Population Health Metrics Research Articles

Coordinates of healthcare systems in Russia and Romania

Abstract The paper discusses the key features of the state of the structure of healthcare in Russia and Romania, the administration of this scheme is assessed and the statistical array takes place over a long period. The health sector’s sustainable status is perceived to be the main target concern of each state. The medical care degree is the country-wide coefficient of well-being. However, not only at the state level, but also at the regional level, the characteristic effect of socio-economic factors on population health metrics is especially pronounced. Without the introduction of modern approaches to management, it’s more professionalization, that is, the building of a management organization that insists on the professionalism of leadership, the solution to the problems of healthcare growth is unlikely. One of the essential national tasks of establishing a plan for the socio-economic growth of Russian and Romanian healthcare and raising the standard and quality of life of the people is to strengthen the healthcare system. The provision and management of the quality of patient services can be considered one of the most significant concerns in the area of healthcare. Moreover, these issues are the most difficult to address, which is why, during a time of economic and social turmoil, their importance grows significantly.

Psychometric Evaluation of the Health State Description Questionnaire in Chile: A Proposal for a Latent Variable Approach for Valuating Health States

BackgroundA few instruments that identify and valuate health states are based on the International Classification of Functioning, Disability and Health States of the World Health Organization. One of them is the Health State Description (HSD) questionnaire first used in the World Health Survey (WHS) initiative (HSD-WHS), whose psychometric properties have not been evaluated in Chile. Additionally, the use of latent variables for the valuation process of health states has been scarcely investigated in the context of population health metrics. We aim to evaluate the psychometric properties and factorial structure of the HSD-WHS for Chile and describe a latent variable method for valuating health states associated with diseases. MethodsWe used data from the second Chilean National Health population-based survey from 2009 to 2010 (N = 5293). We explored the factorial structure of the HSD-WHS through exploratory and confirmatory factor analyses, the reliability, and the discriminant validity of the latent variable of disability. Disability weights for diseases were calculated using a linear regression model. ResultsWe found an adequate goodness of fit for a second-order model with 9 factors corresponding to disability domains (Tucker–Lewis index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.060), and good reliability estimates (standardized α = 0.91). The rescaled (between 0 and 100) latent variable of disability showed significant difference according to the explored variables. We estimated disability weights for the following: (1) depressive episode, 13.6 (12.1-15.2), (2) hypertension, 1.6 (0.0-3.3), and (3) diabetes, 5.0 (2.5-7.4). ConclusionsThis study supports the use of the HSD-WHS questionnaire in the Chilean population and a latent variable approach for valuating health states associated with diseases.

Estimating causes of out-of-hospital deaths in China: application of SmartVA methods

BackgroundMost deaths in China occur at home, making it difficult to collect reliable cause of death (CoD) information. Verbal autopsy (VA) was applied using the SmartVA tool to a sample of home deaths in China to explore its feasibility as a means of improving the quality of CoD data.MethodsThe study was carried out in 22 districts in 9 provinces, located in north-east, central, and western areas of China during 2017 and 2018. Trained interviewers selected suitable respondents in each household to collect information using the Population Health Metrics Research Consortium (PHMRC) shortened and validated electronic VA questionnaire on tablets. The CoD was diagnosed from the interview data using the SmartVA-Analyze 2.0 software (Tariff 2.0).ResultsNon-communicable diseases (NCDs) dominated the leading causes of death in all age groups and for both sexes. After redistribution of undetermined causes, stroke (24%), ischemic heart diseases (IHD) (21%), chronic respiratory diseases (11%), and lung cancer (6%) were the leading causes of death. The cause fractions for level-one cause categories and ranking of specific causes were similar between SmartVA and results from the Global Burden of Disease (GBD) study.ConclusionEvidence from this large pilot study suggests that SmartVA is a feasible and plausible tool and could be a valuable tool to improve the quality and standardization of CoD information across China.

How advanced is the epidemiological transition in Papua New Guinea? New evidence from verbal autopsy.

BackgroundReliable cause of death (COD) data are not available for the majority of deaths in Papua New Guinea (PNG), despite their critical policy value. Automated verbal autopsy (VA) methods, involving an interview and automated analysis to diagnose causes of community deaths, have recently been trialled in PNG. Here, we report VA results from three sites and highlight the utility of these methods to generate information about the leading CODs in the country.MethodsVA methods were introduced in one district in each of three provinces: Alotau in Milne Bay; Tambul-Nebilyer in Western Highlands; and Talasea in West New Britain. VA interviews were conducted using the Population Health Metrics Research Consortium (PHMRC) shortened questionnaire and analysed using the SmartVA automated diagnostic algorithm.ResultsA total of 1655 VAs were collected between June 2018 and November 2019, 87.0% of which related to deaths at age 12 years and over. Our findings suggest a continuing high proportion of deaths due to infectious diseases (27.0%) and a lower proportion of deaths due to non-communicable diseases (NCDs) (50.8%) than estimated by the Global Burden of Disease Study (GBD) 2017: 16.5% infectious diseases and 70.5% NCDs. The proportion of injury deaths was also high compared with GBD: 22.5% versus 13.0%.ConclusionsHealth policy in PNG needs to address a ‘triple burden’ of high infectious mortality, rising NCDs and a high fraction of deaths due to injuries. This study demonstrates the potential of automated VA methods to generate timely, reliable and policy-relevant data on COD patterns in hard-to-reach populations in PNG.

Estimates of insulin needs and dispensation given wastage, alternative glycemic targets, and non-insulin therapies in US populations with type 2 diabetes mellitus: A microsimulation study.

Registries and health plans estimate insulin need for population health metrics. We sought to identify how such estimates affect population- and individual-level estimates of over- and under-treatment. We developed a microsimulation comparing estimated insulin need to dispensation using the National Health and Nutrition Examination Survey (NHANES, 2005-2016, N= 2832) and Medical Expenditure Panel Survey (MEPS, 2005-2016, N = 29,615). From NHANES, ~21.6% of people with type 2 diabetes would require insulin to achieve a HbA1c target of 7% after maximum titration of two non-insulins (60.7 IU/person/day, or 84,629,833 vials of 1000 IU in the US). From MEPS, we observed 57.4 IU/person/day of insulin dispensed (81,585,842 vials). About 29% of people were dispensed at least two standard deviations less than their estimated need, and 22% at least two standard deviations more than estimated need. Population-level need estimates reduced 39.4% if liberalizing HbA1c targets to 8% for people ≥75 years old. Estimated insulin needs of people with type 2 diabetes in the U.S. are consistent with their dispensed insulin at the population level, but are sensitive to HbA1c targets for older adults, and conceal under- and over-treated subpopulations.

Work-related lung cancer concentrate among lower status workers: a quantification approach

Background/Aim: Although the concentration of carcinogenic exposures within specific socio-occupational groups is widely acknowledged, most burden of disease (BOD) approaches focus on the general population, missing the dimension of occupational health inequities. This raises social justice issues as those quantification tools are increasingly used for discussing cancer prevention priorities and occupational disease compensation rules. Our general aim was to explore ways by which population health metrics could integrate occupational inequities, and to identify eventual data gaps. Our specific aim was to compare the burden of work-related lung cancer between different socio-economic groups taking into account three known occupational carcinogens (asbestos, silica and DME) and smoking. Methods: We conducted a secondary analysis of the population-based case-control study ICARE (Investigation sur les Cancers Respiratoires et l’Environnement). The study included 2926 lung cancer cases and 3555 frequency-matched controls covering 13% of the French population. Lifelong occupational exposures were assessed by job-exposure matrices and self-report. We compared sources of variation in the attributable fractions (AF) estimates, including interaction with socio-economic position (SEP) and SEP indicator (education or occupation-based), using STATA. Results: The analysis was based on men-only due to the limited number of exposures among women. The AF for the combined indicator of asbestos, silica and DME exposures was 29.4% (95%CI [23.9-34.4]) overall, while the AF for exposed blue-collar workers compared to non-exposed groups regardless of SEP was 26.9% (95%CI [21.9- 31.7]). AFs were slightly higher when capturing SEP through education. Our results provide quantified evidence that the burden of work-related lung cancer attributable to these 3 known carcinogens concentrates massively (over 90%) among manual workers. Conclusion: They exemplify the need for integrating indicators of occupation and industry sector into BOD estimates to inform decision-making in cancer prevention and compensation. More broadly, they plead for information systems linking SEP, environmental and occupational health.

BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES.

The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data.

Interpretation of Population Health Metrics: Environmental Lead Exposure as Exemplary Case.

Our objective was to gain insight in the calculation and interpretation of population health metrics that inform disease prevention. Using as model environmental exposure to lead (ELE), a global pollutant, we assessed population health metrics derived from the Third National Health and Nutrition Examination Survey (1988 to 1994), the GBD (Global Burden of Disease Study 2010), and the Organization for Economic Co-operation and Development. In the National Health and Nutrition Examination Survey, the hazard ratio relating mortality over 19.3 years of follow-up to a blood lead increase at baseline from 1.0 to 6.7 µg/dL (10th–90th percentile interval) was 1.37 (95% CI, 1.17–1.60). The population-attributable fraction of blood lead was 18.0% (10.9%–26.1%). The number of preventable ELE-related deaths in the United States would be 412 000 per year (250 000–598 000). In GBD 2010, deaths and disability-adjusted life-years globally lost due to ELE were 0.67 million (0.58–0.78 million) and 0.56% (0.47%–0.66%), respectively. According to the 2017 Organization for Economic Co-operation and Development statistics, ELE-related welfare costs were $1 676 224 million worldwide. Extrapolations from the foregoing metrics assumed causality and reversibility of the association between mortality and blood lead, which at present-day ELE levels in developed nations is not established. Other issues limiting the interpretation of ELE-related population health metrics are the inflation of relative risk based on outdated blood lead levels, not differentiating relative from absolute risk, clustering of risk factors and exposures within individuals, residual confounding, and disregarding noncardiovascular disease and immigration in national ELE-associated welfare estimates. In conclusion, this review highlights the importance of critical thinking in translating population health metrics into cost-effective preventive strategies.

Improvement in the medication reconciliation postdischarge quality measure after implementation of a pharmacist-run service

ObjectiveThis study aimed to evaluate the impact of a pharmacist-led postsurgical discharge medication reconciliation program in an accountable care organization–like setting. SettingMultidisciplinary primary care network in Central Ohio. Practice descriptionAmbulatory care pharmacists are integrated in a primary care network, which includes a centralized focus on assistance with population health metrics, along with an interdisciplinary team. Practice innovationIn May 2018, a pharmacist-led medication reconciliation postdischarge (MRP) program was initiated focused on a postsurgical discharge population. EvaluationA retrospective chart review was conducted to compare the 2 primary end points of overall MRP rates from 4 payers in 2017 and 2018, and the postsurgical MRP rates from May 25 to November 30 of 2017 and 2018. Secondary outcomes included description of pharmacist interventions in pharmacy-led service and 30-day readmission or emergency department (ED) utilization rates for postsurgical population. ResultsA total of 5837 hospitalizations were identified as eligible for overall MRP completion in 2017 (n = 2621) and 2018 (n = 3216). A 20% relative increase in overall MRP completion was reported in 2018 (P < 0.001). In 2017, 55.9% of postsurgical MRPs were completed. The implementation of the pharmacy-led MRP program led to a 41% relative increase in postsurgical MRP completion (78.9% in 2018, P < 0.001). Pharmacists completed 220 postsurgical MRPs in the 6-month study period, including 765 total recommendations for intervention and an average of 3 recommendations and 4 medication list updates per MRP. There was no statistically significant reduction in 30-day readmission and ED utilization rates in 2018 (6.8% in 2017 vs. 6.2% in 2018, P = 0.74). ConclusionA pharmacy-led postsurgical discharge program led to an increase in MRP completion rates in the target postsurgical MRP population, which also positively affected the overall MRP completion rate. Pharmacists can be key players in the improvement and quality of MRP measures.

Robustness of the Tariff method for diagnosing verbal autopsies: impact of additional site data on the relationship between symptom and cause

BackgroundVerbal autopsy (VA) is increasingly being considered as a cost-effective method to improve cause of death information in countries with low quality vital registration. VA algorithms that use empirical data have an advantage over expert derived algorithms in that they use responses to the VA instrument as a reference instead of physician opinion. It is unclear how stable these data driven algorithms, such as the Tariff 2.0 method, are to cultural and epidemiological variations in populations where they might be employed.MethodsVAs were conducted in three sites as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) study: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Similar diagnostic criteria and cause lists as the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. We assessed changes in Tariffs by examining the proportion of Tariffs that changed significantly after the addition of the IMMCMC dataset to the PHMRC dataset.ResultsThe IMMCMC study added 3512 deaths to the GS VA database (2491 adults, 320 children, and 701 neonates). Chance-corrected cause specific mortality fractions for Tariff improved with the addition of the IMMCMC dataset for adults (+ 5.0%), children (+ 5.8%), and neonates (+ 1.5%). 97.2% of Tariffs did not change significantly after the addition of the IMMCMC dataset.ConclusionsTariffs generally remained consistent after adding the IMMCMC dataset. Population level performance of the Tariff method for diagnosing VAs improved marginally for all age groups in the combined dataset. These findings suggest that cause-symptom relationships of Tariff 2.0 might well be robust across different population settings in developing countries. Increasing the total number of GS deaths improves the validity of Tariff and provides a foundation for the validation of other empirical algorithms.

Estimating Causes of Out-of-Hospital Deaths in China: Application of SmartVA Methods

Background: Most deaths in China occur at home, making it difficult to collect reliable cause of death (CoD) information. Verbal autopsy (VA) was applied using the SmartVA tool to a sample of home deaths in China to explore its feasibility as a means of improving the quality of CoD data. Methods: The study was carried out in 22 districts in 9 provinces, located in north-east, central, and western areas of China during 2017 and 2018. Trained interviewers selected suitable respondents in each household to collect information using the Population Health Metrics Research Consortium (PHMRC) shortened and validated electronic VA questionnaire on tablets. The CoD was diagnosed from the interview data using the SmartVA-Analyze 2.0 software (Tariff 2.0). Findings: Non-communicable diseases (NCDs) dominated the leading causes of death in all age groups and for both sexes. After redistribution of undetermined causes, stroke (24%), ischemic heart diseases (IHD) (21%), chronic respiratory diseases (11%), and lung cancer (6%) were the leading causes of death. The cause fractions for level-one cause categories and ranking of specific causes were similar between SmartVA and results from the Global Burden of Disease (GBD) study. Interpretation: Evidence from this large pilot study suggests that SmartVA can be a valuable tool to improve the quality and standardization of CoD information across China. Funding Statement: Bloomberg Data for Health Initiative. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The ethics committee from the National Center for Chronic Non-Communicable Disease Control and Prevention (NCNCD) of the Chinese Center for Disease Control and Prevention approved the study. Verbal informed consent was obtained and recorded for all participants.

Population health model for pharmacist assessment and independent prescribing of statins in an ambulatory care setting

ObjectiveThis study aimed to evaluate the impact of a pharmacist population health initiative on the ability to increase the percentage of patients with atherosclerotic cardiovascular disease (ASCVD) who are on an appropriate statin. SettingTen primary care clinics in Southwest Washington. The average payer mix across the included clinics is 47% Medicare, 26% commercial, 22% Medicaid, 2% self-pay, and 3% other. Reimbursement-tied statin quality metrics are present in 14.1% of patients’ insurance contracts. Practice descriptionThe primary care pharmacy collaborative drug therapy agreement allows pharmacists to act as prescribers by permitting initiation, adjustment, and monitoring of medication therapy, with the authority historically stemming from referral by the patient’s primary care provider to the pharmacist. Practice innovationA novel, population health protocolized prescriptive authority (PPA) initiative was implemented, of which a key component was expanding pharmacists’ prescriptive authority to prescribe statins for population health initiatives. Without referral, pharmacists screened, directly outreached to, and prescribed statins for patients with ASCVD who were not on a moderate- or high-intensity statin. Electronic health record (EHR) documentation was updated to better reflect the patient’s history and increase metric accuracy. EvaluationA retrospective analysis of a population health initiative from October 1 to December 31, 2018. The initiative was evaluated on the combined success of initiating patients with ASCVD on moderate- or high-intensity statins and the acceptance rate of EHR corrections. ResultsThe pharmacy team screened 510 patients. Appropriately dosed statins were initiated for 40.0% of patients, and the EHR was accurately updated in 91.9% of instances. These combined efforts demonstrate 50.5% overall success of pharmacist interventions. ConclusionExpanding pharmacists’ authority to PPA for statin medications in patients not meeting quality metrics increased the number of successful interventions. Pharmacists make a major contribution on improving population health metrics for statins.

Urban scaling of health outcomes: a protocol for a scoping review

IntroductionWith most of the world’s population living in urban areas, it is important to understand the health effects of city living. Precise descriptions of the relations of city size and...

Linking the Assessment of the Burden of Work-Related Cancer to Health Inequities Research

TPS 791: Occupational health 1, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Population attributable fractions (PAFs) are increasingly used for discussing cancer prevention priorities. Although conceptual and validity issues have been widely addressed in the epidemiological literature, there is a paucity of attention on the impact of PAFs in decision-making, and the lack of consideration of factors like work or the environment on population health inequalities. Our aims are, first, to gather published estimates of cancer attributed to causal agents in the workplace over the last decades (past); second, to analyze them from the perspective of their potential effects on population health inequalities research (present); and third, to explore ways by which population health metrics could take occupational inequities into account (future). PAFs of work-related cancer ranged from less than 2% to more than 8%, with an average of 4-5%. While most authors acknowledge that exposures concentrate in lower-socioeconomic status workers, occupational group has never been considered as a source of variation in the calculations. This knowledge gap is linked to the paucity of data describing the occupational patterning of exposures and cancer. More globally, the social gradient in cancer is often interpreted in the light of behavioral factors alone, a tendency linked by historians to the very foundations of modern epidemiology, and that may result in what science studies refer to “undone science”. Yet, there is accumulated evidence that work affects health and death through different pathways, which are also relevant to cancer inequalities research and prevention. We hence propose to further the discipline’s reflexivity by changing the focus, scope and metrics in order to assess the burden of work-related cancer in a way that is more meaningful to the most disadvantaged workers. We empirically illustrate some of these conceptual and methodological issues based on the secondary analysis of a large case-control study on lung cancer conducted in France.

Analysis of causes of death using verbal autopsies and vital registration in Hidalgo, Mexico.

IntroductionVerbal autopsy (VA) is a useful tool for evaluating causes of death, especially in places with limited or no vital registration systems. The Population Health Metrics Research Consortium (PHMRC) developed a validated questionnaire and a set of automated methods to determine the cause of death from a VA. However, the application of these methods needs to be tested in a community environment.ObjectiveTo estimate cause-specific mortality fractions (CSMFs) using VAs and compare them against those obtained in the vital statistics of the state of Hidalgo, Mexico.MethodsA random sample of deaths occurred in 2009 was selected from vital statistics in the state of Hidalgo. The full PHMRC validated VA instrument was applied to the relatives of the deceased, and the cause of death was determined using Tariff's automated method. The causes of death were grouped into 34 causes for adults, 21 for children and 6 for newborns. Results were compared with cause of death on death certificates for all deaths.ResultsA total of 1,198 VAs were analyzed. The Tariff method was not able to assign a cause of death in only 9% of adults, 2% of children and 7% of neonatal deaths. The CSMFs obtained from the Tariff method were similar in some cases to those of vital statistics (e.g. cirrhosis), but different in others (e.g. sepsis).ConclusionThe application of VAs in a community sample, analyzed with the Tariff method, allowed assigning a cause of death to most of the cases, with results similar to those of vital statistics for most conditions. This tool can be useful to strengthen the quality of vital statistics.

An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model

BackgroundVerbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts.The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.ResultsThe new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults.The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.ConclusionsDespite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.

Patient Diversity and Population Health-Related Cardiovascular Outcomes Associated with Warfarin Use in Atrial Fibrillation: An Analysis Using Administrative Claims Data.

Anticoagulants are effective for stroke prevention in atrial fibrillation (AF). Data on population health-related cardiovascular outcomes by race/ethnicity and gender are not well described. The aim was to assess the impact of patient diversity on associated cardiovascular outcomes related to warfarin anticoagulation in Medicare beneficiaries with AF. Medicare administrative claims data for years 2000-2010 were used to calculate AF prevalence and rates of new AF cases. Three 20% sample cohorts of new AF beneficiaries for years 2000, 2005, and 2007 were extracted and analyzed in a longitudinal study design. The impact of warfarin on associated cardiovascular outcomes was measured with respect to race/ethnicity and gender. Measured outcomes included the risk of stroke, mortality and hospitalization after adjusting for age, gender, race/ethnicity, CHADS2 score and warfarin. AF prevalence and warfarin use increased while stroke and mortality rates declined across race/ethnicity and gender from 2000 to 2010. Analyses comparing Whites to non-Whites highlighted several disparities: (1) Blacks were 40% (p < 0.0001) more likely to have a stroke even after adjustment for warfarin; (2) in 2007, Hispanics had a 35% (p < 0.01) higher prevalence of stroke and warfarin did not reduce the risk; and (3) Asians had better outcomes. Warfarin reduced stroke less well in women who had a lower risk of death and hospitalization. Despite a > 70% (p < 0.0001) reduction in mortality for warfarin users, Blacks had a 25% (p < 0.0001) higher mortality risk than Whites. Differences in population health metrics across race/ethnicity and gender exist in AF. Across all metrics, Blacks had comparatively worse outcomes. Patient diversity should be a focus for future investigations in AF to improve outcomes in the whole population. National Minority Quality Forum.

Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system

BackgroundDeaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis.MethodsThis study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff.ResultsOf the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen’s kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%).ConclusionThe CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning.

A de-identified database of 11,979 verbal autopsy open-ended responses

As part of the Gates Grand Challenge 13, the Population Health Metrics Research Consortium (PHMRC) collected data to enable the development and validation of methods that measure cause-specific mortality in populations with incomplete or inadequate cause of death coding. This work yielded 11,979 verbal autopsy interviews (VAIs). In each, a field interviewer spoke with an individual familiar with the deceased and their final illness, and used a semi-structured questionnaire to collect information about the symptoms of the deceased in their final illness. The VAI collected demographic characteristics, possible risk factors (such as tobacco use), and other potentially contributing characteristics. It also included the open-ended question, “Could you please summarize, or tell us in your own words, any additional information about the illness and/or death of your loved one?” (open narrative). The VAI data were released in a de-identified format in September 2013 through the Global Health Data Exchange, in files that contain verbal autopsies that were collected at six sites in four countries (India, Mexico, Tanzania, and the Philippines). Due to research interest, we have now created redacted versions of the open narratives from the open-ended question of the questionnaire. We hope that this database will be the source of innovations that increase our knowledge about the causes of ill health and, through this knowledge, produce improvements in health for individuals and populations.

A strengths-based data capture model: mining data-driven and person-centered health assets.

With health care policy directives advancing value-based care, risk assessments and management have permeated health care discourse. The conventional problem-based infrastructure defines what data are employed to build this discourse and how it unfolds. Such a health care model tends to bias data for risk assessment and risk management toward problems and does not capture data about health assets or strengths. The purpose of this article is to explore and illustrate the incorporation of a strengths-based data capture model into risk assessment and management by harnessing data-driven and person-centered health assets using the Omaha System. This strengths-based data capture model encourages and enables use of whole-person data including strengths at the individual level and, in aggregate, at the population level. When aggregated, such data may be used for the development of strengths-based population health metrics that will promote evaluation of data-driven and person-centered care, outcomes, and value.