Consumer Product Injury Statistics: What the NEISS Data Shows

Consumer product injury statistics in the United States almost all trace back to one public dataset: the National Electronic Injury Surveillance System (NEISS), run by the U.S. Consumer Product Safety Commission. When a news story says a particular appliance, scooter or piece of furniture sends tens of thousands of people to the emergency department each year, that number came from weighting a sample of coded emergency department records. This article covers what the published evidence reports about which consumer products injure people, how those figures were produced, and how to retrieve the underlying data yourself.

Where Consumer Product Injury Statistics Actually Come From

NEISS is a stratified probability sample of 100 hospital emergency departments, drawn from a 2020 frame of 4,723 eligible U.S. emergency departments representing roughly 155.7 million annual ED visits. The strata are four annual-ED-volume categories plus a children’s hospital stratum: 43 small, 26 medium, 12 large, 11 very large and 8 children’s hospitals. Trained coders at each hospital review medical records and enter a structured summary of every visit associated with a consumer product. The CPSC describes the purpose as collecting data on injuries “associated with (but not necessarily caused by) specific consumer products,” a phrase worth quoting exactly, because it marks the boundary of what this data can support (CPSC NEISS FAQ).

That is the short version. The full mechanics are in our reference piece on how an ER visit becomes a NEISS record, and the sampling side in how NEISS weights scale a sample to a national estimate. For scale: a scoping review screened 6,944 citations and identified 167 peer-reviewed articles using the NEISS All Injury Program as their primary data source between 2001 and 2021, an average of 8.0 per year across 72 journals [1].

Two Numbers That Are Not the Same: Cases Collected and the National Estimate

This distinction decides whether a statistic is right or wrong, and most coverage gets it backwards.

• Cases collected. The CPSC’s FY2023 Annual Report states that “annually, NEISS supplies information on almost 400,000 product-related cases” (CPSC FY2023 Annual Report). Its OMB supporting statement uses a planning figure of about 470,000 per year, roughly 2,375 of which are selected for follow-back investigation.
• The national weighted estimate. Every case carries a statistical weight, and summing those weights produces a national number. The most recent published CPSC figure for all consumer product-related, ED-treated injuries is 10,994,000 for calendar year 2020 (CPSC Directorate for Epidemiology, September 2021). Two caveats travel with it: 2020 was a pandemic year with depressed ED volumes, and the figure aggregates across product groups.

An unweighted count of NEISS rows is a count of records at about 100 hospitals. It is not a smaller version of the national number, because a record from a small-stratum hospital can carry a far larger weight than one from a very large emergency department. Publishing an unweighted count as if it were national is the most frequent analytic error in this literature.

A second trap: reporting completeness moves year to year. Ninety-six sampled hospitals reported in 2019 and again in 2023, but only 77 of 100 reported in 2024. Weights adjust for non-response, but anyone comparing 2024 against 2019 should know the reporting base shifted.

What CPSC Data Shows About the Largest Product Categories

The CPSC publishes per-category national estimates each spring in its NEISS Data Highlights. The 2024 release gives these figures by product group:

Do not add these rows together. The CPSC states in every Data Highlights release that “the data system allows for reporting of up to three products for each person’s injury, so a person’s injury may be counted in three product groups.” Summing published categories double and triple counts people. It is why the CPSC publishes no de-duplicated “all products” total, and why any article claiming one is misreading the source or inventing it.

Notice what these categories are. The largest consumer product injury statistics in the United States are floors, stairs, beds and chairs, because those are the structures people fall onto and off of. The interesting research question is almost never which product tops the list, it is what circumstances produce these injuries, and that requires the case narratives rather than the coded product field.

What the Published Literature Reports, Product Area by Product Area

Every finding below comes from a peer-reviewed NEISS analysis, reported as its authors reported it, with the unweighted case count and the weighted estimate wherever the paper gives both.

Household structures, furniture and falls

A 2026 analysis of hand and wrist injuries across 2018 to 2022 found knives accounted for 11% of all hand and wrist injuries, with doors next at 15,827 cases (5.8%), and 94% discharged as outpatients. The associated product varied sharply with age: doors for ages 1 to 9, sports equipment for 10 to 14, knives from 15 to 80, and floors and stairs above age 80 [3].

Using 2019 data for ages 15 to 64, researchers estimated 687,902 ED visits for falls on stairs. Patients with alcohol intoxication had a higher prevalence of traumatic brain injury (prevalence ratio 2.7, 95% CI 2.3 to 3.1) and craniomaxillofacial injury (PR 2.5, 95% CI 2.3 to 2.8) [4].

Eye injuries in older adults follow the same pattern. Across 2000 to 2019 in adults 65 and over, 4,953 unweighted cases represented approximately 238,162 injuries nationally, about 12,000 per year, with falls accounting for 11.5% of ED-treated eye injuries in that age group [5].

Nursery and children’s products

The best-documented nursery finding comes from a machine-learning analysis of the narratives. Researchers manually coded 4,546 narratives to train a natural language processing model, then applied it to yield 91,325 coded cases of falls in children under five across 2012 to 2016. Children fell from the bed in 33% of infant fall injuries, and odds of hospitalisation after falling from another person were 2.1 times higher (95% CI 1.6 to 2.7) than falls from other surfaces [6].

Ingested objects are the other classic nursery signal. A 20-year review of children under 18 identified 52,315 unweighted esophageal foreign-body cases, national estimate 1,589,325. The most frequently ingested items were coins (37.6%), toys (13.5%) and batteries (6.8%), and incidence rose from 7.3 to 14.2 per 10,000 children (R-squared 0.8, p<0.0001) [7].

A 10-year analysis of patients under 17 counted 458,433 elbow fractures, mean age 7.4 years (SD 4.1), 55.6% male. Sports and recreation equipment was the most common associated product, and children aged eight and above were injured by it far more often than younger children (79.2% versus 51.1%, p<0.0001) [8].

Kitchen appliances and workshop tools

The pressure cooker study is the cleanest worked example here because every step is documented. Across 2003 to 2019, 759 actual cases yielded a national estimate of 28,337 ED-treated injuries (95% CI 24,588 to 32,086), about 1,667 per year. The authors then read the narratives and classified four precipitating factors that exist nowhere in the coded fields: a burning agent, being struck by the product, injury while opening the device, and other [9].

For power tools, a 2013 to 2022 analysis found hand injuries predominated at 39.08%, followed by finger (19.19%) and lower arm (11.25%). Lacerations were the most frequent injury type (60.89%), and males accounted for 95.97% of injuries [10].

Lithium-ion batteries and e-cigarettes

This is the product area where invented statistics circulate most freely, so the verified numbers matter. FDA authors examined electronic nicotine delivery system (ENDS) battery explosions and burns across 2020 to 2024 and estimated 2,043 emergency department visits (95% CI 1,326 to 2,760) from just 52 unweighted cases. Of those, 72.7% were adults aged 18 to 44, 80.7% male, lower-extremity injuries most common at 54.1%, and 26.3% required hospital admission [11].

An earlier series covering 2015 to 2019 estimated 3,369 ED visits (95% CI 2,020 to 4,718) from e-cigarette explosion and burn injuries, plus 1,550 (95% CI 778 to 2,322) cases of children under five ingesting e-cigarette liquids during 2018 to 2019 [12].

Scale matters. Across 2012 to 2022, ENDS and related products accounted for an estimated 3,142 injuries (95% CI 2,384 to 3,975), against 46,116 (95% CI 38,712 to 53,520) from combustible tobacco paraphernalia [13]. A battery explosion is a vivid hazard and a numerically small one next to lighters and matches. Note also the 52-case foundation under that ENDS estimate: a reminder of how thin the data can be for a narrowly defined product.

Micromobility: e-scooters and e-bikes

Micromobility is the fastest-moving category in recent consumer product injury statistics. Across 2015 to 2019, an estimated 60,554 e-scooter injuries (95% CI 37,525 to 84,594) were treated in U.S. emergency departments. About 19% involved a motor vehicle, and those were admitted more often (14.6% versus 8.8%, p<0.01) [14].

A 2026 CDC-authored analysis widened the lens: across 2020 to 2024, an estimated 2,532,882 ED visits involved e-scooters, e-bikes and bicycles, 20.5% of them with a motor vehicle, and admission was consistently higher when one was involved (for bicycles, 17.7% versus 9.5%) [15].

In children, 669 unweighted e-scooter fractures (national estimate 18,103) rose significantly from 2014 to 2023 (p=0.011), a 2.7-fold increase in annual average incidence from 2017 to 2019 versus 2021 to 2023. Lower arm (27.9%) and wrist (26.3%) were most affected, and 80.2% were falls off the scooter [16].

Alcohol is a documented contributor. Across 4,020 unweighted e-scooter and e-bike injury visits from 2019 to 2022, weighted visits rose roughly three-fold, from 22,835 in 2019 to 65,892 in 2022, and alcohol was reported in 8.6% of e-scooter and 2.5% of e-bike visits [17].

In ages 2 to 18 across 2011 to 2020, e-bike injuries increased (Kendall’s tau 0.73, p=0.004) while pedal bicycle injuries fell (tau -0.91, p=0.0003). Hospitalisation was significantly higher for e-bikes (11.5% versus 7.0% for mopeds and 4.8% for bicycles, p<0.0001), and 97.3% of injured e-bike riders with helmet status recorded were unhelmeted [18].

Sports and recreation equipment

Pickleball shows how fast a category can appear in the data. Across 2005 to 2024, an estimated 3,112 pickleball-related ocular injuries came from 73 unweighted cases, 1,262 of them (33 unweighted) in 2024 alone. Survey-weighted linear regression showed incidence rising by 405 cases per year (95% CI 288 to 522, p=0.004) from 2021 to 2024 [19]. That paper is also the cleanest modern reporting template: unweighted count and weighted estimate side by side, with survey-weighted regression rather than treating the rows as a simple random sample.

How These Consumer Product Injury Statistics Are Produced

If you intend to cite or reproduce any figure above, three mechanics decide whether your number is defensible.

1. The weight and the two design variables beside it. The public file contains Weight, Stratum and PSU (the sampled hospital). A national estimate is the sum of weights across cases meeting your case definition. A weighted point estimate computed without declaring Stratum and PSU as the survey design produces standard errors that are wrong, usually too small. A methods paper using NEISS as its test case found that “in almost all cases, design-corrected approaches contrasted profoundly with standard statistical techniques” [20]. Our walkthrough of what happened when we ran the same NHANES analysis three ways shows how far the answer moves.

2. The CPSC’s own reportability rules. Variance is computed from the sample design and reported as a coefficient of variation (square root of the variance divided by the estimate). The 95% confidence interval is estimate plus or minus (1.96 times estimate times CV). The CPSC treats an estimate as unstable and will not report it when the estimate is under 1,200, contributing cases number fewer than 20, or the CV exceeds 33% (CPSC explanation of NEISS estimates). Mirror those thresholds. Our free incidence rate calculator handles the rate arithmetic once you have a denominator.

3. The coded fields are NEISS’s own, not ICD. NEISS does not use ICD-10-CM or ICD-10-PCS. It uses closed internal code sets: 26 body-part codes, 30 diagnosis codes, four-digit product codes with up to three products per case, plus disposition and location codes, all enumerated in the CPSC Data Dictionary. Plan an ICD-based NEISS study and you will find mid-project that the variable does not exist. Age is coded unusually too: 0 means not recorded, 2 to 120 is years, and 201 to 223 encodes 1 to 23 months.

One more field carries most of the findings above: the narrative, free text capped at 400 characters. It is why several studies here could classify circumstances the coded fields never captured, and why each needed manual review or a trained classifier. Our piece on the free-text narrative field covers what it does and does not contain.

How to Look Up Consumer Product Injury Statistics Yourself

The data is free and needs no registration. The workflow is short:

1. Start in the query builder. The NEISS Estimates Query Builder produces national estimates interactively and serves the downloadable annual files for data years 2006 through 2025 in Excel, tab-delimited text and SAS formats.
2. Identify the right product code. Four-digit product codes are not stable across decades. The Product Code Comparability Table documents every code merged, split, deleted or reactivated since 1972. Query one code across 20 years without it and you will produce artefactual trends.
3. Check the variable years. Body_Part_2, Diagnosis_2, Product_3, Hispanic, Alcohol and Drug exist only from 2019 onward, and a non-binary Sex value is unavailable before 2021.
4. Declare the design, then estimate. In R, svydesign(id = ~PSU, strata = ~Stratum, weights = ~Weight, data = neiss, nest = TRUE). In Stata, svyset PSU [pweight=Weight], strata(Stratum). In SAS, STRATA Stratum; CLUSTER PSU; WEIGHT Wt;.
5. Apply the suppression rules before reporting anything, and read the narratives for any claim about circumstance rather than product.

Still deciding whether NEISS is the right source? Our comparison of which health database your study should use covers what each public dataset can and cannot answer, and how to get the NEISS data files covers retrieval.

What Consumer Product Injury Statistics Cannot Tell You

Every limitation below is documented, and each has ended a study that ignored it too long.

• Emergency department visits only. Injuries treated in urgent care, physician offices or at home are invisible, as are deaths that never reach an ED.
• Association, not causation. A product code on a record means the product was involved in the event, not that it failed or caused the harm.
• No longitudinal follow-up. Each record is a single visit: no patient identifier, no readmission, no post-discharge outcome, no cost field.
• No state or local estimates. The most frequently reported limitation in the published literature: in the scoping review of 167 NEISS-AIP articles, 38 authors flagged that the system produces national estimates only [1].
• Some records are not injuries. A record-linkage validation at a NEISS children’s hospital found 13.32% (385 of 2,890) of sampled cases were non-injuries by the ICD-9-CM diagnosis in the same record. Among true injury cases, agreement was kappa 0.87 (95% CI 0.85 to 0.88), and the authors concluded that best practice “should include manual review of case narratives” [2].
• Small cells are unstable. With roughly 100 hospitals, a narrowly defined product yields very few unweighted cases, as the 52-case ENDS battery estimate illustrates.

For the biases underneath these limits, see the biases baked into NEISS sampling and our assessment of whether NEISS injury statistics are reliable. On the regulatory side, the testing standards these injury reports feed into is the companion piece to this one.

What Is Changing: The NEISS Modernization

In 2026 the CPSC announced a modernization, NEISS-Remodel (NEISS-R), moving from manual medical-record abstraction at roughly 70 hospitals to standardized electronic data exchange covering all 50 states. The announcement notes that under the legacy design 14 states had no participating hospital at all, with Alaska, Hawaii and the Mountain West underrepresented. The modernized system is “expected to become fully effective by the beginning of 2027” (CPSC news release, 2026). Two consequences: state which NEISS vintage your methods section used, and treat trend analyses crossing the transition with the care the 2019 and 2021 variable additions already require.

Common Mistakes When Citing Consumer Product Injury Statistics

1. Reporting the unweighted case count as a national figure, or the reverse. Report both [19].
2. Summing CPSC product categories, which overlap by design.
3. Assuming ICD codes exist. They do not, in this dataset.
4. Claiming a product caused an injury on the strength of a product code.
5. Producing state-level numbers. The design does not support them.
6. Ignoring product code changes across a long time series.
7. Reporting an estimate the CPSC would suppress (under 1,200, fewer than 20 cases, or CV above 33%).
8. Treating a sub-system figure as a NEISS figure. NEISS, NEISS-AIP, NEISS-Work, NEISS-CADES and NEISS-FISS use different hospital subsets and weights.

If the analysis is heading for a journal, the checklist reviewers apply is in our tutorial on the STROBE checklist for observational studies, and the design questions to settle first are in how to design a study around this data.

Tools and Resources

Professional Services for a Product Injury Study

Most consumer product injury projects fail for one of three reasons: the question was already published, the variables do not exist across the years you want, or the analytic sample cannot support the estimate once you narrow it. Those are the checks worth running before committing a year to a paper.

There is no NEISS-specific package, because a NEISS project lands in one of the three above depending on how many years and sources it touches. Everything in this category is listed under Registry and Real-World Data Studies.

Conclusion

The consumer product injury statistics that circulate in the press are real, public, and far more constrained than their headline versions suggest. About 100 emergency departments contribute roughly 400,000 coded product-related cases a year, weights scale those records to national estimates, and every finding above, from the 33% of infant falls from a bed to the 26.3% admission rate after an ENDS battery explosion, came from that process with a confidence interval attached.

The discipline separating a citable figure from a viral one is specific: report the unweighted count alongside the weighted estimate, never sum overlapping product categories, declare the survey design before computing a standard error, and stop short of causation. Do that and this dataset supports genuinely publishable injury research.

Frequently Asked Questions

What are the best sources for consumer product injury statistics in the United States?

The primary source is the National Electronic Injury Surveillance System, operated by the U.S. Consumer Product Safety Commission. The CPSC publishes annual NEISS Data Highlights with per-category national estimates, plus a free query builder and downloadable annual data files for data years 2006 through 2025. Peer-reviewed NEISS analyses in PubMed supply product-specific detail the Data Highlights do not.

How many consumer product injuries does NEISS record each year?

The CPSC’s FY2023 Annual Report states that NEISS supplies information on almost 400,000 product-related cases annually. Those are collected cases, not a national total. The most recent published CPSC national weighted estimate of all consumer product-related, ED-treated injuries is 10,994,000 for calendar year 2020, a pandemic year in which ED volumes were depressed.

Which consumer products are associated with the most emergency department visits?

In the CPSC’s 2024 NEISS Data Highlights, the largest product groups by national estimate were stairs, ramps, landings and floors (3,101,789), beds, mattresses and pillows (1,114,811), chairs, sofas and sofa beds (756,953), bathroom fixtures (658,680), and exercise equipment (564,845). These are structures people fall onto or off of, not products that fail.

Can I add up CPSC product categories to get a national total?

No. Up to three products can be reported for each person’s injury, so one injury may be counted in three product groups. Summing the published categories double and triple counts people, which is why the CPSC publishes no de-duplicated all-products total in that series.

Does NEISS prove that a product caused an injury?

No. The CPSC’s own wording is that NEISS captures injuries “associated with (but not necessarily caused by) specific consumer products.” A product code means the product was present in the event. Establishing that it failed or caused the harm needs follow-back investigation, engineering evidence, or a different study design.

Can I get state-level consumer product injury statistics from NEISS?

No. National estimates only. In a scoping review of 167 NEISS-AIP articles, 38 flagged that the system cannot support state or county-level surveillance. Under the legacy design, 14 states had no participating hospital at all.

What is changing about NEISS in 2027?

The CPSC announced a modernization in 2026, moving from manual medical-record abstraction at roughly 70 hospitals to standardized electronic data exchange covering all 50 states. It is expected to be fully effective by the beginning of 2027. Any study using data from either side of that transition should state which vintage it used.

References

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