Predictors of COVID-19 testing rates: A cross-country comparison

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Citation for this paper:

Marziali, M., Hogg, R. S., Oduwole, O. A., & Card, K. G. (2021). Predictors of COVID-19

testing rates: A cross-country comparison. International Journal of Infectious Diseases, 104,

370-372. https://doi.org/10.1016/j.ijid.2020.12.083.

UVicSPACE: Research & Learning Repository

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Predictors of COVID-19 testing rates: A cross-country comparison

Megan E. Marziali, Robert S. Hogg, Oluwamayowa A. Oduwole, & Kiffer G. Card

March 2021

© 2021 Megan E. Marziali et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License.

https://creativecommons.org/licenses/by-nc-nd/4.0/

This article was originally published at:

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Short

Communication

Predictors

of

COVID-19

testing

rates:

A

cross-country

comparison

Megan

E. Marziali

a,b

,

Robert

S. Hogg

a,c

,

Oluwamayowa

A. Oduwole

c

,

Kiffer

G. Card

c,d,

*

a_Epidemiology_and_Population_Health_Program,_BC_Centre_for_Excellence_in_HIV/AIDS,_Vancouver,_Canada b_Mailman_School_of_Public_Health,_Columbia_University,_New_York_City,_NY,_USA

c

FacultyofHealthSciences,SimonFraserUniversity,Burnaby,Canada

d

SchoolofPublicHealthandSocialPolicy,FacultyofHumanandSocialDevelopment,UniversityofVictoria,Victoria,Canada

ARTICLE INFO Articlehistory:

Received26October2020

Receivedinrevisedform25December2020 Accepted29December2020

Keywords: COVID-19

COVID-19diagnostictesting Humandevelopment

ABSTRACT

Objectives:Cross-countrycomparisonsofcoronavirusdisease(COVID-19)havelargelybeenappliedto mortalityanalyses.ThegoalofthisanalysisistoexplorepredictorsofCOVID-19testingthrough cross-countrycomparisons,tobetterinforminternationalhealthpolicies.

Methods:Testingandcase-baseddatawereamassedfromOurWorldinData,andinformationregarding predictorswasgatheredfromtheWorldBank.WeinvestigateHumanDevelopmentIndex(HDI),health expenditure,universalhealthcoverage(UHC),urbanpopulation,serviceindustryworkers(%),andair pollutionaspredictors.WeexploredtestingdatathroughJuly31,2020,ormostrecentlyavailable,using case-indexingmethods,whichinvolvesynchronizingcountriesbydateofﬁrstreportedCOVID-19caseas anindexdateandnormalizingtothecumulativetests25dayspost-indexdate.Threemultivariablelinear regressionmodelswerebuiltinastepwisefashiontoexploretheassociationbetweentheindexed numberofCOVID-19testsandHDIscores.

Results:Atotalof86countrieswereincludedintheﬁnalanalyticalsample,excludingcountrieswith missingdata.HDIandurbanpopulationwerefoundtobesigniﬁcantlyassociatedwithtestinglevels. Conclusions:Resultssuggestthatsocialconditionsandgovernmentcapacityremainconsistentlysalient intheconsiderationoftestingrates.Internationaleffortstoassistlow-HDIcountriesareneededto supporttheglobalCOVID-19response.

Introduction

Infection and mortality rates due to COVID-19 continue to

surge;nearlyone million(990,586) liveshavebeenlost dueto

COVID-19and32, 662,857 caseshavebeendocumented(Dong

et al., 2020). Robust testing systems are necessary to prevent

localized outbreaks and forward transmission. Cross-country

comparisonshavebeencarriedoutlargelytoinvestigate COVID-19mortality.Previousresearchhasidentiﬁedhealthcarespending to be associated with higher mortality (Squalli, 2020), likely

resulting in enhanced documentation of COVID-19 deaths. A

negative association between mortality and testing exists as a functionof governmenteffectiveness(Liangetal.,2020), which suggests that the national policy plays a large role. Previous

researchhasdemonstratedthathealth expenditureand Human

Development Index (HDI) scores have been associated with

enhanced disease control (Tsai and Tipayamongkholgul, 2020), factorsthatmaybeassociatedwithCOVID-19testing.Additionally, countriesmayperceivetheirpopulationtobeatahigherriskof

respiratory diseases, which results from factors such as the

proportionoftheworkforceclassiﬁedasessentialworkers(The Lancet,2020),whichmayinﬂuencetheestablishmentoftesting

programs. There remains a gap in research regarding factors

associatedwithtestingtobetterunderstandnationaltestingrates. The goal of this analysis is to explore predictors of COVID-19 testing, tobetter inform international health and development policies.

Methods Datasources

Testingandcase-baseddatawereamassedfromOurWorldin Data,anonlinerepositoryoftestingindicatorspercountry(Our WorldinData,2020).Informationwithregardtopredictorswas gatheredfromTheWorldBank(2020).Datafromthemostrecent yearavailablewerecollected(2017–2019).HDIscores(2018)were

*Correspondingauthorat:291AHealthandWellnessBuilding,Victoria,V8P5C2, BC,Canada.

E-mailaddress:kiffercard@uvic.ca(K.G. Card).

https://doi.org/10.1016/j.ijid.2020.12.083

1201-9712/©2021TheAuthor(s).PublishedbyElsevierLtdonbehalfofInternationalSocietyforInfectiousDiseases.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

InternationalJournalofInfectiousDiseases104(2021)370–372

ContentslistsavailableatScienceDirect

International

Journal

of

Infectious

Diseases

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retrieved from the United Nations Development Programme (UNDP)(2019).Alldatawerepubliclyavailable.

Explanatorymeasures

HDI was assessed as a predictor due to the hypothesized

relationshipbetweengovernmentalpolicyinrelationtohealthand COVID-19testing.HDIincludesmultipledimensions,compiledof

sociodemographic measuressuchas life expectancy,education,

and income, and is often employed to compare development,

whichresultsfromnationalpolicies(UnitedNationsDevelopment Programme,2019).Wehypothesizedhealthexpenditurepercapita (USD), universalhealth coverage(UHC),urbanpopulation(% of totalpopulation),peopleemployedintheserviceindustry(%of totallaborforce),andairpollution(meanannualexposure)would impact testing capacity, and thus were included as predictors. HealthexpenditureandUHCwereincludedduetoahypothesized relationshipbetweenthesefactorsandavailabilityand accessibili-tyoftestingprograms.Countriesthatallocateagreaternumberof

resources to populationhealth may have a greater capacity to

handle pandemicconditionsand theimplementationof testing

programsthroughresponsereadinessandexistinghealthsystems.

With regard to UHC,existing or perceived cost of testing may

create a barrier for access. Additionally, the proportion of the populationresidinginurbanareaswasinvestigatedasthevirusis transmittedwithcloseproximity(TheLancetRespiratory Medi-cine,2020);wedidnotinvestigateoverallpopulationsize,because ofpotentialcollinearitywiththeproportionofurbanpopulation. Weinvestigatedtheproportionofthepopulationintheservice industrytoassessameasureofthenumberofessentialworkers, whoareatahigherriskofgettinginfectedwithCOVID-19(The Lancet, 2020). Additionally, air pollution has been found to increaseCOVID-19mortality(Wuetal.,2020).Wehypothesized thatthelatterthreepredictors,whichmayindicateapopulationat greaterrisk forinfection,would resultinhigher testingratesif governing bodiesrecognizedgreaterriskamongthepopulation. Allvariableswereoperationalizedascontinuousmeasures. Statisticalanalysis

Thisanalysisexploredtestingdatamostrecentlyavailableuntil July31,2020.Tofacilitatecountrycomparisons,weemployed case-indexingmethodsasmeasuringabsolutenumberscanleadtobias duetovaryingpopulationsizes(MiddelburgandRosendaal,2020). Thismethodinvolvessynchronizingtheepidemicacrosscountries byusingthedateoftheﬁrstreportedCOVID-19case(s)asanindex

dateand subsequentlynormalizing tocumulativecases25days

postindex date (Middelburg and Rosendaal, 2020). As adapted

here,wenormalizedtocumulativetests25dayspostindexdate.

This resulted in an indexed number of tests that allowed for

comparisonsindependentoftemporalvariation(Middelburgand Rosendaal,2020).Analyseswererestrictedtocountrieswhichhad: availabletestingdata,informationregardingﬁrstreported COVID-19case,andthenumberofcasesinthecountryexactly25days followingitsﬁrstcase.Countrieswithmissingdataforpredictors wereexcluded.

Threemultivariablelinearregressionmodelswerebuiltin a stepwisefashiontoexploretheassociationbetweentheindexed numberofCOVID-19testsandtheHDIscoresofeachcountry.All threemodelsincludedurbanpopulation,percentageoflaborforce

in the service industry, and mean air pollution exposure.

Confounderswereminimizedbecauseofhighcorrelationbetween nationalpredictors.ThemodelﬁtwasassessedbyusingQ-Qplots to test normality, variable inﬂation factors to detect multi-collinearity,andresidualplotstotestforheteroscedasticity. Results

Atotalof87countrieswereconsideredforinclusion;theﬁnal analyticalsampleincluded86countries.AsshowninTable1,we foundthatHDIandurbanpopulationweresigniﬁcantlyassociated (p<0.05)withtesting.TheeffectofHDIwasnotexcludedbecause ofeitherUHCorhealthexpenditure.

Discussion

This analysis explored the potential predictors of COVID-19

testing; HDI and urban population were the only signiﬁcant

predictorsofCOVID-19testing.TheassociationbetweenHDIand testingsuggeststhatcountrieswithalowerHDImayexperiencea disproportionateburdenconductinghighvolumetesting,andthat inequities in testing exist ona global scale. These results may suggestthatlowHDIcountriesmaybefacingbarrierstocontrol theepidemic,imposingseriouslimitationsontheglobalCOVID-19

response. Taken a step further, these results speak of the

importance of governmentcapacity for the creation of testing

interventions.In countriesthatare notabletoestablish robust

testing programs, foreign assistance may be warranted, at the

discretionoflocalgovernance.

Ourresultshaveimplicationsforthecourseofthepandemic

and strategies for worldwide eradication or management. The

possibility of eradication of COVID-19 bears similarities to

smallpox in that worldwide cooperation and efforts will be

required(HeymannandWilder-Smith,2020).Countriesthathave

Table1

BivariableandmultivariablelinearregressionmodelsexploringpredictorsofCOVID-19testingatthecountry-level(N=86). Variable Bivariablemodels Multivariablemodels

Model1(Healthexpenditure only)

Model2(UHConly) Model3(Healthexpenditureand UHC)

^

b SE Z-value ^_b SE Z-value _b^ SE Z-value _b^ SE Z-value HDI 8.05*** 1.95 4.13 7.62* 3.73 2.04 9.07* 3.60 2.52 10.22* 4.06 2.52 UHC 0.06*** 0.02 3.40 – – – 0.06 0.04 1.58 0.06 0.04 1.53 Healthexpenditure(USD) 0.00016*** 0.00 3.33 0.00007 0.00 0.74 – – – 0.00006 0.00 0.63 Urbanpopulation(%oftotalpopulation) 0.05*** 0.01 4.38 0.04* 0.02 2.21 0.05** 0.02 2.64 0.04* 0.02 2.55 Serviceindustry(%totalemployment) 0.05*** 0.01 3.88 0.02 0.03 0.68 0.02 0.03 0.55 0.01 0.03 0.42 Airpollution(ug/m3₎ _0.02 _0.01 _1.87 _0.01 _0.01 _0.796 _0.01 _0.01 _1.13 _0.01 _0.01 _1.15

UHC:UniversalHealthCoverage;HDI:HumanDevelopmentIndex;andSE:StandardError.

* p-value<0.05. ** p-value<0.01. *** p-value<0.0001.

M.E.Marziali,R.S.Hogg,O.A.Oduwoleetal. InternationalJournalofInfectiousDiseases104(2021)370–372

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successfullymanagedCOVID-19inconjunctionwithhightesting coverageshouldconsider,withintheircapacities,lendingaidto countriesexperiencinghightestingburdenswiththediscretionof localgovernance.Thisisparticularly relevanttoconsideraswe lookforwardtovaccinedistribution,ascountrieswitheitherlower testingratesorthathavestruggledwithtestingimplementationas afunctionoflimitedresourcesshouldprepareforchallengeswith vaccine distribution. While testing should remain the focus of preventativeeffortstodetectandtraceCOVID-19cases,itiscrucial tosimultaneouslylookahead and effectively planfor equitable vaccinedistributionamongcountrieswithlowerHDIscoresandin resource-limited settings. Governing of_ﬁcials should consider experiencesfromtheiruniquecommunitieswithregardtotesting, totailorvaccinationstrategies.

Limitationsofthisanalysisincludetherelativelyfewpredictors

included due tochallengeswith completedata. Countrieswith

lowertestingcapabilitiesdidnotmeetinclusioncriteria,creating biastowardcountrieswithmorerobustsystems.Resultsshouldbe interpretedwithcaution.

Conclusions

Signiﬁcant predictors of COVID-19 testing include HDI and

percentage of urbanpopulation. While resultsdemonstratethe

heterogeneity of national data, they also suggest that social

conditionsandgovernmentcapacityremainconsistentlysalientin the consideration of testing rates. International cooperation is neededtosupportlow-HDIcountriesinordertoassistintheglobal

COVID-19response.

Conﬂictofinterest

Noneoftheauthorslistpotentialconﬂictsofinterest. Funding

Thisworkwasnotsupportedbyanyspeciﬁcfundingagency.

Ethicalapproval

No ethics approval was required as the data were publicly

available.

Acknowledgments

Wewould liketosincerely thank Dr.RA Middelburgfor his

contributions toward this report, including sharing code and

methodologyformakingcountrywisecomparisons.

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