Data for: Intermittent preventive treatment of malaria during pregnancy- a generalized linear model with negative binomial distribution
Data for the study is a longitudinal count data obtained from the Sunyani Municipal Hospital in the Brong Ahafo Region, Ghana. The hospital serves approximately 150,000 residents. Count data generally are numbers of events per interval. Mathematically, counts are non-negative integers, since an event cannot happen an incomplete or a negative number of times and essentially, there is no upper boundary to a count, because there can theoretically be close to an infinite number of events taking place. The longitudinal data set comprises vital maternal variables of interest such as antenatal clinic registration of pregnant women, IPTp uptake, age of pregnant women, family planning, number of visits at the clinic, number of pregnancies and births by pregnant women, distance from their homes to the hospital, male partner attendance at clinic were recorded over the period of nine years (from December, 2008 to January, 2017). These events were recorded on a monthly basis. The Negative Binomial method which is an extension of the Generalized Linear Models was used in analyzing the longitudinal data set. This method was chosen because the counts or values recorded for the IPT dosage (IPTp 1 through to IPTp 5) were over-dispersed (i.e. variance is greater than the mean). The distribution of Negative Binomial Model for the over-dispersed count data is expressed as; Pr(Y=y│λ,α)=(Γ(y+α^(-1)))/(y!Γ(α^(-1))) (α^(-1)/(α^(-1)+λ))^(α^(-1) ) (λ/(α^(-1)+λ))^y (1) Where: 𝜆 is the mean or the expected value of the distribution, 𝛼 is the over-dispersion parameter. The Negative Binomial Model (log transformed) for the study is therefore; log〖IPT_i=β_0+β_1 ANC_REG 〗+ β_2 M_ANC+β_3 ANC_V1+β_4 ANC_V2+β_5 ANC_V4+β_6 B_(〖15〗_19 )+β_7 B_(〖20〗_24 )+β_8 B_(〖25〗_29 )+β_9 B_(〖30〗_34 )+β_10 B_35+β_11 M_PARA+β_12 P_ARA (2) For i=1,2,…,5. Where, IPT_i = Intermittent Preventive Treatment 1 through to 5 ANCREG = antenatal clinic registration of pregnant women MANC = male involvement at antenatal clinic B_15_19 = pregnant women of ages 15 to 19 who received IPTp B_20_24 = pregnant women of ages 20 to 24 who received IPTp B_25_29 = pregnant women of ages 25 to 29 who received IPTp B_30_34 = pregnant women of ages 30 to 34 who received IPTp B_35 = pregnant women of age ≥35 years who received IPTp ANC_V1 = one visit to the antenatal clinics by pregnant women ANC_V2 = two visits to the antenatal clinics by pregnant women ANC_V4 = four visits to the antenatal clinics by pregnant women M_PARA = four and greater number of births P_PARA = first birth of a woman.
Steps to reproduce
The data set was obtained from a municipal hospital and spanned a period of nine years (December 2008 to January 2017) and was captured on a monthly basis. We employed the generalized linear modeling approach with negative binomial method to analyze the nine year data set. A log-linear time-series model was introduced which was then used to validate the results obtained from the logistic model.