Estimating The Pattern Of Causes Of Death In Papua New
Kitur et al. BMC Public Health(2019) SEARCH ARTICLEOpen AccessEstimating the pattern of causes of deathin Papua New GuineaUrarang Kitur1,2*, Tim Adair1, Ian Riley1 and Alan D. Lopez1AbstractBackground: Papua New Guinea (PNG) is a diverse country with high mortality and evidence of increased prevalenceof non-communicable diseases (NCDs), but there is no reliable cause of death (COD) data because civil registration isinsufficient and routine health data comprise only a small proportion of deaths. This study aims to estimate causespecific mortality fractions (CSMFs) for five broad groups of causes (endemic infections, emerging infections, endemicNCDs, emerging NCDs and injuries), by sex for each of PNG’s provinces.Methods: CSMFs are calculated as the average of estimates obtained from: (1) Empirical cause method: Utilisingavailable Verbal Autopsy (VA) data and Discharge Health Information System (DHIS) data, and applying statisticalmodels of community versus facility CODs; and (2) Expected cause patterns method: Utilising existing estimates ofmortality levels in each province and statistical models of the relationship between all-cause and cause-specificmortality using Global Burden of Disease (GBD) data.Results: An estimated 41% of male and 49% of female deaths in PNG are due to infectious, maternal (female only),neonatal and nutritional causes. Furthermore, 45% of male and 42% of female deaths arise from NCDs. Infectiousdiseases, maternal, neonatal and nutritional conditions account for more than half the deaths in a number ofprovinces, including lower socioeconomic status provinces of Gulf and Sandaun, while provinces with higher CSMFsfrom emerging NCDs (e.g. ischemic heart disease, stroke) tend to be those where socioeconomic status is comparativelyhigh (e.g. National Capital District, Western Highlands Province, Manus Province, New Ireland Province and East NewBritain Province). Provinces with the highest estimated proportion of deaths from emerging infectious diseases are readilyaccessible by road and have the highest rates of sexually transmitted infections (STIs), while provinces with the highestCSMFs from endemic infectious, maternal, neonatal and nutritional causes are geographically isolated, have high malariaand high all-cause mortality.Conclusions: Infectious, maternal, neonatal and nutritional causes continue to be an important COD in PNG, and arelikely to be higher than what is estimated by the GBD. Nonetheless, there is evidence of the emergence of NCDs inprovinces with higher socioeconomic status. The introduction of routine VA for non-facility deaths should improve CODdata quality to support health policy and planning to control both infectious and NCDs.Keywords: Papua New Guinea, Cause of death, Inequalities, Non-communicable disease, Infectious disease, Cause-specificmortality fractions, Emerging diseases* Correspondence: u.kitur@student.unimelb.edu.au; u.kitur@gmail.com1Melbourne School of Population and Global Health, University ofMelbourne, Melbourne, Carlton, Victoria, Australia2National Department of Health, P.O. Box 807, Waigani, National CapitalDistrict, Papua New Guinea The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication o/1.0/) applies to the data made available in this article, unless otherwise stated.
Kitur et al. BMC Public Health(2019) 19:1322BackgroundAccurate and timely cause of death (COD) data is fundamental for health policy, research and development, butthis is lacking in most middle and low-income countries[1–5]. In a low resource, geographically, culturally andsocioeconomically diverse country like Papua NewGuinea (PNG), understanding COD patterns and differentials in terms of age, sex, ethnicity, socioeconomic status and geography is crucial to identify priority areas fortargeted interventions to improve health [6–8]. However,little is known about the distribution of CODs in PNGbecause the civil registration system does not accuratelyrecord death data; reported deaths from health facilitiescomprise only a small proportion of deaths and there islimited data on the causes of non-hospital deaths [9, 10].COD data in PNG are available from two sources: (1)publications arising from population-based research and(2) routine health facility-based reporting. Populationbased research is limited to small-scale studies in a smallnumber of sites [11–16]. Published data from routinesources are limited to selected hospitals [17, 18] withonly two studies reporting on the quality of medical certification of deaths [19, 20]. All of these published studies report infectious diseases as the predominant causeof death in PNG; however recent findings by Goudaet al. [16] and Rarau et al. [21], suggest the emergence ofsignificant levels of non-communicable diseases (NCDs)and NCD risk factors in parts of the country, albeit fromsites not representative of the entire PNG population.Estimates from the Global Burden of Disease (GBD)study, suggest NCDs are a major COD in PNG, butthese are based on demographic and statistical modellingwith very little empirical or local data for PNG, and needto be verified based on empirical evidence [22]. A studyby Mola and Kirby showed large discrepancies in maternal mortality ratios (MMR) between modelled estimatesand empirical estimates, therefore necessitates the verification of published estimates using local data [23].The GBD Study classifies CODs under three broadgroups: Group I (infectious diseases, maternal, childhealth and conditions of poverty), Group II (Non-communicable diseases), and Group III (Injuries) [24]. In thePNG context, Gouda et al. [16] further categorisedCODs into five groups: (1) endemic infections, (2) emerging infections, (3) endemic NCDs, (4) emerging NCDs,and (5) injuries (see Table 1). This classification dividesinfections into emerging diseases that are recognised forthe first time in the PNG population (e.g., HIV and cervical cancer) or have traditionally affected Papua NewGuineans but are re-emerging (such as tuberculosis orTB) and endemic diseases like malaria, pneumonia, diarrhoea, maternal and other infections that have affectedPNG’s population for longer [25]. TB is an introduceddisease that was thought to be under control in mostPage 2 of 12Table 1 Composition of five broad COD groups [16]GroupCause (ICD-10 code)1. Endemic Infections MalariaDiarrhoea/DysenteryICD-10 code[B50-B54][A00-A09]Pneumonia[J10-J22, J85]Maternal[O00-O99]Other infectious diseasesa2. EmergingInfections3. Endemic NCDsTB[A15–19]HIV[B20-B24]Cervical cancer[C53]Chronic respiratory diseases[J40-J46]Leukaemia/Lymphoma[C81-C85; C91C96]Cirrhosis[K70–76]Renal failure[N17-N19]Breast cancer[C50]Stomach cancer[C16]Oesophageal cancer[C15]Colorectal cancer[C18–21]Prostate cancer[C61]Other NCDsaOther cancersaOther cardiovasculardiseasesa4. Emerging NCDs5. InjuriesDiabetes[E10-E14]Stroke[I60-I69]Ischemic heart diseases[I20-I25]Lung cancer[C34]Homicide[X85-Y09]Falls[W00-W19]Road e of venomous 4]Other injuriesaaCalculated as the difference between total deaths within that sub-categoryand the total of specific causes within that sub-categoryprovinces of PNG in the late 1960s, but the devolutionof health services, increased prevalence of diabetes, anddrug resistance are factors that are responsible for thecurrent epidemic [26]. HIV was unknown in PNG before1987 but its emergence thereafter, in combination withTB, has resulted in a substantial number of hospitaldeaths among young adults [27]. Malaria and pneumoniahave likely been leading CODs in PNG in the last 50
Kitur et al. BMC Public Health(2019) 19:1322Page 3 of 12years; however, deaths from both diseases appear to havedeclined [28, 13].Emerging NCDs include diabetes mellitus, stroke, ischaemic heart disease (IHD) and lung cancer, diseasesthat are largely attributable to individual lifestyle choices,while endemic NCDs comprise chronic respiratory diseases, other cancers, other cardiovascular diseases andall other NCDS. Chronic respiratory diseases amongadults are thought to be common in PNG due more todomestic pollution from burning wood in most traditional societies of PNG than from smoking [29, 30].Diabetes and stroke appear to be increasing in mosturban and peri-urban societies of PNG due to life stylechanges from traditional diets to processed foods, physical inactivity and high rates of cigarette smoking [21].In this study, we have used Gouda et al’s classification tofurther categorise diseases into these broad categorieswithin GBD Groups I and II since doing so is potentiallymore informative for public health policy [16].Given the limited availability of data in PNG, there isenormous uncertainty in COD estimates. This studyaims to provide more reliable estimates by generatingcause-specific mortality fractions (CSMFs) for five broadgroups of causes, by sex, and for each of PNG’s provinces utilising a statistical modelling framework thatencompasses available verbal autopsy (VA) derived CODdata for community (non-facility) deaths from theGouda et al. study [16] and National Department ofHealth (NDoH) Discharge Health Information System(DHIS) routine facility-based data, the GBD database ofCOD and all-cause mortality estimates, and existing estimates of all-cause mortality by age, sex and province inPNG [10, 31]. This approach thus consolidated empiricalevidence on COD and all-cause mortality with expectedcause patterns, given the level of all-cause mortality andsociodemographic development in the country to yieldCOD estimates at national and provincial levels.MethodsData sourcesFacility deathsThe most comprehensive source of facility deaths in PNGis the NDOH Discharge Health Information System(DHIS). Set up in 1968, the DHIS reports deaths from 20provincial hospitals, 635 health centres and clinics out of755 registered health facilities (86% reporting rate) in thecountry [32]. DHIS deaths are those who died in the facilities, except for those who die on arrival, which areregarded as coroner’s cases. Only a very small proportionof deaths are of people residing out of the province, exceptfor the four regional hospitals of Port Moresby in theNational Capital District, Mt Hagen in Western HighlandsProvince (WHP/Jiwaka), Angau in Morobe Province andNonga in East New Britain Province (ENBP). Deaths inTable 2 Estimated CSMFs (%) by age and sex, empirical cause method, expected cause pattern method and final estimates, PNG,2011MethodEmpirical causemethodExpected causepatterns methodFinal estimatesDiseaseClassificationMale (%) 5Female (%)5–4445–6465 All 55–4445–6465 AllEndemic Infections84272524368536222439Emerging Infections2201951232219513Endemic NCDs10183243281021344228Emerging al100100100100100100100100100100Endemic Infections81201212278033141232Emerging Infections213115832717515Endemic NCDs12243639301322353627Emerging 100100100100100100100100100Endemic Infections82241918318235181835Emerging Infections2171551032518514Endemic NCDs11213441291222353927Emerging 0100100100100100100100100100
Kitur et al. BMC Public Health(2019) 19:1322hospitals and health centres are recorded on the standardinternational medical certificate, with information on age,sex, facility/district/province and COD transferred intothe DHIS. Data on deaths in the DHIS are reported usingthe PNG 3-digit shortlist version of the InternationalClassification of Diseases – Tenth Revision (ICD-10), forover 300 causes, and with age recorded for all deaths. Limitations of the DHIS, detailed elsewhere, are that it is unsuitable for population level mortality because of itsexclusion of deaths outside facilities, and that reporting ofdeaths in some facilities is incomplete in some years. Thisstudy uses DHIS data from 2007 to 2013 [32]. The otherNDOH data source, the National Health InformationSystem (NHIS), records more deaths than DHIS but onlyreports deaths based on 26 syndromes. Moreover, 72% ofdeaths do not have an age recorded, greatly limiting theiranalytical and policy utility. A new data source, theeNHIS, developed in 2014 records facility deaths using detailed ICD-10 coding but currently operates only in eightprovinces, and is still in the early stages of development,with limited numbers of death. Neither NHIS nor eNHISdata were used for analysis in this study given theselimitations.Page 4 of 12average of education, economic and fertility indicatorsand is measured for every country-year, and availablerisk factor data (e.g. cigarette consumption for lung cancer) [36].Analytical methodsGiven the potential biases and measurement uncertaintyassociated with extrapolating from the VA samples andalso the local imprecision arising from using globalmodels of mortality developed by the GBD to estimateCOD patterns in PNG, we had no a priori basis tofavour one method over the other and hence the finalestimates of COD suggested by this study were calculated as the simple arithmetic average of estimates obtained from two methods:1) Empirical method: Utilising available data (VA data,DHIS) with statistical models of communitycompared with facility CODs.2) Expected cause patterns method: Utilising existingestimates of mortality levels in each province withstatistical models of the relationship between allcause and cause-specific mortality generated fromGBD data.Community deathsThe only source of data on community CODs in PNGare from four sites in the Gouda et al. study [16]. From2009 to 2014, 1408 community and hospital deaths wererecorded from the sites and diagnosed using SmartVerbal Autopsy (the Population Health Metrics ResearchConsortium (PHMRC) Tariff v.2.0 method). Verbal autopsy is a means of obtaining the probable COD basedon signs and symptoms reported in a standardised interview with a family member of the deceased [33]. TheTariff algorithm estimates the most probable COD froma list of 32 specific causes for adults. Three of thestudy sites (West Hiri in Central Province, Asaro inEastern Highlands Province and Karkar in MadangProvince) are in the top 20 districts in terms of socioeconomic development and access to health care asmeasured by a composite index (described below),while the other site, Hides (Southern Highlands/Hela),is towards the bottom [ 10, 31, 34].Global burden of disease studyThe GBD Study provides estimates of all-cause mortalityand CSMFs by detailed age group and sex for 195 countries and territories for each year 1990–2017 [35]. TheGBD uses the statistical modelling framework Cause ofDeath Ensemble model (CODEm), which combines results from global statistical models of 192 causes ofdeath. For data-scarce countries like PNG, these modelsare based primarily on covariates. Covariates include thesocio-demographic index (SDI), which is the geometricThis approach makes use of the available data, with alltheir limitations, but also draws on what might be theexpected cause patterns given the level of all-cause mortality in each province. CSMFs were estimated for theyear 2011, which is close to the mid-point year of theDHIS and VA data and was used for the all-cause mortality estimates applied in this analysis [10].The basic cause of death measure estimated was theCSMF, defined as the fraction of deaths in the population that is due to each cause. CSMFs were calculatedfor four broad age groups across which the compositionof the leading causes of death was likely to change (0–4,5–44, 45–64, 65 years), and for each sex and eachprovince. The four age groups were chosen becauseleading causes of death commonly vary between eachage group; more detailed age groups could not be usedbecause of the limited data available from VA.Causes of death were first estimated for the five causecategories as defined by Gouda et al.: namely emerginginfections, endemic infections, emerging NCDs, endemicNCDs, and injuries [16]. Table 1 lists the main specificcauses included under each category. A more precisecause listing was not possible again because of the limited data available from VA.Empirical cause methodIn the empirical cause approach, we estimated CODsseparately for facility and community deaths. Facilitydeaths are defined as those reported by the DHIS and
Kitur et al. BMC Public Health(2019) 19:1322Page 5 of 12community deaths are defined as all other deaths.Thirty-eight thousand three hundred three DHIS deathsrecorded from 2007 to 13 were used to calculate facilitybased CSMFs. Deaths recorded in all these years wereused due to the low numbers of deaths in some provinces, especially at older ages, in order to reduce randomerror. The most recent year of data available is 2013.Community CSMFs were estimated for each provinceas follows. For each of the four sites with VA data, theratio of community CSMFs (from VA) to DHIS-derivedCSMFs was calculated, in log space, for each age groupand sex. These ratios were then applied to the DHISCSMFs in each of the provinces to estimate the community CSMFs by age and sex. This method assumes thatthe ratio of community CSMFs to DHIS CSMFs (withineach age and sex group) is constant across provinces.Further detail about the method employed is presentedin the Additional file 1.Once community and DHIS CSMFs were calculated,CSMFs for all deaths by age, sex and province were calculated by weighting them by the proportion of alldeaths that occurred within and outside facilities. Theproportion of all deaths that occurred within and outsidefacilities was calculated, for each age, sex and province,as DHIS deaths divided by total deaths, based on theprovince-specific life tables calculated by Kitur et al. andprovincial population data [10]. This method is described in detail in the Additional file 1.identifiers and no risk to any individual, this was considered a low risk research that required no ethical approval.Verbal approval was granted from NDoH in 2016 to usethe DHIS data.Expected cause patterns methodResultsThe expected cause patterns approach uses data fromthe GBD 2017 study. The GBD data were used to develop linear regressions, for each of the four age groups,with outcome variable of the natural log of the ratio ofeach specific cause to a base cause (endemic NCDs) andcovariates of the natural log of the probability of dyingin that age group (using the all-cause mortality estimatesof Kitur et al), calendar year and SDI [10]. This regression was developed for each sex and cause and thesewere used to predict CSMFs at the national level forPNG. Provincial-specific estimates were made based onregressions as above but excluding the SDI measure because there is no equivalent available for PNG’s provinces. These CSMFs were then predicted for eachprovince, age, sex and then scaled to the national levelCSMFs in PNG. More details about this method can befound in the Additional file 1.National level cause specific mortality fractions (CSMFs)Ethics and data permissionWe used aggregated cause of death (COD) data from apublished PNGIMR study [16], publicly available GlobalBurden of Disease data and from the National Departmentof Health where the lead author works. Since these datasources contained aggregated data with no personalPresentation of findingsThe findings are presented in the main body of the textand the Additional file 1 for each broad age group, sexand province. The plausibility of CSMF estimates andpatterns of geographical distribution were assessed bycomparing provincial CSMFs to a composite index developed by Kitur et al. [10, 31] which measures provincial differences in socioeconomic development andhealth access. The composite index is derived from thearithmetic mean of education, economic, and health access indicators, with each indicator adjusted to be a normally distributed percentage with a mean of 50%. Theeducation indicator measures net admission rate (percentage of children aged 6 years who are admitted intoelementary prep school) and female literacy rate whilethe economic indicator is an average of poverty levels asassessed by the World Bank based on food and nonfood expenditure and the proportion of people engagedin paid work activities from the 2011 census. The healthaccess index was computed based on information aboutnumber of health workers per population and immunizationrates from the 2011 Health Sector Performance AnnualReview [34, 37, 38].In Table 2, the empirical cause method suggests a higherproportion of deaths from infectious diseases, maternal(females only), child health and conditions of povertyand a lower proportion from NCDs, compared with theexpected cause patterns method, at all ages and in bothmales and females. Injury cause-fractions are higher inthe empirical cause method at all ages except 5–44 years.More detailed information is provided in the Additionalfile 1: Table S1. These data show that there are more facility deaths from infections in children under 5 yearsthan in the community and consistently higher injurydeaths in the community.Nationally, at all ages, more females (49%) die from infectious, maternal, neonatal and nutritional conditions(Groups 1 & 2) than males (41%). Conversely, slightlymore males (45%) than females (42%) die from NCDs(Groups 3 & 4). Groups 1 & 2 CSMFs decline with age,and at ages 5–44 are 60% of female deaths, with emerging infections (Group 2) comprising 25%. NCDs, particularly emerging NCDs (Group 4), increase with ageand account for 27% of male deaths and 30% of femaledeaths at ages 65 years. Overall, endemic diseases(Groups 1 & 3) comprise over half of all deaths and
Kitur et al. BMC Public Health(2019) 19:1322Page 6 of 12emerging diseases (Groups 2 & 4) just over one-quarter.At ages 45–64 years, emerging diseases comprise 36% ofdeaths in males (15% emerging infections, 21% emergingNCDs) and 40% of deaths in females (18% emerginginfections, 22% emerging NCDs). Injury CSMFs at ages5–44 years in males (28%) are more than double than infemales (12%).The proportion of deaths from endemic infectious,neonatal, nutritional and maternal causes (females only)are highest in Sandaun Province (43%), Gulf Province(41%), Milne Bay Province (MBP) (40%), East SepikProvince (ESP) (38%) and Morobe Province (37%). Allthese provinces have high levels of all-cause mortality asshown Additional file 1: Figure S1-S3.2. Emerging infections (TB, HIV, cervical cancer)Provincial estimated cause fractions by sexCSMF estimates for the different disease groups varyconsiderably by province (Table 3). We present results primarily for both sexes, but identify sex-specificdifferences of note. Additional file 1: Table S2presents detail information on sex-specific mortalitydifferences.1. Endemic infections (malaria, pneumonia,diarrhoea), neonatal, nutritional and maternalCSMFs from emerging infections are highest in thehighlands of Simbu Province (15%), Enga Province (14%),Southern Highlands Province (SHP/Hela 12%) andEastern Highlands Province (EHP) (12%), in MorobeProvince (14%) and Madang Province (13%) and theNational Capital District (NCD). Apart from the NationalCapital District, all these provinces are linked by theHighlands highway and report the highest rates of sexuallytransmitted infections (STIs) in the country [39].Table 3 Estimated CSMFs for both sexes (%), under-five mortality (per 1000 live births) by sex, life expectancy (years) by sex andcomposite index (%) by province and sex, PNG, demicNCDsEmergingNCDsInjuries Under 5mortality per100011.728.115.610.8Both sexes (%)PNG32.7Lifeexpectancyin yearsCompositeindex (%)Male Female Male Female685862.064.350National Capital District25.212.830.023.09.4342367.070.395East New Britain34.910.427.616.99.6655662.263.975New e35.89.827.414.411.5544362.467.260Eastern 6.913.926.613.010.1847159.761.059Western 8Simbu25.015.431.017.011.0403567.264.257West New Britain37.312.322.614.013.3544563.465.256Milne 517.58.1837562.664.450East unc43.412.423.510.110.713111954.456.829Southern 29.114.817.4696660.762.025aThis table has been sorted by the composite index ranking (%), from the highest rank in level of socioeconomic development and health access (National CapitalDistrict) to the lowest rank (Enga Province). b These provinces were still united at the time of data collection. c Sandaun Province is also known as WestSepik Province
Kitur et al. BMC Public Health(2019) 19:13223. Endemic NCDs (chronic respiratory diseases,cancers, renal failure, liver cirrhosis)A large proportion of deaths from endemic NCDsare from the central highlands provinces of WHP/Jiwaka (35%), Simbu Province (31%), and EHP (31%);Manus Province (32%) and New Ireland Province(NIP) (31%) and National Capital District (30%). Thelowest proportions of deaths from endemic NCDs arefound in Gulf Province and Sandaun Province, although there is relatively little variation amongstprovinces in this cause.4. Emerging NCDs (diabetes, stroke, ischaemic heartdiseases, lung cancer)Provinces with the highest cause fractions for emerging NCDs include National Capital District (23%) andCentral Province (17%), WHP)/Jiwaka (19%), SimbuProvince (17%), and EHP (16%) and Manus Province(18%), ENBP (17%) and NIP (16%). The lowest CSMFsare found in Gulf Province and Sandaun Province, at approximately 10%. The proportion of deaths from emerging NCDs is positively related with a province’s level oflife expectancy. WHP/Jiwaka, Simbu Province, NationalCapital District, Manus Province and NIP report thehighest rates of deaths from NCDs among males and females, at about 50% of deaths for males and slightlylower for females.5. Injuries (homicide, falls, road traffic accidents, bitesof venomous animals, fires, poisoning, suicide)CSMFs for injury deaths are highest in Enga Province(17%), Central Province (17%), EHP (14%), ESP (14%)and West New Britain Province (WNBP) (13%). Theseprovinces have also been found to have the highest ratesof violence, road traffic accidents and falls [16, 43, 44].Relationship of cause fractions with under five mortalityand life expectancyNCDs, especially emerging are higher in low mortalityprovinces and infections are higher in high mortalityprovinces. Table 3 shows great variation in mortalitybetween provinces with the lowest (National CapitalDistrict, male 34/1000, female 23/1000) and highest(Sandaun Province male 131/1000, female 119/1000)level of child mortality estimates. In both sexes, there isa life expectancy gap of about 13 years between theprovince with the lowest levels (Sandaun Province; male54.4 years, female 56.8 years) and the National CapitalDistrict (male 67.0 years, female 70.3 years), the provincewith the highest life expectancy in PNG.Page 7 of 12Composite indexThe level of socioeconomic development and healthaccess varies significantly across PNG as shown inTable 3 and Fig. 1. Development is low in the western(Sandaun Province, Enga Province and WesternProvince) and southern (SHP/Hela, Gulf Province,Central Province and Oro Province) and comparativelyhigh in the National Capital District and in East and WestNew Britain, NIP and Bougainville. It is moderate to highin the central highlands provinces of WHP/Jiwaka, SimbuProvince and EHP, while in the north (Morobe Province,Madang Province and ESP the composite index is low tomoderate. More information is available in the Additionalfile 1: Table S3.The provincial composite index correlates stronglywith CSMFs for both sexes from emerging NCDs (Fig. 2),but less so with other causes (Figs. 2 and 3). There areno clear outliers among any provinces in the relationshipbetween the composite index and emerging NCDs, witha reasonably strong r-squared of 0.57. The weakness ofthe relationship between the composite index and endemic NCDs (r2 0.09), emerging infectious diseases(r2 0.01), and endemic infectious diseases (r2 0.13) isconfirmed by their low r-squared.DiscussionOur findings demonstrate that infectious, maternal(females only), neonatal and nutritional causes (Groups1 & 2) are still an important cause of death in PNG,accounting for more than 50% of all deaths in someprovinces. However, NCDs (Groups 3 & 4), especiallyemerging NCDs (Group 4), are particularly high in provinces with higher levels of socioeconomic development.These results demonstrate the challenge for publichealth policymakers in PNG in addressing the dual burden of infectious diseases and NCDs, particularly withsignificant mortality occurring due to emerging forms ofeach.Our findings from this study show Groups 1 & 2, bothin children and also adults and are much more important as a cause of death accounting for 41% of deaths inmales and 49% of deaths in females. This is almostdouble what is reported by the GBD [35] (23% for malesand 29% for females) and for other Melanesian countries[40, 41]. The GBD Study estimates lower infectious disease mortality for PNG due to the
PNG context, Gouda et al. [16] further categorised CODs into five groups: (1) endemic infections, (2) emer-ging infections, (3) endemic NCDs, (4) emerging NCDs, and (5) injuries (see Table 1). This classification divides infections into emerging diseases that are recognised for the first time i
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