Abstract
Objectives:
Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem.
Methods:
We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases (ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes.
Results:
Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015.
Conclusions:
Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.
Keywords: syndromic surveillance, heroin, opioid, emergency department data
Since 2001, US public health agencies have implemented emergency department (ED) syndromic surveillance systems to collect data for early event detection of infectious diseases and outbreaks.1 These systems quickly demonstrated utility in detecting infectious disease incidents by providing near–real-time data to public health officials for improved situational awareness and preparedness.2 Although data collected by these systems may not initially include a diagnosis, they do provide early information about a patient’s presenting symptoms3 and the number and type of people seeking care in the ED. Although syndromic surveillance systems have primarily been used to detect bioterrorism and infectious disease–related health threats,4 in recent years, their use has expanded to monitor noninfectious disease–related health issues, including heat-related illness,5 carbon monoxide poisoning,6 and drug overdoses.7
The New Hampshire Department of Health and Human Services implemented the Automated Hospital Emergency Department Data (AHEDD) syndromic surveillance system in 2006, starting with 4 hospitals. In 2008, participation in this system became mandatory8; by 2010, all 26 of New Hampshire’s acute care hospitals were participating. Public health authorities have used the system to monitor not only infectious disease syndromes but also heat- and cold-related injuries, motor vehicle collisions and other injuries during snowstorms, and carbon monoxide–poisoning events.9
In September 2013, the New Hampshire Department of Health and Human Services also began using ED data to monitor opioid-related overdoses. Surveillance of opioid-related overdoses was implemented in 2013 because of a rapid increase in opioid-related overdoses and deaths in New Hampshire specifically and the United States generally.10 We describe the methods used to collect and analyze these data to inform New Hampshire’s public health response to the opioid epidemic.
Methods
From January 1, 2011, through December 31, 2015, 25 of 26 acute care hospitals in New Hampshire consistently submitted ED encounter data to the AHEDD system. Records were transmitted from hospitals using Health Level 7 message formatting at scheduled times according to New Hampshire’s local implementation guide.11 Record-level data included the following variables: medical record number, age, sex, city or town and state of residence, encounter date, and encounter number. Of the 25 reporting hospitals, 23 also provided International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)12 codes and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)13 codes beginning October 1, 2015. This work was conducted as part of routine public health surveillance, and institutional review board approval was not required.
Beginning in September 2013, we queried ED data monthly by chief complaint text and ICD codes to identify opioid-related encounters. We shared aggregated data with response partners, including the New Hampshire Department of Health and Human Services, Bureau of Drug and Alcohol Services, and the New Hampshire Department of Safety for use in the public health response to the opioid epidemic. To compare and assess trends in the occurrence of opioid-related ED encounters, we report here on ED data queried for the period of January 1, 2011, through December 31, 2015. We queried chief complaint text for the words “fentanyl,” “heroin,” “opiate,” and “opioid” and their common misspellings (ie, “heroine,” “herione,” “opoid,” “opiod”). We queried hospital data for opioid-related ICD-9-CM codes for the period January 1, 2011, through September 30, 2015 (Table 1). We selected these codes according to consensus recommendations from the Injury Surveillance Workgroup of the Safe States Alliance.14 For the period October 1 through December 31, 2015, we queried hospital data for opioid-related ICD-10-CM codes (Table 1). Because no consensus recommendations for opioid-related ICD-10-CM code queries existed at that time, we mapped the recommended ICD-9-CM codes to the relevant ICD-10-CM codes using the ICD-10-CM Master Mapping Reference Table.15 We eliminated duplicate patient encounters. We calculated frequencies for age, sex, residence, chief complaint text values, ICD-9-CM codes, and ICD-10-CM codes using Microsoft Excel and SAS version 9.3.16 We calculated age-specific and overall incidence rates per 100 000 population using population data from the US Census Bureau.17,18 We used the Mantel-Haenszel χ2 test and standardized Pearson residuals to determine the significance of trends observed over time, with P < .05 considered significant.
Table 1.
Opioid-related International Classification of Diseases codesa
| ICD-9-CM | ICD-10-CM | ||
|---|---|---|---|
| Code | Abbreviated Description | Corresponding Code | Abbreviated Description |
| 304.00-304.02 | Opioid-type dependence | F11.2 | Opioid dependence |
| 304.70-304.72 | Combinations of opioid-type drug with any other drug dependence | F19.2 | Other psychoactive substance dependence |
| 305.50-305.52 | Nondependent opioid abuse | F11.1 F11.9 | Opioid abuse Opioid use, unspecified |
| 965.00 | Poisoning by opium (alkaloids), unspecified | T40.0X (excluding T40.0X6) | Poisoning by and adverse effect of opium |
| 965.01 | Poisoning by heroin | T40.1X | Poisoning by heroin |
| 965.02 | Poisoning by methadone | T40.3X (excluding T40.3X6) | Poisoning by and adverse effect of methadone |
| 965.09 | Poisoning by other opiates and related narcotics | T40.2X (excluding T40.2X6); T40.6 (excluding T40.606 and T40.696) | Poisoning by and adverse effect of other opioids; poisoning by and adverse effect of other or unspecified narcotics |
| E850.0 | Accidental poisoning by heroin | T40.1X | Poisoning by and adverse effect of heroin |
| E850.1 | Accidental poisoning by methadone | T40.3X (excluding T40.3X6) | Poisoning by and adverse effect of methadone |
| E850.2 | Accidental poisoning by other opiates and related narcotics | T40.2X (excluding T40.2X6) | Poisoning by and adverse effect of other opioids |
| E935.0 | Heroin causing adverse effects in therapeutic use | None | |
| E935.1 | Methadone causing adverse effects in therapeutic use | T40.3X | Poisoning by and adverse effect of methadone |
| E935.2 | Other opiates and related narcotics causing adverse effects in therapeutic use of drugs | T40.6 (excluding T40.606 and T40.696) | Poisoning by and adverse effect of other or unspecified narcotics |
Abbreviations: ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification 12; ICD-10-CM, International Classification of Diseases, Tenth Revision, Clinical Modification.13
aThis table reflects the ICD-10-CM code mapping of only the ICD-9-CM codes recommended for monitoring opioid-related encounters.14 Other ICD-9-CM codes that map to the listed ICD-10-CM codes may not be included in this table, because they were not included in the list of recommended ICD-9-CM codes.
Results
From January 1, 2011, through December 31, 2015, the New Hampshire Department of Health and Human Services received 3 101 402 electronic ED encounter records in the AHEDD system; an average of 620 000 records were submitted annually. We identified 20 994 (<1%) opioid-related ED encounters from the 25 New Hampshire hospitals that provided data; of these, we identified 18 554 (88%) using ICD code query alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. The average number of opioid-related ED encounters at each hospital during the study period was 808 (range: 10-4131); 8 of 25 hospitals had >1000 opioid-related ED encounters.
During the 5-year period, opioid-related ED encounters increased by 70%, from 3300 in 2011 to 5603 in 2015. The number of opioid-related ED encounters initially decreased between 2011 and 2012 but increased an average of 20% annually from 2012 to 2015; the largest single-year increase (31%) occurred from 2013 (n = 3813) to 2014 (n = 4994; Table 2).
Table 2.
Number of opioid-related emergency department (ED) encounters, by age, sex, year, and whether related to heroin, New Hampshire, January 1, 2011, through December 31, 2015a
| Encounters, No. (%) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | Total | 2011c | 2012 | 2013c | 2014c | 2015c | P Valueb | Change 2011-2015, % |
| Total | 20 994 | 3300 | 3284 | 3813 | 4994 | 5603 | <.001 | 70 |
| Sex | <.001 | |||||||
| Male | 11 128 (53) | 1627 (49) | 1651 (50) | 1934 (51) | 2686 (54) | 3230 (58) | 99 | |
| Female | 9866 (47) | 1673 (51) | 1633 (50) | 1879 (49) | 2308 (46) | 2373 (42) | 42 | |
| Age group, y | <.001 | |||||||
| 0-12 | 197 (1) | 50 (2) | 38 (1) | 38 (1) | 43 (1) | 28 (<1) | –44 | |
| 13-17 | 265 (1) | 56 (2) | 39 (1) | 44 (1) | 69 (1) | 57 (1) | 2 | |
| 18-29 | 9213 (44) | 1304 (40) | 1384 (42) | 1637 (43) | 2285 (46) | 2603 (46) | 100 | |
| 30-49 | 8166 (39) | 1236 (37) | 1276 (39) | 1479 (39) | 1952 (39) | 2223 (40) | 80 | |
| 50-69 | 2523 (12) | 455 (14) | 432 (13) | 474 (12) | 552 (11) | 610 (11) | 34 | |
| ≥70 | 630 (3) | 199 (6) | 115 (4) | 141 (4) | 93 (2) | 82 (1) | –59 | |
| Heroin relatedd | 3023 (14) | 145 (4) | 184 (6) | 332 (9) | 1018 (20) | 1344 (24) | <.001 | 827 |
aData source: New Hampshire Automated Hospital Emergency Department Data System.
bMantel-Haenszel χ2 test assessing the proportion of encounters attributed to each characteristic during the 5-year period. For total number of ED encounters, the P value is associated with the proportion of opioid-related encounters relative to the total number of ED encounters each year, which increased from 0.5% in 2011 to 0.9% in 2015.
cPercentages may not total to 100 because of rounding.
dHeroin-related ED visits are visits with a heroin-related International Classification of Diseases, Ninth Revision, Clinical Modification 12 code (965.01, E850.0, E935.0), a heroin-related International Classification of Diseases, Tenth Revision, Clinical Modification 13 code (T40.1X including subgroups), or the word “heroin” (or a common misspelling) in the chief complaint text.
Of the 20 994 total opioid-related ED encounters, 3023 (14%) were heroin related. The number of heroin-related ED encounters increased ninefold, from 145 ED encounters in 2011 to 1344 ED encounters in 2015; the largest single-year increase (207%) occurred from 2013 (n = 332) to 2014 (n = 1018; Table 2). Of the 3023 total heroin-related ED encounters, 2459 (81%) were identified using heroin-specific ICD codes, and 564 (19%) were identified using chief complaint text only. A total of 29 fentanyl-related ED encounters were identified by chief complaint text query; none were identified by ICD code due to lack of a fentanyl-specific ICD code.
A total of 153 170 ICD codes were associated with the 20 994 total opioid-related ED encounters, 25 967 (17%) of which were for ICD codes included in our query (Table 3). The 3 codes most commonly associated with the ICD-9-CM query were for nondependent opioid abuse, opioid-type dependence, and poisoning by heroin. The 3 codes most commonly associated with the ICD-10-CM query were opioid dependence, opioid abuse, and poisoning by heroin. Of the 18 554 opioid-related ED encounters we identified using ICD code only, the corresponding chief complaint text included varied and nonspecific words; the most common words were “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). The chief complaints for the remaining records ranged from nausea and infection to psychiatric and dental problems.
Table 3.
Number of opioid-related emergency department encounters, by International Classification of Diseases code, New Hampshire, January 1, 2011, through December 31, 2015a
| ICD Code | Abbreviated Description | No. (%) |
|---|---|---|
| ICD-9-CM | 19 724 (100) | |
| 304.00-304.02 | Opioid-type dependence | 7692 (39) |
| 304.70-304.72 | Combinations of opioid-type drug with any other drug dependence | 782 (4) |
| 305.50-305.52 | Nondependent opioid abuse | 8550 (43) |
| 965.00 | Poisoning by opium (alkaloids), unspecified | 417 (2) |
| 965.01 | Poisoning by heroin | 2310 (12) |
| 965.02 | Poisoning by methadone | 95 (<1) |
| 965.09 | Poisoning by other opiates and related narcotics | 754 (4) |
| E850.0 | Accidental poisoning by heroin | 1107 (6) |
| E850.1 | Accidental poisoning by methadone | 38 (<1) |
| E850.2 | Accidental poisoning by other opiates and related narcotics | 550 (3) |
| E935.0 | Heroin causing adverse effects in therapeutic use | 4 (<1) |
| E935.1 | Methadone causing adverse effects in therapeutic use | 51 (<1) |
| E935.2 | Other opiates and related narcotics causing adverse effects in therapeutic use of drugs | 1476 (7) |
| ICD-10-CM | 1270 (100) | |
| F11.1 | Opioid abuse | 632 (50) |
| F11.2 | Opioid dependence | 657 (52) |
| F11.9 | Opioid use, unspecified | 75 (6) |
| F19.2 | Other psychoactive substance dependence | 91 (7) |
| T40.0X | Poisoning by and adverse effect of opium | 8 (1) |
| T40.1X | Poisoning by heroin | 517 (41) |
| T40.2X | Poisoning by and adverse effect of other opioids | 85 (7) |
| T40.3X | Poisoning by and adverse effect of methadone | 4 (<1) |
| T40.6 | Poisoning by and adverse effect of other or unspecified narcotics | 72 (6) |
Abbreviations: ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification 12; ICD-10-CM, International Classification of Diseases, Tenth Revision, Clinical Modification.13
aA total of 153 170 ICD codes were associated with the 20 994 opioid-related encounters during the study period. Visits may have had >1 ICD code assigned to them; as such, the total number of visits associated with each ICD code does not total to 20 994. ICD-9-CM codes are for encounters from January 1, 2011, through September 30, 2015. ICD-10-CM codes are for encounters from October 1, 2015, through December 31, 2015. Data source: New Hampshire Automated Hospital Emergency Department Data System.
Of the 20 994 opioid-related ED encounters during the study period, overall 11 128 (53%) patients were male; however, the proportion of opioid-related ED encounters made by males increased over time. In 2011, males accounted for 49% (n = 1627 of 3300) of opioid-related ED encounters; by 2015, they accounted for 58% (n = 3230 of 5603) of opioid-related ED encounters. Males accounted for more opioid-related ED encounters than females across all age groups for the 5-year period, except in children aged <18 and adults aged ≥70 (Figure 1). More than 80% of opioid-related ED encounters during the study period were among adults aged 18-49, who comprised approximately 40% of the population in New Hampshire in 2015.18 As a proportion of total opioid-related ED encounters, the proportion of encounters attributed to adults aged 18-29 increased significantly, and the proportion of encounters attributed to adults aged ≥70 decreased significantly from 2011 to 2015. Based on patients’ residences, the incidence of opioid-related ED encounters was 318 per 100 000 population during the 5-year period. Geographic distribution of patient residences was consistent with population density; most encounters occurred in the south-central and southeastern part of the state. The highest number of opioid-related ED encounters occurred in residents of the 2 largest cities, Manchester and Nashua, which together accounted for 6633 (32%) of all 20 994 encounters (Figure 2).
Figure 1.
Incidence of opioid-related emergency department (ED) encounters, by age group and sex, New Hampshire, January 1, 2011, through December 31, 2015. Rates calculated with US Census Bureau population data.17 Population data by age and sex for 2015 were not yet available, so 2014 data were used. Data source: New Hampshire Automated Hospital Emergency Department Data System.
Figure 2.
Geographic distribution of the incidence of opioid-related emergency department (ED) encounters, by county, New Hampshire, January 1, 2011, through December 31, 2015. The total number of opioid-related ED encounters was 20 994; however, 1616 encounters were excluded because the patient’s residence was out of state, and 23 encounters were excluded because the patient’s residence information was missing. Rates were calculated with US Census Bureau population data.18 The rate for Belknap County should be interpreted with caution because 89% of all opioid-related ED encounters were identified by International Classification of Diseases codes, and the 2 hospitals that serve this region did not supply code data. Data source: New Hampshire Automated Hospital Emergency Department Data System.
Discussion
The Centers for Disease Control and Prevention released a report in January 2016 describing the worsening opioid overdose epidemic in the United States, particularly in relation to heroin and illicit fentanyl abuse. The report identified New Hampshire as having the third-highest rate of drug overdose deaths in the country in 2014, with rates rising substantially.19 With a greater need nationally and locally to prevent opioid abuse and target resources for prevention and treatment, we used data from our existing ED syndromic surveillance system to provide timely information to public health partners to support the state’s comprehensive response to the opioid overdose crisis.20 We were able to rapidly implement ED data monitoring early on in the epidemic because the syndromic surveillance system in New Hampshire included functionality to deploy custom chief complaint and ICD queries. Having flexible systems that can monitor emerging issues, such as heroin, fentanyl, synthetic marijuana (eg, “spice”), and other street drugs, can ensure agility as new public health threats emerge and public health priorities change.
Using data acquired from chief complaint text fields and ICD codes from ED encounters, we found that >20 000 total opioid-related ED visits were made in New Hampshire during the study period, the number of opioid-related ED encounters increased by 70%, and the number of heroin-specific encounters increased by 827%. Of the 20 994 opioid-related encounters, 3023 (14%) were heroin related; the proportion of opioid-related encounters that were due to heroin increased disproportionately during the period, from 4% of opioid-related encounters in 2011 to 24% of opioid-related encounters in 2015. The ED data system also helped us to define demographic and geographic impact. We found that the largest increases in opioid-related ED encounters were occurring among adults aged 18-29 and in males. Similar increasing trends in occurrence and demographic characteristics of those most affected have been observed in national data.10,19
These ED data were also combined with other data sources, such as emergency medical services naloxone administration data and data on death from the Office of the Chief Medical Examiner, to provide a comprehensive assessment of the opioid overdose problem in New Hampshire. This information was distributed routinely on a monthly basis to response agencies, including public health and law enforcement, in a report by the New Hampshire Department of Safety.21 The ED data were particularly useful because they were timely and provided demographic analyses that could be used to target interventions toward populations most at risk. Because the ED surveillance data are collected in near-real time, the system is useful for quickly identifying outbreaks of opioid overdose. Local agencies reported using the data to help support requests for additional resources and to target limited resources in their area. The data were also used for public education to gain support for initiatives that addressed drug misuse in targeted areas (Deirdre Boulter, New Hampshire Department of Safety Information and Analysis Center, email to E. R. Daly, May 17, 2016).
The Centers for Medicare & Medicaid Services required hospitals to use ICD-10-CM codes beginning October 1, 2015.22 Because of this transition, all syndromic surveillance queries had to be updated to include ICD-10-CM codes instead of ICD-9-CM codes. Recommendations for the use of ICD-9-CM codes to monitor opioid-related morbidity have been made14; however, no such recommendations existed for ICD-10-CM codes. In the absence of recommendations, we developed an ICD-10-CM code query based on the recommended ICD-9-CM codes.
This report includes 3 months of data based on ICD-10-CM codes (October 1 through December 31, 2015). Given the recent transition from ICD-9-CM to ICD-10-CM codes, it is important to assess differences in their ability to identify opioid-related ED encounters. For example, we identified that 18% of opioid-related ICD-9-CM codes involved heroin, compared with 41% of ICD-10-CM codes. These results are not directly comparable, however, given the limited data for our ICD-10-CM code analysis and the possible temporal and provider billing changes after the conversion from ICD-9-CM to ICD-10-CM codes. Additionally, the number of opioid-related ED encounters identified through the ICD-10-CM query for October 1 through December 31, 2015 (n = 1255), was 14% fewer than the number of opioid-related encounters identified through the ICD-9-CM query for the same period in 2014 (n = 1452). This result was not expected given the consistently increasing number of opioid-related ED encounters overall. Because of these differences, performance of the ICD-10-CM query should continue to be evaluated as more data are collected over time. The new ICD-10-CM codes may provide some advantages by allowing for more detailed analyses of subgroup coding. Gaps persist, however, in the ability to use the codes to monitor opioid overdoses, such as the lack of a fentanyl-specific code. The impact of the transition should be explored in more detail.
Limitations
Our findings demonstrate how ED-based syndromic surveillance systems can be used to monitor opioid-related ED encounters; however, ICD codes and chief complaint text present their own limitations in terms of quality, sensitivity, and specificity. Two hospitals did not provide ICD codes to New Hampshire’s real-time ED data system; among the hospitals that did submit ICD code data, coding data may have arrived up to 2 weeks after the encounter date. The coding can also be incomplete or inaccurate, and overall accuracy was estimated at 83% in 1 review.23 Querying chief complaint text can be useful because data for this field are often available before ICD codes are available; however, because these data are rapidly transmitted shortly after collection, no editing or data quality checks are made before transmission, and some encounters may not be identified because of misspellings, use of generic “drop-down” values, or typographic errors. For these reasons, we queried both ICD codes and chief complaint text to increase overall sensitivity. The ICD queries performed better than the chief complaint text queries for identifying opioid-related ED encounters. The ICD queries captured 97% (20 304 of 20 994) of identified opioid-related ED encounters, whereas chief complaint text queries captured only an additional 3% (690 of 20 994) of opioid-related ED encounters—although, a higher proportion (19%) of heroin-specific ED encounters were identified via chief complaint text versus all opioid-related ED encounters (3%). The chief complaint text query could be improved; however, our analysis showed that most of the records identified through ICD code alone had corresponding chief complaint texts that were generic (eg, “pain”) and not specific to opioid use. As such, their inclusion in the chief complaint text query would result in the inclusion of many nonrelevant records. Developing chief complaint text queries requires consideration for the desired sensitivity and specificity. Given our results, using ICD codes alone or in combination with more specific chief complaint text queries will continue to be used in New Hampshire to identify and report on opioid-related ED encounters. We also recommend that syndromic surveillance ED data systems be used in concert with other data sources to give a more complete picture of opioid-related health outcomes.
Conclusions
Syndromic surveillance data systems offer a rich source of health data to public health agencies. Although the use of ED data is limited, these data are one of the most robust and complete sources of timely data for monitoring current health problems in a population. Syndromic surveillance ED data systems, which were initially developed for bioterrorism and infectious disease early event detection, have now emerged as an important source of data for all-hazards preparedness and response, including noninfectious disease health conditions such as drug overdoses. These systems can provide timely data for use in public health planning and response activities to improve population health.
Acknowledgments
We thank Tylor Young, geographic information systems analyst at the New Hampshire Department of Health and Human Services, who created the geographic distribution map in this article, and Deirdre Boulter at the New Hampshire Department of Safety Information and Analysis Center, who provided information on how ED data were being used in New Hampshire’s response to the opioid epidemic.
Authors’ Note: The findings and conclusions in this article are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Assistant Secretary for Preparedness and Response.
Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported, in part, by the Centers for Disease Control and Prevention, National Syndromic Surveillance Program, and the Hospital Preparedness Program and Public Health Emergency Preparedness cooperative agreements (grants 1U50OE000065-01 and 5U90TP000535-04).
References
- 1. Henning KJ. Overview of syndromic surveillance: what is syndromic surveillance? MMWR Morb Mortal Wkly Rep. 2004;53(suppl):5–11. [Google Scholar]
- 2. Heffernan R, Mostashari F, Das D, et al. Syndromic surveillance in public health practice, New York City. Emerg Infect Dis. 2004;10(5):858–864. [DOI] [PubMed] [Google Scholar]
- 3. Bueller JW, Hopkins RS, Overhage JM, et al. ; CDC Working Group. Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep. 2004;53(RR-5):1–11. [PubMed] [Google Scholar]
- 4. Paterson BJ, Durrheim DN. The remarkable adaptability of syndromic surveillance to meet public health needs. J Epidemiol Glob Health. 2013;3(1):41–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Ridpath A, Driver CR, Nolan ML, et al. ; Centers for Disease Control and Prevention. Illnesses and deaths among persons attending an electronic dance-music festival—New York City, 2013. MMWR Morb Mortal Wkly Rep. 2014;63(50):1195–1198. [PMC free article] [PubMed] [Google Scholar]
- 6. O’Connell EK, Zhang G, Leguen F, et al. Innovative uses for syndromic surveillance. Emerg Infect Dis. 2010;16(4):669–671. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Ising A, Proescholdbell S, Harmon KJ, et al. Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. Inj Prev. 2016;22(suppl 1):i43–i49. [DOI] [PubMed] [Google Scholar]
- 8. NH Code Admin R. He-P 301 Communicable Diseases. He-P 301.14 b.6. #8242; effective June 3, 2008.
- 9. Swenson D, Zhang X, Miller S, et al. Ten years of syndromic surveillance in New Hampshire: innovation, experience and outcomes. Presented at: International Society for Disease Surveillance Conference; December 6-8, 2011; Atlanta, GA. [Google Scholar]
- 10. Jones CM, Logan J, Gladden RM, et al. Vital signs: demographic and substance use trends among heroin users—United States, 2002-2013. MMWR Morb Mortal Wkly Rep. 2015;64(26):719–725. [PMC free article] [PubMed] [Google Scholar]
- 11. New Hampshire Department of Health and Human Services. New Hampshire local implementation guide for syndromic surveillance reporting 2015. http://www.dhhs.nh.gov/dphs/bphsi/documents/sslocalguide.pdf. Published 2015. Accessed March 31, 2017.
- 12. Centers for Disease Control and Prevention. International classification of diseases, ninth revision, clinical modification (ICD-9-CM). https://www.cdc.gov/nchs/icd/icd9 cm.htm. Accessed March 31, 2017. [PubMed]
- 13. Centers for Disease Control and Prevention. International classification of diseases, tenth revision, clinical modification (ICD-10-CM). https://www.cdc.gov/nchs/icd/icd10 cm.htm. Accessed March 31, 2017.
- 14. The Safe States Alliance. Consensus recommendations for national and state poisoning surveillance: report from the Injury Surveillance Workgroup (ISW7). http://c.ymcdn.com/sites/www.safestates.org/resource/resmgr/imported/ISW7%20Full%20Report_3.pdf. Published 2012. Accessed March 31, 2017.
- 15. International Society for Disease Surveillance. ICD-10-CM master mapping reference table. http://www.syndromic.org/resources/icd-10-conversion/icd-10-mmrt . Accessed March 31, 2017.
- 16. SAS Institute, Inc. SAS: Version 9.3. Cary, NC: SAS Institute, Inc; 2011. [Google Scholar]
- 17. US Census Bureau, Population Division. Annual estimates of the resident population for selected age groups by sex for the United States, states, counties, and Puerto Rico commonwealth and municipios: April 1, 2010, to July 1, 2014. https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2014/PEPAGESEX?slice=GEO∼0400000US33. Accessed March 31, 2017.
- 18. US Census Bureau, Population Division. Annual estimates of the resident population: April 1, 2010, to July 1, 2015. https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2015/PEPANNRES/0400000US33|0400000US33.05000. Accessed March 31, 2017.
- 19. Rudd RA, Aleshire N, Zibbell JE, et al. Increases in drug and opioid overdose deaths—United States, 2000-2014. MMWR Morb Mortal Wkly Rep. 2016;64(50-51):1378–1382. [DOI] [PubMed] [Google Scholar]
- 20. New Hampshire Department of Health and Human Services. The opiate/opioid public health crisis: update on the State of New Hampshire’s comprehensive response. http://www.dhhs.nh.gov/dcbcs/bdas/documents/state-response-opioid-crisis.pdf. Accessed March 31, 2017.
- 21. New Hampshire Department of Health and Human Services. New Hampshire drug monitoring initiative. http://www.dhhs.nh.gov/dcbcs/bdas/data.htm. Accessed March 31, 2017.
- 22. 45 CFR part 162 (2009).
- 23. Burns EM, Rigby E, Mamidanna R, et al. Systematic review of discharge coding accuracy. J Public Health (Oxf). 2012;34(1):138–148. [DOI] [PMC free article] [PubMed] [Google Scholar]