Cost of Digital Nomad Health Insurance 2026 — Methodology

What we measured

The dataset covers six carriers chosen to represent the realistic spectrum of products a digital nomad actually evaluates: Passportcard, April International, SafetyWing, Genki, IMG Global, and Cigna Global. These six were selected because together they span the meaningful price-and-structure range nomads face — from low-monthly-subscription nomad-native products through full international private medical insurance (IPMI) policies. Adding more carriers would have widened the dataset without changing the shape of what it shows.

We quoted each carrier across twelve countries: the ten destinations covered editorially on nomadsurance.com — Thailand, Bali (Indonesia), Portugal, Mexico, Vietnam, Spain, Georgia, the United Arab Emirates, Colombia, and Japan — plus two control countries, the United States and Germany. Germany was included as a high-cost European baseline. The United States was included as the single most expensive insured-resident market in the world, to anchor the upper end of the distribution. The US column is therefore a reference, not a recommendation; few nomads buy IPMI as US residents.

We quoted each country-carrier pair at three age brackets: 28, 38, and 50. These were chosen to capture the three broad life stages we see in the nomad population — early-career, mid-career, and later-career nomads. Each quote was standardized to a solo individual on a worldwide-excluding-US tier where available, at the carrier's mid-tier benefit level, on a 12-month policy duration. Where a carrier did not offer a worldwide-excluding-US tier (notably SafetyWing's regional product structure), we quoted the nearest geographic-equivalent tier and flagged the cell in the dataset.

How we collected the data

Where the carrier provided a public live-quote tool, we used it. Inputs were entered manually by the same researcher across all twelve countries for that carrier in a single sitting, to control for platform changes mid-collection. We captured the quoted monthly premium in the carrier's native quote currency, the quote date, and the URL of the quoting tool.

Where the carrier did not expose a public live-quote tool, or where the tool produced an obviously broken result for a given country (a known issue with several legacy IPMI quoting platforms for certain ordinarily-resident-in-X cases), we contacted the carrier's underwriting or broker channel directly, requested a quote against the same standardized inputs, and recorded the response. We documented the channel for each cell so a reader can see which numbers came from a live tool and which came from underwriter response.

We did not scrape any carrier's site programmatically. Every quote was a manual interaction, in keeping with the public terms of the quoting platforms and the principle that the dataset should be reproducible by hand if anyone wants to verify it.

Where the carrier's quoting tool required a residency declaration, we used "ordinary residence" in {{REFERENCERESIDENCECOUNTRY}} as the constant input across carriers. This is a known limitation: residency declaration meaningfully changes the premium quoted by several carriers, and a different reference residence would shift the absolute numbers up or down. The shape of the dataset — the relative cost between carriers and between countries — is preserved across reasonable choices of reference residence.

How to read the table

Each row in the dataset is a country-age combination. Each column is a carrier. Each cell is the monthly premium for that combination, converted to EUR at the European Central Bank reference rate on the quote date. The original quoted amount and quote currency are preserved in the raw CSV for anyone who wants to redo the conversion.

The right way to read the table is for shape, not for absolute price. The shape — which carriers cluster cheap, which cluster expensive, how each carrier's pricing curve changes across the three age brackets, how each country compares to Germany as a baseline — is the durable signal. The absolute number in any individual cell is a snapshot. Premiums change frequently. Several of these carriers re-priced during the collection window itself.

A reader who looks at this dataset and decides "I should buy this exact product at this exact price" is using it wrong. A reader who looks at it and decides "the kind of plan I want sits in this price band, so a live quote that comes back at three times that band is a signal something is different about my inputs" is using it right.

Limitations and caveats

• Prices age fast. By the time you are reading this, individual cells will be wrong. The shape will mostly hold for six to twelve months; individual numbers may not.
• Carrier availability varies by country in ways the quoting tool does not always surface. A quote returned does not always mean the carrier can actually issue a policy at that price for a real applicant with that residency profile.
• Pre-existing conditions are not modelled. Every quote assumes a healthy applicant with no declared conditions. Real applicants with declared conditions will see materially different numbers, often with exclusions or loadings.
• Family pricing is not modelled. Solo only. Family premiums do not scale linearly from the solo number on any of these carriers.
• US-inclusive variants are not modelled. The dataset is worldwide-excluding-US. The same plan with US cover added typically prices {{USINCLUSIONPREMIUM_MULTIPLIER}}× higher.
• Quoting platforms occasionally return different numbers depending on IP, cookies, browser session, and time of day. We controlled for this where we could (same researcher, same browser, single sitting per carrier) but cannot rule out residual variance.
• Deductibles were standardized to the carrier's most common mid-tier default; readers comparing against their own quote should confirm the deductible matches.

What we did NOT measure

This dataset measures price for a healthy 28-, 38-, or 50-year-old at a standardized benefit level. That is one dimension of an insurance product. It is not the only one and it is not the most important one once you are inside a claim.

We did not measure claim-payout reliability. We did not measure customer-service quality. We did not measure time-to-payout on reimbursement claims. We did not measure how thick or thin a given carrier's direct-billing network is inside specific cities — which, as covered elsewhere in our editorial, can be the difference between a cashless admission and a five-figure deposit on a personal card. We did not measure how each carrier handles disputed claims, undisclosed pre-existing-condition findings during a claim, or appeals.

Those dimensions matter at least as much as price. They require different methodology — surveys, structured interviews, claims-experience aggregation — and they are a separate workstream. We expect to publish parts of that work over the next twelve months. Until then, the right way to use this dataset is as a price-discovery tool, paired with editorial coverage and individual carrier reviews that speak to the dimensions price cannot capture.

How to use this responsibly

This dataset tells you where prices cluster across the nomad insurance market in early 2026. It does not tell you what to buy. Use it to set expectations: to recognise when a quote you receive is in line with the market or wildly outside it, to understand the shape of the cost difference between carriers, to anchor a conversation with a broker or an underwriter. Then run a live quote on the specific carriers that look interesting from this snapshot, against your actual profile and actual residency situation, and make the decision on the live numbers — not on ours.