Tuberculosis and HIV/AIDS-attributed mortalities and associated sociodemographic factors in Papua New Guinea: evidence from the comprehensive health and epidemiological surveillance system

Pham, Bang Nguyen; Abori, Norah; Silas, Vinson D.; Jorry, Ronny; Rao, Chalapati; Okely, T.; Pomat, Willie

doi:10.1136/bmjopen-2021-058962

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Using the household characteristics and assets from the household data as a proxy to evaluate socioeconomic status [19,38,44,45], a household wealth index was calculated using principal component analysis [46], The household wealth index was divided into five quintiles, with each quintile, from the first to the fifth, including the lowest or poorest household, lower or poorer households, middle households, higher or wealthier households, and highest or richest households [44,46]. Further information on similar analysis involving the surveillance data are discussed elsewhere [44,45,47,48].…”

Section: Measurementsmentioning

confidence: 99%

Maternal demographic and socioeconomic correlates of child mortality in Papua New Guinea: Evidence from the integrated Health and Demographic Surveillance System

Emori,

Pham,

et al. 2023

Preprint

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Background Papua New Guinea (PNG) continues to struggle with high child U5MRs, with data from the PNG Census reporting 63.9 deaths per 1000 live births. Addressing high child U5MRs remains a health priority for PNG in line with the UN SDG3. This study aimed to examine the socio-demographic correlates associated with U5MR in PNG. Methods The 2015 Women of Reproductive Age (15-49 years) and Household Socio-Economic Status datasets from the PNG Institute of Medical Research (PNGIMR) integrated Health Demographic Surveillance System (iHDSS) were used to examine associations between surveillance site, maternal age, marital status and household wealth quintile and U5MR. The main outcome measure was under-5 mortality (death from between 0-59 months) determined by using data from women of reproductive age who had experienced under-five mortality in the lifetime. Sociodemographic factors were assessed using participant interviews. Chi-square and logistic regressions were used to test the strength of associations between factors and U5MR. Results A total of 5,899 women were included in the study. Factorssignificantly associated with U5MR were maternal age group and marital status. Children born to mothers aged 15-19 [OR: 10.91 (95% CI: 3.70-32.15)] and children born to mothers aged 20-24 [OR: 3.11 (95% CI: 1.65 -5.85)] had higher likelihood of experiencing U5MR compared to children who were born to mothers aged 40-49. Children born to single mothers [OR: 1.91 (95% CI: 1.19-3.05) were more likely to experience U5MR compared to children born to married mothers. Compared with the Hiri surveillance site, U5MR was lower in Asaro [OR: 0.54 (95% CI: 0.32-0.90)] and Hides [OR: 0.25 (95% CI: 0.11-0.59)] surveillance sites. No associations were found between the U5MR and household wealth quintile. Conclusions This study highlights the importance of re-evaluating existing maternal and child health intervention programs. Comprehensive approaches are needed to address associated maternal and household socio-economic factors to improve U5MR in PNG.

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Section: Measurementsmentioning

confidence: 99%

Maternal demographic and socioeconomic correlates of child mortality in Papua New Guinea: Evidence from the integrated Health and Demographic Surveillance System

Emori,

Pham,

et al. 2023

Preprint

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“…As the data were collected in 2021, reflecting the household SES in 2020, the study's results may not fully represent the current COVID-19 situation in PNG, which might have evolved over time. [31][32][33][34][35][36][37][38][39][40][41] Lastly, the study's focus on household-level data may have limited the ability to explore individual-level factors and their associations with COVID-19 health issues comprehensively (online supplementary file 2). Future research may benefit from more representative sampling, improved data collection methods, and a broader scope of individual and community-level factors.…”

Section: Limitationsmentioning

confidence: 99%

Assessing health impact of COVID-19 and associated household socioeconomic factors in Papua New Guinea: evidence from the Comprehensive Health and Epidemiological Surveillance System

Pham,

Aga,

Emori

et al. 2023

bmjph

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IntroductionThe COVID-19 pandemic had an unprecedented impact on the health and well-being of populations worldwide. Few studies have used household data to explore the health risks associated with COVID-19 in low-income and middle-income countries. This study assessed population vulnerability to COVID-19 by examining household socioeconomic factors related to COVID-19 health risks in Papua New Guinea (PNG).MethodsUsing household socioeconomic surveillance data from 2020, encompassing 37 880 residents living within the catchment areas of the Comprehensive Health and Epidemiological Surveillance System, the study assessed COVID-19 health risks based on the socioeconomic demographic characteristics of the surveillance population. Multinomial logistic regression analyses were conducted to determine associated factors and to estimate predictors of COVID-19 health risks.ResultsAmong the surveillance population, more than 9% reported experiencing COVID-19 health risks, including home-based quarantine (9.6%), centre-based quarantine (0.5%), positive COVID-19 test (0.1%), hospitalisation due to COVID-19 (0.3%) and death from COVID-19 (0.3%). People living in semimodern houses (OR 1.47 (95% CI 1.35 to 1.61)) (verse permanent houses), individuals living in houses with 1–2 bedrooms (OR 1.12 (95% CI 1.01 to 1.25)) (verse houses with 4+ bedrooms) and those belonging to the poorest wealth quintile (OR 1.16 (95% CI 1.024 to 1.314)) (verse the richest) were more susceptible to COVID-19 health risks. Protective factors against COVID-19 health risks included urban residence (OR 0.65 (95% CI 0.59 to 0.71)) (verse rurality), aged 0–4 years (OR 0.76 (95% CI 0.64 to 0.91)) (verse aged 55+ years), households with 7–8 members (OR 0.84 (95% CI 0.74 to 0.96)) (verse 10+ members), handwashing with soap (OR 0.3 (95% CI 0.28 to 0.33)) (verse without soap).ConclusionsThe study provides insights into the susceptibility to COVID-19 health risks across socioeconomic groups in PNG. These findings have implications for development of public health policies and interventions that can be extrapolated to similar settings for enhancing preparedness for future public health emergencies.

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“…Recent socioeconomic development was hypothesised as a key driver of the health transition in PNG, 10 which is characterised by an epidemiological shift in causes of death (CODs) from infectious diseases to non-communicable diseases. 11 12 This has contributed to an increase in injury mortality. 13 This epidemiological shift could have resulted in an increase in the burden of disease and pressure on the health system, particularly at the primary health level.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social determinants of injury-attributed mortality in Papua New Guinea: new data from the Comprehensive Health and Epidemiological Surveillance System

et al. 2022

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ObjectiveThis study reported the prevalence and sociodemographic distribution of mortalities attributed to injuries in Papua New Guinea (PNG).SettingAs part of a longitudinal study, mortality data were collected from the population who live in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System, established in six major provinces in PNG. Verbal autopsy (VA) interviews were conducted by the surveillance team with close relatives of the deceased, using the WHO 2016 VA instrument from January 2018 to December 2020.Participant and InterventionMortality data from 926 VA interviews were analysed, using the InterVA-5 diagnostic tool to assign specific cause of death (COD). Distributions of injury-attributed mortality were calculated and multinomial logistic regression analyses were conducted to identify sociodemographic factors and provide ORs, 95% CIs of estimates and p values.ResultInjury-attributed deaths accounted for 13% of the total deaths recorded in the surveillance population, with the highest proportion in Madang (22%), followed by Port Moresby and Central Province (13%). Road traffic accidents were the leading COD, accounting for 43% of the total injury-attributed deaths, followed by assaults (25%) and accidental falls (10%). Young adults (aged 15–24 years) accounted the largest proportion of injury-attributed deaths (34%) and were nearly six times more likely to die from injuries than those aged 75+ years (OR: 5.89 (95% CI: 2.18 to 15.9); p<0.001). Males were twice more likely to die from injuries than females (OR: 2.0 (95% CI: 1.19 to 3.36); p=0.009). Another significant sociodemographic factor associated with the increased injury-attributed mortalities included urban versus rural residence (OR: 2.0 (95% CI: 1.01 to 3.99); p=0.048).ConclusionYoung adults, particularly those who live in urban areas, were at the highest risk of dying from injuries. Public health policies and interventions are needed to reduce premature mortality from injuries in PNG.

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Tuberculosis and HIV/AIDS-attributed mortalities and associated sociodemographic factors in Papua New Guinea: evidence from the comprehensive health and epidemiological surveillance system

Cited by 6 publications

References 21 publications

Maternal demographic and socioeconomic correlates of child mortality in Papua New Guinea: Evidence from the integrated Health and Demographic Surveillance System

Maternal demographic and socioeconomic correlates of child mortality in Papua New Guinea: Evidence from the integrated Health and Demographic Surveillance System

Assessing health impact of COVID-19 and associated household socioeconomic factors in Papua New Guinea: evidence from the Comprehensive Health and Epidemiological Surveillance System

Social determinants of injury-attributed mortality in Papua New Guinea: new data from the Comprehensive Health and Epidemiological Surveillance System

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