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We aimed to analyze the temporal trends in the per capita food (kcal/day/person) and protein (g/day/person) availability at the national level in the Southeast Asian (SEA) countries from 1961 to 2018. To avoid intercountry variations and errors, we used a dataset derived from the FAO’s old and new food balance sheets. We used the joinpoint model and the jump model to analyze the temporal trends. The annual percentage change (APC) was computed for each segment of the trends. Per capita food and protein availability in the SEA countries increased significantly by 0.8% per year (54.0%) and 1.1% per year (85.1%), respectively, from 1961 to 2018. During the 1960s, 1970s, and 1980s the per capita food availability in mainland SEA did not change significantly and was less than 2200 kcal/person/day. Since the early 1990s, food availability increased appreciably in the mainland SEA countries, except for Cambodia, which has experienced the increasing trend from the late 1990s. Distinct from the mainland, maritime SEA countries showed an up–down–up growth trend in their per-capita food availability from 1961 to 2018. Food-availability growth slowed down for Brunei (since the mid-1980s) and Malaysia (since mid-the 1990s) whereas it increased for Indonesia (1.5% per year), Timor-Leste (0.9% per year), and the Philippines (0.8% per year). Per capita protein availability trends in the mainland SEA countries were similar to the countries’ per capita food availability trends. Since the late 1980s, Thailand and since the late 1990s, other mainland SEA countries experienced a significant growth in their per capita protein availability. Since the late 1990s, per capita protein availability in Vietnam increased markedly and reached the highest available amount in the SEA region, following Brunei and Myanmar. Per capita protein availability increased almost continuously among the maritime SEA countries, except for Timor-Leste. Marked inequality did exist between maritime and mainland SEA countries in per capita food-availability growth till the mid-1990s. Considerable increases in per capita food availability have occurred in most of the SEA countries, but growth is inadequate for Timor-Leste and Cambodia.

Background: The aim of this study was to analyze the trends of multiple births at both national and provincial levels in Iran from 2014 to 2023. Methods: Data on the number of live births and multiple births from 2014 to 2023 at national and provincial levels were obtained from the Iran’s Bureau of Vital Statistics. The multiple birth rate (MBR), defined as the number of live births from multiple births per 1,000 live births, was calculated annually. To examine temporal trends and detect significant changes in MBR over the study period, joinpoint regression analysis was performed. Annual percent change (APC) and average annual percent change (AAPC) were calculated for the entire period. Result: The national AAPC in the MBR was estimated at 2.38% (95%CI: 1.95 to 2.76) over the entire study period. A significant shift in trend was identified in 2020. Specifically, the APC from 2014 to 2020 was 0.24%. However, from 2020 to 2023, the APC markedly increased to 6.8%, reflecting a substantial rise in MBR during this latter period. Furthermore, the study findings demonstrated that nearly all provinces across the country have exhibited an upward trend in MBR in recent years. Conclusion: The increasing trend of multiple births in Iran aligns with global patterns. Several factors may have contributed to this rise, including increased maternal age at childbirth, higher prevalence of infertility, expanded use of assisted reproductive technologies (ARTs), and shifts in population policies.  

Background Worldwide, cervical cancer is the second-most-common malignancy of the female reproductive system. Due to its large population, China accounted for 11.9% of cervical cancer deaths, and 12.3% of global cervical cancer DALYs in 2017. In 2009, China launched a nationwide screening program, yet mortality from cervical cancer has shown an upward trend in recent years. The aim of this study was to explore factors affecting cervical cancer mortality rates in China, and contribute to their future reduction. Methods In this descriptive study, a Joinpoint regression analysis and age-period-cohort (APC) model based on the intrinsic estimator (IE) algorithm were utilized. Data from the period 1989–2018 were extracted from the International Agency for Research on Cancer (IARC) Database of WHO (1989–2000) and China Health Statistical Yearbook database (2002–2018). Results Our study found mortality from cervical cancer to have initially declined, but increase thereafter over the entire observation period in both rural and urban China. The influence of age, period and cohort effect on the mortality rate had statistical significance. The effect of age increased with years, becoming a contributing factor in women aged over 45 years countrywide. Conversely, the cohort effect became a protective factor for women born after 1938 in urban areas, and for women born after 1958 in rural areas. The period effect was relatively less impactful. Conclusions The study indicates that organized cervical screening projects facilitated the identification of potential patients, or patients with comorbidities. Correspondingly, mortality was found to increase with incidence, particularly among elderly women, indicating that newly diagnosed patients were at an advanced stage of cervical cancer, or were not receiving appropriate treatment. Therefore, the coverage of cervical cancer screening should be improved, and women’s health awareness promoted. Early diagnosis and treatment is critical to reduce the disease burden and improve outcomes.

Background Retinoblastoma is the most common intraocular malignant tumor occurring among children, with an incidence rate of 1/15 000. This study built a joinpoint regression model to assess the incidence trend of retinoblastoma from 2004 to 2015 and constructed a nomogram to predict the overall survival (OS) in children. Materials and Methods Patients less than 19 years diagnosed with retinoblastoma from 2004 to 2015 were selected from the SEER database. Joinpoint regression analysis (version 4.9.0.0) was performed to evaluate the trends in retinoblastoma incidence rates from 2004 to 2015. Cox Regression Analysis was applied to investigate prognostic risk factors that influence OS. Results Joinpoint regression revealed that retinoblastoma incidence exhibited no significant increase or decrease from 2004 to 2015. As per the multiple Cox regression, tumor size, laterality, and residence (rural-urban continuum code) were correlated with OS and were used to construct a nomogram. The nomogram exhibited a good C-index of 0.71 (95% CI, 0.63 to 0.79), and the calibration curve for survival probability demonstrated that the predictions corresponded well with actual observations. Conclusions and Relevance A prognostic nomogram integrating the risk factors for retinoblastoma was constructed to provide comparatively accurate individual survival predictions. If validated, this type of assessment could be used to guide therapy in patients with retinoblastoma. Retinoblastoma is the most common intraocular malignant tumor diagnosed in children. This study built a joinpoint regression model to assess the incidence trend of retinoblastoma and constructed a nomogram to predict overall survival in children.

Background Rotavirus is the leading global pathogen of diarrhea-associated mortality and poses a great threat to public health in all age groups. This study aimed to explore the global burden and 30-year change patterns of rotavirus infection-associated deaths. Methods Based on the Global Burden of Disease 2019 Study (GBD 2019), we analyzed the age-standardized death rate (ASDR) of rotavirus infection by sex, geographical region, and sociodemographic index (SDI) from 1990 to 2019. A Joinpoint regression model was used to analyze the global trends in rotavirus infection over the 30 years, SaTScan software was used to detect the spatial and temporal aggregations, and a generalized linear model to explore the relationship between sociodemographic factors and death rates of rotavirus infection. Results Globally, rotavirus infection was the leading cause of diarrheal deaths, accounting for 19.11% of deaths from diarrhea in 2019. Rotavirus caused a higher death burden in African, Oceanian, and South Asian countries in the past three decades. The ASDR of rotavirus declined from 11.39 (95% uncertainty interval [95% UI] 5.46–19.48) per 100,000 people in 1990 to 3.41 (95% UI 1.60–6.01) per 100,000 people in 2019, with an average annual percentage change (AAPC) (− 4.07%, P  < 0.05). However, a significant uptrend was found in high-income North America (AAPC = 1.79%, P  < 0.05). The death rate was the highest among children under 5 years worldwide. However, the death rates of elderly individuals over 70 years were higher than those of children under 5 years in 2019 among high, high-middle, middle, and low-middle SDI regions. Current health expenditure, gross domestic product per capita, and the number of physicians per 1000 people were significantly negatively correlated with death rates of rotavirus. Conclusions Although the global trends in the rotavirus burden have decreased substantially over the past three decades, the burden of rotavirus remained high in Africa, Oceania, and South Asia. Children under 5 years and elderly individuals over 70 years were the populations most at risk for rotavirus infection-associated deaths, especially elderly individuals over 70 years in relatively high SDI regions. More attention should be paid to these areas and populations, and effective public health policies should be implemented in the future.

Background Cervical cancer is the fourth most common cancer that occurs to women worldwide. This study aims to assess trends in incidence and mortality of cervical cancer in India and its states over past three decades for tracking the progress of strategies for the prevention and control of cervical cancer. Methods Data on cervical cancer incidence and mortality from 1990 to 2019 for India and its states were extracted from Global Burden of Disease study and were utilized for the analysis. Spatial and rank map has been used to see the changes in incidence and mortality of cervical cancer in different Indian states. Further, joinpoint regression analysis is applied to determine the magnitude of the time trends in the age standardized incidence and mortality rates of cervical cancer. We obtained the average annual percent change (AAPC) and corresponding 95% confidence intervals (CI) for each state. Results Overall, from 1990 to 2019 Jharkhand (Incidence: -50.22%; Mortality: -56.16%) recorded the highest percentage decrement in cervical cancer incidence and mortality followed by the Himachal Pradesh (Incidence: -48.34%; Mortality: -53.37%). Tamilnadu (1 st rank), Jammu & Kashmir and Ladakh (32 nd rank) maintained the same rank over the period of three decade for age standardized cervical cancer incidence and mortality. The regression model showed a significant declining trend in India between 1990 and 2019 for age standardized incidence rate (AAPC: −0.82; 95%CI: −1.39 to −0.25; p < 0.05) with highest decline in the period 1998-2005 (AAPC: −3.22; 95%CI: −3.83 to −2.59; p < 0.05). Similarly, a significant declining trend was observed in the age standardized mortality rate of India between 1990 and 2019(AAPC: −1.35; 95%CI: −1.96 to −0.75; p < 0.05) with highest decline in the period 1998-2005 (AAPC: −3.52; 95%CI: −4.17 to −2.86; p < 0.05). Conclusion Though the incidence and mortality of cervical cancer declined over past three decades but it is still a major public health problem in India. Information, education and communication activities for girls, boys, parents and community for the prevention and control of cervical cancer should be provided throughout the country.

Background Tuberculosis(TB) remains a pressing public health challenge, with multidrug-resistant tuberculosis (MDR-TB) emerging as a major threat. And healthcare authorities require reliable epidemiological evidence as a crucial reference to address this issue effectively. The aim was to offer a comprehensive epidemiological assessment of the global prevalence and burden of MDR-TB from 1990 to 2019. Methods Estimates and 95% uncertainty intervals (UIs) for the age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized disability-adjusted life years rate (ASR of DALYs), and age-standardized death rate (ASDR) of MDR-TB were obtained from the Global Burden of Disease (GBD) 2019 database. The prevalence and burden of MDR-TB in 2019 were illustrated in the population and regional distribution. Temporal trends were analyzed by using Joinpoint regression analysis to calculate the annual percentage change (APC), average annual percentage change (AAPC) and its 95% confidence interval( CI ). Results The estimates of the number of cases were 687,839(95% UIs: 365,512 to 1223,262), the ASPR were 8.26 per 100,000 (95%UIs: 4.61 to 15.20), the ASR of DALYs were 52.38 per 100,000 (95%UIs: 22.64 to 97.60) and the ASDR were 1.36 per 100,000 (95%UIs: 0.54 to 2.59) of MDR-TB at global in 2019. Substantial burden was observed in Africa and Southeast Asia. Males exhibited higher ASPR, ASR of DALYs, and ASDR than females across most age groups, with the burden of MDR-TB increasing with age. Additionally, significant increases were observed globally in the ASIR (AAPC = 5.8; 95% CI : 5.4 to 6.1; P  < 0.001), ASPR (AAPC = 5.9; 95% CI : 5.4 to 6.4; P  < 0.001), ASR of DALYs (AAPC = 4.6; 95% CI : 4.2 to 5.0; P  < 0.001) and ASDR (AAPC = 4.4; 95% CI : 4.0 to 4.8; P  < 0.001) of MDR-TB from 1990 to 2019. Conclusions This study underscored the persistent threat of drug-resistant tuberculosis to public health. It is imperative that countries and organizations worldwide take immediate and concerted action to implement measures aimed at significantly reducing the burden of TB.

Eating disorders (ED) have increasingly become a global topic of concern for public health. A better understanding of ED incidence is a basic requirement for improving its management. However, the temporal trend of ED incidence in China is still unknown. The incidence rates of ED from 1990 to 2017 were collected from the Global Burden of Disease Study 2017 database according to the following: subtype, i.e. anorexia nervosa (AN) and bulimia nervosa (BN); sex; and age group. The average annual percent changes and relative risks were calculated using joinpoint regression and the age-period-cohort model, respectively. From 1990 to 2017, age-standardized incidence rates of ED continued to increase in males and females, and this variation trend was observed in AN and BN. Joinpoint regression analysis showed that the incidence rates increased in all age groups. Adolescents had the highest risk of developing ED, followed by young adults. Age effects were the most influential risk factor for ED incidence. Period effects showed that the risk of developing ED continuously increased with increasing time periods in BN, but not in ED and AN. Concerning the cohort effects, people born after the 1990s presented a higher risk of ED, though they presented a lower risk of BN as compared to the whole cohort. ED incidence rates continue to increase in China, particularly among adolescents and young adults. Further etiological studies are needed to explain these increases and to facilitate the early identification of high-risk individuals.

Joinpoint regression (JR) is implemented in time series analysis to identify trend changes without predefined shift points. This study compares the performance of the Joinpoint Regression Program (JRP) and R “segmented” package in detecting joinpoints using simulated and real-world data on pediatric mental health (MH)–related hospitalizations. Simulated datasets (n = 1000) were generated with varying residual autocorrelation, trend change magnitude, and joinpoint locations to evaluate the performance of both software (accuracy, specificity, coverage of the 95% confidence interval [95% CI] coverage, monthly percent change [MPC], coverage of the last segment). In addition, monthly proportions of pediatric MH-related hospitalizations (January 2016–December 2023) were analyzed to compare the number of joinpoints identified and the average monthly percent change (AMPC). In simulations without residual autocorrelation and no joinpoint, JRP exhibited a specificity of 92.7% compared to 97.9% for R. With an important trend change, the accuracy and 95% CI coverage were 59.0% and 81.7% using JRP and 69.4% and 76.8% using R. The coverage of the MPC of the last time segment varied from 93.0% to 97.9% using JRP and from 0% to 98.3% using R. When residual autocorrelation was introduced with a moderate trend change toward the end of the dataset, the accuracy and 95% CI coverage were 72.6% and 95.0% using JRP and 52.8% and 67.1% using R. The coverage of the MPC of the last time segment varied from 4.8% to 98.6% using JRP and from 0% to 96.9% with R. In the analysis of MH-related hospitalizations, among girls aged 6–11 years, JRP detected four joinpoints (AMPC: 0.11%), while R found 1 (AMPC: 0.05%). For boys aged 12–17 years, JRP identified four joinpoints vs three using R. The choice of JR software should be guided by the characteristics of the dataset. The R “segmented” package may be more appropriate for datasets without residual autocorrelation, whereas JRP appears to provide more reliable estimates when analyzing autocorrelated health care data or data with no underlying trend changes. This study compares two methods for analyzing changes in trends over time. The two tools examined are the Joinpoint Regression Program (JRP) and the R “segmented” package. Using both simulated data and real-world hospital data, we assessed the performance of these tools. The simulated data included two scenarios: one without residual autocorrelation (simpler) and another with residual autocorrelation (where data points are related to previous ones, often seen in health care data). In the simpler scenario, the R package outperformed JRP by being more accurate in detecting changes and avoiding false detections. It also provided more precise estimates, with smaller uncertainty around the change points. However, when dealing with the more complex scenario involving residual autocorrelation, the JRP performed better, especially when changes occurred later in the timeline. However, early trend changes in the time series were challenging to detect for both JRP and R software. When analyzing real-world data on children's mental health hospitalizations, the JRP tended to identify more changes in trends than the R package. However, both softwares produced broadly similar results for the overall trends. The JRP is user-friendly and works well with data that include complex patterns like residual autocorrelation, but it does not account for seasonal effects and may detect more changes than are truly present. The R package, on the other hand, offers greater precision and flexibility for simpler data but requires more advanced programming skills and does not perform as well when handling data with residual autocorrelation. In summary, the choice between these tools should be driven by the type of data being analyzed: the R package is better for straightforward data, while the JRP is more suited for complex datasets such as those found in health care. •In simulations, JRP had better performance metrics in the presence of residual autocorrelation.•In real-world data, JRP consistently identified more joinpoints in the time series.•We provide evidence on the performance of joinpoint analysis in JRP and R software.•The choice of the software should be guided by the characteristics of the time series.•For health-care time series data, joinpoint analysis using JRP is recommended.

Osteoarthritis (OA) is a major global health burden, affecting millions and causing significant disability. Understanding its trends and determinants is crucial for effective management and prevention. We analyzed data from the Global Burden of Diseases (GBD) study 2021 to assess OA incidence, Years Lived with Disability (YLDs), and age-standardized rates (ASIR/ASYR) from 1990 to 2021. We explored trends and determinants across gender, region, and Socio-Demographic Index (SDI) quintiles using Joinpoint regression, Age-Period-Cohort (APC) modeling, decomposition, and inequality analyses. The global incidence of OA surged from 20.9 million in 1990 to 46.6 million cases in 2021, with an AAPC of 0.29%. Correspondingly, YLDs escalated from 8.92 million to 21.30 million, reflecting an AAPC of 0.30%. Disparities exist across SDI quintiles, with higher rates observed in high SDI countries. Women consistently experience a higher burden compared to men. Asian regions demonstrate the fastest rise in ASYR. High BMI contributes significantly to OA burden, particularly in high SDI countries. The rising burden of OA necessitates urgent attention. Interventions targeting modifiable risk factors, such as obesity, and early detection and management strategies are crucial. Addressing gender disparities and health inequalities, particularly in high SDI countries, is essential for effective OA prevention and control.