TIME SERIES ANALYSIS OF HIV/AIDS PREVALENCE RATE IN CHILDREN AGED 15 YEARS AND BELOW ON HEALTH STATUS FROM 2000 TO 2016

LIST OF ACRONYMS AND ABBREVIATION

AIDS                          Acquired Immunodeficiency Syndrome

FANTA                      Food and Nutrition Technical Assistance

FAO                            Food and Agricultural Organization

HAART                      Highly Active Antiretroviral Therapy

HIV                             Human Immunodeficiency Virus

MOH                          Ministry of Health

PEPFAR                    President’s Emergency Plan For AIDS Relief

PLHIV                       People living with HIV

SPSS                           Statistical Package for Social Sciences

TASO                         The AIDS Support Organization

UBOS                         Uganda Bureau of Statistics

UDHS                         Uganda Demographic and Health Survey

UHSBS                       Uganda HIV/AIDS Sero-Behavioural Survey

UNAIDS                    The Joint United Nations Programme on HIV/AIDS

UNWFP                     United Nations World Food Program

USAID                        United States Agency for International Development

UNSSCN                    United Nations system standing committee on Nutrition

WHO                          World Health Organization

CHAPTER ONE

INTRODUCTION

1.1 Back ground

HIV/AIDS has continued to spread across all continents causing the death of millions of adults in their prime age, disrupting and impoverishing families and turning millions of children into orphans, (UNAIDS, 2009). HIV/AIDS affects the most productive segments of the populations, and the epidemic has thus tremendously reduced workforces and reversed many years of economic and social progress and has in some cases posed threat to political stability.

According to the Joint United Nations Programme on HIV/AIDS (UNAIDS, 2009), there were about 39.5 million people living with HIV by the end of 2006. Out of these, 37.2 million were adults and 2.3 million were children below the age of 15 years. There were 4.3 million new infections in 2006. In Sub-Saharan Africa about 2.8 million people were infected with HIV and 24.7 million people were living with HIV. Despite recent improved access to antiretroviral treatment (ART) and care in many of the world’s regions, the epidemic claimed 2.9 million lives in 2006.

Although efforts have been put in place to fight HIV/AIDS in Uganda, about 1million people are leaving with HIV/AIDS (MOH and ORC Macro, 2006). According to Uganda HIV/AIDS sero behavioural survey (2004-2005), the prevalence of HIV among adults (18-59 years ofage) was 6.7 % and the prevalence is higher in Kampala district about 8.5 % than other districts. The high prevalence of HIV/AIDS in this most productive age has great impact on health, economic and social aspects.

The HIV epidemic in Uganda continues to be generalized, and has not changed pattern in the last three decades. The country achieved impressive success in the control of HIV during the 1990’s, bringing down HIV prevalence among adults aged 15-49 years from a national average of 18.5% in 1992 to 6.4% as reported in the 2005 sero-survey. The 2011 AIDS Indicator Survey in Uganda reported HIV prevalence at a national average of 7.3% and important variations by sex and in specific regions. HIV prevalence in Uganda has consistently been higher among women compared to men since the early years of the epidemic. Between the 2004/2005 and 2011 AIS, there was notable decline in HIV prevalence among women in Kampala, Eastern and Central Eastern regions. These improvements may be a reflection of penetration of effective HIV prevention interventions across communities in the respective regions. However, the overall picture is of increased prevalence nationally and across the sexes, (MoH, 2013).

Although this treatment is not curative and also presents new challenges with respect to side effects and drug resistance, it has dramatically reduced rates of morbidity and mortality, have improved the quality of life of people with HIV/AIDS and have revitalized communities (3) Moreover, HIV/AIDS is now perceived as a manageable chronic illness rather than as a plague. Unfortunately, most of the 39.5 million people currently living with HIV/AIDS reside in developing countries and do not share this improvement in prognosis, (UNAIDS, 2009).

The 2011 Uganda Demographic and Health Survey (2011 UDHS) was designed as a follow-up to the 1988/89, 1995, 2000-01 and 2006 Uganda Demographic and Health Surveys with the objective of providing updated estimates of basic demographic and health indicators. However, it is only the 2006 and 2011 that covered the entire country. The 2011 UDHS was conducted under the Uganda Bureau of Statistics, Act 1998. The data collection was carried out from June to December 2011.

Prevalence in 2011 was much higher among women resident in urban areas compared to those in rural areas (10.7 percent and 7.7 percent respectively); but similar for men resident in both settings (6.1 percent). The prevalence rate among urban women declined by 2.1 percentage points, from 12.8 percent in 2004-05 to 10.7 percent in 2011. In contrast, the rate among rural women increased by 1.2 percentage points over the same period; from 6.5 percent in 2004-05 to 7.7 percent in 2011. Over the same period, the prevalence among men evened out from 6.7 percent in urban residents and 4.7 percent in rural residents in 2004-05, to 6.1 percent in both settings (MoH, 2013).

Despite of the great effort by the government of Uganda to fight against HIV/AIDS, the AIDS virus has continued to increase in Uganda , Basing on this background this study therefore intends to investigate into time series analysis of HIV/AIDS in children aged 15 years and below on health status.

1.2 Problem Statement

The high increase in the availability of antiretroviral drugs among adults aged 18-50 years has a great impact on the nutritional status of people living with HIV/AIDS (PLHIV), (MOH and ORC Macro, 2006). HIV infection increases energy requirements and affects nutrition through increasing energy expenditure, reductions in food intake, nutrient malabsorption and loss and complex metabolic alterations (Macallan, 1995; Babamento and Kotler, 1997). The inadequate dietary intake among PLHIV to meet the increased demand for both energy and protein associated with HIV infection result in weight loss (Piwoz and Preble, 2000), this is despite of the government investments in this sector this study therefore intends to investigate into time series analysis of HIV/AIDS in children aged 15 years and below on health status.

1.3 Objective of the Study

1.3.1 General objective

The study intends to assess into time series analysis of HIV/AIDS in children aged 15 years and below on health status.

1.3.2 Specific Objectives

1. To determine the distribution of HIV/AIDS among the children below 15 years.
2. To forecast HIV/AIDS prevalence for children aged below 15 years in Uganda

1.4 Research hypotheses.

1. Ho₁: There is no trend for HIV/AIDS prevalence among the children below 15years.
2. Ho₂: There is no seasonality for HIV/AIDS prevalence among the children

1.5 Scope of the study

The study scope will cover the following aspects;

1.5.1 Study scope

The study scope will cover, the distribution of HIV/AIDS among the children below 15 years, compare HIV/AIDS prevalence by residence and region, and forecast HIV/AIDS prevalence for children aged below 15 years in Uganda

1.5.3 Time scope

The period of data to be considered will be from 2000-2015.

1.6 Significance of the study

1. The study will help other researchers in understanding the nature of the distribution of HIV/AIDS among the children below 15 years.
2. The study wills also the government to compare HIV/AIDS prevalence by residence and region.

• The study will also help other academicians to forecast HIV/AIDS prevalence for children aged below 15 years in Uganda

CHAPTER TWO

LITERATURE REVIEW

2.0 Introductions

This chapter reviews the study according to various authors.

2.1 The distribution of HIV/AIDS among the children below 15 years

AIDS is a global epidemic which is caused by the virus called human immunodeficiency virus (HIV). It will affect the immune system of the body of human beings. The epidemic was firstly recognized in the year 1980. Since then about 20 million people died and 38 million people are estimated living with HIV in the world (MOH, 2005). The rate of infection of the epidemic is still increasing in many countries of the world and it is distributed unevenly.

It is a major development concern in many countries and is destroying the lives and livelihoods of many people around the world. In spite of increased funding, political commitment and progress in expanding access to HIV treatment, the AIDS epidemic continues against the global response. The epidemic remains extremely dynamic. It is expanding fast and also changing its character as the virus exploits new opportunities for transmission. Hence, the number of people living with HIV/AIDS is growing substantially from year to year.

Since HIV/AIDS was acknowledged as a human being problem, the health researchers have been conducting different research in order to tackle or control the epidemic by developing medicine or vaccine. However, due to the very unique nature of the virus they could not succeed in developing a medicine or vaccine that totally cures or protects from the disease. The antiretroviral medicines which are available currently, at best can diminish the infection rate. i.e they are not able to cure people who are infected by this epidemic. More than this, the price of such medicines has been a major problem especially for developing countries (UNAIDS, 2004).

Almost all countries worldwide are affected by the HIV epidemic. No region of the world has been spared. Although the epidemic is global, there is a remarkable regional variation in its distribution. Some regions are highly affected by the epidemic as compared to other regions. Sub-Saharan Africa (SSA) is one of the hot spots where HIV AIDS is widely spread and it is more hard hit by the consequences of epidemic than other parts of the world.

It is the region where the highest number of victims of HIV/AIDS is found. Among all the people who are infected by diseases all over the world, about 68% (22.5 million) are living in this region (UNAIDS, 2010). According to the United Nation classification of ‘generalized epidemic’ about 90% of the countries which are located in SSA are severely affected by the epidemic. This epidemic has remained the major cause of death in this region. Although the region accounts only for 10% of the world population, it comprises almost 25.8 million of the victims of HIV/AIDS in the world. In 2005 an estimated 3.2 million people in the region became newly infected, while 2.4 million died of AIDS. Among the younger generation (15- 24 years) the percentage of HIV infected women and men account for 4.6% and 1.7%, respectively (UNAIDS, 2005). There were 2.7 million new HIV infections in 2010. HIV AIDS accounts for about approximately 90% of all infection.

The important role of knowledge in addressing the HIV/AIDS pandemic has been recognised. Knowledge about HIV/AIDS is considered an important step in behaviour change, while misconceptions can prevent individuals from making informed choices and taking appropriate action. A Joint United Nations Programme on AIDS (UNAIDS, 2005) report revealed that countries that had significantly reduced rates of new HIV/AIDS infections were those that typically invested heavily in AIDS education and awareness initiatives. Studies also show that young people who have been exposed to appropriate sex education tend to delay sex or use condoms (UNAIDS, 2003; UNFPA, 2003), contrary to the fear that sex education leads to greater sexual activity or experimentation.

2.2 Forecast HIV/AIDS prevalence for children aged below 15 years in Uganda

THE TRENDS IN HIV INCIDENCE 2010–2013 USING MATHEMATICAL MODELLING

Source: 2013 MOH Spectrum estimates

The graphical representation trends in HIV/AIDS incidence from 2010-2013

Source: 2013 MOH Spectrum estimates

CHAPTER THREE

METHODOLOGY

3.0 Introduction:

This section presents a detailed description on how the study will be carried out and collecting the necessary data for the study. It therefore covers the research design, study area, data sources, data processing, data analysis techniques and anticipated limitations of the study.

3.1 Data processing and Data analysis techniques.

The process of data processing will involve editing in order to check for errors and omissions and coding to reduce the data to a meaningful pattern of responses. Model specification and soft wares employed in the tabulation and processing of the findings will be done in order to prepare data, analyze and compile a research report.

The study will use time series analysis and descriptive statistics will be used to describe the information got from the field this will be inform of graphs and tables

Data Analysis will involve applying statistical techniques on it for easy presentation. It will include the interpretation of research findings in the light of the research questions, and objectives to determine if the results are consistent with those research questions.

3.2 Descriptive analysis.

3.2.1 Time series analysis

By the nature of data which is the time series

The analysis however will concentrate on trend and seasonality of HIV prevalence

Assuming a multiplicative model, then 𝑌𝑡=𝑇𝑡∗𝑆𝑡

Where 𝑌𝑡 is the mortality series, 𝑇𝑡 is Trend and 𝑆𝑡 is the seasons.

This employs ARIMA modeling and it includes the following data exploration techniques.

1. Graphical presentation

This will involve plotting the series 𝑌𝑡 against time t.

1. Non parametric tests for trend

A run is defined as a series of increasing values or a series of decreasing values. The number of increasing, or decreasing, values is the length of the run. In a random data set, the probability that the (i+1)^th value is larger or smaller than the i^th value follows a binomial distribution, which forms the basis of the runs test. Testing procedure

Ho: the HIV prevalence series is stationary

Ha: the HIV prevalence series is non-stationary.

Autoregressive Integrated Moving Average (ARIMA)

This is also known as the Box-Jenkins model. This methodology will be used to forecast the HIV prevalence for children aged below 15 years. The model is based on the assumption that the time series involved are stationary. Stationary will first be checked and if not found, the series will be differenced d times to make it stationary and then the Autoregressive Moving Average (ARMA) (p, q) will be applied. The ARIMA procedure provides a comprehensive set of tools for univariate time series model identification, parameter estimation, and forecasting, and it offers great flexibility in the kinds of ARIMA models that can be analyzed. The ARIMA procedure supports seasonal, subset, and factored ARIMA models; intervention or interrupted time series models; multiple regression analysis with ARMA errors; and rational transfer function models of any complexity. The Box-Jenkins methodology has four steps that will be followed when forecasting HIV prevalence among children as below;

Identification.0 This involves finding out the values of p, d, and q

where;

p is the number of autoregressive terms

d is the number of times the series is differenced

q is the number of moving average terms

The identification here will be done basing on the correlogram plot obtained. Where both autocorrelation and partial correlation cuts of at a certain point, we conclude that the data follows an autoregressive model. The order p, of the ARIMA model is obtained by identifying the number of lags moving in the same direction. In case the series was non stationary, the number of times we difference the series to obtain stationarity is the value of d.

Estimation. This involves estimation of the parameters of the Autoregressive and Moving average terms in the model. The nonlinear estimation will be used.

Diagnostic checking. Having chosen a particular ARIMA model, and having estimated its parameters, we now examine whether the chosen model fits the data reasonably well. The simple

test of the chosen model will be done to see if the residuals estimated from this model are white noise. If they are, we can accept the particular fit and if not, the model will have to be started over.

Forecasting. Exponential smoothing methods will be used for making forecasts. While exponential smoothing methods do not make any assumptions about correlations between successive values of the time series, in some cases you can make a better predictive model by taking correlations in the data into account. Autoregressive Integrated Moving Average (ARIMA) models include an explicit statistical model for the irregular component of a time series that allows for non-zero autocorrelations in the irregular component.

The forecast for the year 2016 will be done by regressing HIV prevalence against time

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