Composite Health Index Research Articles

Decomposing the association of psychosocial wellbeing with all-cause mortality: the mediating role of physical health and lifestyle factors.

This study quantified the effect of 48 psychosocial constructs on all-cause mortality using data from 7,698 individuals in the U.S. Health and Retirement Study. Latent class analysis was used to divide participants into mutually exclusive psychosocial wellbeing groups (good, average, or poor) which was subsequently considered as the exposure. Mediation analysis was then conducted to determine the direct effect of the psychosocial wellbeing groups and the indirect (mediating) effects of physical health (functional status and comorbid conditions) and lifestyle factors (physical activity, smoking, and alcohol consumption) on overall survival. We also created a composite health index measure representing the summative effect of the mediators. We observed a strong and statistically significant total effect (TE) between survival time and psychosocial wellbeing group (survival time ratio (SR) = 1.73, 95% confidence interval (CI):1.50,2.01 when comparing good to poor). Mediation analysis revealed that the direct effect via psychosocial wellbeing group accounted for more than half of the TE (SR = 1.46, 95% CI:1.27,1.67). The composite health index measure mediated 36.2% of the TE with the natural indirect effect SR of 1.18 (95% CI:1.13,1.22). Our findings demonstrate the interconnectedness between psychosocial wellbeing and physical health and lifestyle factors on survival.

Assessment of ecological civilization construction from the perspective of environment and health in China

This study innovatively evaluated ecological civilization in China from the perspective of environment and health. A Composite Environmental Health Index (CEHI) was constructed based on the Driving force-Pressure-State-Impact-Response (DPSIR) and Coupling Coordination Degree (CCD) models. Results showed that significant and sustained improvements were observed in the ecological environment after ecological civilization, while economic development continued to progress at a steady pace. However, the advancement in population health (impact subsystem), exhibited comparatively modest progress, potentially linked to issues such as demographic aging and the enduring consequences of past exposure to environmental pollutants. At the provincial level, the regional development was uneven. The CEHI performance was highest in the eastern regions, followed by the central regions, with the western regions showing the least progress. Beijing, Guangdong, Jiangsu, Shanghai, and Zhejiang emerged as top performers with higher CEHI scores, which can be attributed to their favorable geographical positioning and the response subsystem. Conversely, northeastern regions (Heilongjiang, Jilin, and Liaoning) and northwestern regions (Shaanxi, Gansu, Ningxia, and Qinghai) experienced limited advancements in post-ecological civilization implementation. For these underperforming regions, there is a pressing need to intensify efforts aimed at enhancing their response subsystems. In summary, China's pursuit of ecological civilization has yielded significant successes, potentially offering valuable insights for other nations striving for sustainable development. The ecological civilization model's integration of ecological environmental protection into economic, political, cultural, and social constructs may serve as a meaningful reference for the sustainable development of other countries.

Malnourished children? Child health disparities and its causes in Eastern India

The disparity in child health outcomes across Indian districts, particularly in Eastern India, is alarming. Approximately half of the districts in this region exhibit very poor child health outcomes, evidenced by alarmingly high neonatal, infant, and under‐five mortality rates. This study analyses the existing levels and patterns of child health outcomes across 112 districts of Eastern India, a region known for its low levels of child health status. To do this, we have constructed a composite child health index (CHI) using nationally representative data from the National Family Health Survey (NFHS‐5). Using Principal Component Analysis, the study uses the CHI values to categorize the districts into high, moderate, and low‐performing districts. The results show a significant variation in CHI outcomes across the districts, with striking regional disparities. Notably, all high‐performing districts were located only in the state of Odisha. In contrast, a substantial proportion of the low‐performing districts were in the state of Bihar, suggesting systemic healthcare deficiencies. Using discriminant function analysis, we obtain the critical determinants of CHI to be sanitation facilities, mother's education, marriage before 18 years, and low body mass index of the mother. Our findings confirm that improvements in these areas are crucial for enhancing child health outcomes and ending intergenerational transmission of poor health outcomes. The implications of this study extend beyond eastern India, offering valuable insights into similar challenges in global low‐ and middle‐income regions.

Live longer, work longer? An investigation of the health capacity to work at older ages in Denmark using combined register and survey data

Publicly funded pension systems are facing the challenge of remaining financially sustainable without lowering pensions. Raising the statutory retirement age gradually in line with the increase in life expectancy has been a key measure to solve the problem. The implicit assumption is that the additional years of life are lived in good health, or as a minimum that health status is compatible with work. However, some individuals may not have the ability to work. Furthermore, a uniform retirement age ignores the different exposures to morbidity and mortality risks across social groups. Consequently, it is important to examine whether the health of older individuals will allow them to continue working and whether there is significant heterogeneity in the ability to work. Combining the Survey of Health, Ageing and Retirement in Europe (SHARE) with data from the Danish registers enables us to create a composite health index that includes an extensive range of health indicators. Utilising the health capacity to work approach, we estimate the health capacity to work among Danes aged 55 and above. We divide health capacity into physical and mental health. We investigate heterogeneity in health capacity across educational and occupational attainment. Substantial additional work capacity is found for older Danes. Depending on the type of health index applied, the health capacrk varies. There is evidence of a socio-economic gradient in work capacity. Results thereby show that policies that intend to utilise the additional work capacity should consider heterogeneity in health.HighlightsWe provide new evidence of the health capacity work in Denmark.The combination of survey and register data allow for comprehensive health indices.There is substantial additional health capacity to work.There is variation across health indices.There is variation across educational and occupational attainment.

Menstrual symptoms and subjective well-being among postmenarchal adolescents

BackgroundMenstrual symptoms are predominantly studied among adults but may occur directly after menarche. Adolescent menstrual health care, however, faces specific obstacles and more research into menstrual symptoms as a determinant of adolescent well-being is therefore advocated. Objective(s)To study menstrual symptoms and their impact on everyday life and well-being among postmenarchal adolescents. Study Design: A survey was delivered to a random sample of 1644 schoolgirls, drawn from a population-based project involving 116 lower secondary education schools (7th-8th grade) in Stockholm, Sweden. Menstrual symptoms (i.e., dysmenorrhea; heavy bleeding; irregular periods; mood disturbance; other general symptoms) were investigated through multiple choice questions and defined according to their impact on everyday life as mild (seldom affected), moderate (affected but possible to cope) and severe (affected and difficult to cope). Subjective well-being was measured with the World Health Organisation Five Well-Being Index (WHO-5). Postmenarchal respondents were eligible for analysis whereas those with incomplete outcome data or using hormonal contraception were excluded. The frequency and severity of symptoms across different postmenarchal years (1st, 2nd, 3rd, 4th, or 5th+ year after menarche) were studied with Chi-square and Kendall's tau statistics. Analysis of variance was used to study the association between menstrual symptoms and WHO-5 scores. A composite Menstrual Health index variable was obtained through principal component analysis and used to study the overall impact of menstrual symptoms on well-being in regression analyses. ResultsOf 1100 postmenarchal girls (mean age 14.1±0.7), 93.2% reported menstrual symptoms, 81.3% had at least one moderate symptom and 31.3% had at least one severe symptom. The most frequent symptoms were dysmenorrhea (80.4%) and mood disturbance (81.1%), followed by irregular periods (67.9%), heavy bleeding (60.4%) and other general symptoms (60.4%). Throughout postmenarchal years, there was a significant increase in frequency and severity (p<.001) of dysmenorrhea (τ=0.148), heavy bleeding (τ=0.134), mood disturbance (τ=0.117) and other general symptoms (τ=0.110), but not irregular periods (τ=-0.0201; p=.434). Girls with menstrual symptoms had significantly lower WHO-5 scores than those without symptoms (mean difference [MD] =17.3; 95%CI 12.3-22.4). Analysis of variance showed significant associations (p<.001) with WHO-5 scores for each of the examined menstrual symptoms. In post-hoc pairwise comparisons with peers without symptoms, the greatest reductions in WHO-5 score were found among girls with severe symptoms (MD for: dysmenorrhea -20.72; heavy bleeding -15.75; irregular periods -13.81; mood disturbance -24.97; other general symptoms -20.29) but significant differences were observed even for moderate or mild symptoms. The composite Menstrual Health index was significantly associated with WHO-5 scores in regression analysis, independently of age, age at menarche, BMI, smoking, physical activity, own and parental country of birth, biparental care, and socioeconomic status. ConclusionsDespite growing awareness about the relevance of menstruation to women's health, unmet menstrual health needs are a potential threat to the well-being of adolescents. Education, screening, and clinical competence are important tools to reduce the burden of menstrual symptoms during adolescence and to prevent long-term consequences. The development of novel person-centered strategies should be a priority for clinical practice and research in adolescent menstrual health.

Online unsupervised optimization framework for machine performance assessment based on distance metric learning

Machine performance assessment is of great significance to machine health management, whose major tasks are incipient fault detection in a normal stage and constructing a health index (HI) to track the degradation status. However, most previous assessment methods need the rich domain knowledge or labeled historical data and are not adaptive to time-varying working conditions. To address these challenges, a novel two-stage online unsupervised optimization framework for machine performance assessment is proposed without relying on any historical abnormal and faulty data. In the first stage, high-dimensional features are extracted from the raw monitoring signals and then projected into the low-dimensional space for feature selection. A distance metric learning-based clustering model considering separability is proposed to simultaneously detect the incipient fault initial time and search the most effective features, which can separate the normal and degradation stages more distinctly. In the second stage, once the incipient fault is identified, a composite HI is generated based on Mahalanobis distance between the normal features and current degradation features. An adaptive information-theoretic metric learning algorithm is developed to iteratively optimize and update the HI for online degradation assessment, whose objective function is formulated based on LogDet divergence, and the monotonicity and robustness properties. Finally, the effectiveness of the proposed method is illustrated through applications to two bearing datasets.

Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study.

Reduced sleep duration and work hour variability contribute to medical error and physician burnout. This study assesses the relationships between physician performance, burnout, and the dimensions of sleep beyond hours slept. This was an ancillary analysis of 3 years of data from an international prospective cohort study: the Intern Health Study. Actigraphy data from 3,654 intern physicians capturing sleep timing, regularity, efficiency, and duration were used individually and combined as a composite sleep health index to measure the association of multidimensional sleep patterns on self-reported medical errors and burnout. From 2017-2019, interns' work hours decreased by 4 hours per week and total sleep time also decreased (6.7 to 5.99 hours), and sleep efficiency, timing, and regularity all worsened (all P < .05). In the 21.2% of participants who committed an error, there was no difference in sleep duration, timing, or regularity. Lower sleep efficiency was associated with higher odds of committing an error (P = .003) and higher burnout scores (P < .001). Although overall sleep quality was poor in the entire cohort, interns in the lowest quintile of sleep duration, regularity, and efficiency had higher burnout scores than those in the best quintile. Sleep efficiency, not duration, was associated with increased self-reported medical errors and burnout in intern physicians. Overall sleep quality and duration worsened despite fewer hours worked. Future studies on physician burnout should measure all aspects of sleep health. Hassinger AB, Velez C, Wang J, Mador MJ, Wilding GE, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. J Clin Sleep Med. 2024;20(2):221-227.

Mitochondrial health index correlates with plasma circulating cell-free mitochondrial DNA in bipolar disorder.

Although mitochondrial dysfunction is known to play an essential role in the pathophysiology of bipolar disorder (BD), there is a glaring gap in our understanding of how mitochondrial dysfunction can modulate clinical phenotypes. An emerging paradigm suggests mitochondria play an important non-energetic role in adaptation to stress, impacting cellular resilience and acting as a source of systemic allostatic load. Known as mitochondrial allostatic load, this (phenomenon) occurs when mitochondria are unable to recalibrate and maintain cell homeostasis. This study aimed to evaluate the composite mitochondrial health index (MHI) in BD subjects and non-psychiatry controls. We will also explore whether lower MIH will be related to higher cell-free mtDNA (ccf-mtDNA) levels and poor clinical outcomes. In this study, 14 BD-I patients and 16 age- and sex-matched non-psychiatry controls were enrolled. Peripheral blood mononuclear cells (PBMCs) were used to measure the enzymatic activities of citrate synthase and complexes I, II, and IV and mtDNA copy number. Ccf-mtDNA was evaluated by qPCR in plasma. Mitochondrial quality control (MQC) proteins were evaluated by western blotting. After adjusting for confounding variables, such as age, sex, body mass index (BMI), and smoking status, patients with BD presented lower MHI compared to non-psychiatry controls, as well as higher ccf-mtDNA levels that negatively correlated with MHI. Because the MQC network is essential to maintain mitochondrial health, MHI and ccf-mtDNA were also examined in relation to several MQC-related proteins, such as Fis-1, Opa-1, and LC3. Our results showed that MHI correlated negatively with Fis-1 and positively with Opa-1 and LC3. Accordingly, ccf-mtDNA had a positive correlation with Fis-1 and a negative correlation with Opa-1 and LC3. Furthermore, we found a noteworthy inverse correlation between illness severity and MHI, with lower MHI and higher ccf-mtDNA levels in subjects with a longer illness duration, worse functional status, and higher depressive symptoms. Our findings indicate that mitochondrial allostatic load contributes to BD, suggesting mitochondria represent a potential biological intersection point that could contribute to impaired cellular resilience and increased vulnerability to stress and mood episodes. Ultimately, by linking mitochondrial dysfunction to disease progression and poor outcomes, we might be able to build a predictive marker that explains how mitochondrial function and its regulation contribute to BD development and that may eventually serve as a treatment guide for both old and new therapeutic targets.

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

Degradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method.

The prevalence of frailty and its relationship with sociodemographic factors, regional healthcare disparities, and healthcare utilization in the aging population across India.

To estimate frailty prevalence and its relationship with the socio-economic and regional factors and health care outcomes. In this study, participants from the harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) were included. The frailty index (FI) was calculated using a 32-variable deficit model, with a value of ≥ 25% considered as frail. Data on demographic (including caste and religion) and socioeconomic profiles and health care utilization were obtained. The state-wise health index maintained by the government based on various health-related parameters was used to group the participants' residential states into high-, intermediate-, and low-performing states. Multivariable and zero-inflated negative binomial regression was used to assess the relationship of frailty index with sociodemographic characteristics, health index, and health care expenditure or hospitalization. Among the 3953 eligible participants, the prevalence of frailty was 42.34% (men = 34.99% and women = 49.35%). Compared to high-performing states, intermediate- and low-performing states had a higher proportion of frail individuals (49.7% vs. 46.8% vs. 34.5%, P < 0.001). In the adjusted analysis, frailty was positively associated with age, female sex, rural locality, lower education level, and caste (scheduled caste and other backward classes). After adjusting for the socio-economic profile, FI was inversely associated with the composite health index of a state (P < 0.001). FI was also significantly correlated with total 1-year health care expenditure and hospitalization (P < 0.001 and 0.020, respectively). There is a high prevalence of frailty among older Indian adults that is associated with sociodemographic factors and regional health care performance. Furthermore, frailty is associated with increased health care utilization and expenditure.

An unsupervised neural network for graphical health index construction and residual life prediction

To better characterize the health status and performing remaining useful life prediction, a composite health index is developed through the fusion of multi-channel signals. However, most of the existing literature limits the data fusion to be linear, which implies that the underlying degradation pattern must follow a linear form. This strong prerequisite of these approaches undermines the effectiveness of existing techniques for capturing the potential nonlinear nature of degradation process. In order to overcome this limitation as well as to improve the predictability, this paper proposes a nonlinear health index construction method achieving by an unsupervised neural network. Specifically, a neural network structure is introduced to approximate the highly nonlinear relationship between signals and health status. Furthermore, we consider the remaining useful life prediction as a binary classification problem, and then propose a maximal classification margin constraint, which is integrated with the monotonicity and minimal variability at the failure time to formulate the novel loss function. To estimate the model parameter, we developed a customized adaptive moment estimation algorithm (Adam). The comprehensive case study is performed based on the benchmark C-MAPSS dataset. As reported in the experiment, the constructed health index can better characterize the underlying degradation process.

STUDY TO EVALUATE THE EFFECTIVENESS OF FERROUS SULPHATE VS FERROUS ASCORBATE IN THE MANAGEMENT OF ANAEMIA IN PREGNANCY AT SELECTED DISTRICTS OF MADHYA PRADESH

Introduction High priority districts (the bottom 25% of the districts) were identied based on composite health index to bring about sharper improvements in health outcomes Ferrous ascorbate supplementation is being carried out in Raisen district, a HPD, when compared to the nearby district Bhopal where Ferrous sulphate is still the preferred modality for prophylaxis and management of anaemia in pregnancy. Studies showing the efcacy of Ferrous ascorbate over ferrous sulphate in pharmacological settings are available in plenty, but the data regarding real time effectiveness is not sufciently available in this part of the country. Objectives To study the sociodemographic factors associated with anaemia in pregnant women. To study the effectiveness of Ferrous sulphate vs Ferrous ascorbate in the management of anaemia in pregnancy. Methods It was a prospective observational study, including 50 pregnant females in each treatment group with gestational week of 12-14 getting themselves registered at the anganwadis (Selected by random selection without replacement method) of urban and rural eld practice area of GMC Bhopal. They were followed up after 1 and 3 months of the initial visit to evaluate their compliance and changes in haemoglobin level. Results The mean change in haemoglobin level among FS group after three visits was found to be 0.30±0.23 gm/dl, while it was 0.54±0.32 gm/dl among FA group. The mean change in haemoglobin level was more in compliant when compared to non-compliant. And the mean change in haemoglobin level in compliant females of FA treatment group was 0.52±0.30 gm/dl, and was higher than that of FS group, i.e. 0.37±0.22 gm/dl. The difference observed was statistically signicant. Conclusion Ferrous ascorbate was found better and effective in improving haemoglobin status, with better compliance and less adverse drug reactions.

A feature-level degradation measurement method for composite health index construction and trend prediction modeling

Accurate measurement of degradation levels and evolution trend for mechanical equipment has great significance to achieve the goal of condition-based maintenance (CBM) and increase the economic benefit. In this paper, a feature-level degradation measurement method is proposed for composite health index (CHI) construction and trend prediction, systematically blending the multi-source signals analysis theory and deep learning technology. First, a CHI is innovatively constructed based on a novel hybrid stacked auto-encoder (HSAE) and self-organizing map (SOM) network to comprehensively evaluate the degradation levels, which achieves the feature-level data fusion for degradation measurement. Second, considering the significant characteristics of strong nonlinear and random fluctuations for the generated CHI sequence, a hybrid algorithm, combining the variational mode decomposition (VMD) and a dynamic step size-based fruit fly optimization algorithm (DSSFOA), is applied for effectively extracting the multiscale frequency components from raw sequence and capturing the inherent laws of health degradation from different scales. Specifically, by designing a hierarchical search structure with dynamic step sizes in DSSFOA, the parameters of VMD can be optimized with high efficiency and accuracy. Finally, a scale-adaptive attention (SAA)-based bidirectional long short-term memory (BiLSTM) network and the corresponding dual-stage training strategy are developed to adaptively estimate the attention weights of different components and further predict the degradation trend in future. Extensive experiments on the C-MAPSS turbofan engine dataset and the PSU dataset validate the more superior performance of the proposed method on both of health index construction and degradation trend prediction than other existing methods.

The Establishment of a New Air Health Index Integrating the Mortality Risks Due to Ambient Air Pollution and Non-Optimum Temperature

A composite Air Health Index (AHI) is helpful for separately emphasizing the health risks of multiple stimuli and communicating the overall risks of an adverse atmospheric environment to the public. We aimed to establish a new AHI by integrating daily mortality risks due to air pollution with those due to non-optimum temperature in China. Based on the exposure-response (E-R) coefficients obtained from time-series models, the new AHI was constructed as the sum of excess mortality risk associated with air pollutants and non-optimum temperature in 272 Chinese cities from 2013 to 2015. We examined the association between the “total AHI” (based on total mortality) and total mortality, and further compared the ability of the “total AHI” to predict specific cardiopulmonary mortality with that of “specific AHIs” (based on specific mortalities). On average, air pollution and non-optimum temperature were associated with 28.23% of daily excess mortality, of which 23.47% was associated with non-optimum temperature while the remainder was associated with fine particulate matter (PM 2.5 ) (1.12%), NO 2 (2.29%,), and O 3 (2.29%). The new AHI uses a 10-point scale and shows an average across all 272 cities of 6 points. The E-R curve for AHI and mortality is approximately linear, without any thresholds. Each one unit increase in “total AHI” is associated with a 0.84% increase in all-cause mortality and 1.01%, 0.98%, 1.02%, 1.66%, and 1.71% increases in cardiovascular disease, coronary heart disease, stroke, respiratory diseases, and chronic obstructive pulmonary disease mortality, respectively. Cause-specific mortality risk estimates using the “total AHI” are similar to those predicted by “specific AHIs.” In conclusion, the “total AHI” proposed herein could be a promising tool for communicating health risks related to exposure to the ambient environment to the public.

Remaining useful life prediction using composite health index and hybrid LSTM‐SVR model

AbstractRemaining useful life (RUL) plays an important role in prognostic and health management to reduce maintenance costs and avoid possible accidents. Massive multi‐sensor data makes it challenging to extract degradation features and predict RUL. This paper develops a novel RUL prediction framework consisted of a multi‐sensor fusion model and a hybrid prediction model. HI curves are constructed by synthesizing multiple metrics including time correlation, monotonicity, robustness, and consistency, making our data fusion method different from existing methods considering only one metric. In many practical situations, the initial degradation levels of obtained HI curves are different, and treating all curves without classification will result in errors in RUL prediction. The K‐means method is used to partition degradation paths into discrete states and the obtained HI curves are grouped by their initial states and then trained separately. A hybrid prediction model combining Long Short‐Term Memory (LSTM) network and Support Vector Regression (SVR) is developed to predict the RUL. The dataset of turbofan engines is used to verify the proposed method. The results show that the proposed method performs better than many existing methods.

P168. Mitochondrial Health Index Correlates With Plasma Circulating Cell-Free Mitochondrial DNA in Bipolar Disorder

Although mitochondria dysfunction is known to play an important role in the pathophysiology of bipolar disorder (BD), there is a glaring gap in our understanding of how mitochondrial dysfunction moderates clinical phenotypes. This study aimed to evaluate the composite mitochondrial health index (MHI) in BD subjects and healthy controls. We will also explore whether lower MIH will be related to higher cell-free mtDNA (ccf-mtDNA) levels and poor clinical outcomes.

An adaptive prognostics method based on a new health index via data fusion and diffusion process

Remaining useful life prediction (RUL) is critical in predictive maintenance for components or systems prone to deterioration. However, direct RUL prediction methods have difficulties tracking health trends and realizing online prognostics. To address this issue, this paper proposes a novel health index (HI) based adaptive prognostics method by leveraging the advantages of both data fusion to handle multi-dimensional data and the adaptive extended Kalman filter (AEKF) algorithm for parameter identification in the diffusion process. A fitness metric is proposed for feature selection, and then the composite HI sequence is constructed via data fusion using the genetic algorithm. Furthermore, a diffusion process model is built to characterize HI degradation while considering multi-source uncertainties. Model parameters are then updated using the fitting-based AEKF method. Finally, the proposed method is validated on a real-world dataset of solid-state drives in data centers, and prediction results and comparative studies verify its superiority.

Newer tools to assess socioeconomic status of Indian households.

“Let there be no distinction between rich and poor, high and low.” – The Collected Works of Mahatma Gandhi, Publications Division XXVI – 199 Dear Editor, Ananthan in May 2021 issue of the Journal highlights modified Kuppuswamy scale for (measuring) socioeconomic status of an Indian family – Update based on new CPI (IW) series from September 2020 (data). Recently Labour Bureau, Government of India revised base year of existing CPI (IW) series from 2001 to 2016 and this letter updates the income ranges for Modified Kuppuswamy scale on this basis.[1] Notwithstanding what effect the economic revision will have on assessing socioeconomic status of an Indian family, we disagree with the basic assumption that this status is one of the most important determinants of health status of a family. If we look at current status of various States of our nation, there is a hidden lesson in relationship (or its lack) between average income per person (Net State domestic Product) and various health indicators. National Institute for Transforming India (NITI Aayog) – a government body – in collaboration with World Bank released Healthy States – Progressive India, which has a Report on the Ranks of States and Union Territories, two years ago. The Report counts on as basic health services indicators as vacancy of Medical Officers at PHCs and of Specialists at District Hospitals, proportion of functional 24 × 7 PHCs and proportion of ANC registered within first trimester against total registrations etc., It has a Figure E1 – Larger States: Incremental scores and ranks with overall performance on page 17. What this figure highlights is that Kerala is the best overall performer in ensuring welfare of its citizens, whereas richer ones like Karnataka and Telangana lag behind. Similarly, Figure E.4 on page 21 of the Report provides us a relationship between composite Health index score (a measurement of health indicators) and per capita State Domestic Product (for income). If health indicators are simple products of income, the relationship should be linear. But surprisingly that is not the case. States higher on the y axis are better social performers where Kerala tops the graph. What is striking to observe is that several other States – despite having similar wealth fare – not as good as this one. We need to learn a simple lesson from this official report[2] and others[3] that higher funds don’t automatically translate into indicators for making our lives better and much complexities exist in between. Another basic indicator of health of a society is that how many women die during childbirth. Here UNICEF provides an updated map of maternal mortality ratio.[4] When we look at it, what is obvious is that Thailand, Sri Lanka. Malaysia and Vietnam have very low figures whereas they don’t have much higher income per person than us. Hence we learn while looking around is that relationship between economic status and health indicators is complex one and several factors, for example improvement of primary and preventive care as well as providing universal basic education play a strong intermediate role to decide final outcome. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. We accessed all the webpages while submitting this letter to the Editor.

Free Papers Compiled

Introduction:The study was done to find out the pathways of care in patients with depressive disorders before reaching psychiatric treatment in a tertiary care Medical College Hospital. Previous studies suggest, only a limited proportion of patients with psychiatric disorders including depression attend the healthcare facilities, and that too when the condition becomes severe. Treatment from unqualified practitioners and faith healers is reported to be a common practice, leading to delay in proper treatment. Highly literate states in India like Kerala, may be have a difference in this.Aim:To study pathways to care of psychiatric patients, and the associated factors based upon the duration of untreated illness, the duration between onset of symptoms and presentation to any mental health professional, total duration of illness, the duration between onset of symptoms and psychiatric consultation, expenses spent before reaching psychiatric care, number of carers seen before consulting psychiatrist, sociodemographic factors and the diagnosis.Methods:A cross-sectional study was conducted to understand the pathway of care adopted by patients with depression attending outpatient departments in tertiary care Psychiatry centers in a rural district of Kerala. The subjects were selected from a semi-structured interview schedule. Severity of depression assessed by Hamilton Rating Scale for Depression. The data was analyzed using SPSS v22 software.Results:Awaiting.Conclusion:Kerala being the leading state in India in literacy rate and composite Health Index scores, might have a pattern on its own in health-seeking behavior. Speedier access to psychiatric care will lead to faster remission, cure and decreased relapses and disability.

Data-model interactive prognosis for multi-sensor monitored stochastic degrading devices

With advances in sensing and monitoring techniques, real time multi-sensor monitoring data of stochastic degrading devices has become the reality. How to effectively fuse these multi-sensor monitoring data to first construct the composite health index (CHI) and then model its degradation evolving process has become the emerging topic in the field of the remaining useful life (RUL) prediction. However, existing works treat the CHI construction and degradation modeling for prognostics under multi-sensor data as the disjoint problems rather than both though they are closely related in nature. To address this issue, this paper presents a novel data-model interactive RUL prediction method for multi-sensor monitored stochastic degrading devices. In the proposed method, based on the CHI extracted from multi-sensor historical data and the associated lifetime prediction via stochastic degradation modeling, an optimization objective function synthesizing the mean squared error between the predicted life and the actual life as well as the variance of the predicted life is constructed. As such, a closed-loop feedback mechanism is established for the CHI constructing and stochastic degradation modeling. Based on this feedback mechanism, the fusion coefficients for multi-sensor data and the failure threshold of the associated CHI are reversely optimized to realize the collaborative interaction between the CHI constructing and stochastic degradation modeling. To do so, the goal of making the constructed CHI automatically match the adopted stochastic degradation model can be achieved naturally. To make the degradation model accurately reflect the current reality of the in-service device, a sequential Bayesian method is proposed to update the degradation model parameters. Based on the updated model, the RUL distribution can be derived under the concept of the first passage time to achieve the prognosis. Finally, through multi-sensor data of aircraft gas turbine engines, we justify the necessity of applying the proposed method in prognosis and show its advantages in the improvements of the signal quality and prognosis accuracy.