Trends and Patterns of Differences in Infectious Disease Mortality Among US Counties, 1980-2014

Charbel el Bcheraoui; Ali H Mokdad; Laura Dwyer-Lindgren; Amelia Bertozzi-Villa; Rebecca W Stubbs; Chloe Morozoff; Shreya Shirude; Mohsen Naghavi; Christopher J L Murray

doi:10.1001/jama.2018.2089

. 2018 Mar 27;319(12):1248–1260. doi: 10.1001/jama.2018.2089

Trends and Patterns of Differences in Infectious Disease Mortality Among US Counties, 1980-2014

Charbel el Bcheraoui ¹, Ali H Mokdad ¹, Laura Dwyer-Lindgren ¹, Amelia Bertozzi-Villa ¹, Rebecca W Stubbs ¹, Chloe Morozoff ¹, Shreya Shirude ¹, Mohsen Naghavi ¹, Christopher J L Murray ^1,^✉

¹Institute for Health Metrics and Evaluation, University of Washington, Seattle

^✉

Corresponding Author: Christopher J. L. Murray, MD, DPhil, Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave, Ste 600, Seattle, WA 98121 (cjlm@uw.edu).

Accepted for Publication: February 16, 2018.

Author Contributions: Dr Murray had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Mokdad, Dwyer-Lindgren, Bertozzi-Villa, Shirude, Naghavi, Murray.

Acquisition, analysis, or interpretation of data: Bcheraoui, Mokdad, Dwyer-Lindgren, Bertozzi-Villa, Morozoff, Naghavi, Murray.

Drafting of the manuscript: Bcheraoui.

Critical revision of the manuscript for important intellectual content: Bcheraoui, Mokdad, Dwyer-Lindgren, Bertozzi-Villa, Morozoff, Shirude, Naghavi, Murray.

Statistical analysis: Dwyer-Lindgren, Bertozzi-Villa, Naghavi.

Obtained funding: Mokdad, Murray.

Administrative, technical, or material support: Mokdad, Morozoff, Shirude.

Supervision: Mokdad, Shirude, Naghavi, Murray.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: Research reported in this publication was supported by the Robert Wood Johnson Foundation (72305), the National Institute on Aging of the National Institutes of Health (5P30AG047845), and John W. Stanton and Theresa E. Gillespie.

Role of the Funder/Sponsor: The Robert Wood Johnson Foundation, the National Institute on Aging, and John W. Stanton and Theresa E. Gillespie had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank Samuel B. Finegold, BA, at the Institute for Health Metrics and Evaluation, University of Washington, who provided assistance in preparing this article and was compensated for his work.

^✉

Corresponding author.

PMCID: PMC5885870 PMID: 29584843

Key Points

Question

What are the spatial and temporal trends in mortality due to lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis among US counties from 1980 to 2014?

Findings

In this study that applied small-area estimation models to deidentified death records from the National Center for Health Statistics, overall mortality due to infectious diseases decreased from 42.95 to 34.10 deaths per 100 000 persons, but with substantial variation among counties. The only category of infectious diseases to increase over this time was diarrheal diseases (from 0.41 to 2.41 deaths per 100 000 persons).

Meaning

Between 1980 and 2014, there were declines in mortality from most categories of infectious disease, but an increase in mortality for diarrheal diseases; however, there were large differences among US counties.

Abstract

Importance

Infectious diseases are mostly preventable but still pose a public health threat in the United States, where estimates of infectious diseases mortality are not available at the county level.

Objective

To estimate age-standardized mortality rates and trends by county from 1980 to 2014 from lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis.

Design and Setting

This study used deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Validated small-area estimation models were applied to these data to estimate county-level infectious disease mortality rates.

Exposures

County of residence.

Main Outcomes and Measures

Age-standardized mortality rates of lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis by county, year, and sex.

Results

Between 1980 and 2014, there were 4 081 546 deaths due to infectious diseases recorded in the United States. In 2014, a total of 113 650 (95% uncertainty interval [UI], 108 764-117 942) deaths or a rate of 34.10 (95% UI, 32.63-35.38) deaths per 100 000 persons were due to infectious diseases in the United States compared to a total of 72 220 (95% UI, 69 887-74 712) deaths or a rate of 41.95 (95% UI, 40.52-43.42) deaths per 100 000 persons in 1980, an overall decrease of 18.73% (95% UI, 14.95%-23.33%). Lower respiratory infections were the leading cause of infectious diseases mortality in 2014 accounting for 26.87 (95% UI, 25.79-28.05) deaths per 100 000 persons (78.80% of total infectious diseases deaths). There were substantial differences among counties in death rates from all infectious diseases. Lower respiratory infection had the largest absolute mortality inequality among counties (difference between the 10th and 90th percentile of the distribution, 24.5 deaths per 100 000 persons). However, HIV/AIDS had the highest relative mortality inequality between counties (10.0 as the ratio of mortality rate in the 90th and 10th percentile of the distribution). Mortality from meningitis and tuberculosis decreased over the study period in all US counties. However, diarrheal diseases were the only cause of infectious diseases mortality to increase from 2000 to 2014, reaching a rate of 2.41 (95% UI, 0.86-2.67) deaths per 100 000 persons, with many counties of high mortality extending from Missouri to the northeastern region of the United States.

Conclusions and Relevance

Between 1980 and 2014, there were declines in mortality from most categories of infectious diseases, with large differences among US counties. However, over this time there was an increase in mortality for diarrheal diseases.

This population epidemiology study estimates county-level patterns in mortality rates from infectious diseases in the United States between 1980 and 2014.

Introduction

The declining trend of infectious disease mortality in the United States was previously reported at the national and state levels. This decline has been previously attributed to several factors, including better health care and preventive measures such as vaccines. However, infectious diseases pose a major health threat because they have the potential of emerging through outbreaks, due to a variety of factors that increase contact between humans and previously unknown pathogens.

There are no comprehensive estimates of infectious diseases mortality available for all counties in the United States, and national- or state-level estimates can be misleading because they can mask variations at the local level. Estimating the burden of infectious disease at the county level can help identify geographic differences and their root causes. Such information is particularly relevant to local public health leaders in making decisions around resource allocation and focusing of treatment efforts. This study used innovative small-area estimation techniques to estimate infectious diseases mortality rates at the county level in the United States from 1980 to 2014. The study examined mortality of 6 major infectious disease groups that each caused 1% or more of the deaths due to infectious diseases between 1980 and 2014: lower respiratory infections, HIV/AIDS, diarrheal diseases, hepatitis, meningitis, and tuberculosis.

Methods

This analysis used methods reported in detail elsewhere; the methods approach and its application to mortality from infectious diseases is briefly described later in this section. This research received institutional review board approval from the University of Washington. Informed consent was not required because the study used deidentified data and was retrospective.

Data

Data were accessed from deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Deaths and population were tabulated by county, age group (0, 1-4, 5-9, …, 70-74, 75-79, and ≥80 years), sex, year, and (in the case of deaths data) cause. County-level information on levels of education, income, race/ethnicity, Native American reservations, and population density derived from US Census Bureau and NCHS data sources were also used (eTable 1 in the Supplement). In a small number of cases, counties were combined to ensure historically stable units of analysis (eTable 2 in the Supplement).

Cause List and Garbage Redistribution

This study used the cause list developed for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). This cause list is arranged hierarchically in 4 levels, and within each level, the list is exhaustive and mutually exclusive. eTable 3 in the Supplement lists all causes in the GBD cause list and the International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 codes that correspond to each cause. The specific focus of this analysis is infectious diseases in level 3 of the GBD cause hierarchy that accounted for at least 1% of the total deaths from infectious diseases between 1980 and 2014 and excluding the other infectious diseases category (eTable 4 in the Supplement): lower respiratory infections, HIV/AIDS, diarrheal diseases, hepatitis, meningitis, and tuberculosis. Deaths from chronic hepatitis B and C were counted under the category of cirrhosis. Although the focus of this study is infectious diseases, all causes of death in the GBD cause list were analyzed concurrently.

Previous studies have documented the existence of “garbage codes” in death registration data (insufficiently specific or implausible cause of death codes), which may lead to misleading spatial and temporal patterns, as well as misleading rankings among causes because the percent of deaths assigned garbage codes varies by location, year, and true underlying cause. This study used garbage redistribution methods developed for the GBD to reallocate deaths assigned garbage codes. First, plausible target causes were identified for each garbage code or group of garbage codes. Second, deaths were reassigned to the specified target codes according to proportions derived in 1 of 4 ways: (1) published literature or expert opinion; (2) regression models; (3) according to the proportions initially observed among targets; and (4) for HIV/AIDS specifically, by comparison to years before HIV/AIDS became widespread.

Small-Area Models

The study estimated spatially explicit Bayesian mixed-effects regression models for each cause, separately for males and females. The model for each cause was specified as

D_j,t,a ∼ Poisson (m_j,t,a · P_j,t,a) log(m_j,t,a) = β₀ + β₁ · X_j,t + γ_1,a,t + γ_2,j + (γ_3,j· t + γ_4,j,t) + (γ_5,j·a + γ_6,j,a)

in which D_j,t,a indicates the number of deaths, P_j,t,a indicates the population, and m_j,t,a indicates the underlying mortality rate for county (j), year (t), and age group (a). The model for m_j,t,a contained 6 components: an intercept (β₀), fixed covariate effects (β₁), random age-time effects (γ_1,_a_,_t), random spatial effects (γ₂_,j), random space-time effects (γ_3,_j and γ_4,_j_,_t), and random space-age effects (γ_5,_j and γ_6,_j_,_a). The model incorporated 7 covariates: the proportion of the adult population that graduated from high school, the proportion of the population that is Hispanic, the proportion of the population that is black, the proportion of the population that is a race other than black or white, the proportion of a county that is contained within a state or federal Native American reservation, the median household income, and the population density. γ₁, γ₂, γ₃ and γ₅ were assumed to follow conditional autoregressive distributions, which allow for smoothing over adjacent age groups and years (γ₁) or counties (γ₂, γ₃, and γ₅). γ₄ and γ₆ were assumed to follow independent mean-zero normal distributions.

Models were fit using the Template Model Builder Package in R statistical software (version 3.2.4). Model predictions were raked (ie, iteratively scaled along multiple dimensions) to simultaneously ensure consistency between levels of the cause hierarchy and between county-level estimates and existing national-level estimates from the GBD. After raking, age-standardized mortality rates were calculated using the 2010 US Census population as the standard, and years of life lost were calculated for each age by multiplying the mortality rate by population by life expectancy at the average age at death in the reference life table used in the GBD and then summed across all ages. When measuring changes over time, a change was considered statistically significant if the posterior probability of an increase (or decrease) was at least 95%. Similarly, differences between males and females were considered statistically significant if the posterior probability that the rate among males was higher than the rate among females (or vice versa) was at least 95%. No explicit correction for multiple testing (ie, across multiple counties) was applied; however, modeling all counties simultaneously is expected to mitigate the risk of spuriously detecting changes due to multiple testing.

County-level inequality in mortality rates was quantified by comparing the 10th and 90th percentile rates among counties. The difference between the 10th and 90th percentile was used as a measure of absolute geographic inequality, and the ratio between the 10th and 90th percentile was used as a measure of relative geographic inequality.

Results

Between 1980 and 2014, there were 4 081 546 deaths due to infectious diseases recorded in the United States. In 2014, a total of 113 650 (95% UI, 108 764-117 942) deaths or a rate of 34.10 (95% UI, 32.63-35.38) deaths per 100 000 persons were due to infectious diseases in the United States compared to a total of 72 220 (95% UI, 69 887-74 712) deaths or a rate of 41.95 (95% UI, 40.52-43.42) deaths per 100 000 persons in 1980, an overall decrease of 18.73% (95% UI, 14.95%-23.33%). These death causes include the 6 categories presented in the following sections (which accounted for 95.9% of the total infectious diseases deaths between 1980 and 2014), several infectious diseases that caused a smaller number of deaths (eTable 4 in the Supplement), and deaths attributed to the other infectious diseases category (which accounted for 2.8% of the total).

Mortality rates from infectious diseases were significantly higher among men than women in all years, but the decline from 1980 to 2014 was more substantial among men (30.42% [95% UI, 25.59%-35.68%]; from 56.37 [95% UI, 53.70-58.97] deaths per 100 000 persons in 1980 to 39.22 [95% UI, 36.94-41.06] deaths per 100 000 persons in 2014) than among women (9.49% [95% UI, 3.73%-16.16%]; from 33.11 [95% UI, 31.57-34.82] deaths per 100 000 persons in 1980 to 29.97 [95% UI, 28.26-31.50] deaths per 100 000 persons in 2014).

Figure 1 shows the age-standardized mortality rate in 2014, relative change in the age-standardized mortality rate between 1980 and 2014, and the age-standardized mortality rates in 1980, 1990, 2000, and 2014 from all infectious diseasess.

The Table summarizes the results by infectious disease in 2014 at the national and county levels. The section on the left summarizes the burden of each cause at the national level in terms of deaths, years of life lost, and the age-standardized mortality rate while the section on the right summarizes the distribution of counties according to the estimated age-standardized mortality rate from each cause. Results by county and by sex for all years are available in an online visualization tool.

Table. National Deaths, Years of Life Lost, Age-Standardized Mortality Rate, and Distribution of Age-Standardized Mortality Rates From Specific Infectious Diseases at the US County Level in 2014.

Cause of Death	US National Levels			US County-Level Mortality Rates
	No. in Thousands (95% Uncertainty Interval)		Mortality Rate, No. of Deaths/100 000 Persons (95% Uncertainty Interval)	No. of Deaths/100 000 Persons						90th/10th Percentile, Ratio^b
	Deaths	Years of Life Lost		Minimum	10th Percentile	Median	90th Percentile	Maximum	90th Minus 10th Percentile^a	90th/10th Percentile, Ratio^b
All infectious diseases	113.65 (108.76-117.94)	1865.53 (1820.40-1932.73)	34.10 (32.63-35.38)	12.2	22.77	34.69	50.74	135.11	27.98	2.23
Lower respiratory infections^c	89.88 (86.25-93.82)	1221.41 (1178.72-1272.52)	26.87 (25.79-28.05)	7.24	18.55	29.03	43.07	87.70	24.52	2.32
Diarrheal diseases^c	8.03 (2.77-8.88)	118.31 (60.32-128.40)	2.41 (0.86-2.67)	0.47	1.37	2.03	3.31	6.14	1.94	2.42
HIV/AIDS^c	7.75 (7.66-7.84)	299.13 (296.03-302.53)	2.40 (2.38-2.43)	0.15	0.31	0.73	3.05	64.87	2.75	9.96
Meningitis^c	1.34 (1.28-1.41)	50.89 (48.72-53.66)	0.41 (0.40-0.43)	0.22	0.29	0.40	0.59	1.23	0.30	2.06
Hepatitis^c	0.97 (0.89-1.07)	27.99 (25.53-30.69)	0.29 (0.27-0.32)	0.06	0.10	0.19	0.36	5.24	0.26	3.67
Tuberculosis^c	0.85 (0.81-0.89)	17.71 (16.93-18.49)	0.25 (0.24-0.27)	0.06	0.09	0.17	0.39	3.51	0.30	4.30

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^{^a}

Measure of absolute geographic inequality.

^{^b}

Measure of relative geographic inequality.

^{^c}

Combined categories accounted for 95.9% of the total infectious diseases deaths between 1980 and 2014. Each category accounted for at least 1% of total infectious disease deaths and are at level 3 of the Global Burden of Diseases, Injuries, and Risk Factors study cause hierarchy (eTable 3 in the Supplement), excluding several infectious diseases that caused a smaller number of deaths (eTable 4 in the Supplement), and the other infectious diseases category, which accounted for 2.8% of the total.

Lower Respiratory Infections

Lower respiratory infections were the leading cause of infectious diseases mortality in 2014, accounting for 78.80% of all infectious diseases mortality (26.87 [95% UI, 25.79-28.05] deaths per 100 000 persons). Among the 6 diseases considered, lower respiratory infection had the highest absolute inequality among counties with a difference of 24.5 deaths per 100 000 persons in mortality rates between counties in the 10th percentile and counties in the 90th percentile. Lower respiratory infection mortality was higher in the eastern half of the United States due to mortality in the upper 5% (>48.98 deaths per 100 000 persons) of the range of lower respiratory infections, except for Florida (Collier County, Florida, had the lowest rate at 7.24 [95% UI, 6.59-7.97] deaths per 100 000 persons) and parts of the Northeast, and peaked in counties in Louisiana (East Feliciana Parish, Louisiana, had the highest mortality rate at 87.70 [95% UI, 77.80-97.64] deaths per 100 000 persons), Arkansas, Alabama, Tennessee, Kentucky, and Georgia (Figure 2). In the western half of the country, many counties with high rates of lower respiratory infection mortality were located in Nevada, Montana, and South Dakota. Overall, lower respiratory infection mortality rates decreased 25.79% (95% UI, 22.02%-29.43%) from 36.21 (95% UI, 34.82-37.64) deaths per 100 000 persons in 1980 to 26.87 (95% UI, 25.79-28.05) deaths per 100 000 persons in 2014. Lower respiratory infection mortality decreased in most counties (75.11%; statistically significant in 56.88% of counties), but counties with increases (24.89%; statistically significant in 12.32% of counties) were found throughout the South and East.

Figure 2. — A, Age-standardized mortality rate for both sexes combined in 2014.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1980 and 2014. A and B, the color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

C, Age-standardized mortality rate for both sexes combined in 1980, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

Diarrheal Diseases

Diarrheal diseases were the second leading cause of infectious diseases mortality in 2014, accounting for 7.07% of infectious disease deaths with 2.41 (95% UI, 0.86-2.67) deaths per 100 000 persons. This category included diarrheal diseases from all etiologies (eTable 5 in the Supplement). The corridor spanning between Missouri and Maine showed a higher number of counties with mortality rates due to diarrheal diseases in the upper 5% of counties (>3.70 deaths per 100 000 persons) (Figure 3). Furthermore, New Mexico, Arizona, California, and Washington states showed an increased number of counties with similar high diarrheal disease mortality. Kalawao and Maui Counties in Hawaii had the lowest mortality rate of 0.47 (95% UI, 0.17-0.78) deaths per 100 000 persons, while Ross County, Ohio, had the highest rate of 6.14 (95% UI, 2.22-8.12) deaths per 100 000 persons. Mortality from diarrheal diseases increased by 483.96% (95% UI, −17.66% to 622.24%) between 1980 (0.41 [95% UI, 0.36-0.84] deaths per 100 000 persons) and 2014 (2.41 [95% UI, 0.86-2.67] deaths per 100 000 persons). Diarrheal diseases were the only cause of infectious diseases mortality to increase from 2000 to 2014. At the county level, diarrheal disease mortality rates increased in nearly all counties (99.97%; statistically significant in 99.71% of counties), with the largest increases mainly observed in the Northeast, Midwest, Southwest, and Pacific coast.

Figure 3. — A, Age-standardized mortality rate for both sexes combined in 2014. The color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1980 and 2014. The color scale is similarly truncated at approximately the 99th percentile, but not at the first percentile, to avoid combining counties where the mortality rate increased with counties where the mortality rate decreased in the same group.

C, Age-standardized mortality rate for both sexes combined in 1980, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

HIV/AIDS

HIV/AIDS caused 7.04% of all deaths from infectious diseases in 2014, accounting for 2.40 (95% UI, 2.38-2.43) deaths per 100 000 persons. Among the diseases considered, HIV/AIDS had the highest relative between-county inequality with a ratio of 10.0 in mortality rates from the 90th percentile compared to the 10th. Mortality rates in 2014 were significantly higher among men (3.51 [95% UI, 3.45-3.56] deaths per 100 000 persons) than among women (1.35 [95% UI, 1.33-1.37] deaths per 100 000 persons). Among counties in 2014, HIV/AIDS mortality was predominantly concentrated in the southeastern region of the United States with mortality in the upper 5% (4.50 deaths per 100 000 persons) of the range of HIV/AIDS (Figure 4). In 2014, Union County, Florida, had the highest rate of HIV/AIDS mortality at 64.87 (95% UI, 55.94-75.06) deaths per 100 000 persons, while the lowest rate was registered in Saint Croix County, Wisconsin, at 0.15 (95% UI, 0.10-0.23) deaths per 100 000 persons. At the national level, the mortality rate increased by 25.82% (95% UI, 23.78%-28.03%) between 1985 and 2014, reflecting a steep increase prior to 1994, when the mortality rate peaked at 15.87 (95% UI, 15.73-16.01) deaths per 100 000 persons, and a steep decrease thereafter. Mortality rates increased in the vast majority of counties since 1985 (97.49%; statistically significant in 85.92% of counties) given that most counties had almost no deaths in that year.

Figure 4. — A, Age-standardized mortality rate for both sexes combined in 2014.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1985 and 2014. A and B, the color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

C, Age-standardized mortality rate for both sexes combined in 1985, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

Meningitis

Meningitis caused 1.21% of all infectious diseases mortality in 2014 with 0.41 (95% UI, 0.40-0.43) deaths per 100 000 persons. In 2014, mortality from meningitis was highest in the southeastern part of the United States, with additional agglomeration of counties with mortality in the upper 5% (>0.66 deaths per 100 000 persons) of the range of meningitis in Arizona, New Mexico, South Dakota, and Alaska (Figure 5). In the Southeast, many counties with higher meningitis mortality were observed in Louisiana, Mississippi, Alabama, Georgia, and South Carolina. Marin County, California, registered the lowest mortality rate of 0.22 (95% UI, 0.18-0.26) deaths per 100 000 persons from meningitis, while Oglala Lakota County, South Dakota, registered the highest rate of 1.23 (95% UI, 0.96-1.52) deaths per 100 000 persons. Nationally, the meningitis mortality rate decreased by 69.55% (95% UI, 66.98%-71.52%) from 1.36 (95% UI, 1.30-1.42) in 1980 to 0.41 (95% UI, 0.40-0.43) in 2014. Over this period, statistically significant declines in the meningitis mortality rate were observed in all counties.

Figure 5. — A, Age-standardized mortality rate for both sexes combined in 2014.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1980 and 2014. A and B, the color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

C, Age-standardized mortality rate for both sexes combined in 1980, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

Hepatitis

Hepatitis (excluding chronic hepatitis B and C which are counted under cirrhosis in the GBD cause list), accounted for 0.86% of infectious diseases mortality with 0.29 (95% UI, 0.27-0.32) deaths per 100 000 persons in 2014. Among men, hepatitis caused 0.41 (95% UI, 0.36-0.46) deaths per 100 000 persons, compared to 0.19 (95% UI, 0.17-0.21) deaths per 100 000 persons among women. In 2014, counties in parts of California, Oregon, Texas, and New Mexico were the most affected due to hepatitis in the upper 5% (>0.45 deaths per 100 000 persons) of the range of hepatitis (Figure 6). Neighboring states also included many counties with high hepatitis mortality. Union County, Florida, had the highest rate of of hepatitis mortality with 5.24 (95% UI, 3.99-7.01) deaths per 100 000 persons, while the lowest rate of 0.06 (95% UI: 0.04–0.08) was registered both in Steele County, North Dakota, and Waukesha County, Wisconsin. Nationally, hepatitis mortality rates increased sharply from 1980 before peaking in 2000 at 1.84 (95% UI, 1.75-1.94) deaths per 100 000 persons and then rapidly decreased. Overall, mortality rates decreased by 40.74% (95% UI, 32.30%-48.49%) from 0.49 (95% UI, 0.46-0.53) in 1980 to 0.29 (95% UI, 0.27-0.32) in 2014. Over the same period, mortality from hepatitis decreased in most counties (98.10%; statistically significant in 82.41% of counties), but counties with increases (1.90%; statistically significant in 0.42% of counties) were found in some southern and western states.

Figure 6. — A, Age-standardized mortality rate for both sexes combined in 2014.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1980 and 2014. A and B, the color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

C, Age-standardized mortality rate for both sexes combined in 1980, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

Tuberculosis

Tuberculosis caused 0.75% of infectious diseases mortality in 2014, with 0.25 (95% UI, 0.24-0.27) deaths per 100 000 persons. As with HIV/AIDS and hepatitis, tuberculosis mortality rates in 2014 were significantly higher among men (0.35 [95% UI, 0.32-0.37] deaths per 100 000 persons) than among women (0.18 [95% UI, 0.17-0.19]). In 2014, counties in southern and southwestern states had noticeably elevated tuberculosis mortality, as did counties in parts of California, Alaska, and South Dakota due to mortality in the upper 5% (>0.50 deaths per 100 000 persons) of the range of tuberculosis (Figure 7). Rich County, Utah and Hinsdale County, Colorado, registered the lowest tuberculosis mortality rate of 0.06 (95% UI, 0.04-0.08) deaths per 100 000, persons, while Oglala Lakota County, South Dakota, registered the highest rate of 3.51 (95% UI, 2.64-4.45) deaths per 100 000 persons. Nationally, tuberculosis mortality decreased by 83.31% (95% UI, 81.97%-84.50%) from 1.52 (95% UI, 1.44-1.61) in 1980 to 0.25 (95% UI, 0.24-0.27) in 2014. As with meningitis, tuberculosis also decreased in all counties between 1980 and 2014 (statistically significant in 99.97% of counties).

Figure 7. — A, Age-standardized mortality rate for both sexes combined in 2014.

B, Relative change in the age-standardized mortality rate for both sexes combined between 1980 and 2014. A and B, the color scale is truncated at approximately the first and 99th percentiles as indicated by the range given in the color scale.

C, Age-standardized mortality rate for both sexes combined in 1980, 1990, 2000, and 2014. The bottom border of the boxes indicates the 25th percentile; middle line, the 50th percentile; and the top border indicates the 75th percentile across all counties; whiskers indicate the full range across counties; and the dots indicate the national-level rate.

Discussion

This study showed declining overall trends of infectious diseases mortality in the United States over the last 35 years, in line with advancements in health care. However, there were large variations between counties in both levels of mortality and rates of change.

Access and quality of health care may explain some of the observed differences. Early diagnosis and proper case management of infectious diseases increase the chances of survival. Certain behaviors increase the risk for contracting several infectious diseases, such as sharing syringes among drug users. Data from the National Survey on Drug Use and Health showed increasing heroin use across the United States, most age groups, and all income levels between 2002 and 2013.

Having the highest mortality rate for a specific infectious diseases can have a specific reason, or a multiplicity of reasons. For instance, HIV/AIDS mortality in Union County, Florida, is likely to be driven by a high proportion of the county’s population being inmates housed at Union Correctional Institution, while tuberculosis mortality in Oglala Lakota County, South Dakota, is driven by a multiplicity of factors. Oglala Lakota County also has the highest rate of mortality from meningitis and is disadvantaged overall (eg, very high rates of poverty, smoking, heavy drinking, and diabetes—all risk factors for tuberculosis), relative to other counties in the United States.

This study also showed some notable geographic patterns. HIV/AIDS mortality increased first in some of the wealthiest US counties on the West Coast, but has shifted to some of the poorest counties in the southeastern United States. Affluent populations might have been able to benefit from advanced medical HIV care and changing risky behaviors for HIV infection. Overall, the trend of HIV/AIDS mortality reflected the availability of and advancement in treatment. HIV/AIDS mortality increased until 1994, when effective treatment and prevention strategies became available.

The increasing trends in hepatitis mortality between 1983 and 2000 have been reported previously, specifically, increased deaths from hepatitis B. This increase might be the reflection of the increase in injection drug use in the previous decades.

The aging population of the United States might have contributed to higher infectious diseases mortality rates. Clostridium difficile infection is a common nosocomial infection in the United States for elderly individuals. Lower respiratory infections are also very common among the elder population through community or nosocomial infections. As the US population ages, infectious diseases among elderly persons will put strains on the medical system.

In 2011, the Emerging Infections Program (Centers for Disease Control and Prevention) found that individuals aged 65 years or older were almost 9 times more likely than their counterparts to get infected with C difficile, and reported 29 000 deaths associated with this infection in the United States despite a reported 8% decrease in incidence since 2011. The Centers for Disease Control and Prevention recommend that clinicians should balance the need for antibiotics and their risks and follow the guidelines of infection control recommendations.

The mortality trends of lower respiratory infection and diarrheal diseases are consistent with their economic burden. The study by Dieleman et al estimated these diseases to account for approximately $46.3 billion of personal health care spending in the United States in 2013. This cost is likely to increase in the future due to aging and growth of the population. Hence, it is necessary to focus on infectious diseases prevention through public health strategies to decrease their burden. There is also a need to improve access to and quality of health care throughout the United States to improve health outcomes and increase immunization coverage for vaccine-preventable diseases such as hepatitis and meningitis. Developing newer medicines and vaccines for diarrheal diseases should also be a priority, as well as better infectious diseases control measures in health care facilities and use of antibiotics.

This study identified local burden of infectious diseasess. The findings are relevant in examining local differences often masked by national or state-level averages. These findings can help local public health authorities in planning and allocating resources to focus on the most burdening infectious diseases.

This study has a number of strengths. First, cause-specific mortality rates were analyzed over an extended period of time using a consistent methodology, which enabled meaningful comparisons among counties over time and between different infectious diseases. Second, garbage code redistributions methods were applied to address known issues with coding in death registration data. In the United States, deaths that occur in hospitals or nursing homes are assigned a code. In most cases of sudden death or home death, an autopsy is performed and a cause of death is then assigned. For analysis in the present study, death certificate data were used. Three methods were used to redistribute causes: (1) proportional analysis; (2) regression methods; and (3) literature review. Third, validated small-area models were used to produce reasonably precise estimates of cause-specific mortality rates without resorting to combining counties or pooling data across multiple years.

Limitations

This study also has several limitations. First, the deaths, population, and covariates data used in this analysis are all subject to error. Second, the garbage code redistribution methods used in this analysis have not been validated due to insufficient criterion standard (autopsy) data. Third, uncertainty due to the garbage code redistributions methods is difficult to quantify and has not been accounted for in the uncertainty intervals reported for this analysis. Fourth, the small-area estimation models smooth over time, space, and age groups and may in some cases attenuate unusually low or high mortality rates, particularly in counties with small populations, resulting in an underestimation of geographic differences. Fifth, for HIV/AIDS estimates, data on HIV/AIDS deaths is not reliable for the years prior to 1985, which is why estimates for HIV/AIDS were limited to this period of time.

Conclusions

Supplement.

eTable 1. Data Sources Used for Covariates

eTable 2. Counties Combined to Ensure Historically Stable Units of Analysis

eTable 3. GBD Cause List and Associated ICD-9 and ICD-10 Codes

eTable 4. Observed Deaths Due to Infectious Diseases by Cause, United States, 1980-2014

eTable 5. Diarrheal Diseases and Associated ICD-9 and ICD-10 Codes

Click here for additional data file.^{(704.5KB, pdf)}

References

1.Pinner RW, Teutsch SM, Simonsen L, et al. . Trends in infectious diseases mortality in the United States. JAMA. 1996;275(3):189-193. [PubMed] [Google Scholar]
2.Armstrong GL, Conn LA, Pinner RW. Trends in infectious disease mortality in the United States during the 20th century. JAMA. 1999;281(1):61-66. [DOI] [PubMed] [Google Scholar]
3.Centers for Disease Control and Prevention (CDC) Control of infectious diseases. MMWR Morb Mortal Wkly Rep. 1999;48(29):621-629. [PubMed] [Google Scholar]
4.Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerg Infect Dis. 2007;13(1):1-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lederberg J, Shope RE, Oaks SC Jr, Institute of Medicine; Committee on Emerging Microbial Threats to Health eds. Emerging Infections: Microbial Threats to Health in the United States. Washington, DC: National Academy Press; 1992. [PubMed] [Google Scholar]
6.Morse SS. Factors in the emergence of infectious diseases. Emerg Infect Dis. 1995;1(1):7-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, et al. . US county-level trends in mortality rates for major causes of death, 1980-2014. JAMA. 2016;316(22):2385-2401. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.National Vital Statistics System Multiple cause of death data file. (1980-2014). https://www.cdc.gov/nchs/nvss/mortality_public_use_data.htm. Accessed February 8, 2018.
9.United States Census Bureau County intercensal estimates by age, sex, race: 1980-1989. Published 2009. https://www.census.gov/data/tables/time-series/demo/popest/1980s-county.html. Accessed January 8, 2018.
10.National Center for Health Statistics Bridged-race intercensal estimates of the resident population of the United States for July 1, 2000-July 1, 2009, by year, county, single-year of age (0, 1, 2, ..., 85 years and over), bridged race, Hispanic origin, and sex. 2012. http://www.cdc.gov/nchs/nvss/bridged_race/data_documentation.htm#july2000-2009. Accessed January 8, 2018.
11.National Center for Health Statistics Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, ..., 85 years and over), bridged race, Hispanic origin, and sex. 2015. http://www.cdc.gov/nchs/nvss/bridged_race/data_documentation.htm#Vintage2014. Accessed January 8, 2018.
12.University of California Berkeley, Max Planck Institute for Demographic Research Human mortality database. http://www.mortality.org/. Accessed January 8, 2018.
13.Wang H, Naghavi M, Allen C, et al. ; GBD 2015 Mortality and Causes of Death Collaborators . Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459-1544. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Naghavi M, Makela S, Foreman K, O’Brien J, Pourmalek F, Lozano R. Algorithms for enhancing public health utility of national causes-of-death data. Popul Health Metr. 2010;8:9. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Leroux BG, Lei X, Breslow N. Estimation of disease rates in small areas: a new mixed model for spatial dependence In: Statistical Models in Epidemiology, the Environment, and Clinical Trials. Vol 116 New York, NY: Springer-Verlag; 2000:179-191. [Google Scholar]
16.Knorr-Held L. Bayesian modelling of inseparable space-time variation in disease risk. Stat Med. 2000;19(17-18):2555-2567. [DOI] [PubMed] [Google Scholar]
17.Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: automatic differentiation and laplace approximation. J Stat Softw. 2016;70(5). doi: 10.18637/jss.v070.i05 [DOI] [Google Scholar]
18.R Core Team R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. http://www.R-project.org/. Accessed January 8, 2018.
19.University of Washington Institute for Health Metrics and Evaluation US health map. https://vizhub.healthdata.org/subnational/usa. Accessed February 27, 2018.
20.Collins IJ, Cairns J, Ngo-Giang-Huong N, et al. ; Programme for HIV Prevention and Treatment Study Team . Cost-effectiveness of early infant HIV diagnosis of HIV-exposed infants and immediate antiretroviral therapy in HIV-infected children under 24 months in Thailand. PLoS One. 2014;9(3):e91004. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Caliendo AM, Gilbert DN, Ginocchio CC, et al. ; Infectious Diseases Society of America (IDSA) . Better tests, better care: improved diagnostics for infectious diseases. Clin Infect Dis. 2013;57(suppl 3):S139-S170. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.May M, Gompels M, Delpech V, et al. . Impact of late diagnosis and treatment on life expectancy in people with HIV-1: UK Collaborative HIV Cohort (UK CHIC) Study. BMJ. 2011;Oct 11(343):d6016. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Pouget ER, Hagan H, Des Jarlais DC. Meta-analysis of hepatitis C seroconversion in relation to shared syringes and drug preparation equipment. Addiction. 2012;107(6):1057-1065. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Centers for Disease Control and Prevention Today’s heroin epidemic: more people at risk, multiple drugs abused. Published July 7, 2015. https://www.cdc.gov/vitalsigns/heroin/index.html. Accessed January 8, 2018.
25.Hedden SL, Kennet J, Medley G, Tice P; Substance Abuse and Mental Health Services Administration . Behavioral health trends in the United States: results from the 2014 National Survey on Drug Use and Health; 2015:1-64. https://www.samhsa.gov/data/sites/default/files/NSDUH-FRR1-2014/NSDUH-FRR1-2014.pdf. Accessed August 17, 2017.
26.Dwyer-Lindgren L, Mackenbach JP, van Lenthe FJ, Flaxman AD, Mokdad AH. Diagnosed and undiagnosed diabetes prevalence by county in the US, 1999-2012. Diabetes Care. 2016;39(9):1556-1562. [DOI] [PubMed] [Google Scholar]
27.Dwyer-Lindgren L, Flaxman AD, Ng M, Hansen GM, Murray CJL, Mokdad AH. Drinking patterns in US counties From 2002 to 2012. Am J Public Health. 2015;105(6):1120-1127. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Dwyer-Lindgren L, Mokdad AH, Srebotnjak T, Flaxman AD, Hansen GM, Murray CJ. Cigarette smoking prevalence in US counties: 1996-2012. Popul Health Metr. 2014;12(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.US Census Bureau Small area income and poverty estimates data. https://www.census.gov/programs-surveys/saipe/data.html. Accessed January 8, 2018.
30.Riley ED, Gandhi M, Hare C, Cohen J, Hwang S. Poverty, unstable housing, and HIV infection among women living in the United States. Curr HIV/AIDS Rep. 2007;4(4):181-186. [DOI] [PubMed] [Google Scholar]
31.Centers for Disease Control and Prevention Twenty-five years of HIV/AIDS—United States, 1981–2006. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5521a1.htm. Accessed January 8, 2018.
32.Kim WR. Epidemiology of hepatitis B in the United States. Hepatology. 2009;49(5)(suppl):S28-S34. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Armstrong GL. Injection drug users in the United States, 1979-2002: an aging population. Arch Intern Med. 2007;167(2):166-173. [DOI] [PubMed] [Google Scholar]
34.Jump RL. Clostridium difficile infection in older adults. Aging Health. 2013;9(4):403-414. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Montoya A, Mody L. Common infections in nursing homes: a review of current issues and challenges. Aging Health. 2011;7(6):889-899. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Lessa FC, Mu Y, Bamberg WM, et al. . Burden of Clostridium difficile infection in the United States. N Engl J Med. 2015;372(9):825-834. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Centers for Disease Control and Prevention 2014 National and state healthcare-associated infections progress report: 2016. https://www.cdc.gov/hai/surveillance/progress-report/index.html. Accessed January 8, 2018.
38.Centers for Disease Control and Prevention Antibiotic resistance threats in the United States, 2013.; 2013:1-114. https://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=49. Accessed April 14, 2017.
39.Dieleman JL, Baral R, Birger M, et al. . US spending on personal health care and public health, 1996-2013. JAMA. 2016;316(24):2627-2646. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Caucci L, Warner M; Centers for Disease Control and Prevention . Table 1: selected characteristic of death requiring investigation by state, 2013. https://www.cdc.gov/phlp/docs/coroner/table1-investigation.pdf. Accessed February 8, 2018.
41.Caucci L, Warner M; Centers for Disease Control and Prevention . Table 2: characteristic of deaths requiring autopsy by state, 2013. https://www.cdc.gov/phlp/docs/coroner/table2-autopsy.pdf. Accessed February 8, 2018.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eTable 1. Data Sources Used for Covariates

eTable 2. Counties Combined to Ensure Historically Stable Units of Analysis

eTable 3. GBD Cause List and Associated ICD-9 and ICD-10 Codes

eTable 4. Observed Deaths Due to Infectious Diseases by Cause, United States, 1980-2014

eTable 5. Diarrheal Diseases and Associated ICD-9 and ICD-10 Codes

Click here for additional data file.^{(704.5KB, pdf)}

[joi180023r1] 1.Pinner RW, Teutsch SM, Simonsen L, et al. . Trends in infectious diseases mortality in the United States. JAMA. 1996;275(3):189-193. [PubMed] [Google Scholar]

[joi180023r2] 2.Armstrong GL, Conn LA, Pinner RW. Trends in infectious disease mortality in the United States during the 20th century. JAMA. 1999;281(1):61-66. [DOI] [PubMed] [Google Scholar]

[joi180023r3] 3.Centers for Disease Control and Prevention (CDC) Control of infectious diseases. MMWR Morb Mortal Wkly Rep. 1999;48(29):621-629. [PubMed] [Google Scholar]

[joi180023r4] 4.Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerg Infect Dis. 2007;13(1):1-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r5] 5.Lederberg J, Shope RE, Oaks SC Jr, Institute of Medicine; Committee on Emerging Microbial Threats to Health eds. Emerging Infections: Microbial Threats to Health in the United States. Washington, DC: National Academy Press; 1992. [PubMed] [Google Scholar]

[joi180023r6] 6.Morse SS. Factors in the emergence of infectious diseases. Emerg Infect Dis. 1995;1(1):7-15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r7] 7.Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, et al. . US county-level trends in mortality rates for major causes of death, 1980-2014. JAMA. 2016;316(22):2385-2401. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r8] 8.National Vital Statistics System Multiple cause of death data file. (1980-2014). https://www.cdc.gov/nchs/nvss/mortality_public_use_data.htm. Accessed February 8, 2018.

[joi180023r9] 9.United States Census Bureau County intercensal estimates by age, sex, race: 1980-1989. Published 2009. https://www.census.gov/data/tables/time-series/demo/popest/1980s-county.html. Accessed January 8, 2018.

[joi180023r10] 10.National Center for Health Statistics Bridged-race intercensal estimates of the resident population of the United States for July 1, 2000-July 1, 2009, by year, county, single-year of age (0, 1, 2, ..., 85 years and over), bridged race, Hispanic origin, and sex. 2012. http://www.cdc.gov/nchs/nvss/bridged_race/data_documentation.htm#july2000-2009. Accessed January 8, 2018.

[joi180023r11] 11.National Center for Health Statistics Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, ..., 85 years and over), bridged race, Hispanic origin, and sex. 2015. http://www.cdc.gov/nchs/nvss/bridged_race/data_documentation.htm#Vintage2014. Accessed January 8, 2018.

[joi180023r12] 12.University of California Berkeley, Max Planck Institute for Demographic Research Human mortality database. http://www.mortality.org/. Accessed January 8, 2018.

[joi180023r13] 13.Wang H, Naghavi M, Allen C, et al. ; GBD 2015 Mortality and Causes of Death Collaborators . Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459-1544. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r14] 14.Naghavi M, Makela S, Foreman K, O’Brien J, Pourmalek F, Lozano R. Algorithms for enhancing public health utility of national causes-of-death data. Popul Health Metr. 2010;8:9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r15] 15.Leroux BG, Lei X, Breslow N. Estimation of disease rates in small areas: a new mixed model for spatial dependence In: Statistical Models in Epidemiology, the Environment, and Clinical Trials. Vol 116 New York, NY: Springer-Verlag; 2000:179-191. [Google Scholar]

[joi180023r16] 16.Knorr-Held L. Bayesian modelling of inseparable space-time variation in disease risk. Stat Med. 2000;19(17-18):2555-2567. [DOI] [PubMed] [Google Scholar]

[joi180023r17] 17.Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: automatic differentiation and laplace approximation. J Stat Softw. 2016;70(5). doi: 10.18637/jss.v070.i05 [DOI] [Google Scholar]

[joi180023r18] 18.R Core Team R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. http://www.R-project.org/. Accessed January 8, 2018.

[joi180023r19] 19.University of Washington Institute for Health Metrics and Evaluation US health map. https://vizhub.healthdata.org/subnational/usa. Accessed February 27, 2018.

[joi180023r20] 20.Collins IJ, Cairns J, Ngo-Giang-Huong N, et al. ; Programme for HIV Prevention and Treatment Study Team . Cost-effectiveness of early infant HIV diagnosis of HIV-exposed infants and immediate antiretroviral therapy in HIV-infected children under 24 months in Thailand. PLoS One. 2014;9(3):e91004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r21] 21.Caliendo AM, Gilbert DN, Ginocchio CC, et al. ; Infectious Diseases Society of America (IDSA) . Better tests, better care: improved diagnostics for infectious diseases. Clin Infect Dis. 2013;57(suppl 3):S139-S170. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r22] 22.May M, Gompels M, Delpech V, et al. . Impact of late diagnosis and treatment on life expectancy in people with HIV-1: UK Collaborative HIV Cohort (UK CHIC) Study. BMJ. 2011;Oct 11(343):d6016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r23] 23.Pouget ER, Hagan H, Des Jarlais DC. Meta-analysis of hepatitis C seroconversion in relation to shared syringes and drug preparation equipment. Addiction. 2012;107(6):1057-1065. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r24] 24.Centers for Disease Control and Prevention Today’s heroin epidemic: more people at risk, multiple drugs abused. Published July 7, 2015. https://www.cdc.gov/vitalsigns/heroin/index.html. Accessed January 8, 2018.

[joi180023r25] 25.Hedden SL, Kennet J, Medley G, Tice P; Substance Abuse and Mental Health Services Administration . Behavioral health trends in the United States: results from the 2014 National Survey on Drug Use and Health; 2015:1-64. https://www.samhsa.gov/data/sites/default/files/NSDUH-FRR1-2014/NSDUH-FRR1-2014.pdf. Accessed August 17, 2017.

[joi180023r26] 26.Dwyer-Lindgren L, Mackenbach JP, van Lenthe FJ, Flaxman AD, Mokdad AH. Diagnosed and undiagnosed diabetes prevalence by county in the US, 1999-2012. Diabetes Care. 2016;39(9):1556-1562. [DOI] [PubMed] [Google Scholar]

[joi180023r27] 27.Dwyer-Lindgren L, Flaxman AD, Ng M, Hansen GM, Murray CJL, Mokdad AH. Drinking patterns in US counties From 2002 to 2012. Am J Public Health. 2015;105(6):1120-1127. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r28] 28.Dwyer-Lindgren L, Mokdad AH, Srebotnjak T, Flaxman AD, Hansen GM, Murray CJ. Cigarette smoking prevalence in US counties: 1996-2012. Popul Health Metr. 2014;12(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r29] 29.US Census Bureau Small area income and poverty estimates data. https://www.census.gov/programs-surveys/saipe/data.html. Accessed January 8, 2018.

[joi180023r30] 30.Riley ED, Gandhi M, Hare C, Cohen J, Hwang S. Poverty, unstable housing, and HIV infection among women living in the United States. Curr HIV/AIDS Rep. 2007;4(4):181-186. [DOI] [PubMed] [Google Scholar]

[joi180023r31] 31.Centers for Disease Control and Prevention Twenty-five years of HIV/AIDS—United States, 1981–2006. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5521a1.htm. Accessed January 8, 2018.

[joi180023r32] 32.Kim WR. Epidemiology of hepatitis B in the United States. Hepatology. 2009;49(5)(suppl):S28-S34. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r33] 33.Armstrong GL. Injection drug users in the United States, 1979-2002: an aging population. Arch Intern Med. 2007;167(2):166-173. [DOI] [PubMed] [Google Scholar]

[joi180023r34] 34.Jump RL. Clostridium difficile infection in older adults. Aging Health. 2013;9(4):403-414. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r35] 35.Montoya A, Mody L. Common infections in nursing homes: a review of current issues and challenges. Aging Health. 2011;7(6):889-899. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r36] 36.Lessa FC, Mu Y, Bamberg WM, et al. . Burden of Clostridium difficile infection in the United States. N Engl J Med. 2015;372(9):825-834. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r37] 37.Centers for Disease Control and Prevention 2014 National and state healthcare-associated infections progress report: 2016. https://www.cdc.gov/hai/surveillance/progress-report/index.html. Accessed January 8, 2018.

[joi180023r38] 38.Centers for Disease Control and Prevention Antibiotic resistance threats in the United States, 2013.; 2013:1-114. https://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=49. Accessed April 14, 2017.

[joi180023r39] 39.Dieleman JL, Baral R, Birger M, et al. . US spending on personal health care and public health, 1996-2013. JAMA. 2016;316(24):2627-2646. [DOI] [PMC free article] [PubMed] [Google Scholar]

[joi180023r40] 40.Caucci L, Warner M; Centers for Disease Control and Prevention . Table 1: selected characteristic of death requiring investigation by state, 2013. https://www.cdc.gov/phlp/docs/coroner/table1-investigation.pdf. Accessed February 8, 2018.

[joi180023r41] 41.Caucci L, Warner M; Centers for Disease Control and Prevention . Table 2: characteristic of deaths requiring autopsy by state, 2013. https://www.cdc.gov/phlp/docs/coroner/table2-autopsy.pdf. Accessed February 8, 2018.

PERMALINK

Trends and Patterns of Differences in Infectious Disease Mortality Among US Counties, 1980-2014

Charbel el Bcheraoui, PhD

Ali H Mokdad, PhD

Laura Dwyer-Lindgren, PhD

Amelia Bertozzi-Villa, MPH

Rebecca W Stubbs, MA

Chloe Morozoff, MPH

Shreya Shirude, MPH

Mohsen Naghavi, MD, PhD

Christopher J L Murray, MD, DPhil

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design and Setting

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Data

Cause List and Garbage Redistribution

Small-Area Models

Results

Figure 1. County-Level Mortality From All Infectious Diseases.

Table. National Deaths, Years of Life Lost, Age-Standardized Mortality Rate, and Distribution of Age-Standardized Mortality Rates From Specific Infectious Diseases at the US County Level in 2014.

Lower Respiratory Infections

Figure 2. County-Level Mortality From Lower Respiratory Infections.

Diarrheal Diseases

Figure 3. County-Level Mortality From Diarrheal Diseases.

HIV/AIDS

Figure 4. County-Level Mortality From HIV/AIDS.

Meningitis

Figure 5. County-Level Mortality From Meningitis.

Hepatitis

Figure 6. County-Level Mortality From Hepatitis.

Tuberculosis

Figure 7. County-Level Mortality From Tuberculosis.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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