MPH 515 Week 7 Data Interpretation Paper Example

Data Interpretation: What Bellamy County's 2024 Overdose Tables Say About Where Overdoses Reach the Emergency Department and Where They Do Not

Student Name

Chamberlain University

MPH 515: Epidemiology and Biostatistics for Public Health Application I

Instructor Name

Month Day, Year

Data Interpretation: What Bellamy County's 2024 Overdose Tables Say About Where Overdoses Reach the Emergency Department and Where They Do Not

Purpose

Planners at the county health department asked for an interpretation of its 2024 overdose surveillance tables to guide where to place a new overdose response position. The tables give first nonfatal opioid overdose emergency department visits from syndromic surveillance and opioid overdose deaths from vital records, by the county's three planning areas. Of the many figures available, this paper selects three findings that bear on the placement question and leaves the rest in the tables. The question is a practical one: one new staff member, three planning areas and a limited budget, so the interpretation has to end in a choice.

Table 1

Nonfatal Opioid Overdose Visits and Overdose Deaths by Planning Area, Bellamy County, 2024

Note. Note. Composite surveillance and vital records data constructed for this course; confidence intervals for rates use an approximation for Poisson counts.

Finding 1: The Urban Core Has the Most Visits

The urban core's rate of first nonfatal overdose visits, 196.4 per 100,000, was about 1.8 times the inner suburbs' rate and 2.1 times the rural rate, and its confidence interval does not overlap theirs. It accounted for 330 of 586 first visits, 56%, with 40% of the county's population. On visits alone, the urban core is the obvious place for a new position.

Finding 2: The Rural South Has Few Visits but as Many Deaths per Resident

The rural south had the lowest visit rate, 92.6 per 100,000, but its overdose death rate, 27.4 per 100,000, was at least as high as the urban core's, 24.4. The intervals for the two death rates overlap widely, so the data do not show the rural rate is higher; they show it is not lower, despite far fewer visits. The resulting ratio stands out: about 0.30 deaths for every nonfatal visit in the rural south, compared with about 0.12 in the urban core and 0.13 in the suburbs. In plain terms, for every ten rural residents who reached an emergency department alive after an overdose, three died, while in the urban core the figure was a little over one.

Finding 3: The Magnitude Is Uncertain

The rural figures rest on 26 deaths and 88 visits, small numbers whose intervals are wide. A difference of a few deaths would change the ratio noticeably. The finding should therefore be read as a strong signal requiring confirmation, not as an established fact.

Alternative Explanations Worked Through

Several explanations could produce the rural pattern. Each predicts something different, which helps decide among them.

Distance and response time. If emergency medical services take longer to reach rural homes and the nearest emergency department is farther, more overdoses would end in death before arrival. This predicts that rural deaths more often occur at home and that rural emergency visits arrive by ambulance after longer transport times, both checkable in the data.

Less bystander naloxone. If fewer rural residents carry naloxone, fewer overdoses would be reversed and more would be fatal. This predicts low naloxone distribution in rural ZIP codes, also checkable.

Surveillance artifact. Rural residents may be treated in a hospital across the county line, whose visits do not appear in the county's syndromic data. This predicts that adding the neighboring hospital's data would raise the rural visit count and lower the ratio. If the artifact explains most of the pattern, the ratio is an illusion.

Different drugs or use patterns. A more potent supply or more use alone in rural areas could increase lethality. This is harder to check but could be explored through toxicology reports from death investigations.

A Favored Reading, Defended

The favored reading is that rural overdoses are less likely to reach care in time, through a combination of distance and lower naloxone access, with some contribution from missed out-of-county visits. Distance and naloxone fit the ratio and match what is known about rural services in the county: the rural south has one ambulance base and no pharmacy naloxone program. Evidence from Massachusetts showed lower overdose death rates in communities with more naloxone distribution (Walley et al., 2013), which supports naloxone access as part of the explanation. The surveillance artifact is unlikely to explain all of the pattern, because the rural death rate itself, which comes from vital records and does not depend on where residents were treated, is as high as the urban rate.

Limitations Specific to These Data

The tables cover one year, so the rural pattern may be unstable. Deaths are counted by residence and visits by residence, which is consistent, but the visit data depend on emergency department coding, which differs between hospitals. Rates are not adjusted for age, and the rural population is older, which could lower its visit rate, because the county's overdose visits cluster in adults between 25 and 44. Age adjustment might narrow the gap in visit rates and strengthen the death finding.

The Practical Question Answered

For the placement decision, the data support splitting the new position rather than placing it wholly in the urban core. Visits point to the urban core, where a response worker could reach the most people after an emergency visit; patients treated in emergency departments after nonfatal overdose face high short-term mortality (Weiner et al., 2020). Deaths per visit point to the rural south, where the problem may be that overdoses never reach the emergency department, so the worker's role there would be naloxone distribution and outreach rather than emergency department follow-up. Before the decision is final, the health department should obtain the neighboring hospital's data for rural residents and repeat the comparison with 2025 data.

What Would Change This Reading

If the neighboring hospital's data added more than about 40 rural visits, the rural ratio would fall toward the urban figure and the favored reading would weaken. If 2025 data showed a similar ratio in the rural south, the reading would strengthen considerably.

References

Walley, A. Y., Xuan, Z., Hackman, H. H., Quinn, E., Doe-Simkins, M., Sorensen-Alawad, A., Ruiz, S., & Ozonoff, A. (2013). Opioid overdose rates and implementation of overdose education and nasal naloxone distribution in Massachusetts: Interrupted time series analysis. BMJ, 346, f174. https://doi.org/10.1136/bmj.f174

Weiner, S. G., Baker, O., Bernson, D., & Schuur, J. D. (2020). One-year mortality of patients after emergency department treatment for nonfatal opioid overdose. Annals of Emergency Medicine, 75(1), 13-17. https://doi.org/10.1016/j.annemergmed.2019.04.020