visits per thousand

How to Calculate Admits Per 1000

by Victoria Lee Blackstone

Published on 8 Nov 2018

"Admits per 1,000" is a term that represents how many patients are admitted to a hospital, healthcare facility or treatment center for every 1,000 people who seek help there. Many patients enter through the emergency department or in-patient admissions at regional hospitals, but other types of medical providers also are included, such as psychiatric and chemical-dependency facilities. Regardless of the type of medical facility, admits per 1,000 is a ratio that's calculated using a simple math equation.

You can calculate the number of admits per 1,000 visits by taking the number of admits over a given time, multiplying it by 1,000, and then dividing it by the total number of people who visited the facility during that identical duration of time.

How to Calculate Admits Per Thousand

In order to calculate the number of admits per thousand, you must first determine the number of patients admitted to a hospital, healthcare facility or treatment center in a given time period. Next, multiply this number by 1,000. Lastly, divide the result by the total number of people who visited that medical provider, including those who were not ultimately admitted to the facility.

By using the calculation above, if a hospital admits 500 patients from a total of 800 people who visited the hospital, the number of admits per thousand is 625 (500 x 1,000 divided by 800 = 625).

Why Is This Calculation Important?

Hospitals and other in-house medical facilities face ongoing challenges as they prepare budgets and cost estimates for future years. Admits per 1,000 is one way they can project a future year's financial needs based on the past year's actual patient statistics. This simple equation can help healthcare centers find solutions to minimize their costs, manage their supplies, modify their medical practices and adjust their budgets.

Benchmarking to Compare Standards

Admits per 1,000 also provides a benchmark, or point of reference, from which one hospital may measure its performance based on one or more other hospitals. Benchmarking can compare one hospital's statistics to other hospitals, which may be in the same community/county, state or national database. Hospitals may use the benchmarking tool to help them identify areas that need improvement toward their ongoing goal of providing the best patient care at the best costs.

Identifying Proactive Benefits

Although monitoring expenses is a primary focus of calculating admits per 1,000, hospitals can also use this calculation to help identify industry trends as they begin to emerge. This proactive management tool allows hospitals to stay on the forefront of subtle shifts in patient care so they can quickly identify and eliminate wasteful spending and unnecessary supply costs and other expenses. With a total of 140-plus million hospital visits each year just to emergency departments, according to a 2014 Centers for Disease Control and Prevention report, the data collected from a hospital's admits-per-1,000 calculations have significant potential to reduce national healthcare costs.

• HEDIS Measures and Technical Resources
• Emergency Department Utilization

Emergency Department Utilization (EDU)

Assesses emergency department (ED) utilization among commercial (18 and older) and Medicare (18 and older) health plan members. Plans report observed rates of ED use and a predicted rate of ED use based on the health of the member population. The observed and expected rates are used to calculate a calibrated observed-to-expected ratio that assesses whether plans had more, the same or less emergency department visits than expected, while accounting for incremental improvements across all plans over time. The observed-to-expected ratio is multiplied by the emergency department visit rate across all health plans to produce a risk-standardized rate which allows for national comparison.

Why It Matters

ED visits are a high-intensity service and a cost burden on the health care system, as well as on patients. Some ED events may be attributed to preventable or treatable conditions . A high rate of ED utilization may indicate poor care management, inadequate access to care or poor patient choices, resulting in ED visits that could be prevented. 1,2 Plans can ensure that members receive appropriate, coordinated primary care to address preventable ED visits.

Results – National Averages

Emergency department utilization total rate.

*Lower rates signify better performance.

§  Not available due to CMS suspension of data reporting during COVID-19 pandemic.

This State of Healthcare Quality Report classifies health plans differently than NCQA’s Quality Compass. HMO corresponds to All LOBs (excluding PPO and EPO) within Quality Compass. PPO corresponds to PPO and EPO within Quality Compass.

Figures do not account for changes in the underlying measure that could break trending. Contact Information Products via  my.ncqa.org  for analysis that accounts for trend breaks.

• Dowd, B., M. Karmarker, T. Swenson, et al. 2014. “Emergency department utilization as a measure of physician performance.” American Journal of Medical Quality 29 (2), 135–43. http://ajm.sagepub.com/content/29/2/135.long
• Agency for Healthcare Research and Quality. 2015. Measures of Care Coordination: Preventable Emergency Department Visits. Accessed at https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure2.html

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• Healthcare Glossary
• PLEXIS Support

Days (Or Visits) Per Thousand

A standard unit of measurement of utilization. Refers to an annualized use of the hospital or other institutional care. It is the number of hospital days that are used in a year for each thousand covered lives. The formula used to calculate days per thousand is as follows: (# of days/member months) x (1000 members) x (# of months). An indicator calculated by taking the total number of days (for inpatient, residential, or partial hospitalization) or visits (for outpatient) received by a specific group for a specific period of time (usually one year). A measure used to evaluate utilization management performance.

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The Latest Emergency Department Utilization Numbers Are In

T he Centers for Disease Control and Prevention (CDC) released its statistical survey of emergency department visits for 2016 on April 1. Called the National Hospital Ambulatory Medical Care Survey (NHAMCS), it is a wealth of information for emergency physicians and will guide the data and trends for the emergency services for which they are responsible. 1

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The numbers.

ED visit estimates increased from 136.9 million in 2015 to 145.6 million in 2016, a jump of 6.4 percent. The 10-year volume change is 24.7 percent, and for the past 20 years, the increase has totaled 61.2 percent (the 1996 ED visit estimate was 90.3 million). The past 15 years of volume estimates appear in Table 1.

These data may not match the experience in every emergency department and every community. First, the CDC typically estimates the lowest volume of ED visits, and the NHAMCS does not include visits to freestanding emergency departments. Second, there are changing patterns of ED use based on community sources of unscheduled care. Third, the patchwork of primary care systems in the country influences the number of ED visits locally.

(click for larger image) Table 1: Estimated Annual ED Visits

What is apparent from the CDC data is that the trend of emergency departments seeing older, sicker patients, combined with continued growth in retail clinics, telehealth, and other sources of care for nonemergent problems, will yield a net increase in the average severity and complexity of patients seen in full-service emergency departments.

Who Are the Patients?

ED visits increased from 369 to 458 visits per 1,000 people between 1995 and 2016. High utilizers continue to include infants, nursing home residents, the homeless, black persons, and people over age 75.

Infants under age 1 had 987 visits per 1,000 persons. This is relatively high utilization and represents an opportunity for parent education.

There were roughly 2.2 million visits for patients who reside in nursing homes, for a utilization of 1,594 visits per 1,000 residents. Approximately 33 percent of nursing home patient ED visits resulted in hospital admission (739,000), with an average length of hospital stay of 5.7 days.

Persons classified as homeless represented a larger visit load for EDs compared with prior years. In 2016, homeless persons accounted for an estimated 1,446,000 visits, a rate of 2,630 visits per 1,000 estimated number of homeless persons. Those visits equal roughly 1 percent of total ED visits.

The CDC also categorized visit rates for white, black, Hispanic, and other races/ethnicities. The visit rate was 435 visits per 1,000 white people, 404 visits per 1,000 Hispanics, and 804 visits per 1,000 black people. The visit rate was 172 visits per 1,000 persons of other races (ie, Asian, native Hawaiian or other Pacific Islander, American Indian or Alaska native, and persons with more than one race).

The ED population is aging in line with national demographics. Persons over age 65 accounted for 15.8 percent of ED visits, and persons age 75 and older had 605 visits per 1,000 in 2016. Thus, emergency departments must prepare for larger numbers of patients and develop processes tailored to older persons. In addition, older patients require more workup, treatment, and, thus, more time in the department.

Finally, because older patients are admitted to the hospital more often, they spend more time as ED boarders. Planning for new or renovating old emergency departments should account for these shifting demographics.

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Topics: Centers for Disease Control and Prevention Emergency Department Benchmarking Alliance Utilization

A Sobering Year for Emergency Departments and Their Patients

A First Look at Emergency Department Data for 2022

Emergency Department Patient Challenges to Come

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ACEP Now: Vol 43 – No 04 – April 2024

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About the Author

James J. Augustine, MD, FACEP

James J. Augustine, MD, FACEP, is national director of prehospital strategy for US Acute Care Solutions in Canton, Ohio; clinical professor of emergency medicine at Wright State University in Dayton, Ohio; and vice president of the Emergency Department Benchmarking Alliance.

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• Introduction
• Conclusions
• Article Information

Total visits (A), injuries and not-preventable or avoidable visits (B), and visits related to mental health and substance use disorders (C). Q1 indicates quarter 1.

Nonemergent (A), emergent but preventable or avoidable (B), and emergent but primary care–treatable conditions (C). Q1 indicates quarter 1.

eFigure. Total Annual Number of Emergency Department Visits Across States From 2011 to 2017

eTable 1. Indirect Test of the Parallel Trends Assumption in the Pre-ACA Implementation Period

eTable 2. Difference-in-Differences Regression Analyses Estimating Postexpansion Years Separately: Total ED Visits per 1000 Population and Stratified by Medical Urgency

eTable 3. Difference-in-Differences Regression Analyses: Total ED Visits per 1000 Population and Stratified by Medical Urgency Between Florida and New York

• Medicaid Expansion Use and Avoidable Emergency Department Use JAMA Network Open Invited Commentary June 14, 2022 Amber K. Sabbatini, MD, MPH; Jerome Dugan, PhD

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Giannouchos TV , Ukert B , Andrews C. Association of Medicaid Expansion With Emergency Department Visits by Medical Urgency. JAMA Netw Open. 2022;5(6):e2216913. doi:10.1001/jamanetworkopen.2022.16913

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Association of Medicaid Expansion With Emergency Department Visits by Medical Urgency

• 1 Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, Columbia
• 2 Department of Health Policy & Management, School of Public Health, Texas A&M University, College Station
• Invited Commentary Medicaid Expansion Use and Avoidable Emergency Department Use Amber K. Sabbatini, MD, MPH; Jerome Dugan, PhD JAMA Network Open

Question   Is Medicaid eligibility expansion associated with changes in emergency department (ED) visits based on the medical urgency of the conditions?

Findings   In this cross-sectional study of 80.6 million ED visits across 4 US states, ED visits per 1000 population decreased for states that expanded Medicaid compared with states that did not. This decrease was associated with decreases in ED visits for less-emergent or nonemergent conditions.

Meaning   The findings of this study suggest that expanding Medicaid might reduce ED visits for conditions that could be treated in outpatient settings.

Importance   Relatively little is known about the association of the Medicaid eligibility expansion under the Patient Protection and Affordable Care Act with emergency department (ED) visits categorized by medical urgency.

Objective   To estimate the association between state Medicaid expansions and ED visits by the urgency of presenting conditions.

Design, Setting, and Participants   This cross-sectional study used the Healthcare Cost and Utilization Project State Emergency Department Databases from January 2011 to December 2017 for 2 states that expanded Medicaid in 2014 (New York and Massachusetts) and 2 states that did not (Florida and Georgia). Difference-in-differences regression models were used to estimate the changes in ED visits overall and further stratified by the urgency of the conditions using an updated version of the New York University ED algorithm between the states that expanded Medicaid and those that did not, before and after the expansion. Data were analyzed between June 7 and December 12, 2021.

Exposure   State-level Medicaid eligibility expansion.

Main Outcomes and Measures   Emergency department visits per 1000 population overall and stratified by medical urgency of the conditions.

Results   In total, 80.6 million ED visits by 26.0 million individuals were analyzed. Emergency department visits were concentrated among women (59.3%), non-Hispanic Black individuals (28.3%), non-Hispanic White individuals (47.8%), and those aged 18 to 34 years (47.5%) and 35 to 44 years (20.4%). The rates of ED visits increased by a mean of 2.4 visits in nonexpansion states and decreased by a mean of 2.2 visits in expansion states after 2014, resulting in a significant regression-adjusted decrease of 4.7 visits per 1000 population (95% CI, −7.7 to −1.5; P  = .003) in expansion states. Most of this decrease was associated with decreases in ED visits by conditions classified as not emergent (−1.5 visits; 95% CI, −2.4 to −0.7; P  < .001), primary care treatable (−1.1 visits; 95% CI, −1.6 to −0.5; P  < .001), and potentially preventable (−0.3 visits; 95% CI, −0.5 to −0.1; P  = .02). No significant changes were observed for ED visits related to injuries and conditions classified as not preventable (−1.4; 95% CI, −3.1 to 0.3; P  = .10), as well as for substance use and mental health disorders (0.0; 95% CI, −0.2 to 0.2; P  = .94).

Conclusions and Relevance   The findings of this study suggest that Medicaid expansion was associated with decreases in ED visits, for which decreases in ED visits for less medically emergent ED conditions may have been a factor.

Emergency departments (EDs) are a vital component of the US health care system, with more than 140 million visits in 2018 incurring a total cost of approximately $75 billion. 1 Emergency department visits are expected to increase even further in the years ahead, by a projected 12% by 2030. 2 , 3 Although EDs treat patients with acute and unexpected health care conditions, they often serve as a safety net for individuals who are unable to access other health care settings. The use of EDs for nonemergent and preventable medical conditions has been a long-standing challenge in the US health care system. Every year, it is estimated that one-third of all ED visits occur for care that is treatable in primary care settings and preventable. 4 - 6 At an average cost that is many times higher compared with treatment provided at a physician’s office or an urgent care center, some of these visits suggest inefficient allocation of resources and present a cost-saving opportunity. 7

Limited or lack of insurance coverage is a key factor for many ED visits. Uninsured individuals often use the ED as a place to receive routine health care services and are also more likely to experience a health care crisis requiring emergent care owing to the lack of access to outpatient health services and preventive care. 8 Social determinants of health among low-income beneficiaries that perpetuate difficulties in receiving routine health care services could also result in increased ED use for similar reasons. 9 - 11

The expansion of Medicaid eligibility afforded by the Patient Protection and Affordable Care Act of 2010 (ACA) presented a unique opportunity to address this challenge by providing millions of US residents with health insurance coverage that could improve access to routine health care and preventive services. Since 2014, 35 US states have adopted the Medicaid eligibility expansion option, resulting in health insurance uptake for more than 20 million US residents. 12 , 13 Larger gains in enrollment were documented in states that decided to expand Medicaid eligibility to adults less than age 65 years with incomes up to 138% of the federal poverty level. 14 - 17 The implementation of the Medicaid expansion has also been associated with increased use of preventive services, access to primary care clinicians, affordability, and quality of care. 13 , 16 - 23

However, the evidence on the association between Medicaid expansion and ED use is inconclusive. 18 , 24 Limited or no cost-sharing for preventive and primary care services could lead to a shift of care from the ED setting to outpatient care, after obtaining new or more generous health care coverage. Recent studies reported that Medicaid expansion was associated with decreases in ED visits among individuals who previously reported barriers to outpatient care and those related to opioid use. 25 , 26 However, the expansion could have also increased ED use by eliminating or reducing cost barriers to receiving care in the ED. 18 , 27 - 31 A recent study by Garthwaite and colleagues 32 noted that Medicaid expansion was associated with increases in ED visits for deferrable medical conditions, defined as visits for which the patient has some discretion regarding when and where to seek care.

These results highlight a complex relationship between health insurance and ED use. 33 However, analyses examining only total ED visits may conceal important changes in the composition of the ED visits by medical urgency. A recent study analyzing ED visits after Medicaid expansion highlighted the importance of further studies that examine nonemergent ED use. 24 Hence, there remains a need to categorize ED visits by medical urgency because parameters that divert individuals with different conditions from use of EDs might vary. 34

To address this gap in the literature, we estimated the association between the ACA Medicaid expansion and ED visits, stratified by medical urgency of the visits, using a validated algorithm, and compared 2 Medicaid-expansion states with 2 nonexpansion states.

Our main databases were the Healthcare Cost and Utilization Project State Emergency Department Databases from January 2011 to December 2017. 35 These longitudinal administrative secondary databases include all-payer discharge information for nearly all outpatient (treat and release) ED visits across every general and acute care hospital within a state, similar to previous work. 31 We focused on outpatient ED visits, which account for almost 90% of all ED visits, to identify encounters that can be classified as less emergent. 36 We included data from 4 states (Florida, Georgia, Massachusetts, and New York), which account for almost one-fifth of the US population. Two of these states expanded Medicaid in 2014 (Massachusetts and New York), and the others did not. We included in-state residents aged 18 to 64 years who were covered by Medicaid, private plans or other local, state, or federal plans, or who were uninsured throughout the study period. Medicare enrollees were not included in the study given that individuals enrolled in the program were not directly affected by the Medicaid eligibility expansion and would be unlikely to transition from Medicare to Medicaid as a result of the policy change. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for reporting observational studies. The study was determined to be not human subjects research by the Texas A&M University Institutional Review Board.

Our outcomes of interest were total ED visits and ED visits classified by medical urgency per 1000 population. We obtained publicly available annual state-level population counts for the corresponding age groups to generate ED visits per 1000 residents. 37 We then used the updated version of the New York University ED algorithm to classify each ED visit by medical urgency, based on the primary diagnosis code from the International Classification of Diseases, 9th Revision (January 2011 to September 2015) or International Statistical Classification of Diseases, 10th Revision (October 2015 to December 2017). 6 , 38 This algorithm assigns probabilities and classifies each ED visit into 1 or multiple probability-adjusted categories: emergent and not-preventable or avoidable (eg, chest pain, tachycardia); emergent but preventable or avoidable (eg, dehydration); emergent but primary care–treatable (eg, muscle strain); not emergent (eg, low back pain, headache); injury; and alcohol, drug use, and mental health–related issues. We averaged the algorithmically assigned probabilities and grouped ED visits into 5 categories to reflect conditions by medical urgency: (1) not preventable and injury-related, (2) emergent but potentially preventable, (3) emergent but primary care treatable, (4) not emergent, and (5) mental health and substance use disorders. 28 We aggregated data at the state year-quarter level similar to previous work. 24 , 26

The primary exposure was state Medicaid expansion status. We obtained and identified the status of the decisions to expand Medicaid from publicly available resources. 39 Massachusetts and New York adopted the ACA Medicaid expansion provision in January 2014; Georgia and Florida did not.

We also included publicly available, time-varying, state-level variables in our analyses that have been commonly associated with ED use as control variables (percentages of women; non-Hispanic Black, Hispanic, and non-Hispanic White individuals; age-group distributions; annual unemployment rate; and percentage of population under 200% of the federal poverty level). 37 , 40 , 41 Race and ethnicity data were obtained from publicly available resources at the state-year level to control for state-level variation. The categorization is how this information was available on the publicly available resource.

We conducted a descriptive analysis for all 4 states and then stratified by expansion status. We then performed difference-in-differences regression analyses to estimate the association of the Medicaid expansion with ED visits that were weighted by each state’s population with state year-quarter as the unit of analysis. 31 This approach enabled us to compare pre-ACA expansion vs post-ACA expansion outcomes in states that implemented the Medicaid expansion (treatment group) with states that did not (control group). Our adjusted regression analyses included all covariates. Robust SEs were used. We also conducted sensitivity analyses without state-level population weights to evaluate the sensitivity of the findings. Moreover, we evaluated the year-by-year difference-in-differences in ED visits by interacting the Medicaid expansion indicator of the states with each year separately. Because Massachusetts implemented a large health reform before the ACA to provide near-universal health insurance coverage, we conducted supplemental analyses by only comparing New York with Florida. These 2 states had similar rates of ED visits in the preexpansion period.

One critical assumption of the difference-in-differences model is that both the treatment and control groups exhibited parallel trends in the prepolicy implementation period. 42 We examined trends in the pre-ACA expansion period (2011-2013) by conducting regressions across all outcomes with an interaction term between year-quarters and the expansion status dummy as the primary independent variable. The results showed little evidence of diverging trends and provided support of the parallel trends assumption (eTable 1 in the Supplement ). Two-tailed tests were used, and statistical significance was considered at P  < .05. We managed the data using SAS, version 9.4 (SAS Institute Inc) and all statistical analyses were performed between June 7 and December 12, 2021, using Stata, version 17.0 (StataCorp LLC).

Our study included 80.6 million ED visits by 26.0 million individuals. Table 1 presents descriptive information for all states and stratified by Medicaid expansion status from 2011 to 2017. Overall, 59.3% of visits were by women and 40.7% by men, with 67.9% of the visits by those aged between 18 and 44 years (18-34 years, 47.5%; 35-44 years, 20.4%). With classification by race and ethnicity status, 28.3% of the visits were by non-Hispanic Black individuals, 15.8% by Hispanic individuals, 47.8% by non-Hispanic White individuals, and 8.1% by those of other racial and ethnic groups (ie, Asian/Pacific Islander, Native American, or other race and ethnicity). Across all states, about one-third (32.1%) of the ED visits were for injury-related or not-preventable conditions, 22.9% were classified as not emergent, 21.8% as primary care treatable, and 5.3% as potentially preventable. Visits for mental health and substance use disorders accounted for 4.8% of all visits but were relatively higher in expansion states compared with nonexpansion states (6.4% vs 3.3%; P  < .001). The share of Medicaid-paid ED visits was disproportionately higher in expansion states compared with nonexpansion states (41.5% vs 25.4%; P  < .001), and the opposite was observed for the uninsured population (11.2% vs 35.1%; P  < .001).

Overall, total ED visits per 1000 population remained similar in the pre- and post-ACA periods (52.1 vs 52.3 visits) while the total number of ED visits increased by approximately 1% per year on average ( Figure 1 ; eFigure in the Supplement ). Figure 1 presents the trends in total ED visits, injuries and not-preventable or avoidable issues, and mental health–related or substance use issues, and Figure 2 shows the trends by medical urgency per 1000 population from 2011 to 2017 in expansion and nonexpansion states. Across all outcomes, states had relatively similar ED visit trends before January 2014 (preexpansion period). Total ED visits per 1000 population decreased after 2014 in expansion states (4.3% mean relative decrease) and increased in nonexpansion states (4.5% mean relative increase). Emergency department visits for conditions classified as potentially preventable (6.1% mean relative decrease) and primary care treatable (3.2% mean relative decrease) decreased only in expansion states, and those for not-emergent conditions decreased in both expansion (10.3% mean relative decrease) and nonexpansion (0.7% mean relative decrease) states. Visits for injuries and not-preventable conditions decreased in both expansion and nonexpansion states (mean relative decrease 11.3% and 2.0%), and those related to mental health and substance use disorders followed relatively flat trends across expansion and nonexpansion states.

Table 2 reports unadjusted and adjusted regression difference-in-differences results. Total ED visits per 1000 population increased by 2.4 visits in nonexpansion states and decreased by 2.2 visits in Medicaid expansion states after 2014 compared with the pre-ACA period. This change resulted in a significant regression-adjusted decrease of 4.7 ED visits per 1000 population (95% CI, −7.7 to −1.5; P  = .003). Compared with nonexpansion states, the ACA was associated with decreases of 1.5 ED visits per 1000 population (95% CI, −2.4 to −0.7; P  < .001) for not-emergent, 1.1 ED visits per 1000 population (95% CI, −1.6 to −0.5; P  < .001) for primary care–treatable, and 0.3 ED visits per 1000 population (95% CI, −0.5 to −0.1; P  = .02) for potentially preventable conditions in states that opted in the expansion of Medicaid. We did not observe any significant differences in ED visits for injuries or not-preventable conditions (−1.4; 95% CI, −3.1 to 0.3; P  = .10), and visits related to mental health and substance disorders (0.0; 95% CI, −0.2 to 0.2; P  = .94). The year-by-year trend difference-in-differences in the post–Medicaid expansion period showed that ED visits overall decreased each year as well as for less-emergent conditions (eTable 2 in the Supplement ). Medicaid expansion was also associated with decreases in ED visits for injuries and not-preventable conditions, but only after the third year of the policy implementation (2016: −2.6; 95% CI, −4.3 to −0.8; P  = .004; 2017: −3.3; 95% CI, −5.6 to −1.1; P  = .004). In addition, the sensitivity analyses without state population size-adjusted weights and the sample comparing only New York with Florida yielded similar results (eTable 3 in the Supplement ).

In this analysis of 80.6 million ED visits from 2011 to 2017 in 4 states, we found that Medicaid expansion was associated with a significant decrease of 4.7 ED visits per 1000 population in states that expanded Medicaid compared with states that did not, for which decreases in ED visits for less-emergent or not-emergent conditions may be a factor. We did not observe any major policy-related differences in ED visits for mental health and substance use disorders, injuries, and nonpreventable conditions overall.

Our results are consistent with previous work that found significant decreases in ED visits following the ACA implementation in states that expanded Medicaid. 17 , 25 , 42 , 43 Our study expands the evidence using 4 postexpansion years and focuses on populous states that might more specifically represent the national trends in ED visits. 44 Previous work with diverging results generally assessed changes in ED visits after 1 or 2 years postexpansion, focused their analysis on different states and populations, or reviewed outcomes using different definitions of not-emergent ED visits. 25 , 31 , 32

The observed decreases in ED visits concurrently occurred with increases in availability and access to primary care, and more primary care professionals accepting patients with Medicaid coverage to higher Medicaid payment rates. 17 , 25 , 43 , 45 - 47 Furthermore, decreases in Medicaid expansion states were concentrated in conditions that were less-emergent or not medically emergent, suggesting that access to preventive services could have substituted ED visits, improved health, and stabilized health conditions, which rendered ED use not necessary. 25 , 48 - 51 Before implementation of the ACA, EDs were the only access point for many individuals owing to financial difficulties in identifying cost-effective avenues of care. 52

However, ED visits for potentially preventable, primary care–treatable, and not-emergent conditions made up more than 40% of all ED visits even after states expanded Medicaid. 4 , 5 Although expanding health insurance coverage may be important, it does not guarantee access to medical care. Time and access barriers to outpatient care, such as appointment availability, inconvenient office hours, underinsurance, infrastructure barriers (eg, waiting times and lack of diagnostic capabilities of primary care offices), and prevailing social needs (eg, housing and food instability), are commonly cited reasons for nonemergent presentations to EDs even among insured individuals. 4 , 5 , 9 , 10 , 25 , 33

The high rates of ED visits for not-emergent conditions also raise concerns about the quality of care that some patients receive in primary care and outpatient settings. Dissatisfaction with primary care professionals, language barriers and unclear instructions, physician referrals to the ED, convenient operating hours, and the need for a second opinion are additional factors that predispose ED use. 4 , 10 Furthermore, many individuals are not equipped to accurately perceive the severity of their condition and might overestimate the need for emergency care, particularly after regular office hours or when outpatient care is not available. In addition, lack of knowledge about viable alternatives and limited information about outpatient clinicians’ resources may further predispose nonemergent ED presentations. 4 , 25 , 53 , 54

We also observed a small increase in ED visits related to mental health and substance use disorders across both expansion and nonexpansion states. 23 Our data fall within the years of the second wave of the opioid epidemic in the US, when ED visits for substance use disorders increased by more than 30%. 55 , 56 Factors such as fear of stigmatization, service availability, and health plan benefit constraints are additional barriers and considerations that these individuals face before entering treatments. 57 - 59

Beyond the ACA expansion, we also noted an annual increase in the total number of ED visits of approximately 1%, almost twice the rate of annual population growth. 2 The increasing demand for ED services warrants new and revised treatment protocols and models of care among emergency medicine professionals. Policies that might contain ED use include higher outpatient Medicaid reimbursement rates for behavioral and substance use treatment services and promoting the use of telemedicine. 16 , 31 , 46 , 55 , 60 In addition, targeted outreach efforts to increase health plan enrollment may yield long-term benefits, as more than half of the uninsured population is eligible for Medicaid or subsidized coverage. 61

However, addressing only medical and health care system factors will not reverse the social and economic circumstances that exacerbate access to care and chronic health problems. Synergies between medical and social needs demonstrated that coordination of medical, behavioral, and social services can improve health outcomes and contain ED use. 62 Investments in social welfare services, data sharing, and integration of primary care, housing, food, and psychosocial services are needed to improve health and allocate resources more effectively. In addition, investments in health care professional education to promote patient engagement in the development of personal care plans with need-oriented goal setting are necessary to enable the health care system to evolve from medical care centered to patient centered.

Our study has limitations. First, we used data from only 4 states that exhibit similar ED visit rates with national trends, but findings may differ by state, as previous work using similar methods but different states found increases in ED visits. 31 However, the difference in the results might be associated with either the use of more states or the use of data only including the first year of the Medicaid expansion. In addition, although the State Emergency Department Databases data represent almost 90% of all ED visits, we did not include data on ED visits that resulted in hospital admissions; thus, findings might not be generalizable to all ED visits. Second, there are differences in the proportion of each state’s population that became eligible for Medicaid in 2014. 24 Massachusetts implemented a partial Medicaid expansion in 2006 and consequently added far fewer residents to Medicaid in 2014. However, this limitation is likely to result in an underestimation of the association of Medicaid expansion with observed trends rather than an overestimation. Third, the transition from International Classification of Diseases, 9th Revision to International Statistical Classification of Diseases, 10th Revision occurred during the study period, resulting in a shift in the codes used to identify visit types. Nonetheless, sensitivity tests conducted to check for bias in visit types, including unclassified visits, did not result in any significant change in our study findings. Fourth, owing to the nature of the New York University algorithm, it is possible that some categories were nonmutually exclusive and, thus, some conditions could be assigned to more than one group, which could bias our estimates. Fifth, the discharge diagnoses based on the retrospective assignment of probabilities by the New York University algorithm do not capture the patients’ perception of risk at the time of the episode and ED visit. Sixth, the retrospective design may be subject to potential unobserved confounders that could bias our results.

The findings of this study suggest that the Medicaid expansion that occurred with implementation of the ACA was associated with significant reductions in ED visits in states that expanded Medicaid, for which decreases in ED visits for less medically emergent conditions, some of which could potentially be treated in other settings, may be a factor. However, ED visits for potentially preventable and primary care–treatable conditions continued to account for a large share of all ED visits, even in expansion states. As policy makers debate the future of the ACA and public support for a single-payer national health plan increases, our findings provide further data suggesting that investing in health insurance can reduce ED use for nonemergent medical conditions.

Accepted for Publication: April 19, 2022.

Published: June 14, 2022. doi:10.1001/jamanetworkopen.2022.16913

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Giannouchos TV et al. JAMA Network Open .

Corresponding Author: Theodoros V. Giannouchos, PhD, MS, Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, 915 Greene St, Columbia, SC 29208 ( [email protected] ).

Author Contributions : Drs Giannouchos, Ukert had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Giannouchos, Ukert.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Giannouchos, Ukert.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Giannouchos.

Administrative, technical, or material support: Giannouchos.

Supervision: Giannouchos, Ukert.

Conflict of Interest Disclosures: None reported.

Additional Information: The Population Informatics Lab provided access to the data and the Texas Virtual Data Library (ViDaL) funded by the Texas A&M University Research Development Fund the secure computing infrastructure required in this research.

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Trends in United States emergency department visits and associated charges from 2010 to 2016

Affiliations.

• 1 Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH, USA.
• 2 Department of Health Service Administration, Xavier University, Cincinnati, OH, USA.
• 3 Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH, USA; Department of Health Service Administration, Xavier University, Cincinnati, OH, USA. Electronic address: [email protected].
• PMID: 31519380
• DOI: 10.1016/j.ajem.2019.158423

Background: Demographic shifts and care delivery system evolution affect the number of Emergency Department (ED) visits and associated costs. Recent aggregate trends in ED visit rates and charges between 2010 and 2016 have not been evaluated.

Methods: Data from the National Emergency Department Sample, comprising approximately 30 million annual patient visits, were used to estimate the ED visit rate and charges per visit from 2010 to 2016. ED visits were grouped into 144 mutually exclusive clinical categories. Visit rates, compound annual growth rates (CAGRs), and per visit charges were estimated.

Results: From 2010 to 2016, the number of ED visits increased from 128.97 million to 144.82 million; the cumulative growth was 12.29% and the CAGR was 1.95%, while the population grew at a CAGR of 0.73%. Expressed as a population rate, ED visits per 1000 persons increased from 416.92 in 2010 to 448.19 in 2016 (p value <0.001). The mean charges per visit increased from $2061 (standard deviation $2962) in 2010 to $3516 (standard deviation $2962) in 2016; the CAGR was 9.31% (p value <0.001). Of 144 clinical categories, 140 categories had a CAGR for mean charges per visit of at least 5%.

Conclusion: The rate of ED visits per 1000 persons and the mean charge per ED visit increased significantly between 2010 and 2016. Mean charges increased for both high- and low-acuity clinical categories. Visits for the 5 most common clinical categories comprise about 30% of ED visits, and may represent focus areas for increasing the value of ED care.

Keywords: After-hours care; Emergency medicine; Health care costs; Health care economics and organization; Health care expenditures; Hospital charges.

Copyright © 2019 Elsevier Inc. All rights reserved.

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Emergency department visit rate 72.2 per 1,000 adults with diabetes in 2020 to 2021

by Elana Gotkine

In 2020 to 2021, the emergency department visit rate was 72.2 visits per 1,000 adults with diabetes, with the rate increasing with age, according to a December data brief published by the U.S. Centers for Disease Control and Prevention National Center for Health Statistics.

Loredana Santo, M.D., M.P.H., from the National Center for Health Statistics in Hyattsville, Maryland, and colleagues used data from the National Hospital Ambulatory Medical Care Survey to describe emergency department visits made by adults with diabetes.

The researchers found that the emergency department visit rate by adults with diabetes was 72.2 per 1,000 adults in 2020 to 2021, and the rate increased with age. The highest emergency department visit rates by adults with diabetes were seen among Black non-Hispanic people (136.6 per 1,000 adults per year) and were higher among white non-Hispanic than Hispanic individuals (69.9 versus 52.3 visits per 1,000 adults per year).

For adults with diabetes and two to four chronic conditions , the emergency department visit rate was 541.4 per 1,000 adults per year in 2020 to 2021 and increased with age. For adults with diabetes, there was an increase seen in emergency department visit rates from 48.6 to 74.9 visits per 1,000 adults in 2012 and 2021, respectively.

"From 2012 through 2021, emergency department visit rates among all adults with diabetes increased, as well as among adults age 45 and older," the authors write. "Among adults ages 18 to 44, emergency department visit rates increased during 2012 to 2016 and then remained stable during 2016 to 2021."

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• Open access
• Published: 26 April 2024

Association between maternal health service utilization and under-five mortality rate in China and its provinces, 1990–2017

• Jingya Zhang 1 ,
• Haoran Li 1 ,
• Bincai Wei 2 ,
• Rongxin He 3 ,
• Bin Zhu 2 ,
• Ning Zhang 3 &
• Ying Mao 1  

BMC Pregnancy and Childbirth volume  24 , Article number:  326 ( 2024 ) Cite this article

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Metrics details

The United Nations (UN) Sustainable Development Goal − 3.2 aims to eliminate all preventable under-five mortality rate (U5MR). In China, government have made efforts to provide maternal health services and reduce U5MR. Hence, we aimed to explore maternal health service utilization in relation to U5MR in China and its provinces in 1990–2017.

We obtained data from Global Burden of Disease 2017, China Health Statistics Yearbook, China Statistical Yearbook, and Human Development Report China Special Edition. The trend of U5MR in each province of China from 1990 to 2017 was analyzed using Joinpoint Regression model. We measured the inequities in maternal health services using HEAT Plus, a health inequity measurement tool developed by the UN. The generalized estimating equation model was used to explore the association between maternal health service utilization (including prenatal screening, hospital delivery and postpartum visits) and U5MR.

First, in China, the U5MR per 1000 live births decreased from 50 in 1990 to 12 in 2017 and the average annual percentage change (AAPC) was − 5.2 ( p  < 0.05). Secondly, China had a high maternal health service utilization in 2017, with 96.5% for prenatal visits, 99.9% for hospital delivery, and 94% for postnatal visits. Inequity in maternal health services between provinces is declining, with hospital delivery rate showing the greatest decrease (SII, 14.01 to 1.87, 2010 to 2017). Third, an increase in the rate of hospital delivery rate can significantly reduce U5MR (OR 0.991, 95%CI 0.987 to 0.995). Postpartum visits rate with a one-year lag can reduce U5MR (OR 0.993, 95%CI 0.987 to 0.999). However, prenatal screening rate did not have a significant effect on U5MR.

The decline in U5MR in China was associated with hospital delivery and postpartum visits. The design and implementation of maternal health services may provide references to other low-income and middle-income countries.

Peer Review reports

Children’s health is widely recognized as a public health priority in every country. The under–five mortality rate (U5MR), which estimates the probability of dying between birth and the fifth birthday (usually expressed per 1000 live births), is a useful indicator that measures not only the level of child health, but also the economy, education, and medical care in a country [ 1 ]. Sustainable and Development Goals (SDG) 3.2, set by United Nations (UN), calls for an end to avoidable deaths of children, with all countries aiming to decrease U5MR to at least as low as 25 deaths per 1,000 live births by 2030 [ 2 , 3 ].

Over the past several decades, the world has recorded remarkable progress on child survival. This global U5MR decreased from 71.2/1000 live births in 2000 to 37.1/1000 live births in 2019 [ 4 ]. Although U5MR has been significantly reduced globally, it still falls short of the SDG target. Even some developing countries face poor child survival. Sub-Saharan Africa remains the region with the highest U5MR in the world, 76 deaths/1000 live births, which is equivalent to 1 child in 13 dying before reaching the age of 5 [ 5 ]. The U5MR also remains a major public-health issue in some Belt and Road Initiative countries [ 6 ]. As one of the few countries that have already achieved the third SDG child health goal, China’s practices deserve further evaluation, both to understand the Chinese experience and to provide lessons for other developing countries undergoing health reform alongside rapid social and economic development.

The U5MR in a region is closely related to the state of socio-economic development [ 7 , 8 ]. For example, regional gross domestic product (GDP) [ 9 ], national educational attainment [ 10 ], gender inequality index [ 11 ], and health care policies [ 12 ] have all been verified to be associated with U5MR. Mosley & Chen [ 13 ] propose that all social and economic determinants of child mortality must work through a common set of biological mechanisms or proximate determinants to have an impact on mortality. These proximate determinants include maternal factors, environmental contamination, nutrient deficiency, injury, and personal illness control. For children under five years of age, maternal factors are the most important proximate determinants because, lacking self-awareness, they are more dependent on maternal guidance for nutrient deficiency, injury, and personal illness control. Mothers are the primary care-givers of children under the age of five. Their health-seeking behavior during and after pregnancy tends to influence the chances of child survival during the first five years of life [ 14 , 15 , 16 ]. For example, place of delivery [ 17 ], birth interval [ 18 ], breastfeeding [ 19 ], and behavioral habits during pregnancy [ 20 ] (e.g., smoking and drinking) can affect the child’s health. In such an association framework, the utilization of maternal health services by mothers has an impact on U5MR that cannot be ignored.

In many countries and regions, maternal health services are considered as an important component of primary health care policies and are closely related to the overall level of economic development of the country. Maternal health services can directly affect a variety of maternal characteristics. Prenatal screening [ 21 ] is available for genetic and infectious diseases, continuous monitoring of various health indicators and guidance on various physiological hygiene and nutritional. Hospital delivery is equipped with professional delivery rooms, delivery equipment and midwives, which can effectively eliminate neonatal tetanus [ 22 ], etc. The postpartum visits can keep track of the changes in the newborn’s signs and breastfeeding, so that problems can be detected and health guidance can be given [ 23 ].

Maternal health services in China have covered prenatal screening, hospital delivery, and postpartum visits. In 2009, China launched a comprehensive health care reform that included maternal health services as part of the basic public health service program, which is provided free of charge by the government. Services related to maternal health [ 24 ] in basic public health services include free prenatal screening to promote healthy childbirth and child development for all rural couples, subsidized hospital delivery for rural women, and free postpartum visits by maternal and child doctors at the community health services in their resting places. Postpartum visits are carried out in conjunction with maternal health management, and newborn visits are also carried out to strengthen newborn care guidance. It is the responsibility of mothers, where health facilities are available and accessible, to visit these to receive proper medical care during pregnancy, at delivery and after childbirth to promote good health and preserve the lives of herself and her child. With these reform measures, China has made great strides in improving the health of women and children. Overall, maternal mortality declined substantially and rapidly [ 25 ], from 108.7 per 100 000 live births in 1996 to 21.8 per 100 000 live births in 2015, making the deceleration rate 8.5%. After years of effort, the urban–rural disparity of maternal mortality in China has also been greatly narrowed. The maternal mortality between urban and rural areas changed from 1:2.37 in 2000 to 1:1.05 in 2015.

In recent decades, China has seen a dramatic decline in U5MR and a significant increase in maternal health service utilization. Despite these achievements, the effectiveness of maternal health service utilization in reducing under-five mortality has not been measured, so we explored the association between maternal health service utilization and under-five mortality. Determining the relationship between U5MR and prenatal screening, hospital delivery, and postnatal visits is key to planning and implementing interventions. Therefore, this study aimed to determine: (1) the trends of U5MR in various provinces of China. (2) the degree of inequity in maternal health services utilization between provinces and its trends. (3) the association between U5MR and maternal health service utilization in China.

Study design and data sources

We obtained U5MR data from Global Burden of Disease (GBD) 2017, which assess U5MR at the provincial level in China from 1990–2017 [ 26 ]. This analysis includes 34 province-level administrative units in China (Xinjiang Production and Construction Corps is excluded). These province-level units consist of 23 provinces, five autonomous regions, four municipalities, and two special administrative regions but are all termed provinces. GBD is a widely used database coordinated by the Institute for Health Metrics and Evaluation [ 27 ], with data downloaded from the Global Burden of Disease Results ( https://vizhub.healthdata.org/gbd-Results// ).

Maternal health service utilization rate data were obtained from the China Health Statistics Yearbook, including prenatal screening rate, hospital delivery rate, and postpartum visits rate. The prenatal screening rate refers to the ratio of the number of mothers who received one or more prenatal checkups to the number of live births during the year, in %. The hospital delivery rate refers to the ratio of the number of live births delivered in institutions qualified for midwifery technology to the number of all live births during the year, in %. The postpartum visits rate refers to the ratio of the number of women who had one or more postpartum visits to the number of live births during the year, in %.

Data on demographic characteristics, socioeconomic status, and health services were obtained from the China Statistical Yearbook, the China Health Statistics Yearbook, and the Human Development Report China Special Edition, and we collected data from 2002 to 2017 based on data availability and completeness, which were used as covariates in our model.

We matched U5MR data, maternal health service utilization data (including prenatal screening rate, hospital delivery rate, and postpartum visits rate.), demographic characteristics data (including years of education per capita, birth rate), socioeconomic status data (including GDP per capita, disposable income per capita, emissions of particulate matter in exhaust gases), and health services data (including total number of health workers) based on two variables: region and year.

Data analysis

Joinpoint model was used to analyze the trend of U5MR from 1990 to 2017 for the country and each province, respectively. Joinpoint analysis was based on a Poisson regression model, and the optimal number of connection points was selected by a substitution test [ 28 ]. In this study, the natural logarithm of U5MR was selected as the response variable and the notification year was used as the independent variable. The annual percent change (APC), average annual percentage change (AAPC) and its 95% Confidence Interval (CI) were reported. If the lower CI of AAPC is above 0, it reveals an uphill tendency of the indicator, and; if the upper CI of AAPC is below 0, it indicates a downward trend of the indicator. Each P-value was found calculated using the Monte Carlo methods, and the overall asymptotic significance level was maintained through a Bonferroni correction. P value of less than 0.05 was considered statistically significant. Additionally, if the confidence interval contains 0, it indicates that the trend of change is not statistically significant.

The UN’s Health Inequity Toolkit HEAT Plus was used to measure the inequity of maternal health services (including prenatal screening, hospital delivery, and postpartum visits) in each province in China, comparing its change between 2010 and 2017. The UN’s Health Inequities Toolkit is available at the website ( https://www.who.int/data/inequality-monitor ). First, we finished that the uploaded data passed validation using HEAT Plus template and validation before performing analysis. The UN’s Health Inequity Toolkit can export a range of health inequity indicators such as Difference (D), Absolute concentration index (ACI), Population attributable fraction (PAF), Population attributable risk (PAR). Ratio (R), Relative concentration index (RCI), and other health inequity indicators. This study additionally calculated the slope index of inequality (SII) to measure the absolute difference between the highest and lowest levels of maternal health care between provinces. The x-axis represents the relative order of each province (weighted order after population weighting), and the y-axis represents the U5MR for each province. The relative order of provinces is the midpoint of the cumulative population fraction. The SII is the slope of the regression line of y versus x. The determination of the relative order in this study included the following steps: first, all provinces were ranked from lowest to highest according to the Human Development Index (HDI); then, the relative order of the subgroup was determined based on the population fraction of each province. For all measures of inequity, the lower the value, the more equitable it is.

To assess the association between maternal health service utilization (including prenatal screening, hospital delivery, and postpartum visits) and U5MR, we applied the generalized estimating equation model, a widely used linear model for longitudinal data analysis with repeated measures over time [ 29 ]. The generalized estimating equation (GEE) model used a gamma distribution and log-link function to control for the skewed nature of mortality. The dependent variable refers to ln (U5MR). In multivariable models, we controlled for year, years of schooling per capita, GDP per capita, disposable income per capita, birth rate, total number of health workers and emissions of particulate matter in exhaust gas. To strengthen the longitudinal analysis, we also examined the lagged effects [ 30 , 31 , 32 ] of prenatal visits and postnatal visits. To test the robustness of the results, in sensitivity analyses we replaced gamma distribution with Poisson distribution, gaussian distribution, and negative binomial distribution for the GEE model analysis, respectively. STATA version 13.0 was used in this study, and statistical significance was attributed to P values < 0.05. All figures were drawn by using OriginPro (version 2023b).

Table  1 showed the U5MR in China and each province in 2017 and the trend from 1990 to 2017. The overall U5MR in China in 2017 was 12 per 1000, which was in a decreasing trend from 1990 to 2017 (AAPC − 5.2, 95%CI −5.3 to −5.1). Most provinces met the SDG 3 of reducing U5MR to less than 25 per 1,000 live births, with only two provinces, Xinjiang (28 per 1000) and Tibet (36 per 1000), which were in remote western regions, still having relatively high U5MR. Beijing, as the capital city, was rich in various social resources and has the lowest U5MR of 5per 1000. Some east economically developed regions also had relatively low U5MR, such as Shanghai (7 per 1000), Jiangsu (7 per 1000), Zhejiang (7 per 1000), Guangdong (7 per 1000), Fujian (7 per 1000), and Liaoning (7 per 1000). Each province in China had seen a decrease in U5MR from 1990 to 2017. The central and western regions were decreasing at a faster rate, such as Guizhou (AAPC − 6.7, 95%CI −6.8 to −6.6). The eastern regions were decreasing at a relatively slow rate, such as Tianjin (AAPC − 2.8, 95%CI −3.0 to −2.6).

Figure  1 showed the maternal health service utilization in each province from 2002 to 2017. Considering prenatal screening, the prenatal screening rate in China was 96.5% in 2017, and the prenatal screening rate was generally high in all provinces, with only Tibet had a low prenatal screening rate of 89.5%. Most provinces were seeing an increase in the rate of prenatal screening, especially at a faster average annual growth rate in some western provinces, such as Tibet (2.52%) and Qinghai (1.29%). It was worth noting that Hainan, as an eastern city, also had a relatively high average annual growth rate of 1.32%. As for the hospital delivery, the hospital delivery rate in China was 99.9% in 2017, with many provinces reaching 100% hospital delivery rate. Most provinces had a hospital birth rate of over 96%, with only Tibet having a relatively low rate of 92.5%. The hospital delivery showed varying degrees of increase in each province from 2002 to 2017, with the fastest annual growth rates in some western provinces, such as Guizhou (8.06%) and Tibet (7.05%). The average annual rate of change in hospital delivery rates was the fastest growing, much higher than prenatal screening and postpartum visits. Judging from postpartum visits, the postpartum visit rate in China was 94% in 2017. Similarly, Tibet had the lowest postpartum visits rate of 83.4%. Most provinces achieved postpartum visits above 92%, with some western provinces such as Hainan (89.7%), Henan (89.9%), Anhui (90%), Guangxi (90.2%), Guizhou (91.3%), and Shanxi (91.9%) having relatively low postpartum visits rates. Most provinces were seeing a rising trend in postpartum visits rate from 2002 to 2017, especially Tibet and Hainan with a faster rate of increase, with an average annual growth rate of 2.57% and 2.14%, respectively.

Maternal health service utilization rate by province in China, 2002–2017. Notes ( A ) Prenatal screening rates by province in China, 2017; ( B ) Hospital delivery rates by province in China, 2017; ( C ) Postnatal visits rates by province in China, 2017; ( D ) Average annual change in prenatal screening rate, hospital delivery rate, and postpartum visits rate by province, China, 2002–2017

Table  2 ; Fig.  2 showed the inequity of maternal health services in each province in China. Looking at prenatal screening, the values of the indicators measuring the inequity of prenatal screening in China, including D, ACI, PAF, R, RCI, and SII, had all been decreasing, especially SII decreased from 11.12 in 2010 to 4.06 in 2017. From hospital delivery, the values of each inequity indicator, including D, ACI, PAF, R, RCI, and SII, were also decreasing, especially SII from 14.01 in 2010 to 1.87 in 2017. Similarly, the values of each inequity indicator for postpartum visits, including D, ACI, PAF, R, RCI, and SII, had decreased, especially SII from 15.21 in 2010 to 5.80 in 2017.

The SII of maternal health services by province in China. Notes ( A ) The SII of prenatal screening by province in China, 2017; ( B ) The SII of hospital delivery by province in China, 2017; ( C ) The SII of postpartum visits by province in China, 2017; ( D ) The SII of prenatal screening by province in China, 2010; ( E ) The SII of hospital delivery by province in China, 2010; ( F ) The SII of postpartum visits by province in China, 2010

Table  3 presented the association between maternal health service utilization and U5MR. In multivariate generalized estimating equation models, a negative association was observed between hospital delivery and U5MR (OR 0.991, 95%CI 0.987 to 0.995). The association of prenatal screening and postnatal visits with U5MR was not significant. Tables  4 and 5 further demonstrated the lagged effect of prenatal screening and postnatal visits on U5MR. The association between the previous year’s prenatal visit rate and the current year’s U5MR was not significant, however, the association between postpartum visits in the previous year and U5MR in the current year was significant (OR 0.993, 95%CI 0.987 to 0.999). The robustness results show that the significance and direction of the coefficients in the model were consistent we replaced gamma distribution with Poisson distribution, gaussian distribution, and negative binomial distribution for the GEE model analysis, which indicated that the estimates and results of the association between the dependent and independent variables in this study were robust and reliable.

The U5MR in China was declining, the utilization rate of maternal health services was increasing, and inequalities in maternal health services between provinces were slowly narrowing. The experience of hospital delivery and postpartum visits, both of which had been shown to have a significant impact on the reduction of under-five mortality, might serve as a reference for other countries.

Changes in U5MR

According to GBD2017 results the U5MR is declining in China and its provinces, which means that child health outcomes are being improved. At the provincial level, only two provinces, Xinjiang (36 per 1000) and Tibet (28 per 1000), did not meet the SDG target. But looking at time trends, the U5MR in Xinjiang and Tibet are on a steady decline, proving that child health outcomes in these areas are getting better. Tibet is in remote areas of China, where the land is vast and the people are sparse, making it more difficult to provide maternal health services as part of basic public health services, but the coverage rate has been increasing, so the health of maternal and child is improving. In some remote rural areas of Tibet, there is still a shortage of human resources for health and health infrastructure [ 33 ], and the quality of existing maternal health service provision needs to be improved [ 34 ], which can lead to poor maternal and child health outcomes. We should adhere to The Pregnancy and Village Outreach Tibet program [ 35 ] and continue to provide families with maternal-newborn health education, skills training, and resources. However, the utilization of maternal health services in Xinjiang is at normal values, which is not quite consistent with its significantly low U5MR. Other factors influencing child health outcomes in Xinjiang still need to be further explored. Some environmental influences can be considered, such as the long summers and low economic performance in some areas of Xinjiang, which are key factors in the high rate of child drowning mortality [ 36 ]. In addition, the analysis from intra-provincial suggests that we should also pay attention to U5MR in special populations. At the intra-provincial level. A study [ 37 ] based in Zhejiang Province showed that the U5MR among the migrant population was more than twice that of native children (7.82 per 1,000 to 3.89 per 1,000). A study [ 38 ] from Henan province shows that although U5MR have declined in recent years, U5MR remain high in rural areas. A study [ 39 ] from Sichuan showed that there are ethnic disparities in pneumonia-specific mortality rates among children under 5 years of age in Sichuan, with emphasis on child health in minority counties.

Inequalities and current status of maternal health services utilization rate

We found that maternal health services utilization is becoming better in every province in China, with prenatal screening rate, hospital delivery rate and postpartum visits rate all gradually increasing, which is consistent with the previous studies [ 40 ]. In terms of inequity, the inequity in maternal health services utilization between provinces is decreasing, and the gap in prenatal screening rates, hospital delivery rates, and postpartum visits rates is gradually narrowing across provinces. One of the possible reasons for this is that as Chinese women become more educated, more and more families are paying more attention to maternal and child health, more willing to invest in health care, and increase their use of health care services [ 41 ]. At the same time, the country is vigorously promoting fourteen basic public health services, of which maternal health is one of the crucial ones. As a public service, the government invests funds and various administrative efforts to encourage the residents to utilize maternal health services. The dual effect has led to more women taking the initiative to participate in maternal health services.

In particular, the rate of hospital delivery is the highest coverage among the three maternal health services, even reaching 100% hospital delivery of pregnant women in many provinces. There are also a variety of reasons why hospital delivery, a maternal health service, is doing best. One possible reason for this is that incentives vary across service provision. The prenatal screening and postpartum visits provided by primary health care institutions free of charge are a service incentive, while the government provides direct subsidies for hospital delivery as a monetary incentive [ 42 ], prompting more rural women to be more willing to take advantage of hospital delivery services, which is one reason why hospital delivery services are better implemented. Compared to service incentives, monetary incentives are more likely to influence people’s behavioral choices [ 43 ].

U5MR and maternal health services utilization rate

We found that hospital delivery in maternal health services significantly reduced U5MR, which is consistent with previous studies [ 44 , 45 ]. Studies [ 46 , 47 ] from different regions have shown that neonatal disorders remained the leading cause of death in children younger than 5 years, and the proportion of U5MR occurring in the neonatal period is increasing [ 48 ]. Hospital delivery reduce U5MR by reducing neonatal mortality substantially. Medical facilities usually have specialized maternal and child doctors and a more childbirth-friendly environment, which means that the mother is face-to-face with a specialized doctor, whose midwifery practices and resuscitation of emergencies make the improvement in the health of the mother and child more intuitive for everyone. The opposite of hospital delivery is home delivery. Home delivery in the Chinese context is usually performed by acquaintances who have experience in delivering babies, the midwives do not have expertise in childbirth, and the families do not have a safe environment for delivery, resulting in problems such as neonatal asphyxia [ 49 ] infection and tetanus in many home births, which will result in the death of the baby. From the international experience, a meta-analysis [ 50 ] provides the strongest evidence so far that hospital delivery can, after all, be beneficial to newborn babies. The research from several industrialized countries (the United States, Canada, Australia, Sweden, the Netherlands, and Switzerland), with a total sample of 500,000 newborns, had shown that planned home births to healthy and low-risk mothers compared with planned hospital births in the same group of women doubled the risk of neonatal deaths (0.2% vs. 0.09%). However, this is not absolute, and it is beneficial in some cases to deliver at home. Studies [ 51 ] from countries such as Australia, the Netherlands, and United Kingdom show that home birth can provide advantages to the mother and the newborn. It needs to be provided with sufficient material means, and should be attended by trained and accredited professionals, and needs to be perfectly coordinated with the hospital obstetrics and neonatology units, in order to guarantee its safety. However, in China, there are no safety data or sufficient scientific evidence to support home births at present.

Prenatal screening and postpartum visits are more about health guidance, a role that has long-term effects [ 44 ] and does not improve maternal and infant health outcomes as quickly and intuitively as hospital births. We validated the lagged effect of prenatal visits and postnatal visits on child health outcomes. The effect of a one-year lag in prenatal visits on U5MR remained insignificant. Prenatal screening, as a health care behavior during the mother’s pregnancy, is a relatively indirect effect on children, which could explain their lack of a statistically significant effect on U5MR [ 16 ]. At the same time, there are also problems such as unqualified diagnostic services in prenatal screening in some areas (e.g., delayed prenatal diagnosis [ 52 ], biased diagnostic results by physicians [ 53 , 54 ], etc.), and further standardization of services is still needed. However, the effect of a one-year lag in postpartum visits on U5MR was significant and it could reduce U5MR. The potential of timely, quality postnatal visits in reducing U5MR is well documented [ 55 , 56 ]. In addition to neonatal diseases, the common causes of U5MR are important infections like lower respiratory infections, pneumonia [ 57 ], diarrhoea, and meningitis [ 58 ], which a relatively long duration and can be effectively prevented by means of scientific feeding and care. The study [ 59 ] from China showed that all mothers, whether first or second time mothers, were unsure of their infant care skills. They expressed concerns about infant feeding, defecation, and illness, suggesting that health professionals should provide postpartum mothers with the knowledge and skills they need to care for their newborns, which can be accomplished through postpartum visits. One of the very important tasks of the post-natal visit in the National Basic Public Health Service is the newborn visit [ 60 ], which provides guidance on feeding and caring for newborns. Several lifesaving newborn behaviors can be promoted, and interventions delivered, through early postnatal care. These include an assessment of the baby and treatment or referral, and counselling on breastfeeding, thermal care, hygiene, cord care and on danger signs. These measures may prevent health problems from becoming long-term, with effects on women, their babies, and their families [ 61 ].

There are some limitations that need to be considered. First, the GBD 2017 reports estimated data. Owing to the poor availability of data in some regions or countries, there may be bias between the reported and actual values. Therefore, there may be some statistical bias in our analysis. Second, in exploring the association between maternal health service utilization and U5MR, we included a limited number of control variables. We are unable to include all potential confounders due to constraints imposed by the availability of province-level data. It is possible that some variables were not measured, which may have biased the results.

The decline in U5MR in China was associated with hospital delivery rate and postpartum visits rate. Hospital delivery can reduce U5MR by reducing neonatal mortality. postnatal visits have a long-term impact on reducing U5MR. The design and implementation of maternal health services may provide references to other low-income and middle-income countries.

Data availability

All the data used in the article is publicly available and accessible in the following websites: “ https://www.healthdata.org/ ” “ https://www.stats.gov.cn/sj/ndsj/ ” “ http://www.nhc.gov.cn/mohwsbwstjxxzx/tjzxtjsj/tjsj_list.shtml ” “ https://www.undp.org/china/publications/national-human-development-report-special-edition ”.

Abbreviations

United Nations

under-five mortality rate

Sustainable and Development Goals

Global Burden of Disease

annual percentage change

average annual percentage change

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Acknowledgements

We would like to thank all involved in the study, especially appreciate the works by the Global Burden of Disease Study 2017 collaborators.

This research was funded by the education department of Guangdong province (grant number 2022WTSCX100) and Guangdong Provincial Natural Science Funds (grant number 2022A1515011871).

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JY contributed to study conduct, study design, data collection, data analysis, data interpretation, and writing of the manuscript. HR contributed data analysis, data interpretation and review. BC contributed data interpretation and review. RX contributed data interpretation and review. ZB contributed data interpretation and review. ZN contributed study concept and design, chairing of steering committee, oversight of study implementation, extensive data analysis and interpretation, and approving of final version of manuscript. MY contributed study concept and design, chairing of steering committee, oversight of study implementation, extensive data analysis and interpretation, and approving of final version of manuscript. The other authors read and approved the final manuscript.

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Zhang, J., Li, H., Wei, B. et al. Association between maternal health service utilization and under-five mortality rate in China and its provinces, 1990–2017. BMC Pregnancy Childbirth 24 , 326 (2024). https://doi.org/10.1186/s12884-024-06437-8

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Emergency Department Visit Rates by Adults With Diabetes: United States, 2020–2021

NCHS Data Brief No. 487, December 2023

PDF Version (427 KB)

Loredana Santo, M.D., M.P.H., Susan M. Schappert, M.A., and Jill J. Ashman, Ph.D.

• Key findings

What was the ED visit rate by adults with diabetes in 2020–2021 and did it differ by sex and age?

Did the ed visit rate by adults with diabetes differ by race and ethnicity and age, did the ed visit rate by adults with diabetes differ by number of other chronic conditions and age, did ed visit rates by adults with diabetes change over the past decade, definitions, data source and methods, about the authors, suggested citation.

Data from the National Hospital Ambulatory Medical Care Survey

In 2020–2021, the emergency department visit rate by adults with diabetes was 72.2 visits per 1,000 adults, and the rate increased with age.

Emergency department visit rates by adults with diabetes were highest among Black non-Hispanic people (136.6 visits per 1,000 adults per year) and higher among White non-Hispanic people (69.9) compared with Hispanic people (52.3).

In 2020–2021, the emergency department visit rate by adults with diabetes and two to four other chronic conditions was 541.4 per 1,000 adults per year and increased with age.

Emergency department visit rates by adults with diabetes increased from 48.6 visits per 1,000 adults in 2012 to 74.9 visits per 1,000 adults in 2021.

In 2021, diabetes was the eighth leading cause of death in the United States ( 1 ). Over 37 million Americans have diabetes ( 2 ). While it most often develops in people older than age 45 ( 3 ), its frequency is increasing in young adults ( 4 ). Among people with diabetes, increasing age is a risk factor for hospitalization ( 5 ). Emergency department (ED) visits by people with diabetes have been used to monitor access to care and healthcare use ( 6 ). This report describes ED visits made by adults with diabetes, and presents selected characteristics by age.

Keywords : emergency care, National Hospital Ambulatory Medical Care Survey

• In 2020–2021, the annual ED visit rate by adults with diabetes was 72.2 visits per 1,000 adults ( Figure 1 ).
• ED visit rates by adults with diabetes did not differ significantly by sex, with 75.1 visits per 1,000 women and 69.1 visits per 1,000 men.
• Among adults ages 18–44 with diabetes, ED visit rates were higher among women (32.2) than men (19.5). Among adults ages 45–64 with diabetes, ED rates were similar for women (86.2) and men (84.4). Among adults age 65 and older with diabetes, the observed difference in ED rates between men (159.9) and women (142.8) was not significant.
• ED visit rates by adults with diabetes increased with age for both women and men.

Figure 1. Emergency department visit rate among adults with diabetes, by sex and age group: United States, 2020–2021

• In 2020–2021, ED visit rates by adults with diabetes were highest among Black non-Hispanic people (subsequently, Black) (136.6 visits per 1,000 adults), followed by White non-Hispanic (subsequently, White) (69.9) and Hispanic (52.3) people ( Figure 2 ).
• For each age group, ED rates by adults with diabetes were highest among Black people. Differences between Hispanic and White people were not significant.
• ED visit rates by adults with diabetes increased with age among Black, White, and Hispanic people.

Figure 2. Emergency department visit rate among adults with diabetes, by race and ethnicity and age group: United States, 2020–2021

• Most ED visits by adults with diabetes were made by patients with two to four other reported chronic conditions (541.4 visits per 1,000 visits) ( Figure 3 ). Rates by patients with no other chronic conditions were lowest (90.2).
• Among adults with diabetes ages 18–44, ED visit rates were highest among those with two to four other chronic conditions (402.0) and lowest among those with five or more other conditions (93.8).
• Among adults with diabetes ages 45–64, ED visit rates were highest among those with two to four other chronic conditions (526.4) and lowest among those with no other conditions (87.7).
• Among adults with diabetes age 65 and older, ED visit rates were highest among those with two to four other conditions (605.2), followed by those with five or more conditions (217.7), one other chronic condition (140.6), and no other conditions (36.5).
• The ED visit rate by adults with diabetes and two to four or five or more other chronic conditions increased by age, but the rates for those with no other chronic conditions or one other condition decreased with age.

Figure 3. Emergency department visit rate among adults with diabetes, by number of additional chronic conditions and age group: United States, 2020–2021

• ED visit rates by adults with diabetes increased from 48.6 visits per 1,000 adults in 2012 to 74.9 in 2021 ( Figure 4 ).
• ED visit rates by adults with diabetes age 65 and older were higher than all other age groups during 2012–2021, and increased from 113.4 in 2012 to 156.8 in 2021.
• ED visit rates by adults with diabetes ages 45–64 increased from 53.1 in 2012 to 89.2 in 2021.
• ED visit rates by adults with diabetes ages 18–44 increased from 20.9 in 2012 to 26.4 in 2016, but their rates remained stable for 2016–2021.

Figure 4. Emergency department visit rate among adults with diabetes, by age group: United States, 2012–2021

In 2020–2021, the annual ED visit rate by adults with diabetes was 72.2 visits per 1,000 adults. ED visit rates by adults with diabetes increased with age for both women and men. ED visit rates were highest among Black people, and higher among White people compared with Hispanic people. Among each race and ethnicity group, ED visit rates increased with age. Rates by adults with two or more other chronic conditions were higher than rates for adults with one or no other chronic conditions. From 2012 through 2021, ED visit rates among all adults with diabetes increased, as well as among adults age 45 and older. Among adults ages 18–44, ED visit rates increased during 2012–2016 and then remained stable during 2016–2021. This report includes the most recent estimates of ED visits by adults with diabetes from the National Hospital Ambulatory Medical Care Survey (NHAMCS) and shows an increasing trend in rates by adults with diabetes in the ED setting.

ED visits by adults with diabetes: In both 2020 and 2021, a checkbox item was included in NHAMCS that asked, “Does the patient have (mark all that apply).” The list included three checkboxes for diabetes: diabetes mellitus, type 1; diabetes mellitus, type 2; and diabetes mellitus, type unspecified. A separate item, provider’s diagnosis, collected information on up to five diagnoses related to the current visit, using verbatim text entries from the patient’s medical record, which were later coded by National Center for Health Statistics medical coders using the International Classification of Diseases , Ninth Revision , Clinical Modification (for survey years 2012–2015) and the International Classification of Diseases , 10th Revision, Clinical Modification (for 2016–2021) ( 7 , 8 ). To be sure that the chronic condition checkbox data were complete, a consistency check was performed during data editing to review responses to the provider’s diagnosis item. If any of the chronic conditions from the checkbox item were also listed in the diagnosis item, the relevant checkbox item was also checked. This report includes ED visits made by adults (age 18 and older) that had either a reported diagnosis of diabetes ( International Classification of Diseases , Ninth Revision , Clinical Modification codes 249–250 or International Classification of Diseases , 10th Revision, Clinical Modification codes E08–E13) or a checkbox category of diabetes. Adults that either had diabetes diagnosed at the ED visit or had pre-existing diabetes that was documented in the medical record were included in the analysis.

ED visits by adults with diabetes and additional chronic conditions reported : The number of chronic conditions, excluding diabetes, was grouped into none (referring to adults with only diabetes), one, two to four, and five or more of the conditions collected in NHAMCS. Beside the checkboxes for diabetes described above, checkboxes for alcohol misuse, abuse, or dependence; Alzheimer disease or dementia; asthma; cancer; cerebrovascular disease or history of stroke or transient ischemic attack; chronic kidney disease; chronic obstructive pulmonary disease; congestive heart failure; coronary artery disease, ischemic heart disease or history of myocardial infarction; depression; end-stage renal disease; history of pulmonary embolism or deep vein thrombosis; HIV; hyperlipidemia; hypertension; obesity; obstructive sleep apnea; osteoporosis; and substance use disorders were included in NHAMCS in 2020 and 2021. The consistency check described above was performed during data editing for each checkbox.

ED visit rate : Calculated by dividing the number of ED visits by adults with diabetes, by the July 1, 2012–2021, sets of estimates of the U.S. civilian noninstitutionalized population (obtained from the U.S. Census Bureau’s Population Division) for each demographic group. Visit rates for adults with chronic conditions are calculated by dividing the number of ED visits made by adults with diabetes and a specified number of other chronic conditions by the total number of ED visits made by adults with diabetes in 2020 and 2021. Visit rates are presented as annual average rates per year.

Race and ethnicity : Race and Hispanic ethnicity were collected separately and converted into a single combined variable that includes Hispanic, Black non-Hispanic, White non-Hispanic, and other races non-Hispanic. Other races includes American Indian and Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander people, and people of two or more races, representing 4.5% of all ED visits by adults with diabetes. Data for other races are included in the denominator but not shown. For 2020–2021, 13.6% of weighted race data and 10.7% of weighted ethnicity data were missing for ED visits by adults with diabetes; race and ethnicity were imputed for these missing records ( 7 , 8 ).

This report analyzed data from NHAMCS, a nationally representative annual survey of nonfederal, general, and short-stay hospitals. NHAMCS uses a multistage probability design with samples of geographic primary sampling units, hospitals within primary sampling units, and patient visits within EDs. Analyses for this report were conducted using data from restricted-use data files. Public-use versions of these files are available from: https://www.cdc.gov/nchs/ahcd/datasets_documentation_related.htm . Count estimates and measures of variance could differ between the restricted-use and public-use files. Information for accessing the restricted-use data file is available from: https://www.cdc.gov/rdc/index.htm . Additional information on the methodology of NHAMCS is available online ( 7 , 8 ). This report presents results combining data for 2020 and 2021 for more detailed subgroup analyses. In addition to the 2020–2021 data, Figure 4 includes individual data years from the 2012–2021 NHAMCS.

Data analyses were performed using the statistical packages SAS version 9.4 (SAS Institute, Cary, N.C.) and SAS-callable SUDAAN version 11.0 (RTI International, Research Triangle Park, N.C.). To test for linear and quadratic trends over time, the null hypothesis of nonlinear or quadratic trend was examined using the POLY option in SUDAAN. If a quadratic trend was significant, Joinpoint software ( 9 ) was used to determine the change point in the trend line. Piecewise linear regression was used to test the significance of slopes according to National Center for Health Statistics trend analysis guidelines ( 10 ). Differences among groups were evaluated using two-sided significance tests at the p < 0.05 level.

Loredana Santo, Susan M. Schappert, and Jill J. Ashman are with the National Center for Health Statistics, Division of Health Care Statistics.

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• Uppal TS, Chehal PK, Fernandes G, Haw JS, Shah M, Turbow S, et al. Trends and variations in emergency department use associated with diabetes in the U.S. by sociodemographic factors, 2008–2017. JAMA Netw Open 5(5):e2213867. 2022.
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Santo L, Schappert SM, Ashman JJ. Emergency department visit rates by adults with diabetes: United States, 2020–2021. NCHS Data Brief, no 487. Hyattsville, MD: National Center for Health Statistics. 2023. DOI: https://dx.doi.org/10.15620/cdc:134505 .

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