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Why Quality Data is Important Not good and inaccurate information are blocking or stopping global aid efforts to enhance the lives of the poor or unfortunate

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Why Quality Data is Important
Not good and inaccurate information are blocking or stopping global aid efforts to enhance the lives of the poor or unfortunate, Good public health decision is relying on accuracy and timely statistics and data. It is dangerous that quality health data be acquired in order to measure the amount and the delivery of the disease problem, so that programs can be developed to tackle health needs worldwide. In a clinical setting, quality data is important because it can improve the care provided. For example, a study on child mental health services showed that 58% of the patients had improved outcomes after a data quality improvement project was implemented.(6) It is also equally important that the researcher does not utilize inaccurate data for programming or planning purposes since the associated medical errors can lead to long term damage or death in patients, as well as economic losses.(7)
Challenges With Obtaining Quality Data in Resource-Poor Settings
Lack of Data
All high-income countries have national civil registration systems that record births and deaths, and the countries generate statistics. Unfortunately, these statistics and registration systems are not usually available in lower income countries where premature mortality and infant mortality are highest. “Too many people, especially the poor, are never counted; they are born, and live and die uncounted and ignored. It is a fundamental principle of human rights that every life counts, that every individual matters. If we are to give life to such principles, it is time to start counting everyone.”(8) Developing countries currently account for 99% of unregistered births worldwide, totaling an estimated 48 million unregistered births. Country data for %age of births registered showed that the 42 countries with complete birth registration had a mean purchasing power parity of $17,357, while the 57 countries which reported lower levels of birth registration, had a mean purchasing power parity of only $2,675.(9) In addition, over half the countries in Africa and Southeast Asia record no data on cause of death.(10)
It is especially important that resource-poor settings begin to create civil registration systems in order to measure vital statistics. Vital statistics, such as birth and death rates and cause of death, are critical for targeting and assessing the effectiveness of public health initiatives.(11) It is also important to obtain quality health data because public health decisions can be driven in wrong directions when whole categories of data are not identified.(12) It is important to note, however, that the value of data lies in their use and not in their collection. Data must not be left unanalyzed, which is often what happens in resource-poor settings.
Lack of Infrastructure
In resource-poor settings, poor roads, political instability and crime may reduce the completeness and accuracy of the collected data. Lack of technology and data management infrastructure also present a challenge to data management and collection. For example, specimen collection and processing of bodily fluids is especially dependent on timely and reliable analysis, which is not always possible in resource poor settings due to the lack of infrastructure and technology. Lack of cell phones and harsh climate also impede data flow. (13)
Population Demographics
In resource-poor settings, populations are often highly mobile. Thus, problems collecting data may arise when the household heads are away for extended periods of time in search of food, water, or causal work. (14) In addition, cultural and linguistic differences between the research staff and respondents may lead to misreporting. For example, a pilot study in The Gambia asked people to record their spending by using a grid with pictures of items and pictures of currency. This was a linguistically competent initiative because it enabled even the illiterate to participate and record their spending, but it was not culturally accurate. To represent livestock, the researchers drew a cow with one hump. They later realized that the reason few people were recording spending money on livestock was because the cow was missing a hump.(15) In addition, when researchers from developed countries arrive in a resource-poor country to conduct research, local community members might often treat them with skepticism. Thus, if possible, a community advisory board should be consulted to enhance the cultural appropriateness of questionnaires, and introduce the study to the community, thereby reducing community members’ worries and suspicions.(16)