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"This study investigates the relationship between the level of socio-economic development and infant and child mortality in India. The perspective of this study is based on the "Theory of Demographic Transition" which states that improved public health programs and technological and medical advances bring down the level of mortality. The study tests the following major hypothesis: the higher level the level of socio-economic development, the lower the infant and child mortality rates among the states in India. The study applies correlation and multiple regression analysis to data collected by the National Family Health Survey 1992-1993, one of the most comprehensive surveys of its kind ever conducted in India by the Ministry of Health and Family Welfare Government of India. The finding support the theory of demographic transition in large measure revealing that the overall socio-economic development is inversely related to infant and child mortality rates among the states of India."

"Angka kematian bayi di Indonesia tergolong tinggi jika dibandingkan negara-negara anggota ASEAN. Untuk menurunkan angka kematian bayi diperlukan suatu pemahaman yang komprehensif tentang determinannya. Penelitian ini secara umum bertujuan untuk mempelajari kejadian kematian dan tingkat benahan hidup bayi berdasarkan faktor-faktor kesehatan dan demografi. Metode analisis yang digunakan dalam penelitian ini adalah analisis deskriptif dan analisis inferensial yang terdiri dari analisis regresi logistik untuk mempelajari pengaruh faktor-faktor kesehatan dan demografi terhadap kejadian kematian bayi serta proporsional hazard model untuk mempelajari pengaruh faktor-faktor kesehatan dan demografi terhadap tingkat bertahan hidup bayi yang menggunakan data hasil SDKI 2007. Temuan analisis deskriptif menunjukkan bahwa kejadian kematian bayi lebih banyak pada ibu yang berpendidikan dan kemampuan ekonomi rendah, tinggal di perdesaan, tidak melakukan pemeriksaan kehamilan memenuhi ST, penolong persalinan bukan tenaga kcsehatan, tidak melakukan pemeriksaan bayi setelah lahir, melahirkan pada umur yang berisiko, jumlah anak tiga ke atas, jarak kelahiran di bawah dua puluh empat bulan, serta jenis kelamin anaknya laki-laki. Hasil analisis regresi logistik menunjukkan bahwa kematian neonatal dipengaruhi pemeriksaan bayi setelah lahir, umur ibu saat melahirkan, urutan ke1ahiran,jarak kelahiran, dan jenis kelamin anak. Untuk kematian postneonatal, faktor-faktor yang mempengaruhi adalah penolong persalinan, pemeriksaan bayi setelah lahir, umur ibu saat melahirkan, urutan kelahiran, jarak kelahiran, dan daerah tempat tinggal. Untuk kematian bayi, faktor-faktor yang mempengaruhi adalah pemeriksaan bayi setelah lahir, umur ibu saat melahirkan, urutan kelahizan, jarak kelahiran, indeks kekayaan kuantil dan daerah tempat tinggal. Hasil analisis proporsional hazard model menunjukkan bahwa tingkat bertahan hidup postneonatal dipengaruhi oleh penolong persalinan, pemeriksaan bayi setelah lahir, umur ibu saat melahirkan, urutan kelahiran, jarak kelahiran, pcndidikan ibu dan daerah tempat tinggal. Untuk tingkat bertahan hidup bayi, faktor-faktor yang mempengaruhinya adalah pemeriksaan bayi setelah lahir, umur ibu saat melahirkan, urutan kelahiran, jarak kelahiran, jenis kelamin anak, pendidikan ibu dan daerah tempat tinggal.

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Compared to other ASEAN countries, infant mortality in Indonesia is higher. To reduce infant mortality, we need comprehensive understanding about its determinants. Generally, this research’s aim is to study infant mortality and its survival rate based on health and demographic factors. In this research, method of analyses are descriptive analysis, logistic regression analysis and proportional hazard model analysis. Logistic regression analysis is used to examine influence of health and demographic factors on infant mortality. Proportional hazard model is used to investigate the impacts of health and demographic factors on survival rate of infant. This research uses the results of the 2007 Indonesia Demographic and Health Survey Data (IDI-IS).

The results of descriptive analysis show that the incidence of infant mortality is higher among infants from mothers with lower education and economic status. In addition, infant mortality is higher among babies from mother who lived in rural areas, had no antenatal care, were assisted non-professional health worker at delivery, had no postnatal check, gave births at high risk age, had more than three children, and had less than 24 months birth interval. Futhermore, baby boys had higher had higher mortality than baby girls.

The results of regression analysis on the factors of infant mortality show some interesting results. Neonatal mortality is influenced by the existence of postnatal check, mother‘s age at delivery, birth order, birth interval, and the sex of the baby. In posmeonatal case, the mortality is affected by the type of assistance at delivery, the existence of postnatal check, mother’s age at delivery, birth order, birth interval, and mother’s place of residence. In infant case, the mortality is influenced by the existence of postnatal check, mother’s age at delivery, birth order, birth interval, quintile index of welfare and mother’s place of residence.

The results of proportional hazard model show that survival rate of postneonatal is influenced by the type of assistance at delivery, the existence of postnatal check, birth order, birth interval, mother’s education level, and mother’s place of residence. Survival rate of infant is affected by the existence of postnatal check, mother’s age at delivery, birth order, birth interval, sex of infant, mother’s education level, and mother’s place of residence."

"ABSTRACTBackground: to identify other factors other than the TIMI scores that can be used as predictors of 30-day mortality in STEMI patients by including variables of left ventricle ejection fraction (LVEF) and glomerulus filtration rate (GFR) at Cipto Mangunkusumo National Central General Hospital. Methods: a retrospective cohort study was conducted in 487 STEMI patients who were hospitalized at RSUPN Cipto Mangunkusumo between 2004 and 2013. Sample size was calculated using the rule of thumbs formula. Data were obtained from medical records and analyzed with bivariate and multivariate method using Coxs Proportional Hazard Regression Model. Subsequently, a new scoring system was developed to predict 30-day mortality rate in STEMI patients. Calibration and discrimination features of the new model were assessed using Hosmer-Lemeshow test and area under receiver operating characteristic curve (AUC). Results: bivariate and multivariate analyses showed that only two variables in the new score system model were statistically significant, i.e. the Killip class II to IV and GFR with a range of total score between 0 and 4,6. Thirty-day mortality risk stratification for STEMI patient included high, moderate and low risks. The risk was considered high when the total score was >3,5 (46,5%). It was considered moderate if the total score was between 2,5 and 3,5 (23,2%) and low if the total score was <2,5 (5,95%). Both variables of the score had satisfactory calibration (p > 0,05) and discrimination (AUC 0,816 (0,756-0,875; CI 95%). Conclusion: There are two new score variables that can be used as predictors of 30-day mortality risks for STEMI patients, i.e. the Killip class and GFR with satisfactory calibration and discrimination rate."

"Pendahuluan:Peningkatan kadar laktat pada saat masuk UPI secara independent berhubungan dengan outcome yang buruk. Kadar laktat sebagai parameter prognostik di UPI RSCM belum pemah diteliti sebelumnya. Pada penelitian pendahulu, skor SAPS II menunjukkan kemampuan yang baik dalam memprediksi mortalitas di UPI. Penelitian ini bertujuan membandingkan kadar laktat arteri inisial dengan SAPS II sebagai prediktor mortalitas di UPI RSCM.Pasien & Metode:Suatu studi observasi yang prospektif selama periode bulan April sampai Juni 2006 yang dilakukan di UPI bedah-medik. Data dikumpulkan dari 153 pasien yang memenuhi kriteria penerimaan. Data dasar: kadar laktat arteri inisial pada sate jam pertama masuk UPI dan 24 jam pertama untuk skor SAPS II. Mortalitas UPI pun dicatat. Analisis statistik menggunakan Uji Student t and chi-square. Kurva ROC (Receiver Operating Curve) dibuat dan titik potong optimal ditetapkan serta luas daerah di bawah kurva dihitung, untuk menilai untuk nilai prognostik kadar laktat arteri inisial dan SAPS II. Koefisien Pearson digunakan un tuk menganalisa hubungan antara kadar laktat inisial dan skor SAPS II.Hasil:Dari 153 pasien yang memenuhi kriteria, 16 pasien (10,5%) mengalami kematian di UPI. Kelompok survivor memiliki rerata kadar laktat arteri inisial dan skor SAPS II yang lebih rendah dibandingkan kelompok nonsurvivor. Terdapat perbedaan yang berrnakna antara kadar laktat dan mortalitas UPI (p=0,001). Titik potong ditetapkan 3 mmolll. Analisis ROC menunjukkan bahwa kadar laktat arteri inisial (leas daerah di bawah kurva=0,732) tidak lebih baik bila dibandingkan dengan skor SAPS II (luas daerah di bawah kurva=0,915) sebagai prediktor mortalitas di UPI. Terdapat hubungan yang lemah antara kadar laktat arteri inisial dan SAPS II (p=0,002).Kesimpulan:Kadar laktat arteri inisial dan skor SAPS H yang tinggi secara independent berhubungan dengan peningkatan mortalitas UPI di UPI RSCM.

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Introduction:

Elevated lactate levels on ICU admission have been independently associated with poor outcome. The prognostic values of this value have not been investigated in Cipto Mangunkusumo Hospital's ICU

Patients & Methods:

A prospective observational study over a periode from April to June 2006 was conducted in a medical-surgical ICU. Data were extracted from ICU data base: arterial blood lactate at the first hour on admission and the worst clinical & laboratory findings in the first 24 hours for SAPS II scoring. ICU mortality are also recorded. Statistical analyses were performed using Student t-test and chi-square tests_ Receiver Operating Curve were constructed, the optimal cut off point have been obtained and area under curve was used to assess the prognostic value of initial arterial lactate and SAPS H. The coefficient of Pearson were analyzed to assess the relation between initial lactate levels and SAPS II score.

Main Outcome:

Of the 153 evaluable patients, 16 patients (10.5%) were died in ICU Survivor had a lower mean of arterial lactate levels and SAPS II score than nonsurvivor). The mean of initial arterial lactate in survivor group is low than the nonsurvivor. There are a sign{flcant differences between initial lactate level and ICU mortality (p=0,001). The cut off point was obtained at 3.0 mmolll. ROC analysis demonstrated that initial arterial lactate level (AUC=0.732) is worsen than SAPS II Score (AUC=0,915) as a predictor of ICU mortality. There is a weak correlation between initial lactate and SAPS II score.

Conclusion:

An high initial arterial lactate and SAP II score are independently associated with increased ICU mortality in Cipto Mangunkusumo Central Hospital."