Population Health Metrics最新文献

Background: Obtaining representative abortion incidence estimates is challenging in restrictive contexts. While the confidante method has been increasingly used to collect this data in such settings, there are several biases commonly associated with this method. Further, there are significant variations in how researchers have implemented the method and assessed/adjusted for potential biases, limiting the comparability and interpretation of existing estimates. This study presents a standardized approach to analyzing confidante method data, generates comparable abortion incidence estimates from previously published studies and recommends standards for reporting bias assessments and adjustments for future confidante method studies.

Methods: We used data from previous applications of the confidante method in Côte d'Ivoire, Ethiopia, Ghana, Java (Indonesia), Nigeria, Uganda, and Rajasthan (India). We estimated one-year induced abortion incidence rates for confidantes in each context, attempting to adjust for selection, reporting and transmission bias in a standardized manner.

Findings: In each setting, majority of the foundational confidante method assumptions were violated. Adjusting for transmission bias using self-reported abortions consistently yielded the highest incidence estimates compared with other published approaches. Differences in analytic decisions and bias assessments resulted in the incidence estimates from our standardized analysis varying widely from originally published rates.

Interpretation: We recommend that future studies clearly state which biases were assessed, if associated assumptions were violated, and how violations were adjusted for. This will improve the utility of confidante method estimates for national-level decision making and as inputs for global or regional model-based estimates of abortion.

Background: Full birth histories (FBHs) are a key tool for estimating fertility and child mortality in low- and middle-income countries, but they are lengthy to collect. This is not desirable, especially for rapid turnaround surveys that ought to be short (e.g., mobile phone surveys). To reduce the length of the interview, some surveys resort to truncated birth histories (TBHs), where questions are asked only on recent births.

Methods: We used 32 Malaria Indicator Surveys that included TBHs from 18 countries in sub-Saharan Africa. Each set of TBHs was paired and compared to an overlapping set of FBHs (typically from a standard Demographic and Health Survey). We conducted a variety of data checks, including a comparison of the proportion of children reported in the reference period and a comparison of the fertility and mortality estimates.

Results: Fertility and mortality estimates from TBHs are lower than those based on FBHs. These differences are driven by the omission of events and the displacement of births backward and out of the reference period.

Conclusions: TBHs are prone to misreporting errors that will bias both fertility and mortality estimates. While we find a few significant associations between outcomes measured and interviewer's characteristics, data quality markers correlate more consistently with respondent attributes, suggesting that truncation creates confusion among mothers being interviewed. Rigorous data quality checks should be put in place when collecting data through this instrument in future surveys.

Background: During the COVID-19 pandemic, governments and researchers have used routine health data to estimate potential declines in the delivery and uptake of essential health services. This research relies on the data being high quality and, crucially, on the data quality not changing because of the pandemic. In this paper, we investigated those assumptions and assessed data quality before and during COVID-19.

Methods: We obtained routine health data from the DHIS2 platforms in Ethiopia, Haiti, Lao People's Democratic Republic, Nepal, and South Africa (KwaZulu-Natal province) for a range of 40 indicators on essential health services and institutional deaths. We extracted data over 24 months (January 2019-December 2020) including pre-pandemic data and the first 9 months of the pandemic. We assessed four dimensions of data quality: reporting completeness, presence of outliers, internal consistency, and external consistency.

Results: We found high reporting completeness across countries and services and few declines in reporting at the onset of the pandemic. Positive outliers represented fewer than 1% of facility-month observations across services. Assessment of internal consistency across vaccine indicators found similar reporting of vaccines in all countries. Comparing cesarean section rates in the HMIS to those from population-representative surveys, we found high external consistency in all countries analyzed.

Conclusions: While efforts remain to improve the quality of these data, our results show that several indicators in the HMIS can be reliably used to monitor service provision over time in these five countries.

Background: Low birth weight (LBW) is a significant public health concern given its association with early-life mortality and other adverse health consequences that can impact the entire life cycle. In many countries, accurate estimates of LBW prevalence are lacking due to inaccuracies in collection and gaps in available data. Our study aimed to determine LBW prevalence among facility-born infants in selected areas of Kenya and Tanzania and to assess whether the introduction of an intervention to improve the accuracy of birth weight measurement would result in a meaningfully different estimate of LBW prevalence than current practice.

Methods: We carried out a historically controlled intervention study in 22 health facilities in Kenya and three health facilities in Tanzania. The intervention included: provision of high-quality digital scales, training of nursing staff on accurate birth weight measurement, recording and scale calibration practices, and quality maintenance support that consisted of enhanced supervision and feedback (prospective arm). The historically controlled data were birth weights from the same facilities recorded in maternity registers for the same calendar months from the previous year measured using routine practices and manual scales. We calculated mean birth weight (95% confidence interval CI), mean difference in LBW prevalence, and respective risk ratio (95% CI) between study arms.

Results: Between October 2019 and February 2020, we prospectively collected birth weights from 8441 newborns in Kenya and 4294 in Tanzania. Historical data were available from 9318 newborns in Kenya and 12,007 in Tanzania. In the prospective sample, the prevalence of LBW was 12.6% (95% confidence intervals [CI]: 10.9%-14.4%) in Kenya and 18.2% (12.2%-24.2%) in Tanzania. In the historical sample, the corresponding prevalence estimates were 7.8% (6.5%-9.2%) and 10.0% (8.6%-11.4%). Compared to the retrospective sample, the LBW prevalence in the prospective sample was 4.8% points (3.2%-6.4%) higher in Kenya and 8.2% points (2.3%-14.0%) higher in Tanzania, corresponding to a risk ratio of 1.61 (1.38-1.88) in Kenya and 1.81 (1.30-2.52) in Tanzania.

Conclusion: Routine birth weight records underestimate the risk of LBW among facility-born infants in Kenya and Tanzania. The quality of birth weight data can be improved by a simple intervention consisting of provision of digital scales and supportive training.

Background: Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population.

Methods: We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death).

Results: A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state "mild anemia" (DW = 0.005, 95% UI 0.000-0.027) or "allergic rhinitis (hay fever)" (0.005, 95% UI 0.000-0.029) had the lowest DW and "heroin and other opioid dependence, severe" had the highest DW (0.699, 95% UI 0.579-0.827). A high correlation coefficient (Pearson's r = 0.876; P < 0.001) for DWs of same health states was observed between Wuhan's survey and GBD 2013 DW survey. Health states referred to mental symptom, fatigue, and the residual category of other physical symptoms were statistically significantly associated with a lower Wuhan's DWs than the GBD's DWs. Health states with disfigurement and substance use symptom had a higher DW in Wuhan population than the GBD 2013 study.

Conclusions: This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD's survey and Wuhan's survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases.

Introduction: Low back pain (LBP), neck pain (NKP), osteoarthritis (OST) and rheumatoid arthritis (RHE) are among the musculoskeletal (MSK) disorders causing the greatest disability in terms of Years Lived with Disability. The current study aims to analyze the health and economic impact of these MSK disorders in Belgium, providing a summary of morbidity and mortality outcomes from 2013 to 2018, as well as direct and indirect costs from 2013 to 2017.

Methods: The health burden of LBP, NKP, OST and RHE in Belgium from 2013 to 2018 was summarized in terms of prevalence and disability-adjusted life years (DALY) using data from the Belgian health interview surveys (BHIS), the INTEGO database (Belgian registration network for general practitioners) and the Global Burden of Diseases study 2019. The economic burden included estimates of direct medical costs and indirect costs, measured by cost of work absenteeism. For this purpose, data of the respondents to the BHIS-2013 were linked with the national health insurance data (intermutualistic agency [IMA] database) 2013-2017.

Results: In 2018, 2.5 million Belgians were affected by at least one MSK disorder. OST represented the disorder with the highest number of cases for both men and women, followed by LBP. In the same year, MSK disorders contributed to a total of 180,746 DALYs for female and 116,063 DALYs for men. LBP appeared to be the largest contributor to the health burden of MSK. Having at least one MSK disorder costed on average 3 billion € in medical expenses and 2 billion € in indirect costs per year, with LBP being the most costly.

Conclusion: MSK disorders represent a major health and economic burden in Belgium. As their burden will probably continue to increase in the future, acting on the risk factors associated to these disorders is crucial to mitigate both the health and economic burden.

Background: This descriptive study assessed the completeness, agreement, and representativeness of ethnicity recording in the United Kingdom (UK) Clinical Practice Research Datalink (CPRD) primary care databases alone and, for those patients registered with a GP in England, when linked to secondary care data from Hospital Episode Statistics (HES).

Methods: Ethnicity records were assessed for all patients in the May 2021 builds of the CPRD GOLD and CPRD Aurum databases for all UK patients. In analyses of the UK, English data was from combined CPRD-HES, whereas data from Northern Ireland, Scotland, and Wales drew from CPRD only. The agreement of ethnicity records per patient was assessed within each dataset (CPRD GOLD, CPRD Aurum, and HES datasets) and between datasets at the highest level ethnicity categorisation ('Asian', 'black', 'mixed', 'white', 'other'). Representativeness was assessed by comparing the ethnic distributions at the highest-level categorisation of CPRD-HES to those from the Census 2011 across the UK's devolved administrations. Additionally, CPRD-HES was compared to the experimental ethnic distributions for England and Wales from the Office for National Statistics in 2019 (ONS2019) and the English ethnic distribution from May 2021 from NHS Digital's General Practice Extraction Service Data for Pandemic Planning and Research with HES data linkage (GDPPR-HES).

Results: In CPRD-HES, 81.7% of currently registered patients in the UK had ethnicity recorded in primary care. For patients with multiple ethnicity records, mismatched ethnicity within individual primary and secondary care datasets was < 10%. Of English patients with ethnicity recorded in both CPRD and HES, 93.3% of records matched at the highest-level categorisation; however, the level of agreement was markedly lower in the 'mixed' and 'other' ethnic groups. CPRD-HES was less proportionately 'white' compared to the UK Census 2011 (80.3% vs. 87.2%) and experimental ONS2019 data (80.4% vs. 84.3%). CPRD-HES was aligned with the ethnic distribution from GDPPR-HES ('white' 80.4% vs. 80.7%); however, with a smaller proportion classified as 'other' (1.1% vs. 2.8%).

Conclusions: CPRD-HES has suitable representation of all ethnic categories with some overrepresentation of minority ethnic groups and a smaller proportion classified as 'other' compared to the UK general population from other data sources. CPRD-HES data is useful for studying health risks and outcomes in typically underrepresented groups.

Background: AMI and stroke are the leading causes of premature mortality and hospitalizations in China. Incidence data at the population level for the two diseases is limited and the reliability and completeness of the existing incidence registry have not been investigated. We aim to assess if the completeness of case ascertainment of AMI and stroke incidence has improved since the implementation of electronic reporting and to estimate the incidence of AMI and stroke in Tianjin, China.

Methods: We applied the DisMod II program to model the incidence of AMI and stroke from other epidemiological indicators. Inputs include mortality rates from Tianjin's mortality surveillance system, and the point prevalence, remission rates and relative risks taken from IHME's Global Burden of Disease studies. The completeness of AMI and stroke incidence reporting was assessed by comparing the sex and age-specific incidence rates derived from the incidence surveillance system with the modeled incidence rates.

Results: The age and sex standardized modeled incidence per 100,000 person-year decreased (p < 0.0001) from 138 in 2007 to 119 in 2015 for AMI and increased (p < 0.0001) from 520 in 2007 to 534 in 2015 for stroke. The overall completeness of incidence report was 36% (95% CI 35-38%) for AMI and 54% (95% CI 53-55%) for stroke. The completeness was higher in men than in women for both AMI (42% vs 30%, p < 0.0001) and stroke (55% vs 53%, p < 0.0001) and was higher in residents aged 30-59 than those aged 60 or older for AMI (57% vs 38%, p < 0.0001). The completeness of reporting increased by 7.2 (95% CI 4.6-9.7) and 15.7 (95% CI 14.4-16.9) percentage points for AMI and stroke, respectively, from 2007 to 2015 among those aged 30 or above. The increases were observed in both men and women (p < 0.0001) and were more profound (p < 0.0001) among those aged between 30 and 59 and occurred primarily during the 2010 and 2015 period.

Conclusions: Completeness of AMI and stroke incidence surveillance was low in Tianjin but has improved in recent years primarily owing to the incorporation of an automatic reporting component into the information systems of health facilities.

Aim: We aimed to combine Global Burden of Disease (GBD) Study data and local data to identify the highest priority intervention domains for preventing cardiovascular disease (CVD) in the case study country of Aotearoa New Zealand (NZ).

Methods: Risk factor data for CVD in NZ were extracted from the GBD using the "GBD Results Tool." We prioritized risk factor domains based on consideration of the size of the health burden (disability-adjusted life years [DALYs]) and then by the domain-specific interventions that delivered the highest health gains and cost-savings.

Results: Based on the size of the CVD health burden in DALYs, the five top prioritized risk factor domains were: high systolic blood pressure (84,800 DALYs; 5400 deaths in 2019), then dietary risk factors, then high LDL cholesterol, then high BMI and then tobacco (30,400 DALYs; 1400 deaths). But if policy-makers aimed to maximize health gain and cost-savings from specific interventions that have been studied, then they would favor the dietary risk domain (e.g., a combined fruit and vegetable subsidy plus a sugar tax produced estimated lifetime savings of 894,000 health-adjusted life years and health system cost-savings of US$11.0 billion; both 3% discount rate). Other potential considerations for prioritization included the potential for total health gain that includes non-CVD health loss and potential for achieving relatively greater per capita health gain for Māori (Indigenous) to reduce health inequities.

Conclusions: We were able to show how CVD risk factor domains could be systematically prioritized using a mix of GBD and country-level data. Addressing high systolic blood pressure would be the top ranked domain if policy-makers focused just on the size of the health loss. But if policy-makers wished to maximize health gain and cost-savings using evaluated interventions, dietary interventions would be prioritized, e.g., food taxes and subsidies.

Background: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically.

Methods: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts.

Results: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states.

Conclusions: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care.

Trial registration: Not applicable.