Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the ...Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.展开更多
Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immun...Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.展开更多
BACKGROUND Immunization is a key component of primary health care and an indisputable human right.Vaccines are critical to the prevention and control of infectious disease outbreaks.The coronavirus disease 2019(COVID-...BACKGROUND Immunization is a key component of primary health care and an indisputable human right.Vaccines are critical to the prevention and control of infectious disease outbreaks.The coronavirus disease 2019(COVID-19)pandemic and associated disruptions over the past two years have strained the health systems,with many children missing out on essential childhood vaccines.AIM To evaluate the immunization coverage among 12-23-month-old children in the rural areas of Community Health Centre(CHC)Dighal and to determine the factors influencing the existing immunization coverage.METHODS A coverage evaluation survey was conducted according to the 30-cluster sampling technique,which is the standard methodology for such surveys devised by World Health Organization.A total of 300 children aged 12-23 months were included,whose immunization details were noted from their immunization cards.RESULTS Full immunization rate was noted in 86.7%of the children,with partial and non-immunized children accounting for 9%and 4.3%respectively.The full immunization dropout rate was 4.2%.The common reasons for partial or non-immunization were family problem including illness of mother,vaccine not being available and child being ill.Place of birth(P=0.014)and availability of immunization card(P<0.001)were significant predictors of the immunization status.Since the study was conducted in 2020/2021,health services were disrupted due to the COVID-19 lockdown.CONCLUSION Due to the coverage being higher than the national average,it was concluded that the immunization coverage was optimal and not affected by the COVID-19 pandemic.展开更多
BACKGROUND Acute respiratory infections(ARI)and diarrhoea are among the leading causes of infant and under-five mortality worldwide.Zinc,the second most abundant trace element in the human body,is widely used in the t...BACKGROUND Acute respiratory infections(ARI)and diarrhoea are among the leading causes of infant and under-five mortality worldwide.Zinc,the second most abundant trace element in the human body,is widely used in the treatment of both conditions.It mitigates diarrhoea by restoring mucosal integrity and enhancing enterocyte brush border enzyme activity.In ARI,zinc boosts immune function,promotes epithelial regeneration,and inhibits the replication of respiratory viruses.AIM To assess the effectiveness of prophylactic intermittent zinc supplementation in preventing acute diarrhoea and ARI in infants.METHODS This open-label,randomized controlled trial with a 1:1 allocation ratio was conducted over 15 months(October 2022 to December 2023)at a tertiary care hospital in Eastern India.A total of 320 infants attending the outpatient department for routine vaccinations were enrolled and randomly assigned to intervention and control groups.The intervention group received zinc drops for two weeks,with the regimen repeated one month later and again at six months during subsequent vaccination visits.The control group received no placebo or alternative treatment.Outcomes were assessed after the final follow-up at nine months.RESULTS The mean annual incidence of ARI and diarrhoea was significantly lower in the zinc group than in the control group[ARI:0.25±0.61 vs 0.92±1.22;mean difference=-0.67(95%CI:-0.88 to-0.45),P<0.001,Cohen’s d=-0.69]and[diarrhoea:1.04±1.30 vs 2.07±2.09;mean difference=-1.03(95%CI:-1.42 to-0.65),P<0.001,Cohen's d=-0.59],respectively.Additionally,the zinc group showed significantly greater gains in length[10±0.6 cm vs 8.6±0.4 cm;mean difference=1.4(95%CI:1.3-1.5),P<0.001,Cohen’s d=2.74]and weight[3150±108 g vs 2818±76 g;mean difference=332(95%CI:310-352),P<0.001,Cohen's d=3.56].CONCLUSION Prophylactic intermittent zinc supplementation administered alongside routine immunization substantially reduces the incidence of ARI and diarrhoea in infants and promotes improved growth.This affordable strategy holds promise for reducing infant morbidity and mortality without increasing healthcare burdens.展开更多
基金This study was funded by GCRF UK and was carried out as part of project CoNTINuE-Capacity building in technology-driven innovation in healthcare.
文摘Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency DevelopmentProgram for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.
文摘BACKGROUND Immunization is a key component of primary health care and an indisputable human right.Vaccines are critical to the prevention and control of infectious disease outbreaks.The coronavirus disease 2019(COVID-19)pandemic and associated disruptions over the past two years have strained the health systems,with many children missing out on essential childhood vaccines.AIM To evaluate the immunization coverage among 12-23-month-old children in the rural areas of Community Health Centre(CHC)Dighal and to determine the factors influencing the existing immunization coverage.METHODS A coverage evaluation survey was conducted according to the 30-cluster sampling technique,which is the standard methodology for such surveys devised by World Health Organization.A total of 300 children aged 12-23 months were included,whose immunization details were noted from their immunization cards.RESULTS Full immunization rate was noted in 86.7%of the children,with partial and non-immunized children accounting for 9%and 4.3%respectively.The full immunization dropout rate was 4.2%.The common reasons for partial or non-immunization were family problem including illness of mother,vaccine not being available and child being ill.Place of birth(P=0.014)and availability of immunization card(P<0.001)were significant predictors of the immunization status.Since the study was conducted in 2020/2021,health services were disrupted due to the COVID-19 lockdown.CONCLUSION Due to the coverage being higher than the national average,it was concluded that the immunization coverage was optimal and not affected by the COVID-19 pandemic.
文摘BACKGROUND Acute respiratory infections(ARI)and diarrhoea are among the leading causes of infant and under-five mortality worldwide.Zinc,the second most abundant trace element in the human body,is widely used in the treatment of both conditions.It mitigates diarrhoea by restoring mucosal integrity and enhancing enterocyte brush border enzyme activity.In ARI,zinc boosts immune function,promotes epithelial regeneration,and inhibits the replication of respiratory viruses.AIM To assess the effectiveness of prophylactic intermittent zinc supplementation in preventing acute diarrhoea and ARI in infants.METHODS This open-label,randomized controlled trial with a 1:1 allocation ratio was conducted over 15 months(October 2022 to December 2023)at a tertiary care hospital in Eastern India.A total of 320 infants attending the outpatient department for routine vaccinations were enrolled and randomly assigned to intervention and control groups.The intervention group received zinc drops for two weeks,with the regimen repeated one month later and again at six months during subsequent vaccination visits.The control group received no placebo or alternative treatment.Outcomes were assessed after the final follow-up at nine months.RESULTS The mean annual incidence of ARI and diarrhoea was significantly lower in the zinc group than in the control group[ARI:0.25±0.61 vs 0.92±1.22;mean difference=-0.67(95%CI:-0.88 to-0.45),P<0.001,Cohen’s d=-0.69]and[diarrhoea:1.04±1.30 vs 2.07±2.09;mean difference=-1.03(95%CI:-1.42 to-0.65),P<0.001,Cohen's d=-0.59],respectively.Additionally,the zinc group showed significantly greater gains in length[10±0.6 cm vs 8.6±0.4 cm;mean difference=1.4(95%CI:1.3-1.5),P<0.001,Cohen’s d=2.74]and weight[3150±108 g vs 2818±76 g;mean difference=332(95%CI:310-352),P<0.001,Cohen's d=3.56].CONCLUSION Prophylactic intermittent zinc supplementation administered alongside routine immunization substantially reduces the incidence of ARI and diarrhoea in infants and promotes improved growth.This affordable strategy holds promise for reducing infant morbidity and mortality without increasing healthcare burdens.
