An Introduction to Bayesian Spatial Smoothing Methods for Disease Mapping: Modeling County Firearm Suicide Mortality Rates.

This article introduces Bayesian spatial smoothing models for disease mapping, a specific application of small area estimation where the full universe of data is known, to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York and Mollié (BYM) Poisson spatial and space-time smoothing models were fit to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0-24.81 deaths per 10,000 people. However, the highest mortality rate was highly unstable based on only 2 deaths in a population of approximately 800, and 82.4% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06-4.05 deaths per 10,000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gained precision from adjacent neighbors and their own rates in adjacent years. Bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R have made fitting these models more accessible to researchers.

Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis.

BACKGROUND: The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes. METHODS: Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05. FINDINGS: Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures. INTERPRETATION: COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

COVID-19 impact on life expectancy in Mexico. An analysis based on the Global Burden of Disease 2021 study.

BACKGROUND: Between 2020 and 2021, Mexico documented 2.21 million fatalities, out of which 25.3% were attributable to SARS-COV-2 infection. OBJECTIVES: To evaluate COVID-19 mortality during 2020-2021, determine its impact on national- and state-level life expectancy at birth, and in a group of selected countries of the region, as well as to analyze it according to sociodemographic profiles. MATERIAL AND METHODS: Data from the Global Burden of Disease 2021 study were used to report mortality, the impact on life expectancy and underlying causes between 2019 and 2021. These data were evaluated from the perspective of response to the pandemic and according to the sociodemographic structure based on a quadratic regression model. RESULTS: Between 2020 and 2021, 708,971 excess deaths were recorded, which decreased life expectancy at birth by 4.6 years; 76% of this reduction was attributed to COVID-19. The COVID-19 mortality rate was higher than expected according to the sociodemographic conditions of the states. CONCLUSIONS: In Mexico and the countries of the region, the pandemic was devastating and generated regressions in life expectancy at birth, which varied from two to nine years. It is not clear why the effect was so different between countries and within Mexico.

ANTECEDENTES: Entre 2020 y 2021, México experimentó 2.21 millones de defunciones, de las cuales 25.3 % estuvo relacionado con infección por SARS-COV-2. OBJETIVOS: Evaluar la mortalidad por COVID-19 en 2020-2021, determinar su influencia en la esperanza de vida al nacer a nivel nacional, estatal y en países seleccionados de la región, así como analizarla en función del perfil sociodemográfico. MATERIAL Y MÉTODOS: Se utilizaron datos del Global Burden of Disease 2021 para reportar la mortalidad, el impacto en la esperanza de vida y las causas subyacentes entre 2019 y 2021. Se usó una regresión cuadrática para evaluar la mortalidad en exceso como indicador de la respuesta de los estados a la pandemia, considerando su estructura sociodemográfica. RESULTADOS: Entre 2020 y 2021, se registraron 708 971 muertes en exceso, que disminuyeron la esperanza de vida al nacer en 4.6 años; 76 % de esta reducción se atribuyó a COVID-19. La tasa de mortalidad por COVID-19 fue superior a la esperada conforme a las condiciones sociodemográficas de las entidades. CONCLUSIONES: En México y los países de la región, la pandemia fue devastadora y generó regresiones en la esperanza de vida al nacer, que variaron de dos a nueve años. Se requiere más investigación para entender las variaciones en sus efectos.

Adapting and validating the log quadratic model to derive under-five age- and cause-specific mortality (U5ACSM): a preliminary analysis.

BACKGROUND: The mortality pattern from birth to age five is known to vary by underlying cause of mortality, which has been documented in multiple instances. Many countries without high functioning vital registration systems could benefit from estimates of age- and cause-specific mortality to inform health programming, however, to date the causes of under-five death have only been described for broad age categories such as for neonates (0-27 days), infants (0-11 months), and children age 12-59 months. METHODS: We adapt the log quadratic model to mortality patterns for children under five to all-cause child mortality and then to age- and cause-specific mortality (U5ACSM). We apply these methods to empirical sample registration system mortality data in China from 1996 to 2015. Based on these empirical data, we simulate probabilities of mortality in the case when the true relationships between age and mortality by cause are known. RESULTS: We estimate U5ACSM within 0.1-0.7 deaths per 1000 livebirths in hold out strata for life tables constructed from the China sample registration system, representing considerable improvement compared to an error of 1.2 per 1000 livebirths using a standard approach. This improved prediction error for U5ACSM is consistently demonstrated for all-cause as well as pneumonia- and injury-specific mortality. We also consistently identified cause-specific mortality patterns in simulated mortality scenarios. CONCLUSION: The log quadratic model is a significant improvement over the standard approach for deriving U5ACSM based on both simulation and empirical results.

A FLEXIBLE BAYESIAN FRAMEWORK TO ESTIMATE AGE- AND CAUSE-SPECIFIC CHILD MORTALITY OVER TIME FROM SAMPLE REGISTRATION DATA.

In order to implement disease-specific interventions in young age groups, policy makers in low- and middle-income countries require timely and accurate estimates of age- and cause-specific child mortality. High-quality data is not available in settings where these interventions are most needed, but there is a push to create sample registration systems that collect detailed mortality information. current methods that estimate mortality from this data employ multistage frameworks without rigorous statistical justification that separately estimate all-cause and cause-specific mortality and are not sufficiently adaptable to capture important features of the data. We propose a flexible Bayesian modeling framework to estimate age- and cause-specific child mortality from sample registration data. We provide a theoretical justification for the framework, explore its properties via simulation, and use it to estimate mortality trends using data from the Maternal and Child Health Surveillance System in China.

Delayed diagnosis of palatal adenoid cystic carcinoma: Review of diagnostic workup and image features of perineural spread.

OBJECTIVES: Adenoid cystic carcinoma (ACC) is the malignancy most likely to spread perineurally. Delayed diagnosis often leads to undetected perineural spread (PNS). Better understanding of diagnostic processes, clinical and imaging features in ACC may allow earlier diagnoses. MATERIALS AND METHODS: A retrospective records search of the University of Washington Radiology archive identified patients with palatal ACC diagnosis and pre-treatment MDCT and/or MRI. Demographic data, clinical findings, diagnostic workup history and image features including the presence of PNS were recorded. RESULTS: 44 patients met inclusion and exclusion criteria. Symptoms included pain, mass, numbness, and sinonasal congestion. The most common finding on clinical examination was visible or palpable palatal mass. 55% of patients were evaluated by a dentist pre-diagnosis. Most common initial impressions were infection and/or dental disease. PNS was identified in 81.8% of patients, most commonly at pterygopalatine fossa, palatine foramina/canals, Vidian canal, or foramen rotundum. PNS was statistically significantly associated with paresthesia (p = 0.003) but not with tumor size, age, gender, or tobacco history. 44% of patients were diagnosed ≥1 year from initial symptoms, with a mean delay of 18.3 (range < 1-72) months. CONCLUSIONS: This study represents the first analysis of the nature of the diagnostic process of palatal ACC. Majorities of patients were evaluated by a general dentist pre-diagnosis. PNS was identified in a majority of patients and all the patients who presented with paresthesia had evidence of PNS. Despite patients presenting with paresthesia, palatal ACC is often initially misdiagnosed resulting in delayed diagnosis.

Infarct Collagen Topography Regulates Fibroblast Fate via p38-Yes-Associated Protein Transcriptional Enhanced Associate Domain Signals.

RATIONALE: Myocardial infarction causes spatial variation in collagen organization and phenotypic diversity in fibroblasts, which regulate the heart's ECM (extracellular matrix). The relationship between collagen structure and fibroblast phenotype is poorly understood but could provide insights regarding the mechanistic basis for myofibroblast heterogeneity in the injured heart. OBJECTIVE: To investigate the role of collagen organization in cardiac fibroblast fate determination. METHODS AND RESULTS: Biomimetic topographies were nanofabricated to recapitulate differential collagen organization in the infarcted mouse heart. Here, adult cardiac fibroblasts were freshly isolated and cultured on ECM topographical mimetics for 72 hours. Aligned mimetics caused cardiac fibroblasts to elongate while randomly organized topographies induced circular morphology similar to the disparate myofibroblast morphologies measured in vivo. Alignment cues also induced myofibroblast differentiation, as >60% of fibroblasts formed αSMA (α-smooth muscle actin) stress fibers and expressed myofibroblast-specific ECM genes like Postn (periostin). By contrast, random organization caused 38% of cardiac fibroblasts to express αSMA albeit with downregulated myofibroblast-specific ECM genes. Coupling topographical cues with the profibrotic agonist, TGFß (transforming growth factor beta), additively upregulated myofibroblast-specific ECM genes independent of topography, but only fibroblasts on flat and randomly oriented mimetics had increased percentages of fibroblasts with αSMA stress fibers. Increased tension sensation at focal adhesions induced myofibroblast differentiation on aligned mimetics. These signals were transduced by p38-YAP (yes-associated protein)-TEAD (transcriptional enhanced associate domain) interactions, in which both p38 and YAP-TEAD (yes-associated protein transcriptional enhanced associate domain) binding were required for myofibroblast differentiation. By contrast, randomly oriented mimetics did not change focal adhesion tension sensation or enrich for p38-YAP-TEAD interactions, which explains the topography-dependent diversity in fibroblast phenotypes observed here. CONCLUSIONS: Spatial variations in collagen organization regulate cardiac fibroblast phenotype through mechanical activation of p38-YAP-TEAD signaling, which likely contribute to myofibroblast heterogeneity in the infarcted myocardium.

Osteoarthritis of the temporomandibular joint and increase of the horizontal condylar angle: a longitudinal study.

OBJECTIVE: Our previous study of patients with unilateral temporomandibular joint (TMJ) osteoarthritis (OA) showed that the affected joints had greater horizontal condylar angle (HCA) compared with the contralateral unaffected joints. However, it was unclear whether the HCA changes preceded or were the result of OA changes. The aim of this longitudinal study was to investigate the relationship between HCA and OA progression. STUDY DESIGN: In total, 127 patients (with or without TMJ disorders) completed baseline and follow-up examinations (average time to follow-up 7.9 years). Generalized estimating equation models were used to account for correlation of observations within the same patients. RESULTS: (1) HCA was greater in OA-affected joints than in unaffected joints (P = .04). (2) Increased HCA at follow-up was associated with change in joint status from no OA to OA. (P = .001). (3) Baseline HCA value alone did not predict future OA diagnosis. (4) All OA changes in fossa/articular eminence morphology, and some combinations of condylar changes, were associated with a greater HCA. (5) OA diagnosis was associated with pain during maximum opening (P = .005) and pain history (P = .002). (6) Aging alone was not correlated with increased HCA. CONCLUSIONS: Clinical progression of OA preceded increases in HCA. HCA alone did not predict OA development.

Under-5 mortality in 2851 Chinese counties, 1996-2012: a subnational assessment of achieving MDG 4 goals in China.

BACKGROUND: In the past two decades, the under-5 mortality rate in China has fallen substantially, but progress with regards to the Millennium Development Goal (MDG) 4 at the subnational level has not been quantified. We aimed to estimate under-5 mortality rates in mainland China for the years 1970 to 2012. METHODS: We estimated the under-5 mortality rate for 31 provinces in mainland China between 1970 and 2013 with data from censuses, surveys, surveillance sites, and disease surveillance points. We estimated under-5 mortality rates for 2851 counties in China from 1996 to 2012 with the reported child mortality numbers from the Annual Report System on Maternal and Child Health. We used a small area mortality estimation model, spatiotemporal smoothing, and Gaussian process regression to synthesise data and generate consistent provincial and county-level estimates. We compared progress at the county level with what was expected on the basis of income and educational attainment using an econometric model. We computed Gini coefficients to study the inequality of under-5 mortality rates across counties. FINDINGS: In 2012, the lowest provincial level under-5 mortality rate in China was about five per 1000 livebirths, lower than in Canada, New Zealand, and the USA. The highest provincial level under-5 mortality rate in China was higher than that of Bangladesh. 29 provinces achieved a decrease in under-5 mortality rates twice as fast as the MDG 4 target rate; only two provinces will not achieve MDG 4 by 2015. Although some counties in China have under-5 mortality rates similar to those in the most developed nations in 2012, some have similar rates to those recorded in Burkina Faso and Cameroon. Despite wide differences, the inter-county Gini coefficient has been decreasing. Improvement in maternal education and the economic boom have contributed to the fall in child mortality; more than 60% of the counties in China had rates of decline in under-5 mortality rates significantly faster than expected. Fast reduction in under-5 mortality rates have been recorded not only in the Han population, the dominant ethnic majority in China, but also in the minority populations. All top ten minority groups in terms of population sizes have experienced annual reductions in under-5 mortality rates faster than the MDG 4 target at 4.4%. INTERPRETATION: The reduction of under-5 mortality rates in China at the country, provincial, and county level is an extraordinary success story. Reductions of under-5 mortality rates faster than 8.8% (twice MDG 4 pace) are possible. Extremely rapid declines seem to be related to public policy in addition to socioeconomic progress. Lessons from successful counties should prove valuable for China to intensify efforts for those with unacceptably high under-5 mortality rates. FUNDING: National "Twelfth Five-Year" Plan for Science and Technology Support, National Health and Family Planning Commission of The People's Republic of China, Program for Changjiang Scholars and Innovative Research Team in University, the National Institute on Aging, and the Bill & Melinda Gates Foundation.

Global, regional, and national levels of neonatal, infant, and under-5 mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

BACKGROUND: Remarkable financial and political efforts have been focused on the reduction of child mortality during the past few decades. Timely measurements of levels and trends in under-5 mortality are important to assess progress towards the Millennium Development Goal 4 (MDG 4) target of reduction of child mortality by two thirds from 1990 to 2015, and to identify models of success. METHODS: We generated updated estimates of child mortality in early neonatal (age 0-6 days), late neonatal (7-28 days), postneonatal (29-364 days), childhood (1-4 years), and under-5 (0-4 years) age groups for 188 countries from 1970 to 2013, with more than 29,000 survey, census, vital registration, and sample registration datapoints. We used Gaussian process regression with adjustments for bias and non-sampling error to synthesise the data for under-5 mortality for each country, and a separate model to estimate mortality for more detailed age groups. We used explanatory mixed effects regression models to assess the association between under-5 mortality and income per person, maternal education, HIV child death rates, secular shifts, and other factors. To quantify the contribution of these different factors and birth numbers to the change in numbers of deaths in under-5 age groups from 1990 to 2013, we used Shapley decomposition. We used estimated rates of change between 2000 and 2013 to construct under-5 mortality rate scenarios out to 2030. FINDINGS: We estimated that 6·3 million (95% UI 6·0-6·6) children under-5 died in 2013, a 64% reduction from 17·6 million (17·1-18·1) in 1970. In 2013, child mortality rates ranged from 152·5 per 1000 livebirths (130·6-177·4) in Guinea-Bissau to 2·3 (1·8-2·9) per 1000 in Singapore. The annualised rates of change from 1990 to 2013 ranged from -6·8% to 0·1%. 99 of 188 countries, including 43 of 48 countries in sub-Saharan Africa, had faster decreases in child mortality during 2000-13 than during 1990-2000. In 2013, neonatal deaths accounted for 41·6% of under-5 deaths compared with 37·4% in 1990. Compared with 1990, in 2013, rising numbers of births, especially in sub-Saharan Africa, led to 1·4 million more child deaths, and rising income per person and maternal education led to 0·9 million and 2·2 million fewer deaths, respectively. Changes in secular trends led to 4·2 million fewer deaths. Unexplained factors accounted for only -1% of the change in child deaths. In 30 developing countries, decreases since 2000 have been faster than predicted attributable to income, education, and secular shift alone. INTERPRETATION: Only 27 developing countries are expected to achieve MDG 4. Decreases since 2000 in under-5 mortality rates are accelerating in many developing countries, especially in sub-Saharan Africa. The Millennium Declaration and increased development assistance for health might have been a factor in faster decreases in some developing countries. Without further accelerated progress, many countries in west and central Africa will still have high levels of under-5 mortality in 2030. FUNDING: Bill & Melinda Gates Foundation, US Agency for International Development.

Left behind: widening disparities for males and females in US county life expectancy, 1985-2010.

BACKGROUND: The United States spends more than any other country on health care. The poor relative performance of the US compared to other high-income countries has attracted attention and raised questions about the performance of the US health system. An important dimension to poor national performance is the large disparities in life expectancy. METHODS: We applied a mixed effects Poisson statistical model and Gaussian Process Regression to estimate age-specific mortality rates for US counties from 1985 to 2010. We generated uncertainty distributions for life expectancy at each age using standard simulation methods. RESULTS: Female life expectancy in the United States increased from 78.0 years in 1985 to 80.9 years in 2010, while male life expectancy increased from 71.0 years in 1985 to 76.3 years in 2010. The gap between female and male life expectancy in the United States was 7.0 years in 1985, narrowing to 4.6 years in 2010. For males at the county level, the highest life expectancy steadily increased from 75.5 in 1985 to 81.7 in 2010, while the lowest life expectancy remained under 65. For females at the county level, the highest life expectancy increased from 81.1 to 85.0, and the lowest life expectancy remained around 73. For male life expectancy at the county level, there have been three phases in the evolution of inequality: a period of rising inequality from 1985 to 1993, a period of stable inequality from 1993 to 2002, and rising inequality from 2002 to 2010. For females, in contrast, inequality has steadily increased during the 25-year period. Compared to only 154 counties where male life expectancy remained stagnant or declined, 1,405 out of 3,143 counties (45%) have seen no significant change or a significant decline in female life expectancy from 1985 to 2010. In all time periods, the lowest county-level life expectancies are seen in the South, the Mississippi basin, West Virginia, Kentucky, and selected counties with large Native American populations. CONCLUSIONS: The reduction in the number of counties where female life expectancy at birth is declining in the most recent period is welcome news. However, the widening disparities between counties and the slow rate of increase compared to other countries should be viewed as a call for action. An increased focus on factors affecting health outcomes, morbidity, and mortality such as socioeconomic factors, difficulty of access to and poor quality of health care, and behavioral, environmental, and metabolic risk factors is urgently required.