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Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8-98·1) in Iceland, followed by 96·6 (94·9-97·9) in Norway and 96·1 (94·5-97·3) in the Netherlands, to values as low as 18·6 (13·1-24·4) in the Central African Republic, 19·0 (14·3-23·7) in Somalia, and 23·4 (20·2-26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1-93·6) in Beijing to 48·0 (43·4-53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6-68·8) in Goa to 34·0 (30·3-38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle- SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view – and subsequent provision – of quality health care for all populations.
Drawing from methods established in GBD 2015,20 our analysis involved four steps: mapping the Nolte and McKee cause list to GBD causes; constructing MIRs for cancers and risk-standardising non-cancer deaths to remove variations in mortality not directly amenable to health care; calculating the HAQ Index on the basis of principal components analysis (PCA), providing an overall score of personal health-care access and quality on a scale of 0–100; and examining associations between national HAQ Index scores and potential correlates of performance. Our study draws from GBD 2016 results,31, 32, 33 which entail several improvements since GBD 2015, including 169 new country-years of vital registration data, 528 new cancer-registry years with a total of 92 countries’ cancer registries,31 five new risk factors,32 and cause-specific mortality modelling updates (eg, cancers, tuberculosis).31 Further information can be found in the appendix (pp 12–89) and the GBD 2016 capstone series.31, 32, 33 In addition to national and aggregated HAQ Index results, we report estimates at the subnational level for Brazil (26 states and the Federal District), China (33 provinces and special administrative regions), England (nine regions and 150 local government areas), India (31 states and union territories), Japan (47 prefectures), Mexico (32 states), and the USA (50 states and the District of Columbia). As with all GBD revisions, GBD 2016 HAQ Index estimates for the full time series published here supersede previous iterations. This analysis complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER);34 additional information is found in the appendix (pp 5–7). We mapped 32 of 33 causes from the Nolte and McKee cause list6, 7, 8, 9 to GBD causes in accordance with International Classification of Diseases codes (table 1; appendix p 156). GBD includes thyroid diseases within a larger residual category, and only non-fatal outcomes are estimated for benign prostatic hyperplasia; consequently, these causes were not included in our analyses. GBD provides separate estimates for diphtheria and tetanus, so we disaggregated these causes from the original Nolte and McKee list. Causes for which mortality is amenable to health care, mapped to GBD causes, and amenable age range Although 0 (at birth) to 1 are listed as the lower bound of age ranges, age restrictions are applied for many causes such that mortality estimates are not produced before a given age group (eg, 15–19 years for many non-communicable diseases). Causes are ordered on the basis of the GBD cause list and corresponding group hierarchies. GBD=Global Burden of Disease. GBD cancer mortality estimates are informed by MIRs, which are derived from incidence and mortality data recorded in cancer registries; more detail on MIR estimation is in the appendix (pp 41–49).31 MIRs provide a good approximation of cancer survival and have been used to identify countries with higher or lower cancer mortality relative to incidence.22, 35 Because of the improved quantity and quality of cancer registry data from GBD 2016, we used cancer-specific MIRs instead of risk-standardised death rates. As detailed in the appendix (pp 10–11), cancer-specific MIRs were more strongly correlated with the Socio-demographic Index (SDI), a measure of overall development, than were risk-standardised death rates. These results, and the distribution of MIRs by SDI quintile (appendix pp 96–111), showed that cancer MIRs provide a more robust signal of cancer care access and quality than do risk-standardised death rates. To better isolate differences in mortality associated with health-care access and quality from differences associated with underlying risk exposure, we risk-standardised cause-specific deaths to global levels of risk exposure.32 We did not risk-standardise differences in exposure to three metabolic risk factors (high systolic blood pressure, high total cholesterol, and high fasting plasma glucose) given their amenability to health care (eg, diagnosis and treatment of hypertension in primary care). For the 24 non-cancer causes, we risk-standardised deaths by removing the joint effects of location-specific behavioural and environmental risk exposure, and replaced these estimates with the global level of joint risk exposure (appendix pp 9–10). Joint population attributable fraction (PAF) estimation accounts for effects of multiple risks combined, including the mediation of different risk factors through each other. More detail on the PAF calculations and risk-standardisation is provided in the appendix (pp 9–10). Since GBD 2015,36 five risk factors were added, most notably low birthweight and short gestation,32 which enabled the risk-standardisation of neonatal disorder deaths. Risk-standardised deaths equalled observed deaths for causes in which no risk–outcome pairs have met evidence thresholds for inclusion in GBD (eg, diphtheria, appendicitis). Using the GBD world population data,37 we age-standardised risk-standardised death rates, as well as cancer mortality and incidence estimates, before producing MIRs. We rescaled age weights to equal 1, by cause, a necessary step since included age groups represented a subset of the age groups comprising the world population standard. By cause, we log-transformed age-standardised risk-standardised death rates (or MIRs for cancers) and scaled them from 0 to 100 across locations from 1990–2016. Zero was determined by the first percentile observed (ie, highest death rates or MIRs), and 100 was applied to the 99th percentile (ie, lowest death rates or MIRs). This scaling approach differs somewhat from that of GBD 2015,20 wherein maximum values determined zero and minimum values set 100. Using a percentile-based approach more closely aligns with other index construction methods used in GBD,38 and is less sensitive to outliers or fluctuations in estimates over time. We then applied cause-specific thresholds set by the national level to subnational locations. We used PCA to construct the HAQ Index on the basis of scaled cause values, resulting in an overall score on a scale of 0–100. The GBD 2016 HAQ Index differed in three main ways from GBD 2015. First, no cause had negative PCA weights (ie, implying that higher death rates were associated with access to higher-quality health care), so all causes contributed to the final index. In GBD 2015, colon and breast cancers had negative PCA weights in the first PCA iteration, so their weights were ultimately set to zero. Second, some cancers had PCA weights more similar to communicable, maternal, and neonatal causes, which meant these causes were weighted more equally (appendix p 157). Finally, we derived PCA weights from country-level estimates and applied them to subnational results; this approach provides greater stability across GBD iterations, particularly as the GBD continues to expand its subnational assessments. The HAQ Index reflects many factors that affect service access and quality across the continuums of care and therapeutic areas, and thus it is challenging to distinguish the unique contribution of access versus quality from other potential drivers.39 To provide an initial examination of correlates with HAQ Index performance, we ran Pearson correlations between location-specific HAQ Index values with financial measures (eg, total health spending per capita),40 and health system inputs and outputs (eg, outpatient and inpatient utilisation).33 We selected these indicators on the basis of data availability in relation to GBD locations, and thus they do not represent all possible correlates. As well as examining global patterns, we report differences in the HAQ Index across levels of development. To do this, we used SDI, a summary measure of overall development based on average income per capita, educational attainment, and total fertility rates.41 Countries are grouped by SDI quintiles, as established in GBD 2016, on the basis of their 2016 SDI values.31 GBD aims to propagate uncertainty throughout its estimation process, which results in uncertainty intervals (UIs) accompanying each estimate. We estimated the HAQ Index for each location-year on the basis of 1000 draws from the posterior distribution for each included cause of death. 95% UIs were based on the 2·5th and 97·5th quantiles of the draws for each measure. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
