous 12 months was on target (0.40-0.79 mmol/L; British National Formulary/National Institute for Health and Care Excellence criteria), 90% remained within the target range at 6 months. Neither age nor duration of lithium therapy had any significant effect on lithium level stability. Levels within the 0.80-0.99 mmol/L category were linked to a higher probability of moving to the ≥1.0 mmol/L category (10%) compared with those in the 0.4-0.79 mmol/L group (2%), irrespective of testing frequency. CONCLUSION: We propose that for those who achieve 12 months of lithium tests within the 0.40-0.79 mmol/L range, the interval between tests could increase to 6 months, irrespective of age. Where lithium levels are 0.80-0.99 mmol/L, the test interval should remain at 3 months. This could reduce lithium test numbers by 15% and costs by ~$0.4 m p.a.