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Background Injecting drug use is associated with considerable morbidity and mortality. Estimates of the size of the population of people who inject drugs are critical to inform service planning and estimate disease burden due to injecting drug use. We aimed to estimate the size of the population of people who inject drugs in Australia. Methods We applied a multiplier method which used benchmark data (number of people in opioid substitution therapy (OST) on a snapshot day in 2014) and multiplied it by a factor derived from the prevalence of current OST among people who inject drugs participating in the Australian Needle and Syringe Program Survey in 2014. Estimates of the total population of people who inject drugs were calculated in each state and territory and summed to produce a national estimate. We used the sex and age group distribution seen in datasets relating to people who inject drugs to derive sex- and age-stratified estimates, and calculated prevalence per 1000 population. Results Between 68,000 and 118,000 people aged 15–64 years inject drugs in Australia. The population prevalence of injecting drug use was 6.0 (lower and upper uncertainty intervals of 4.3 and 7.6) per 1000 people aged 15–64 years. Injecting drug use was more common among men than women, and most common among those aged 35–44 years. Comparison of expected drug-related deaths based on these estimates to actual deaths suggest that these figures may be underestimates. Conclusions These are the first indirect prevalence estimates of injecting drug use in Australia in over a decade. This work has identified that there are limited data available to inform estimates of this population. These estimates can be used as a basis for further work estimating injecting drug use in Australia.

Background Survey studies in medical and health sciences predominantly apply a conventional direct questioning (DQ) format to gather private and highly personal information. If the topic under investigation is sensitive or even stigmatizing, such as COVID-19-related health behaviors and adherence to non-pharmaceutical interventions in general, DQ surveys can lead to nonresponse and untruthful answers due to the influence of social desirability bias (SDB). These effects seriously threaten the validity of the results obtained, potentially leading to distorted prevalence estimates for behaviors for which the prevalence in the population is unknown. While this issue cannot be completely avoided, indirect questioning techniques (IQTs) offer a means to mitigate the harmful influence of SDB by guaranteeing the confidentiality of individual responses. The present study aims at assessing the validity of a recently proposed IQT, the Cheating Detection Triangular Model (CDTRM), in estimating the prevalence of COVID-19-related health behaviors while accounting for cheaters who disregard the instructions. Methods In an online survey of 1,714 participants in Taiwan, we obtained CDTRM prevalence estimates via an Expectation-Maximization algorithm for three COVID-19-related health behaviors with different levels of sensitivity. The CDTRM estimates were compared to DQ estimates and to available official statistics provided by the Taiwan Centers for Disease Control. Additionally, the CDTRM allowed us to estimate the share of cheaters who disregarded the instructions and adjust the prevalence estimates for the COVID-19-related health behaviors accordingly. Results For a behavior with low sensitivity, CDTRM and DQ estimates were expectedly comparable and in line with official statistics. However, for behaviors with medium and high sensitivity, CDTRM estimates were higher and thus presumably more valid than DQ estimates. Analogously, the estimated cheating rate increased with higher sensitivity of the behavior under study. Conclusions Our findings strongly support the assumption that the CDTRM successfully controlled for the validity-threatening influence of SDB in a survey on three COVID-19-related health behaviors. Consequently, the CDTRM appears to be a promising technique to increase estimation validity compared to conventional DQ for health-related behaviors, and sensitive attributes in general, for which a strong influence of SDB is to be expected.

Background Owing to migration, female genital mutilation or cutting (FGM/C) has become a growing concern in host countries in which FGM/C is not familiar. There is a need for reliable estimates of FGM/C prevalence to inform medical and public health policy. We aimed to advance methodology for estimating the prevalence of FGM/C in diaspora by determining the prevalence of FGM/C among women giving birth in the Netherlands. Methods Two methods were applied to estimate the prevalence of FGM/C in women giving birth: (I) direct estimation of FGM/C was performed through a nationwide survey of all midwifery practices in the Netherlands and (II) the extrapolation model was adopted for indirect estimation of FGM/C, by applying population-based-survey data on FGM/C in country of origin to migrant women who gave birth in 2018 in the Netherlands. Results A nationwide survey among primary care midwifery practices that provided care for 57.5% of all deliveries in 2018 in the Netherlands, reported 523 cases of FGM/C, constituting FGM/C prevalence of 0.54%. The indirect estimation of FGM/C in an extrapolation-model resulted in an estimated prevalence of 1.55%. Possible reasons for the difference in FGM/C prevalence between direct- and indirect estimation include that the midwives were not being able to recognize, record or classify FGM/C, referral to an obstetrician before assessing FGM/C status of women and selective responding to the survey. Also, migrants might differ from people in their country of origin in terms of acculturation toward discontinuation of the practice. This may have contributed to the higher indirect-estimation of FGM/C compared to direct estimation of FGM/C. Conclusions The current study has provided insight into direct estimation of FGM/C through a survey of midwifery practices in the Netherlands. Evidence based on midwifery practices data can be regarded as a minimum benchmark for actual prevalence among the subpopulation of women who gave birth in a given year.

Background The prevalence of type 2 diabetes mellitus (T2DM) in the population covered by the Tehran University of Medical Sciences is unclear but crucial for healthcare programs. This study aims to validate four non-laboratory risk-score models, the American Diabetes Association (ADA) Risk Score, Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), Finnish Diabetes Risk Score (FINDRISC), and TOPICS Diabetes Screening Score, for identifying undiagnosed diabetes and indirectly estimate the prevalence of T2DM in a subset of the Tehranian population using the selected model. Methods This research consisted of two main parts. In the first part, non-laboratory risk-score models to identify undiagnosed T2DM were validated using Iranian data from STEPs 2016 survey. The model performance was evaluated through the Area Under the Curve (AUC) and calibration via the observed-to-expected (O/E) ratio. Additional independent data from STEPs 2011 survey in Iran were utilized to test the model results by comparing indirect prevalence estimates with observed estimates. In the second part, the prevalence of T2DM was estimated indirectly by applying the selected model to a representative random sample from a Tehranian population telephone survey conducted in 2023. Results Among the different models used, AUSDRISK showed the best performance in both discrimination (AUC (95% confidence interval (CI)): 0.80 (0.78, 0.81)) and calibration (O/E ratio = 1.01). After updating the original model, there was no change in the AUC value or calibration. Additionally, our findings indicate that the indirect estimates are nearly identical to the observed values in STEPs 2011 survey. In the second part of the study, by applying the recalibrated model to a subsample, the indirect prevalence of undiagnosed diabetes and T2DM (95% CI) were estimated at 4.18% (3.87, 4.49) and 11.1% (9.34, 13.1), respectively. Conclusion Given the strong performance of the model, it appears that indirect method can provide a cost-effective and simple approach to assess disease prevalence and intervention effectiveness. Graphical Abstract

Background Migraine is a primary headache disorder which affects all aspects of life. The financial burden of migraine imposed on the society might be substantial. This study aims at estimating the economic cost of migraine in Latvia and Lithuania, including both direct and indirect costs. Direct costs encompass the costs of migraine-related health care resource utilization. Indirect costs are related to productivity loss, the potential or expected earnings lost due to migraine. Methods Direct cost is assessed by using the prevalence method, a widely used cost-of-illness approach. The prevalence rate of migraine and the migraine-related health care resource utilization are proxied from the literature, whereas unit cost of medical services and procedures are retrieved from national databases and providers. For estimating the indirect cost of migraine, we follow the human capital approach. We quantify three components of indirect costs: reduced labour force participation, absence from work and reduced productivity while at work. The number of unemployed migraineurs, days missed from work and days lost due to impairment while at work are drawn from the literature. Unemployment rate and average income in Latvia and Lithuania are then inserted to assess indirect costs. Results We find that the mean per-person total cost of migraine is €801 annually in Latvia, and €721 in Lithuania. In both countries around 30% of total cost is direct cost; cost related to a wide array of migraine-related medical services and interventions. The total cost of migraine is €112.26 million in Latvia, corresponding to 0.42% of Latvia’s GDP. The total cost of migraine is €149.62 million in Lithuania, corresponding to 0.35% of Lithuania’s GDP. In both countries two thirds of total cost is related to lost workdays due to absenteeism and presenteeism. Conclusions The financial burden of migraine imposed on the society is substantial in Latvia and Lithuania. Improvements in care for patients with migraine, such as easier access to structured headache assessment services, wider availability of various procedures and preventive medications would significantly increase direct costs. Nevertheless, this cost increase might be far outweighed by lower migraine-related productivity loss, especially as the prevalence of migraine is the highest in the most productive years of life.

Introduction China’s Hunan Province, known for its extensive lake and marshland areas, continues to face considerable challenges in eliminating schistosomiasis. This study aims to examine the epidemiological characteristics of schistosomiasis in the province, focusing on seropositive rates across various demographic groups, spatial distribution, and identifying key associated factors to inform targeted control measures. Methods From 2020 to 2022, the number of people screened each year using the indirect hemagglutination assay (IHA) was 1,053,973, 682,921, and 729,782, respectively. The Cochran-Armitage test for trend and chi-square test were employed to assess differences in seropositive rates among different times, age groups, genders, educational levels, and occupations. Spatial autocorrelation analysis was conducted to identify clusters of seropositive rates at the village level. A multiple logistic model was used to identify associated factors and generalized estimating equation (GEE) was used to obtain the parameter estimates. Results From 2020 to 2022, the seropositive rate of schistosomiasis in Hunan Province were 1.53% (95% CI: 1.51–1.55), 2.22% (95% CI: 2.19–2.26), and 2.06% (95% CI: 2.03–2.10), respectively. The seropositive rate in Hunan Province was spatially clustered in each year, with high-high clustering areas mainly distributed around the southern Dongting Lake region, the tributary areas of Dongting Lake, as well as along the Yangtze River. The seropositive rate increased with age, with individuals aged 60–69 showing the highest seropositive rate (adjusted odds ratio [OR] when compared to < 10 years old: 47.94; 95% CI: 30.04–76.52). Males had higher seropositive rate compared to females (adjusted OR: 1.72; 95% CI: 1.69–1.76). Compared to farmers, fishermen (adjusted OR: 2.54; 95% CI: 2.40–2.70) and business/service staff (adjusted OR: 1.63; 95% CI: 1.52–1.74) had higher seropositive rate. The seropositive rate decreased with increasing educational level. Individuals using tap water and sanitary toilets had lower seropositive rate compared to those who did not use (tap water: adjusted OR: 0.66; 95% CI: 0.64–0.68; sanitary toilets: adjusted OR: 0.95; 95% CI: 0.93–0.97). Additionally, those who raised sheep had a higher seropositive rate compared to those who did not (adjusted OR: 4.67; 95% CI: 4.04–5.39). Conclusions Schistosomiasis remains a significant public health issue in Hunan Province, with the seropositive rate remaining clustered in certain regions and high-risk populations. Achieving schistosomiasis elimination requires sustained targeted interventions, improved sanitation infrastructure, enhanced health education, and long-term monitoring and comprehensive control measures for high-risk areas and vulnerable populations to reduce transmission risk and ensure sustainable disease elimination.

Background In populations that lack vital registration systems, under-5 mortality (U5M) is commonly estimated using survey-based approaches, including indirect methods. One assumption of indirect methods is that a mother’s survival and her children’s survival are not correlated, but in populations affected by HIV/AIDS this assumption is violated, and thus indirect estimates are biased. Our goal was to estimate the magnitude of the bias, and to create a predictive model to correct it. Methods We used an individual-level, discrete time-step simulation model to measure how the bias in indirect estimates of U5M changes under various fertility rates, mortality rates, HIV/AIDS rates, and levels of antiretroviral therapy. We simulated 4480 populations in total and measured the amount of bias in U5M due to HIV/AIDS. We also developed a generalized linear model via penalized maximum likelihood to correct this bias. Results We found that indirect methods can underestimate U5M by 0–41% in populations with HIV prevalence of 0–40%. Applying our model to 2010 survey data from Malawi and Tanzania, we show that indirect methods would underestimate U5M by up to 7.7% in those countries at that time. Our best fitting model to correct bias in U5M had a root median square error of 0.0012. Conclusions Indirect estimates of U5M can be significantly biased in populations affected by HIV/AIDS. Our predictive model allows scholars and practitioners to correct that bias using commonly measured population characteristics. Policies and programs based on indirect estimates of U5M in populations with generalized HIV epidemics may need to be reevaluated after accounting for estimation bias.

ObjectivesTo calculate the prevalence of disabling chronic pain (DCP) and to offer a more representative and accurate estimation by applying different calibration techniques.Settings2011 Andalusian Health Survey, a cross-sectional population survey based on face-to-face home interviews.Participants6507 people aged 16 years or older and living in Andalusia, Spain.OutcomesDesign weights, linear calibration based on marginals and on crossings, and model-assisted calibration were used to estimate the prevalence and variance of DCP, for the whole sample and for the domains of sex and age groups (16–44; 45–64; +65).ResultsCalibration variables were sex, age groups and educational level. In the whole sample, DCP prevalence calibration reduced by more than 5.2% and 8.2% the estimated prevalences and variances, respectively, obtained with the design weights. Regarding the domains, prevalence reductions are from 33% to 1%, and variance reductions are from 0.2% to 1%. Model-assisted calibration is the best technique to estimate DCP prevalence for the whole population and crossing calibration for their domains, although with almost no differences compared to marginal calibration.ConclusionsThe validity and accuracy of estimations of DCP prevalence are improved by calibration adjustments. Model-assisted calibrated prevalence of DCP is 10.78% for the whole population, being at least 2-fold higher in women in all age groups. The results and methodology developed could be useful in clinical and population-based studies on chronic pain and disability.